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PMC10769261	38187637	[ "<title>Introduction</title>", "<p id=\"P4\">Advances in experimental RNA structure determination methods, particularly Cryo-EM [##REF##35197441##1##], have resulted in over 1600 RNA chains being deposited into the Protein Data Bank (PDB) database [##REF##14634627##2##]. Despite these strides, functional annotations of experimental RNA structures are glaringly absent in both the PDB and secondary databases. The PDB database merely includes the bare minimum annotations for RNA chains, such as their names, lengths, and species. Downstream databases like NAKB (formerly known as NDB) [##UREF##0##3##], DNATCO [##REF##32876056##4##], and BGSU RNA [##REF##23970545##5##] offer more annotations for base pairing, backbone torsions, and 3D motifs, yet the annotation of the RNAs' biological roles is still wanting. The MeRNA database [##REF##16381830##6##] was probably the only database dedicated to function (in this case, metal ion binding sites) for experimental RNA structures, but it has long been defunct and was limited to 256 RNAs and their binding to metal ions. Simultaneously, recent studies have confirmed that many non-coding RNAs play vital roles in numerous biological events, particularly those involved in gene expression regulations [##UREF##1##7##], making RNA structures ideal targets for drug design [##REF##27444227##8##]. This fresh understanding underscores the importance of annotating RNA functions for the RNA biology community.</p>", "<p id=\"P5\">In contrast to the stark lack of a functional database for RNA structures, several databases to annotate protein functions have already been established. Databases such as PDBsum [##REF##28875543##9##] and SIFTS [##REF##30445541##10##] annotate protein chains in the PDB using Gene Ontology (GO) terms and Enzyme Commission (EC) numbers by mapping PDB chains to UniProt [##REF##33237286##11##] proteins and InterPro [##REF##33156333##12##] families. The PDBbind-CN [##REF##25301850##13##], BindingDB [##REF##26481362##14##], and Binding MOAD [##REF##31125569##15##] databases collect protein-ligand interactions with known affinity data. The PDBe-KB [##REF##34755867##16##] database features ligand binding sites and post-translational modification sites for all PDB proteins. The FireDB [##REF##24243844##17##] and IBIS [##REF##22102591##18##] database curate protein-ligand interaction data in the PDB. Most recently, BioLiP2 [##UREF##2##19##] was developed as a comprehensive database covering almost all functional aspects of PDB proteins, including GO terms, EC numbers, ligand binding sites, binding affinities, and cross-reference to external databases.</p>", "<p id=\"P6\">Inspired by BioLiP2, we created FURNA, the first database in the field to offer comprehensive functional annotations for all RNA chains in the PDB database. Function annotations in FURNA include GO terms, EC numbers, Rfam [##REF##33211869##20##] RNA families, RNA motifs for protein binding, species, literature, and cross references to external databases like PDBsum, NAKB, DNATCO, BGSU RNA, ChEMBL [##REF##30398643##21##], DrugBank [##REF##29126136##22##], ZINC [##REF##33118813##23##], and RNAcentral [##REF##33106848##24##]. Unlike protein-ligand interaction databases such as BioLiP, FireDB, and PDBbind-CN, which consider receptor-ligand contacts within each asymmetric unit, FURNA determines RNA-ligand interactions based on the biological assembly (i.e., biounit). This approach situates RNA-ligand interactions within the context of its quaternary structure. FURNA is available both as an open-source software package and as a browsable and searchable web service at <ext-link xlink:href=\"https://seq2fun.dcmb.med.umich.edu/furna/\" ext-link-type=\"uri\">https://seq2fun.dcmb.med.umich.edu/furna/</ext-link>.</p>" ]	[ "<title>Materials and Methods</title>", "<p id=\"P17\">Each entry in FURNA corresponds to one RNA chain in the PDB database. To this end, we first download the mmCIF files of all structures containing nucleic acid from the PDB database and split them into individual chains using a modified version of the BeEM tool [##REF##37340457##39##]. An RNA chain is defined by possessing more ribonucleotides than deoxyribonucleotides and amino acids. RNA chains with ten or more nucleotides become entries in FURNA, but oligo-ribonucleotide fragments with fewer than ten nucleotides are only included as \"ligands\" if they bind to an RNA chain with ten or more nucleotides. The curation of an RNA chain involves several steps: annotating GO terms and EC numbers, mapping RNA-protein binding motifs, extracting RNA-ligand interactions, and assigning RNA secondary structures.</p>", "<title>GO term and EC number annotation</title>", "<p id=\"P18\">We employ two complementary strategies to obtain GO terms for an RNA chain. First, we search the RNA sequence against the most current version of the Rfam database (Rfam 14.9, with covariance models for 4108 families) using Infernal [##REF##24008419##40##], utilizing the parameters: <monospace>cmsearch --cpu 4 -Z 1 --toponly</monospace>. Then, we transfer the GO terms related to each Rfam family hit (<ext-link xlink:href=\"http://current.geneontology.org/ontology/external2go/rfam2go\" ext-link-type=\"uri\">http://current.geneontology.org/ontology/external2go/rfam2go</ext-link>) to the query RNA. Second, we map RNA chains to RNAcentral sequences based on the mapping file provided by RNAcentral (<ext-link xlink:href=\"http://ftp.ebi.ac.uk/pub/databases/RNAcentral/current_release/id_mapping/database_mappings/pdb.tsv\" ext-link-type=\"uri\">http://ftp.ebi.ac.uk/pub/databases/RNAcentral/current_release/id_mapping/database_mappings/pdb.tsv</ext-link>). If the RNAcentral entry has GO terms in the Gene Ontology Annotation (GOA) project (<ext-link xlink:href=\"http://ftp.ebi.ac.uk/pub/databases/GO/goa/UNIPROT/goa_uniprot_all.gaf.gz\" ext-link-type=\"uri\">http://ftp.ebi.ac.uk/pub/databases/GO/goa/UNIPROT/goa_uniprot_all.gaf.gz</ext-link>), we also transfer these GO terms to the FURNA entry. We utilize Graphviz [##UREF##5##33##] to plot the direct acyclic graphs showcasing the relationships among an RNA's GO terms (including their parent terms). For the subset of RNAs with annotated catalytic activities, we convert their Enzyme Commission (EC) numbers from GO terms using the EC2GO mapping (<ext-link xlink:href=\"https://www.ebi.ac.uk/GOA/EC2GO\" ext-link-type=\"uri\">https://www.ebi.ac.uk/GOA/EC2GO</ext-link>). For RNA-binding proteins, their UniProt accessions, GO terms and EC numbers are directly obtained through the SIFTS [##REF##30445541##10##] database.</p>", "<title>RNA-protein binding motif mapping</title>", "<p id=\"P19\">To identify RNA motifs corresponding to known recognition sites for RNA-binding proteins, we download the position weight matrices (PWMs) for all 1583 protein-binding motifs from the latest ATtRACT database (version 0.99β). These motifs and the query RNAs collected by FURNA are grouped by species. Here, we extract the species information of an RNA chain from the respective mmCIF file, specifically from records such as \"gene_src_ncbi_taxonomy_id\", \"ncbi_taxonomy_id\", \"pdbx_gene_src_ncbi_taxonomy_id\", or \"pdbx_ncbi_taxonomy_id\". For any species that has at least one ATtRACT motif and one FURNA RNA chain, we download its transcriptome from the NCBI FTP site (<ext-link xlink:href=\"ftp://ftp.ncbi.nlm.nih.gov/genomes/all/annotation_releases/\" ext-link-type=\"ftp\">ftp://ftp.ncbi.nlm.nih.gov/genomes/all/annotation_releases/</ext-link>) to determine its background distribution of the four nucleotide types (A, C, G, U). This background information is ascertained using the fasta-get-markov program of the MEME suite [##REF##25953851##41##]. Subsequently, this background file is used by the FIMO program [##REF##21330290##42##] of the MEME suite when it searches the motif PWMs against the FURNA RNAs with the parameters: <monospace>--norc --bfile</monospace>, to enable the motifs to align with the RNAs.</p>", "<title>Ligand-RNA interaction extraction</title>", "<p id=\"P20\">For each query RNA included in the FURNA database, we gather its interaction partners from the mmCIF format biological assembly file (i.e., biounit) that contains the pertinent RNA chain. As an example, the asymmetric unit of PDB 1a9n (the spliceosomal U2B\"-U2A' protein complex bound to a fragment of U2 small nuclear RNA) contains six chains, which comprises four protein chains (Chains A, B, C, and D) and two RNA chains (Chains Q and R). This PDB correlates with two different biological assemblies: assembly 1 includes chains A, B, and Q; assembly 2 incorporates chains C, D, and R. Consequently, to extract ligand-RNA interactions for 1a9n chain R, we only consider assembly 2.</p>", "<p id=\"P21\">Starting from the biological assembly file selected for a query RNA, we employ a modified version of the BeEM program [##REF##37340457##39##] to split it into different chains. For each chain, we further split the macromolecule part and the small molecule parts, where the former and latter are labeled by numerical values and a period (\".\"), respectively, in the \"label_seq_id\" record of the mmCIF file. Next, we collect all non-water ligands from all chains in the mmCIF file, including small molecules and metal ions, proteins, DNAs, and RNAs (excluding the query RNA). For each query RNA-ligand pair, we calculate all inter-molecular atomic contacts, i.e., atom pairs within the sum of the van der Waals radii plus 0.5 Å, among non-hydrogen atoms. We label a nucleotide on the query RNA as a ligand binding residue if it has two or more inter-molecular atomic contacts with a ligand. We group any collection of two or more ligand binding residues for the same query RNA-ligand pair into a binding site. Ligands without a binding site are excluded.</p>", "<p id=\"P22\">For a small molecule ligand, we extract the name, synonyms, chemical formula, and linear descriptions (including SMILES, InChI, and InChIKey) from the Chemical Component Dictionary (CCD) provided by the PDB database. We perform mappings from PDB ligand IDs (i.e., CCD IDs) to ligand IDs in the ChEMBL, DrugBank, and ZINC databases using the UniChem database [##REF##23317286##43##]. For protein ligands, we retrieve their GO terms, EC numbers, species, and UniProt accessions from the SIFTS [##REF##30445541##10##] database. For DNA ligands, we retrieve the species from the mmCIF file of the asymmetric unit, similar to how we obtain species information for RNA chains.</p>", "<title>RNA secondary structure assignment</title>", "<p id=\"P23\">FURNA assigns RNA secondary structures in dot-bracket format for canonical base pairs (Watson-Crick pairs and G:U Wobble pairs) in the experimental 3D structure, using two complementary methods: CSSR [##REF##35362469##44##] and DSSR [##REF##26184874##45##]. CSSR is our in-house program, optimized for coarse-grained RNA structures. It can assign secondary structures even when the nucleotides have missing atoms. Conversely, DSSR only functions when the nucleobase of the nucleotide is fully atomic and its RMSD to the standard nucleobase conformation is less than 0.28 Å. Due to these stringent requirements, DSSR-assigned secondary structures might have missing positions compared to the input RNA. To ensure consistency between DSSR input and output, we utilize Arena [##REF##37479079##46##] to fill in missing atoms and rectify unphysical nucleobase conformations for all RNA chains before we execute the DSSR assignment. For an RNA-RNA interaction involving two RNA chains, we assign secondary structures to both the individual RNAs and the RNA pair.</p>", "<title>Infernal database construction</title>", "<p id=\"P24\">For users to perform sensitive Infernal searches of query RNA sequences through FURNA, a database in the Infernal [##REF##24008419##40##] format must be pre-constructed. To accomplish this, we first obtain a non-redundant set of RNAs, which is generated by collapsing multiple FURNA RNAs with identical sequences into one entry. For each RNA in the non-redundant set, the CSSR-assigned secondary structure in dot bracket is collected, and any pseudoknots present in the secondary structures are removed. Subsequently, the secondary structure and sequence are converted by the “cmbuild” tool of the Infernal package into the uncalibrated Infernal format covariance model. This covariance model is then calibrated by the “cmcalibrate” tool of the Infernal package. The calibrated covariance models for all non-redundant FURNA RNA chains are concatenated into the Infernal format database. This database can be utilized by the “cmscan” tool of the Infernal package, allowing a user to perform Infernal searches of query RNA sequences through FURNA.</p>", "<title>US-align database construction</title>", "<p id=\"P25\">Since conducting a tertiary structure search of all RNA chains in FURNA is more time-consuming than a sequence search, two procedures are implemented to reduce the size of the structure database used for US-align search. First, the non-redundant set of RNAs with non-identical sequences is isolated, from which the coordinates of the C3’ atoms are extracted. The exclusion of atoms other than C3’ does not affect US-align, which only considers C3’ atoms for RNA structure alignment. Second, we utilize the qTMclust tool [##REF##36038728##47##] from the US-align package to cluster the structures of the non-redundant RNAs. This results in a set of representative RNA structures with a pairwise TM-score [##REF##31161212##48##] less than 0.5. These representative RNA structures form the US-align database. When a user carries out an RNA structure query through the FURNA website, this query structure will be searched using US-align through the database of representative structures to report the top 100 hits with the highest TM-scores. Meanwhile, the RNAs belonging to the same structure clusters will also be reported.</p>" ]	[ "<title>Results</title>", "<title>Overall statistics</title>", "<p id=\"P7\">At the time of writing this manuscript (Oct 2023), FURNA includes 16154 RNAs involved in 380680 ligand-RNA interactions; the online version of the database is updated on a weekly basis. Among these interactions, 186025, 138245, 31659, 24056 and 695 are interactions with metal ions, proteins, “regular” small molecule compounds excluding metal ions, other RNAs, and DNAs, respectively. Unlike BioLiP, FURNA does not attempt to exclude “biologically irrelevant” RNA-associated molecules from the database apart from removal of water molecules. This is because the biological relevance of ligands, especially metal ions, are less clearly defined for RNAs than for proteins. For example, calcium ions (Ca<sup>2+</sup>) are usually biologically irrelevant artifacts added for purification and/or crystallization purposes for proteins, but they are used to substitute magnesium ions (Mg<sup>2+</sup>) that are critical to maintain the folding of RNAs in pre-catalytic states [##REF##23101623##25##]. Similarly, while potassium ions (K<sup>+</sup>) are a simple buffer additive for many proteins, they critical for the folding of many large RNAs where potassium ions stabilize juxtaposition of nucleotides with large sequence separation by neutralizing charge density [##REF##31175275##26##]. This is why a significant portion (48.9%) of ligand-RNA interactions in FURNA are metal ions, among which 91.4% are magnesium ions, which are the most critical ion for RNA folding (##FIG##0##Fig 1A##). For non-ion small molecule ligands, the most frequent compound is osmium (III) hexammine (Table S1), which is a crystallization additive used to determine the RNA structure by multiwavelength anomalous diffraction (MAD) phasing [##REF##8939748##27##].</p>", "<p id=\"P8\">Among the 10561 RNAs with GO annotations in FURNA, 9288, 5311 and 7014 have annotations in Molecular Function (MF), Biological Process (BP), and Cellular Component (CC) aspects, respectively (##FIG##0##Fig 1B##). Out of the RNAs with MF terms, 58.0% are rRNAs (denoted by GO:0003735 \"structural constituent of ribosome\") and 23.5% are tRNAs (indicated by GO:0030533 \"triplet codon-amino acid adaptor activity\") (##FIG##0##Fig 1C##). This suggests that the distribution of RNA families among experimentally determined RNA structures is highly biased, consistent with MF annotations for RNA-binding proteins where GO:0019843 \"rRNA binding\" and GO:0000049 \"tRNA binding\" are among the most common GO terms. It is worth noting that, on average, the similarity of BP GO term annotations between an interacting RNA-protein pair is significantly higher than a random RNA-RNA pair or a random RNA-protein pair (Figure S1, Mann-Whitney U test p-value<1E-300 and p-value=1.0E-20, respectively). This suggests that RNA-protein interactions will be useful for RNA BP term prediction, similar to the utility of protein-protein interactions in protein function prediction [##REF##28472402##28##–##REF##31106361##30##].</p>", "<title>Web Interface</title>", "<p id=\"P9\">The FURNA website provides three primary interfaces: SEARCH, BROWSE, and DOWNLOAD. The functionalities of these interfaces are elaborated upon below.</p>", "<title>BROWSE.</title>", "<p id=\"P10\">Each entry in the FURNA database represents one RNA chain in the PDB. For each of these RNA chains, the BROWSE interface displays the PDB ID and chain ID, resolution, EC number, GO terms, RNAcentral accessions, Rfam families, species, PubMed citations, and protein-binding motifs found in the ATtrRACT [##UREF##3##31##] database (##FIG##1##Fig 2A##). Additionally, if the RNA chain has a non-water ligand, the BROWSE interface also provides information on the ligand ID, the chain and residue sequence number of the ligand, the ligand-binding nucleotides on the query RNA, and the biological assembly information where interaction with that ligand was retrieved.</p>", "<p id=\"P11\">Individual pages, accessed by clicking on the ligand in the last column of the BROWSE table, offer detailed structure and function information of each ligand-RNA interaction. These individual pages include the 3D structures of the RNA chain on its own, the full biological assembly, the RNA-ligand pair, and the local structure of the ligand binding site. These are displayed via four JSmol [##UREF##4##32##] applets. Where available, GO terms for Molecular Function, Biological Process, and Cellular Component are presented in three directed acyclic graphs created using Graphviz [##UREF##5##33##], illustrating the relationships among different GO terms. The GO terms, as well as EC numbers, are also listed as tables. Additional information is also provided, including the RNA sequence and secondary structure, resolution, the structure's name, species, ATtRACT motifs, PubMed citations, and crosslinks to other databases. In case of a small molecule ligand or an ion, the page exhibits the 2D diagram, ligand IDs (including PDB CCD ID, ChEMBL ID, DrugBank ID, and ZINC ID), the chemical formula, ligand name, and linear descriptions of the molecules (##FIG##1##Fig 2B##). For RNA, DNA, or protein ligands, additional details such as the sequence, name, and species, as well as relevant function annotations such as GO terms and EC numbers, are provided when available (Figure S2). For FURNA entries without ligand interactions, the structure and function details of the RNA can be viewed by clicking on the first column of the BROWSE interface.</p>", "<title>SEARCH.</title>", "<p id=\"P12\">The \"SEARCH\" interface provides four methods to explore FURNA: 'Search by name', 'Quick sequence search (via BLAST)', 'Sensitive sequence search (via Infernal)', and 'Search by structure'. Firstly, users can query FURNA using PDB ID, PDB chain ID, ligand ID (as defined by the 3-letter code in the PDB database's Chemical Compound Dictionary), ligand name, RNAcentral accession, Rfam family, EC number, GO term, ATtRACT motif, taxonomy, PubMed ID, or any combination of these. Secondly, FURNA can employ NCBI BLAST+ to search its entries using RNA, DNA, or protein sequences through a local non-redundant sequence database where identical sequences are merged into the same entry. In the search results, both representative hits found in the non-redundant database and members from the same sequence clusters are displayed (##FIG##2##Fig 3A##). Thirdly, to address the issue of a BLAST search's low sensitivity for nucleic acid sequences, FURNA offers an alternative, more sensitive RNA sequence search option using Infernal (See <xref rid=\"S10\" ref-type=\"sec\">Materials and Methods</xref>, ##FIG##2##Fig 3B##-##FIG##2##C##). Lastly, users can search the tertiary structure of a query RNA (in PDB format) through the FURNA database using US-align (See <xref rid=\"S10\" ref-type=\"sec\">Materials and Methods</xref>).</p>", "<title>DOWNLOAD.</title>", "<p id=\"P13\">All data from FURNA can be downloaded in bulk through the \"DOWNLOAD\" interface. Functional annotations for each RNA chain and each ligand-RNA interaction are available in tab-separated tables. The FASTA sequences of RNAs, plus those of RNA-binding proteins and RNA-binding DNAs, are also provided. The coordinates of the RNAs and all non-water ligands are supplied in PDB format files. Furthermore, the link to the source codes for database curation and website display is also located on this page.</p>", "<title>Case study on TPP riboswitches</title>", "<p id=\"P14\">To illustrate FURNA's utility in RNA function annotation, we conducted a case study involving the TPP (thiamin pyrophosphate) binding riboswitches, also known as the THI element or Thi-box riboswitch. This well-known family of riboswitches binds to thiamine pyrophosphate (TPP) to regulate the expression of its downstream gene [##REF##23332744##34##, ##REF##20009507##35##]. In <italic toggle=\"yes\">Escherichia coli</italic>, one such riboswitch is located upstream of the Hydroxyethylthiazole kinase (<italic toggle=\"yes\">thiM</italic>) coding sequence [##REF##16728979##36##, ##REF##16962976##37##] (##FIG##1##Fig. 2B##, Table S2). Unsurprisingly, both BLAST (##FIG##2##Fig. 3A##) and Infernal (##FIG##2##Fig. 3B##) searches of the <italic toggle=\"yes\">E. coli</italic> TPP riboswitch through FURNA return hits for many TPP riboswitches, including those from <italic toggle=\"yes\">E. coli</italic>. Similar results can be obtained by searching the region upstream of the <italic toggle=\"yes\">thiM</italic> gene of <italic toggle=\"yes\">Siccibacter turicensis</italic>, which also belongs to the Enterobacteriaceae family (Table S2).</p>", "<p id=\"P15\">Based on gene function and the general prevalence of Thi-box riboswitches, we suspected the presence of riboswitches at several locations in <italic toggle=\"yes\">Bacillus subtilis</italic>, e.g., one situated upstream of the coding sequence of the HMP/thiamine-binding protein (<italic toggle=\"yes\">ykoF</italic>) and the other situated upstream of the aminopyrimidine aminohydrolase (tenA, Table S2). Indeed, the <italic toggle=\"yes\">tenA</italic> riboswitch has been previously reported [##REF##12464185##38##], whereas a riboswitch upstream of <italic toggle=\"yes\">ykoF</italic> has not, to our knowledge, been previously reported in the literature, although its presence is indicated in the RNAcentral database (<ext-link xlink:href=\"https://rnacentral.org/rna/URS000005CA97\" ext-link-type=\"uri\">https://rnacentral.org/rna/URS000005CA97</ext-link>). When using FURNA to perform a BLAST sequence search of the putative <italic toggle=\"yes\">B. subtilis</italic> TPP riboswitches, no hits are returned, not to the <italic toggle=\"yes\">E. coli thiM</italic> riboswitch. This outcome is not unexpected considering <italic toggle=\"yes\">E. coli</italic> and <italic toggle=\"yes\">B. subtilis</italic> are gram-negative and gram-positive bacteria, respectively, and have evolved separately for billions of years. In contrast, a sensitive Infernal search using either of the potential <italic toggle=\"yes\">B. subtilis</italic> TPP riboswitches does yield hits to other TPP riboswitches, including the <italic toggle=\"yes\">E. coli thiM</italic> riboswitch (##FIG##2##Fig. 3C##-##FIG##2##D##). These findings highlights FURNA's capability for function annotations of low-homology RNAs using its sensitive sequence search option, providing a unified interface for obtaining functional information on a new RNA of interest.</p>" ]	[ "<title>Discussion and Conclusions</title>", "<p id=\"P16\">We introduce FURNA, the first comprehensive structure database for ligand-RNA interactions and RNA function annotations. Compared to existing RNA structure and function databases, FURNA stands out in several ways. Firstly, it is the only database to utilize standard function vocabularies (GO terms and EC numbers) for the annotation of RNA tertiary structures. Secondly, it outlines ligand-RNA interactions based on biological assembly, which enhances the investigational context of interactions within the complete RNA-containing complex. Thirdly, FURNA offers user-friendly database search capabilities at varying levels of sensitivity, ensuring its relevance in annotating even remote RNA homologs. Fourthly, its data curation code is modular and fully open source, thereby simplifying regular data updates and future development. These unique aspects of FURNA position it as a valuable resource for the biological community, aiding in summarizing known RNA biological functions, creating functional hypotheses for poorly characterized RNAs, and developing new algorithms for ligand-RNA docking, virtual screening, and structure-based RNA function annotation. Nonetheless, FURNA does present a challenge in its lack of a clear definition of the biological relevance of ligand-RNA interactions, an issue we plan to address in our future work.</p>" ]	[ "<title>Discussion and Conclusions</title>", "<p id=\"P16\">We introduce FURNA, the first comprehensive structure database for ligand-RNA interactions and RNA function annotations. Compared to existing RNA structure and function databases, FURNA stands out in several ways. Firstly, it is the only database to utilize standard function vocabularies (GO terms and EC numbers) for the annotation of RNA tertiary structures. Secondly, it outlines ligand-RNA interactions based on biological assembly, which enhances the investigational context of interactions within the complete RNA-containing complex. Thirdly, FURNA offers user-friendly database search capabilities at varying levels of sensitivity, ensuring its relevance in annotating even remote RNA homologs. Fourthly, its data curation code is modular and fully open source, thereby simplifying regular data updates and future development. These unique aspects of FURNA position it as a valuable resource for the biological community, aiding in summarizing known RNA biological functions, creating functional hypotheses for poorly characterized RNAs, and developing new algorithms for ligand-RNA docking, virtual screening, and structure-based RNA function annotation. Nonetheless, FURNA does present a challenge in its lack of a clear definition of the biological relevance of ligand-RNA interactions, an issue we plan to address in our future work.</p>" ]	[ "<p id=\"P1\">Author contributions</p>", "<p id=\"P2\">C.Z. conceived the project and developed the method. C.Z. and P.L.F designed the experiment, performed the data analysis, and wrote the manuscript.</p>", "<p id=\"P3\">Despite the increasing number of 3D RNA structures in the Protein Data Bank, the majority of experimental RNA structures lack thorough functional annotations. As the significance of the functional roles played by non-coding RNAs becomes increasingly apparent, comprehensive annotation of RNA function is becoming a pressing concern. In response to this need, we have developed FURNA (<underline>Fu</underline>nctions of <underline>RNA</underline>s), the first database for experimental RNA structures that aims to provide a comprehensive repository of high-quality functional annotations. These include Gene Ontology terms, Enzyme Commission numbers, ligand binding sites, RNA families, protein binding motifs, and cross-references to related databases. FURNA is available at <ext-link xlink:href=\"https://seq2fun.dcmb.med.umich.edu/furna/\" ext-link-type=\"uri\">https://seq2fun.dcmb.med.umich.edu/furna/</ext-link> to enable quick discovery of RNA functions from their structures and sequences.</p>" ]	[]	[ "<title>Acknowledgements</title>", "<p id=\"P26\">We thank Dr Xiaoqiong Wei for insightful discussions. This work was directly supported by NIH R01 AI134678 (to PLF). This work also used the Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS) program, which is supported by National Science Foundation (Grant nos. 2138259, 2138286, 2138307, 2137603, and 2138296).</p>" ]	[ "<fig position=\"float\" id=\"F1\"><label>Fig 1.</label><caption><title>Overall statistics for RNAs and ligand-RNA interactions in FURNA.</title><p id=\"P27\">(A) Pie chart for the breakdown of ligand types in ligand-RNA interactions. (B) Venn graph showing the numbers of RNAs with GO terms in the MF, BP and/or CC aspects. (C) Top GO terms in RNAs and proteins collected by FURNA.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Fig 2.</label><caption><title>Browsing function annotations in FURNA.</title><p id=\"P28\">(A) Summary table. (B) Individual web page for small molecule-RNA interaction between thiamine diphosphate and TPP riboswitch (PDB 2gdi chain X, <ext-link xlink:href=\"https://seq2fun.dcmb.med.umich.edu/furna/pdb.cgi?pdbid=2gdi&chain=X&lig3=TPP&ligCha=X&ligIdx=1\" ext-link-type=\"uri\">https://seq2fun.dcmb.med.umich.edu/furna/pdb.cgi?pdbid=2gdi&chain=X&lig3=TPP&ligCha=X&ligIdx=1</ext-link>).</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Fig 3.</label><caption><title>FURNA database sequence search results for TPP riboswitches in different bacteria.</title><p id=\"P29\">(A-B) Top 10 BLAST (A) and Infernal (B) search hits for the <italic toggle=\"yes\">E. coli</italic> thiM riboswitch. (C-D) Top 10 Infernal search results for ykoF riboswitch (C) and tenA riboswitch (D) from <italic toggle=\"yes\">B. subtilis</italic>. BLAST search result is not shown for the <italic toggle=\"yes\">B. subtilis</italic> riboswitches because there are no BLAST hits.</p></caption></fig>" ]	[]	[]	[]	[]	[]	[]	[]	[]	[ "<graphic xlink:href=\"nihpp-2023.12.19.572314v1-f0001\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-2023.12.19.572314v1-f0002\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-2023.12.19.572314v1-f0003\" position=\"float\"/>" ]	[]	[{"label": ["3."], "surname": ["Berman", "Lawson", "Schneider"], "given-names": ["HM", "CL", "B"], "article-title": ["Developing Community Resources for Nucleic Acid Structures."], "source": ["Life (Basel)."], "year": ["2022"], "volume": ["12"], "issue": ["4"], "comment": ["Epub 20220406."], "pub-id": ["10.3390/life12040540"]}, {"label": ["7."], "surname": ["Fernandes", "Acuna", "Aoki", "Floeter-Winter", "Muxel"], "given-names": ["JCR", "SM", "JI", "LM", "SM"], "article-title": ["Long Non-Coding RNAs in the Regulation of Gene Expression: Physiology and Disease."], "source": ["Noncoding RNA."], "year": ["2019"], "volume": ["5"], "issue": ["1"], "comment": ["Epub 20190217."], "pub-id": ["10.3390/ncrna5010017"]}, {"label": ["19."], "surname": ["Zhang", "Zhang", "Freddolino", "Zhang"], "given-names": ["C", "X", "PL", "Y"], "article-title": ["BioLiP2: an updated structure database for biologically relevant ligand-protein interactions."], "source": ["Nucleic Acids Res."], "year": ["2023"], "comment": ["Epub 20230731."], "pub-id": ["10.1093/nar/gkad630"]}, {"label": ["31."], "surname": ["Giudice", "Sanchez-Cabo", "Torroja", "Lara-Pezzi"], "given-names": ["G", "F", "C", "E"], "article-title": ["ATtRACT-a database of RNA-binding proteins and associated motifs."], "source": ["Database (Oxford)."], "year": ["2016"], "volume": ["2016"], "comment": ["Epub 2016/04/09."], "pub-id": ["10.1093/database/baw035"]}, {"label": ["32."], "surname": ["Hanson", "Prilusky", "Renjian", "Nakane", "Sussman"], "given-names": ["RM", "J", "Z", "T", "JL"], "article-title": ["JSmol and the Next-Generation Web-Based Representation of 3D Molecular Structure as Applied to Proteopedia."], "source": ["Isr J Chem."], "year": ["2013"], "volume": ["53"], "issue": ["3\u20134"], "fpage": ["207"], "lpage": ["16"], "pub-id": ["10.1002/ijch.201300024"]}, {"label": ["33."], "surname": ["Ellson", "Gansner", "Koutsofios", "North", "Woodhull"], "given-names": ["J", "ER", "E", "SC", "G"], "article-title": ["Graphviz and dynagraph - Static and dynamic graph drawing tools."], "source": ["Math Vis."], "year": ["2004"], "fpage": ["127"], "lpage": ["48"]}]	{ "acronym": [], "definition": [] }	48	CC BY	no	2024-01-13 23:49:38	bioRxiv. 2023 Dec 19;:2023.12.19.572314	oa_package/62/53/PMC10769261.tar.gz
PMC10769273	38187747	[]	[]	[]	[]	[]	[ "<p id=\"P1\">The majority of bacteriophage diversity remains uncharacterised, and new intriguing mechanisms of their biology are being continually described. Members of some phage lineages, such as the <italic toggle=\"yes\">Crassvirales</italic>, repurpose stop codons to encode an amino acid by using alternate genetic codes. Here, we investigated the prevalence of stop codon reassignment in phage genomes and subsequent impacts on functional annotation. We predicted 76 genomes within INPHARED and 712 vOTUs from the Unified Human Gut Virome catalogue (UHGV) that repurpose a stop codon to encode an amino acid. We re-annotated these sequences with modified versions of Pharokka and Prokka, called Pharokka-gv and Prokka-gv, to automatically predict stop codon reassignment prior to annotation. Both tools significantly improved the quality of annotations, with Pharokka-gv performing best. For sequences predicted to repurpose TAG to glutamine (translation table 15), Pharokka-gv increased the median gene length (median of per genome medians) from 287 to 481 bp for UHGV sequences (67.8% increase) and from 318 to 550 bp for INPHARED sequences (72.9% increase). The re-annotation increased mean coding density from 66.8% to 90.0%, and from 69.0% to 89.8% for UHGV and INPHARED sequences. Furthermore, the proportion of genes that could be assigned functional annotation increased, including an increase in the number of major capsid proteins that could be identified. We propose that automatic prediction of stop codon reassignment before annotation is beneficial to downstream viral genomic and metagenomic analyses.</p>" ]	[ "<p id=\"P2\">Bacteriophages, hereafter phages, are increasingly recognised as a vital component of microbial communities in all environments where they have been studied in detail. Phages are known to drive bacterial evolution and community composition through predator-prey dynamics and their potential as agents of horizontal gene transfer. The use of viral metagenomics, or viromics, has massively expanded our understanding of global viral diversity and shed light on the ecological roles that phages play.</p>", "<p id=\"P3\">Much of the study into viral communities has been conducted on the human gut. Here, viromics has uncovered ecologically important viruses that are difficult to bring into culture using standard laboratory techniques<sup>##UREF##0##1##</sup>, shown potential roles of viruses in disease states<sup>##REF##31757768##2##</sup>, and allowed for the recovery of enormous phage genomes larger than any brought into culture<sup>##UREF##1##3##</sup>. As the majority of phage diversity remains uncharacterised, new and enigmatic diversification mechanisms are being described continually, including the potential use of alternative translation tables.</p>", "<p id=\"P4\">Lineage-specific stop codon reassignment has been described previously in bacteriophages<sup>##REF##24855270##4##,##REF##33594055##5##</sup>, whereby a stop codon is repurposed to encode an amino acid. Notably, annotations of Lak “megaphages” assembled from metagenomes were observed to exhibit unusually low coding density (~70%) when genes are predicted using the standard bacterial, archaeal and plant plastid genetic code (translation table 11)<sup>##UREF##1##3##</sup>, much lower than the value observed for most cultured phages of ~90%<sup>##REF##36159887##6##</sup>. The Lak megaphages were predicted to repurpose the TAG stop codon into an as-of-yet unknown amino acid<sup>##UREF##1##3##</sup>. More recently, uncultured members of <italic toggle=\"yes\">Crassvirales</italic> have been predicted to repurpose TAG to glutamine (translation table 15), and TGA to tryptophan (translation table 4)<sup>##REF##33594055##5##</sup>, and since then the use of translation table 15 has been experimentally validated in two phages belonging to <italic toggle=\"yes\">Crassvirales</italic><sup>##UREF##2##7##</sup>. As this feature may be widespread in human gut viruses, we trained a fork of Prodigal<sup>##REF##20043860##8##</sup>, named prodigal-gv, to predict stop codon reassignment in phages<sup>##UREF##3##9##</sup> and implemented in the pyrodigal-gv library to provide efficient Cython bindings to Prodigal-gv with pyrodigal<sup>##UREF##4##10##</sup>. Additionally, the virus discovery tool geNomad incorporates pyrodigal-gv to predict stop codon reassignment for viral sequences identified in metagenomes and viromes<sup>##UREF##3##9##</sup>. However, the detection of translation table 15 still has limited support in many tools, and the impacts of stop codon reassignment are rarely considered in viral genomics and metagenomics.</p>", "<p id=\"P5\">To assess the extent of stop codon reassignment in studied phage genomes and the impacts on functional annotation, we extracted phage genomes from INPHARED<sup>##REF##36159887##6##</sup> and predicted those using alternative stop codons. We also added high-quality and complete vOTUs from the Unified Human Gut Virome Catalog (UHGV; <ext-link xlink:href=\"https://github.com/snayfach/UHGV\" ext-link-type=\"uri\">https://github.com/snayfach/UHGV</ext-link>) predicted to use alternative codons. The viral genomes were re-annotated using modified versions of the commonly used annotation pipelines Prokka<sup>##REF##24642063##11##</sup>, and Pharokka<sup>##UREF##5##12##</sup> implementing prodigal-gv/pyrodigal-gv for gene prediction (##SUPPL##1##Supplementary Methods##). Hereafter, the modified versions are referred to Prokka-gv and Pharokka-gv.</p>", "<p id=\"P6\">From INPHARED, 49 genomes (0.24%) were predicted to use translation table 15, and 27 (0.13%) were predicted to use translation table 4. From the UHGV, 666 vOTUs (1.2%) were predicted to use translation table 15 and 46 (0.08%) were predicted to use translation table 4. These genomes and vOTUs were not constrained to one particular clade of viruses, being predicted to occur on both dsDNA viruses of the realm <italic toggle=\"yes\">Duplodnaviria</italic> and ssDNA viruses of the realm <italic toggle=\"yes\">Monodnaviria</italic>, suggesting it is a phenomenon that has arisen on at least two occasions (##SUPPL##0##Supplementary Table 1##). The lower frequency of these genomes in cultured isolates (INPHARED) versus human viromes (UHGV) may be due to culturing and sequencing biases, perhaps including modifications to DNA that are known to be recalcitrant to sequencing.</p>", "<p id=\"P7\">Although the mechanism for stop codon reassignment in phages is not fully understood, suppressor tRNAs are suggested to play a role<sup>##REF##24855270##4##,##UREF##6##13##</sup>. Consistent with previous findings, we found 375/715 (52.4%) phages predicted to use translation table 15 encoded at least one suppressor tRNA corresponding to the <italic toggle=\"yes\">amber</italic> stop codon (Sup-CTA tRNA), and 11/73 (15.1%) of those predicted to use translation table 4 encoded at least one suppressor tRNA corresponding to the opal stop codon (Sup-TCA tRNA)<sup>##REF##24855270##4##,##UREF##6##13##,##REF##31020551##14##</sup>. Although fewer of those predicted to use translation table 4 encoded the relevant suppressor tRNA, 22/27 (81%) of the INPHARED phages predicted to use translation table 4 were viruses of <italic toggle=\"yes\">Mycoplasma</italic> or <italic toggle=\"yes\">Spiroplasma</italic>. As <italic toggle=\"yes\">Mycoplasma</italic> and <italic toggle=\"yes\">Sprioplasma</italic> are known to use translation table 4, many of the viruses predicted to use translation table 4 may be simply using the same translation table as their host.</p>", "<p id=\"P8\">Prediction of stop codon reassignment led to improved annotations for both Prokka and Pharokka, although the extent of this varied with the two datasets, translation tables, and annotation pipelines tested. As Pharokka-gv outperformed Prokka-gv on all metrics tested, only Pharokka-gv is discussed further, and the equivalent results for Prokka-gv can be found in ##SUPPL##1##Supplementary Results##.</p>", "<p id=\"P9\">The largest differences were observed for sequences predicted to use translation table 15, for which Pharokka-gv increased the median gene length (median of per genome medians) from 287 to 481 bp for UHGV sequences (67.8% increase) and from 318 to 550 bp for INPHARED sequences (72.9% increase; ##FIG##0##Figure 1A##). This was also reflected in an increase of median coding capacity from 66.8% to 90.0% for UHGV, and 69.0% to 89.8% for INPHARED (##FIG##0##Figure 1B##). Overall, these improved gene calls led to an increased gene length, and a reduction in the number of predicted genes per kb and the number of genes that could not be assigned functional annotations (Supplementary Figure 2; ##SUPPL##0##Supplementary Table 2##). As it is commonly used as a phylogenetic marker for bacteriophages, we investigated how commonly the major capsid protein (MCP) could be identified with and without predicted stop codon reassignment<sup>##REF##36780432##15##</sup>. For those viruses we predicted to use translation table 15, annotation using the default translation table 11 only resulted in the MCP being identified in 407/715 (56.9%) of the genomes. In contrast, using translation table 15 with Pharokka-gv, we could identify the MCP in 475/715 (66.4%).</p>", "<p id=\"P10\">When investigating the sequences for which translation table 4 was predicted to be optimal, a substantial increase was also observed for UHGV sequences, with Pharokka-gv increasing median gene length (median of per genome medians) from 350 to 518 bp (a 48.0% increase in length; ##FIG##0##Figure 1A##), resulting in an increase of coding capacity from 78.0% to 90.4% (##FIG##0##Figure 1B##). However, the same was not observed for the 27 INPHARED genomes predicted to use translation table 4. Reannotation resulted in a modest increase in median gene length (median of per genome medians) from 573 to 588 bp (a 2.6% increase in length; ##FIG##0##Figure 1A##). Median coding capacity was not increased, with both Pharokka and Pharokka-gv obtaining 89.1% (##FIG##0##Figure 1B##). As the median gene length and coding capacity for INPHARED sequences predicted to use translation table 4 are in line with expected values, their prediction may be a false positive. Reassuringly, the prediction of translation table 4 has not hindered the quality of annotations where it may be a false positive.</p>", "<p id=\"P11\">The analysis of viral (meta)genomes relies on accurate protein predictions, with predicted ORFs being used in common analyses, including (pro)phage prediction, functional annotation, and phylogenetic analyses. The clear differences in protein predictions with/without predicted stop codon reassignment will likely have downstream impacts upon these analyses. However, this phenomenon is not yet widely considered in viral (meta)genomics. We have demonstrated the impacts of stop codon reassignment in the functional annotation of phages, and provide tools for the automatic prediction and annotation of viral genomes that repurpose stop codons. Our analysis highlights the need for accurate viral ORF prediction, and further experimental validation to elucidate the mechanisms of stop codon reassignment.</p>", "<title>Supplementary Material</title>" ]	[ "<title>Data Availability</title>", "<p id=\"P12\">The genomes used in this analysis are from two publicly available datasets; INPHARED (<ext-link xlink:href=\"https://github.com/RyanCook94/inphared\" ext-link-type=\"uri\">https://github.com/RyanCook94/inphared</ext-link>) and the Unified Human Gut Virome (UHGV; <ext-link xlink:href=\"https://github.com/snayfach/UHGV\" ext-link-type=\"uri\">https://github.com/snayfach/UHGV</ext-link>). The details of included sequences are shown in ##SUPPL##0##Supplementary Table 1##. The code for Prokka-gv is available on GitHub (<ext-link xlink:href=\"https://github.com/telatin/metaprokka\" ext-link-type=\"uri\">https://github.com/telatin/metaprokka</ext-link>). The code for Pharokka is available on GitHub (<ext-link xlink:href=\"https://github.com/gbouras13/pharokka\" ext-link-type=\"uri\">https://github.com/gbouras13/pharokka</ext-link>). The code for Prodigal-gv is available on GitHub (<ext-link xlink:href=\"https://github.com/apcamargo/prodigal-gv\" ext-link-type=\"uri\">https://github.com/apcamargo/prodigal-gv</ext-link>). The code for Pyrodigal-gv is available on GitHub (<ext-link xlink:href=\"https://github.com/althonos/pyrodigal-gv\" ext-link-type=\"uri\">https://github.com/althonos/pyrodigal-gv</ext-link>).</p>" ]	[ "<fig position=\"float\" id=\"F1\"><label>Figure 1.</label><caption><p id=\"P15\">Re-annotating with predicted stop codon reassignment increases the quality of annotations. Comparison of (<bold>A</bold>) median predicted gene length (bp) and (<bold>B</bold>) coding capacity (%) for INPHARED genomes and UHGV vOTUs annotated with Pharokka (translation table 11 only) and Pharokka-gv (prediction of stop codon reassignment), grouped by dataset and predicted stop codon reassignment. Asterisk indicates significance at P ≤ 10e-10 with P determined by a simple T test and adjusted with the Benjamini-Hochberg procedure.</p></caption></fig>" ]	[]	[]	[]	[]	[]	[]	[ "<supplementary-material id=\"SD1\" position=\"float\" content-type=\"local-data\"><label>Supplement 1</label></supplementary-material>", "<supplementary-material id=\"SD2\" position=\"float\" content-type=\"local-data\"><label>Supplement 2</label></supplementary-material>" ]	[ "<fn-group><fn fn-type=\"COI-statement\" id=\"FN1\"><p id=\"P13\">Competing Interests</p><p id=\"P14\">The authors have nothing to declare.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"nihpp-2023.12.19.572299v1-f0001\" position=\"float\"/>" ]	[ "<media xlink:href=\"media-1.xlsx\" id=\"d64e319\" position=\"anchor\"/>", "<media xlink:href=\"NIHPP2023.12.19.572299v1-supplement-2.pdf\" id=\"d64e322\" position=\"anchor\"/>" ]	[{"label": ["1"], "surname": ["Dutilh"], "given-names": ["B. E."], "source": ["Nature Communications"], "volume": ["5"], "fpage": ["4498"], "year": ["2014"]}, {"label": ["3"], "surname": ["Devoto"], "given-names": ["A. E."], "source": ["Nature Microbiology"], "year": ["2019"]}, {"label": ["7"], "surname": ["Peters"], "given-names": ["S. L."], "article-title": ["Experimental validation that human microbiome phages use alternative genetic coding."], "source": ["Nature Communications"], "volume": ["13"], "fpage": ["5710"], "year": ["2022"], "pub-id": ["10.1038/s41467-022-32979-6"]}, {"label": ["9"], "surname": ["Camargo"], "given-names": ["A. P."], "article-title": ["Identification of mobile genetic elements with geNomad."], "source": ["Nat Biotechnol"], "year": ["2023"], "pub-id": ["10.1038/s41587-023-01953-y"]}, {"label": ["10"], "surname": ["Larralde"], "given-names": ["M."], "article-title": ["Pyrodigal: Python bindings and interface to Prodigal, an efficient method for gene prediction in prokaryotes."], "source": ["Journal of Open Source Software"], "volume": ["7"], "fpage": ["4296"], "year": ["2022"], "pub-id": ["10.21105/joss.04296"]}, {"label": ["12"], "surname": ["Bouras"], "given-names": ["G."], "article-title": ["Pharokka: a fast scalable bacteriophage annotation tool."], "source": ["Bioinformatics"], "volume": ["39"], "year": ["2022"], "pub-id": ["10.1093/bioinformatics/btac776"]}, {"label": ["13"], "surname": ["Pfennig", "Lomsadze", "Borodovsky"], "given-names": ["A.", "A.", "M."], "article-title": ["Annotation of Phage Genomes with Multiple Genetic Codes."], "source": ["bioRxiv"], "comment": ["2022.2006.2029.495998"], "year": ["2022"], "pub-id": ["10.1101/2022.06.29.495998"]}, {"label": ["17"], "surname": ["Terzian"], "given-names": ["P."], "source": ["NAR Genomics and Bioinformatics"], "volume": ["3"], "publisher-name": ["Oxford Academic"], "year": ["2021"]}, {"label": ["18"], "surname": ["Team"], "given-names": ["R. C. R"], "source": ["A language and environment for statistical computing."], "publisher-name": ["R Foundation for Statistical Computing"], "year": ["2018"]}, {"label": ["19"], "surname": ["Benjamini", "Hochberg"], "given-names": ["Y.", "Y."], "source": ["Journal of the Royal Statistical Society: Series B (Methodological)"], "volume": ["57"], "fpage": ["289"], "lpage": ["300"], "publisher-name": ["John Wiley & Sons, Ltd"], "year": ["1995"]}, {"label": ["20"], "surname": ["Wickham"], "given-names": ["H."], "source": ["Ggplot2: Elegant graphics for data analysis."], "edition": ["2"], "publisher-name": ["Springer International Publishing"], "year": ["2016"]}]	{ "acronym": [], "definition": [] }	20	CC BY	no	2024-01-13 23:49:38	bioRxiv. 2023 Dec 19;:2023.12.19.572299	oa_package/dd/95/PMC10769273.tar.gz
PMC10769279	38187600	[ "<title>Introduction</title>", "<p id=\"P4\">Viruses are metabolically inert and must rely on host cell metabolic events to generate the necessary building blocks to multiply (##REF##33613518##1##). Historically, host metabolism has been thought to play only host-specific roles in cellular homeostasis, the immune response, and autophagy (##REF##28514672##2##–##REF##26177004##3##). However, recent studies have shown that pathogens such as parasites, bacteria, and viruses influence host cell metabolism (##REF##26439298##4##–##UREF##0##6##) to create a more favorable environment to ensure their own optimal replication (##UREF##1##15##). Many investigations within the past decade have examined how viruses alter the host cellular metabolic profile and identified some of the metabolic pathways important during virus infection. These studies have shown that a common consequence of viral infection is induction of high glucose metabolism, which can lead to aerobic glycolysis, or the Warburg effect (##REF##31319842##7##). In addition, other pathways such as glutaminolysis, the pentose phosphate pathway (PPP), fatty acid synthesis, and tricarboxylic acid cycle (TCA) activity may also be altered, thus highlighting that central carbon metabolism is significantly perturbed during many viral infections (##REF##31319842##7##). Viruses often hijack these pathways to divert the production of nucleotides, lipids, amino acids, and other metabolites away from host processes toward virus particle construction. Virus-induced alterations to host metabolism can be shared among different viruses but are usually context dependent and variable between specific virus families or infected host cell types. For example, glucose deprivation significantly decreases dengue virus replication, while lack of glutamine does not (##REF##25505078##8##). In contrast, glutamine deprivation significantly reduces vaccinia virus replication, while glucose deprivation has no effect (##REF##24501408##9##). Thus, dengue virus and vaccinia virus show opposite dependencies on host glycolysis and glutaminolysis during infection. Other examples of virus-induced changes in host metabolism come from adenovirus, human cytomegalovirus, chikungunya virus, Zika virus, SARS-CoV-2, rhinovirus, lytic gammaherpesvirus, both latent and lytic Kaposi sarcoma-associated herpes virus and hepatitis C virus (##REF##30827860##10##–##REF##19854061##14##, ##REF##29987044##33##, ##REF##28275189##36##, ##REF##37191529##38##–##REF##13593191##39##, ##REF##33767183##68##). While multiple studies have reported that metabolic pathways are altered during virus infection, the mechanistic details of how viruses achieve these changes remain elusive. Increased investigation into how viruses reprogram and usurp host metabolic pathways with an emphasis on mechanistic insights may reveal innovative therapeutic targets and provide a deeper understanding of specific viral replicative cycles.</p>", "<p id=\"P5\">Noroviruses (NoVs) are positive-sense single-stranded RNA viruses and the leading cause of acute non-bacterial gastroenteritis worldwide (##UREF##2##16##). Globally, human NoV (HNoV) infections are extremely common, with estimated cases reaching ~685 million per year. Annually, HNoV infections result in ~200,000 fatalities, mostly in infants but also in immunocompromised individuals and in older adults (##REF##24981041##17##). Additionally, HNoV infections result in serious annual economic burdens, with global economic costs surpassing US$60 billion (##REF##27115736##18##). In the United States alone, HNoV infections cause ~21 million cases of gastroenteritis and are the leading cause of death in older adults with viral gastroenteritis (##REF##27211790##19##–##REF##21192848##20##). Although HNoV infections are self-limiting in most individuals, the intense vomiting, diarrhea, and abdominal pain associated with this infection can be debilitating. However, despite the devastating public health and economic burdens caused by HNoV, no approved vaccines or antivirals against this virus exist (##REF##30584800##21##), and development of anti-NoV therapeutics has been hampered by the lack of a cell culture model for HNoV. Although human intestinal enteroids (HIEs) and human B cells support varying degrees of infection, a cell culture–derived HNoV stock is still not available (##REF##27562956##22##–##REF##35404121##24##). To overcome the limitations inherent to HNoV research, murine NoV (MNV) is used as a model system to study general NoV biology because MNV readily replicates in cell culture, is genetically similar to HNoV, and has a genetically tractable small animal model and infectious clones available (##REF##16698991##25##). MNV strains, although genetically closely related, fall into two phenotypic groups. The acute strain, MNV-1, is cleared from infected mice within one week, while persistent strains, including MNV-CR6 (CR6) and MNV-CR3 (CR3), are shed for months (##REF##17652401##26##). The strains also differ in their <italic toggle=\"yes\">in vivo</italic> tropism, in which CR6 infects tuft cells while MNV-1 infects immune cells (macrophages, dendritic cells, and lymphocytes) (##REF##37703370##27##,##UREF##3##28##).</p>", "<p id=\"P6\">We previously performed a metabolomic screen of MNV-1–infected macrophages, which revealed that metabolites in many pathways were significantly upregulated, including those integral to central carbon metabolism (##REF##30862747##29##). Our screen identified glycolysis, nucleotide biosynthesis via the PPP, and oxidative phosphorylation (OXPHOS) as being required for optimal MNV-1 replication in murine macrophages based on experiments using common metabolic inhibitors (##REF##30862747##29##). We further determined that glycolysis is important for the replication step in the MNV lifecycle since treatment with the hexokinase inhibitor 2-deoxyglucose (2DG) led to a decrease in viral protein and RNA synthesis (##REF##30862747##29##). However, the requirement for glycolysis was independent of the host antiviral type I interferon response, and the underlying mechanisms behind NoV-induced upregulation of host metabolism and the role that host metabolic pathways plays in persistent MNV replication are not known. Thus, the goals of this current study were to further define the role of host metabolism in NoV replication, explore the role of host metabolism for persistent MNV strains, and begin to uncover the underlying mechanisms of NoV-induced metabolic alterations. Untangling the process of virus-induced metabolic alterations may enable development of more efficient HNoV cultivation systems and identify innovative metabolic therapeutic targets aimed at reducing persistent NoV infections.</p>", "<p id=\"P7\">With these goals in mind, we investigated the dependence of persistent strains CR3 and CR6 on host cell glycolysis, the PPP, and OXPHOS. While MNV-1, CR3, and CR6 all relied on glycolysis and nucleotide biosynthesis, OXHPOS was not required for replication of persistent strains. We also performed the first metabolic flux analysis of MNV-1–infected macrophages, which revealed a concurrent increase in glycolysis and glutaminolysis. Reducing host glutaminolysis via pharmacological inhibition with the inhibitor CB839 and via glutamine deprivation showed that both acute and persistent MNV strains rely on glutamine metabolism, in particular for viral genome replication, which has repercussions for later steps in the viral life cycle. Early mechanistic investigations revealed that the observed increase in glutaminolysis during MNV infection is driven in large part by the viral non-structural protein NS1/2 that caused increased glutaminase (GLS) activity, the rate limiting enzyme within the glutamine catabolic pathway (##REF##24140288##30##). Overall, our findings highlight the importance of pathways in central carbon metabolism in NoV infection, albeit with strain-specific differences, and show that glutaminolysis is universally required for optimal MNV replication. Our finding that glutaminolysis is modulated by the viral protein NS1/2 provides a foundation for detailed mechanistic studies in the future, which may reveal novel chokepoints for therapeutic intervention.</p>" ]	[ "<title>Methods</title>", "<title>Compounds and reagents:</title>", "<p id=\"P32\">2-Deoxyglucose (2DG) (Sigma #D8375) was solubilized fresh for each experiment in cell culture medium to 100 mM and added to the culture medium at a final concentration of 10 mM. CB839 (Cayman Chemical #22038) was solubilized in DMSO at 10 mM and used at final concentrations of 5, 10, or 15 μM. 6-Aminonicotinamide (6AN) (Cayman #10009315) was solubilized in DMSO at 500 mM and used at 500 or 750 μM. Oligomycin A (Cayman #11342) was solubilized in DMSO at 5 mM and used at 1 μM. Glutamine-free media was prepared fresh for each experiment using DMEM-10 medium (Gibco DMEM medium #11995–044 with 4.5 g/L D-Glucose, 10% dialyzed fetal bovine serum (Thermo Fischer Scientific #A3382001), and 1% HEPES buffer (1M, Gibco #15630–080). MNV-1 NS1/2, NS3, and NS5 plasmids were a kind gift from Dr. Jason Mackenzie (University of Melbourne, AUS) and previously described (##REF##20674956##83##). Flag-tagged MNV-1 NS4, NS6, and NS7 plasmids were a kind gift from Dr. Ian Goodfellow (University of Cambridge, UK) and previously described (##REF##36943868##84##).</p>", "<title>Cell culture and virus strains:</title>", "<p id=\"P33\">The RAW 264.7 macrophage cell line (referred to herein as RAW cells) (ATCC TIB-71) and CD300lf-expressing Huh-7 cells were maintained in DMEM-10 medium (Gibco DMEM medium #11995–065 with 4.5 g/L D-Glucose and 110 mg/L Sodium Pyruvate, 10% Fetal Bovine Serum [HyClone #SH30396.03], 1% HEPES buffer [1M, Gibco #15630–080], 1% Non-Essential Amino Acids [100X, Gibco #11140–050] and 1% L-Glutamine [200 mM, Gibco #25030–081]) in treated tissue culture flasks at 37°C/5% CO<sub>2</sub>. CD300lf-expressing Huh-7 cells were a gift from Dr. Stefan Taube (University of Lübeck, Germany) and were previously described (##REF##32251490##63##). Primary bone marrow-derived macrophages (BMDM) were differentiated from male Balb/C mouse femur and tibia bone marrow in 20% L929 medium (Gibco DMEM medium, 20% FBS [HyClone #SH30396.03], 30% L9 supernatant, 1% L-Glutamine, 1% Sodium Pyruvate, 0.25 mL β-mercaptoethanol/L and 2% Penicillin/Streptomycin). All experiments using primary cells were performed with 10% L929 working medium (same as 20% L929 medium but with 10% L929 supernatant). The plaque purified MNV-1 clone (2002/USA) MNV-1.CW3 (referred herein as MNV-1) was used at passage 6 in all experiments. CR3 and CR6 were also used at passage 6 in all experiments (##REF##17652401##64##).</p>", "<title>Virus infections and plaque assay:</title>", "<p id=\"P34\">All MNV infections were performed in the RAW 264.7 cell line, Balb/C primary bone marrow-derived macrophages (BMDM), or CD300lf-expressing Huh-7 cells. Cells were grown in 12-well tissue culture plates seeded at 5x10<sup>5</sup> cells/well. At the time of infection, the medium was replaced with 1 mL of media containing MNV-1, CR3, or CR6 at the indicated MOI. Plates were rocked for 1 hour on ice. Then, cells were washed 3X with cold DPBS++ (+Calcium and +Magnesium Chloride—Gibco #14040), fresh medium was added containing metabolic inhibitors at the indicated concentrations, vehicle control, or glutamine-free media. Cells were incubated for indicated times. Cells were then frozen at −80°C and freeze-thawed two times before lysates were analyzed by plaque assay as previously described (##REF##22951568##65##). Vehicle control experiments were performed using DMSO in a v/v match to the volume of metabolic inhibitors. Primary cell infections were done the same as RAW infections except in medium containing 10% L929 supernatant.</p>", "<title>RNA extraction and RT-qPCR:</title>", "<p id=\"P35\">Experiments to quantify MNV genome copies and glutaminase expression were performed on MNV- or mock-infected RAW cells as indicated above. At time of RNA extraction, cells were washed 1X with cold DPBS++ and then 500 μL of Zymo Research TriReagent (#R2050–1) was added. Extraction was performed per manufacturer’s directions using the Zymo Research Direct-zol RNA MiniPrep Plus (#R2072) and then used for One-Step TaqMan Assay. Primers used to measure murine glutaminase transcript and MNV genome levels were previously described (##REF##9170104##66##, ##REF##37478303##78##).</p>", "<title>Protein extraction, SDS-PAGE, and immunoblotting:</title>", "<p id=\"P36\">Experiments were performed as described above in 12-well or 6-well tissue culture plates. At time of harvest, cells were washed 2X with cold DPBS++ and RIPA buffer (Pierce #89900) containing complete EDTA-free protease inhibitor cocktail (Roche #11873580001) was added to wells. Cells were scraped, moved to Eppendorf tubes, and incubated on ice for 15 minutes. Cells were then spun at 4<sup>o</sup>C at 14,000 x g for 15 minutes. Lysates were moved to fresh tubes, and Laemmli buffer with β-mercaptoethanol was added at 3:1 lysate to buffer ratio before freezing the sample until analysis. SDS-PAGE was performed with BioRad 4–20% Mini-Protean TGX gels (BioRad #456–1096) per standard SDS-PAGE procedures (##REF##20512673##79##). Gels were transferred to Immobilon-FL transfer membranes (#IPFL00010, pore size 0.45 μm) using a Semi-Dry transfer at 10V for 60 minutes. Membranes were blocked in PBS+0.05% Tween + 1% low-fat milk for 1 hour at room temp, then primary antibodies were added in the same buffer and membranes were rocked at 4°C overnight. Membranes were washed 3X with 1X PBS, then secondary LI-COR fluorescent antibodies were added for 1 hour at room temp and then visualized on the LI-COR Odyssey Imager. Western blots were quantified by densitometry using ImageJ and normalizing bands to β-actin. Antibodies used: mouse mAb β<bold>–</bold>Actin (clone 8H10D10, Cell Signaling #3700) at 1:10,000 dilution; rabbit mAb β-Actin (clone 13E5, Cell Signaling #8457) at 1:10,000 dilution; anti-rabbit polyclonal glutaminase (Proteintech #12855–1-AP) at 1:1000 dilution; anti-mouse monoclonal FLAG (Sigma #F1804) at 1:3000 dilution. The rabbit polyclonal anti-MNV-1 capsid antibody (used at 1:500 dilution) was described previously (##REF##30862747##29##). The mouse monoclonal anti-NS1/2 and anti-NS5 antibodies (both used at 1:3000 dilution) were a kind gift from Dr. Vernon Ward (University of Otago, New Zealand) and previously described (##UREF##6##85##).</p>", "<title>Cell Viability Assay:</title>", "<p id=\"P37\">Cell viability was tested with the WST-1 Cell Proliferation Reagent (Sigma #5015944001) or Resazurin Cell Viability Assay Kit (Biotium #30025–1). Briefly, RAW cells, primary BMDMS, or CD300lf-expressing Huh-7 cells were plated at 2x10<sup>5</sup> per well of a 24-well plate. After overnight growth at 37°C/5% CO<sub>2</sub>, medium was replaced with DMEM-10 medium containing a specific pharmacological inhibitor. Treated cells were then placed back at 37°C/5% CO<sub>2</sub> for a 24-hour incubation period. The following day, cell viability was calculated according to the manufacturer’s recommendations.</p>", "<p id=\"P38\">To measure the viability of RAW cells in glutamine-free media, cells were plated at 5*10^5 per well in a 6-well plate. After overnight growth at 37°C/5% CO<sub>2</sub>, media was replaced with glutamine-free DMEM-10 medium for 8 hours. After the incubation, cells were scrapped with a cell scrapper and cell viability was measured using trypan blue staining on a Life Technologies Countess 3 automated cell counter assay platform. Cell viability was calculated as the percent of live cells in glutamine-free media treated vs. untreated controls.</p>", "<title>Metabolic Flux Analysis:</title>", "<p id=\"P39\">5x10<sup>5</sup> RAW cells were plated in 6-well plates and infected with MNV-1 or mock-infected as described above. Following the removal of the virus inoculum, fresh medium was added containing uniformly labeled <sup>13</sup>C<sub>5</sub> glucose or glutamine and incubated at 37°C/5% CO<sub>2</sub> for 8 hours. Following the 8-hour incubation, cells were washed 2x DPBS (+Calcium and +Magnesium Chloride – Gibco #14040) and 300 μL of ice-cold methanol was added. Wells were scraped with a cell lifter and the volume was transferred to a fresh Eppendorf tube where 300 μL of water containing 1µg of norvaline internal standard was added to each tube. Next, 600µL of high-performance liquid-chromatography grade chloroform was added to each tube to isolate nonpolar lipid content from the sample matrix. Tubes were then vortexed at 4°C for 30 minutes and centrifuged at 17,000 x g for 15 minutes at 4°C to separate contents into an upper aqueous layer and lower chloroform layer. The upper phase was collected into new tubes which were then dried by vacuum centrifugation in a SpeedVac for 5 hours at room temperature. After drying, samples were stored at −80°C until GC-MS analysis. For polar metabolite analysis, dried samples were derivatized with 30µL of 2% methoxyamine hydrochloride in pyridine at 45°C for 1 hour under constant shaking. Then 30 µL of N-tert-butyldimethylsilyl-N-methyltrifluoroacetamide (MBTSTFA) + 1% tertbutyldimetheylchlorosilane (TBDMCS) was added, and samples were further incubated at 45°C for 30 min. Derivatized samples were then transferred to GC vials with glass inserts and loaded for autosampler injection. GC-MS analysis was performed using an Agilent 7890 GC equipped with a 30m DB-35MS UI capillary column connected to an Agilent 5977B MS. Samples were run with 1 mL/min helium flow with the following heating cycle for the GC oven: 100 °C for 1 minute, ramp of 3.5 °C/min to 255 °C, ramp of 15 °C to 320 °C, then held at 320 °C for 3 min to a total run time of 52.6 min. MS source was held at 230 °C and quadrupole at 150 °C. Data was acquired in scan mode (70–600 m/z). The relative abundance of metabolites was calculated from the integrated signal of all potentially labeled ions for each metabolite fragment. Metabolite levels were normalized to the norvaline internal standard and quantified using 10-point calibration with external standards for 36 polar metabolites. Mass Isotopomer Distributions (MIDs) were corrected for natural isotope abundances and tracer purity using IsoCor.</p>", "<title>Overexpression of Viral Proteins:</title>", "<p id=\"P40\">A total of 2.0 μg of plasmid DNA harboring sequences for individual MNV non-structural proteins or green fluorescent protein (GFP) was added to 100 μL of Opti-MEM media (Thermo Fischer Scientific #11058–02 with L-Glutamine and HEPES). Then, 8 μL of FuGENE HD Transfection reagent (FuGENE #0000553572) was added to the Opti-MEM plasmid mix and centrifuged for 10 s at 8000 x g. Plasmid mix was then incubated for 15 minutes at room temperature. After the incubation, the plasmid mix was added to a separate Eppendorf tube containing 1.6x10<sup>6</sup> CD300lf-expressing Huh-7 cells and incubated for 10 minutes at room temperature. After the incubation, 500 μL of the cell suspension was plated per well in a 6-well plate and incubated at 37°C/5% CO<sub>2</sub> for 24–48 hours. After the incubation period, two of the wells were used to confirm successful expression of the viral protein via western blot analysis as described above. The remaining well was used to analyze glutaminase activity with the commercially available Cohesion Biosciences Microplate Assay Kit as described above.</p>", "<title>Glutaminase Activity Assay:</title>", "<p id=\"P41\">Glutaminase enzymatic activity was assessed with the commercially available Cohesion Biosciences Microplate Assay Kit (#CAK1065). Briefly, RAW or CD300lf-expressing Huh-7 cells were either mock- or MNV-infected as described above. After 8 hours of incubation at 37°C/5% CO<sub>2</sub>, cells were sonicated for 10 seconds 30x and kit contents added per the manufacturer’s instructions. Samples were transferred to a 96-wellplate and absorbance at 620 nm was measured in a Synergy H1 plate reader. Glutaminase activity was calculated following the manufacturer’s instructions.</p>", "<title>Statistical Analysis:</title>", "<p id=\"P42\">For all experiments, data were analyzed in Prism9 using the tests as indicated in figure legends.</p>" ]	[ "<title>Results</title>", "<title>Persistent MNV strains CR6 and CR3 rely on glycolysis and nucleotide biosynthesis, but not OXPHOS, for optimal replication.</title>", "<p id=\"P8\">We previously performed a metabolomics screen of MNV-1–infected macrophages, which identified increased metabolites from glycolysis, PPP, and OXPHOS in infected cells (##REF##30862747##29##). Inhibition of these pathways resulted in significantly lower MNV titers, ranging from an 0.5 to 2-log<sub>10</sub> reduction (##REF##30862747##29##). However, whether the genetically closely related persistent MNV strains CR3 and CR6 also rely on these important metabolic pathways for optimal replication was not known. To investigate whether acute and persistent MNV strains have a common dependence on host cell metabolism, RAW 264.7 (RAW) cells were inoculated with MNV-1, CR3, and CR6 at an MOI of 5 for 1 hour. Medium containing the glycolysis inhibitor 2DG, the PPP inhibitor 6-Aminonicotinamide (6AN), or the OXPHOS inhibitor oligomycin-A was then added after inoculation, and cells were incubated for 8 hours, corresponding to approximately one round of viral replication. Non-toxic concentrations of 2DG and 6AN were previously determined (##REF##30862747##29##), and cell viability assays were performed to ensure the concentration of oligomycin-A used would maintain >80% cell viability (##SUPPL##0##Fig S1A##). Infectious titers were measured after 8 hours via plaque assay. A significant (>2 log<sub>10</sub>) decrease was observed in the number of infectious MNV-1, CR3, and CR6 titers in 2DG-treated cells (##FIG##0##Fig. 1A##). Treatment with 6AN also resulted in significantly decreased MNV-1, CR3, and CR6 titers; however, only a 1 log<sub>10</sub> decrease in infectious particles was observed (##FIG##0##Fig 1B##). Additionally, the 1 versus 2 log<sub>10</sub> decrease in viral titers observed after 6AN and 2DG treatment, respectively, suggested that all three MNV strains depend more on glycolysis than the PPP for optimal reproduction.</p>", "<p id=\"P9\">Because active viral replication requires large amounts of host energy, we also investigated whether CR3 and CR6 require OXPHOS for optimal replication. Surprisingly, we observed that CR3 and CR6 infection did not depend on OXPHOS because viral titers remained similar between oligomycin-A treated and untreated cells; however, acute strain MNV-1 showed an 0.5-log<sub>10</sub> titer decrease. Lack of a significant reduction in viral titers of persistent MNV strains during oligomycin-A treatment suggests that glycolysis-derived ATP is sufficient to meet the energetic requirements for sustaining optimal CR3 and CR6 replication. These data highlight strain-specific dependencies on individual metabolic pathways for efficient MNV virion production.</p>", "<p id=\"P10\">Taken together, these data demonstrate that like MNV-1, the persistent strains CR3 and CR6 require host glycolysis and nucleotide biosynthesis for optimal replication; however, unlike MNV-1, OXPHOS is dispensable for the persistent strains.</p>", "<title>MNV-1 infection upregulates metabolite flux through glycolysis and glutaminolysis.</title>", "<p id=\"P11\">Our previous static metabolomic screen (##REF##30862747##29##) analyzed the intracellular concentrations of metabolites but did not measure metabolite flux or metabolite turnover. To this end, we performed a metabolic flux analysis, which uses uniformly labeled metabolites measured via gas chromatography mass spectrometry (GC-MS) to track incorporation of molecules into various metabolic pathways. Because glucose and glutamine are the two leading carbon sources used by mammalian cells (##REF##29540733##31##), we analyzed their incorporation during MNV-1 infection to determine whether infection mediates an increase in their catabolism (##FIG##1##Fig. 2##). RAW cells were infected for 1 hour with MNV-1 or mock lysate at an MOI of 5. After a 1-hour incubation, the virus inoculum was replaced with medium containing either <sup>13</sup>C<sub>5</sub>-glucose or <sup>13</sup>C<sub>5</sub>-glutamine. Samples were collected and analyzed after an 8-hour incubation. Through analysis of the mass isotopomer distribution (MID), we observed higher glucose metabolism in MNV-1–infected cells than in mock-infected cells as seen by increased incorporation of glucose into lactate, a common glycolytic byproduct, and into citrate, a downstream metabolite within the TCA cycle that can be generated from the final glycolytic product pyruvate through acetyl co-enzyme A (##FIG##1##Fig. 2A##). These findings are consistent with our previous metabolic screen that showed higher concentrations of several glycolytic intermediates such as 2- and 3-phosphoglycerate and fructose-bisphosphate in infected cells (##REF##30862747##29##) and confirmed that MNV-1 induces host glucose metabolism during its replicative cycle. Additionally, we further observed increased glutamine metabolism in MNV-1–infected cells relative to mock-infected cells. Glutamine undergoes a deaminase reaction to produce glutamate followed by another deaminase reaction to produce alpha-ketoglutarate (aKG), an intermediate that can enter the TCA cycle (##FIG##1##Fig. 2B##). In MNV-1–infected cells, higher production of both metabolites was observed, thus showing increased glutamine metabolism (##FIG##1##Fig. 2B##). Given this finding, we revisited our previous metabolomic screen and investigated whether the concentrations of glutamate or aKG were significantly altered during MNV-1 infection. While aKG was not included in the screen, glutamate levels were significantly higher during infection (##REF##30862747##29##). Taken together, our previous metabolomic screen (##REF##30862747##29##) and current flux analysis provide strong evidence that glutamine metabolism is upregulated during MNV infection. As a control to ensure that the presence of uniformly labeled glucose and glutamine did not negatively affect virus replication, we titered MNV-infected RAW cells in the presence of the labeled metabolites and measured viral replication via plaque assay (##FIG##1##Fig. 2C##). We observed no negative effects from the uniformly labeled metabolites on virus replication, with a >6 log<sub>10</sub> growth after 8 hours (##FIG##1##Fig. 2C##), which is similar to titers obtained in unlabeled medium (##FIG##0##Fig. 1##).</p>", "<p id=\"P12\">Activated macrophages can dramatically upregulate immunoresponsive gene 1 (IRG1) expression leading to itaconate production from cis-aconitate in the TCA cycle (##REF##21919507##74##–##REF##30705422##76##). Furthermore, itaconate can play diverse roles in the immune response, including inhibition of succinate dehydrogenase in the TCA cycle (##REF##27189937##77##). Consistent with previous reports, we measured approximately two-fold higher itaconate and succinate abundances (##FIG##1##Fig. 2D##) with a larger fraction being glutamine-derived in MNV-1 infected cells (##SUPPL##0##Fig. S2A##). To determine how itaconate production might affect mitochondrial metabolism in macrophages, we analyzed the utilization of reductive carboxylation in MNV-1 infected cells. Reductive carboxylation is a glutamine-dependent metabolism favored by cells when the oxidative mitochondrial metabolism is dysfunctional (##REF##22101431##69##). We reasoned that production of itaconate during viral infection may reduce reliance on oxidative metabolism. Indeed, we measured a decrease in the ratio of oxidative to reductive metabolism in MNV-1 infected cells as measured by the ratio of oxidative-derived M4 citrate, M4 fumarate, and M4 malate to reductive-derived M5 citrate, M3 fumarate, and M3 malate (##FIG##1##Fig. 2E##).</p>", "<p id=\"P13\">Overall, flux analysis of MNV-1 infection demonstrates production of itaconate coupled with reductive TCA cycle activity and reprogramming of glucose and glutamine metabolism, which are all hallmarks of virus-induced metabolic reprograming of infected cells (##REF##35337009##67##).</p>", "<title>Inhibition of glutaminolysis significantly reduces MNV replication.</title>", "<p id=\"P14\">Glutaminolysis catabolizes glutamine for anaplerosis and provides a nitrogen source to fuel nucleotide and amino acid biosynthesis, key building blocks required for viral replication (##REF##26323613##32##). The rate-limiting enzyme within the pathway is glutaminase (GLS), which catalyzes the first deaminase reaction (##REF##24140288##30##). Since we uncovered higher glutamine flux in MNV-1 infected cells (##FIG##1##Fig. 2##), we hypothesized that this pathway would be required for optimal MNV replication. To test this, we infected RAW cells and primary bone marrow-derived macrophages (BMDMs) with MNV-1, CR3, and CR6 at an MOI of 5 for 1 hour. Medium containing CB839, a non-competitive GLS inhibitor, was thus added after infection and infectious titers measured after 8 hours by plaque assay. The concentrations of CB839 used in both RAW cells and primary BMDMs were non-toxic and maintained >80% cell viability (##SUPPL##0##Fig. S1B##, ##SUPPL##0##C##). Cells treated with CB839 had significantly lower MNV titers (by ~1.5-log<sub>10</sub>) than cells that were treated with vehicle control (##FIG##2##Fig. 3A##). RAW cells are transformed macrophages, and transformed cells can have altered metabolic processes (##REF##29889899##80##). Thus, to confirm the phenotype observed in RAW cells, we repeated infections in primary BMDMs. MNV-infected primary BMDMs treated with CB839 harbored significantly lower MNV titers (by >1.0-log<sub>10</sub>) than vehicle control (DMSO) cells for all strains despite using a slightly higher non-toxic concentration of CB839 (##FIG##2##Fig. 3B##). The results in BMDMs confirmed what was seen in RAW cells and showed that glutaminolysis is required for optimal replication of acute and persistent MNV strains.</p>", "<p id=\"P15\">Pharmacologic inhibitors can result in off-target effects. Hence, we repeated infections in RAW cells with medium lacking glutamine. Infections were performed as before, and viral titers were measured by plaque assay at 8 hpi. While glutamine deprivation has been reported to negatively affect cell viability after 48 hours in numerous cell types (##REF##30877243##40##–##REF##30917872##41##), we confirmed that 8-hour incubation without extracellular glutamine did not negatively affect RAW cell viability (> 80% viability) (##SUPPL##0##Fig. S1D##). Glutamine deprivation resulted in significantly lower (by 2–2.5-log<sub>10</sub>) MNV titers for all strains tested (##FIG##2##Fig. 3C##).</p>", "<p id=\"P16\">Taken together, these results demonstrate that acute and persistent MNV strains have a similar dependence on glutaminolysis for optimal replication.</p>", "<title>MNV genome replication is the stage in the viral life cycle most dependent upon glutaminolysis.</title>", "<p id=\"P17\">Typical of a positive-sense, single-stranded virus, the MNV life cycle involves the following steps: host cell uptake of viral particles, uncoating of the positive-strand viral RNA (vRNA) genome, direct translation of the positive-sense vRNA to produce nonstructural proteins, and synthesis of viral negative-sense RNA strand for eventual production of new positive-strand vRNA, translation of structural proteins, followed by progeny virion assembly, maturation, and finally egress. To identify the stage within the MNV lifecycle that is most dependent upon glutaminolysis, we continued investigating infection under glutamine-starved conditions to avoid potential off-target effects of CB839. Since glutamine can be used as a nitrogen source for nucleotide biosynthesis (##REF##26323613##32##), we first sought to analyze the role of glutaminolysis on viral genome replication. To test this, RAW cells were infected with MNV-1, CR3, or CR6 for 1 hour at an MOI of 5. After 1 hour, the virus inoculum was replaced with glutamine-free medium, and cells were incubated for 8 hours. After the incubation period, we extracted RNA and assessed viral genome levels via reverse transcriptase quantitative polymerase chain reaction (RT-qPCR). Glutamine-deprived cells had significantly fewer genome copies for all three strains, a 1.8–2.0-log<sub>10</sub> decrease (##FIG##3##Fig. 4A##).</p>", "<p id=\"P18\">Glutamine can also be used for amino acid synthesis (##UREF##5##81##). Thus, we next investigated whether MNV protein synthesis is dependent on host glutaminolysis. RAW cells were infected as before, and after the 8 hr incubation period, levels of the non-structural protein NS1/2 and the capsid protein were measured via western blot (##FIG##3##Fig. 4B##). NS1/2 protein levels were low in samples from infections with glutamine-containing medium, but not detectable in protein samples from infections with glutamine-free media (##FIG##3##Fig. 4B## left panel). Quantification of NS1/2 protein signals from three independent replicates indicated a >90% decrease for all strains tested when grown in glutamine-free medium (##FIG##3##Fig. 4B## middle panel), indicating that glutaminolysis is required for NS1/2 synthesis. Quantification of the capsid protein also showed significantly lower levels of this protein during glutamine starvation (##FIG##3##Fig. 4B## left panel). For MNV-1 and CR6 infected cells starved for glutamine, we observed a ~60% reduction in capsid protein levels compared to infections in replete media, while a ~40% reduction was observed for CR3-infected glutamine-starved cells (##FIG##3##Fig. 4B## right panel). These data suggested that glutaminolysis is important for MNV viral protein synthesis, although CR3 was slightly more resistant to glutamine starvation than MNV-1 and CR6 (##FIG##3##Fig. 4B##).</p>", "<p id=\"P19\">Last, we investigated viral assembly and egress, the end stages of infection. RAW cells were infected with MNV-1, CR3, or CR6 as before in replete and glutamine-starved media. After the 8 hr incubation period, supernatants and cell monolayers were collected separately to measure viral titers and calculate the released virus. In the cell-associated fraction, about a 2.0-log<sub>10</sub> decrease in viral titers was observed during glutamine starvation vs. replete media for all three strains tested (##FIG##3##Fig. 4C## left panel), which was similar to the results obtained for total MNV titers (##FIG##2##Fig. 3C##). The significant decrease in cell-associated MNV titers during glutamine starvation suggests that glutaminolysis is required for MNV assembly in both persistent and acute strains. However, analysis of extracellular MNV showed a significant decrease of MNV titers in glutamine-depleted media only for the persistent strains (##FIG##3##Fig. 4C## middle panel). Specifically, we observed a 0.75-log<sub>10</sub> decrease in extracellular CR3 and CR6 titers but no significant decrease for MNV-1 titers (##FIG##3##Fig. 4C## middle panel), highlighting strain-specific dependencies on glutaminolysis. Additionally, we calculated the ratio of released-to-total viral titers to investigate whether glutamine deprivation affects MNV release efficiency. Surprisingly, glutamine deprivation led to increased release efficiency in all strains, with the highest increase in release efficiency observed in MNV-1 infected cells (##FIG##3##Fig. 4C## right panel).</p>", "<p id=\"P20\">In summary, because glutamine can be used for nucleotide synthesis but no change in the intracellular amino acid pool was detected in MNV-1–infected cells in our flux analysis (##SUPPL##0##Fig. S2B##), we conclude that genome replication is the stage of the MNV lifecycle that most imminently relies on host glutaminolysis. All other phenotypes observed during later stages of the viral life cycle are most likely a consequence of this initial effect.</p>", "<title>Glutaminase activity is upregulated during MNV infection.</title>", "<p id=\"P21\">Our previous data indicated that glutaminolysis is upregulated during and required for optimal MNV replication. Therefore, we were interested in whether MNV infection increases glutaminolysis through changes in GLS expression. We first directed our attention to GLS transcript and protein levels, since HCMV and HIV have previously been shown to increase GLS protein levels and mRNA expression, respectively (##REF##19939921##35##, ##REF##22479354##43##). To test whether MNV infection modulates GLS expression, we infected RAW cells with MNV-1, CR3, and CR6 for 1 hour at an MOI of 5. After 8 hours, we assessed GLS transcript and protein levels via RT-qPCR and western blot, respectively. We observed that <italic toggle=\"yes\">GLS</italic> transcript levels were significantly higher in MNV-infected cells compared to mock-infected cells (##FIG##4##Fig. 5A##). Using the housekeeping gene beta-actin as a measure of baseline transcription, we observed some strain-specific differences, with MNV-1 infection leading to a 3-fold increase in <italic toggle=\"yes\">GLS</italic> transcript levels and the persistent strains leading to a 0.5–1-fold increase (##FIG##4##Fig. 5A##). Western blot analysis of GLS protein levels resulted in no observable difference between MNV and mock-infected cells (##FIG##4##Fig. 5B##). The two bands present in the immunoblot potentially represent the two isoforms of GLS, KGA and GAC, which are identical in all aspects except the C-terminal domain (##REF##28111979##45##). Surprisingly, quantification of GLS protein levels revealed a small but significant decrease (5–7%) in GLS protein levels in MNV-infected relative to mock-infected cells (##FIG##4##Fig. 5B##). From these data, we conclude that the upregulation of glutamine metabolism during MNV infection is not due to increased GLS mRNA or protein expression.</p>", "<p id=\"P22\">We next investigated whether GLS enzymatic activity was increased during MNV infection, which would be consistent with our flux analysis results showing increased glutamine catabolism during MNV infection. RAW cells were infected with MNV-1, CR3, and CR6 for 8 hours as before and GLS enzymatic activity was analyzed with a commercially available kit that measures ammonia, the byproduct of the reaction that GLS catalyzes (##REF##28111979##45##). We observed higher levels of GLS enzyme activity in MNV-infected cells than in mock-infected cells (##FIG##4##Fig. 5C##). When analyzing the fold change in GLS activity over mock infected cells, an approximately 0.75-fold increase was detected for all three MNV strains, with each strain increasing GLS activity to a similar extent (##FIG##4##Fig. 5C##).</p>", "<p id=\"P23\">Overall, we conclude that increased rates of glutaminolysis during MNV infection in macrophages is the result of increases host cell GLS enzymatic activity, but not due to changes in GLS transcript or protein levels.</p>", "<title>NS1/2 is a viral mediator of increased GLS activity.</title>", "<p id=\"P24\">Viral proteins can mediate changes to host metabolism to ensure optimal infection. For example, dengue virus NS1 interacts with glyceraldehyde-3-phosphate dehydrogenase to upregulate glycolysis (##REF##26378175##46##). Therefore, we investigated whether increased GLS activity in MNV-infected cells is mediated by a viral protein. To test this, we overexpressed individual MNV non-structural proteins in Huh-7 cells expressing the viral receptor CD300lf and measured GLS activity as before. As a control, we first tested whether MNV infection of CD300lf-expressing Huh-7 cells would be sensitive to glutaminolysis inhibition. Cell viability studies determined the concentration of CB839 at which >80% cell viability is maintained to be 5 μM (##SUPPL##0##Fig. S1E##). We then infected the cells with MNV-1, CR3, and CR6 for 1 hour at an MOI of 5 before adding medium containing 5 μM CB839 or vehicle control (DMSO) for 8 hrs. Viral titers were measured via plaque assay. We observed a 0.5–1-log<sub>10</sub> decrease in MNV titers when glutaminolysis was inhibited, confirming that similar to infected macrophages CD300lf-expressing Huh-7 cells are sensitive to glutaminolysis inhibition (##FIG##5##Fig. 6A##) and provide an efficient cell line for protein overexpression.</p>", "<p id=\"P25\">Having confirmed the importance of glutaminolysis during MNV infection in CD300lf-expressing Huh-7 cells, we investigated whether the expression of an individual viral protein would alter GLS activity. To this end, we transfected CD300lf expressing Huh-7 cells with plasmids for the expression of 6 MNV-1 non-structural proteins (NS1/2 and NS3 to NS7) or green fluorescent protein (GFP) as a negative control. Transfected cells were incubated for 24–48 hours, and cell lysates were first tested for successful protein expression via western blot (##SUPPL##0##Fig. S3##). After confirming expression of the proteins of interest, cell lysates were analyzed for GLS activity. We observed increased GLS activity in cells expressing NS1/2 (##FIG##5##Fig. 6B## left panel), with an approximately 0.5-fold change in GLS activity (##FIG##5##Fig. 6B##, right panel), over cells expressing GFP. This is slightly less than the 0.75-fold increase in GLS activity observed during MNV-1 infection (##FIG##4##Fig. 5C##). NS7 overexpression resulted in highly variable GLS activities but was not statistically significant (##FIG##5##Fig. 6B##). Thus, other viral proteins, e.g. NS7 or structural proteins, may contribute to the full increase in GLS activity observed in MNV-infected macrophages.</p>", "<p id=\"P26\">Taken together, these data demonstrate that the MNV structural protein NS1/2 mediates an increase in GLS activity and is a viral factor upregulating glutaminolysis during macrophage infection.</p>" ]	[ "<title>Discussion</title>", "<p id=\"P27\">Viruses have evolved numerous mechanisms for manipulating host cellular metabolism to create a more favorable intracellular environment to support optimal replication. Our previous study showed that MNV-1 infection significantly alters numerous host metabolic pathways, including glycolysis, the PPP, and OXHPOS, thereby supporting the energetic and biosynthetic needs for optimal virion production (##REF##30862747##29##). In our present study, we extended our investigation to include two persistent MNV strains, CR3 and CR6, and observed strain-dependent differences compared to MNV-1 in that while these strains also required host glycolysis and the PPP for optimal replication, they did not require OXPHOS. To support the previous static metabolomic analysis, we furthermore performed metabolic flux analysis to measure the incorporation of labeled carbon from glucose and glutamine. These data showed significantly higher glucose and glutamine catabolism during MNV-1 infection, thus supporting the observation that MNV infection upregulates both metabolic pathways concurrently. Having previously investigated the role of glycolysis during MNV infection, we focused on the role of glutaminolysis during MNV infection in this study. Glutamine deprivation and pharmacological inhibition of glutamine catabolism resulted in significantly lower MNV-1, CR3, and CR6 viral titers in multiple cell types, thus revealing that glutaminolysis is required for optimal MNV replication. Our results also showed that MNV genome replication is the first step in the viral life cycle that depends on glutaminolysis and our mechanistic studies point to NS1/2 as a viral protein that mediates upregulation of GLS activity, the key enzyme in glutaminolysis. Thus, in addition to glycolysis, glutaminolysis is another intrinsic host metabolic factor that contributes to optimal MNV replication. Collectively, our investigation has revealed both shared and strain-specific metabolic dependencies that may underly the different pathogenic phenotypes of various MNV strains.</p>", "<p id=\"P28\">Glycolysis and glutaminolysis are the catabolic pathways for glucose and glutamine, respectively, and these molecules are the main carbon sources used by mammalian cells to perform a myriad of cellular processes. Importantly, these pathways are often concurrently rewired by viruses, since metabolites from the glycolytic pathway can not only be used for energy production via OXPHOS, but also can be used within the PPP for nucleotide synthesis, molecules that viruses need for genome replication. Additionally, glycolytic intermediates can be used in lipid biosynthesis, and when glycolytic intermediates are used more for lipid biosynthesis or lactic acid assembly rather than energy production, aKG, a glutaminolysis product, can be shuttled into the TCA cycle to ensure continuous downstream ATP production via anaplerosis. This phenotype is observed in HCMV infections (##REF##19939921##35##). Glutamine catabolism also provides nitrogen-containing metabolites for amino acid and nucleotide biosynthesis (##REF##26323613##32##). Together, glycolysis and glutaminolysis provide the necessary building blocks and energetic needs for optimal progeny virion production. Hence, viruses may target both pathways to promote optimal replication. In the present study we observed increased glycolysis and glutaminolysis during MNV-1 replication in murine macrophages. Glutamine deprivation and treatment with the pharmacological inhibitor CB839 significantly decreased virion production of MNV-1, CR3, and CR6 through reduced genome replication, which resulted in lower levels of non-structural and structural protein synthesis, viral assembly, and release. Diverse viruses such as HIV-1, white-spot syndrome virus, hepatitis C virus, influenza virus, and adenovirus also upregulate both glycolysis and glutaminolysis during infection (##REF##24501408##9##, ##REF##35017663##47##–##REF##28538182##55##). However, the molecular mechanisms underlying the upregulation of these two key metabolic pathways and how this metabolic rewiring affects virus replication vary by virus and host cell type. Uncovering these mechanisms may reveal shared metabolic dependencies and therapeutic chokepoints.</p>", "<p id=\"P29\">As obligate intracellular parasites, viruses rely on the metabolic products of host cells and have evolved capabilities to hijack metabolic resources and stimulate specific metabolic pathways required for replication. However, the viral proteins responsible for metabolic control are mostly unknown. In this study, we identified the NoV non-structural protein NS1/2 as being involved in host cell metabolic modulation. This protein is released from the viral polyprotein precursor via proteolytic activity of the viral protease NS6 (##REF##16873239##82##). Our results strongly suggest that upon release from the polyprotein one function of the NS1/2 protein is to enhance GLS enzymatic activity, leading to increased glutaminolysis. Other viral proteins known to mediate changes to host metabolism come from diverse virus families. For example, three different DNA viruses use non-structural proteins to modulate host metabolism. Epstein-Barr virus increases fatty acid synthase expression during lytic replication through the immediate-early non-structural protein BRLF1, which works in a p38 stress mitogen-activated protein kinase-dependent manner to increase fatty acid production (##REF##15047835##56##). Hepatitis B virus uses viral protein X to reprogram liver glucose metabolism through increased expression of key gluconeogenic enzymes (##REF##21690090##57##). And adenoviruses use the E4ORF1 gene product through a direct interaction with c-Myc to increase anabolic glucose metabolism and glutaminolysis (##REF##24501408##9##,##REF##26561297##49##). Enterovirus A71, on the other hand, affects host cell metabolism through its structural protein VP1, which directly binds to trifunctional carbamoyl-phosphate synthetase 2, aspartate transcarbamylase, and dihydroorotase to promote increased pyrimidine synthesis (##REF##32085644##37##). These examples highlight that both non-structural and structural viral proteins from diverse viral families can contribute to altering host metabolism during viral infection. However, our work on NS1/2 increasing GLS activity provides the first example of an RNA virus that upregulates glutaminolysis through a specific non-structural viral protein. Although we cannot rule out that NS1/2 is the only MNV viral protein that increases GLS activity. Future investigations into the detailed mechanism of NS1/2-mediated increase in GLS enzymatic activity are needed and have the potential to reveal fundamental insights into norovirus-host interactions and pathogenesis.</p>", "<p id=\"P30\">Macrophages are highly plastic immune cells that adapt to different physiological microenvironments. These cells are often parsed into two major categories: pro-inflammatory (M1) and anti-inflammatory/pro-resolving (M2) macrophages (##UREF##4##58##). Importantly, these two macrophage phenotypes are associated with distinct metabolic profiles. Hallmarks of M1 macrophages include high rates of glycolysis, fatty acid synthesis, and pentose phosphate activity. In contrast, hallmarks of M2 macrophages include high rates of glutaminolysis, fatty acid oxidation, and OXPHOS (##UREF##4##58##). Our previous (##REF##30862747##29##) and current metabolomic analyses revealed significant upregulation of central carbon metabolism and increased carbon flow through glycolysis and glutaminolysis during MNV infection of macrophages. Since upregulation of glycolysis, the PPP, and increased succinate production are hallmarks of M1 macrophages, while upregulation of glutaminolysis and OXPHOS are hallmarks of an M2 macrophage, MNV-infected macrophages display a hybrid metabolic profile during infection. Intriguingly, the underlying metabolic program is crucial for macrophage function (##UREF##4##58##). However, how the metabolic alterations induced by MNV infection impact macrophage function remains unknown. Like MNV, bacteria also rewire macrophage metabolism to grow and evade innate immunity. <italic toggle=\"yes\">Legionella pneumophila, Brucella abortus,</italic> and <italic toggle=\"yes\">Listeria monocytogenes</italic> rewire macrophages towards aerobic glycolysis, and <italic toggle=\"yes\">L. pneumophila</italic> enhances glycolysis by a yet-to-be-determined mechanism (##REF##28867389##59##). <italic toggle=\"yes\">L. monocytogenes</italic> uses a bacterial toxin to induce mitochondrial fragmentation and takes advantage of increased glycolysis in M1 macrophages to efficiently proliferate (##REF##21321208##60##). While chronic <italic toggle=\"yes\">B. abortus</italic> infection preferentially occurs in M2 macrophages, it requires PPARγ to increase glucose availability (##REF##23954155##61##). Parasites can also alter macrophage metabolism during intracellular infection. For example, <italic toggle=\"yes\">Leishmania spp</italic>. are protozoan parasites that infect macrophages and activate HIF-1α to upregulate HIF-1α target genes, including glucose transporters and glycolytic enzymes, resulting in increased glucose uptake, glycolysis, and activation of the PPP (##REF##33898331##62##). These examples suggest that while MNV infection increases the availability of resources for optimal infection, rewired macrophage metabolism may also promote changes to the host immune response. Disentangling which metabolic pathways are directly altered by MNV and which are consequences of macrophage host defenses is an important area for future investigations.</p>", "<p id=\"P31\">In conclusion, we have shown that glutaminolysis, in addition to glycolysis, is an intrinsic host factor promoting optimal replication of MNV. Our data are consistent with a model whereby MNV uses the NS1/2 protein to upregulate GLS activity during infection of macrophages, which increases glutamine catabolism. Our previous and current findings reveal that central carbon metabolism plays an important role in NoV replication, and these findings may uncover novel chokepoints for therapeutic intervention and new avenues for improving HNoV cultivation.</p>" ]	[]	[ "<p id=\"P1\">Current address: Department of Microbiology, Immunology, and Inflammation, University of Illinois, Chicago, Illinois, USA</p>", "<p id=\"P2\">Viruses are obligate intracellular parasites that rely on host cell metabolism for successful replication. Thus, viruses rewire host cell pathways involved in central carbon metabolism to increase the availability of building blocks for replication. However, the underlying mechanisms of virus-induced alterations to host metabolism are largely unknown. Noroviruses (NoVs) are highly prevalent pathogens that cause sporadic and epidemic viral gastroenteritis. In the present study, we uncovered several strain-specific and shared host cell metabolic requirements of three murine norovirus (MNV) strains, the acute MNV-1 strain and the persistent CR3 and CR6 strains. While all three strains required glycolysis, glutaminolysis, and the pentose phosphate pathway for optimal infection of macrophages, only MNV-1 relied on host oxidative phosphorylation. Furthermore, the first metabolic flux analysis of NoV-infected cells revealed that both glycolysis and glutaminolysis are upregulated during MNV-1 infection of macrophages. Glutamine deprivation affected the MNV lifecycle at the stage of genome replication, resulting in decreased non-structural and structural protein synthesis, viral assembly, and egress. Mechanistic studies further showed that MNV infection and overexpression of the MNV non-structural protein NS1/2 increased the enzymatic activity of the rate-limiting enzyme glutaminase. In conclusion, the inaugural investigation of NoV-induced alterations to host glutaminolysis identified the first viral regulator of glutaminolysis for RNA viruses, which increases our fundamental understanding of virus-induced metabolic alterations.</p>", "<title>Author Summary:</title>", "<p id=\"P3\">All viruses critically depend on the host cells they infect to provide the necessary machinery and building blocks for successful replication. Thus, viruses often alter host metabolic pathways to increase the availability of key metabolites they require. Human noroviruses (HNoVs) are a major cause of acute non-bacterial gastroenteritis, leading to significant morbidity and economic burdens. To date, no vaccines or antivirals are available against NoVs, which demonstrates a need to better understand NoV biology, including the role host metabolism plays during infection. Using the murine norovirus (MNV) model, we show that host cell glutaminolysis is upregulated and required for optimal virus infection of macrophages. Additional data point to a model whereby the viral non-structural protein NS1/2 upregulates the enzymatic activity of glutaminase, the rate-limiting enzyme in glutaminolysis. Insights gained through investigating the role host metabolism plays in MNV replication may assist with improving HNoV cultivation methods and development of novel therapies.</p>" ]	[ "<title>Supplementary Material</title>" ]	[ "<title>Acknowledgements</title>", "<p id=\"P46\">These studies were funded by the University of Michigan Pandemic Relief fund to C.E.W. D.N. and N.M. are supported by NCI grant nos. R01CA227622 and R01CA204969. D.N. is also supported by grants from the Rogel Cancer Center and the Forbes Institute for Cancer Discovery. A.H. was supported by the Molecular Mechanisms of Microbial Pathogenesis Training Grant (5T32AI007528-24). We thank past and present members of the Wobus lab for helpful discussions, and Drs. Ian Goodfellow, Vernon Ward, Jason Mackenzie, and Stefan Taube for the indicated reagents.</p>" ]	[ "<fig position=\"float\" id=\"F1\"><label>Figure 1:</label><caption><title>Persistent strains CR3 and CR6 rely on host glycolysis and nucleotide biosynthesis, but not OXPHOS, for optimal replication.</title><p id=\"P47\">RAW 264.7 cells were infected for 1 hour at an MOI of 5 with either MNV-1, CR3, or CR6. Virus inoculum was removed and replaced with medium containing <bold>(A)</bold> 10 mM 2-deoxuglucose (2DG), <bold>(B)</bold> 500 μM 6-aminonicotinamide (6AN), <bold>(C)</bold> 1 μM oligomycin-A (Oligo), or vehicle control (DMSO). Infected cells were incubated for 8 hours and infectious MNV titers were measured via plaque assay. Experiments represent combined data from at least three independent experiments. Statistical analysis was performed using Two-tailed Students-t tests. *, <italic toggle=\"yes\">P<</italic>0.001; , <italic toggle=\"yes\">P</italic> <0.01; , <italic toggle=\"yes\">P<</italic>0.05; ns, not significant.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2:</label><caption><title>MNV-1 infection upregulates glycolysis and glutaminolysis in macrophages.</title><p id=\"P48\">RAW 264.7 cells were mock-infected or infected with MNV-1 for 1 hour at an MOI of 5. The virus inoculum was removed and replaced with medium containing (<bold>A</bold>) <sup>13</sup>C<sub>5</sub>-glucose or (<bold>B-E</bold>) <sup>13</sup>C<sub>5</sub>-glutamine for 8 hours. After 8 hrs, intracellular metabolites were extracted with ice-cold methanol. (<bold>D</bold>) RAW 264.7 cells were infected as before, and MNV-1 titers measured via plaque assay. Experiments represent combined data from at least two independent experiments with at least two technical replicates. Statistical analysis was performed by multiple unpaired t-tests. *, <italic toggle=\"yes\">P<</italic>0.0001; , <italic toggle=\"yes\">P<</italic>0.001; , <italic toggle=\"yes\">P</italic> <0.01; , <italic toggle=\"yes\">P<</italic>0.05; ns, not significant.</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3:</label><caption><title>Inhibition of glutaminolysis significantly reduces MNV replication in both primary and transformed macrophages.</title><p id=\"P49\">(<bold>A</bold>) RAW 264.7 cells or (<bold>B</bold>) primary bone marrow-derived macrophages were infected for 1 hour at an MOI of 5 with either MNV-1, CR3, or CR6. Virus inoculum was removed and replaced with medium containing (<bold>A</bold>) 10 μM or (<bold>B</bold>) 15 μM CB839 or vehicle control (DMSO). (<bold>C</bold>) RAW 264.7 cells were infected as before but infection was performed with glutamine-free or replete medium. After an 8 hr incubation, MNV titers were measured via plaque assay. Experiments represent combined data from at least three independent experiments. Statistical analysis was performed using Two-tailed Students-t tests. *, <italic toggle=\"yes\">P<</italic>0.001; , <italic toggle=\"yes\">P</italic> <0.01; , <italic toggle=\"yes\">P<</italic>0.05; ns, not significant.</p></caption></fig>", "<fig position=\"float\" id=\"F4\"><label>Figure 4:</label><caption><title>Viral genome replication is the stage of the MNV lifecycle that is most dependent on host glutaminolysis.</title><p id=\"P50\">(<bold>A</bold>) RAW 264.7 cells were infected for 1 hour at an MOI of 5 with either MNV-1, CR3, or CR6. Virus inoculum was removed and replaced with glutamine-free or replete medium. Infected cells were incubated for 8 hours. RNA was extracted and MNV genome levels were assessed via qRT-PCR. (<bold>B</bold>) RAW 264.7 cells were infected as above, and Western blot analysis was performed for MNV viral proteins NS1/2 and capsid. β-actin was used as a loading control. Data shown are representative Western blots from 3 independent experiments. Numbers below blots indicate densitometry measurement of protein level relative to MNV-infected cells receiving replete medium. (<bold>C</bold>) RAW 264.7 cells were infected as before. Supernatants and cell-associated virus were measured separately via plaque assay. Experiments represent combined data from at least three independent experiments. Statistical analysis was performed using Two-tailed Students-t tests and One-Way ANOVA. , <italic toggle=\"yes\">P<</italic>0.001; , <italic toggle=\"yes\">P</italic> <0.01; , <italic toggle=\"yes\">P<</italic>0.05; ns, not significant.</p></caption></fig>", "<fig position=\"float\" id=\"F5\"><label>Figure 5:</label><caption><title>Glutaminase activity is upregulated during MNV infection in macrophages.</title><p id=\"P51\"><bold>(A)</bold> RAW 264.7 cells were infected for 1 hour at an MOI of 5 with either MNV-1, CR3, or CR6. Virus inoculum was removed and replaced with replete medium. Infected cells were incubated for 8 hours. RNA was extracted and glutaminase transcripts were assessed via qRT-PCR. <bold>(B)</bold> RAW 264.7 cells were infected as before. Western blot analysis was then performed for glutaminase protein levels. β-actin was used as a loading control. A representative Western blot is shown on the left and quantification from 3 independent experiments on the right. <bold>(C)</bold> RAW 264.7 cells were infected as before. Glutaminase activity was analyzed utilizing the Cohesion Biosciences Glutaminase Microassay kit. Experiments represent combined data from at least three independent experiments. Experiments represent combined data from at least three independent experiments. Statistical analysis was performed using Two-tailed Students-t tests. , <italic toggle=\"yes\">P<</italic>0.001; , <italic toggle=\"yes\">P</italic> <0.01; , <italic toggle=\"yes\">P<</italic>0.05; ns, not significant.</p></caption></fig>", "<fig position=\"float\" id=\"F6\"><label>Figure 6:</label><caption><title>NS1/2 is a viral mediator of increased glutaminase activity in macrophages.</title><p id=\"P52\">(<bold>A</bold>) Huh-7 cells expressing the viral receptor CD300lf were infected for 1 hour at an MOI of 5 with either MNV-1, CR3, or CR6. Virus inoculum was removed and replaced with medium containing 5 μM CB839 or vehicle control (DMSO). Infected cells were incubated for 8 hours and MNV titers were measured via plaque assay. (<bold>B</bold>) Huh-7 CD300lf cells were transfected with plasmids encoding the indicated MNV-1 non-structural protein or green fluorescent protein. Transfected cells were incubated for 24–48 hours. Glutaminase activity was analyzed utilizing the Cohesion Biosciences Glutaminase Microassay kit. Experiments represent combined data from at least three independent experiments. Statistical analysis was performed using Two-tailed Students-t tests. , <italic toggle=\"yes\">P</italic> <0.01; , <italic toggle=\"yes\">P<</italic>0.05; ns, not significant.</p></caption></fig>" ]	[]	[]	[]	[]	[]	[]	[ "<supplementary-material id=\"SD1\" position=\"float\" content-type=\"local-data\"><label>Supplement 1</label><caption><p id=\"P43\"><bold>Supplementary Figure 1: Cell viability assays of indicated cell lines. (A-B)</bold> RAW 264.7 cells were treated with indicated concentrations of <bold>(A)</bold> Oligomycin-A, <bold>(B)</bold> CB839, or vehicle control (DMSO) for either 8 or 24 hours, respectively. Cell viability was measured using Resazurin or WST-1 reagent. <bold>(C)</bold> Primary bone marrow-derived macrophages were treated with CB839 or vehicle control at the indicated concentrations for 24 hours. Cell viability was measured using WST-1 reagent. <bold>(D)</bold> RAW 264.7 cells were incubated with glutamine free or replete medium for 8 hours. Cell viability was measured using trypan blue staining on a Life Technologies Countess 3 automated cell counter assay platform. <bold>(E)</bold> Huh-7 CD300lf cells were treated with indicated concentrations of CB839 for 24 hrs. Cell viability was measured using WST-1 reagent. Experiments represent combined data from at least two independent experiments with two technical replicates each.</p><p id=\"P44\"><bold>Supplementary figure 2: MNV-1 infection does not alter the intracellular amino acid pool. (A-B)</bold> RAW 264.7 cells were either mock-infected or infected with MNV-1 for 1 hour at an MOI of 5. The virus inoculum was removed and replaced with medium containing <sup>13</sup>C<sub>5</sub>-glutamine for 8 hours. Intracellular metabolites and amino acids were extracted with ice-cold methanol and measured by mass spectrometry. Experiments represent combined data from two independent experiments with four technical repeats.</p><p id=\"P45\"><bold>Successful expression of MNV viral proteins.</bold> Validation of MNV-1 nonstructural protein expression. (<bold>A-E</bold>) Huh-7 CD300lf cells were transfected with plasmids encoding the indicated MNV-1 nonstructural protein or green fluorescent protein. Transfected cells were incubated for 24–48 hours. Western blot analysis was performed to confirm successful expression. β-actin was used as a loading control. Data shows representative Western blots from 3 independent experiments.</p></caption></supplementary-material>" ]	[]	[ "<graphic xlink:href=\"nihpp-2023.12.19.572316v1-f0001\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-2023.12.19.572316v1-f0002\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-2023.12.19.572316v1-f0003\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-2023.12.19.572316v1-f0004\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-2023.12.19.572316v1-f0005\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-2023.12.19.572316v1-f0006\" position=\"float\"/>" ]	[ "<media xlink:href=\"NIHPP2023.12.19.572316V1-supplement-1.pdf\" id=\"d64e779\" position=\"anchor\"/>" ]	[{"label": ["6."], "surname": ["Bravo-Santano", "Ellis", "Mateos", "Calle", "Keun", "Behrends", "Letek"], "given-names": ["N", "JK", "LM", "Y", "HC", "V", "M"], "year": ["2018"], "article-title": ["Intracellular Staphylococcus aureus modulates host central carbon metabolism to activate autophagy."], "source": ["mSphere"], "volume": ["3"], "fpage": ["e00174"], "lpage": ["18"]}, {"label": ["15."], "surname": ["Sanchez", "Lagunoff"], "given-names": ["EL", "M"], "article-title": ["Viral activation of cellular metabolism."], "source": ["Virology."], "year": ["2015"], "month": ["May"], "volume": ["479\u2013480"], "fpage": ["609"], "lpage": ["18"]}, {"label": ["16."], "surname": ["Carvajal", "Avellaneda", "Escobar", "Covi\u00e1n", "Kalergis", "Lay"], "given-names": ["J. J.", "A. M.", "D.", "C.", "A. M.", "M. K."], "comment": ["2019"], "article-title": ["Human Norovirus Proteins: Implications in the Replicative Cycle, Pathogenesis, and the Host Immune Response."], "source": ["Frontiers in Immunol."], "year": ["2020"], "month": ["Jun"], "day": ["16"], "volume": ["11"], "fpage": ["961"]}, {"label": ["28."], "surname": ["Wobus"], "given-names": ["CE"], "article-title": ["The Dual Tropism of Noroviruses."], "source": ["J Virol."], "year": ["2018"], "month": ["Jul"], "day": ["31"], "volume": ["92"], "issue": ["16"], "fpage": ["e01010"], "lpage": ["17"]}, {"label": ["58."], "surname": ["Viola", "Munari", "Scolaro", "Castegna"], "given-names": ["A.", "F.", "T.", "A."], "year": ["2019"], "article-title": ["The Metabolic Signature of Macrophage Responses."], "source": ["Frontiers in Immunology"], "volume": ["10"], "fpage": ["466337"]}, {"label": ["81."], "surname": ["Attarwala", "Zhang", "Lee", "Joseph"], "given-names": ["Nabeel", "Cissy", "Anne", "Jez"], "part-title": ["Diseases & Disorders \| Therapies Targeting Glutamine Addiction in Cancer."], "source": ["Encyclopedia of Biological Chemistry III"], "edition": ["Third"], "publisher-name": ["Elsevier"], "year": ["2021"], "fpage": ["452"], "lpage": ["461"]}, {"label": ["85."], "surname": ["Baker"], "given-names": ["E."], "source": ["Characterization of the NS1\u20132 and NS4 proteins of murine norovirus: PhD Thesis."], "publisher-name": ["University of Otago, Microbiology & Immunology"], "year": ["2012"]}]	{ "acronym": [], "definition": [] }	85	CC BY	no	2024-01-13 23:49:37	bioRxiv. 2023 Dec 19;:2023.12.19.572316	oa_package/f7/5c/PMC10769279.tar.gz
PMC10769303	38187564	[ "<title>INTRODUCTION</title>", "<p id=\"P5\">Estradiol (E2) is mainly produced by the ovaries and is the most physiologically relevant estrogen (others include estrone and estriol). E2 exerts control over numerous biological functions by binding to the intracellular estrogen receptors alpha and beta (ERα and ERβ) [##REF##9048584##1##–##REF##19803851##5##] and the G-protein coupled receptor GPR30/GPER1 [##REF##15539556##6##].</p>", "<p id=\"P6\">E2 levels fluctuate throughout the female menstrual cycle and, as women age and go through natural menopause, E2 levels progressively decline. As a reflection of the broad roles of E2, adverse physiological and psychiatric effects accompany the natural decline of E2 levels. These include vasomotor symptoms (hot flashes, night sweats) [##REF##17666595##7##, ##REF##16735636##8##], sleep disturbances [##REF##28944165##9##], somatic symptoms (pains, aches) [##REF##17666595##7##], cognitive performance decline [##REF##12525723##10##], anxiety [##REF##17666595##7##], and depression [##REF##17666595##7##]. Such effects are expected given the E2’s neural role in mediating synaptic plasticity [##UREF##0##11##–##REF##1353794##16##], increasing dendritic spine density, long-term potentiation (LTP) [##REF##24112361##17##], neuroprotective effects [##REF##31364065##18##] and improving cognitive performance [##REF##21285321##19##, ##REF##25205317##20##].</p>", "<p id=\"P7\">A lack of E2 right after menopause negatively affects learning and memory and increases the risk of neurodegenerative diseases, such as Alzheimer’s disease (AD) [##REF##20442496##21##, ##REF##29311911##22##]. The incidence of AD and related dementias is two to three times higher in women than in men, and premature menopause increases this risk [##REF##27870425##23##, ##REF##32210745##24##]. Although surrounded by a lot of debate [##REF##24042430##25##, ##REF##12771112##26##], there is much evidence to suggest that E2 replacement therapy, administered immediately at menopause [##REF##29526116##27##–##REF##23100399##35##], may improve cognitive performance and reduce risk for onset and development of AD [##REF##15668595##3##–##REF##19803851##5##, ##REF##32210745##24##]. These effects highlight E2’s role in preserving cognitive function and overall well-being.</p>", "<p id=\"P8\">Although it does not exactly replicate the natural decline in E2 as seen in healthy women, ovariectomy (Ov) and ovohysterectomy (OvH) have been widely used in preclinical models to investigate the physiological and neural adaptations that take place when the ovarian E2 supply is removed [##REF##31364065##18##]. It should be noted that, although there is neuronal production of E2 with important neuromodulator and neuroprotective functions [##UREF##2##36##, ##REF##30728170##37##], its source are androgens, which are mostly produced by the ovaries [##REF##33488324##38##]. Ov and Ov-HRT are implemented as effective cancer treatment and are commonly used for benign gynecologic conditions in women 40 years and older [##REF##21518944##39##]. Furthermore, women who underwent Ov showed a higher risk for development of dementia, but not if they received Ov-HRT treatment at the time of surgery [##REF##24508665##33##]. Thus, preclinical animal surgical menopause animal models become excellent models in which to examine the negative impact of reduced E2 concentrations on molecular and physiological processes, as well as the potential benefits of hormonal replacement therapy (HRT), in women who have experienced abrupt E2 removal.</p>", "<p id=\"P9\">Among the current preclinical models, nonhuman primates (NHPs) are highly valuable for this research because of their very similar physiology to humans and because females undergo a typical menopausal transition [##REF##16837643##40##]. Our own studies, and others, demonstrated that immediate Ov-HRT in aged surgically menopausal rhesus macaque females showed positive effects on memory [##REF##27707975##30##, ##REF##29952604##41##] and favorable effects on cognition in aged females under an obesogenic diet [##REF##32670182##42##]. Using brain samples from the same females, we identified differential gene expression in the occipital (OC), prefrontal cortex (PFC), hippocampus (HIP) and amygdala (AMG), with an enrichment in neuroinflammation in OC and HIP, but an inhibition in the AMG with Ov-HRT. Synaptogenesis, circadian rhythm, mitochondrial dysfunction, mTOR, glutamate, serotonin, GABA, dopamine, epinephrine/norepinephrine, glucocorticoid receptor signaling, neuronal NOS, and amyloid processing were exclusively enriched in AMG. As compared to the control group, most of these signaling pathways are downregulated after Ov-HRT, suggesting a protective effect of E2 in Ov-HRT females under a Western-style diet. A follow up study, using the contralateral AMG from these same females, as well as from a separate cohort of females under a regular chow diet, showed that Ov-HRT (immediate treatment) had lower histological amyloid β plaque density as compared to placebo females [##UREF##3##43##]. Furthermore, our own studies showed that E2 treatment clearly improved cognitive performance in the same animals included in the current study [##REF##27707975##30##]. In the present study, we sought to elucidate the molecular pathways in two cognitive-relevant cortical regions that could be altering brain function and ultimaltely contributing to such cognitive benefits.</p>", "<p id=\"P10\">It is well known that E2 binds to ERα and ERβ, and through the canonical mechanism of action, the E2-ER complex binds to estrogen-responsive elements (ERE) at promoters of target genes regulating their expression. In addition, E2, through binding to EREs, regulates gene expression through neuroepigenetic regulation [##UREF##4##44##]. After binding to ERE, the ligand-bound ERs recruit chromatin remodelers, such are BAF60 or recruiting CREB binding protein (CBP), that regulate DNA and histone modifications [##REF##18369406##45##–##REF##22566700##49##]. Intrahippocampal E2 increases DNMT3a and 3b levels and activity, decreased HDAC2 expression, increased H3 and H4 acetylation, altering memory in ovariectomized mice. Furthermore, DNMTs inhibiton by 5-AZA inhibited recognition memory [##REF##35360070##50##, ##REF##20212170##51##]. These prior findings support a critical role of DNAm in mediating the effects of E2 in brain function.</p>", "<p id=\"P11\">Iin the present study, we characterize the transcriptomic and methylomic profile of the brain between elderly ovary intact (OI), Ov females without HRT under a regular chow diet to determine the molecular signatures associated with an abrupt depletion of E2 at a peri-menopausal age. We next evaluate if HRT can revert any of these changes to maintain an age-matched molecular profile. We focus on two cortical brain regions associated with cognitive function and known to be impacted in aging and dementias. The OC is involved in visuospatial processing, distance and depth perception, color determination, object and face recognition, and memory formation (Rehman & Al Khalili, 2021; Stufflebeam & Rosen, 2007). Damage in this area is linked to hallucinations in dementia patients. The PFC is a central brain structure involved in working memory, temporal processing, decision making, flexibility, and goal-oriented behavior [##REF##23684970##52##]. In the context of AD, neurodegeneration and neural damage spreads from the PFC to occipital lobes.</p>", "<p id=\"P12\">The present study focused on elucidating the molecular effects of E2 on the primate brain by examining the differential gene expression and DNAm patterns in OI and following Ov and subsequent E2 treatment. With this model, we have identified a number of dysregulated molecular networks that are associated with Ov and are shared across two different regions of the brain, OC and PFC. The latter being particularly susceptible to age-associated neuropathologies such as AD and frontotemporal lobar degeneration (FTLD). Our results suggest extensive molecular differences in the brain induced by E2 depletion. We have also identified a number of Ov-related molecular differences that appear to be modulated by Ov-HRT treatment. These changes offer valuable insights into the neurobiological consequences of E2 deficiency, and potential alternative therapeutics that could be more targeted.</p>" ]	[ "<title>MATERIALS AND METHODS</title>", "<title>Subjects</title>", "<p id=\"P31\">This study was approved by the Oregon National Primate Research Center (ONPRC) Institutional Animal Care and Use Committee and used 19 old (range = 15.4–19.2 years, at the beginning of the study) female rhesus macaques (<italic toggle=\"yes\">Macaca mulatta</italic>). The maximum lifespan of this species is in the early 40s, so these animals were proportionally in late-middle to early-old age [##UREF##13##103##] and in the range of pre- to peri-menopausal endocrine status [##REF##16837643##40##, ##REF##9241047##104##]. The animals were socially housed indoors in paired cages under controlled environmental conditions: 24 °C temperature; 12-h light and 12-h darkness photoperiods (lights on at 07:00 h) and were cared for by the ONPRC Division of Comparative Medicine in accordance with the National Research Council’s Guide for the Care and Use of Laboratory Animals. Daily meals at ~08:00 h and ~ 15:00 h were supplemented with fresh fruits or vegetables; fresh drinking water was available ad libitum. Diet was monkey chow which provides calories with 13% fat, 69% complex carbohydrates (includes 6% sugars), and 18% protein. Additional enrichment included watching video programs and interactions with the Behavioral Science Unit staff and animal care technicians.</p>", "<title>Ovariectomy and estradiol supplementation</title>", "<p id=\"P32\">Before ovariectomy, all of the females were showing menstrual cycles and were therefore considered to be premenopausal at the beginning of the study. Except for the ovary intact (OI, <italic toggle=\"yes\">n</italic> = 4) females, the rest of the animals were Ov, resulting in E2 levels below 20 pg/mL. Half of the females (<italic toggle=\"yes\">n</italic> = 6, Ov-HRT) were immediately started on HRT in the form of E2-containing elastomer capsules, which achieved serum E2 concentrations of 94.3 ± 20.5 pg/ mL; the other half (<italic toggle=\"yes\">n</italic> = 8) received empty capsules (placebo), which achieved serum E2 concentrations of <30 pg/mL on average across ~48 months (age at end of study, 19.4–23.2 years). Serum E2 was measured every 2 months and the capsule replaced or its size adjusted as deemed appropriate [##REF##27707975##30##].</p>", "<title>Euthanasia</title>", "<p id=\"P33\">After the ~4-years duration of the study, a detailed necropsy protocol previously used in our laboratory was used to systematically collect brain tissues from all subjects; other body tissues were made available to other investigators for unrelated postmortem studies. Briefly, monkeys were sedated with ketamine (10 mg/kg), and administered pentobarbital, followed by exsanguination, as recommended by the 2013 Edition of the American Veterinary Medical Association Guidelines for the Euthanasia of Animals. Brains were quickly removed and the right hemisphere was dissected to isolate the different brain regions. Briefly, the dorsal and ventral banks of the dorsolateral prefrontal cortex (PFC) were collected around the primary sulcus. The OC was removed from the caudal tip of the occipital lobe. All tissues were wrapped in aluminum foil and immediately frozen in liquid nitrogen, and then archived at −80 °C.</p>", "<title>DNA/RNA isolation</title>", "<p id=\"P34\">Genomic DNA and RNA were extracted from each brain region using the All-Prep DNA/RNA/miRNA Universal kit (Qiagen Sciences Inc., Germantown, MD) following the manufacturer’s recommendations. Briefly, each brain region was pulverized and ~30 mg of tissue was used for DNA/RNA isolation.</p>", "<title>cDNA library construction and sequencing</title>", "<p id=\"P35\">For stranded RNA-seq, cDNA libraries were prepared with the TruSeq stranded mRNA library prep Kit (cat# RS-122–2101, Illumina, San Diego, CA, USA). The resulting libraries were sequenced on a HiSeq 4000 (Genomics & Cell Characterization Core Facility, University of Oregon) using a paired-end run (2 × 150 bases). A minimum of 100 M reads was generated from each library.</p>", "<title>RNA-Seq Processing and Calling</title>", "<p id=\"P36\">Raw sequences were examined for quality using FASTQC [##UREF##14##105##]. Phred scores (probability a base was called correctly) and GC content were observed for abnormalities. After initial quality control of the reads was completed, alignment was performed using STAR two pass alignment [##REF##23104886##106##]. Reads were aligned to the <italic toggle=\"yes\">Macaca mulatta</italic> assembly (Mmul_10) and scored on how well they corresponded to the reference genome and whether or not they map to multiple positions across the genome. Low scoring reads, usually short and poor-quality reads, were not retained (Mapping Quality Score < 2). Post-alignment, reads were quantified at the gene level using the program featureCounts [##REF##24227677##107##], and DESeq2 [##REF##25516281##108##] was used to transform gene counts and estimate fold-change values for differentially expressed genes. Genes that had either an average read count below 5, missing values for more than one-third of the samples, or a coefficient of variation greater than 50 in the control samples were dropped to remove noisy and lowly expressed genes.</p>", "<title>Differential Expression Analysis</title>", "<p id=\"P37\">Differential expression analysis was computed in DESeq2 where the gene expression values were evaluated as the outcome in a negative binomial generalized linear model [##REF##25516281##108##]. Whether or not the animal had undergone Ov was the predictor of the primary model we computed. This analysis was computed only in animals that had not received Ov-HRT treatment. Given no significant differences in age between animals and all of them are all much older females (~25 yrs) in the same stage of life and the limited power of the study we did not adjust for age as a covariate. A Benjamini-Hochberg false discovery rate (FDR) was applied to the unadjusted p-value to account for multiple comparisons [##UREF##15##109##]. We repeated this same analysis in animals treated with OV-HRT. In addition to these models to find differences directly related to Ov, we tested the interaction between Ov-HRT and Ov. To enable this analysis, we replicated the control samples (OI) into two groups, one labeled as having received Ov-HRT treatment and the other as having not received Ov-HRT treatment. We recognize that comparing both Ov groups to the same exact set of controls (OI) will lead to false positives, but we primarily used the test of interaction as a way of rapidly identifying genes associated with Ov that are potentially modified by OV-HRT treatment. As expected, FDR adjustment left no interaction results, so we considered interaction results that met an unadjusted <italic toggle=\"yes\">p</italic> < 0.05, particularly because computing the interaction globally for all genes was not our main interest. We were primarily interested in genes that showed expression changes related to Ov that no longer showed association with Ov with Ov-HRT treatment. To obtain a general set of genes that fit this criterion, we filtered the results to include only genes that showed suggestive evidence of Ov association (unadjusted <italic toggle=\"yes\">p</italic> < 0.05) without Ov-HRT treatment and little evidence of Ov association with Ov-HRT treatment (unadjusted <italic toggle=\"yes\">p</italic> > 0.1). From this reduced set of genes, we further filtered down to only those results that had at least suggestive evidence (<italic toggle=\"yes\">p</italic> < 0.05) of interaction between Ov and Ov-HRT treatment.</p>", "<title>Differential Exon Usage Analysis</title>", "<p id=\"P38\">The DEXSeq pipeline was applied (with the default parameters) to analyze the aligned reads and obtain exon level counts [##REF##22722343##110##]. The exon level counts were loaded and inspected in R (4.1.1) as DEXSeq objects before being normalized with DESeq2’s normalization algorithm [##REF##25516281##108##]. DEXSeq, similar to DESeq2, computes negative binomial regression and shares dispersion estimation across features. The program is designed to estimate differences in exon usage within a particular gene across conditions and will not identify genes with global differences in exon expression across a given gene (i.e., the genes identified by DEU analysis will be different than those identified in the DE analysis). We computed a model where the predictors included dummy variable for the exon, an indicator variable for whether the animal had undergone Ov, as well as the interaction between the two to assess DEU age associated with Ov. Results with an FDR adjusted <italic toggle=\"yes\">p</italic> < 0.05 were retained for pathway analysis. Top results were also overlapped with DMR genes to assess whether changes in methylation appeared to be affecting exon usage of any genes.</p>", "<title>Genome-wide DNA methylation profiling</title>", "<p id=\"P39\">Genomic DNA was checked for quality by electrophoresis on a 0.7% agarose gel, using a NanoDrop 8000 spectrophotometer (Thermo Scientific, Wilmington, DE, USA) and quantified using a Qubit (Thermo Scientific, Wilmington, DE, USA). Five hundred nanograms of genomic DNA was sheared using a Bioruptor UCD200 (Diagenode, Denville, NJ, USA), generating fragments ~180 bp. The Illumina TruSeq Methyl Capture EPIC library prep kit (Illumina, Santa Clara, CA, USA) was used following the manufacturer’s instructions. The EPIC probes interrogate >3.3 million individual CpG sites per sample at single-nucleotide resolution. After end repair, 3’ A-tailing, and adaptor ligation, libraries were pooled in groups of four, followed by two rounds of hybridization and capture using the EPIC probes, bisulfite conversion and final amplification. After library quantification using a 2100 Bioanalyzer (Agilent Technologies), DNA libraries were sequenced (3 libraries per 150PE lane) on an Illumina HiSeq4000 at the University of Oregon Genomics & Cell Characterization Core Facility (GC3F). Five percent PhiX DNA (Illumina Inc.) was added to each library pool during cluster amplification to boost diversity. Cases and control samples were mixed within lanes and sequenced together on the same flow-cell to reduce the impact of batch effects on data. The quality of the bisulfite-converted sequencing reads was assessed with FastQC [##UREF##14##105##]. Reads were trimmed and aligned to the macaque reference genome (Mmul10), and then the bisulfite conversion rates were evaluated, insuring all libraries were >98% converted, and CpG methylation was evaluated using Bismark [##REF##21493656##111##]. The methylation rates were calculated as the ratio of methylated reads over the total number of reads. Methylation rates for CpGs with fewer than 10 reads were excluded from further analysis. We next removed CpG sites on sex chromosomes. The remaining ~2.8–3.0 million CpGs per sample (OC and PFC; respectively) post-filtering were used for downstream analyses.</p>", "<p id=\"P40\">All sequence reads were submitted to the Sequence Read Archive at NCBI under project accession number TBD.</p>", "<p id=\"P41\">The differential methylation analysis was carried out by applying a generalized linear mixed effects model (GLMM) implemented in R package PQLseq (version 1.2.1) [##UREF##16##112##, ##UREF##17##113##] separately for each CpG site. PQLseq models the technical sampling variation in bisulfite sequencing data with a binomial distribution, effects of biological and technical covariates with the linear model, and the random effects with a correlated multivariate normal distribution. The most common methylation proportion values are 0 and 1, which are problematic in the context of generalized linear models with the logit link function (infinite in the logit-transformed space). We used a common pseudo-count transformation to avoid both extremes, as recommended, for example, by the developers of PQLseq [##UREF##18##114##]. This was done after adding + 1 to the numbers of methylated reads and +2 to the total numbers of reads to avoid modeling methylation proportions that are exactly 0 or 1, as recommended by the authors of PQLseq [##UREF##18##114##]. This pseudo-count transformation was only applied to non-missing values (coverage >10x).</p>", "<p id=\"P42\">Each nominal p-value was corrected for multiple comparisons by the False Discovery Rate (FDR). In parallel, the nominal p-value was used as input for Comb-p [##REF##22954632##115##] analysis to identify differentially methylated regions (DMRs) between controls and AUD subjects as previously described [##REF##28072409##116##].</p>", "<title>Network analysis</title>", "<p id=\"P43\">Significant DMRs that had gene annotations, as well as DE genes and genes demonstrating DEU were combined for each brain region. Those gene lists were again combined across brain regions to identify significantly altered genes that replicated across both, OC and PFC. Genes that were DE in both tissues were only retained if they were altered in consistent directions across both brain regions.</p>", "<p id=\"P44\">Results were analyzed in KEGG, STRING, and MCODE to find biological pathways enriched between groups (##FIG##0##Figure 1##, Figure S2-S3, ##TAB##0##Table 1##, ##TAB##1##Table 2##) [##REF##33237311##117##, ##REF##12525261##118##]. STRING was used to obtain protein-protein interactions for all genes that met our filtering criteria for each omic analysis. STRING was applied to find only “High confidence” protein-protein interactions with options for “textmining” and “neighborhood” disabled [##REF##33237311##117##]. MCODE was applied to the remaining interactions to obtain a set of highly interconnected gene clusters [##REF##12525261##118##] and the biological functions of each clusters with MCODE scores greater than 4.0 were identified through the KEGG pathways [##REF##26476454##119##].</p>", "<title>Functional promoter/enhancer assay</title>", "<p id=\"P45\">To determine the promoter or enhancer activity capacity of two DMRs located in the promoter and overlapping with exon 1 of the rhesus macaque <italic toggle=\"yes\">LTBR</italic> and overlapping with the last exon of <italic toggle=\"yes\">MZF1</italic> and in the promoter of <italic toggle=\"yes\">UBE2M</italic> genes, we cloned the corresponding macaque DMR regions (<italic toggle=\"yes\">LTBR</italic> (PFC), chr11:6528520–6529383; <italic toggle=\"yes\">UBE2M</italic> (PFC), chr19:58128610–58130108 & (OC), chr19: 58128532–58130175) in the luciferase reporter vector pGL3 (Promega) and transfected HEK293 cells (HEK 293, obtained from the Wake Core Repository). In addition, we transfected cells with the basic and control pGL3 as negative and positive controls; respectively. HEK293 cells were seeded in 96-well plates at 10.000 cells/well density and cultured in Dulbecco’s modified Eagle medium (DMEM) containing high glucose (4.5 g/L) supplemented with 10% fetal bovine serum (FBS) and maintained at 37°C and 5% CO<sub>2</sub>. Twenty-four hours later, cells were transfected using 90ng of each corresponding vector diluted in 10ul of opti-medium and 0.3ul of X-treme GENE HP DNA Transfection Reagent (Roche). 10ng of Renilla vector (Promega) was co-transfected and used for normalization. After 48 hours of transfection, Dual-Glo<sup>®</sup> Reagent equal to the volume of culture medium was added to each well. After 10 minutes, firefly luminescence was measured in a luminometer (SpectraMax iD3).</p>" ]	[ "<title>RESULTS</title>", "<title>Differential Expression Analysis</title>", "<p id=\"P13\">In the OC, 14,842 genes met the filtering criteria while 14,590 genes met the filtering criteria in the PFC. After computing association testing with each gene from each of those sets, we identified 150 and 128 differentially expressed (DE) genes associated with Ov (FDR < 0.05) in the OC and PFC, respectively (Table S1).</p>", "<p id=\"P14\">To explore if Ov-HRT treatment modulates the effect of Ov, we computed an interaction test between Ov-HRT treatment and Ov status. Instead of globally testing for an interaction between Ov-HRT and Ov status, we were primarily interested in genes that showed significant expression changes related to Ov that no longer showed a statistical association with Ov once the animals received Ov-HRT treatment. To obtain a general set of genes that fit this criterion, we filtered the results to include only genes that showed suggestive evidence of Ov association (unadjusted <italic toggle=\"yes\">p</italic> < 0.05) without Ov-HRT treatment and little evidence of Ov association with Ov-HRT treatment (unadjusted <italic toggle=\"yes\">p</italic> > 0.1). From this reduced set of 884 (PFC) and 663 genes (OC), we further filtered down to only those results that had suggestive evidence (<italic toggle=\"yes\">p</italic> < 0.05) of interaction between Ov and Ov-HRT treatment. In the OC and PFC, we identified 19 and 10 genes, respectively, with suggestive evidence for Ov-HRT effects (##TAB##0##Table 1##). Ten of these genes are known to interact with the estrogen receptors or its expression being associated with the levels of estrogen. These include the transient receptor potential vanilloid 6 (<italic toggle=\"yes\">TRVP6</italic>), adrenomedullin (<italic toggle=\"yes\">ADM</italic>), the glucose transporter 12 (<italic toggle=\"yes\">SLC2A12</italic>), supervillin (<italic toggle=\"yes\">SVIL</italic>), acyl-CoA synthetase 2 (<italic toggle=\"yes\">ACSF2</italic>), lymphotoxin B receptor (<italic toggle=\"yes\">LTBR</italic>), the hematopoietic PBX-interacting protein 1 (<italic toggle=\"yes\">PBXIP1</italic>), the fucosyltransferase 1 (<italic toggle=\"yes\">FUT1</italic>), neuromedin U (<italic toggle=\"yes\">NMU</italic>) and the Purkinje cell protein 4 (<italic toggle=\"yes\">PCP4</italic>). Furthermore, <italic toggle=\"yes\">TRVP6</italic> is known to contain estrogen-responsive element in its promoter. In the OC-specific network, <italic toggle=\"yes\">ADM</italic> was part of the insulin secretion pathway. And in the OC/PFC combined network, <italic toggle=\"yes\">PBXIP1</italic> was a member of the AMPK signaling pathway.</p>", "<title>Differential Exon Usage Analysis</title>", "<p id=\"P15\">These represent a unique set of genes that are being alternatively spliced in Ov animals and are mutually exclusive from the set of DE genes. Among these genes, <italic toggle=\"yes\">HADHB, MDH2</italic> and <italic toggle=\"yes\">ELMO1</italic> are known to interact with ERs and/or have EREs (i.e. <italic toggle=\"yes\">ELMO1</italic>). No exons were identified as significant in the test of interaction between Ov and Ov-HTR. Given the massive number of tests computed, the smaller sample size of the study, and the additional degrees of freedom needed to test the interaction between exon and Ov status (see <xref rid=\"S12\" ref-type=\"sec\">Methods</xref>), it is likely that we are underpowered to compute DEU analysis at this scale. Nonetheless, the 15 unique genes demonstrating significant DEU were included in the network analysis (##FIG##0##Figure 1##). <italic toggle=\"yes\">BMS1</italic>, <italic toggle=\"yes\">CACNA2D3</italic>, <italic toggle=\"yes\">DAPK1</italic> and <italic toggle=\"yes\">RGS6</italic> clustered into the MAPK signaling and ribosome biogenesis networks (##FIG##0##Figure 1##). In addition, we overlapped each of the 15 DEU genes with significant DMRs in the OC (FDR<0.05 and Sidak<0.05), because exon usage can often be influenced by DNAm. We did not identify any overlapping results between the genes that were mapped to our significant DMRs and the DEU genes.</p>", "<title>Differential Methylation Analysis</title>", "<p id=\"P16\">In the OC and PFC, 2.6 million and 2.9 million CpGs met the filtering (no missing values across samples and standard deviation of CpG methylation rate across all samples less than 5%), respectively. After computing association testing with each CpG in from each of those sets, and aggregating the CpG results into DMRs, we identified 254 (OC) and 457 (PFC) significant (Sidak’s <italic toggle=\"yes\">p</italic> < 0.05) DMRs associated with Ov (Table S1). 18 DMRs were shared across both brain regions (##TAB##1##Table 2##). All of them showed the same direction of change in DNAm, except for the E3 ubiquitin protein ligase, <italic toggle=\"yes\">TRIM36</italic>, and the SH3 binding kinase 1, <italic toggle=\"yes\">SBK1</italic>, that were hypomethylated in OC. Furthermore, <italic toggle=\"yes\">SBK1</italic> is both hypermethylated and downregulated in the PFC. In the PFC, 5 DMRs showed overlap with DE genes (##TAB##1##Table 2##). The <italic toggle=\"yes\">UBEM2</italic> (ubiquitin conjugating enzyme 2) DMR is very strongly replicated across both brain regions, which again suggests this is unlikely to be a false positive (##FIG##1##Figure 2##). <italic toggle=\"yes\">UBE2M</italic> also shows suggestive evidence of differential expression in the OC (##FIG##1##Figure 2##). In this case, the DMR (OC: 157 DMCs, PFC: 145 DMCs) is hypomethylated in both brain areas with Ov as compared to OI, and the gene is upregulated in the OC of Ov with or without HRT. The DMR is located ~3 kb upstream of the <italic toggle=\"yes\">UBE2M</italic> transcription start site, and mapping to the last exon of the <italic toggle=\"yes\">MZF1</italic> gene. Differential expression was detected for <italic toggle=\"yes\">UBE2M</italic> but not for the <italic toggle=\"yes\">MZF1</italic> gene. According to the reporter assay results, the subregion within this DMR has promoter activity (Figure S1), as shown by an increased luciferase relative light units (RLUs) as compared to the control vectors (PGL3 enhancer vector, Figure S1).</p>", "<p id=\"P17\">To explore if Ov-HRT treatment modulates the effect of Ov, we also computed an interaction test between Ov-HRT treatment and Ov status. As expected for the sample size and the nature of combining CpGs to build DMRs, FDR adjustment left no DMRs. <italic toggle=\"yes\">LTBR</italic>, as mentioned above, is a DE gene that is also a significant DMR in the PFC (##FIG##2##Figure 3##). The DMR contains 57 DMCs, and maps to the promoter and the first alternative exons of this gene. The DMR is significantly hypermethylated in Ov samples as compared to OI samples (average DNAm = 27% vs 14%; respectively), while in Ov-HRT the methylation level was not different to OI or Ov (average DNAm = 20%). The promoter/enhancer assays and the position of the DMR suggest that this DMR is functioning as a promoter (Figures S1). In agreement with this DMR functioning as a promoter, we observed a downregulation of <italic toggle=\"yes\">LTBR</italic> with the hypermethylated DMR in Ov (##FIG##2##Figure 3##), while the expression in OI and Ov-HRT did not differ.</p>", "<title>Network analysis</title>", "<p id=\"P18\">Network analysis for each brain region was completed independently for each tissue. In the OC we identified 150 DE genes and 254 DMRs, while we identified 128 DE genes and 457 DMRs significantly associated with Ov in the PFC (Table S1). In the OC, 23 genes clustered into 3 MCODE clusters (MCODE score > 4.0) and in the PFC, 48 genes clustered into 5 MCODE clusters (MCODE score > 4.0) (Figures S2 & S3). Pathway analysis shows different pathways enriched in a tissue-specific manner, highlighting the particular function of each brain region. For instance, in the OC there was an enrichment in the regulation of insulin secretion, with <italic toggle=\"yes\">UBE2M</italic> and <italic toggle=\"yes\">ADM</italic> being hypermethylated and the estrogen receptor <italic toggle=\"yes\">ESR2</italic> being hypomethylated in Ov. In the PFC, there was an enrichment in GPCR signaling, which included the pro-opiomelanocortin <italic toggle=\"yes\">POMC</italic>, the G protein signaling 17 (<italic toggle=\"yes\">RGS17</italic>) or the G protein subunit gamma 2 and 7 (<italic toggle=\"yes\">GNG2</italic> and <italic toggle=\"yes\">GNG7</italic>), among others. The ankyrin signaling pathway and the HSP90 chaperone mediated activation of steroid hormone receptors were also enriched in the PFC with Ov. These pathways included <italic toggle=\"yes\">ANK1</italic> and <italic toggle=\"yes\">ANK3</italic>, several dyneins (<italic toggle=\"yes\">DYNLL2</italic>, <italic toggle=\"yes\">DYNLT3</italic> and <italic toggle=\"yes\">DYNC1LI2</italic>), and the FK506-binding protein 4 (<italic toggle=\"yes\">FKBP4</italic>).</p>", "<p id=\"P19\">Given that the different brain regions under study play critical roles in processing cognitive functions, after filtering and manual curation to identify genes that were replicated across brain regions for omics, 891 total genes were submitted for a global integrated network analysis (##FIG##0##Figure 1##). Of the 891 genes, 127 genes clustered in 7 MCODE clusters (MCODE score > 4.0). The largest of the clusters contained 67 genes and was strongly enriched for “MAPK signaling”. Genes with differential expression and methylation from both brain regions are similarly represented in this pathway. For instance, and within the regulators of G protein signaling (RGS) subcluster, all three members of the R12 family (<italic toggle=\"yes\">RGS10</italic>, <italic toggle=\"yes\">12</italic> and <italic toggle=\"yes\">14</italic>) were hypermethylated in Ov in the PFC. Exon 24 of the <italic toggle=\"yes\">RGS6</italic> was downregulated in the OC leading to the production of different RGS6 transcripts under Ov conditions (Figure S4). This brain specificity in expression of RGS members may suggest activation/inhibition of different intracellular signaling cascades by brain region. One of the strongest results from the DNAm analyses, <italic toggle=\"yes\">UBE2M</italic>, appears to play a key role in the regulation of this network (##FIG##0##Figure 1##). <italic toggle=\"yes\">UBE2M</italic> interestingly links into the pathway through 17β-estradiol receptor, <italic toggle=\"yes\">ESR2</italic>, which is hypermethylated in Ov in the OC. Other key networks worth highlighting include those that were enriched for “AMPK signaling” and several signaling pathways involved in transcription and translation regulation (i.e. ribosome biogenesis; ##FIG##0##Figure 1##). Interestingly, an enrichment in “Dopaminergic synapse” was, almost exclusively, found in the PFC. Within this network, the family of kinesin motor proteins, KIF, were primarily downregulated (<italic toggle=\"yes\">KIF5A-C</italic>; <italic toggle=\"yes\">KIF1B</italic>, <italic toggle=\"yes\">KIF4A</italic>, <italic toggle=\"yes\">KIFAP3</italic>) in Ov. Among this family, <italic toggle=\"yes\">KIF26B</italic> was hypermethylated in Ov, but DNAm levels went down with Ov-HRT, more similar to the levels in OI.</p>", "<p id=\"P20\">Using the 26 unique annotated genes from the interaction analysis across both brain regions, pathway enrichment suggests only two potential networks, HIF-1 signaling (<italic toggle=\"yes\">p</italic>=0.0088, <italic toggle=\"yes\">LTBR</italic>, <italic toggle=\"yes\">TIMP1</italic>) and neuroactive ligand-receptor interaction (<italic toggle=\"yes\">p</italic>=0.0096, <italic toggle=\"yes\">P2RX1</italic>, <italic toggle=\"yes\">NMU</italic>, <italic toggle=\"yes\">ADM</italic>), suggesting that these genes and pathways may be working together in mediating the effects of HRT in Ov (##TAB##0##Table 1##).</p>", "<p id=\"P21\">28 genes showed overlap in consistent directions either across the two brain regions or both omics. These genes replicated across both regions or showed significant methylation and expression effects (##TAB##1##Table 2##). The network analysis highlights <italic toggle=\"yes\">UBE2M</italic>, <italic toggle=\"yes\">AURKC</italic>, <italic toggle=\"yes\">SGTA</italic>, <italic toggle=\"yes\">RAB12</italic>, <italic toggle=\"yes\">KIFAP3</italic>, <italic toggle=\"yes\">NCOR2</italic>, <italic toggle=\"yes\">TAF1B</italic>, <italic toggle=\"yes\">ZBTB7A</italic>, and <italic toggle=\"yes\">KCNG2</italic> as genes with potentially key biological importance related to Ov across both brain regions (##FIG##0##Figure 1##).</p>" ]	[ "<title>DISCUSSION</title>", "<title>Overall goals</title>", "<p id=\"P22\">This study examined the molecular effects of estrogen depletion at an older age on two different cortical regions, the OC and PFC, implicated in cognitive function. The OC controls visuospatial processing, distance and depth perception, color determination, object and face recognition, and memory formation [##UREF##5##53##, ##REF##17983964##54##]. While the PFC is involved in working memory, temporal processing, decision making, flexibility, and goal-oriented behavior [##REF##23684970##52##]. Damage to these brain regions contributes to cognitive decline in dementia patients. While limited in sample size, this study leveraged an NHP model that was sufficiently powered to detect significant differences in gene expression and DNAm because of our ability to tightly regulate the environment and obtain high quality and highly reproducible brain samples. With this NHP model of middle-aged female rhesus macaques, we identified highly translatable molecular changes in the brain that are linked with the E2 depletion associated with Ov. Given the natural depletion of E2 that occurs with age, these results present a novel understanding of the role E2 plays in the aging brain and how long-term immediate HRT treatment (~4 years) can reverse or palliate those changes to maintain the brain in an age-matched molecular profile.</p>", "<p id=\"P23\">Because of the established relationship between E2 levels, its broad molecular regulatory function, and cognition [##REF##25205317##20##, ##REF##27707975##30##], we expected to identify robust molecular differences in these cognitive-relevant brain regions associated with Ov. For this reason, we completed RNA-sequencing to determine the genes that were DE and genome-wide DNAm sequencing to determine the genomic regions that were differentially methylated in the brains of animals that had undergone Ov for ~4 years prior to necropsy. As expected, we detected a large number of DMRs as well as DE genes – sometimes genes that were both DE and differentially methylated in their respective promoter/enhancer regions (##FIG##0##Figures 1##–##FIG##1##2##). We note, however, that because these results are derived from heterogeneous bulk tissue that contains many cell types, we are unable to attribute these differences to actual changes in the cell-type molecular mechanisms linked to Ov. Instead, it is possible the changes we identify are driven by differences in the proportions of particular cell types. For example, <italic toggle=\"yes\">LTBR</italic> is a gene that is primarily expressed in microglial cells. The differences we see in DNAm and expression between groups may either be attributed to a change in the abundance of microglia seen between groups or an actual shift in DNAmlevels across cell types (or even just a large DNAm shift in microglia). A follow up study using single-cell RNA-Seq would need to be completed to determine what the real drivers are in most of these cases.</p>", "<title>Ov is associated with dramatic changes in neural signaling pathways</title>", "<p id=\"P24\">Pathway analysis revealed several networks of genes that changed with Ov (##FIG##0##Figure 1##) in both brain regions. Importantly, all the identified networks had at least one gene that was DE and/or differentially methylated in both brain regions, suggesting a common link to the same pathways across them. These include the voltage-gated potassium channel encoded by <italic toggle=\"yes\">KCNG2</italic> (proteasome, p=2.53×10<sup>−2</sup>), the TATA-box binding protein associated factor (<italic toggle=\"yes\">TAF1B</italic>, ribosome biogenesis, <italic toggle=\"yes\">p</italic>=8.41×10<sup>−6</sup>), the nuclear receptor corepressor 2 (<italic toggle=\"yes\">NCOR2</italic>, NOTCH signaling, <italic toggle=\"yes\">p</italic>=2.65×10<sup>−2</sup>), the Ras-related protein 12 (<italic toggle=\"yes\">RAB12</italic>, AMPK signaling, <italic toggle=\"yes\">p</italic>=2.96×10<sup>−2</sup>), and the kinesin associated protein 3 (<italic toggle=\"yes\">KIFAP3</italic>, dopaminergic synapse, <italic toggle=\"yes\">p</italic>=2.37×10<sup>−6</sup>). Interestingly, <italic toggle=\"yes\">KIFAP3</italic> was downregulated in the PFC of Ov-HRT females under a chronic obesogenic diet [##REF##34642852##55##]. In the current study, <italic toggle=\"yes\">KIFAP3</italic> was hypermethylated in the PFC with Ov as compared to OI, while the DNAm levels were similar to the group receiving HRT. These results highlight the role of this gene in the PFC and its responsiveness to the presence of E2, independently of the diet. Within the basal transcription factors network (<italic toggle=\"yes\">p</italic>=4.43×10<sup>−4</sup>), the zinc finger and BTB domain containing 7A factor (<italic toggle=\"yes\">ZBTB7A</italic>) is known to transcriptionally upregulate ERα expression by directly binding to the <italic toggle=\"yes\">ESR1</italic> promoter. In addition, ERα potentiates <italic toggle=\"yes\">ZBTB7A</italic> expression via a positive loop in breast cancer [##REF##30265334##56##]. In the PFC, <italic toggle=\"yes\">ZBTB7A</italic> was downregulated, probably due to the absence of E2 in Ov, and, given the role of this transcription factor in metabolism [##REF##36626981##57##], this downregulation could have implications in the regulation of brain metabolism.</p>", "<p id=\"P25\">The dopaminergic synapse pathway contained the <italic toggle=\"yes\">KIFAP3</italic> gene which was hypermethylated in both brain regions and contained a number of other members of the kinesin heavy chain proteins (i.e. <italic toggle=\"yes\">KIF1B</italic>, <italic toggle=\"yes\">4A</italic>, <italic toggle=\"yes\">5A, 5B</italic> and <italic toggle=\"yes\">5C</italic> were all downregulated in the PFC). Kinesins are molecular motors that transport cargos along microtubules [##UREF##6##58##]. These kinesin members are involved in transporting mitochondria, amyloid precursor protein vesicles, GABA and dopamine receptors, lysosomes, choline acetyl transferase and dopamine [##REF##30664247##59##–##REF##19451621##65##]. Our results suggest that with Ov there is a downregulation in kinesin expression that could be contributing to alterations in intracellular protein trafficking that could impact synaptic transmission.</p>", "<p id=\"P26\">One of the strongest biological pathways enriched in Ov was the MAPK signaling (enrichment <italic toggle=\"yes\">p</italic>=2.3×10<sup>−10</sup>). Prior evidence showed that E2 alters cellular components required for maintaining balance between active and inactive MAPKs. For example, crosstalk between phosphorylation and ubiquitination pathways can exert long-term changes in cellular processes through multiple feedback loops that ultimately impact apoptosis and cell proliferation [##REF##23902637##66##]. Among the members of the MAPK signaling pathway, the ERK 1 gene (<italic toggle=\"yes\">MAPK3</italic>) and the ubiquitination gene <italic toggle=\"yes\">UBE2M</italic> were hypomethylated, and several RGS proteins (<italic toggle=\"yes\">RGS 10, 12</italic> and <italic toggle=\"yes\">14</italic>) were hypermethylated in the PFC with Ov. Activation of ERK 1/2 subjected to G-protein coupled receptor-mediated signaling is regulated through RGS proteins [##REF##18047785##67##, ##REF##10936173##68##]. Although additional studies analyzing protein levels and activation/inhibition ratio of these molecules are needed, our results suggest that RGS protein activity might be downregulated, leading to less inhibition of ERK 1, which would be consequently upregulated (supported by the hypomethylated DMR mapping to <italic toggle=\"yes\">MAPK3</italic>). In addition, three genes are differentially methylated in both brain regions, <italic toggle=\"yes\">UBE2M</italic>, <italic toggle=\"yes\">SGTA</italic> and <italic toggle=\"yes\">AURKC</italic>. While little is known about the neural function of aurora kinase C (<italic toggle=\"yes\">AURKC</italic>), the <italic toggle=\"yes\">SGTA</italic> (small glutamine-rich tetratricopeptide repeat-containing protein alpha) encodes for a molecular co-chaperone that interacts with steroid receptors and heat shock chaperone proteins, i.e. HSP90AA1 (##FIG##0##Figure 1##), to regulate steroid receptor signaling, protein folding and conformation state, receptor stability, subcellular localization and intracellular trafficking [##REF##23818240##69##]. A study in yeast showed that Hsp90 functions to maintain the estrogen receptors in a high affinity hormone-binding conformation [##REF##10822011##70##]. Interestingly, <italic toggle=\"yes\">UBE2M</italic> involvement in the MAPK signaling network is through its interaction with the ERβ gene (<italic toggle=\"yes\">ESR2</italic>), that was hypermethylated in Ov (##FIG##0##Figure 1##). Given its robust association with Ov across both brain regions and both the transcriptome and methylome, <italic toggle=\"yes\">UBE2M</italic> is a strong result that should be heavily considered for further investigation. We confirmed that the DMR proximal to <italic toggle=\"yes\">UBE2M</italic> functions as a promoter (Figure S1), suggesting that changes in DNAm in this DMR may contribute to regulating its expression. UBE2M’s primary function is as a ubiquitin-protein transferase, involved in protein neddylation, which is a post-translational ubiquitin-like protein modification that plays pivotal roles in protein quality control and homeostasis. Neddylation, involving UBE2M and other enzymes, is a critical mechanism for targeting and degrading misfolded or damaged proteins, helping to maintain protein quality control within brain cells [##UREF##8##71##]. For instance, during the initial stages of AD, ubiquitin-proteasome proteolysis degrades the abnormal amyloid β peptides and hyperphosphorylated tau. But as the disease progresses, ubiquitination becomes ineffective at degrading the accumulating insoluble proteins, and neddylation seems to contribute to degradation with these abnormal proteins. In AD patients, neddylation mechanisms are dysregulated [##REF##15634231##72##], and neurons show accumulation of the neddylation enzyme NEDD8 in the cytoplasm and colocalization with ubiquitin and proteasome components in protein inclusions in the brain [##REF##15634231##72##, ##REF##12533840##73##]. Our results showed hypomethylation in both brain regions and upregulation of <italic toggle=\"yes\">UBE2M</italic> in the OC. While additional studies are needed to determine the cellular localization of <italic toggle=\"yes\">UBE2M</italic> and its role in protein neddylation, our results suggest that dysregulation of <italic toggle=\"yes\">UBE2M</italic> associated with E2 depletion may be a key player mediating the negative effects that lack of E2 has on brain function [##REF##26109339##74##]. Together, these results emphasize <italic toggle=\"yes\">SGTA</italic> and <italic toggle=\"yes\">UBE2M</italic>, through their direct (<italic toggle=\"yes\">ESR2)</italic> or indirect connections with the estrogen system, as critical mediators of the MAPK signaling cascade and its connections with synaptic function, neuroinflammation and neurodegeneration across brain regions [##REF##32707231##75##–##REF##31840000##78##].</p>", "<title>Immediate estradiol supplementation ameliorates molecular alterations linked to Ov</title>", "<p id=\"P27\">After identifying molecular changes associated with Ov, we were interested in understanding whether immediate (right after Ov) and long-term (over 4 years) E2 treatment reverses any of the changes linked to Ov. By identifying Ov-linked effects ameliorated by E2 treatment, the biological pathways that are directly impacted by estrogen levels become clearer. While E2 treatment ameliorates some of the behavioral and physiological changes seen following menopause in humans, the effects of E2 treatment on cognitive performance are still mixed [##REF##31364065##18##]. Such inconclusive results could be due to the differences in HRT timing, diet, and other confounding variables common in human studies. Contrarily, results in NHPs on the beneficial effects of HRT on cognition are more consistent [##REF##27707975##30##, ##REF##29952604##41##], probably due to the controlled experimental conditions. Thus, by understanding the specific genes altered by E2 in the brain with this animal model, we can begin to understand the biological pathways estrogen impacts and thereby develop more targeted therapeutics that specifically improve brain function and ultimately cognitive performance.</p>", "<p id=\"P28\">In the OC and PFC, we identified 19 and 10 genes, respectively, with suggestive evidence for E2 effects, where E2 appears to restore expression levels to a level that is similar to OI (##TAB##1##Table 2##). Among these 29 genes, the following are known to interact with the estrogen receptors, or its expression being associated with the levels of estrogen. The transient receptor potential vanilloid 6 (<italic toggle=\"yes\">TRPV6</italic>) is a highly Ca<sup>2+</sup>-selective channel that contains an ERE in its promoter [##REF##11741335##79##], and its regulation by estrogen has been proposed in peripheral tissues [##REF##17374692##80##] and the CNS, including cortex [##REF##28039038##81##]. Furthermore, in mice, hypothalamic levels of Trpv6 are susceptible to estradiol oscillations through the estrous cycle, with higher Trpv6 levels at the proestrous phase where estrogen levels are at their highest [##REF##28039038##81##]. In the OC, <italic toggle=\"yes\">TRPV6</italic> was upregulated with Ov, and the levels decreased with HRT. These results disagree with previously reported findings in the hippocampus [##REF##28039038##81##] and could stem from brain-specific differences in <italic toggle=\"yes\">TRPV6</italic> regulation. Adrenomedullin (<italic toggle=\"yes\">ADM</italic>) is a peptide exerting important functions in the periphery and CNS. In the uterus, studies revealed that ADM promoter is recognized by the ER in a ligand-dependent manner, and that there is a positive correlation between estrogen levels and <italic toggle=\"yes\">ADM</italic> gene expression [##REF##11739094##82##–##REF##16461929##85##]. In the CNS, ADM is known to contribute to the activation of the hypothalamic-pituitary-adrenal (HPA) axis through release of CRH [##REF##15271873##86##], and thus contributing to regulating hormonal responses to stress. In breast cancer cell lines, the insulin-sensitive glucose transporter 12 (<italic toggle=\"yes\">SLC2A12</italic>) protein levels are increased with estradiol [##REF##15389572##87##]. The fucosyltransferase 1 (<italic toggle=\"yes\">FUT1</italic>) is involved in the creation of a precursor of the H antigen, which is required for the final step in the synthesis of soluble A and B antigens, and its expression is mediated by the ESR2 [##REF##18434428##88##]. Neuromedin U (<italic toggle=\"yes\">NMU</italic>) is a gonadal peptide, which receptor, neuromedin U2 is expressed throughout the brain [##REF##15018811##89##]. It is suggested that <italic toggle=\"yes\">NMU</italic> has a protective role in neurodegenerative diseases [##REF##18336945##90##]. In particular, <italic toggle=\"yes\">NMU</italic> protects neuronal cell viability, and inhibits inflammation-induced memory impairments [##REF##18336945##90##]. Others have shown that estradiol levels following Ov in rats resulted alterations in <italic toggle=\"yes\">NMU</italic> levels in the brain [##REF##27872704##91##, ##REF##17726140##92##]. Although this effect seems to be estradiol dose-dependent, with low estradiol levels increasing <italic toggle=\"yes\">NMU</italic> expression levels, while this increment was only previously observed with progesterone supplementation [##REF##27872704##91##, ##REF##17726140##92##]. The Purkinje cell protein 4 (<italic toggle=\"yes\">PCP4</italic>) is a calmodulin-binding anti-apoptotic peptide in neural cells and an estrogen-inducible peptide in breast cancer cell lines [##REF##15514030##93##]. The hematopoietic PBX-interacting protein 1 (<italic toggle=\"yes\">PBXIP1</italic>) is a transcription factor involved in extracellular matrix organization and chromatin regulation. It is an estrogen receptor (ER) interacting protein that regulates estrogen-mediated breast cancer cell proliferation and tumorigenesis [##REF##34302919##94##]. <italic toggle=\"yes\">PBXIP1</italic> is a key gene in the AMPK signaling network that was altered by Ov (##FIG##0##Figure 1##). While the specific function of these genes in the cortex, or even in the brain, remains to be investigated, our results indicate that Ov and E2 treatment alter these genes (and likely associated pathways) to normalize them to those levels of age matched controls.</p>", "<p id=\"P29\">Enrichment analysis of these 29 DE genes revealed two small networks that were significant: HIF-1 signaling (<italic toggle=\"yes\">p</italic>=0.0088, <italic toggle=\"yes\">LTBR</italic>, <italic toggle=\"yes\">TIMP1</italic>) and neuroactive ligand-receptor interaction (<italic toggle=\"yes\">p</italic>=0.0096, <italic toggle=\"yes\">P2RX1</italic>, <italic toggle=\"yes\">NMU</italic>, <italic toggle=\"yes\">ADM</italic>). HIF-1 signaling has been previously linked to estradiol [##REF##26598706##95##] and has been shown to regulate neuroinflammation in traumatic brain injury [##REF##36815939##96##]. This network contains the lymphotoxin B receptor (<italic toggle=\"yes\">LTBR</italic>), which is one of our most promising results that demonstrates differential expression effects in both brain areas. We also identified a DMR mapping to <italic toggle=\"yes\">LTBR</italic> in the PFC (##FIG##1##Figure 2##). We confirmed that the DMR proximal to <italic toggle=\"yes\">LTBR</italic> is a promoter (Figure S2). LTBR has been mostly studied in lymphoid tissues, with data supporting its role as a regulator of inflammation [##REF##34335629##97##], a process known to be modulated by estrogen as well [##UREF##10##98##]. In fact, LTBR signaling can activate both canonical and alternative NF-κβ signaling to induce proinflammatory chemokines and cytokines [##REF##12387745##99##]. Although studies on the role of <italic toggle=\"yes\">LTBR</italic> in brain function remain mostly unexplored, a recent transcriptomic study identified <italic toggle=\"yes\">LTBR</italic> as a neuroinflammatory biomarker of AD [##UREF##11##100##]. More specifically, it has been recently shown that <italic toggle=\"yes\">LTBR</italic> is upregulated in microglia of aged (mean age of 94 years) brains as compared to middle-aged (mean age of 53 years) human brains [##UREF##12##101##], which could suggest that upregulated <italic toggle=\"yes\">LTBR</italic> levels might be mediating age-related neuroinflammation through microglial function. However, the relationship between <italic toggle=\"yes\">LTBR</italic> expression/function and microglial activation status remains unknown, and additional studies are needed to determine how or whether <italic toggle=\"yes\">LTBR</italic> mediates microglial activation. Others have shown that estrogen can inactivate microglia through the ERβ [##REF##23401502##102##]. Our results show increased DNAm levels and decreased levels of <italic toggle=\"yes\">LTBR</italic> expression with Ov (##FIG##1##Figure 2##), with <italic toggle=\"yes\">LTBR</italic> expression levels reverting to OI levels with HRT. As discussed earlier, it is possible that the changes in DNAm and gene expression seen with Ov-HRT are associated with a change in the proportions of cell types, namely microglia, with Ov-HRT. This is a limitation in this study, and we cannot exclude that such molecular changes are due to a change in the number of microglia present, their activation status or a combination of all. Nonetheless, it is evident from our results that understanding the role of <italic toggle=\"yes\">LTBR</italic> in microglia function would be of critical importance to understanding estrogen’s relationship to brain function.</p>" ]	[ "<title>CONCLUSIONS</title>", "<p id=\"P30\">This work highlights the importance of multiple omics across multiple brain regions to identify robust molecular signals linked to E2 regulation in the aging brain. By including multiple omics and more than one brain area, we were able to home in on biological effects that replicated across brain regions and thereby reduce noise from false positives. Importantly, this work represents a major step towards understanding molecular changes in the brain that are linked to Ov and how HRT may revert or protect against the negative consequences of a depletion in E2. Our findings indicate that the molecular profile of the cortical regions (OC and PFC) in the absence of E2 may lead to neuroinflammation or the dysregulation of critical processes, like intracellular axonal protein trafficking or protein ubiquitination, that are necessary for proper function of brain cells. Immediate HRT reverted these effects, at least partially, by bringing the epigenetic/transcriptomic profile of genes involved in neuroinflammation and other unknown functions, to that profile of age-matched OI females. It remains to be known if the molecular profile of the brain after HRT is more similar to that of younger OI brains, which we are currently investigating. Nonetheless, our results present real opportunities to discover novel therapeutics to slow cognitive decline caused by the lack of E2. Although our work needs further validation with larger cohorts, it also requires focused investigation of some of the genes we identified with very robust effects like <italic toggle=\"yes\">LTBR</italic> and <italic toggle=\"yes\">UBE2M</italic>. Moreover, because our studies were performed in a NHP preclinical animal model of human aging, the findings may have more immediate translational potential to clinical studies involving postmenopausal women.</p>" ]	[ "<p id=\"P1\">equal contribution</p>", "<p id=\"P2\">Author contributions:</p>", "<p id=\"P3\">SGK, HFU and RCJ designed the experiments. SGK and HFU provided the rhesus macaque samples. RCJ isolated all the DNA and RNA samples, and prepared all the omics libraries. DZ and JB conducted the reporter assays. DNA methylation and gene expression bioinformatic analyses were performed by KDZ and LJW, and KDZ advised and conducted on the appropriate statistical analyses to be performed in all the experiments. SGK, HFU, KDZ and RCJ and RCJ wrote the manuscript with the help of all the authors.</p>", "<p id=\"P4\">The postmenopausal decrease in circulating estradiol (E2) levels has been shown to contribute to several adverse physiological and psychiatric effects. To elucidate the molecular effects of E2 on the brain, we examined differential gene expression and DNA methylation (DNAm) patterns in the nonhuman primate brain following ovariectomy (Ov) and subsequent E2 treatment. We identified several dysregulated molecular networks, including MAPK signaling and dopaminergic synapse response, that are associated with ovariectomy and shared across two different brain areas, the occipital cortex (OC) and prefrontal cortex (PFC). The finding that hypomethylation (<italic toggle=\"yes\">p</italic>=1.6×10<sup>−51</sup>) and upregulation (<italic toggle=\"yes\">p</italic>=3.8×10<sup>−3</sup>) of <italic toggle=\"yes\">UBE2M</italic> across both brain regions, provide strong evidence for molecular differences in the brain induced by E2 depletion. Additionally, differential expression (<italic toggle=\"yes\">p</italic>=1.9×10<sup>−4</sup>; interaction <italic toggle=\"yes\">p</italic>=3.5×10<sup>−2</sup>) of <italic toggle=\"yes\">LTBR</italic> in the PFC, provides further support for the role E2 plays in the brain, by demonstrating that the regulation of some genes that are altered by ovariectomy may also be modulated by Ov followed by hormone replacement therapy (HRT). These results present real opportunities to understand the specific biological mechanisms that are altered with depleted E2. Given E2’s potential role in cognitive decline and neuroinflammation, our findings could lead to the discovery of novel therapeutics to slow cognitive decline. Together, this work represents a major step towards understanding molecular changes in the brain that are caused by ovariectomy and how E2 treatment may revert or protect against the negative neuro-related consequences caused by a depletion in estrogen as women approach menopause.</p>" ]	[]	[ "<title>Grant support:</title>", "<p id=\"P46\">National Institutes of Health Grants: P30 AG066518, P51 OD011092, RF1 AG062220</p>", "<title>Data Sharing:</title>", "<p id=\"P47\">The data that support the findings of this study are available on GEO under the following accession number: TBD</p>" ]	[ "<fig position=\"float\" id=\"F1\"><label>FIGURE 1 –</label><caption><title>Biological networks of ovariectomy-related changes in gene expression and DNA methylation.</title><p id=\"P49\">Protein interactions were obtained from STRING’s protein interaction database. MCODE was used to find tightly connected clusters of interactions that are labeled according to function defined in Gene Ontology biological processes (enrichment p-value listed). The color of the nodes reflects the tissue where the gene was identified while the shape of the nodes reflect which omics analysis the gene was identified in. The size of the node reflects statistical significance with larger nodes, like <italic toggle=\"yes\">UBE2M</italic>, being more significant.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>FIGURE 2 –</label><caption><title>Ovariectomy-related changes in gene expression and methylation of <italic toggle=\"yes\">UBE2M</italic>.</title><p id=\"P50\"><italic toggle=\"yes\">UBE2M</italic> was a gene that demonstrated significant differences in methylation across both brain regions. In addition, it demonstrated significant differences in expression in the OC. The DMR and CpG island that was annotated to <italic toggle=\"yes\">UBE2M</italic> resides upstream of <italic toggle=\"yes\">UBE2M</italic> in the same genomic region as the <italic toggle=\"yes\">MZF1</italic> gene and appears to be hypo-methylated with ovariectomy. Expression of <italic toggle=\"yes\">UBE2M</italic> increases with ovariectomy, regardless of E2 treatment and average DNA methylation rates are not significantly altered with E2 treatment.</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>FIGURE 3 –</label><caption><title>Ovariectomy-related changes in gene expression and methylation of <italic toggle=\"yes\">LTBR</italic>.</title><p id=\"P51\"><italic toggle=\"yes\">LTBR</italic> was a gene that demonstrated differences in expression associated with ovariectomy across both brain regions. In addition, it demonstrated significant differences in methylation in the PFC (but no significant DMR was identified in the OC). The DMR and CpG island that was annotated to <italic toggle=\"yes\">LTBR</italic> resides just upstream of <italic toggle=\"yes\">LTBR</italic> and appears to be hyper-methylated with ovariectomy. With average methylation rates, it appears that the hyper-methylation in this region that is caused by Ov is modulated by estradiol treatment. Corresponding expression of <italic toggle=\"yes\">LTBR</italic> decreases with ovariectomy, but appears to be rescued to some degree with E2 treatment, particularly in the Occipital cortex. Near the same region of the DMR, lie a number of cis-acting eQTLs associated with <italic toggle=\"yes\">LTBR</italic> expression that are also known GWAS variants for a variety of disorders – many of which are related to immune-disorders.</p></caption></fig>" ]	[ "<table-wrap position=\"float\" id=\"T1\"><label>TABLE 1 –</label><caption><title>Interaction Results.</title><p id=\"P52\">Genes that demonstrate suggestive evidence (<italic toggle=\"yes\">p</italic> < 0.05) for a modifying E2 result.</p></caption><table frame=\"box\" rules=\"all\"><colgroup span=\"1\"><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/></colgroup><thead><tr><th align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Ensembl ID</th><th align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Gene Name</th><th align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">OVX log2FC</th><th align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">OVX <italic toggle=\"yes\">p</italic></th><th align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Estradiol log2FC</th><th align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Estradiol <italic toggle=\"yes\">p</italic></th><th align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Interaction <italic toggle=\"yes\">p</italic></th><th align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Tissue</th></tr></thead><tbody><tr><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">ENSMMUG00000008260</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">\n<bold>\n<italic toggle=\"yes\">P2RX1</italic>\n</bold>\n</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">−1.65</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">6.10×10<sup>−05</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">0.37</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">5.81×10<sup>−01</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">9.64×10<sup>−04</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">OC</td></tr><tr><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">ENSMMUG00000016219</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">TRPV6</italic>\n</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">0.93</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">1.58×10<sup>−02</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">−0.43</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">4.67×10<sup>−01</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">2.00×10<sup>−03</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">OC</td></tr><tr><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">ENSMMUG00000049314</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">DUSP2</italic>\n</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">0.61</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">1.27×10<sup>−02</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">−0.13</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">6.35×10<sup>−01</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">2.46×10<sup>−03</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">OC</td></tr><tr><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">ENSMMUG00000020213</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">LTBR</italic>\n</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">−0.72</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">2.90×10<sup>−03</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">0.18</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">6.06×10<sup>−01</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">4.09×10<sup>−03</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">OC</td></tr><tr><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">ENSMMUG00000011426</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">TIMP1</italic>\n</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">−0.40</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">2.97×10<sup>−02</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">0.23</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">1.66×10<sup>−01</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">4.16×10<sup>−03</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">PFC</td></tr><tr><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">ENSMMUG00000063192</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">SOX17</italic>\n</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">−0.98</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">8.29×10<sup>−03</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">0.08</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">8.63×10<sup>−01</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">7.62×10<sup>−03</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">OC</td></tr><tr><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">ENSMMUG00000000662</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">GBP3</italic>\n</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">−0.43</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">6.81×10<sup>−03</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">0.45</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">1.51×10<sup>−01</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">1.10×10<sup>−02</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">PFC</td></tr><tr><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">ENSMMUG00000007604</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">PODN</italic>\n</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">−0.77</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">2.92×10<sup>−02</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">0.52</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">3.32×10<sup>−01</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">1.14×10<sup>−02</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">OC</td></tr><tr><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">ENSMMUG00000016387</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">ADM</italic>\n</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">0.61</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">4.31×10<sup>−02</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">−0.18</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">5.82×10<sup>−01</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">1.29×10<sup>−02</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">OC</td></tr><tr><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">ENSMMUG00000016027</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">SAD1</italic>\n</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">0.49</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">3.25×10<sup>−02</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">0.02</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">9.32×10<sup>−01</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">1.63×10<sup>−02</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">OC</td></tr><tr><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">ENSMMUG00000017327</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">SLAMF7</italic>\n</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">−1.72</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">1.62×10<sup>−02</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">0.35</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">6.04×10<sup>−01</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">1.79×10<sup>−02</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">PFC</td></tr><tr><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">ENSMMUG00000022921</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">SLC2A12</italic>\n</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">−0.44</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">1.41×10<sup>−02</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">0.06</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">7.74×10<sup>−01</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">1.81×10<sup>−02</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">OC</td></tr><tr><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">ENSMMUG00000002422</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">AEBP1</italic>\n</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">−0.93</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">5.65×10<sup>−03</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">0.43</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">3.82×10<sup>−01</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">2.63×10<sup>−02</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">OC</td></tr><tr><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">ENSMMUG00000001545</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\"/><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">0.64</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">1.22×10<sup>−02</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">−0.02</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">9.47×10<sup>−01</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">2.94×10<sup>−02</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">OC</td></tr><tr><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">ENSMMUG00000020346</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">CPXM2</italic>\n</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">−1.15</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">3.70×10<sup>−03</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">0.24</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">5.62×10<sup>−01</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">2.99×10<sup>−02</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">OC</td></tr><tr><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">ENSMMUG00000025057</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">SNORD14</italic>\n</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">0.70</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">3.37×10<sup>−02</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">−0.30</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">4.81×10<sup>−01</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">3.10×10<sup>−02</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">PFC</td></tr><tr><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">ENSMMUG00000064609</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">LIN28B</italic>\n</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">0.87</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">1.51×10<sup>−03</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">0.11</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">7.45×10<sup>−01</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">3.15×10<sup>−02</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">PFC</td></tr><tr><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">ENSMMUG00000022741</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">WFIKKN2</italic>\n</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">−0.94</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">7.49×10<sup>−03</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">0.07</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">8.89×10<sup>−01</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">3.33×10<sup>−02</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">OC</td></tr><tr><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">ENSMMUG00000001374</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">SVIL</italic>\n</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">−0.47</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">2.61×10<sup>−02</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">0.13</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">6.37×10<sup>−01</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">3.56×10<sup>−02</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">OC</td></tr><tr><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">ENSMMUG00000017236</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">ACSF2</italic>\n</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">−0.31</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">2.90×10<sup>−02</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">0.19</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">4.00×10<sup>−01</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">3.58×10<sup>−02</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">PFC</td></tr><tr><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">ENSMMUG00000020213</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">\n<bold>\n<italic toggle=\"yes\">LTBR</italic>\n</bold>\n</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">−0.86</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">1.90×10<sup>−04</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">−0.04</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">9.01×10<sup>−01</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">3.59×10<sup>−02</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">PFC</td></tr><tr><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">ENSMMUG00000006576</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">\n<bold>\n<italic toggle=\"yes\">PBXIP1</italic>\n</bold>\n</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">−0.59</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">1.35×10<sup>−04</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">−0.12</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">5.76×10<sup>−01</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">4.08×10<sup>−02</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">PFC</td></tr><tr><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">ENSMMUG00000004020</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">FUT1</italic>\n</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">0.58</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">4.57×10<sup>−02</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">−0.12</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">6.88×10<sup>−01</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">4.19×10<sup>−02</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">OC</td></tr><tr><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">ENSMMUG00000043692</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">NMU</italic>\n</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">1.30</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">1.41×10<sup>−03</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">0.36</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">1.74×10<sup>−01</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">4.21×10<sup>−02</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">OC</td></tr><tr><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">ENSMMUG00000041181</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">PCP4</italic>\n</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">0.72</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">3.37×10<sup>−02</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">−0.05</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">8.57×10<sup>−01</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">4.36×10<sup>−02</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">OC</td></tr><tr><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">ENSMMUG00000000984</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">VILL</italic>\n</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">1.07</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">1.81×10<sup>−02</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">0.21</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">5.86×10<sup>−01</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">4.51×10<sup>−02</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">OC</td></tr><tr><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">ENSMMUG00000017360</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">NT5DC2</italic>\n</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">−0.30</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">3.20×10<sup>−02</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">0.19</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">4.20×10<sup>−01</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">4.54×10<sup>−02</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">PFC</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T2\"><label>TABLE 2 –</label><caption><title>Overlapping Results.</title><p id=\"P54\">Genes that demonstrate either differential expression or methylation across both the occipital cortex and the prefrontal cortex. DMR stands for differentially methylated region and DEG stands for differentially expressed gene. “Effect” stands for either a log<sub>2</sub>(fold-change) or a difference in methylation rates. A positive effect is indicative of an increase with ovariectomy, while a negative effect indicates a decrease with ovariectomy. “Adjusted <italic toggle=\"yes\">p</italic>” is the <italic toggle=\"yes\">p</italic>-value adjusted for multiple comparisons, either an FDR adjustment for DEGs or Sidak’s adjustment for DMRs. For results that are both a DEG & DMR in the same tissue, the DMR effect and p-value are listed.</p></caption><table frame=\"box\" rules=\"groups\"><colgroup span=\"1\"><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/></colgroup><thead><tr><th rowspan=\"2\" align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" colspan=\"1\">Gene Name</th><th colspan=\"3\" align=\"center\" valign=\"bottom\" style=\"border-bottom: solid 1px; border-right: solid 1px\" rowspan=\"1\">Occipital Cortex</th><th colspan=\"3\" align=\"center\" valign=\"bottom\" style=\"border-bottom: solid 1px\" rowspan=\"1\">Prefrontal Cortex</th></tr><tr><th align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Effect</th><th align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Adjusted <italic toggle=\"yes\">p</italic></th><th align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">Dataset</th><th align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Effect</th><th align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Adjusted <italic toggle=\"yes\">p</italic></th><th align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Dataset</th></tr></thead><tbody><tr><td align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">UBE2M</italic>\n</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">−0.17</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">1.65×10<sup>−51</sup></td><td align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">DMR</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">−0.13</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">9.63×10<sup>−32</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">DMR</td></tr><tr><td align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">PLD6</italic>\n</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">−0.16</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">1.16×10<sup>−20</sup></td><td align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">DMR</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">−0.11</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">5.08×10<sup>−03</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">DMR</td></tr><tr><td align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">KCNG2</italic>\n</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">−0.08</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">4.74×10<sup>−19</sup></td><td align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">DMR</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">−0.05</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">1.33×10<sup>−08</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">DMR</td></tr><tr><td align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">RNF157</italic>\n</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">−0.14</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">5.94×10<sup>−12</sup></td><td align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">DMR</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">−0.42</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">1.34×10<sup>−02</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">DEG</td></tr><tr><td align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">PPIAL4G</italic>\n</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">−0.15</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">9.66×10<sup>−11</sup></td><td align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">DMR</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">−0.24</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">5.36×10<sup>−15</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">DMR</td></tr><tr><td align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">PTPRU</italic>\n</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">0.24</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">2.43×10<sup>−06</sup></td><td align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">DMR</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">0.11</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">7.68×10<sup>−11</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">DMR</td></tr><tr><td align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">CHSY1</italic>\n</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">0.10</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">4.88×10<sup>−05</sup></td><td align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">DMR</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">0.08</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">8.27×10<sup>−05</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">DMR</td></tr><tr><td align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">STX2</italic>\n</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">−0.06</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">6.29×10<sup>−05</sup></td><td align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">DMR</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">−0.13</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">2.70×10<sup>−03</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">DMR</td></tr><tr><td align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">TRIM36</italic>\n</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">−0.95</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">2.78×10<sup>−04</sup></td><td align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">DEG</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">0.12</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">1.03×10<sup>−02</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">DMR</td></tr><tr><td align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">KLK4</italic>\n</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">0.16</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">8.52×10<sup>−04</sup></td><td align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">DMR</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">0.09</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">1.44×10<sup>−03</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">DMR</td></tr><tr><td align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">NCOR2</italic>\n</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">−0.08</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">1.11×10<sup>−03</sup></td><td align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">DMR</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">−0.08</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">1.37×10<sup>−03</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">DMR</td></tr><tr><td align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">SBK1</italic>\n</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">−0.10</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">1.19×10<sup>−03</sup></td><td align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">DMR</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">0.10</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">1.54×10<sup>−05</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">DEG & DMR</td></tr><tr><td align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">KIFAP3</italic>\n</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">0.03</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">1.35×10<sup>−03</sup></td><td align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">DMR</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">0.05</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">8.88×10<sup>−03</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">DMR</td></tr><tr><td align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">GAS6</italic>\n</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">−0.22</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">2.77×10<sup>−03</sup></td><td align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">DMR</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">−0.06</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">8.21×10<sup>−03</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">DMR</td></tr><tr><td align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">HAPLN4</italic>\n</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">0.15</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">3.58×10<sup>−03</sup></td><td align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">DMR</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">0.10</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">2.02×10<sup>−04</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">DMR</td></tr><tr><td align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">RAB12</italic>\n</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">−0.13</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">5.24×10<sup>−03</sup></td><td align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">DMR</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">−0.08</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">1.90×10<sup>−03</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">DMR</td></tr><tr><td align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">TAF1B</italic>\n</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">0.11</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">6.58×10<sup>−03</sup></td><td align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">DMR</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">0.12</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">1.84×10<sup>−16</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">DMR</td></tr><tr><td align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">ADGRD1</italic>\n</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">0.10</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">7.40×10<sup>−03</sup></td><td align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">DMR</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">0.10</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">2.84×10<sup>−03</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">DMR</td></tr><tr><td align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">SGTA</italic>\n</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">0.07</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">1.73×10<sup>−02</sup></td><td align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">DMR</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">0.09</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">3.24×10<sup>−02</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">DMR</td></tr><tr><td align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">ZBTB7A</italic>\n</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">−0.12</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">2.30×10<sup>−02</sup></td><td align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">DMR</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">−0.83</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">3.62×10<sup>−02</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">DEG</td></tr><tr><td align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">CEP170</italic>\n</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">−0.58</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">3.98×10<sup>−02</sup></td><td align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">DEG</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">−0.15</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">4.78×10<sup>−24</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">DMR</td></tr><tr><td align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">AURKC</italic>\n</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">0.16</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">4.21×10<sup>−02</sup></td><td align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">DMR</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">0.10</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">6.35×10<sup>−05</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">DMR</td></tr><tr><td align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">NUDT10</italic>\n</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">0.82</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">4.29×10<sup>−02</sup></td><td align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">DEG</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">1.00</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">3.82×10<sup>−02</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">DEG</td></tr><tr><td align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">MEIS2</italic>\n</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">−0.11</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">4.94×10<sup>−02</sup></td><td align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">DMR</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">−0.09</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">1.76×10<sup>−02</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">DMR</td></tr><tr><td align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">FBXO31</italic>\n</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">0.09</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">5.73×10<sup>−03</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">DEG & DMR</td></tr><tr><td align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">LTBR</italic>\n</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">0.12</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">1.88×10<sup>−16</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">DEG & DMR</td></tr><tr><td align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">ZNF423</italic>\n</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">0.07</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">3.90×10<sup>−03</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">DEG & DMR</td></tr><tr><td align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">AATF</italic>\n</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"bottom\" style=\"border-right: solid 1px\" rowspan=\"1\" colspan=\"1\">-</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">0.12</td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">3.02×10<sup>−02</sup></td><td align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">DEG & DMR</td></tr></tbody></table></table-wrap>" ]	[]	[]	[]	[]	[]	[]	[ "<fn-group><fn fn-type=\"COI-statement\" id=\"FN3\"><p id=\"P48\">Conflict of Interest statement: The authors declare no conflict of interest</p></fn></fn-group>", "<table-wrap-foot><fn id=\"TFN1\"><p id=\"P53\">Genes in bold were DE and/or differentially methylated across brain areas.</p></fn></table-wrap-foot>" ]	[ "<graphic xlink:href=\"nihpp-2023.12.18.572105v1-f0001\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-2023.12.18.572105v1-f0002\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-2023.12.18.572105v1-f0003\" position=\"float\"/>" ]	[]	[{"label": ["11."], "surname": ["Boulware", "Heisler", "Frick"], "given-names": ["MI", "JD", "KM"], "article-title": ["The memory-enhancing effects of hippocampal estrogen receptor activation involve metabotropic glutamate receptor signaling. The Journal of neuroscience : the official journal of the"], "source": ["Society for Neuroscience"], "year": ["2013"], "volume": ["33"], "issue": ["38"], "fpage": ["15184"], "lpage": ["94"], "comment": ["Epub 2013/09/21."], "pub-id": ["10.1523/JNEUROSCI.1716-13.2013"]}, {"label": ["29."], "surname": ["Khadilkar"], "given-names": ["SS"], "article-title": ["Post-reproductive Health: Window of Opportunity for Preventing Comorbidities"], "source": ["J Obstet Gynaecol India"], "year": ["2019"], "volume": ["69"], "issue": ["1"], "fpage": ["1"], "lpage": ["5"], "comment": ["Epub 2019/03/01."], "pub-id": ["10.1007/s13224-019-01202-w"]}, {"label": ["36."], "surname": ["Brann", "Lu", "Wang", "Sareddy", "Pratap", "Zhang"], "given-names": ["DW", "Y", "J", "GR", "UP", "Q"], "article-title": ["Neuron-Derived Estrogen-A Key Neuromodulator in Synaptic Function and Memory"], "source": ["Int J Mol Sci"], "year": ["2021"], "volume": ["22"], "issue": ["24"], "comment": ["Epub 2021/12/25."], "pub-id": ["10.3390/ijms222413242"]}, {"label": ["43."], "surname": ["Urbanski", "Appleman", "Nilaver", "Kohama"], "given-names": ["HF", "M. L.", "B.", "S. 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PMC10769310	38187645	[ "<title>Introduction</title>", "<p id=\"P4\">Transposable elements (TEs) are mobile, repetitive DNA sequences that are found in nearly all eukaryotic genomes. Typically, TEs are vertically inherited from parents to offspring within a species (##REF##32955944##Wells and Feschotte, 2020##). However, transmission of TEs between different species can also occur through the process of horizontal transposon transfer (HTT) (##REF##2155157##Daniels et al., 1990##). With the widespread availability of whole genome sequencing data, HTT has been increasingly recognized as an important phenomenon in the evolution of eukaryotic genomes (##REF##20591532##Schaack et al., 2010##; ##UREF##8##Wallau et al., 2012##; ##UREF##0##Gilbert and Feschotte, 2018##; ##REF##29422954##Wallau et al., 2018##).</p>", "<p id=\"P5\">HTT events can be identified using different sources of evolutionary evidence including phylogenetic incongruence between host genomes and TE sequences, unexpectedly high sequence similarity between TEs from divergent species, or the patchy distribution of a TE family across a set of closely-related lineages (##UREF##8##Wallau et al., 2012##; ##UREF##5##Peccoud et al., 2018##). Based on these criteria, an increasing number of HTT events have been identified across the tree of life, with many examples in plants and animals (##REF##25940562##Dotto et al., 2015##). While the number of HTT events detected in fungi is still relatively rare (##REF##7679935##Dobinson et al., 1993##; ##REF##11919292##Daboussi et al., 2002##; ##REF##18677522##Novikova et al., 2009##; ##REF##22800085##Amyotte et al., 2012##; ##UREF##7##Sarilar et al., 2015##), a growing number of HTT events have been identified among <italic toggle=\"yes\">Saccharomyces</italic> yeast species (##REF##15704235##Liti et al., 2005##; ##REF##23226439##Carr et al., 2012##; ##REF##29942366##Bergman, 2018##; ##REF##32084126##Czaja et al., 2020##; ##REF##34115140##Bleykasten-Grosshans et al., 2021##). As in other taxa, previous studies reporting HTT in yeast have typically focused on detecting their existence. However, the timing, geographic location, identity of donor/recipient lineages, and consequences of these HTT events remain unknown.</p>", "<p id=\"P6\">The Ty4 long-terminal repeat (LTR) retrotransposon family is a promising model to understand the process and impact of HTT in <italic toggle=\"yes\">Saccharomyces</italic> yeasts. Ty4 elements – like other members of the Ty1/Copia superfamily – are composed of two overlapping ORFs (<italic toggle=\"yes\">gag</italic> and <italic toggle=\"yes\">pol</italic>) flanked by LTRs (##REF##1328182##Janetzky and Lehle, 1992##; ##REF##1333437##Stucka et al., 1992##). The founding member of the Ty4 family was first identified in <italic toggle=\"yes\">S. cerevisiae</italic> (##REF##2548153##Stucka et al., 1989##) and defined the eponymous Ty4 subfamily. After the initial discovery of the Ty4 subfamily, ##REF##12045146##Neuveglise et al. (2002)## described a related subfamily called Tsu4 in the distant congener <italic toggle=\"yes\">S. uvarum</italic>. More recently, ##REF##29942366##Bergman (2018)## reported the unexpected presence of Tsu4 sequences in <italic toggle=\"yes\">S. paradoxus, S. cerevisiae</italic>, and <italic toggle=\"yes\">S. mikatae</italic> and proposed these observations could be explained by HTT events from a donor related to <italic toggle=\"yes\">S. uvarum</italic> or its sister species <italic toggle=\"yes\">S. eubayanus</italic>. The strongest evidence for HTT involving the Tsu4 subfamily was found in <italic toggle=\"yes\">S. paradoxus</italic> based on its patchy distribution among divergent <italic toggle=\"yes\">S. paradoxus</italic> lineages, a high similarity between <italic toggle=\"yes\">S. paradoxus</italic> Tsu4 elements and those from <italic toggle=\"yes\">S. uvarum</italic> and <italic toggle=\"yes\">S. eubayanus</italic>, and discordance between the phylogeny of Tsu4 elements and the host tree of <italic toggle=\"yes\">Saccharomyces</italic> species. ##REF##29942366##Bergman (2018)## also reported evidence for a potential Tsu4 HTT event involving <italic toggle=\"yes\">S. cerevisiae</italic> based on the presence of one Tsu4 full-length element (FLE) in a single strain (called 245) and its high sequence similarity with the Tsu4 sequences recently introduced into <italic toggle=\"yes\">S. paradoxus</italic>. Subsequently, ##REF##37524789##O’Donnell et al. (2023)## confirmed the presence of Tsu4 sequences in <italic toggle=\"yes\">S. cerevisiae</italic> in a different strain (called CQS), although it is currently unclear whether Tsu4 in strains 245 and CQS arose from the same or different HTT events. Likewise, ##REF##29942366##Bergman (2018)## provided limited evidence for a possible third Tsu4 HTT event in <italic toggle=\"yes\">S. mikatae</italic> based on discordance between the phylogeny of Tsu4 elements and the accepted tree for the genus <italic toggle=\"yes\">Saccharomyces</italic> (##REF##25657346##Borneman and Pretorius, 2015##).</p>", "<p id=\"P7\">Despite these advances, several important aspects of how HTT events affect the evolution of the Ty4 family in <italic toggle=\"yes\">Saccharomyces</italic> yeasts remain unresolved. First, the samples of <italic toggle=\"yes\">S. cerevisiae</italic> and <italic toggle=\"yes\">S. paradoxus</italic> genomes previously studied did not span the global diversity of these species. Thus the timing, geographic origin, and number of Tsu4 HTT events in these species is not fully understood. Second, previous inferences about the potential donor species for the Tsu4 HTT event into <italic toggle=\"yes\">S. paradoxus</italic> were limited by the lack of high-quality whole genome assemblies (WGAs) for <italic toggle=\"yes\">S. uvarum</italic> and <italic toggle=\"yes\">S. eubayanus</italic>. For example, the inference that Tsu4 elements from <italic toggle=\"yes\">S. eubayanus</italic> are most closely related to those transferred in <italic toggle=\"yes\">S. paradoxus</italic> (##REF##29942366##Bergman, 2018##) was made indirectly using genome data from the interspecific hybrid species <italic toggle=\"yes\">S. pastorianus</italic>, which contains subgenomes from <italic toggle=\"yes\">S. eubayanus</italic> and <italic toggle=\"yes\">S. cerevisiae</italic> (##REF##21873232##Libkind et al., 2011##; ##REF##26269586##Baker et al., 2015##; ##REF##26732986##Okuno et al., 2016##). Third, evidence for the putative Tsu4 HTT in <italic toggle=\"yes\">S. mikatae</italic> was based on a single element from a highly fragmented draft WGA (##REF##12775844##Cliften et al., 2003##), which are known to have incompletely reconstructed TE sequences (##REF##10731133##Myers et al., 2000##). Finally, the single Ty4 family member identified in a draft genome for <italic toggle=\"yes\">S. kudriavzevii</italic> (which is a key outgroup species to <italic toggle=\"yes\">S. cerevisiae</italic>, <italic toggle=\"yes\">S. paradoxus</italic>, and <italic toggle=\"yes\">S. mikatae</italic>) was found to be in an intermediate phylogenetic position to both the Ty4 and Tsu4 subfamilies (##REF##29942366##Bergman, 2018##). These results suggest that additional subfamilies in the Ty4 family remain to be discovered, or that recombination occurred between the Ty4 and Tsu4 lineages. Here we use large-scale short-read resequencing data and high-quality long-read WGAs from multiple species in the genus <italic toggle=\"yes\">Saccharomyces</italic> to address the impact of HTT on the evolution of the Ty4 family. Our results support a complex model for Ty4 family evolution in yeast that is shaped by recurrent HTT events involving the Tsu4 subfamily, lineage-specific extinction events, and creation of new retrotransposon clades through recombination between pre-existing subfamilies that co-occur in the same species because of HTT.</p>" ]	[ "<title>Materials and Methods</title>", "<title>DNA preparation, PacBio sequencing and genome assembly of <italic toggle=\"yes\">S. mikatae</italic> strains IFO 1815 and NBRC 10994</title>", "<p id=\"P37\">To prepare DNA for PacBio sequencing, single colonies of strain IFO 1815 (NCYC 2888) and NBRC 10994 was inoculated in 7 ml yeast extract-peptone-dextrose (YPD) liquid broth and cultured for ~24 hours at 30°C. DNA was isolated using the Wizard genomic DNA purification kit (Promega), and a PacBio library was prepared using the SMRTbell Express template prep kit (Pacific Biosciences), following the >15-kb size-selection protocol that includes Covaris g-TUBE shearing. PacBio sequencing was performed using the Sequel II instrument (sequencing kit v2.1). Whole genome assemblies were generated by performing Flye (v2.9) (##REF##30936562##Kolmogorov et al., 2019##) with parameters “–pacbio-raw -g 12m.” Raw PacBio reads and genome assemblies of both <italic toggle=\"yes\">S. mikatae</italic> strains have been submitted to NCBI under BioProject PRJNA934353.</p>", "<title>Genome sequence datasets</title>", "<p id=\"P38\">We compiled public short-read paired-end WGS datasets of multiple <italic toggle=\"yes\">Saccharomyces</italic> species to generate intraspecific phylogenies and survey Ty4/Tsu4 subfamily content within species. WGS datasets for each <italic toggle=\"yes\">Saccharomyces</italic> species were cleaned and normalized using the following steps: (i) raw reads with identical BioSample accession, strain name and sequencing strategy (i.e., sequencing instrument and layout) were merged according to the metatable provided by NCBI SRA; (ii) for the NCBI BioSample accessions that have multiple records after merging, only the record with the highest sequencing depth was retained; and (iii) all samples with sequencing depth <10× were removed. After these quality control processes, the integrated WGS dataset used in this study includes 2,404 <italic toggle=\"yes\">S. cerevisiae</italic> strains (##REF##23720455##Skelly et al., 2013##; ##REF##24425782##Bergstrom et al., 2014##; ##REF##26248006##Almeida et al., 2015##; ##REF##25750179##Marsit et al., 2015##; ##REF##25781462##Song et al., 2015##; ##REF##25840857##Strope et al., 2015##; ##REF##26782936##Barbosa et al., 2016##; ##REF##27152522##Drozdova et al., 2016##; ##REF##27610566##Gallone et al., 2016##; ##REF##27744274##Gayevskiy et al., 2016##; ##REF##27720622##Goncalves et al., 2016##; ##REF##27317778##Zhu et al., 2016##; ##REF##28192619##Coi et al., 2017##; ##UREF##2##Istace et al., 2017##; ##REF##29183975##Kita et al., 2017##; ##REF##28472365##Maclean et al., 2017##; ##REF##28416820##Yue et al., 2017##; ##REF##29982460##Barbosa et al., 2018##; ##REF##30002370##Duan et al., 2018##; ##REF##29746697##Legras et al., 2018##; ##REF##29643504##Peter et al., 2018##; ##REF##30835725##Fay et al., 2019##; ##REF##30715293##Kang et al., 2019##; ##REF##30246283##Ramazzotti et al., 2019##; ##REF##33653526##Basile et al., 2021##; ##REF##33301710##Bigey et al., 2021##; ##REF##33679656##Han et al., 2021##), 370 <italic toggle=\"yes\">S. paradoxus</italic> strains (##REF##24425782##Bergstrom et al., 2014##; ##UREF##4##Leducq et al., 2016##; ##REF##27988980##Xia et al., 2017##; ##REF##30804385##Eberlein et al., 2019##; ##REF##33011797##Koufopanou et al., 2020##; ##REF##34961959##He et al., 2022##; ##REF##36755033##Peris et al., 2023##), 18 <italic toggle=\"yes\">S. mikatae</italic> strains (##REF##36755033##Peris et al., 2023##), 62 <italic toggle=\"yes\">S. uvarum</italic> strains (##REF##22384314##Scannell et al., 2011##; ##REF##24887054##Almeida et al., 2014##; ##REF##34762651##Macias et al., 2021##; ##REF##36755033##Peris et al., 2023##), and 292 <italic toggle=\"yes\">S. eubayanus</italic> strains (##REF##27385107##Peris et al., 2016##; ##REF##31519660##Brouwers et al., 2019##; ##REF##31791228##Salazar et al., 2019##; ##REF##32251477##Langdon et al., 2020##; ##REF##32357148##Nespolo et al., 2020##; ##REF##36473696##Bergin et al., 2022##; ##REF##33755311##Mardones et al., 2022##; ##REF##35396494##Molinet et al., 2022##; ##REF##36755033##Peris et al., 2023##).</p>", "<p id=\"P39\">We complied public high-quality WGAs of <italic toggle=\"yes\">Saccharomyces</italic> species to identify Ty4/Tsu4 copies and extract FLEs for phylogenetic analysis. These WGAs were generated mostly with long-read sequencing data (PacBio or ONT), however we also included three WGAs generated with short-read sequencing data for <italic toggle=\"yes\">S. cerevisiae</italic> that showed evidence of Tsu4 internal regions (strain 245 from (##REF##25750179##Marsit et al., 2015##); strains AFQ and CDM from (##REF##29643504##Peter et al., 2018##)) and two WGAs generated with short-read sequencing data for <italic toggle=\"yes\">S. arboricola</italic> (strain H-6 from (##REF##23368932##Liti et al., 2013##) and strain ZP960 from (##REF##36755033##Peris et al., 2023##)) that represent the best available WGAs for this species. In total, we analyzed 183 <italic toggle=\"yes\">S. cerevisiae</italic> WGAs (##REF##25750179##Marsit et al., 2015##; ##REF##28416820##Yue et al., 2017##; ##REF##29643504##Peter et al., 2018##; ##REF##37524789##O’Donnell et al., 2023##), 12 <italic toggle=\"yes\">S. paradoxus</italic> WGAs (##REF##28416820##Yue et al., 2017##; ##REF##30804385##Eberlein et al., 2019##; ##REF##35049349##Chen et al., 2022b##), two <italic toggle=\"yes\">S. mikatae</italic> WGAs (this study), two <italic toggle=\"yes\">S. jurei</italic> WGAs (##REF##30097472##Naseeb et al., 2018##), three <italic toggle=\"yes\">S. kudriavzevii</italic> WGAs (##REF##30505317##Boonekamp et al., 2018##; ##REF##36441813##Salzberg et al., 2022##), two <italic toggle=\"yes\">S. arboricola</italic> WGAs (##REF##23368932##Liti et al., 2013##; ##REF##36755033##Peris et al., 2023##), two <italic toggle=\"yes\">S. uvarum</italic> WGAs (##REF##34989601##Chen et al., 2022a##; ##REF##36441813##Salzberg et al., 2022##), and four <italic toggle=\"yes\">S. eubayanus</italic> WGAs (##REF##30147677##Brickwedde et al., 2018##; ##REF##31519660##Brouwers et al., 2019##; ##REF##33755311##Mardones et al., 2022##).</p>", "<title>Ty4/Tsu4 copy number estimates</title>", "<p id=\"P40\">Copy number of LTRs and internal regions for the Ty4 and Tsu4 subfamilies were estimated by the coverage module of McClintock 2 (##REF##37452430##Chen et al., 2023##) using public short-read WGS datasets compiled above. For this analysis, we used McClintock revision 7aa5298 with parameters “--keep_intermediate minimal,coverage -m coverage”. Reference genomes used for WGS based copy number were as follows: <italic toggle=\"yes\">S. cerevisiae</italic> laboratory strain S288c (UCSC version sacCer2); <italic toggle=\"yes\">S. paradoxus</italic> European strain CBS432 (GCA_002079055.1) (##REF##28416820##Yue et al., 2017##); <italic toggle=\"yes\">S. uvarum</italic> European strain CBS 7001 (GCA_019953615.1) (##REF##34989601##Chen et al., 2022a##); <italic toggle=\"yes\">S. eubayanus</italic> Patagonia strain FM1318 (GCA_001298625.1) (##REF##26269586##Baker et al., 2015##). The Ty query library used for this analysis is the same as in ##REF##32084126##Czaja et al. (2020)##. The edge-trimming option in the McClintock coverage module was disabled by specifying “omit_edges” as “False” in the configuration file “config/coverage/coverage.py”. To reduce the influence of variable coverage and computing resources, samples with original fold-coverage greater than 100× were down-sampled to 100× using seqtk (v1.3) (<ext-link xlink:href=\"https://github.com/lh3/seqtk\" ext-link-type=\"uri\">https://github.com/lh3/seqtk</ext-link>).</p>", "<title>Phylogenetic analysis of host species</title>", "<p id=\"P41\">For each species, multi-sample variant calling was performed with BCFtools (v1.16, “bcftools mpileup -a 'FORMAT/DP' -Q 20 -q 20”; “bcftools call -f GQ,GP -mv – skip-variants indels”) (##REF##21903627##Li, 2011##) using BAM files generated by McClintock 2 (##REF##37452430##Chen et al., 2023##). Alignments with mapping quality less than 20, or bases with quality score less than 20, were removed. All indels were excluded from variant calling. Subsequently, the SNP matrix was filtered with BCFtools filter (v1.16) to discard sites with polymorphic probabilities under 99%; or genotypes with average supporting read depth less than 10×. Vcf2phylip (revision 0eb1b80) (<ext-link xlink:href=\"https://github.com/edgardomortiz/vcf2phylip/tree/v2.0\" ext-link-type=\"uri\">https://github.com/edgardomortiz/vcf2phylip/tree/v2.0</ext-link>) was executed to create multi-sequence alignments from the filtered VCF file. For all species other than <italic toggle=\"yes\">S. cerevisiae</italic>, maximum likelihood (ML) phylogenetic analysis was performed with RAxML (v8.2.12, “-f a -x 23333 -p 2333 –no-bfgs”) (##REF##24451623##Stamatakis, 2014##) applying GTRGAMMA model and 100 times of bootstrap resampling. The host species trees were mid-point rooted and visualized in R (v4.2.3) using packages phytools (v1.5_1) (##UREF##6##Revell, 2012##) and ggtree (v3.6.0) (##UREF##9##Yu et al., 2017##), respectively. For <italic toggle=\"yes\">S. cerevisiae</italic>, we followed the workflow in ##REF##29643504##Peter et al. (2018)##: “snpgdsVCF2GDS” and “snpgdsDiss” from package SNPRelate (v1.32.0) (##REF##23060615##Zheng et al., 2012##) were used to create the distance matrix from SNP data, and then function “bionj” from ape (v5.7_1) (##REF##14734327##Paradis et al., 2004##) was used to reconstruct the neighbor joining (NJ) tree.</p>", "<title>Annotation and sequence analysis of full-length elements from whole genome assemblies</title>", "<p id=\"P42\">Ty elements were annotated in WGAs using a RepeatMasker-based pipeline previously described in ##REF##32084126##Czaja et al. (2020)## updated to use RepeatMasker v4.0.9. Three <italic toggle=\"yes\">S. cerevisiae</italic> Ty4 elements with secondary FLE insertions from other Ty families were excluded from the final dataset to prevent multi-sequence alignment artifacts. <italic toggle=\"yes\">De novo</italic> LTR element prediction was performed using LTRharvest (“-seed 100 -minlenltr 100 -maxlenltr 1000 -mindistltr 1500 -maxdistltr 15000 -similar 80.0 -xdrop 5 -mat 2 -mis -2 -ins -3 -del -3 -mintsd 5 -maxtsd 5 -motif tgca -motifmis 0 -vic 60 -overlaps best”) followed by LTRdigest (PFAM models: PF00078, PF00665, PF01021, PF03732, PF07727, PF12384, PF13976) in GenomeTools 1.6.1 (##REF##18194517##Ellinghaus et al., 2008##; ##REF##19786494##Steinbiss et al., 2009##; ##REF##24091398##Gremme et al., 2013##; ##REF##33125078##Mistry et al., 2021##). Multi-sequence alignments of annotated FLEs were generated using MAFFT (v7.508) with default parameters (##REF##23329690##Katoh and Standley, 2013##). Sub-regions of FLEs in alignments were identified by aligning the “Tsu4p_nw” sequence from a public database of annotated canonical yeast transposons (<ext-link xlink:href=\"https://github.com/bergmanlab/yeast-transposons\" ext-link-type=\"uri\">https://github.com/bergmanlab/yeast-transposons</ext-link>) with the FLE dataset, then selecting sub-regions with seqkit subseq (v0.16.1) (##REF##27706213##Shen et al., 2016##). ML phylogenetic trees were reconstructed using RAxML (v8.2.12, “-f a -x 23333 -p 2333 –no-bfgs”) (##REF##24451623##Stamatakis, 2014##) with GTRGAMMA model and 100 bootstrap replicates. Phylogenetic network analysis was performed with SplitsTree4 (v4.15.1) (##REF##16221896##Huson and Bryant, 2006##) applying the “Uncorrect_P” model and “NeighborNet” method. Pairwise sequence divergence was calculated based on Kimura’s 2-parameter substitution model with 50-bp sliding window size and 10-bp step size with R package spider (GitHub revision e93c5b4) (##REF##22243808##Brown et al., 2012##) and phangorn (v2.11.1) (##REF##21169378##Schliep, 2011##) in R (v4.2.3).</p>", "<title>Phylogenetic analysis of Tsu4 strain-specific consensus sequences</title>", "<p id=\"P43\">Strain-specific consensus sequences were generated with BCFtools (v1.16, “bcftools mpileup -a 'FORMAT/DP' -Q 20 -q 20”; “bcftools call -f GQ,GP -mv–skip-variants indels; bcftools consensus”) (##REF##21903627##Li, 2011##) using BAM files previously generated by McClintock 2 (##REF##37452430##Chen et al., 2023##). The percentage of bases supported by mapped reads (i.e., breadth) was calculated with BEDtools (v2.30.0, “bedtools genomecov -d -split”) (##REF##20110278##Quinlan and Hall, 2010##). To avoid generating consensus sequences that are biased towards the Tsu4 reference sequence, samples with normalized Tsu4 depth less than 0.75 (estimated by McClintock coverage module) or breadth less than 0.9 (estimated by BEDtools genomecov) were removed from consensus sequence analysis. A multi-sequence alignment of strain-specific consensus sequences was generated using MAFFT (v7.508) (##REF##23329690##Katoh and Standley, 2013##) with default parameters. ML phylogenetic analysis was performed using RAxML (v8.2.12, “-f a -x 23333 -p 2333 –no-bfgs”) applying GTRGAMMA model and 100 bootstrap replicates (##REF##24451623##Stamatakis, 2014##). The ML tree was mid-point rooted using R package phytools (v1.2_0) (##UREF##6##Revell, 2012##) and then visualized using ggtree (v3.6.0) (##UREF##9##Yu et al., 2017##).</p>" ]	[ "<title>Results and Discussion</title>", "<p id=\"P8\">To better understand the history and impact of HTT events involving the Ty4 family in <italic toggle=\"yes\">Saccharomyces</italic>, we used four complementary genomic strategies. First, to more accurately infer the biogeographic distribution and ancestral states of the Ty4/Tsu4 subfamilies in <italic toggle=\"yes\">S. paradoxus</italic> and <italic toggle=\"yes\">S. cerevisiae</italic>, we investigated the presence/absence of Ty4/Tsu4 subfamily sequences in worldwide phylogenies for both species using unassembled short-read WGS datasets. For these analyses, we estimated copy number for LTRs and internal regions separately because recombination between LTRs within FLEs frequently excises internal sequences creating solo LTRs (##REF##6250062##Farabaugh and Fink, 1980##). Doing this allows us to interpret the presence of internal sequences as evidence of recent activity, and the presence of LTRs as evidence of both recent and past activity in a strain. Second, we annotated Ty4/Tsu4 copies in a dataset of over 200 high-quality WGAs for all species in the <italic toggle=\"yes\">Saccharomyces</italic> genus which allowed us to cross-validate Ty4/Tsu4 subfamily presence/absence data based on short-read WGS data, and to classify annotated copies at higher resolution into FLEs, truncated elements, and solo LTRs. We interpret the presence of FLEs in a WGA as evidence for recent activity in a strain, while truncated elements and solo LTRs represent past activity. Third, we generated phylogenetic networks and trees for internal coding regions of FLEs extracted from WGAs, which allowed us to directly investigate the molecular evolution of the Ty4 family across the entire <italic toggle=\"yes\">Saccharomyces</italic> genus. Fourth, we generated strain-specific consensus sequences from unassembled short-read WGS datasets, which allowed us to study Tsu4 subfamily evolution among <italic toggle=\"yes\">S. paradoxus</italic>, <italic toggle=\"yes\">S. cerevisiae</italic>, <italic toggle=\"yes\">S. eubayanus</italic> and <italic toggle=\"yes\">S. uvarum</italic> using larger samples of strains that lack high-quality WGAs.</p>", "<title>An ancestral Tsu4 HTT event occurred prior to radiation of indigenous American <italic toggle=\"yes\">S. paradoxus</italic> lineages</title>", "<p id=\"P9\">Using short-read WGS datasets for 370 <italic toggle=\"yes\">S. paradoxus</italic> strains, we reconstructed a maximum likelihood (ML) phylogenetic tree based on 713,556 SNPs that confirmed all major known lineages, sub-lineages, and their relationships (##FIG##0##Figure 1A##) (##REF##9103619##Naumov et al., 1997##; ##REF##17028086##Koufopanou et al., 2006##; ##REF##17306538##Kuehne et al., 2007##; ##REF##19212322##Liti et al., 2009##; ##UREF##4##Leducq et al., 2016##; ##UREF##1##Henault et al., 2019##; ##REF##30804385##Eberlein et al., 2019##; ##REF##34961959##He et al., 2022##). Worldwide diversity in <italic toggle=\"yes\">S. paradoxus</italic> splits into the two major known lineages: American and Eurasian. The American lineage includes several indigenous North American sub-lineages (SpB, SpC, SpC* and SpD), as well a lineage with a single strain from Hawaii. SpC* and SpD are hybrid lineages derived from crosses between SpB and SpC, and between SpB and SpC, respectively (##UREF##4##Leducq et al., 2016##; ##UREF##1##Henault et al., 2019##; ##REF##30804385##Eberlein et al., 2019##). The Hawaiian lineage has been reported to share similarity with either the SpB (##UREF##3##Leducq et al., 2014##) or SpC/SpC lineages (##REF##34961959##He et al., 2022##; ##REF##36755033##Peris et al., 2023##). Our analysis places the Hawaiian lineage as an outgroup to the SpC/SpC* lineages (circled number 3, ##FIG##0##Figure 1A##). Importantly, we note that <italic toggle=\"yes\">S. paradoxus</italic> lineage from S. America (circled number 2, ##FIG##0##Figure 1A##) – which was formerly considered a distinct species called <italic toggle=\"yes\">S. cariocanus</italic> (##REF##11034507##Naumov et al., 2000##) – is contained within the North American SpB sub-lineage (##REF##17028086##Koufopanou et al., 2006##; ##REF##16951060##Liti et al., 2006##, ##REF##19212322##2009##; ##REF##23286354##Hyma and Fay, 2013##; ##UREF##3##Leducq et al., 2014##; ##REF##34961959##He et al., 2022##). The Eurasian lineage includes sub-lineages indigenous to Europe, Far East Asia, and China, as well as a sub-lineage (SpA) composed of strains from North America that descend from a recent trans-oceanic migration event (##REF##17306538##Kuehne et al., 2007##; ##UREF##4##Leducq et al., 2016##).</p>", "<p id=\"P10\">By mapping estimated Ty4/Tsu4 subfamily copy number onto the global phylogeny for <italic toggle=\"yes\">S. paradoxus</italic>, we observe that Ty4 LTR sequences are present in all <italic toggle=\"yes\">S. paradoxus</italic> strains from both the Americas and Eurasia (##FIG##0##Figure 1B##). Strains from the Far East sub-lineage show a significantly higher Ty4 LTR copy number in comparison to other sub-lineages. Ty4 internal regions are essentially absent across the species, except in the LTR-rich Far East sub-lineage (##FIG##0##Figure 1B##). In contrast, Tsu4 LTR sequences are only found in indigenous American strains and absent from strains with a Eurasian origin (##FIG##0##Figure 1C##). Tsu4 internal sequences are found in all indigenous American sub-lineages (SpB, SpC, SpC, SpD, and Hawaii) with highly variable copy number (##FIG##0##Figure 1C##). Notably, we found no Tsu4 sequences in the Eurasian-derived North American SpA sub-lineage (see also (##REF##32955438##Henault et al., 2020##)), which shows no evidence of admixture with indigenous American sub-lineages after secondary contact (##REF##17306538##Kuehne et al., 2007##; ##REF##23286354##Hyma and Fay, 2013##; ##UREF##3##Leducq et al., 2014##, ##UREF##4##2016##; ##REF##34961959##He et al., 2022##).</p>", "<p id=\"P11\">Analysis of Ty4/Tsu4 subfamily content in Ty4 sequences in a smaller dataset of 12 long-read WGAs that samples all major <italic toggle=\"yes\">S. paradoxus</italic> lineages cross-validated results based on short-read WGS data. Ty4 solo LTRs were found in all strains but Ty4 FLEs were only found in the Far East strain N44 (##SUPPL##0##Table S1##). In contrast, Tsu4 solo LTRs are identified in the nine indigenous American <italic toggle=\"yes\">S. paradoxus</italic> strains, and are absent from the other three strains with Eurasian origin (CBS432, N44, and LL2012_001) (##SUPPL##0##Table S1##). At least one Tsu4 FLE is identified in all indigenous American <italic toggle=\"yes\">S. paradoxus</italic> strains with WGAs except for the SpB strain DG1768 that is commonly used in retromobility studies (##REF##35049349##Chen et al., 2022b##) (circled number 1, ##FIG##0##Figure 1A##). As previously reported (##REF##29942366##Bergman, 2018##), Tsu4 copy number in the South American SpB strain UFRJ50916 is much higher than other <italic toggle=\"yes\">S. paradoxus</italic> strains with WGAs.</p>", "<p id=\"P12\">These data indicate that the Ty4 subfamily was present in the most recent common ancestor (MRCA) of all <italic toggle=\"yes\">S. paradoxus</italic> lineages prior to global dispersal, and therefore represents the ancestral subfamily in <italic toggle=\"yes\">S. paradoxus</italic>. The Ty4 subfamily subsequently went extinct in most recognized <italic toggle=\"yes\">S. paradoxus</italic> sub-lineages except for the Far East sub-lineage where it remains active. In contrast, the lack of Tsu4 sequences in Eurasian <italic toggle=\"yes\">S. paradoxus</italic> and the Eurasian-derived SpA sub-lineage indicates this subfamily has never existed in Eurasia and therefore was not present in the MRCA of all <italic toggle=\"yes\">S. paradoxus</italic> strains. Our results support the interpretation that a Tsu4 HTT event occurred in an ancestor of all indigenous American <italic toggle=\"yes\">S. paradoxus</italic> sub-lineages after the divergence of American from Eurasian lineages. This HTT event most likely occurred in an ancestral lineage where the Ty4 subfamily had already gone extinct, thus explaining why Ty4 and Tsu4 FLEs have never been observed in the same <italic toggle=\"yes\">S. paradoxus</italic> strain. Since this ancestral HTT event, Tsu4 has maintained activity in all indigenous American <italic toggle=\"yes\">S. paradoxus</italic> sub-lineages. However, Tsu4 has secondarily gone extinct or expanded to very high copy-number in many strains in each American <italic toggle=\"yes\">S. paradoxus</italic> sub-lineage. We note that this parsimonious scenario does not exclude the possibility of additional recent Tsu4 HTT events into indigenous American <italic toggle=\"yes\">S. paradoxus</italic> lineages that are obscured by this initial ancestral HTT event.</p>", "<title>Recent HTT has introduced Tsu4 into a small number of Central/South American <italic toggle=\"yes\">S. cerevisiae</italic> strains</title>", "<p id=\"P13\">Using a similar short-read WGS-based approach as was used for <italic toggle=\"yes\">S. paradoxus</italic>, we reconstructed a species-wide phylogeny for <italic toggle=\"yes\">S. cerevisiae</italic> based on 2,787,577 genome-wide SNPs from 2,404 strains (##FIG##1##Figure 2##). In contrast to the ML approach used for <italic toggle=\"yes\">S. paradoxus</italic> where admixture among lineages is rare, we followed ##REF##29643504##Peter et al. (2018)## in using a neighbor-joining (NJ) approach to generate the <italic toggle=\"yes\">S. cerevisiae</italic> phylogeny which accommodate the well-established existence of admixed strains in this species (##REF##19212322##Liti et al., 2009##; ##REF##29643504##Peter et al., 2018##). Despite using nearly twice as many strains, our phylogenetic tree of <italic toggle=\"yes\">S. cerevisiae</italic> strains shows a similar topology as ##REF##29643504##Peter et al. (2018)##, who identified a complex population structure including more than 26 distinct lineages plus many mosaic strains derived from admixture between these lineages. Strains in our integrated dataset that are not present in ##REF##29643504##Peter et al. (2018)## – such as those from ##REF##30002370##Duan et al. (2018)## and ##REF##26782936##Barbosa et al. (2016)## – cluster with known lineages previously characterized by ##REF##29643504##Peter et al. (2018)##. For instance, “activated dry yeast” strains from ##REF##30002370##Duan et al. (2018)## cluster in the “mixed origin” lineage from ##REF##29643504##Peter et al. (2018)## (##FIG##1##Figure 2##).</p>", "<p id=\"P14\">We then visualized the presence/absence of LTR and internal regions for Ty4/Tsu4 subfamilies on the phylogeny inferred for <italic toggle=\"yes\">S. cerevisiae</italic> from short-read WGS data. This analysis revealed that Ty4 LTR and internal sequences are present in all <italic toggle=\"yes\">S. cerevisiae</italic> lineages (##FIG##1##Figure 2A##,##FIG##1##B##). In contrast, Tsu4 LTR sequences are restricted to ~2% of strains surveyed (49/2,404) all of which are found in Central/South America (specifically French Guiana, Mexico, Brazil, and the French West Indies) (##FIG##1##Figure 2C##,##FIG##1##D##). Tsu4 sequences are completely absent from most other <italic toggle=\"yes\">S. cerevisiae</italic> lineages, including the most ancestral Chinese lineages (##REF##22913817##Wang et al., 2012##; ##REF##30002370##Duan et al., 2018##). We identified six <italic toggle=\"yes\">S. cerevisiae</italic> strains that contain evidence of Tsu4 internal regions (245, AFQ and CDM from Mosaic lineage 2; CQS from the French Guiana lineage; UFMG-CM-Y641 and UFMG-CM-Y642 from the Brazil 3 lineage) (##REF##25750179##Marsit et al., 2015##; ##REF##29643504##Peter et al., 2018##; ##REF##26782936##Barbosa et al., 2016##). Three of these <italic toggle=\"yes\">S. cerevisiae</italic> strains (AFQ, CDM and CQS) also contain internal regions for Ty4.</p>", "<p id=\"P15\">Next, we analyzed Ty4/Tsu4 subfamily content in WGAs for a global sample of 183 <italic toggle=\"yes\">S. cerevisiae</italic> strains (##SUPPL##0##Table S1## and ##SUPPL##0##Figure S1##), which confirmed results based on short-read WGS data. Ty4 subfamily sequences were found in all <italic toggle=\"yes\">S. cerevisiae</italic> WGAs analyzed, while Tsu4 subfamily sequences were absent from the majority of <italic toggle=\"yes\">S. cerevisiae</italic> WGAs. CQS is the only strain assembled using long-read data for which we identify FLEs for Tsu4 (n=9), confirming previous observations (##REF##37524789##O’Donnell et al., 2023##). We also identified one full-length (245 and AFQ) or truncated (CDM) Tsu4 copy in short-read WGAs (##SUPPL##0##Table S1##) for three <italic toggle=\"yes\">S. cerevisiae</italic> strains that we identified previously as containing Tsu4 internal regions in the short-read WGS scan. No publicly-available WGAs are available for the two Brazil 3 strains (UFMG-CM-Y641 and UFMG-CM-Y642, (##REF##26782936##Barbosa et al., 2016##)) with evidence of Tsu4 internal regions based on WGS data. All three <italic toggle=\"yes\">S. cerevisiae</italic> strains with Tsu4 FLEs in WGAs are geographically restricted to Central/South America. Importantly, we note that FLEs for both the Ty4 and Tsu4 subfamilies were identified in the long-read WGA for strain CQS.</p>", "<p id=\"P16\">The prevalence of the Ty4 subfamily in most <italic toggle=\"yes\">S. cerevisiae</italic> lineages – including ancestral Chinese lineages (##REF##22913817##Wang et al., 2012##; ##REF##30002370##Duan et al., 2018##) – indicates that the Ty4 subfamily was present in the MRCA of this species. However, despite being broadly active at the species level, the absence of Ty4 internal regions and FLEs in many strains indicates this subfamily has undergone many local extinction events (see also (##REF##34115140##Bleykasten-Grosshans et al., 2021##)). In contrast, the absence of Tsu4 in most lineages (including ancestral Chinese lineages) strongly indicates that this subfamily was not present in the MRCA of <italic toggle=\"yes\">S. cerevisiae</italic>. The small number of strains that do contain Tsu4 in <italic toggle=\"yes\">S. cerevisiae</italic> do not form a single monophyletic group, which is consistent either with one HTT event followed by admixture among lineages, or multiple recent HTT events that have introduced Tsu4 into different lineages of <italic toggle=\"yes\">S. cerevisiae</italic> in Central/South America. Finally, the observation of <italic toggle=\"yes\">S. cerevisiae</italic> strains with FLEs for both Tsu4 and Ty4 subfamilies (i.e., CQS) demonstrates that FLEs from the Ty4 and Tsu4 subfamilies can co-exist in the same <italic toggle=\"yes\">Saccharomyces</italic> strain.</p>", "<title>Multiple HTT events have transferred Tsu4 into <italic toggle=\"yes\">S. paradoxus</italic> and <italic toggle=\"yes\">S. cerevisiae</italic></title>", "<p id=\"P17\">The short-read WGS strategy used above allowed us to establish Ty4 as the ancestral subfamily in <italic toggle=\"yes\">S. paradoxus</italic> and <italic toggle=\"yes\">S. cerevisiae</italic>, and to identify at least one Tsu4 HTT event in both species. However, this approach cannot resolve how many Tsu4 HTT events occurred in either species, nor can it identify the potential donor lineages for these HTT events. To investigate whether the presence of Tsu4 in <italic toggle=\"yes\">S. paradoxus</italic> and <italic toggle=\"yes\">S. cerevisiae</italic> can be explained by one or more Tsu4 HTT event, and to identify the most likely donor lineage(s) for these HTT events, we analyzed the molecular evolution of all Ty4 family FLEs identified in a integrated dataset of 210 high-quality WGAs for all recognized species in the genus <italic toggle=\"yes\">Saccharomyces</italic>. The majority of WGAs in this dataset were generated using PacBio or Oxford Nanopore Technology (ONT) long reads, including new high-quality WGAs for <italic toggle=\"yes\">S. mikatae</italic> strains IFO 1815 and NBRC 10994 that we generated using PacBio long reads (##SUPPL##0##Table S2##). Phylogenetic analysis of our new <italic toggle=\"yes\">S. mikatae</italic> WGAs confirmed the taxonomic placement of IFO1815 in the Asia A clade (##REF##36755033##Peris et al., 2023##) and revealed that NBRC 10994 should be placed in a new clade (Asia C) (##SUPPL##0##Figure S2##). In addition, we also included WGAs based on short-read data for three <italic toggle=\"yes\">S. cerevisiae</italic> strains with Tsu4 internal sequences (245, AFQ and CDM; see above) and for two strains of <italic toggle=\"yes\">S. arboricola</italic> (H-6 and ZP960) that represent the best available WGAs for this species.</p>", "<p id=\"P18\">In total, we identified 247 FLEs for the Ty4 subfamily and 124 FLEs for the Tsu4 subfamily in this integrated dataset (##SUPPL##0##Table S2##, ##SUPPL##0##Figure S1##). No FLEs for either subfamily were identified <italic toggle=\"yes\">S. arboricola</italic> and <italic toggle=\"yes\">S. kudriavzevii</italic> using our current query sequences. The absence of Ty4 family FLEs in <italic toggle=\"yes\">S. arboricola</italic> may simply reflect the lack of high-quality WGAs for this species. However, the absence of Ty4 family FLEs in <italic toggle=\"yes\">S. kudriavzevii</italic> is likely an artifact of divergence between our current query sequences and Ty4 family FLEs in this species. In <italic toggle=\"yes\">S. kudriavzevii</italic> strain (IFO1802) we observed a high copy number of truncated Tsu4 elements (n=8) that, upon further inspection, revealed five nearly full-length elements that were highly similar to one another, dispersed throughout the IFO1802 genome, and overlapped full-length <italic toggle=\"yes\">de novo</italic> LTRharvest predictions (##REF##18194517##Ellinghaus et al., 2008##). We concluded that these five <italic toggle=\"yes\">S. kudriavzevii</italic> elements represented FLEs from a novel branch in the Ty4 family and included them in our phylogenetic analysis of FLEs.</p>", "<p id=\"P19\">We next created a multiple sequence alignment and reconstructed phylogenetic networks and trees based on internal coding regions of all 376 FLEs in our integrated dataset (##FIG##2##Figure 3A##, ##FIG##2##B##). We excluded LTR and untranslated sequences from this analysis, which exhibited poor alignment due to higher divergence in noncoding regions. This analysis identified 14 well-supported clades of FLEs each found in a single species, plus two branches with singleton FLEs from the Hawaiian <italic toggle=\"yes\">S. paradoxus</italic> strain UWOPS91-917.1 and Asia C <italic toggle=\"yes\">S. mikatae</italic> strain NBRC 10994, respectively. Two clades (Clades 11 and 12) with FLEs from either <italic toggle=\"yes\">S. mikatae</italic> or <italic toggle=\"yes\">S. kudriavzevii</italic> exhibit evidence of reticulation between the Ty4 and Tsu4 clades (##FIG##2##Figure 3A##), which we interpret as being caused by recombination between these subfamilies (see detailed analysis below). Exclusion of these clades eliminated the major signal for reticulation between the Ty4 and Tsu4 subfamilies in the phylogenetic network (##SUPPL##0##Figure S3A##) and increased bootstrap support for clades in <italic toggle=\"yes\">S. jurei</italic> and <italic toggle=\"yes\">S. mikatae</italic>, but did not alter the topological relationships of other clades in the ML tree (##SUPPL##0##Figure S3B##). The Ty4 subfamily is represented by only two clades with FLEs from only <italic toggle=\"yes\">S. cerevisiae</italic> (Clade 13) or <italic toggle=\"yes\">S. paradoxus</italic> (Clade 14), respectively. In contrast, the Tsu4 subfamily is represented by ten species-specific clades (1-10) with FLEs from all species except <italic toggle=\"yes\">S. kudriavzevii</italic> (##FIG##2##Figure 3A##, ##SUPPL##0##S4##, ##SUPPL##0##S5##, ##SUPPL##0##S6##). Two clades each of Tsu4 FLEs from <italic toggle=\"yes\">S. paradoxus</italic> (Clades 1 and 3) and <italic toggle=\"yes\">S. cerevisiae</italic> (Clades 2 and 4) together with the singleton FLE from <italic toggle=\"yes\">S. paradoxus</italic> UWOPS91-917.1 form a monophyletic group. <italic toggle=\"yes\">S. eubayanus</italic> is represented two Tsu4 FLEs clades from the Holarctic (Clade 5) and Patagonian (Clade 6) lineages, respectively. Tsu4 FLEs from <italic toggle=\"yes\">S. uvarum</italic> form two clades (Clades 7 and 8) that are both found in a single strain from the Holarctic lineage. Tsu4 FLEs from European <italic toggle=\"yes\">S. jurei</italic> (Clade 9) cluster with Tsu4 FLEs from the <italic toggle=\"yes\">S. mikatae</italic> Asia A lineage (Clade 10) and the singleton branch from the <italic toggle=\"yes\">S. mikatae</italic> Asia C lineage.</p>", "<p id=\"P20\">Previous analysis of Tsu4 HTT events in <italic toggle=\"yes\">S. paradoxus</italic> and <italic toggle=\"yes\">S. cerevisiae</italic> using a smaller dataset of FLEs (##REF##29942366##Bergman, 2018##) suggested one primary HTT occurred in the ancestor of American <italic toggle=\"yes\">S. paradoxus</italic> lineages followed by one secondary HTT from <italic toggle=\"yes\">S. paradoxus</italic> into <italic toggle=\"yes\">S. cerevisiae</italic>. This hypothesis predicts that Tsu4 FLEs from <italic toggle=\"yes\">S. paradoxus</italic> and <italic toggle=\"yes\">S. cerevisiae</italic> should form a single clade with <italic toggle=\"yes\">S. cerevisiae</italic> FLEs forming a single sub-clade somewhere within the broader diversity of <italic toggle=\"yes\">S. paradoxus</italic> FLEs. Two key features of the Tsu4 FLE phylogeny in our expanded dataset are inconsistent with this hypothesis (##FIG##2##Figure 3##). First, we observe two distinct clades of FLEs for both <italic toggle=\"yes\">S. paradoxus</italic> and <italic toggle=\"yes\">S. cerevisiae</italic>, each of whose closest sampled relatives are from a different species. Namely, <italic toggle=\"yes\">S. cerevisiae</italic> Clade 2 (from the French Guiana strain CQS) clusters with S. American <italic toggle=\"yes\">S. paradoxus</italic> Clade 1, while <italic toggle=\"yes\">S. cerevisiae</italic> Clade 4 (from Mosaic 2 strains 245 and AFQ) clusters with the N. American <italic toggle=\"yes\">S. paradoxus</italic> Clade 3. Second, the observed topology of Tsu4 FLEs in <italic toggle=\"yes\">S. paradoxus</italic> does not strictly follow the host phylogeny for the SpB sub-lineage strain UFRJ50816. Specifically, Tsu4 FLEs from the S. American <italic toggle=\"yes\">S. paradoxus</italic> strain UFRJ50816 form their own divergent Clade 1 instead of grouping as expected with Tsu4 FLEs from other SpB strains from N. America (MSH-604 and YPS138) in Clade 3 (##SUPPL##0##Figure S4##).</p>", "<p id=\"P21\">We interpret the phylogeny of Tsu4 FLEs in <italic toggle=\"yes\">S. paradoxus</italic> and <italic toggle=\"yes\">S. cerevisiae</italic> to be consistent with at least two Tsu4 HTT events into each species. In <italic toggle=\"yes\">S. paradoxus</italic>, we infer one ancestral Tsu4 HTT event creating Clade 3 FLEs that are present in all SpB, SpC, SpC and SpD strains (except UFRJ50816), and one recent HTT event creating Clade 1 FLEs present in the S. American sub-lineage containing UFRJ50816 (where Clade 3 FLEs had already gone extinct). Likewise, in <italic toggle=\"yes\">S. cerevisiae</italic>, we infer one recent HTT into the French Guiana lineage creating Clade 2 FLEs, and another recent HTT into the Mosaic 2 lineage creating Clade 4 FLEs. These data suggest that when conditions are favorable for HTT events in <italic toggle=\"yes\">Saccharomyces</italic>, they can occur more frequently than the most parsimonious interpretation based on presence/absence data would imply.</p>", "<title>Tsu4 in <italic toggle=\"yes\">S. paradoxus</italic>, <italic toggle=\"yes\">S. cerevisiae</italic>, and Holarctic <italic toggle=\"yes\">S. eubayanus</italic> were transferred from an unknown donor.</title>", "<p id=\"P22\">The collective monophyly of the Tsu4 clades found in <italic toggle=\"yes\">S. paradoxus</italic> and <italic toggle=\"yes\">S. cerevisiae</italic> suggests the Tsu4 HTT events in these species ultimately arose from a similar donor lineage, with distinct clades being formed at different times or in different geographic regions. Using indirect data from the hybrid species <italic toggle=\"yes\">S. pastorianus</italic> that contains subgenomes from <italic toggle=\"yes\">S. eubayanus</italic> and <italic toggle=\"yes\">S. cerevisiae</italic> (##REF##21873232##Libkind et al., 2011##; ##REF##26269586##Baker et al., 2015##; ##REF##26732986##Okuno et al., 2016##), ##REF##29942366##Bergman (2018)## concluded that the Holarctic lineage of <italic toggle=\"yes\">S. eubayanus</italic> contains the most closely related Tsu4 sequences to those in <italic toggle=\"yes\">S. paradoxus</italic> and <italic toggle=\"yes\">S. cerevisiae</italic>. Our current analysis provides direct evidence for this conclusion, with Clade 5 FLEs from CDFM21L.1 in the the Holarctic <italic toggle=\"yes\">S. eubayanus</italic> lineage clustering most closely with the common ancestor of all Tsu4 FLEs in <italic toggle=\"yes\">S. paradoxus</italic> and <italic toggle=\"yes\">S. cerevisiae</italic> (##FIG##2##Figure 3##). Taken at face value, this result implies that Holarctic <italic toggle=\"yes\">S. eubayanus</italic> represents the most likely donor lineage for the multiple HTT events observed in <italic toggle=\"yes\">S. paradoxus</italic> and <italic toggle=\"yes\">S. cerevisiae</italic>.</p>", "<p id=\"P23\">However, several features of the Tsu4 FLE phylogeny suggest that the Holarctic <italic toggle=\"yes\">S. eubayanus</italic> lineage is not the direct donor for the HTT events in <italic toggle=\"yes\">S. paradoxus</italic> and <italic toggle=\"yes\">S. cerevisiae</italic> (##FIG##2##Figure 3##). First, Tsu4 clades in <italic toggle=\"yes\">S. paradoxus</italic> and <italic toggle=\"yes\">S. cerevisiae</italic> are not nested within the diversity of FLEs from Holarctic <italic toggle=\"yes\">S. eubayanus</italic>, but rather form a sister group separated by substantial divergence. Second, bootstrap support for the clustering of Holarctic <italic toggle=\"yes\">S. eubayanus</italic> FLEs with the ancestor of FLEs from <italic toggle=\"yes\">S. cerevisiae</italic> and <italic toggle=\"yes\">S. paradoxus</italic> is relatively weak (>66%). The alternative clustering of FLEs from the Holarctic and Patagonia-B <italic toggle=\"yes\">S. eubayanus</italic> lineages together would suggest that the donor into <italic toggle=\"yes\">S. paradoxus</italic> and <italic toggle=\"yes\">S. cerevisiae</italic> is from a currently-unsampled lineage of <italic toggle=\"yes\">S. eubayanus</italic>, or a species closely related to <italic toggle=\"yes\">S. eubayanus</italic>. Third, Tsu4 FLEs from Holarctic <italic toggle=\"yes\">S. eubayanus</italic> are not nested with in the diversity of <italic toggle=\"yes\">S. eubayanus</italic> Patagonian FLEs (##FIG##2##Figure 3##, ##SUPPL##0##S5##), as is expected since Holarctic <italic toggle=\"yes\">S. eubayanus</italic> is known to be a sub-lineage of the Patagonia-B lineage (##SUPPL##0##Figure S7##) (##REF##27385107##Peris et al., 2016##). Discordance between the <italic toggle=\"yes\">S. eubayanus</italic> Tsu4 and host strain phylogenies suggests the possibility of a previously-undetected Tsu4 HTT event into Holarctic <italic toggle=\"yes\">S. eubayanus</italic>, which could lead to the false conclusion that the Holarctic <italic toggle=\"yes\">S. eubayanus</italic> lineage is the most likely donor for HTT events into <italic toggle=\"yes\">S. paradoxus</italic> and <italic toggle=\"yes\">S. cerevisiae</italic>.</p>", "<p id=\"P24\">To test for a previously-undetected Tsu4 HTT event in the Holarctic <italic toggle=\"yes\">S. eubayanus</italic> lineage, we developed a novel approach to study Tsu4 sequence evolution using strain-specific consensus sequences inferred from short-read WGS data. Importantly, this approach bypasses the limited number of WGAs available in <italic toggle=\"yes\">S. eubayanus</italic> and other potential donor species and allows us to generalize results across larger samples of host strains and lineages. The premise behind this approach is based on the observation that Tsu4 FLEs typically cluster first within the same strain before clustering with FLEs from other strains (##FIG##2##Figure 3##). Thus, strain-specific Tsu4 consensus sequences should be a reasonable proxy for the common ancestor of elements within a strain, and can themselves be used for evolutionary inference across strains and species.</p>", "<p id=\"P25\">Using the short-read based WGS approach as above for <italic toggle=\"yes\">S. paradoxus</italic> and <italic toggle=\"yes\">S. cerevisiae</italic>, we first estimated Ty4/Tsu4 LTR and internal copy numbers in the context of host phylogenies for <italic toggle=\"yes\">S. eubayanus</italic> (##SUPPL##0##Figure S7##) and <italic toggle=\"yes\">S. uvarum</italic> (##SUPPL##0##Figure S8##), respectively. These results reveal that Tsu4 was present and Ty4 was absent in the ancestors of both <italic toggle=\"yes\">S. eubayanus</italic> and <italic toggle=\"yes\">S. uvarum</italic>, that Tsu4 is broadly active in both species, and that the Holarctic <italic toggle=\"yes\">S. eubayanus</italic> lineage has the highest LTR copy number of Tsu4 in either species. We then computed consensus sequences for Tsu4 internal regions in all <italic toggle=\"yes\">S. paradoxus</italic>, <italic toggle=\"yes\">S. cerevisiae</italic>, <italic toggle=\"yes\">S. eubayanus</italic> and <italic toggle=\"yes\">S. uvarum</italic> strains with an estimated copy number of >0.75 and generated a ML tree of strain-specific sequences across the combined dataset of four species (##FIG##3##Figure 4##).</p>", "<p id=\"P26\">Key groupings in the strain-specific Tsu4 consensus tree (##FIG##3##Figure 4##) agreed with the phylogeny of Tsu4 FLEs based on a smaller number of strains above (##FIG##2##Figure 3##), cross-validating both approaches. Notably, Tsu4 consensus sequences for all N. American <italic toggle=\"yes\">S. paradoxus</italic> strains (together with the two <italic toggle=\"yes\">S. cerevisiae</italic> Brazil 3 strains, UFMG-CM-Y641 and UFMG-CM-Y642) form a large monophyletic group (corresponding to Clade 3 in the FLE tree) that clusters most closely with consensus sequences from Mosaic 2 <italic toggle=\"yes\">S. cerevisiae</italic> strains (corresponding to Clade 4). Likewise, the consensus sequence for the S. American <italic toggle=\"yes\">S. paradoxus</italic> strain UFRJ50916 (corresponding to Clade 1) clusters with the S. American <italic toggle=\"yes\">S. cerevisiae</italic> strain CQS (corresponding to Clade 2). All seven Holarctic <italic toggle=\"yes\">S. eubayanus</italic> strains form a monophyletic group (corresponding to Clade 5) that clusters with <italic toggle=\"yes\">S. paradoxus</italic> and <italic toggle=\"yes\">S. cerevisiae</italic> consensus sequences and is distinct from Tsu4 sequences found in all other <italic toggle=\"yes\">S. eubayanus</italic> or <italic toggle=\"yes\">S. uvarum</italic> strains.</p>", "<p id=\"P27\">Phylogenetic analysis of strain-specific consensus sequences also revealed two other Tsu4 HTT events that could not be detected in the FLE phylogeny because of limited samples of WGAs. The first involves the two <italic toggle=\"yes\">S. cerevisiae</italic> Brazil 3 strains (UFMG-CM-Y641 and UFMG-CM-Y642). Based on the location of Brazil 3 samples in the consensus sequence tree (##FIG##3##Figure 4##) and the pattern of variation in the consensus sequences of all six <italic toggle=\"yes\">S. cerevisiae</italic> strains that have Tsu4 (##SUPPL##0##Figure S9##), we conclude that Tsu4 sequences in the <italic toggle=\"yes\">S. cerevisiae</italic> Brazil 3 lineage arose from a HTT event that is distinct from those detected using FLEs in the Mosaic 2 or French Guiana lineages. The second peviously-undetected Tsu4 HTT event involves the <italic toggle=\"yes\">S. eubayanus</italic> Patagonia B strain yHAB565, whose Tsu4 consensus sequence is placed within the <italic toggle=\"yes\">S. uvarum</italic> cluster. <italic toggle=\"yes\">S. eubayanus</italic> yHAB565 is placed correctly in the Patagonia B lineage in our host strain tree (##SUPPL##0##Figure S7##), which rules out the possibility of sample mixups during sequencing or bioinformatic analysis and supports a recent HTT Tsu4 event from <italic toggle=\"yes\">S. uvarum</italic> into <italic toggle=\"yes\">S. eubayanus</italic>.</p>", "<p id=\"P28\">Taken together, our results suggest that the similarity between the Tsu4 clades in Holarctic <italic toggle=\"yes\">S. eubayanus</italic>, <italic toggle=\"yes\">S. paradoxus</italic>, and <italic toggle=\"yes\">S. cerevisiae</italic> arose from parallel HTT events donated by a common but as-yet-unidentified <italic toggle=\"yes\">Saccharomyces</italic> lineage. The substantial divergence between non-Holarctic <italic toggle=\"yes\">S. eubayanus</italic> or <italic toggle=\"yes\">S. uvarum</italic> and the clade comprised of Holarctic <italic toggle=\"yes\">S. eubayanus</italic>, <italic toggle=\"yes\">S. paradoxus</italic> and <italic toggle=\"yes\">S. cerevisiae</italic> suggests that this unknown donor is either a currently-unsequenced lineage of <italic toggle=\"yes\">S. eubayanus</italic> or <italic toggle=\"yes\">S. uvarum</italic> (e.g., West China <italic toggle=\"yes\">S. eubayanus</italic> (##REF##24845661##Bing et al., 2014##)) or potentially an undiscovered species related to <italic toggle=\"yes\">S. eubayanus</italic> and <italic toggle=\"yes\">S. uvarum</italic>. Additionally, we show that our novel strain-specific consensus sequence approach complements analysis of FLEs from WGAs and can reveal previously undetected cases of Tsu4 HTT in the abundant WGS datasets that are available in multiple yeast species.</p>", "<title>Tsu4 HTT fuels the evolution of recombinant clades in <italic toggle=\"yes\">S. mikatae</italic> and <italic toggle=\"yes\">S. kudriavzevii</italic></title>", "<p id=\"P29\">Our phylogenetic network analysis of FLEs from the Ty4 family above revealed evidence of reticulation in <italic toggle=\"yes\">S. mikatae</italic> Clade 11 and <italic toggle=\"yes\">S. kudriavzevii</italic> Clade 12 (##FIG##2##Figure 3A##) that could be caused by recombination between the Ty4 and Tsu4 subfamilies (##REF##16221896##Huson and Bryant, 2006##). In addition, the coexistence of FLEs for both Ty4 and Tsu4 in <italic toggle=\"yes\">S. cerevisiae</italic> strain CQS indicates that conditions for recombination between Ty4 and Tsu4 subfamilies can occur in nature. To provide further evidence for recombination between the Ty4 and Tsu4 subfamilies, we first selected representative FLEs for “pure” Tsu4 (f32 from the <italic toggle=\"yes\">S. uvarum</italic> Clade 8) and “pure” Ty4 (f49 from the <italic toggle=\"yes\">S. cerevisiae</italic> Clade 13) from outgroup species not involved in the putative recombination events. We then plotted a sliding window of pairwise sequence divergence between these representative pure Ty4 and Tsu4 FLEs and a putatively “pure” <italic toggle=\"yes\">S. mikatae</italic> Clade 10 FLE (f286 from IFO 1815) or a putatively “recombinant” <italic toggle=\"yes\">S. mikatae</italic> Clade 11 FLE (f256 from IFO 1815) (##SUPPL##0##Figure S10##). This analysis revealed that that the 5’ internal region – including the complete <italic toggle=\"yes\">gag</italic> gene and the first ~500bp of <italic toggle=\"yes\">pol</italic> – shows lower levels of divergence between <italic toggle=\"yes\">S. uvarum</italic> Tsu4 and pure <italic toggle=\"yes\">S. mikatae</italic> Clade 10 (##SUPPL##0##Figure S10A##) than recombinant <italic toggle=\"yes\">S. mikatae</italic> Clade 11 (##SUPPL##0##Figure S10B##). Conversely, the same 5’ internal region shows higher levels of divergence between <italic toggle=\"yes\">S. cerevisiae</italic> Ty4 and pure <italic toggle=\"yes\">S. mikatae</italic> Clade 10 (##SUPPL##0##Figure S10D##) than recombinant <italic toggle=\"yes\">S. mikatae</italic> Clade 11 (##SUPPL##0##Figure S10E##). These data indicate that the 5’ internal segment in Clade 11 is derived from the Ty4 subfamily, while the rest of the Clade 11 internal region is derived from the Tsu4 subfamily.</p>", "<p id=\"P30\">We then partitioned the multiple sequence alignment of Ty4 family FLE internal regions into 5’ and 3’ segments, and reconstructed ML phylogenies for both partitions from representative clades (##FIG##4##Figure 5##). A striking discordance can be observed in phylogenies reconstructed from 5’ and 3’ internal regions of Clade 11 sequences. In the 5’ partition, pure Clade 10 FLEs cluster with Tsu4 FLEs from <italic toggle=\"yes\">S. jurei</italic> and <italic toggle=\"yes\">S. uvarum</italic>, while recombinant Clade 11 FLEs cluster with strong support as a sister group to from <italic toggle=\"yes\">S. paradoxus</italic>/<italic toggle=\"yes\">S. cerevisiae</italic> in the Ty4 subfamily (##FIG##4##Figure 5A##). In contrast, in the tree reconstructed from the 3’ partition <italic toggle=\"yes\">S. mikatae</italic> Clades 10 and 11 form a single monophyletic group that is closely related to Tsu4 sequences from <italic toggle=\"yes\">S. jurei</italic> (##FIG##4##Figure 5B##).</p>", "<p id=\"P31\">Based on these results and presence of Ty4 solo LTRs in WGAs from <italic toggle=\"yes\">S. mikatae</italic> and <italic toggle=\"yes\">S. jurei</italic> (##SUPPL##0##Table S1##), we propose the following scenario for the evolution of <italic toggle=\"yes\">S. mikatae</italic> Clades 10 and 11. A divergent Ty4 subfamily was previously active in an ancestor of <italic toggle=\"yes\">S. mikatae</italic> and <italic toggle=\"yes\">S. jurei</italic>, which has subsequently gone extinct in both species but left Ty4 internal sequences that were retained in the <italic toggle=\"yes\">S. mikatae</italic> genome for some period of time. A HTT event introduced the Tsu4 subfamily prior to the speciation of <italic toggle=\"yes\">S. mikatae</italic> and <italic toggle=\"yes\">S. jurei</italic>, which evolved into Clade 10 in <italic toggle=\"yes\">S. mikatae</italic> and Clade 9 in <italic toggle=\"yes\">S. jurei</italic>. This Tsu4 HTT event explains the discordance between the Ty4 family and host species phylogenies previously reported for <italic toggle=\"yes\">S. mikatae</italic>\n##REF##29942366##Bergman (2018)##. The donor for this Tsu4 HTT into the ancestor of <italic toggle=\"yes\">S. mikatae</italic> and <italic toggle=\"yes\">S. jurei</italic> is unknown but related to the ancestor to all extant Tsu4 FLEs in <italic toggle=\"yes\">S. eubayanus</italic>, <italic toggle=\"yes\">S. uvarum</italic>, <italic toggle=\"yes\">S. paradoxus</italic> and <italic toggle=\"yes\">S. cerevisiae</italic>. Recombination of the 5’ internal region from the now-extinct Ty4 subfamily in <italic toggle=\"yes\">S. mikatae</italic> onto a Clade 10-like pure Tsu4 FLE created the “recombinant” Tsu4 Clade 11. This model explains the lack of Ty4 FLEs in <italic toggle=\"yes\">S. mikatae</italic> and <italic toggle=\"yes\">S. jurei</italic> (##SUPPL##0##Table S1##), the coexistence of two highly divergent clades in <italic toggle=\"yes\">S. mikatae</italic> genomes (##FIG##2##Figure 3B##), the long branch leading to Clade 11 in the FLE phylogeny (##FIG##2##Figure 3B##), and reticulation between Ty4 and Tsu4 subfamilies for Clade 11 in the phylogenetic network (##FIG##2##Figure 3A##).</p>", "<p id=\"P32\">We applied similar approaches to investigate whether reticulation in the phylogenetic network observed for <italic toggle=\"yes\">S. kudriavzevii</italic> Clade 12 FLEs (##FIG##2##Figure 3A##) also is caused by recombination between Ty4 and Tsu4 subfamilies. Sliding window analysis of a representative Clade 12 FLE versus pure Tsu4 and Ty4 from outgroup species revealed an ~2kb segment starting at the beginning of Pol that shows very high similarity to Tsu4 (##SUPPL##0##Figure S10C##). Phylogenetic analysis of partitions corresponding to the “left,” “middle,” and “right” segments of FLE internal regions for representative clades reveals that the middle internal segment of Clade 12 is derived from the Tsu4 subfamily, while the left and right segments are divergent representatives of the Ty4 subfamily. Based on these results and presence of Ty4 LTRs in WGAs from <italic toggle=\"yes\">S. kudriavzevii</italic> (##SUPPL##0##Table S1##), we propose that <italic toggle=\"yes\">S. kudriavzevii</italic> ancestrally contained a divergent Ty4 subfamily which acquired a middle segment from a horizontally-transferred Tsu4 by recombination. The Tsu4 clade that was horizontally transferred into <italic toggle=\"yes\">S. kudriavzevii</italic> and the original pure <italic toggle=\"yes\">S. kudriavzevii</italic> Ty4 clade have both subsequently gone extinct, leaving the recombinant Clade 12 as the only extant representative of the Ty4 family currently identified in <italic toggle=\"yes\">S. kudriavzevii</italic> . Based on clustering of the middle internal region (##FIG##5##Figure 6B##), the donor lineage for the Tsu4 HTT into <italic toggle=\"yes\">S. kudriavzevii</italic> is related to the donor for Tsu4 HTT in <italic toggle=\"yes\">S. mikatae</italic> and <italic toggle=\"yes\">S. jurei</italic>. Together, the recombinant clades in <italic toggle=\"yes\">S. mikatae</italic> and <italic toggle=\"yes\">S. kudriavzevii</italic> support the conclusion that co-existence of Ty4 and Tsu4 subfamily sequences in the same genome mediated by HTT provides substrate for recombination to generate new retrotransposon clades in <italic toggle=\"yes\">Saccharomyces</italic>.</p>" ]	[ "<title>Results and Discussion</title>", "<p id=\"P8\">To better understand the history and impact of HTT events involving the Ty4 family in <italic toggle=\"yes\">Saccharomyces</italic>, we used four complementary genomic strategies. First, to more accurately infer the biogeographic distribution and ancestral states of the Ty4/Tsu4 subfamilies in <italic toggle=\"yes\">S. paradoxus</italic> and <italic toggle=\"yes\">S. cerevisiae</italic>, we investigated the presence/absence of Ty4/Tsu4 subfamily sequences in worldwide phylogenies for both species using unassembled short-read WGS datasets. For these analyses, we estimated copy number for LTRs and internal regions separately because recombination between LTRs within FLEs frequently excises internal sequences creating solo LTRs (##REF##6250062##Farabaugh and Fink, 1980##). Doing this allows us to interpret the presence of internal sequences as evidence of recent activity, and the presence of LTRs as evidence of both recent and past activity in a strain. Second, we annotated Ty4/Tsu4 copies in a dataset of over 200 high-quality WGAs for all species in the <italic toggle=\"yes\">Saccharomyces</italic> genus which allowed us to cross-validate Ty4/Tsu4 subfamily presence/absence data based on short-read WGS data, and to classify annotated copies at higher resolution into FLEs, truncated elements, and solo LTRs. We interpret the presence of FLEs in a WGA as evidence for recent activity in a strain, while truncated elements and solo LTRs represent past activity. Third, we generated phylogenetic networks and trees for internal coding regions of FLEs extracted from WGAs, which allowed us to directly investigate the molecular evolution of the Ty4 family across the entire <italic toggle=\"yes\">Saccharomyces</italic> genus. Fourth, we generated strain-specific consensus sequences from unassembled short-read WGS datasets, which allowed us to study Tsu4 subfamily evolution among <italic toggle=\"yes\">S. paradoxus</italic>, <italic toggle=\"yes\">S. cerevisiae</italic>, <italic toggle=\"yes\">S. eubayanus</italic> and <italic toggle=\"yes\">S. uvarum</italic> using larger samples of strains that lack high-quality WGAs.</p>", "<title>An ancestral Tsu4 HTT event occurred prior to radiation of indigenous American <italic toggle=\"yes\">S. paradoxus</italic> lineages</title>", "<p id=\"P9\">Using short-read WGS datasets for 370 <italic toggle=\"yes\">S. paradoxus</italic> strains, we reconstructed a maximum likelihood (ML) phylogenetic tree based on 713,556 SNPs that confirmed all major known lineages, sub-lineages, and their relationships (##FIG##0##Figure 1A##) (##REF##9103619##Naumov et al., 1997##; ##REF##17028086##Koufopanou et al., 2006##; ##REF##17306538##Kuehne et al., 2007##; ##REF##19212322##Liti et al., 2009##; ##UREF##4##Leducq et al., 2016##; ##UREF##1##Henault et al., 2019##; ##REF##30804385##Eberlein et al., 2019##; ##REF##34961959##He et al., 2022##). Worldwide diversity in <italic toggle=\"yes\">S. paradoxus</italic> splits into the two major known lineages: American and Eurasian. The American lineage includes several indigenous North American sub-lineages (SpB, SpC, SpC* and SpD), as well a lineage with a single strain from Hawaii. SpC* and SpD are hybrid lineages derived from crosses between SpB and SpC, and between SpB and SpC, respectively (##UREF##4##Leducq et al., 2016##; ##UREF##1##Henault et al., 2019##; ##REF##30804385##Eberlein et al., 2019##). The Hawaiian lineage has been reported to share similarity with either the SpB (##UREF##3##Leducq et al., 2014##) or SpC/SpC lineages (##REF##34961959##He et al., 2022##; ##REF##36755033##Peris et al., 2023##). Our analysis places the Hawaiian lineage as an outgroup to the SpC/SpC* lineages (circled number 3, ##FIG##0##Figure 1A##). Importantly, we note that <italic toggle=\"yes\">S. paradoxus</italic> lineage from S. America (circled number 2, ##FIG##0##Figure 1A##) – which was formerly considered a distinct species called <italic toggle=\"yes\">S. cariocanus</italic> (##REF##11034507##Naumov et al., 2000##) – is contained within the North American SpB sub-lineage (##REF##17028086##Koufopanou et al., 2006##; ##REF##16951060##Liti et al., 2006##, ##REF##19212322##2009##; ##REF##23286354##Hyma and Fay, 2013##; ##UREF##3##Leducq et al., 2014##; ##REF##34961959##He et al., 2022##). The Eurasian lineage includes sub-lineages indigenous to Europe, Far East Asia, and China, as well as a sub-lineage (SpA) composed of strains from North America that descend from a recent trans-oceanic migration event (##REF##17306538##Kuehne et al., 2007##; ##UREF##4##Leducq et al., 2016##).</p>", "<p id=\"P10\">By mapping estimated Ty4/Tsu4 subfamily copy number onto the global phylogeny for <italic toggle=\"yes\">S. paradoxus</italic>, we observe that Ty4 LTR sequences are present in all <italic toggle=\"yes\">S. paradoxus</italic> strains from both the Americas and Eurasia (##FIG##0##Figure 1B##). Strains from the Far East sub-lineage show a significantly higher Ty4 LTR copy number in comparison to other sub-lineages. Ty4 internal regions are essentially absent across the species, except in the LTR-rich Far East sub-lineage (##FIG##0##Figure 1B##). In contrast, Tsu4 LTR sequences are only found in indigenous American strains and absent from strains with a Eurasian origin (##FIG##0##Figure 1C##). Tsu4 internal sequences are found in all indigenous American sub-lineages (SpB, SpC, SpC, SpD, and Hawaii) with highly variable copy number (##FIG##0##Figure 1C##). Notably, we found no Tsu4 sequences in the Eurasian-derived North American SpA sub-lineage (see also (##REF##32955438##Henault et al., 2020##)), which shows no evidence of admixture with indigenous American sub-lineages after secondary contact (##REF##17306538##Kuehne et al., 2007##; ##REF##23286354##Hyma and Fay, 2013##; ##UREF##3##Leducq et al., 2014##, ##UREF##4##2016##; ##REF##34961959##He et al., 2022##).</p>", "<p id=\"P11\">Analysis of Ty4/Tsu4 subfamily content in Ty4 sequences in a smaller dataset of 12 long-read WGAs that samples all major <italic toggle=\"yes\">S. paradoxus</italic> lineages cross-validated results based on short-read WGS data. Ty4 solo LTRs were found in all strains but Ty4 FLEs were only found in the Far East strain N44 (##SUPPL##0##Table S1##). In contrast, Tsu4 solo LTRs are identified in the nine indigenous American <italic toggle=\"yes\">S. paradoxus</italic> strains, and are absent from the other three strains with Eurasian origin (CBS432, N44, and LL2012_001) (##SUPPL##0##Table S1##). At least one Tsu4 FLE is identified in all indigenous American <italic toggle=\"yes\">S. paradoxus</italic> strains with WGAs except for the SpB strain DG1768 that is commonly used in retromobility studies (##REF##35049349##Chen et al., 2022b##) (circled number 1, ##FIG##0##Figure 1A##). As previously reported (##REF##29942366##Bergman, 2018##), Tsu4 copy number in the South American SpB strain UFRJ50916 is much higher than other <italic toggle=\"yes\">S. paradoxus</italic> strains with WGAs.</p>", "<p id=\"P12\">These data indicate that the Ty4 subfamily was present in the most recent common ancestor (MRCA) of all <italic toggle=\"yes\">S. paradoxus</italic> lineages prior to global dispersal, and therefore represents the ancestral subfamily in <italic toggle=\"yes\">S. paradoxus</italic>. The Ty4 subfamily subsequently went extinct in most recognized <italic toggle=\"yes\">S. paradoxus</italic> sub-lineages except for the Far East sub-lineage where it remains active. In contrast, the lack of Tsu4 sequences in Eurasian <italic toggle=\"yes\">S. paradoxus</italic> and the Eurasian-derived SpA sub-lineage indicates this subfamily has never existed in Eurasia and therefore was not present in the MRCA of all <italic toggle=\"yes\">S. paradoxus</italic> strains. Our results support the interpretation that a Tsu4 HTT event occurred in an ancestor of all indigenous American <italic toggle=\"yes\">S. paradoxus</italic> sub-lineages after the divergence of American from Eurasian lineages. This HTT event most likely occurred in an ancestral lineage where the Ty4 subfamily had already gone extinct, thus explaining why Ty4 and Tsu4 FLEs have never been observed in the same <italic toggle=\"yes\">S. paradoxus</italic> strain. Since this ancestral HTT event, Tsu4 has maintained activity in all indigenous American <italic toggle=\"yes\">S. paradoxus</italic> sub-lineages. However, Tsu4 has secondarily gone extinct or expanded to very high copy-number in many strains in each American <italic toggle=\"yes\">S. paradoxus</italic> sub-lineage. We note that this parsimonious scenario does not exclude the possibility of additional recent Tsu4 HTT events into indigenous American <italic toggle=\"yes\">S. paradoxus</italic> lineages that are obscured by this initial ancestral HTT event.</p>", "<title>Recent HTT has introduced Tsu4 into a small number of Central/South American <italic toggle=\"yes\">S. cerevisiae</italic> strains</title>", "<p id=\"P13\">Using a similar short-read WGS-based approach as was used for <italic toggle=\"yes\">S. paradoxus</italic>, we reconstructed a species-wide phylogeny for <italic toggle=\"yes\">S. cerevisiae</italic> based on 2,787,577 genome-wide SNPs from 2,404 strains (##FIG##1##Figure 2##). In contrast to the ML approach used for <italic toggle=\"yes\">S. paradoxus</italic> where admixture among lineages is rare, we followed ##REF##29643504##Peter et al. (2018)## in using a neighbor-joining (NJ) approach to generate the <italic toggle=\"yes\">S. cerevisiae</italic> phylogeny which accommodate the well-established existence of admixed strains in this species (##REF##19212322##Liti et al., 2009##; ##REF##29643504##Peter et al., 2018##). Despite using nearly twice as many strains, our phylogenetic tree of <italic toggle=\"yes\">S. cerevisiae</italic> strains shows a similar topology as ##REF##29643504##Peter et al. (2018)##, who identified a complex population structure including more than 26 distinct lineages plus many mosaic strains derived from admixture between these lineages. Strains in our integrated dataset that are not present in ##REF##29643504##Peter et al. (2018)## – such as those from ##REF##30002370##Duan et al. (2018)## and ##REF##26782936##Barbosa et al. (2016)## – cluster with known lineages previously characterized by ##REF##29643504##Peter et al. (2018)##. For instance, “activated dry yeast” strains from ##REF##30002370##Duan et al. (2018)## cluster in the “mixed origin” lineage from ##REF##29643504##Peter et al. (2018)## (##FIG##1##Figure 2##).</p>", "<p id=\"P14\">We then visualized the presence/absence of LTR and internal regions for Ty4/Tsu4 subfamilies on the phylogeny inferred for <italic toggle=\"yes\">S. cerevisiae</italic> from short-read WGS data. This analysis revealed that Ty4 LTR and internal sequences are present in all <italic toggle=\"yes\">S. cerevisiae</italic> lineages (##FIG##1##Figure 2A##,##FIG##1##B##). In contrast, Tsu4 LTR sequences are restricted to ~2% of strains surveyed (49/2,404) all of which are found in Central/South America (specifically French Guiana, Mexico, Brazil, and the French West Indies) (##FIG##1##Figure 2C##,##FIG##1##D##). Tsu4 sequences are completely absent from most other <italic toggle=\"yes\">S. cerevisiae</italic> lineages, including the most ancestral Chinese lineages (##REF##22913817##Wang et al., 2012##; ##REF##30002370##Duan et al., 2018##). We identified six <italic toggle=\"yes\">S. cerevisiae</italic> strains that contain evidence of Tsu4 internal regions (245, AFQ and CDM from Mosaic lineage 2; CQS from the French Guiana lineage; UFMG-CM-Y641 and UFMG-CM-Y642 from the Brazil 3 lineage) (##REF##25750179##Marsit et al., 2015##; ##REF##29643504##Peter et al., 2018##; ##REF##26782936##Barbosa et al., 2016##). Three of these <italic toggle=\"yes\">S. cerevisiae</italic> strains (AFQ, CDM and CQS) also contain internal regions for Ty4.</p>", "<p id=\"P15\">Next, we analyzed Ty4/Tsu4 subfamily content in WGAs for a global sample of 183 <italic toggle=\"yes\">S. cerevisiae</italic> strains (##SUPPL##0##Table S1## and ##SUPPL##0##Figure S1##), which confirmed results based on short-read WGS data. Ty4 subfamily sequences were found in all <italic toggle=\"yes\">S. cerevisiae</italic> WGAs analyzed, while Tsu4 subfamily sequences were absent from the majority of <italic toggle=\"yes\">S. cerevisiae</italic> WGAs. CQS is the only strain assembled using long-read data for which we identify FLEs for Tsu4 (n=9), confirming previous observations (##REF##37524789##O’Donnell et al., 2023##). We also identified one full-length (245 and AFQ) or truncated (CDM) Tsu4 copy in short-read WGAs (##SUPPL##0##Table S1##) for three <italic toggle=\"yes\">S. cerevisiae</italic> strains that we identified previously as containing Tsu4 internal regions in the short-read WGS scan. No publicly-available WGAs are available for the two Brazil 3 strains (UFMG-CM-Y641 and UFMG-CM-Y642, (##REF##26782936##Barbosa et al., 2016##)) with evidence of Tsu4 internal regions based on WGS data. All three <italic toggle=\"yes\">S. cerevisiae</italic> strains with Tsu4 FLEs in WGAs are geographically restricted to Central/South America. Importantly, we note that FLEs for both the Ty4 and Tsu4 subfamilies were identified in the long-read WGA for strain CQS.</p>", "<p id=\"P16\">The prevalence of the Ty4 subfamily in most <italic toggle=\"yes\">S. cerevisiae</italic> lineages – including ancestral Chinese lineages (##REF##22913817##Wang et al., 2012##; ##REF##30002370##Duan et al., 2018##) – indicates that the Ty4 subfamily was present in the MRCA of this species. However, despite being broadly active at the species level, the absence of Ty4 internal regions and FLEs in many strains indicates this subfamily has undergone many local extinction events (see also (##REF##34115140##Bleykasten-Grosshans et al., 2021##)). In contrast, the absence of Tsu4 in most lineages (including ancestral Chinese lineages) strongly indicates that this subfamily was not present in the MRCA of <italic toggle=\"yes\">S. cerevisiae</italic>. The small number of strains that do contain Tsu4 in <italic toggle=\"yes\">S. cerevisiae</italic> do not form a single monophyletic group, which is consistent either with one HTT event followed by admixture among lineages, or multiple recent HTT events that have introduced Tsu4 into different lineages of <italic toggle=\"yes\">S. cerevisiae</italic> in Central/South America. Finally, the observation of <italic toggle=\"yes\">S. cerevisiae</italic> strains with FLEs for both Tsu4 and Ty4 subfamilies (i.e., CQS) demonstrates that FLEs from the Ty4 and Tsu4 subfamilies can co-exist in the same <italic toggle=\"yes\">Saccharomyces</italic> strain.</p>", "<title>Multiple HTT events have transferred Tsu4 into <italic toggle=\"yes\">S. paradoxus</italic> and <italic toggle=\"yes\">S. cerevisiae</italic></title>", "<p id=\"P17\">The short-read WGS strategy used above allowed us to establish Ty4 as the ancestral subfamily in <italic toggle=\"yes\">S. paradoxus</italic> and <italic toggle=\"yes\">S. cerevisiae</italic>, and to identify at least one Tsu4 HTT event in both species. However, this approach cannot resolve how many Tsu4 HTT events occurred in either species, nor can it identify the potential donor lineages for these HTT events. To investigate whether the presence of Tsu4 in <italic toggle=\"yes\">S. paradoxus</italic> and <italic toggle=\"yes\">S. cerevisiae</italic> can be explained by one or more Tsu4 HTT event, and to identify the most likely donor lineage(s) for these HTT events, we analyzed the molecular evolution of all Ty4 family FLEs identified in a integrated dataset of 210 high-quality WGAs for all recognized species in the genus <italic toggle=\"yes\">Saccharomyces</italic>. The majority of WGAs in this dataset were generated using PacBio or Oxford Nanopore Technology (ONT) long reads, including new high-quality WGAs for <italic toggle=\"yes\">S. mikatae</italic> strains IFO 1815 and NBRC 10994 that we generated using PacBio long reads (##SUPPL##0##Table S2##). Phylogenetic analysis of our new <italic toggle=\"yes\">S. mikatae</italic> WGAs confirmed the taxonomic placement of IFO1815 in the Asia A clade (##REF##36755033##Peris et al., 2023##) and revealed that NBRC 10994 should be placed in a new clade (Asia C) (##SUPPL##0##Figure S2##). In addition, we also included WGAs based on short-read data for three <italic toggle=\"yes\">S. cerevisiae</italic> strains with Tsu4 internal sequences (245, AFQ and CDM; see above) and for two strains of <italic toggle=\"yes\">S. arboricola</italic> (H-6 and ZP960) that represent the best available WGAs for this species.</p>", "<p id=\"P18\">In total, we identified 247 FLEs for the Ty4 subfamily and 124 FLEs for the Tsu4 subfamily in this integrated dataset (##SUPPL##0##Table S2##, ##SUPPL##0##Figure S1##). No FLEs for either subfamily were identified <italic toggle=\"yes\">S. arboricola</italic> and <italic toggle=\"yes\">S. kudriavzevii</italic> using our current query sequences. The absence of Ty4 family FLEs in <italic toggle=\"yes\">S. arboricola</italic> may simply reflect the lack of high-quality WGAs for this species. However, the absence of Ty4 family FLEs in <italic toggle=\"yes\">S. kudriavzevii</italic> is likely an artifact of divergence between our current query sequences and Ty4 family FLEs in this species. In <italic toggle=\"yes\">S. kudriavzevii</italic> strain (IFO1802) we observed a high copy number of truncated Tsu4 elements (n=8) that, upon further inspection, revealed five nearly full-length elements that were highly similar to one another, dispersed throughout the IFO1802 genome, and overlapped full-length <italic toggle=\"yes\">de novo</italic> LTRharvest predictions (##REF##18194517##Ellinghaus et al., 2008##). We concluded that these five <italic toggle=\"yes\">S. kudriavzevii</italic> elements represented FLEs from a novel branch in the Ty4 family and included them in our phylogenetic analysis of FLEs.</p>", "<p id=\"P19\">We next created a multiple sequence alignment and reconstructed phylogenetic networks and trees based on internal coding regions of all 376 FLEs in our integrated dataset (##FIG##2##Figure 3A##, ##FIG##2##B##). We excluded LTR and untranslated sequences from this analysis, which exhibited poor alignment due to higher divergence in noncoding regions. This analysis identified 14 well-supported clades of FLEs each found in a single species, plus two branches with singleton FLEs from the Hawaiian <italic toggle=\"yes\">S. paradoxus</italic> strain UWOPS91-917.1 and Asia C <italic toggle=\"yes\">S. mikatae</italic> strain NBRC 10994, respectively. Two clades (Clades 11 and 12) with FLEs from either <italic toggle=\"yes\">S. mikatae</italic> or <italic toggle=\"yes\">S. kudriavzevii</italic> exhibit evidence of reticulation between the Ty4 and Tsu4 clades (##FIG##2##Figure 3A##), which we interpret as being caused by recombination between these subfamilies (see detailed analysis below). Exclusion of these clades eliminated the major signal for reticulation between the Ty4 and Tsu4 subfamilies in the phylogenetic network (##SUPPL##0##Figure S3A##) and increased bootstrap support for clades in <italic toggle=\"yes\">S. jurei</italic> and <italic toggle=\"yes\">S. mikatae</italic>, but did not alter the topological relationships of other clades in the ML tree (##SUPPL##0##Figure S3B##). The Ty4 subfamily is represented by only two clades with FLEs from only <italic toggle=\"yes\">S. cerevisiae</italic> (Clade 13) or <italic toggle=\"yes\">S. paradoxus</italic> (Clade 14), respectively. In contrast, the Tsu4 subfamily is represented by ten species-specific clades (1-10) with FLEs from all species except <italic toggle=\"yes\">S. kudriavzevii</italic> (##FIG##2##Figure 3A##, ##SUPPL##0##S4##, ##SUPPL##0##S5##, ##SUPPL##0##S6##). Two clades each of Tsu4 FLEs from <italic toggle=\"yes\">S. paradoxus</italic> (Clades 1 and 3) and <italic toggle=\"yes\">S. cerevisiae</italic> (Clades 2 and 4) together with the singleton FLE from <italic toggle=\"yes\">S. paradoxus</italic> UWOPS91-917.1 form a monophyletic group. <italic toggle=\"yes\">S. eubayanus</italic> is represented two Tsu4 FLEs clades from the Holarctic (Clade 5) and Patagonian (Clade 6) lineages, respectively. Tsu4 FLEs from <italic toggle=\"yes\">S. uvarum</italic> form two clades (Clades 7 and 8) that are both found in a single strain from the Holarctic lineage. Tsu4 FLEs from European <italic toggle=\"yes\">S. jurei</italic> (Clade 9) cluster with Tsu4 FLEs from the <italic toggle=\"yes\">S. mikatae</italic> Asia A lineage (Clade 10) and the singleton branch from the <italic toggle=\"yes\">S. mikatae</italic> Asia C lineage.</p>", "<p id=\"P20\">Previous analysis of Tsu4 HTT events in <italic toggle=\"yes\">S. paradoxus</italic> and <italic toggle=\"yes\">S. cerevisiae</italic> using a smaller dataset of FLEs (##REF##29942366##Bergman, 2018##) suggested one primary HTT occurred in the ancestor of American <italic toggle=\"yes\">S. paradoxus</italic> lineages followed by one secondary HTT from <italic toggle=\"yes\">S. paradoxus</italic> into <italic toggle=\"yes\">S. cerevisiae</italic>. This hypothesis predicts that Tsu4 FLEs from <italic toggle=\"yes\">S. paradoxus</italic> and <italic toggle=\"yes\">S. cerevisiae</italic> should form a single clade with <italic toggle=\"yes\">S. cerevisiae</italic> FLEs forming a single sub-clade somewhere within the broader diversity of <italic toggle=\"yes\">S. paradoxus</italic> FLEs. Two key features of the Tsu4 FLE phylogeny in our expanded dataset are inconsistent with this hypothesis (##FIG##2##Figure 3##). First, we observe two distinct clades of FLEs for both <italic toggle=\"yes\">S. paradoxus</italic> and <italic toggle=\"yes\">S. cerevisiae</italic>, each of whose closest sampled relatives are from a different species. Namely, <italic toggle=\"yes\">S. cerevisiae</italic> Clade 2 (from the French Guiana strain CQS) clusters with S. American <italic toggle=\"yes\">S. paradoxus</italic> Clade 1, while <italic toggle=\"yes\">S. cerevisiae</italic> Clade 4 (from Mosaic 2 strains 245 and AFQ) clusters with the N. American <italic toggle=\"yes\">S. paradoxus</italic> Clade 3. Second, the observed topology of Tsu4 FLEs in <italic toggle=\"yes\">S. paradoxus</italic> does not strictly follow the host phylogeny for the SpB sub-lineage strain UFRJ50816. Specifically, Tsu4 FLEs from the S. American <italic toggle=\"yes\">S. paradoxus</italic> strain UFRJ50816 form their own divergent Clade 1 instead of grouping as expected with Tsu4 FLEs from other SpB strains from N. America (MSH-604 and YPS138) in Clade 3 (##SUPPL##0##Figure S4##).</p>", "<p id=\"P21\">We interpret the phylogeny of Tsu4 FLEs in <italic toggle=\"yes\">S. paradoxus</italic> and <italic toggle=\"yes\">S. cerevisiae</italic> to be consistent with at least two Tsu4 HTT events into each species. In <italic toggle=\"yes\">S. paradoxus</italic>, we infer one ancestral Tsu4 HTT event creating Clade 3 FLEs that are present in all SpB, SpC, SpC and SpD strains (except UFRJ50816), and one recent HTT event creating Clade 1 FLEs present in the S. American sub-lineage containing UFRJ50816 (where Clade 3 FLEs had already gone extinct). Likewise, in <italic toggle=\"yes\">S. cerevisiae</italic>, we infer one recent HTT into the French Guiana lineage creating Clade 2 FLEs, and another recent HTT into the Mosaic 2 lineage creating Clade 4 FLEs. These data suggest that when conditions are favorable for HTT events in <italic toggle=\"yes\">Saccharomyces</italic>, they can occur more frequently than the most parsimonious interpretation based on presence/absence data would imply.</p>", "<title>Tsu4 in <italic toggle=\"yes\">S. paradoxus</italic>, <italic toggle=\"yes\">S. cerevisiae</italic>, and Holarctic <italic toggle=\"yes\">S. eubayanus</italic> were transferred from an unknown donor.</title>", "<p id=\"P22\">The collective monophyly of the Tsu4 clades found in <italic toggle=\"yes\">S. paradoxus</italic> and <italic toggle=\"yes\">S. cerevisiae</italic> suggests the Tsu4 HTT events in these species ultimately arose from a similar donor lineage, with distinct clades being formed at different times or in different geographic regions. Using indirect data from the hybrid species <italic toggle=\"yes\">S. pastorianus</italic> that contains subgenomes from <italic toggle=\"yes\">S. eubayanus</italic> and <italic toggle=\"yes\">S. cerevisiae</italic> (##REF##21873232##Libkind et al., 2011##; ##REF##26269586##Baker et al., 2015##; ##REF##26732986##Okuno et al., 2016##), ##REF##29942366##Bergman (2018)## concluded that the Holarctic lineage of <italic toggle=\"yes\">S. eubayanus</italic> contains the most closely related Tsu4 sequences to those in <italic toggle=\"yes\">S. paradoxus</italic> and <italic toggle=\"yes\">S. cerevisiae</italic>. Our current analysis provides direct evidence for this conclusion, with Clade 5 FLEs from CDFM21L.1 in the the Holarctic <italic toggle=\"yes\">S. eubayanus</italic> lineage clustering most closely with the common ancestor of all Tsu4 FLEs in <italic toggle=\"yes\">S. paradoxus</italic> and <italic toggle=\"yes\">S. cerevisiae</italic> (##FIG##2##Figure 3##). Taken at face value, this result implies that Holarctic <italic toggle=\"yes\">S. eubayanus</italic> represents the most likely donor lineage for the multiple HTT events observed in <italic toggle=\"yes\">S. paradoxus</italic> and <italic toggle=\"yes\">S. cerevisiae</italic>.</p>", "<p id=\"P23\">However, several features of the Tsu4 FLE phylogeny suggest that the Holarctic <italic toggle=\"yes\">S. eubayanus</italic> lineage is not the direct donor for the HTT events in <italic toggle=\"yes\">S. paradoxus</italic> and <italic toggle=\"yes\">S. cerevisiae</italic> (##FIG##2##Figure 3##). First, Tsu4 clades in <italic toggle=\"yes\">S. paradoxus</italic> and <italic toggle=\"yes\">S. cerevisiae</italic> are not nested within the diversity of FLEs from Holarctic <italic toggle=\"yes\">S. eubayanus</italic>, but rather form a sister group separated by substantial divergence. Second, bootstrap support for the clustering of Holarctic <italic toggle=\"yes\">S. eubayanus</italic> FLEs with the ancestor of FLEs from <italic toggle=\"yes\">S. cerevisiae</italic> and <italic toggle=\"yes\">S. paradoxus</italic> is relatively weak (>66%). The alternative clustering of FLEs from the Holarctic and Patagonia-B <italic toggle=\"yes\">S. eubayanus</italic> lineages together would suggest that the donor into <italic toggle=\"yes\">S. paradoxus</italic> and <italic toggle=\"yes\">S. cerevisiae</italic> is from a currently-unsampled lineage of <italic toggle=\"yes\">S. eubayanus</italic>, or a species closely related to <italic toggle=\"yes\">S. eubayanus</italic>. Third, Tsu4 FLEs from Holarctic <italic toggle=\"yes\">S. eubayanus</italic> are not nested with in the diversity of <italic toggle=\"yes\">S. eubayanus</italic> Patagonian FLEs (##FIG##2##Figure 3##, ##SUPPL##0##S5##), as is expected since Holarctic <italic toggle=\"yes\">S. eubayanus</italic> is known to be a sub-lineage of the Patagonia-B lineage (##SUPPL##0##Figure S7##) (##REF##27385107##Peris et al., 2016##). Discordance between the <italic toggle=\"yes\">S. eubayanus</italic> Tsu4 and host strain phylogenies suggests the possibility of a previously-undetected Tsu4 HTT event into Holarctic <italic toggle=\"yes\">S. eubayanus</italic>, which could lead to the false conclusion that the Holarctic <italic toggle=\"yes\">S. eubayanus</italic> lineage is the most likely donor for HTT events into <italic toggle=\"yes\">S. paradoxus</italic> and <italic toggle=\"yes\">S. cerevisiae</italic>.</p>", "<p id=\"P24\">To test for a previously-undetected Tsu4 HTT event in the Holarctic <italic toggle=\"yes\">S. eubayanus</italic> lineage, we developed a novel approach to study Tsu4 sequence evolution using strain-specific consensus sequences inferred from short-read WGS data. Importantly, this approach bypasses the limited number of WGAs available in <italic toggle=\"yes\">S. eubayanus</italic> and other potential donor species and allows us to generalize results across larger samples of host strains and lineages. The premise behind this approach is based on the observation that Tsu4 FLEs typically cluster first within the same strain before clustering with FLEs from other strains (##FIG##2##Figure 3##). Thus, strain-specific Tsu4 consensus sequences should be a reasonable proxy for the common ancestor of elements within a strain, and can themselves be used for evolutionary inference across strains and species.</p>", "<p id=\"P25\">Using the short-read based WGS approach as above for <italic toggle=\"yes\">S. paradoxus</italic> and <italic toggle=\"yes\">S. cerevisiae</italic>, we first estimated Ty4/Tsu4 LTR and internal copy numbers in the context of host phylogenies for <italic toggle=\"yes\">S. eubayanus</italic> (##SUPPL##0##Figure S7##) and <italic toggle=\"yes\">S. uvarum</italic> (##SUPPL##0##Figure S8##), respectively. These results reveal that Tsu4 was present and Ty4 was absent in the ancestors of both <italic toggle=\"yes\">S. eubayanus</italic> and <italic toggle=\"yes\">S. uvarum</italic>, that Tsu4 is broadly active in both species, and that the Holarctic <italic toggle=\"yes\">S. eubayanus</italic> lineage has the highest LTR copy number of Tsu4 in either species. We then computed consensus sequences for Tsu4 internal regions in all <italic toggle=\"yes\">S. paradoxus</italic>, <italic toggle=\"yes\">S. cerevisiae</italic>, <italic toggle=\"yes\">S. eubayanus</italic> and <italic toggle=\"yes\">S. uvarum</italic> strains with an estimated copy number of >0.75 and generated a ML tree of strain-specific sequences across the combined dataset of four species (##FIG##3##Figure 4##).</p>", "<p id=\"P26\">Key groupings in the strain-specific Tsu4 consensus tree (##FIG##3##Figure 4##) agreed with the phylogeny of Tsu4 FLEs based on a smaller number of strains above (##FIG##2##Figure 3##), cross-validating both approaches. Notably, Tsu4 consensus sequences for all N. American <italic toggle=\"yes\">S. paradoxus</italic> strains (together with the two <italic toggle=\"yes\">S. cerevisiae</italic> Brazil 3 strains, UFMG-CM-Y641 and UFMG-CM-Y642) form a large monophyletic group (corresponding to Clade 3 in the FLE tree) that clusters most closely with consensus sequences from Mosaic 2 <italic toggle=\"yes\">S. cerevisiae</italic> strains (corresponding to Clade 4). Likewise, the consensus sequence for the S. American <italic toggle=\"yes\">S. paradoxus</italic> strain UFRJ50916 (corresponding to Clade 1) clusters with the S. American <italic toggle=\"yes\">S. cerevisiae</italic> strain CQS (corresponding to Clade 2). All seven Holarctic <italic toggle=\"yes\">S. eubayanus</italic> strains form a monophyletic group (corresponding to Clade 5) that clusters with <italic toggle=\"yes\">S. paradoxus</italic> and <italic toggle=\"yes\">S. cerevisiae</italic> consensus sequences and is distinct from Tsu4 sequences found in all other <italic toggle=\"yes\">S. eubayanus</italic> or <italic toggle=\"yes\">S. uvarum</italic> strains.</p>", "<p id=\"P27\">Phylogenetic analysis of strain-specific consensus sequences also revealed two other Tsu4 HTT events that could not be detected in the FLE phylogeny because of limited samples of WGAs. The first involves the two <italic toggle=\"yes\">S. cerevisiae</italic> Brazil 3 strains (UFMG-CM-Y641 and UFMG-CM-Y642). Based on the location of Brazil 3 samples in the consensus sequence tree (##FIG##3##Figure 4##) and the pattern of variation in the consensus sequences of all six <italic toggle=\"yes\">S. cerevisiae</italic> strains that have Tsu4 (##SUPPL##0##Figure S9##), we conclude that Tsu4 sequences in the <italic toggle=\"yes\">S. cerevisiae</italic> Brazil 3 lineage arose from a HTT event that is distinct from those detected using FLEs in the Mosaic 2 or French Guiana lineages. The second peviously-undetected Tsu4 HTT event involves the <italic toggle=\"yes\">S. eubayanus</italic> Patagonia B strain yHAB565, whose Tsu4 consensus sequence is placed within the <italic toggle=\"yes\">S. uvarum</italic> cluster. <italic toggle=\"yes\">S. eubayanus</italic> yHAB565 is placed correctly in the Patagonia B lineage in our host strain tree (##SUPPL##0##Figure S7##), which rules out the possibility of sample mixups during sequencing or bioinformatic analysis and supports a recent HTT Tsu4 event from <italic toggle=\"yes\">S. uvarum</italic> into <italic toggle=\"yes\">S. eubayanus</italic>.</p>", "<p id=\"P28\">Taken together, our results suggest that the similarity between the Tsu4 clades in Holarctic <italic toggle=\"yes\">S. eubayanus</italic>, <italic toggle=\"yes\">S. paradoxus</italic>, and <italic toggle=\"yes\">S. cerevisiae</italic> arose from parallel HTT events donated by a common but as-yet-unidentified <italic toggle=\"yes\">Saccharomyces</italic> lineage. The substantial divergence between non-Holarctic <italic toggle=\"yes\">S. eubayanus</italic> or <italic toggle=\"yes\">S. uvarum</italic> and the clade comprised of Holarctic <italic toggle=\"yes\">S. eubayanus</italic>, <italic toggle=\"yes\">S. paradoxus</italic> and <italic toggle=\"yes\">S. cerevisiae</italic> suggests that this unknown donor is either a currently-unsequenced lineage of <italic toggle=\"yes\">S. eubayanus</italic> or <italic toggle=\"yes\">S. uvarum</italic> (e.g., West China <italic toggle=\"yes\">S. eubayanus</italic> (##REF##24845661##Bing et al., 2014##)) or potentially an undiscovered species related to <italic toggle=\"yes\">S. eubayanus</italic> and <italic toggle=\"yes\">S. uvarum</italic>. Additionally, we show that our novel strain-specific consensus sequence approach complements analysis of FLEs from WGAs and can reveal previously undetected cases of Tsu4 HTT in the abundant WGS datasets that are available in multiple yeast species.</p>", "<title>Tsu4 HTT fuels the evolution of recombinant clades in <italic toggle=\"yes\">S. mikatae</italic> and <italic toggle=\"yes\">S. kudriavzevii</italic></title>", "<p id=\"P29\">Our phylogenetic network analysis of FLEs from the Ty4 family above revealed evidence of reticulation in <italic toggle=\"yes\">S. mikatae</italic> Clade 11 and <italic toggle=\"yes\">S. kudriavzevii</italic> Clade 12 (##FIG##2##Figure 3A##) that could be caused by recombination between the Ty4 and Tsu4 subfamilies (##REF##16221896##Huson and Bryant, 2006##). In addition, the coexistence of FLEs for both Ty4 and Tsu4 in <italic toggle=\"yes\">S. cerevisiae</italic> strain CQS indicates that conditions for recombination between Ty4 and Tsu4 subfamilies can occur in nature. To provide further evidence for recombination between the Ty4 and Tsu4 subfamilies, we first selected representative FLEs for “pure” Tsu4 (f32 from the <italic toggle=\"yes\">S. uvarum</italic> Clade 8) and “pure” Ty4 (f49 from the <italic toggle=\"yes\">S. cerevisiae</italic> Clade 13) from outgroup species not involved in the putative recombination events. We then plotted a sliding window of pairwise sequence divergence between these representative pure Ty4 and Tsu4 FLEs and a putatively “pure” <italic toggle=\"yes\">S. mikatae</italic> Clade 10 FLE (f286 from IFO 1815) or a putatively “recombinant” <italic toggle=\"yes\">S. mikatae</italic> Clade 11 FLE (f256 from IFO 1815) (##SUPPL##0##Figure S10##). This analysis revealed that that the 5’ internal region – including the complete <italic toggle=\"yes\">gag</italic> gene and the first ~500bp of <italic toggle=\"yes\">pol</italic> – shows lower levels of divergence between <italic toggle=\"yes\">S. uvarum</italic> Tsu4 and pure <italic toggle=\"yes\">S. mikatae</italic> Clade 10 (##SUPPL##0##Figure S10A##) than recombinant <italic toggle=\"yes\">S. mikatae</italic> Clade 11 (##SUPPL##0##Figure S10B##). Conversely, the same 5’ internal region shows higher levels of divergence between <italic toggle=\"yes\">S. cerevisiae</italic> Ty4 and pure <italic toggle=\"yes\">S. mikatae</italic> Clade 10 (##SUPPL##0##Figure S10D##) than recombinant <italic toggle=\"yes\">S. mikatae</italic> Clade 11 (##SUPPL##0##Figure S10E##). These data indicate that the 5’ internal segment in Clade 11 is derived from the Ty4 subfamily, while the rest of the Clade 11 internal region is derived from the Tsu4 subfamily.</p>", "<p id=\"P30\">We then partitioned the multiple sequence alignment of Ty4 family FLE internal regions into 5’ and 3’ segments, and reconstructed ML phylogenies for both partitions from representative clades (##FIG##4##Figure 5##). A striking discordance can be observed in phylogenies reconstructed from 5’ and 3’ internal regions of Clade 11 sequences. In the 5’ partition, pure Clade 10 FLEs cluster with Tsu4 FLEs from <italic toggle=\"yes\">S. jurei</italic> and <italic toggle=\"yes\">S. uvarum</italic>, while recombinant Clade 11 FLEs cluster with strong support as a sister group to from <italic toggle=\"yes\">S. paradoxus</italic>/<italic toggle=\"yes\">S. cerevisiae</italic> in the Ty4 subfamily (##FIG##4##Figure 5A##). In contrast, in the tree reconstructed from the 3’ partition <italic toggle=\"yes\">S. mikatae</italic> Clades 10 and 11 form a single monophyletic group that is closely related to Tsu4 sequences from <italic toggle=\"yes\">S. jurei</italic> (##FIG##4##Figure 5B##).</p>", "<p id=\"P31\">Based on these results and presence of Ty4 solo LTRs in WGAs from <italic toggle=\"yes\">S. mikatae</italic> and <italic toggle=\"yes\">S. jurei</italic> (##SUPPL##0##Table S1##), we propose the following scenario for the evolution of <italic toggle=\"yes\">S. mikatae</italic> Clades 10 and 11. A divergent Ty4 subfamily was previously active in an ancestor of <italic toggle=\"yes\">S. mikatae</italic> and <italic toggle=\"yes\">S. jurei</italic>, which has subsequently gone extinct in both species but left Ty4 internal sequences that were retained in the <italic toggle=\"yes\">S. mikatae</italic> genome for some period of time. A HTT event introduced the Tsu4 subfamily prior to the speciation of <italic toggle=\"yes\">S. mikatae</italic> and <italic toggle=\"yes\">S. jurei</italic>, which evolved into Clade 10 in <italic toggle=\"yes\">S. mikatae</italic> and Clade 9 in <italic toggle=\"yes\">S. jurei</italic>. This Tsu4 HTT event explains the discordance between the Ty4 family and host species phylogenies previously reported for <italic toggle=\"yes\">S. mikatae</italic>\n##REF##29942366##Bergman (2018)##. The donor for this Tsu4 HTT into the ancestor of <italic toggle=\"yes\">S. mikatae</italic> and <italic toggle=\"yes\">S. jurei</italic> is unknown but related to the ancestor to all extant Tsu4 FLEs in <italic toggle=\"yes\">S. eubayanus</italic>, <italic toggle=\"yes\">S. uvarum</italic>, <italic toggle=\"yes\">S. paradoxus</italic> and <italic toggle=\"yes\">S. cerevisiae</italic>. Recombination of the 5’ internal region from the now-extinct Ty4 subfamily in <italic toggle=\"yes\">S. mikatae</italic> onto a Clade 10-like pure Tsu4 FLE created the “recombinant” Tsu4 Clade 11. This model explains the lack of Ty4 FLEs in <italic toggle=\"yes\">S. mikatae</italic> and <italic toggle=\"yes\">S. jurei</italic> (##SUPPL##0##Table S1##), the coexistence of two highly divergent clades in <italic toggle=\"yes\">S. mikatae</italic> genomes (##FIG##2##Figure 3B##), the long branch leading to Clade 11 in the FLE phylogeny (##FIG##2##Figure 3B##), and reticulation between Ty4 and Tsu4 subfamilies for Clade 11 in the phylogenetic network (##FIG##2##Figure 3A##).</p>", "<p id=\"P32\">We applied similar approaches to investigate whether reticulation in the phylogenetic network observed for <italic toggle=\"yes\">S. kudriavzevii</italic> Clade 12 FLEs (##FIG##2##Figure 3A##) also is caused by recombination between Ty4 and Tsu4 subfamilies. Sliding window analysis of a representative Clade 12 FLE versus pure Tsu4 and Ty4 from outgroup species revealed an ~2kb segment starting at the beginning of Pol that shows very high similarity to Tsu4 (##SUPPL##0##Figure S10C##). Phylogenetic analysis of partitions corresponding to the “left,” “middle,” and “right” segments of FLE internal regions for representative clades reveals that the middle internal segment of Clade 12 is derived from the Tsu4 subfamily, while the left and right segments are divergent representatives of the Ty4 subfamily. Based on these results and presence of Ty4 LTRs in WGAs from <italic toggle=\"yes\">S. kudriavzevii</italic> (##SUPPL##0##Table S1##), we propose that <italic toggle=\"yes\">S. kudriavzevii</italic> ancestrally contained a divergent Ty4 subfamily which acquired a middle segment from a horizontally-transferred Tsu4 by recombination. The Tsu4 clade that was horizontally transferred into <italic toggle=\"yes\">S. kudriavzevii</italic> and the original pure <italic toggle=\"yes\">S. kudriavzevii</italic> Ty4 clade have both subsequently gone extinct, leaving the recombinant Clade 12 as the only extant representative of the Ty4 family currently identified in <italic toggle=\"yes\">S. kudriavzevii</italic> . Based on clustering of the middle internal region (##FIG##5##Figure 6B##), the donor lineage for the Tsu4 HTT into <italic toggle=\"yes\">S. kudriavzevii</italic> is related to the donor for Tsu4 HTT in <italic toggle=\"yes\">S. mikatae</italic> and <italic toggle=\"yes\">S. jurei</italic>. Together, the recombinant clades in <italic toggle=\"yes\">S. mikatae</italic> and <italic toggle=\"yes\">S. kudriavzevii</italic> support the conclusion that co-existence of Ty4 and Tsu4 subfamily sequences in the same genome mediated by HTT provides substrate for recombination to generate new retrotransposon clades in <italic toggle=\"yes\">Saccharomyces</italic>.</p>" ]	[ "<title>Conclusions</title>", "<p id=\"P33\">Here we address open questions concerning the impact of HTT on the evolution of the Ty4 family in <italic toggle=\"yes\">Saccharomyces</italic> by integrating large-scale short-read WGS data and high-quality long-read WGAs from multiple <italic toggle=\"yes\">Saccharomyces</italic> species. We show that the previously detected Tsu4 HTT event in <italic toggle=\"yes\">S. paradoxus</italic> (##REF##29942366##Bergman, 2018##) occurred in the ancestor of all American lineages and report new evidence for a second recent Tsu4 HTT in the South American lineage of <italic toggle=\"yes\">S. paradoxus</italic>. We also show that the previously reported presence of Tsu4 in <italic toggle=\"yes\">S. cerevisiae</italic> (##REF##29942366##Bergman, 2018##; ##REF##37524789##O’Donnell et al., 2023##) is explained by at least three independent recent HTT events into <italic toggle=\"yes\">S. cerevisiae</italic> in Central/South America, at least one of which (into the French Guiana lineage) is also associated with HTT of another retrotransposon family (Ty1) and introgression of host genes from <italic toggle=\"yes\">S. paradoxus</italic> (##REF##29643504##Peter et al., 2018##; ##REF##34115140##Bleykasten-Grosshans et al., 2021##). We confirm that the Holarctic lineage of <italic toggle=\"yes\">S. eubayanus</italic> contains Tsu4 elements that are most closely related to those in <italic toggle=\"yes\">S. paradoxus</italic> and <italic toggle=\"yes\">S. cerevisiae</italic> (##REF##29942366##Bergman, 2018##) but conclude that this similarity is caused by an independent Tsu4 HTT into the Holarctic <italic toggle=\"yes\">S. eubayanus</italic> lineage from an unidentified donor. Recurrent HTT of Tsu4 into different host species and lineages provides a mechanism to explain why this subfamily is more clade-rich than the Ty4 subfamily in the Ty4 family phylogeny.</p>", "<p id=\"P34\">Additionally, we investigate the putative Tsu4 HTT event reported for <italic toggle=\"yes\">S. mikatae</italic> (##REF##29942366##Bergman, 2018##) by generating new PacBio WGAs for two strains in this species (IFO 1815 and NBRC 10994), which revealed the presence of two active Ty4 family clades in <italic toggle=\"yes\">S. mikatae</italic>. The first is a pure Tsu4 clade that clusters with FLEs from <italic toggle=\"yes\">S. jurei</italic>, providing evidence for an ancestral Tsu4 HTT event prior to the divergence of <italic toggle=\"yes\">S. mikatae</italic> and <italic toggle=\"yes\">S. jurei</italic> that explains the discordance between Ty4 family and host species phylogenies for <italic toggle=\"yes\">S. mikatae</italic> (##REF##29942366##Bergman, 2018##). We also find a second recombinant clade in <italic toggle=\"yes\">S. mikatae</italic> that shares similarity to the Ty4 and Tsu4 subfamilies in different parts of the internal region. The recombinant Clade 11 implies the co-existence of internal sequences for both subfamilies in the <italic toggle=\"yes\">S. mikatae</italic> genome at some point in history. Likewise, we identify a novel clade in <italic toggle=\"yes\">S. kudriavzevii</italic> that similarly exhibits recombination between the Ty4 and Tsu4 subfamilies, and explains the divergent position of <italic toggle=\"yes\">S. kudriavzevii</italic> FLEs in the Ty4 family phylogeny (##REF##29942366##Bergman, 2018##).</p>", "<p id=\"P35\">The discovery of novel Ty4 family clades in <italic toggle=\"yes\">S. mikatae</italic> and <italic toggle=\"yes\">S. kudriavzevii</italic> that were generated by recombination between resident (ancestral) and horizontally transferred (derived) retrotransposon subfamilies can be used to generalize prior results reported for the Ty1/Ty2 superfamily in <italic toggle=\"yes\">S. cerevisiae</italic> (##REF##9664692##Jordan and McDonald, 1998##; ##REF##32084126##Czaja et al., 2020##; ##REF##34115140##Bleykasten-Grosshans et al., 2021##). The canonical Ty1 subfamily found in <italic toggle=\"yes\">S. cerevisiae</italic> evolved from an ancestral Ty1’ subfamily by independently acquiring segments from horizontally-transferred European <italic toggle=\"yes\">S. paradoxus</italic> Ty1 and Ty2 elements by recombination (##REF##9664692##Jordan and McDonald, 1998##; ##REF##32084126##Czaja et al., 2020##; ##REF##34115140##Bleykasten-Grosshans et al., 2021##). Similarly, the Ty101 subfamily evolved in <italic toggle=\"yes\">S. cerevisiae</italic> through recombination between the ancestral Ty1’ subfamily and a horizontal transferred South American <italic toggle=\"yes\">S. paradoxus</italic> Ty1 element (##REF##34115140##Bleykasten-Grosshans et al., 2021##). Together these results suggest that recombination among divergent subfamilies that co-occur in the same species because of HTT may be a common mechanisms for the evolution of new of new retrotransposon lineages in <italic toggle=\"yes\">Saccharomyces</italic>. In addition, Ty1c and Ty101 in <italic toggle=\"yes\">S. cerevisiae</italic> and the recombinant Clade 11 in <italic toggle=\"yes\">S. mikatae</italic> both retain a complete <italic toggle=\"yes\">gag</italic> gene from the horizontally-transferred element. A truncated product from Ty1c <italic toggle=\"yes\">gag</italic> has been shown to encode a copy-number dependent repressor of Ty1c transposition in <italic toggle=\"yes\">S. cerevisiae</italic> and <italic toggle=\"yes\">S. paradoxus</italic> (##REF##25609815##Saha et al., 2015##; ##REF##34552077##Cottee et al., 2021##). Thus, determining the functional significance of recombinant elements with respect to fitness or transposition control mechanisms may be fruitful areas for future research.</p>", "<p id=\"P36\">Finally, the observation of multiple independent Tsu4 HTT events in both <italic toggle=\"yes\">S. paradoxus</italic> and <italic toggle=\"yes\">S. cerevisiae</italic> reinforces prior observations of parallel HTT events involving the Ty1 family in different lineages of <italic toggle=\"yes\">S. cerevisiae</italic> (##REF##32084126##Czaja et al., 2020##; ##REF##34115140##Bleykasten-Grosshans et al., 2021##). Together, these results suggest that parallel HTT events in different parts of a species range may be a common occurrence in <italic toggle=\"yes\">Saccharomyces</italic>. If so, the number of HTT events in <italic toggle=\"yes\">Saccharomyces</italic> species cannot be reliably inferred from simple presence/absence data within species, and reconstructing the complex history of HTT events will require high-resolution phylogenetic data from large samples of FLEs or strain-specific consensus sequences. Similarly, the observation of parallel HTT events on short timescales for multiple yeast TE families suggests that large-scale surveys that detect HTT among distantly related taxa may underestimate the frequency of HTT in eukaryotic genome evolution (##REF##28416702##Peccoud et al., 2017##, ##UREF##5##2018##).</p>" ]	[ "<p id=\"P1\">Author Contributions</p>", "<p id=\"P2\">C.M.B. designed the experiments and computational analyses with input from J.C. and D.J.G. D.J.G. performed the cell culture experiments. J.C. developed computational pipelines for analysis of unassembled short read data, whole genome assembly, and phylogenetic analyses. C.M.B. developed computational pipelines for annotation of whole-genome assemblies. J.C. and C.M.B analyzed the data. J.C. and C.M.B. drafted the manuscript with contributions from D.J.G. All authors revised and approved the final manuscript.</p>", "<p id=\"P3\">Horizontal transposon transfer (HTT) plays an important role in the evolution of eukaryotic genomes, however the detailed evolutionary history and impact of most HTT events remain to be elucidated. To better understand the process of HTT in closely-related microbial eukaryotes, we studied Ty4 retrotransposon subfamily content and sequence evolution across the genus <italic toggle=\"yes\">Saccharomyces</italic> using short- and long-read whole genome sequence data, including new PacBio genome assemblies for two <italic toggle=\"yes\">S. mikatae</italic> strains. We find evidence for multiple independent HTT events introducing the Tsu4 subfamily into specific lineages of <italic toggle=\"yes\">S. paradoxus, S. cerevisiae, S. eubayanus, S. kudriavzevii</italic> and the ancestor of the <italic toggle=\"yes\">S. mikatae/S. jurei</italic> species pair. In both <italic toggle=\"yes\">S. mikatae</italic> and <italic toggle=\"yes\">S. kudriavzevii</italic>, we identified novel Ty4 clades that were independently generated through recombination between resident and horizontally-transferred subfamilies. Our results reveal that recurrent HTT and lineage-specific extinction events lead to a complex pattern of Ty4 subfamily content across the genus <italic toggle=\"yes\">Saccharomyces</italic>. Moreover, our results demonstrate how HTT can lead to coexistence of related retrotransposon subfamilies in the same genome that can fuel evolution of new retrotransposon clades <italic toggle=\"yes\">via</italic> recombination.</p>" ]	[ "<title>Supplementary Material</title>" ]	[ "<title>Acknowledgments</title>", "<p id=\"P44\">We thank members of the Bergman and Garfinkel Labs for helpful discussion and comments during the project; the University of Georgia Genomics and Bioinformatics Core Facility (RRID:SCR_010994) for assistance with DNA extraction, PacBio library preparation and sequencing; and the Georgia Advanced Computing Resource Center for technical supporting and computational resources. This work was funded by the University of Georgia Research Foundation (CMB) and NIH grant R01GM124216 (DJG and CMB).</p>" ]	[ "<fig position=\"float\" id=\"F1\"><label>Figure 1:</label><caption><title>Host phylogeny of <italic toggle=\"yes\">S. paradoxus</italic> annotated with estimated Ty4/Tsu4 copy number.</title><p id=\"P47\">(A) Midpoint rooted ML phylogenetic tree of 370 <italic toggle=\"yes\">S. paradoxus</italic> strains integrated from multiple public short-read WGS datasets (see <xref rid=\"S9\" ref-type=\"sec\">Materials and Methods</xref>). Bootstrap support is annotated for key nodes. Major lineages and sub-lineages are annotated according to previously-reported population structure (##REF##30804385##Eberlein et al., 2019##; ##REF##34961959##He et al., 2022##). The N. American strain DG1768 used in retromobility studies (##REF##35049349##Chen et al., 2022b##) is found in the SpB sub-lineage and is indicated by the circled number 1. The S. American strain UFRJ50916 is found in the SpB sub-lineage and is indicated by the circled number 2. The Hawaiian strain UWOPS91-917.1 is found in its own sub-lineage indicated by the circled number 3. (B) Copy number estimates for the Ty4 subfamily. (C) Copy number estimates for the Tsu4 subfamily. In (B) and (C), gray bars represent copy number estimates for LTRs, whereas black bars represent estimated copy number for internal regions.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2:</label><caption><title>Host phylogeny of <italic toggle=\"yes\">S. cerevisiae</italic> annotated with the presence or absence of Ty4/Tsu4 subfamilies.</title><p id=\"P48\">(A-D) NJ phylogenetic tree reconstructed using 2,787,577 genome-wide SNPs from 2,404 <italic toggle=\"yes\">S. cerevisiae</italic> strains (see <xref rid=\"S9\" ref-type=\"sec\">Materials and Methods</xref>). Major lineages are annotated with light gray shading based on previously-reported population structure (##REF##29643504##Peter et al., 2018##; ##REF##30002370##Duan et al., 2018##). Colored dots indicate the presence of Ty4 (panels A and B) and Tsu4 (panels C and D) sequences for each <italic toggle=\"yes\">S. cerevisiae</italic> strain. The presence of Ty4/Tsu4 subfamilies in a strain was inferred when copy number estimates were >1 for LTRs (annotated with orange dots in panel A and C) and >0.5 for internal regions (annotated with purple dots in panel B and D).</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3:</label><caption><title>Phylogenetic network and tree of FLEs from the Ty4 family in <italic toggle=\"yes\">Saccharomyces</italic>.</title><p id=\"P49\">(A) Phylogenetic network for internal coding regions of Ty4/Tsu4 FLEs based on the NeighborNet algorithm. To simplify visualization, this network only includes Ty4 subfamily FLEs from WGAs reported in ##REF##28416820##Yue et al. (2017)##. Lineages in the network are labeled according to monophyletic groups identified in panel B. (B) Midpoint rooted ML phylogeny of internal coding regions from Ty4/Tsu4 FLEs. Bootstrap support based on 100 replicates is shown for major nodes. The scale bar for branch lengths is in units of substitutions per site. All monophyletic groups are collapsed as triangles. Two singleton Tsu4 elements (f267 from Hawaiian <italic toggle=\"yes\">S. paradoxus</italic> strain UWOPS91-917.1 and f256 from <italic toggle=\"yes\">S. mikatae</italic> strain NBRC 10994) are denoted as dots at tips. Triangles, tip dots, and ranges are colored for each species. Vertical heights of triangles are proportional to the number of taxa. Horizontal widths of triangles are equal to the maximum branch length within the clade. Note that the monophyletic clade for the Ty4 subfamily from <italic toggle=\"yes\">S. cerevisiae</italic> (annotated with an asterisk) is re-scaled to 5% of the real sample size both horizontally and vertically, due to the large number of Ty4 sequences (n=244) in <italic toggle=\"yes\">S. cerevisiae</italic> genomes.</p></caption></fig>", "<fig position=\"float\" id=\"F4\"><label>Figure 4:</label><caption><title>ML phylogeny of strain-specific consensus sequences for Tsu4 internal regions in all <italic toggle=\"yes\">S. paradoxus, S. cerevisiae, S. eubayanus</italic> and <italic toggle=\"yes\">S. uvarum</italic>.</title><p id=\"P50\">Shown is the phylogeny reconstructed with 1,817 distinct alignment sites from 419 strain-specific consensus sequences. The consensus sequence is computed for each <italic toggle=\"yes\">S. paradoxus</italic>, <italic toggle=\"yes\">S. cerevisiae</italic>, <italic toggle=\"yes\">S. eubayanus</italic>, and <italic toggle=\"yes\">S. uvarum</italic> strain that has >0.75 depth and >0.9 breadth in its Tsu4 internal region. Tip points are colored by species. The phylogeny is midpoint rooted. Bootstrap supporting values are annotated for key nodes. Key clades are annotated with host lineage and/or clade numbers from the Tsu4 FLE phylogeny.</p></caption></fig>", "<fig position=\"float\" id=\"F5\"><label>Figure 5:</label><caption><title>ML phylogeny for 5’ and 3’ internal regions from Tsu4 FLEs in <italic toggle=\"yes\">S. mikatae, S. jurei</italic>, and <italic toggle=\"yes\">S. uvarum</italic>, plus representatives of Ty4 elements.</title><p id=\"P51\">Panel (A) shows the ML phylogeny for 5’ internal region containing 459 distinct alignment sites; Panel (B) shows 3’ internal region containing 455 distinct alignment sites. Internal coding regions from 71 Ty4 and Tsu4 FLEs are included in both panels. Both trees are midpoint rooted, and visualized in the same tree scale which is shown in units of substitutions per site. Bootstrap supporting values are annotated for key nodes.</p></caption></fig>", "<fig position=\"float\" id=\"F6\"><label>Figure 6:</label><caption><title>ML phylogeny for partitioned internal regions from recombinants in <italic toggle=\"yes\">S. kudriavzevii</italic>, pure Tsu4 FLEs in <italic toggle=\"yes\">S. mikatae, S. jurei</italic>, and <italic toggle=\"yes\">S. uvarum</italic>, plus representatives of Ty4 elements.</title><p id=\"P52\">Panel (A) shows the ML phylogeny for left-side internal region based on 357 distinct alignment sites; Panel (B) for middle internal region based on 367 distinct alignment sites; Panel (C) for right-side internal region based on 423 distinct alignment sites. In all three panels, internal regions from 47 Ty4 and Tsu4 FLEs are included. All trees are midpoint rooted, and visualized in the same tree scale which is shown in units of substitutions per site. Bootstrap supporting values are annotated for key nodes.</p></caption></fig>" ]	[]	[]	[]	[]	[]	[]	[ "<supplementary-material id=\"SD1\" position=\"float\" content-type=\"local-data\"><label>Supplement 1</label></supplementary-material>", "<supplementary-material id=\"SD2\" position=\"float\" content-type=\"local-data\"><label>Supplement 2</label></supplementary-material>", "<supplementary-material id=\"SD3\" position=\"float\" content-type=\"local-data\"><label>Supplement 3</label></supplementary-material>", "<supplementary-material id=\"SD4\" position=\"float\" content-type=\"local-data\"><label>Supplement 4</label></supplementary-material>", "<supplementary-material id=\"SD5\" position=\"float\" content-type=\"local-data\"><label>Supplement 5</label></supplementary-material>" ]	[ "<fn-group><fn fn-type=\"COI-statement\" id=\"FN2\"><p id=\"P45\">Conflicts of interest</p><p id=\"P46\">N.A.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"nihpp-2023.12.20.572574v1-f0001\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-2023.12.20.572574v1-f0002\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-2023.12.20.572574v1-f0003\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-2023.12.20.572574v1-f0004\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-2023.12.20.572574v1-f0005\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-2023.12.20.572574v1-f0006\" position=\"float\"/>" ]	[ "<media xlink:href=\"media-1.pdf\" id=\"d64e2272\" position=\"anchor\"/>", "<media xlink:href=\"media-2.csv\" id=\"d64e2275\" position=\"anchor\"/>", "<media xlink:href=\"media-3.tsv\" id=\"d64e2278\" position=\"anchor\"/>", "<media xlink:href=\"media-4.zip\" id=\"d64e2281\" position=\"anchor\"/>", "<media xlink:href=\"media-5.zip\" id=\"d64e2284\" position=\"anchor\"/>" ]	[{"surname": ["Gilbert", "Feschotte"], "given-names": ["C.", "C."], "year": ["2018"], "article-title": ["Horizontal acquisition of transposable elements and viral sequences: patterns and consequences"], "source": ["Curr Opin Genetics Dev"], "volume": ["49"], "fpage": ["15"], "lpage": ["24"]}, {"surname": ["Henault", "Eberlein", "Charron", "Durand", "Nielly-Thibault", "Martin", "Landry", "Polz", "Rajora"], "given-names": ["M.", "C.", "G.", "E.", "L.", "H.", "C. R.", "M. F.", "O. P."], "year": ["2019"], "part-title": ["Yeast population genomics goes wild: The case of Saccharomyces paradoxus"], "source": ["Population Genomics: Microorganisms"], "fpage": ["207"], "lpage": ["230"], "publisher-name": ["Springer International Publishing"], "publisher-loc": ["Cham"]}, {"surname": ["Istace", "Friedrich", "d\u2019Agata", "Faye", "Payen", "Beluche", "Caradec", "Davidas", "Cruaud", "Liti", "Lemainque", "Engelen", "Wincker", "Schacherer", "Aury"], "given-names": ["B.", "A.", "L.", "S.", "E.", "O.", "C.", "S.", "C.", "G.", "A.", "S.", "P.", "J.", "J.-M."], "year": ["2017"], "article-title": ["De novo assembly and population genomic survey of natural yeast isolates with the Oxford Nanopore MinION sequencer"], "source": ["Gigascience"], "volume": ["6"], "fpage": ["1"], "lpage": ["13"]}, {"surname": ["Leducq", "Charron", "Samani", "Dub\u00e9", "Sylvester", "James", "Almeida", "Sampaio", "Hittinger", "Bell", "Landry"], "given-names": ["J.-B.", "G.", "P.", "A. K.", "K.", "B.", "P.", "J. P.", "C. T.", "G.", "C. R."], "year": ["2014"], "article-title": ["Local climatic adaptation in a widespread microorganism"], "source": ["Proceedings of the Royal Society B: Biological Sciences"], "volume": ["281"], "fpage": ["20132472"]}, {"surname": ["Leducq", "Nielly-Thibault", "Charron", "Eberlein", "Verta", "Samani", "Sylvester", "Hittinger", "Bell", "Landry"], "given-names": ["J.-B.", "L.", "G.", "C.", "J.-P.", "P.", "K.", "C. T.", "G.", "C. R."], "year": ["2016"], "article-title": ["Speciation driven by hybridization and chromosomal plasticity in a wild yeast"], "source": ["Nature Microbiology"], "volume": ["1"], "fpage": ["15003"]}, {"surname": ["Peccoud", "Cordaux", "Gilbert"], "given-names": ["J.", "R.", "C."], "year": ["2018"], "article-title": ["Analyzing horizontal transfer of transposable elements on a large scale: challenges and prospects"], "source": ["BioEssays"], "volume": ["40"], "fpage": ["1700177"]}, {"surname": ["Revell"], "given-names": ["L. J."], "year": ["2012"], "article-title": ["phytools: an R package for phylogenetic comparative biology (and other things)"], "source": ["Methods Ecol Evol"], "volume": ["3"], "fpage": ["217"], "lpage": ["223"]}, {"surname": ["Sarilar", "Bleykasten-Grosshans", "Neuveglise"], "given-names": ["V.", "C.", "C."], "year": ["2015"], "article-title": ["Evolutionary dynamics of hAT DNA transposon families in Saccharomycetaceae"], "source": ["Genome Biol Evol"], "volume": ["7"], "fpage": ["172"], "lpage": ["190"]}, {"surname": ["Wallau", "Ortiz", "Loreto"], "given-names": ["G. L.", "M. F.", "E. L. S."], "year": ["2012"], "article-title": ["Horizontal transposon transfer in eukarya: detection, bias, and perspectives"], "source": ["Genome Biol Evol"], "volume": ["4"], "fpage": ["801"], "lpage": ["811"]}, {"surname": ["Yu", "Smith", "Zhu", "Guan", "Lam"], "given-names": ["G.", "D. K.", "H.", "Y.", "T. T.-Y."], "year": ["2017"], "article-title": ["ggtree: an r package for visualization and annotation of phylogenetic trees with their covariates and other associated data"], "source": ["Methods Ecol Evol"], "volume": ["8"], "fpage": ["28"], "lpage": ["36"]}]	{ "acronym": [], "definition": [] }	115	CC BY	no	2024-01-13 00:14:50	bioRxiv. 2023 Dec 20;:2023.12.20.572574	oa_package/ad/ef/PMC10769310.tar.gz
PMC10769311	38187701	[ "<title>INTRODUCTION</title>", "<p id=\"P2\">Interleukin-6 (IL-6) and interleukin-8 (IL-8) play key roles in inflammation and have been implicated in cancer progression. IL-6 is a pro-inflammatory cytokine that, along with TNFα and IL-1β, contributes to the early response to infection [##REF##16199153##1##]. IL-6 is produced by macrophages, dendritic cells, and epithelial cells in response to pathogens, and this cytokine drives T cell differentiation as well as plasma B cell differentiation [##REF##17849470##2##]. IL-8 (CXCL8) is a chemokine produced by monocytes, macrophages, fibroblasts, and other cells [##REF##8765209##3##–##UREF##1##5##], and it is responsible for attracting leukocytes (typically but not exclusively neutrophils) to the site of inflammation by enhancing extravasation and by chemoattraction within tissue [##UREF##0##4##].</p>", "<p id=\"P3\">Both IL-6 and IL-8 have been implicated in the pathogenesis and progression of several solid tumor types, including breast [##REF##16927176##6##], prostate [##REF##31604846##7##], colon [##REF##20648559##8##], and pancreatic [##REF##25100121##9##] cancers, and indeed elevated expression of both molecules has been associated with increased cancer aggressiveness and metastatic burden [##REF##20648559##8##,##REF##17621625##10##,##REF##22651903##11##]. It has recently been demonstrated that IL-6 and IL-8 paracrine signaling in metastatic cancer cells increases motility in a cell density-dependent manner [##UREF##2##12##]. Above a threshold cell density, cancer cells secrete IL-6 and IL-8, which synergistically activate a complex paracrine signaling pathway via Janus kinase (JAK2) and signal transducer and activator of transcription 3 (STAT3) that prompts cancer cells to form <italic toggle=\"yes\">Arp2/3</italic>-dependent dendritic protrusions and undergo migration. Simultaneous inhibition of the IL-6/IL-8 signaling network with tocilizumab, a monoclonal anti-IL-6 receptor-alpha (IL-6Rα, hereafter denoted IL-6R) antibody primarily used to treat rheumatoid arthritis [##REF##20952462##13##], and reparixin, a small molecule allosteric inhibitor of the IL-8 receptor (IL-8R) that recently completed phase II clinical trials against breast cancer [##UREF##3##14##], was found to decrease <italic toggle=\"yes\">in vitro</italic> cell migration and significantly decrease <italic toggle=\"yes\">in vivo</italic> metastasis without affecting rates of tumor growth [##UREF##2##12##,##REF##30220965##15##]. Collectively, these data suggest that targeting IL-6, IL-8, and their receptors is a promising approach to inhibiting tumor metastasis and cancer lethality.</p>", "<p id=\"P4\">Monoclonal antibodies (mAbs) have been a fixture in anti-cancer therapeutic regimens over the past 20 years [##REF##33463090##16##,##UREF##4##17##] due to their target specificity, <italic toggle=\"yes\">in vivo</italic> stability, modular construction, and multi-faceted actions. However, monospecific mAbs have limitations, including the emergence of acquired resistance as cancer cells mutate [##REF##31118560##18##]. Bispecific antibodies (BsAbs), antibodies engineered to simultaneously engage two different target molecules, demonstrate great potential to overcome the shortcomings of antibody drugs [##REF##15109810##19##–##REF##29403265##21##]. Binding in <italic toggle=\"yes\">cis</italic>, i.e., with the BsAb bridging different receptors on the same cell, confers avidity, improved tissue selectivity, and reduced off-target side effects [##REF##25730144##22##–##REF##26910134##24##], while also reducing the likelihood of drug resistance [##REF##27216193##25##]. Additionally, concurrent binding to separate targets can prevent receptor homodimerization [##UREF##5##26##] and increase treatment potency in target tissues [##REF##31175342##27##].</p>", "<p id=\"P5\">To study and potentially better target the IL-6/IL-8 signaling network for metastasis inhibition, Yang and colleagues recently engineered a novel bispecific antibody, BS1, against IL-6R and IL-8RB (also known as CXCR2, hereafter called IL-8R) [##REF##35841152##28##]. BS1 contains arms with variable domains of two distinct antibodies: the anti-IL-6R antibody tocilizumab and the anti-IL-8R antibody 10H2 [##UREF##1##5##,##UREF##6##29##] [##FIG##0##Figure 1A##]. BS1 significantly reduced <italic toggle=\"yes\">in vitro</italic> cancer cell migration, effecting greater inhibition of migration than either the combination of tocilizumab and reparixin or the combination of tocilizumab and 10H2 [##REF##35841152##28##]. Furthermore, BS1 potently decreased metastatic burden <italic toggle=\"yes\">in vivo</italic> in orthotopic mouse xenograft models and, when paired with the anti-proliferative agent gemcitabine, significantly decreased both metastasis and tumor growth [##REF##35841152##28##]. In all studies, BS1 outperformed combination treatments, demonstrating the effectiveness of bispecific agents in targeting complex signaling networks.</p>", "<p id=\"P6\">However, while the dual-targeting ability of BsAbs holds promise, it will be crucial to understand the mechanisms underpinning BsAb binding and how those mechanisms differ from treatment with a combination of monospecific mAbs in order to maximize efficacy. As antibodies are multivalent, their binding is driven both by the inherent affinity of each binding domain for its target antigen and by avidity, the accumulated binding strength from each of the individual molecular interactions [##UREF##7##30##–##REF##35790857##32##]. While it has been established that avidity plays a key role in BsAb tissue selectivity and therapeutic efficacy [##REF##28127051##23##,##REF##26260789##33##], the interplay of individual domain affinity, overall avidity, target expression, and therapeutic concentration in the context of cell binding remains poorly understood. Mechanistic computational models of antibody-target interactions can address this knowledge gap—by incorporating parameters for both monovalent binding affinity and multivalent binding avidity with differential equations describing the binding kinetics, we can investigate the influence of these factors on the binding of monospecific and bispecific antibodies [##REF##33205425##34##–##UREF##9##36##].</p>", "<p id=\"P7\">Mathematical models of the kinetics of heterobivalent antibody binding to cell surface targets have been characterized previously [##REF##26910134##24##,##REF##23330947##37##–##REF##34637774##41##], providing a general framework for modeling multivalent binding. Here, we extend existing modeling frameworks to build a quantitative, computational model for a specific therapeutic target: antibodies targeting the IL-6 and IL-8 receptors for the prevention of cancer metastasis. To therapeutically inhibit IL-6/IL-8-mediated metastasis, we are examining two existing monospecific antibodies, tocilizumab (denoted anti-IL-6R) [##REF##28841363##42##] and 10H2 (denoted anti-IL-8R) [##UREF##1##5##,##UREF##6##29##], and the novel bispecific antibody, BS1 (denoted anti-IL-6R/anti-IL-8R), first described by Yang et al. [##REF##35841152##28##] [##FIG##0##Figure 1A##]. Binding mechanics (receptor complex formation) of each antibody are expressed in a series of coupled differential equations, creating a complete model of antibody interactions that is faithful to the biophysics.</p>", "<p id=\"P8\">Our detailed simulations of the <italic toggle=\"yes\">in vitro</italic> monovalent and bivalent binding interactions between different antibody constructs and the target receptors, IL-6R and IL-8R, establish how binary (antibody-receptor) and ternary (receptor-antibody-receptor) complex formation drives target inhibition. We demonstrate that the ratio between expression levels of IL-6R and IL-8R is crucial to bispecific antibody binding <italic toggle=\"yes\">in vitro</italic> and leads to significant differences in monospecific and bispecific antibody behavior. Simulations also predict necessary antibody concentrations for optimal binding, namely that the most stable antibody-receptor complex formation occurs at high receptor concentrations and intermediate antibody concentrations. Overall, our model simulations with different antibody constructs clarify the effects of binding domain affinity and target expression on receptor inhibition, providing insight that is applicable not only to our particular BsAb system, but also more broadly to bispecific antibody therapeutic design.</p>" ]	[ "<title>METHODS</title>", "<title>Monospecific and Bispecific Antibodies</title>", "<p id=\"P9\">The three key antibodies of interest we are using to target the receptors in the IL-6/IL-8 system are tocilizumab (anti-IL-6Rα), 10H2 (anti-IL-8RB), and a novel bispecific antibody developed by Yang et al. [##REF##35841152##28##], BS1 (anti-IL-6Rα/anti-IL-8RB) [##FIG##0##Figure 1A##]. BS1 is a human immunoglobulin G (IgG)-based bispecific antibody synthesized by combining the knobs-into-holes strategy [##UREF##10##43##] with single-chain Fab design [##REF##25481745##44##], and it was developed to increase high-affinity selective targeting of IL-6R and IL-8R, decrease off-target toxicity, and reduce risk of acquired resistance [##REF##35841152##28##]. The IL-6Rα-blocking arm of BS1 comes from tocilizumab, a monoclonal anti-IL-6Rα antibody used to treat rheumatoid arthritis [##REF##20952462##13##]. There are no clinically-approved anti-IL-8R antibodies, but the experimental anti-IL-8RB antibody 10H2 blocks IL-8 binding and activity [##UREF##1##5##,##UREF##6##29##,##UREF##11##45##] and is used for the IL-8RB-blocking arm of BS1. BS1 is bivalent (one anti-IL-6Rα domain and one anti-IL-8RB domain) and interacts with IL-6Rα<sup>+</sup>/IL-8RB<sup>+</sup>-transduced HEK 293T cells in flow cytometry-based binding studies (K<sub>D</sub> = 14.4 nM) [##REF##35841152##28##].</p>", "<title>Binding Model Equations</title>", "<p id=\"P10\">To describe the ligand-receptor and antibody-receptor binding kinetics, we built a coupled set of ordinary differential equations (ODEs) using the law of mass action. Each individual ODE describes one molecule or molecular complex, with terms representing each binding interaction (binding and unbinding processes) in the system [##FIG##0##Figures 1B##, ##SUPPL##0##S1##].</p>", "<p id=\"P11\">The equations take the form:\n\n</p>", "<p id=\"P12\">These equations represent the antibody first binding one receptor and then binding a second receptor (of the same or different type, depending on the antibody). The “second binding” events, describing the cross-linking of an antibody-receptor complex with an additional receptor, are indicated by asterisks in the equations. The full model equations are included in the ##SUPPL##0##Supplemental Information##. The experimental data to which we are comparing our model simulations [##REF##35841152##28##] were acquired at 4°C. As a result, other processes that could have been incorporated into the model, including receptor synthesis, internalization, and degradation, were assumed to be negligible because they are typically suppressed at low temperatures.</p>", "<title>Rate Constant Values and Similarity of Binding Sites</title>", "<p id=\"P13\">To simulate the complete mechanistic ODE model (see ##SUPPL##0##Supplemental Information##) for three different antibodies requires values for the many parameters in the model—primarily, rate constants. The number of unique parameters for which we need values can be reduced using (a) the identification of a thermodynamic cycle and (b) assumptions of the similarity of binding sites across antibodies, as described in the next two sections.</p>", "<title>BsAb-Receptor Binding Reaction Cycle</title>", "<p id=\"P14\">The bivalent antibody reactions function in a cycle where all of the species are linked by the reactions. Given the reactions:\n\n\n\n</p>", "<p id=\"P15\">These reactions form a cycle because it is possible to find a path through the reactions that leads back to the starting point. For example, starting with the antibody in ##FORMU##2##Reaction 1##, the forward reaction (binding) produces Ab⋅R<sub>1</sub>. Then, the forward reaction in ##FORMU##5##Reaction 4## creates R<sub>1</sub>⋅Ab⋅R<sub>2</sub>. Using the reverse reaction in ##FORMU##4##Reaction 3##, Ab·R<sub>2</sub> is obtained. Finally, through the reverse reaction in ##FORMU##3##Reaction 2##, the original antibody is returned. Because there is a path through the reactions that returns the original reactant and does not repeat any reactions, the reactions form a cycle, with the forward reactions of ##FORMU##2##Reactions 1## and ##FORMU##5##4## and the reverse reactions of ##FORMU##3##Reactions 2## and ##FORMU##4##3##.</p>", "<p id=\"P16\">For each of these reactions, at equilibrium, the rate of the forward reaction must be equal to the rate of the reverse reaction by the principle of detailed balance [##REF##125099##46##,##REF##15189850##47##]. Thus, for each reaction, the concentrations at equilibrium and the rate constants can be related:\n</p>", "<p id=\"P17\">To relate the rate constants for the full reaction cycle, we can multiply the equilibrium constants for each reaction, using the inverse equilibrium constant for each reaction that is reversed (##FORMU##3##Reactions 2## and ##FORMU##4##3##):\n</p>", "<p id=\"P18\">All of the concentrations in ##FORMU##7##Equation 4## cancel out, and the product of the equilibrium constants for the cycle reactions is unity. This is an established behavior for cycles where ligand is bound and released into a single volume with no other reactions [##REF##2453276##48##,##UREF##12##49##]. Although ##FORMU##7##Equation 4## was derived from the equilibrium relationships, the result only involves the system constants, and thus it applies even when the system is not in equilibrium [##REF##2453276##48##].</p>", "<title>Similarity of Binding Sites</title>", "<p id=\"P19\">Using the relationship of the equilibrium constants to the rate constants, we can rewrite the cycle constraint:\n</p>", "<p id=\"P20\">Due to the similarity of the dissociation reaction kinetics between the different antibody domains [##REF##1638531##50##], the parameter space can be simplified by assuming that the k<sub>off</sub> values for the first binding and second binding reactions are equal (i.e., k<sub>off,R1</sub> = k<sub>off,R1</sub> and k<sub>off,R2</sub> = k<sub>off,R2</sub>). This assumption further simplifies the rate constant relationship to:\n</p>", "<p id=\"P21\">In their bivalent antibody model, Harms et al. termed this ratio of k<sub>on,R</sub> to k<sub>on,R</sub> as the “cross-arm binding efficiency” (χ) [##REF##23872324##38##,##UREF##13##51##]. This value combines multiple factors that impact multivalent binding, including the increased rate of binding due to the restriction of the bound antibody to a small volume adjacent to the cell membrane and the decreased flexibility and rotational freedom of the tethered antibody. Greater values of χ indicate stronger cross-arm binding, leading to greater rates of ternary complex formation.</p>", "<p id=\"P22\">With the reaction cycle constraint and above k<sub>off</sub> assumption, the number of unique parameters needed to describe an individual bivalent antibody is reduced from eight to five—three k<sub>on</sub> values and two k<sub>off</sub> values.</p>", "<p id=\"P23\">We can further simplify the overall number of parameters describing the binding of the three antibodies being studied here by noting that BS1 is synthesized using the IL-6Rα and IL-8RB binding domains of tocilizumab and 10H2, respectively. Thus, we made an additional simplifying assumption that the association rate constants were equal for the similar binding domains. With this assumption, the independent association rate constants become:\n\n\n\n</p>", "<p id=\"P24\">With the previous parameter space reduction from the binding reaction cycle constraint and the assumption that first and second k<sub>off</sub> values are equal, the full parameter space for tocilizumab, 10H2, and BS1 is reduced to five independent parameters: k<sub>on,6R</sub>, k<sub>on,8R</sub>, k<sub>on,6R</sub>, k<sub>off,6R</sub>, and k<sub>off,8R</sub>, with k<sub>on,8R</sub> being dependent on the values of the other association rates.</p>", "<title>IL-6Rα and IL-8RB HEK 293T Cell Surface Binding Assays</title>", "<p id=\"P25\">The association and dissociation rate constants for the antibody-receptor complexes were estimated here using experimental data from <italic toggle=\"yes\">in vitro</italic> cell surface binding flow cytometry assays; these data were previously reported [##REF##35841152##28##] and detailed methods can be found in the original publication. Briefly, the IL-6Rα and IL-8RB genes were transduced into HEK 293T cells, generating four cell lines: IL-6Rα<sup>+</sup>/IL-8RB<sup>−</sup>, IL-6Rα<sup>−</sup>/IL-8RB<sup>+</sup>, IL-6Rα<sup>+</sup>/IL-8RB<sup>+</sup>, and IL-6Rα<sup>−</sup>/IL-8RB<sup>−</sup>. The receptor expression on the transduced cell lines was quantified through flow cytometry [##TAB##0##Table 1##].</p>", "<p id=\"P26\">The transduced cell lines were placed into 96-well plates (1 × 10<sup>5</sup> cells per well) and incubated with doses of monoclonal or bispecific antibodies (tocilizumab, 10H2, and BS1) from 10<sup>−2</sup> to 10<sup>3</sup> nM for two hours at 4°C. Cells were then washed and incubated with an allophycocyanin (APC)-conjugated anti-human IgG1 antibody for 15 min at 4°C. Antibodies binding to the receptors were quantified with flow cytometry and reported as Mean Fluorescent Intensity (MFI) detected. The experiments were performed with three technical repeats, and data from all three replicates was used for the binding parameter optimization.</p>", "<title>Model Parameterization: Optimization of Binding Rate Constants</title>", "<p id=\"P27\">We fit the antibody-receptor binding model to the flow cytometry binding assay data by estimating the association and dissociation rate constant values for each binding step. To reduce the number of parameters required to fully characterize the model, multiple simplifying assumptions were made about similarities in binding domain structure and protein geometry, and from the thermodynamic cycle constraint, as described in the previous sections. Thus, to describe the system fully for tocilizumab, 10H2, and BS1, we need values for five unique parameters: k<sub>on,6R</sub>, k<sub>on,8R</sub>, k<sub>on,6R</sub>, k<sub>off,6R</sub>, and k<sub>off,8R</sub>, with the value of k<sub>on,8R*</sub> being dependent on the values of the other parameters [##FIG##0##Figures 1B##, ##FIG##0##1C##].</p>", "<p id=\"P28\">We created MATLAB code to describe the ordinary differential equation (ODE) model as a system of equations and used the ode15s solver to simulate the system over time. Simulations were performed under conditions replicating the <italic toggle=\"yes\">in vitro</italic> experiments as closely as possible. For example, the initial concentration of receptors for each cell line was set to values from the transduced HEK293T cells [##TAB##0##Table 1##], and the initial antibody concentrations used in the simulation were the same as the range of the experimental values. The antibody was added at time = 0, and the free antibody concentration was set to 0 at time = 2 hours to simulate the washing out of unbound antibody. The simulation was continued for 15 minutes after the washout to mimic the incubation with the APC-conjugated antibody. For the optimization, the total bound antibody at the final simulation time point was compared to the Mean Fluorescent Intensity (MFI) values from the flow cytometry binding assays, which represent the total bound antibody in that experiment.</p>", "<p id=\"P29\">We used the non-linear least squares optimization function lsqnonlin to determine the parameter values (i.e., rate constant values) that minimized the sum of the squared differences between the simulation output and the experimental data points. Three hundred sets of initial guesses for the parameter values were generated using Latin Hypercube Sampling, using a log-uniform distribution for each parameter over the ranges listed in ##TAB##1##Table 2##. The optimization process was repeated for each set and for each type of simulation normalization (described below), and optimizations that did not converge or that did not vary from the initial guesses were discarded (approximately 13% of the total optimization runs).</p>", "<p id=\"P30\">The MFI values from the flow cytometry binding assays were normalized against the values for bound BS1 at the initial antibody concentrations where binding reached saturation. The MFI values for BS1 where binding reached saturation were averaged and used as the denominator to normalize all of the data for all antibodies in a single cell line; each cell line was normalized separately. For the simulation results, four different normalization schemes were tested in the parameter optimization; these normalizations are summarized in ##TAB##2##Table 3## and are defined as follows. <italic toggle=\"yes\">BS1</italic> indicates simulations that were normalized against the amount of bound BS1 in the corresponding cell line, and <italic toggle=\"yes\">Ab</italic> indicates simulations where each antibody was normalized against the amount of that specific antibody bound in the corresponding cell line. <italic toggle=\"yes\">Data</italic> indicates simulations that were normalized using output at the same concentrations that were used to normalize the experimental data, and <italic toggle=\"yes\">Max</italic> indicates simulations that were normalized using the output at the maximum antibody concentration.</p>", "<p id=\"P31\">As discussed in the <italic toggle=\"yes\">Results</italic>, the optimizations performed with normalization against BS1 at binding saturation (labeled “BS1, Data”) show stronger convergence around a single optimal value for each parameter and less dependence on the initial value used, so this normalization scheme was selected for the parameter optimization. The cost of the parameter set was calculated as the sum of the squared difference between the normalized model output and normalized experimental binding at each input concentration. The best fit parameter set was used for further binding model simulations [##TAB##1##Table 2##].</p>", "<title>Simulation Output</title>", "<p id=\"P32\">As described above, for the optimization of the binding parameters, the total receptor-bound antibody at the final simulation time point was output from the model simulations for comparison to the experimental MFI values, which represent the total bound antibody in the flow cytometry assays. For the subsequent model simulations, as bivalent antibodies can form both binary (antibody-receptor) and ternary (receptor-antibody-receptor) complexes, quantifying the amount of antibody-bound receptor (as distinct from receptor-bound antibody) provides more information about the inhibition of the system. Thus, for the simulations using the parameterized model, we output the concentration of antibody-bound receptor (in # receptors/cell). Results are given as the concentration of receptor bound in a particular complex type, either binary complexes (with a single receptor) [##FORMU##14##Equation 11##] or ternary complexes (with two receptors) [##FORMU##15##Equation 12##], or the total bound receptor [##FORMU##16##Equation 13##], which is the sum of receptor bound in binary and ternary complexes. Results are also shown for the receptor fractional occupancy, which is calculated as the fraction of the total receptor concentration in the system that is bound either in a specific complex type or overall. Unless stated otherwise, the bound receptor refers to the sum of IL-6R and IL-8R bound in a particular complex type or overall.</p>", "<title>Univariate Sensitivity Analysis</title>", "<p id=\"P33\">We performed local and global univariate sensitivity analyses of the binding model rate constants and the species concentrations to determine the effects of the individual parameters on the model output. For the local sensitivity analysis, simulations were performed for two hours after antibody dosing, with a baseline BS1 concentration of 10 nM and baseline receptor concentrations of 5 × 10<sup>4</sup> receptors/cell for both IL-6R and IL-8R. Each rate constant and initial concentration was varied 10 percent above its baseline value, with each parameter varied individually in separate simulations. The Area Under the Curve (AUC), calculated as the integration of the BS1-receptor complex concentration over time, was output for both ternary complexes and total bound receptors. The sensitivity for each parameter-output combination was calculated as the percentage change in the output value divided by the percentage change in the parameter value (10 percent for all simulations).</p>", "<p id=\"P34\">In the global sensitivity analysis, simulations were performed for 24 hours after antibody dosing, with constant receptor concentrations of 5 × 10<sup>4</sup> receptors/cell for both IL-6R and IL-8R. The longer simulation time was selected for this analysis to examine the model output closer to equilibrium. Each association and dissociation rate constant was separately varied over two orders of magnitude below and above its optimized value. The receptor fractional occupancy was output for ternary complexes and total bound receptor, with fractional occupancy calculated as the fraction of the total receptor (IL-6R + IL-8R) that is bound in ternary complexes or bound in total in either binary or ternary complexes.</p>" ]	[ "<title>RESULTS</title>", "<title>Binding Parameter Optimization and Parameter Identifiability</title>", "<p id=\"P35\">The optimization of the association and dissociation rate constants for the antibody-receptor binding model generated a range of optimal parameter sets depending on the initial guesses used [##FIG##1##Figure 2A##]. About half of the optimized parameter sets, and in particular those of the lowest cost (i.e., best fit), resulted in consistent parameter values (horizontal patterns on the graph) that are independent of the initial guess values. Some of the optimization results do give parameter sets that are correlated to initial guesses, but these are higher in cost (i.e., poorer fit overall) and fewer in number; since each point is moderately transparent in the graph, darker regions indicate many overlapping optimized values. All five of the parameters optimized show consistent optimal parameter values obtained from a wide range of initial guesses. This is evidence of good parameter identifiability—given five parameters being optimized against seven sets of data points from experiments across three cell types and three antibodies.</p>", "<p id=\"P36\">We tested whether the choice of simulation normalization scheme, as described in the <xref rid=\"S2\" ref-type=\"sec\">Methods</xref>, influenced the optimization. The optimizations performed with normalization against BS1 show stronger convergence around a single optimal value for each parameter and less dependence on the initial value used [##FIG##1##Figure 2A##, <bold>lower panel</bold>], compared to simulations normalized to results for each antibody individually, which show a wider spread in the optimal values obtained and a greater reliance on the value of the initial guess [##SUPPL##0##Figures S2##, ##SUPPL##0##S4##]. Further discussion of the results from the different normalization schemes is included in the ##SUPPL##0##Supplemental Information##.</p>", "<p id=\"P37\">The frequency distributions of the optimized parameter values are narrow, again supporting good parameter identifiability and indicating that the parameters are well-constrained by the data [##FIG##1##Figures 2B##, ##SUPPL##0##S3##]; the location of the best-fit (i.e., lowest cost) value is marked for each parameter. The parameter distributions contain multiple small peaks, expressing separate reoccurring optimal values, but each parameter demonstrates a distinct, most frequent value that also corresponds with the lowest cost value. Of note, the first association steps (k<sub>on</sub>) are better constrained, while the second steps (k<sub>on</sub>) have a slightly larger range of potential values [##TAB##1##Table 2##].</p>", "<p id=\"P38\">Across the normalization schemes tested, the most frequent parameter set corresponded well with the lowest cost parameter set [##FIG##1##Figure 2C##]. In this visualization, optimized parameters with the same value and cost were grouped into a single point, and the area of the point was scaled with the number of parameters in the group – in other words, large dots represent parameter value-cost pairs that occur more frequently across the 300 optimizations. All six parameters (with five parameters being optimized and k<sub>on,8R</sub> being calculated from the thermodynamic cycle constraint) show an optimal lowest-cost point that occurs most frequently, with a spread of less frequently occurring values around this central value.</p>", "<p id=\"P39\">Based on its low dependence on initial guess, high proportion of low-cost optimal parameter sets, and narrow distribution of optimal values, the normalization scheme with BS1 data at binding saturation was selected as the primary normalization method for the remaining analysis. The lowest cost parameter set from the optimizations performed with this normalization scheme was selected for the binding model parameter values [##TAB##1##Table 2##] and is indicated on the frequency distribution [##FIG##1##Figure 2B##].</p>", "<title>Best-Fit Parameters Recapitulate Experimental Observations</title>", "<p id=\"P40\">The best-fit association and dissociation constants [##TAB##1##Table 2##] fall within the range of typical values for antibody binding [##REF##28802648##52##]. The association rates for binding to IL-6R and IL-8R are very close in value, and there is not a substantial difference in the monovalent binding domain affinities for either receptor [##TAB##3##Table 4##], as was observed experimentally [##REF##35841152##28##]. Moreover, the calculated monovalent affinities are consistent with the results from the experimental characterization of BS1 [##SUPPL##0##Table S2##] [##REF##35841152##28##]. The second binding step, where the binary antibody-receptor complex cross-links with an additional receptor to form a ternary receptor-antibody-receptor complex, is substantially faster than the initial binding. This is expected for bivalent binding, as the antibody is tethered to the cell surface and held in close proximity to the membrane receptors, promoting interaction with a second receptor. The cross-linking equilibrium constants are in the sub-picomolar range, and the ratio of k<sub>on,R</sub> to k<sub>on,R</sub>, sometimes termed the “cross-arm binding efficiency” [##REF##23872324##38##], is 1.6 × 10<sup>4</sup>, indicating strong avidity of BS1 binding.</p>", "<p id=\"P41\">Simulations using this best-fit parameter set indeed recreate the experimental <italic toggle=\"yes\">in vitro</italic> flow cytometry data (i.e., binding of antibody to the cell surface) that was used to fit the parameter values very well [##FIG##2##Figure 3##]. The dose-dependence, antibody-dependence, and cell-type-dependence (i.e., receptor-expression dependence) of the data were all captured in the simulation.</p>", "<p id=\"P42\">In the single-receptor-positive cell lines (denoted IL-6R<sup>+</sup> and IL-8R<sup>+</sup>), at low antibody concentrations, there is greater binding of the monospecific antibodies than of BS1 because each monospecific antibody molecule has two binding sites (doubling overall likelihood of binding), plus avidity effects promote increased binding. As the antibody concentration increases within the experimentally-tested range, the monospecific antibody curves appear to saturate at a lower level of total antibody bound than BS1 (consistent with the experimental measurements) because much of the monospecific antibody is bound bivalently, with a single antibody occupying two receptors. In contrast, BS1 can only bind monovalently because its second binding site is for a receptor not expressed on that cell type. Thus, BS1 forms antibody-receptor complexes whereas tocilizumab and 10H2 form receptor-antibody-receptor complexes, resulting in a lower measured signal since binding is quantified by the number of antibodies bound.</p>", "<p id=\"P43\">At simulated antibody concentrations higher than those experimentally tested [##FIG##2##Figure 3##, <bold>dashed lines</bold>], we predict further increased binding of the monospecific antibodies; at very high antibody concentrations, the antibody is present in such excess that all of the antibody is bound monovalently, matching the behavior of BS1, i.e., receptor-antibody-receptor complexes are lost in favor of antibody-receptor complexes. As a result, at the highest concentrations, the overall binding of each antibody is predicted to be equivalent; however, at practical experimental concentrations, we can see and explain the higher binding of BS1.</p>", "<p id=\"P44\">In these single-receptor-positive cell lines, BS1 has a sigmoidal binding curve because it is effectively monovalent; however, in the double-receptor-positive cell line (denoted IL-6R<sup>+</sup>/IL-8R<sup>+</sup>, which expresses about twice as many IL-8 as IL-6 receptors [##TAB##0##Table 1##] [##REF##35841152##28##]), BS1 is now effectively bivalent and exhibits a binding curve similar to and higher than the individual tocilizumab and 10H2 curves. This shape is due to BS1 bivalently binding both IL-6R and IL-8R simultaneously, and the total BS1 binding at the highest levels [##FIG##2##Figure 3##, <bold>dashed lines</bold>] is higher than either tocilizumab or 10H2 because it can bind to either IL-6R or IL-8R. The bound receptor concentrations can be further separated by complex type, distinguishing binary antibody-receptor complexes from ternary receptor-antibody-receptor complexes [##SUPPL##0##Figure S6##]. The separation of complex types confirms that the shape of the binding curve in the double-receptor-positive cell line is due to the formation of ternary complexes through bivalent binding.</p>", "<p id=\"P45\">We also simulated the exposure of each of the three cell types to a combination of the two monospecific antibodies (tocilizumab + 10H2), and, as expected, in simulations of single-receptor-positive cell lines, the combination behaved similarly to the single monospecific; while in the double-receptor-positive cell lines, the combination behaved similar to the bispecific. This has potentially useful implications for the ability of the bispecific (vs the monospecific combination) to bind to and inhibit receptors on cells expressing these two receptors at different levels.</p>", "<title>Bivalent Antibody Binding over Time</title>", "<p id=\"P46\">We simulated the formation of the BS1-receptor complexes over time [##FIG##3##Figure 4##], using an equivalent timeline to the experiments: an initial 2-hour binding period, followed by a washout of all free (unbound) BS1 from the system at t = 2 hours to simulate dissociation of the antibody-receptor complexes. The initial antibody concentration was set to 100 nM to ensure the antibody fully saturated the available receptor by 2 hours; similar results are also demonstrated for lower antibody concentrations [##SUPPL##0##Figure S8A##].</p>", "<p id=\"P47\">Binding of BS1 in the single receptor-positive cell lines, IL-6R<sup>+</sup> and IL-8R<sup>+</sup>, shows formation of binary antibody-receptor complexes in the association phase, followed by dissociation of those complexes in the washout phase. Binding is greater in the IL-8R<sup>+</sup> cell line because there is a higher receptor expression in those cells than in the other cell lines [##TAB##0##Table 1##].</p>", "<p id=\"P48\">In the double-receptor-positive cell line, IL-6R<sup>+</sup>/IL-8R<sup>+</sup>, the association phase shows substantial ternary IL-6R-BS1-IL-8R complex formation [##FIG##3##Figure 4##, <bold>purple line</bold>], with fewer binary BS1-IL-8R complexes and almost no BS1-IL-6R complexes being formed. Initially, formation of the ternary complexes progresses rapidly, quickly reaching a steady concentration. After the first 20 minutes, as the free IL-6R becomes saturated with antibody, more binary BS1-IL-8R complex formation occurs because there is no free IL-6R remaining to form ternary complexes. A small amount of binary BS1-IL-6R complex forms near the end of the association phase, but there is much greater binary BS1-IL-8R complex formation because IL-8R is in excess of IL-6R in this cell line.</p>", "<p id=\"P49\">During the dissociation phase in the IL-6R<sup>+</sup>/IL-8R<sup>+</sup> cells, the concentration of the binary BS1-IL-6R and BS1-IL-8R complexes decreases as antibody unbinds due to mass action following removal of the excess free (unbound) antibody. Perhaps counterintuitively, the concentration of the ternary IL-6R-BS1-IL-8R complexes actually slightly increases in this phase, as more receptors are freed and become available to bind to existing binary complexes, and the ternary complex concentration achieves a steady value and does not decrease during the simulation time period. This same behavior is also observed in binding of the two monospecific antibodies, tocilizumab and 10H2, to their target receptors [##SUPPL##0##Figures S8B##, ##SUPPL##0##S8C##]. This illustrates the importance of avidity in bivalent antibody binding; each of the antibodies is able to form ternary complexes as more receptor becomes available for binding, and these complexes persist at a consistent concentration for a significant period of time after antibody removal.</p>", "<title>Effect of Varying Antibody Concentration and Receptor Expression</title>", "<p id=\"P50\">To better understand the impact of the antibody concentration on bispecific antibody-receptor complex formation, and in particular on the relative formation of binary (BS1-IL-6R or BS1-IL-8R) versus ternary (IL-6R-BS1-IL-8R) complexes, we performed simulations of BS1 binding over a range of initial antibody concentrations and for cells with differing levels of combined IL-6R and IL-8R expression [##FIG##4##Figure 5##]. Because BS1 requires both IL-6R and IL-8R available to form ternary complexes, BS1-receptor complex formation is very sensitive to the ratio of IL-6R to IL-8R expression in the system. Thus, to specifically isolate the impact of overall receptor expression on BS1 binding, IL-6R and IL-8R were kept in a 1:1 ratio for these simulations. In these results and the results that follow, the bound receptor is reported as the receptor fractional occupancy, which is calculated as the fraction of the total receptor concentration (IL-6R + IL-8R) that is bound in a particular complex type (i.e., binary or ternary) or that is bound overall (i.e., total bound).</p>", "<p id=\"P51\">At higher antibody concentrations and lower receptor expression levels, the antibody levels are saturating and very little of the antibody is consumed in the bound complexes [##FIG##4##Figures 5A##, ##FIG##4##5B##]. At lower antibody concentrations and higher receptor expression levels, however, the free receptor is in excess of the free antibody and a substantial fraction of the antibody is bound to receptor (up to 100 percent at the highest simulated receptor expression level).</p>", "<p id=\"P52\">The relative excess of antibody or receptor is important for the proportion of binary antibody-receptor and ternary receptor-antibody-receptor complexes that are formed. Ternary complexes require two dissociation reactions to fully separate, and binary complexes tether the antibodies in close proximity to free receptor, promoting recreation of ternary complexes when they do dissociate; thus, ternary complexes represent a more stable antibody binding format relative to binary complexes. When the antibody is present in excess of the receptor (i.e., at high antibody concentrations and low receptor expression), the majority of complexes that form are the less-stable binary complexes. Additionally, at any given receptor density, as the concentration of antibody is increased, more binary complexes are created [##FIG##4##Figures 5C##, ##FIG##4##5D##].</p>", "<p id=\"P53\">Ternary receptor-antibody-receptor complexes, in contrast, are favored at intermediate antibody concentrations, around 10<sup>0</sup> to 10<sup>2</sup> nM (recall ##FIG##2##Figure 3##). Initially, increasing the antibody concentration causes more ternary complexes to form, but, past a certain threshold, the free antibody overwhelms the number of available receptors. At these higher antibody concentrations, there are few remaining receptors available for the second binding reaction to convert binary complexes into ternary complexes [##FIG##4##Figures 5C##, ##FIG##4##5D##]. This bell-shaped relationship between ternary systems and bivalent molecule concentration has been described previously [##REF##23544844##53##]; the decreased ternary complex formation at high concentrations is termed “autoinhibition.”</p>", "<p id=\"P54\">Interestingly, model simulations demonstrate that, at a constant antibody concentration, as the receptor expression is increased, a greater fraction of the receptor is bound to antibody [##FIG##4##Figure 5D##]. This initially appears counterintuitive because increasing receptor expression means the system contains more binding sites for the same amount of antibody. However, this result can be rationalized by the fact that the increase in receptor expression causes a greater proportion of the bound complexes to be of the higher-stability ternary format, which benefit from avidity effects and therefore readily rebind if one arm dissociates, making them less likely to fully dissociate.</p>", "<p id=\"P55\">Increased proportion of bound receptor with increasing receptor concentration is only observed when the antibody amount is still in excess of the receptor. At higher receptor expression levels with lower antibody concentrations, the receptor becomes the excess molecule, resulting in a greater proportion of the receptor remaining unbound [##FIG##4##Figure 5D##].</p>", "<p id=\"P56\">The combined impact of antibody concentration and receptor expression creates “zones”, wherein different antibody-receptor complex types are favored [##FIG##4##Figure 5E##]. Overall, increasing the antibody concentration causes more of the receptor to be bound in total. Binary antibody-receptor complexes are favored at higher antibody concentrations and lower receptor expression levels; whereas, ternary receptor-antibody-receptor complexes are the predominant type at high receptor expression levels with intermediate to high antibody concentrations. This same pattern is also observed in simulations of combination treatment with the two monospecific antibodies, tocilizumab and 10H2, modeled together in a 1:1 concentration ratio [##SUPPL##0##Figure S9##].</p>", "<title>Monovalent and Bivalent Binding</title>", "<p id=\"P57\">To further explore how ternary receptor-antibody-receptor complex formation leads to greater fractional receptor binding as receptor expression increases, we compared the bivalent antibody binding behavior to simulations of BS1 that we artificially restricted to monovalent binding only [##FIG##5##Figure 6##]. In these simulations, the association rate constants for the second binding step (k<sub>on,6R</sub> and k<sub>on,8R*</sub>) were fixed at 0 to prevent ternary complex formation and restrict BS1 to monovalent binding only. The rate constants for the initial association into binary complexes (k<sub>on,6R</sub> and k<sub>on,8R</sub>) and for the dissociation of antibody-receptor complexes (k<sub>off,6R</sub> and k<sub>off,8R</sub>) were kept at their previous values [##TAB##1##Table 2##].</p>", "<p id=\"P58\">Similar to the simulations of bivalent BS1 binding, at lower receptor expression levels, the antibody is present in excess of the receptor and fully saturates the receptor [##FIG##5##Figures 6A##, ##SUPPL##0##S10##]. At the highest receptor expression levels, more of the antibody is consumed in the binding, but there is still a substantial fraction of free antibody available. Unlike the previous bivalent binding simulations, however, the simulations of monovalent binding show that the fraction of receptor that is bound to antibody is nearly entirely independent of the receptor expression. For most of the receptor levels, the bound receptor fraction varies only with the initial antibody concentration and remains constant as receptor expression is increased [##FIG##5##Figures 6B##, ##FIG##5##6C##]. The only deviation from this pattern is at the highest receptor expression levels for intermediate antibody concentrations, wherein the antibody is no longer saturating the receptor, leading to a greater proportion of free receptor remaining.</p>", "<p id=\"P59\">When the antibody is restricted to only binding monovalently, as long as the antibody is present in excess of the receptor, varying the receptor expression level does not change the proportion of the receptor that is bound. In contrast, when the antibody binds bivalently, the receptor expression level in the system determines the proportion of binary and ternary complexes that form [##FIG##5##Figure 6D##]. At the lower receptor expression levels, the antibody fully saturates the receptor and the receptor is primarily bound in binary antibody-receptor complexes. As the receptor expression increases, the proportion of receptor in binary complexes decreases and ternary complexes begin to dominate. Because ternary complexes are the more stable form, the proportion of bound receptor overall also increases, leading to the previously illustrated pattern of greater fractional receptor binding with increasing receptor expression for a given antibody concentration. While these results focus on the bispecific antibody BS1, this is a general pattern of bivalent antibody binding and is seen for the monospecific antibodies tocilizumab and 10H2 as well [##SUPPL##0##Figure S11##].</p>", "<title>Comparison between Monospecific and Bispecific Antibodies</title>", "<p id=\"P60\">Tocilizumab, 10H2, and BS1 are all bivalent antibodies that can form both binary antibody-receptor and ternary receptor-antibody-receptor complexes when exposed to their respective target receptors. Thus, they all share the previously discussed binding behaviors across varying total antibody and receptor concentrations. However, BS1 differs from tocilizumab and 10H2 in that it is bispecific and simultaneously binds to both IL-6R and IL-8R. To further examine how antibody-receptor complex formation compares between monospecific and bispecific antibodies, we simulated BS1 and the combination of tocilizumab and 10H2 over a range of different IL-6R and IL-8R expression levels [##FIG##6##Figure 7A##]. In these simulations, the total initial antibody concentration was held constant at 10 nM, and tocilizumab and 10H2 were combined in a 1:1 ratio. The combination of tocilizumab and 10H2 targets both IL-6R and IL-8R but differs from BS1 in that each antibody can only bind one type of receptor. Although earlier results were presented for an initial antibody concentration of 100 nM [##FIG##3##Figure 4##], the overall binding is strong at 100 nM and the differences between the antibody types are less apparent. Results for additional antibody concentrations are included in the ##SUPPL##0##Supplemental Information## [##SUPPL##0##Figure S12##].</p>", "<p id=\"P61\">As was demonstrated previously, when the receptors are present in a 1:1 ratio, the complex formation is identical between BS1 and the combination of monospecific antibodies [##FIG##4##Figures 5E##, ##SUPPL##0##S9##], and this behavior holds across antibody concentrations [##SUPPL##0##Figure S12##]. Outside of a 1:1 IL-6R:IL-8R ratio, however, the antibodies demonstrate very different binding behaviors. BS1 requires both IL-6R and IL-8R to be available to form ternary complexes, so BS1 ternary complex formation is favored when the receptors are present in a 1:1 ratio [##FIG##6##Figure 7A##]. When either receptor is present in excess of the other, ternary complex formation is limited by the lower-expressed receptor. In this case, the excess receptor will only be able to bind to BS1 in a less-stable binary complex, leading to lower binding overall.</p>", "<p id=\"P62\">In contrast, because tocilizumab and 10H2 each bind to one type of receptor, the ratio of IL-6R to IL-8R expression does not impact their binding; only the total receptor expression has an effect on antibody-receptor complex formation for the monospecific antibodies [##FIG##6##Figure 7A##]. At lower receptor levels where the antibodies are present in substantial excess over the receptors, binary complexes are favored. As the total receptor expression increases, more receptors are available to form ternary complexes, and complex binding shifts to favor the ternary form.</p>", "<p id=\"P63\">Similar trends are observed at different total antibody concentrations as well [##SUPPL##0##Figure S12##]. At lower antibody concentrations, there is less complex formation overall for all antibodies. Notably, at the highest receptor levels, the receptor is present in excess of the antibody, leading to a low fractional occupancy of the receptor. In comparison, higher antibody concentrations lead to greater antibody-receptor complex formation across all receptor expression levels, but otherwise show the same pattern of binding behavior.</p>", "<p id=\"P64\">When the complex formation is examined for each receptor separately, however, a distinct pattern emerges [##FIG##6##Figure 7B##]. In these simulations, the concentration of IL-6R was held constant at 10<sup>3</sup> receptors per cell, while the concentration of IL-8R ranged from 10<sup>2</sup> to 10<sup>7</sup> receptors per cell. The proportion of each individual receptor that is bound is reported. IL-8R was simulated as the excess receptor because it has been shown to be up-regulated relative to IL-6R in breast cancer [##REF##35841152##28##], and similar results are shown when for simulations with IL-6R in excess [##SUPPL##0##Figure S13##]. The monospecific antibodies (tocilizumab and 10H2) each bind to a single receptor type, so the binding of each receptor is independent. Thus, for the combination treatment, varying IL-8R concentration has no effect on the amount of bound IL-6R, and the bound IL-8R concentration shows identical behavior to the simulations where the total receptor concentration was varied [##FIG##6##Figures 7B##, ##SUPPL##0##S9##].</p>", "<p id=\"P65\">The binding of BS1, however, is highly dependent on the ratio of IL-6R to IL-8R expression. As the concentration of IL-8R (the excess receptor in this case) increases, BS1 shows increasing occupancy of IL-6R, the limited receptor [##FIG##6##Figure 7B##]. In these simulations, the concentration of IL-6R was not varied, so the change in IL-6R binding is driven entirely by the increased IL-8R concentration. As greater IL-8R is present in the system, BS1 forms more binary BS1-IL-8R complexes, tethering it to the cell surface and bringing it within close proximity of the free IL-6R. This increases BS1 binding to IL-6R, even when it is present at substantially lower concentrations than the other receptor. This behavior is observed only for the bispecific antibody and is driven by the initial interaction with the excess receptor. The tradeoff, however, is that BS1 shows lower occupancy of the excess receptor, IL-8R, at intermediate antibody concentrations, compared to the combination of monospecific antibodies, and the occupancy declines as the IL-8R concentration increases and the ratio of IL-6R to IL-8R moves further away from 1:1.</p>", "<p id=\"P66\">The impact of these differing binding patterns is apparent when the monospecific and bispecific antibodies are compared directly [##FIG##6##Figure 7C##]. The relative binding output from these simulations quantifies the fold-change in fractional occupancy of each receptor when BS1 binds compared to the binding of the combination of tocilizumab and 10H2. Similar to the previous results, for the overall bound receptor, the monospecific and bispecific antibodies show the same results when the receptors are present at a 1:1 ratio, and the monospecific antibodies show greater binding outside of this ratio. However, the occupancy of the individual receptors reveals that, when one receptor is present in excess of the other, the monospecific antibodies show greater binding to the excess receptor, while BS1 binds more of the limited receptor. The fold-change in binding between the antibody types increases as the ratio moves further away from equal receptor expression [##FIG##6##Figure 7C##]. This pattern is observed for additional antibody concentrations as well, but the differences between antibody types diminish as the concentration increases because the overall binding is high [##SUPPL##0##Figure S14##]. Overall, these results suggest the binding of the bispecific antibody (but not monospecific antibodies) to a low concentration receptor is enriched by the presence of a high concentration of the other target molecule.</p>", "<title>Univariate Sensitivity</title>", "<p id=\"P67\">To analyze the impact of individual model parameters on the bispecific antibody binding, we performed local and global univariate sensitivity analyses of the model output [##FIG##7##Figure 8##]. First, we examined the local sensitivity of ternary IL-6R-BS1-IL-8R complex formation and total receptor binding to changes in the values of the association and dissociation rate constants along with initial antibody and receptor concentrations [##FIG##7##Figure 8A##].</p>", "<p id=\"P68\">Overall, the antibody-receptor complex formation is more sensitive to the initial antibody and receptor concentrations than it is to the binding rates, with the BS1 and IL-8R concentrations having the greatest impact on binding. At two hours after antibody dosing, the amount of bound complex is still increasing [##FIG##3##Figures 4##, ##SUPPL##0##S8##], so increasing the concentration any of the molecules in the system will lead to greater binding. The concentration of IL-8R is slightly more impactful than that of IL-6R because the binding to IL-8R is estimated here to be slightly faster than the binding to IL-6R [##TAB##1##Table 2##], leading to more binary complexes and then more ternary complexes being formed.</p>", "<p id=\"P69\">Of the association and dissociation rate constants, the model output is most sensitive to the initial binding step to form binary complexes, which is the rate-limiting step. The rate of the second receptor binding leading to ternary complexes is so fast that the binding is able to progress immediately after binary complexes are formed, and it does not have a significant impact on the total amount of bound receptor. Increasing the dissociation rates has a very slight negative effect on the receptor binding, but the rates are so slow that little dissociation occurs during the two-hour simulation period and the impact of varying the rate constants is minimal.</p>", "<p id=\"P70\">With global sensitivity analysis of the model parameters, we examined the impact of varying the rate constant values over a wider range [##FIG##7##Figure 8B##]. The results similarly demonstrate that the total bound receptor is most sensitive to increasing the rate of the first binding step. This is especially true for the intermediate antibody concentrations where neither the antibody nor the receptor are significantly in excess. Decreasing either of the first binding step rate constants from their baseline (i.e., moving left of zero on the x-axis for k<sub>on,6R</sub> or k<sub>on,8R</sub>) individually does not have a large impact on the total receptor binding because the antibody can still bind to the opposite receptor to form binary complexes.</p>", "<p id=\"P71\">Some of those binary complexes may progress forward to forming ternary complexes [##FIG##0##Figure 1C##], but the ternary complex concentration shows very distinct behaviors depending on the antibody concentration. For lower antibody concentrations, increasing the rate of the first binding step leads to more ternary binding, as the limiting factor is the binary complex concentration. For higher antibody concentrations, however, the opposite effect is observed: increasing the rate of binary binding leads to fewer ternary complexes, due to auto-inhibition with the binary complexes consuming all of the available receptor. This is consistent with “zones” of different dominant complex types when the concentrations are varied [##FIG##4##Figure 5E##] and suggests bispecific antibody binding to form ternary complexes is dependent on the balance of binding site affinity and species concentrations.</p>", "<p id=\"P72\">For the second binding step leading to the ternary complex, the concentrations of all complexes are sensitive to decreasing the rate constant from the baseline but less so to increasing it [##FIG##7##Figure 8B##]. The formation of ternary complexes progresses incredibly quickly relative to the binary complex binding, so increasing their binding rate has little effect. However, slowing the rate of ternary complex binding causes more receptor to be consumed by less-stable binary form, leading to less receptor being available for ternary complex binding and less bound receptor overall. Finally, the amount of bound receptor is only sensitive to increasing the dissociation rate at high magnitudes [##FIG##7##Figure 8B##]. Generally, the dissociation is so slow relative to the association that the specific value does not have a significant impact on the receptor binding, but, at very high rates, enough dissociation occurs that it decreases the number of bound complexes that are present. These results agree with the results from the local sensitivity analysis, collectively demonstrating that the first binding step is the rate-limiting step and that only extreme values of the second binding step and the dissociation rates have a substantial impact on receptor binding.</p>" ]	[ "<title>DISCUSSION</title>", "<p id=\"P73\">In this study, we have developed a model of a bispecific antibody (BS1) targeting two key cell surface receptors, IL-6Rα and IL-8RB, which were recently implicated in a synergistic pathway that drives tumor metastasis [##UREF##2##12##,##REF##35841152##28##]. Our model is comprised of a series of ODEs for each of the receptors, antibodies, and antibody-receptor complexes studied. Each association and dissociation process in the system is represented as a set of terms in the ODEs, with separate terms for the formation of binary antibody-receptor and ternary receptor-antibody-receptor complexes. Related approaches have been used in other mechanistic models of bivalent antibody binding [##REF##26910134##24##,##REF##23872324##38##,##REF##27022022##39##], and below we describe the differences between those works and this. We used <italic toggle=\"yes\">in vitro</italic> experimental data to estimate values for the binding parameters of the model; we observed that the simulations match the experimental results and the data constrains the parameters well. Deploying the model to simulate antibody binding to cells that express one or other or both of the target receptors, and comparing to simulations of combinations of monospecific antibodies, we gleaned insights into the mechanistic differences between these potential treatments.</p>", "<p id=\"P74\">Once one arm of the antibody binds a cell-surface receptor, the second receptor must be within reach to allow for a second binding event. The distance between antigen-binding domains of IgG antibodies generally ranges from 6 to 12 nm [##REF##8612574##54##–##REF##1737031##56##], but the arms are joined by a highly flexible hinge region that can allow the arms to reach up to 17 to 18 nm when fully extended [##REF##12051932##57##]. Assuming uniform receptor distribution and a surface area of 1000 μm<sup>2</sup>, there would be an average distance between receptors of around 50 nm for 10<sup>5</sup> receptors/cell or 500 nm for 10<sup>3</sup> receptors/cell. However, bispecific antibodies have been demonstrated to simultaneously engage two receptors at these receptor densities [##REF##26260789##33##,##REF##25621507##58##]. It has been hypothesized that this is due to fast diffusion of receptors in the cell membrane [##REF##27097222##40##,##UREF##14##59##] and non-uniform receptor distribution, with receptors being co-localized in lipid rafts and other membrane structures, increasing local density [##REF##16150967##60##,##REF##21194489##61##].</p>", "<p id=\"P75\">Binding of one arm of the antibody increases the local concentration of the antibody at the surface, leading to a significantly stronger apparent affinity for second arm binding (compared to first arm binding) to form the ternary complex [##UREF##7##30##,##REF##1638531##50##]. This may be partly reduced by a loss in rotational flexibility and by steric hindrance from other antibody-receptor complexes in the vicinity [##REF##23330947##37##,##UREF##15##62##], but, on balance, we expect the second binding event to be effectively stronger than the first.</p>", "<p id=\"P76\">Previous mechanistic bivalent binding models have incorporated the effects of binding avidity in various ways. The models presented by van Steeg et al. [##REF##26910134##24##] and Rhoden et al. [##REF##27022022##39##] use the same association rates for both binary and ternary complex formation, but they instead use the effective local antigen concentration within reach of the bound antibody in the rate equation for the second binding event. Flexibility limitations and steric constraints are not explicitly included in these models. Vauquelin and Charlton [##REF##23330947##37##] likewise scaled the ternary complex formation by the effective local concentration, but they also incorporate a “penalty factor” to the second association constant to account for the limited rotational freedom of the bound molecule. Finally, Harms et al. [##REF##23872324##38##] incorporated both the heightened effective concentration and the restricted flexibility for bivalent binding into a single parameter they termed as the “cross-arm binding efficiency” (χ). They defined χ as the ratio of the k<sub>on</sub> for ternary complex formation to the k<sub>on</sub> for the initial binary complex binding (represented in our model as k<sub>on,R*</sub> and k<sub>on,R</sub>, respectively) and hypothesized that χ was an epitope-dependent property of a bivalent antibody, independent of the antibody’s monovalent affinity for its target [##UREF##13##51##].</p>", "<p id=\"P77\">Since we had an experimental data set for our system, we did not need to make assumptions about the relative strength of first versus second binding, and instead estimated independent values for these steps using empirical data. The ratio between these first and second binding rate constants is the χ parameter defined by Harms et al. [##REF##23872324##38##], and the value we obtained (1.6 × 10<sup>4</sup>) was consistent with the range of values for χ they reported (10<sup>2</sup> to 10<sup>5</sup>). Larger values indicate stronger cross-arm binding, consistent with our results demonstrating that BS1 binds bivalently to IL-6R and IL-8R with high avidity [##FIG##2##Figures 3##, ##FIG##4##5A##, ##FIG##4##5C##].</p>", "<p id=\"P78\">For our model, several key assumptions made it possible to reduce the number of unknown parameters. First, because BS1 was constructed using the binding domains from tocilizumab and 10H2, we assumed that the antibodies share rate constants for the first binding step to the same receptor. Second, since all three antibodies are bivalent and IgG-based, we assumed the geometries were similar enough that they share rate constants for the second binding step to the same receptor (but note that the first and second step rate constants are different). Third, we assumed that the dissociation rate constants were shared for unbinding events from a specific receptor. The rate constant values are further constrained by detailed balance (see <xref rid=\"S2\" ref-type=\"sec\">Methods</xref>). These assumptions are supported by the strong fit of the model to experimental data [##FIG##2##Figures 3##, ##SUPPL##0##S7##], and the well-constrained parameter sets [##FIG##1##Figures 2##, ##SUPPL##0##S4##]. If the assumptions were relaxed, the values of the parameters would be less constrained and thus more uncertain.</p>", "<p id=\"P79\">Simulations of the formation of antibody-receptor complexes over time revealed the binding dynamics in the system [##FIG##3##Figures 4##, ##SUPPL##0##S8##]. Initially, binary antibody-receptor complex formation progresses quickly, followed by conversion of binary complexes into ternary receptor-antibody-receptor complexes shortly thereafter. When one receptor is in excess of the other, as IL-8R was in our IL-6R<sup>+</sup>/IL-8R<sup>+</sup> experimental cell line, we found that binary complexes with the excess receptor will continue to accumulate after the ternary complex concentration reaches steady state due to the consumption of the limited receptor. Perhaps surprisingly, after the free antibody concentration is washed out of the system, the ternary complex concentration continues to increase, as binary complexes dissociate and free receptor becomes available for binding again. Increased ternary complex formation as the free antibody concentration declines demonstrates the impact of avidity in bivalent antibody binding – in the single-receptor-positive cell lines where only one domain is capable of binding, there is no increase in bound receptor after the washout phase [##FIG##3##Figure 4##]. This “washout” simulates the effects of decreasing antibody concentration, as might be seen physiologically in the window between therapeutic doses as antibody is cleared, internalized, and degraded. When ternary complexes first dissociate, the antibody remains tethered to the cell surface through its remaining receptor bond, promoting rebinding of the antibody and increasing antibody residence time [##UREF##8##31##,##REF##23330947##37##].</p>", "<p id=\"P80\">We also demonstrated that the combined effects of antibody concentration and receptor expression level determine the relative balance of binary and ternary complex formation [##FIG##4##Figure 5##], creating different “zones” wherein different complex types dominate. As ternary complexes are thought to be the key pharmacologically relevant species [##REF##29403265##21##,##UREF##5##26##,##REF##35790857##32##], elucidating the mechanisms underlying complex type distribution is important for successful bispecific targeting. Our simulations show that when antibody (either monospecific or bispecific) is present in excess of the receptor concentration, the less-stable binary complex form is favored; whereas the ternary complex form is dominant in the window of intermediate antibody concentrations and higher receptor expression levels [##FIG##4##Figure 5E##]. This result suggests an optimal therapeutic window for the bispecific antibody therapeutic where maximal binding can be achieved.</p>", "<p id=\"P81\">Intriguingly, our results also revealed that as surface receptor expression levels increased for a given antibody concentration (monospecific or bispecific), the fractional occupancy of the receptor also increased [##FIG##4##Figures 5B##, ##FIG##4##5D##, ##SUPPL##0##S9##]. At first, this behavior seems counterintuitive, since the system is gaining more binding sites for the same number of antibody molecules. To clarify why this pattern appears, we simulated BS1 binding compared to the binding of a theoretical “monovalent” antibody that was restricted to forming only binary complexes [##FIG##5##Figure 6##]. Over the majority of the receptor concentration range tested, the monovalent-restricted antibody binding is independent of the varying receptor level [##FIG##5##Figure 6B##], indicating that it is specifically the formation of ternary complexes that drives the increased receptor occupancy at higher receptor concentrations [##FIG##5##Figure 6D##].</p>", "<p id=\"P82\">Sensitivity analysis of model binding parameters showed that maximal formation of ternary complexes depends on the balance of antibody concentration and the rates of initial association to form the binary antibody-receptor complexes [##FIG##7##Figure 8B##]. Ternary complex formation by lower affinity antibodies is concentration-limited, with increasing antibody concentration leading to more ternary complex binding. Higher affinity antibodies, however, show “auto-inhibition”, where increasing the amount of antibody leads to the receptor getting overwhelmed by binary complexes, with no free binding sites remaining available for the second association. Ternary binding by these higher affinity antibodies benefits more than ternary binding by lower affinity antibodies from an increased rate of conversion of binary complexes to ternary complexes, which defines the “cross-arm binding efficiency”.</p>", "<p id=\"P83\">Overall, our results suggest that the binding of bivalent (both monospecific and bispecific) antibodies depends on the interplay of the antibody’s inherent affinity for the target and its cross-linking efficiency, along with both antibody concentration and receptor expression level. There appears to be a “Goldilocks” effect, wherein binding is maximized when affinity, cross-linking, and concentration are all balanced, with none too high or too low. This “affinity and avidity window” has been hypothesized to drive the <italic toggle=\"yes\">in vivo</italic> selectivity of antibodies for target tissues [##REF##28127051##23##,##REF##26260789##33##,##REF##21150278##63##]. Antibodies with intermediate affinity rely on bivalent interactions for stable binding, as opposed to high affinity antibodies that show a greater predilection for monovalent interactions. The requirement for intermediate-affinity antibodies to utilize bivalent interactions may drive selective binding to tissues with greater expression of the target antigen, leading to fewer off-target toxic side effects [##REF##21150278##63##,##REF##11156404##64##]. This is particularly important in cancer, as many of the treatment targets are molecules that are up-regulated in cancerous cells but are still expressed on healthy tissues. These results also suggest a benefit for developing “affinity-modulated” bispecific antibodies, with lower inherent affinities, in order to maximize treatment selectivity. Computational bivalent binding models based on the framework presented here can be applied to specific therapeutic targets to predict optimal affinities (including relative affinities between different binding arms) and cross-linking capacities for maximal binding.</p>", "<p id=\"P84\">While the total level of receptor expression is a key determinant of bivalent antibody binding, the relative amount of the two different target receptors is also critical for bispecific antibodies. Our simulations revealed that when IL-6R and IL-8R are present in a 1:1 ratio, bispecific antibody binding is nearly identical to the binding of the combination of anti-IL-6R and anti-IL-8R monospecific antibodies [##FIG##6##Figure 7A##]. However, when one receptor is present in excess of the other on the same cell, BS1 shows significantly different behavior at sub-saturating concentrations. Increasing the concentration of the receptor in excess leads to BS1 forming more binary complexes with that receptor. This in turn tethers BS1 to the cell surface, allowing it to rapidly cross-link with the limiting receptor to form the stable ternary complex. The bispecific antibody demonstrates a heightened apparent affinity for the less expressed receptor that increases with the excess receptor concentration [##FIG##6##Figure 7B##]. The monospecific antibodies do not have this advantage; each monospecific antibody has an independent target, and varying expression of one target does not affect binding to the other. This is a key distinction between the monospecific and bispecific antibodies – when the receptor levels are imbalanced, the monospecific antibodies demonstrate greater binding to the receptor in excess, while the bispecific antibody binds more of the limited receptor [##FIG##6##Figure 7C##]. However, the distinctions between monospecific antibody combinations and bispecific antibodies disappear at saturating antibody concentrations, where full receptor occupancy is achieved in both scenarios.</p>", "<p id=\"P85\">The distinction between monospecific antibody combinations and bispecific antibodies at sub-saturating concentrations is potentially very significant for therapeutic design in the context of heterogeneous expression of receptors. Tissues comprise a mix of cells expressing a single receptor, neither, or both (in various ratios). Individual cells also show significant heterogeneity in membrane receptor expression, particularly cancerous cells with mutations that alter gene expression. Both IL-6R and IL-8R are known to be overexpressed in cancer [##UREF##2##12##,##REF##35841152##28##], and IL-6R has been quantified around 10<sup>3</sup> receptors per cell in different carcinoma cell lines [##REF##8364912##65##]. IL-8R has not been directly quantified in solid tumors, but it is found on monocytes and neutrophils at levels of 10<sup>4</sup> to 10<sup>5</sup> receptors per cell, and it was shown to be up-regulated relative to IL-6R in primary breast cancer tumor samples [##REF##35841152##28##]. If an imbalance in receptor levels is present, it could be important to increase the apparent affinity for the less expressed receptor to achieve sufficient inhibition of the system. BS1 demonstrated stronger inhibition of migration and decreased metastatic burden <italic toggle=\"yes\">in vivo</italic> compared to the combination of tocilizumab and 10H2 [##REF##35841152##28##], and our results suggest that avidity effects may contribute to the underlying mechanism behind the superior performance of the bispecific antibody.</p>", "<p id=\"P86\">While our model provides significant insight into the mechanisms of bivalent and bispecific antibody binding, expanding the model to include additional processes and other cell types has the potential to give even more insight into these treatments. Physiologically, monoclonal antibodies are ultimately eliminated from the body via receptor-mediated endocytosis and subsequent intracellular catabolism [##REF##18784655##66##], and higher affinity antibody binding leads to greater rates of endocytosis and degradation [##REF##21406401##67##]. While receptor synthesis, internalization, and degradation were assumed to be negligible in modeling antibodies binding to cultured cells, given the importance of antibody-receptor complex internalization in determining the drug concentration profile and localization within tissues [##REF##21406401##67##–##REF##30480383##69##], it would be informative to extend the bivalent binding model presented here to include these processes. This modeling could in turn identify optimal bispecific design parameters to balance avidity with the rate of antibody endocytosis and elimination from the system. Additionally, as the aim of BS1 treatment is to inhibit IL-6/IL-8-driven metastasis, adding IL-6 and IL-8 secretion and binding to this system will allow us to directly model how antibody binding leads to ligand inhibition.</p>", "<p id=\"P87\">Our simulation results showed that receptor expression is critical for bispecific antibody binding, both in terms of total receptor levels and in the relative amounts of the individual targets. As IL-6 and IL-8 are both pleiotropic immune factors, their receptors are also expressed on healthy white blood cells and other tissues. Modeling multiple cell types with different receptor levels could allow us to quantify how the affinity and avidity effects presented here impact target tissue selectivity and further clarify differences between monospecific and bispecific antibodies [##REF##28127051##23##,##REF##26910134##24##]. Based on our current results, we hypothesize that cells that express a high concentration of only one receptor may act as “sinks” for monospecific antibodies, while the bispecific antibodies could preferentially bind to cells that express both receptors. Beyond local tissue binding, target expression on healthy cells could also impact antibody distribution and clearance from the body. Pharmacokinetic models of bispecific antibodies have previously been described [##REF##30480383##69##,##UREF##16##70##], and extending our model to include circulation of the drug and its transport into the target tissue would enable study of the full treatment dynamics for monospecific versus bispecific antibodies.</p>" ]	[ "<title>CONCLUSION</title>", "<p id=\"P88\">The binding of bispecific antibodies is governed by the intricate relationships between inherent binding affinity, combined multivalent avidity, therapeutic concentration, and target expression. Here we presented a mechanistic, computational model for antibodies targeting IL-6R and IL-8R, comprised of a series of ordinary differential equations describing antibody binding dynamics. We fully parameterized the model from existing data, and our simulations closely match experimental data of monospecific and bispecific antibodies binding to cells expressing different levels of the IL-6 and IL-8 receptors. Model results describe the system dynamics and reveal key mechanisms underlying bispecific antibody behavior. The model also predicts the consequent receptor occupancy due to the antibodies, as well as the distribution of receptors into binary (antibody-receptor) and ternary (receptor-antibody-receptor) complexes; ultimately, the impact on receptor complex formation, rather than the amount of receptor binding, is critical for therapeutic performance. We observed that the bispecific antibody studied demonstrates strong cross-linking and avidity effects, which increase receptor residence time. Ternary complex formation is maximized when binding affinity is balanced with antibody concentration (for both monospecific and bispecific antibodies) and target expression level. When the target receptors are present in unequal amounts, monospecific and bispecific antibodies demonstrate distinct binding patterns – monospecific antibodies bind more strongly to the excess target, whereas bispecific antibodies show greater apparent affinity for the limited target at sub-saturating concentrations. Overall, our quantitative model of anti-IL-6R/anti-IL-8R antibodies provides clear mechanistic insight into the dynamics of homo- and heterobivalent antibodies and leads to actionable predictions of optimal therapeutic design for maximal binding. The results provided here include specific parameter values for these antibodies for IL-6R and IL-8R, but many of the insights can be applied generally to other bispecific antibodies, and the model itself can be repurposed to analyze other therapeutic systems of interest.</p>" ]	[ "<p id=\"P1\">The spread of cancer from organ to organ (metastasis) is responsible for the vast majority of cancer deaths; however, most current anti-cancer drugs are designed to arrest or reverse tumor growth without directly addressing disease spread. It was recently discovered that tumor cell-secreted interleukin-6 (IL-6) and interleukin-8 (IL-8) synergize to enhance cancer metastasis in a cell-density dependent manner, and blockade of the IL-6 and IL-8 receptors (IL-6R and IL-8R) with a novel bispecific antibody, BS1, significantly reduced metastatic burden in multiple preclinical mouse models of cancer. Bispecific antibodies (BsAbs), which combine two different antigen-binding sites into one molecule, are a promising modality for drug development due to their enhanced avidity and dual targeting effects. However, while BsAbs have tremendous therapeutic potential, elucidating the mechanisms underlying their binding and inhibition will be critical for maximizing the efficacy of new BsAb treatments. Here, we describe a quantitative, computational model of the BS1 BsAb, exhibiting how modeling multivalent binding provides key insights into antibody affinity and avidity effects and can guide therapeutic design. We present detailed simulations of the monovalent and bivalent binding interactions between different antibody constructs and the IL-6 and IL-8 receptors to establish how antibody properties and system conditions impact the formation of binary (antibody-receptor) and ternary (receptor-antibody-receptor) complexes. Model results demonstrate how the balance of these complex types drives receptor inhibition, providing important and generalizable predictions for effective therapeutic design.</p>" ]	[ "<title>Supplementary Material</title>" ]	[ "<title>FUNDING</title>", "<p id=\"P90\">This material is based upon work supported by the NIH grant T32 GM136577 to C.M.P.R. The authors acknowledge the Emerson Collective Cancer Research Fund, a Bisciotti Foundation Translational Fund award, and a Maryland Innovation Initiative TEDCO Phase I Project award. H.Y. is the recipient of a National Science Foundation Graduate Research Fellowship Program award. This work was carried out at the Advanced Research Computing at Hopkins (ARCH) core facility (<ext-link xlink:href=\"https://rockfish.jhu.edu\" ext-link-type=\"uri\">rockfish.jhu.edu</ext-link>), which is supported by the National Science Foundation (NSF) grant number OAC 1920103. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.</p>", "<title>DATA AND CODE AVAILABILITY</title>", "<p id=\"P89\">All code written in support of this publication is publicly available at <ext-link xlink:href=\"https://github.com/christyray/bispecific-binding-model\" ext-link-type=\"uri\">https://github.com/christyray/bispecific-binding-model</ext-link>, and we have archived our code on Zenodo (DOI: <ext-link xlink:href=\"10.5281/zenodo.10396436\" ext-link-type=\"doi\">10.5281/zenodo.10396436</ext-link>). Experimental data, simulation input files, and generated data are available on Zenodo at <ext-link xlink:href=\"10.5281/zenodo.10393562\" ext-link-type=\"doi\">https://doi.org/10.5281/zenodo.10393562</ext-link>. Code for model simulations is written in MATLAB using version R2022a.</p>" ]	[ "<fig position=\"float\" id=\"F1\"><label>Figure 1.</label><caption><title>Bivalent antibody binding model antibodies, rate constants, and reactions.</title><p id=\"P91\"><bold>A,</bold> Monoclonal (mAb) and bispecific (BsAb) antibodies simulated in our computational model. Tocilizumab is a recombinant humanized mAb with two anti-IL-6Rα (denoted anti-IL-6R) binding domains; 10H2 is a mAb with two anti-IL-8RB (denoted anti-IL-8R) binding domains; BS1 is an anti-IL-6Rα/anti-IL-8RB BsAb synthesized from the binding domains of tocilizumab and 10H2 by combining the knobs-into-holes and single-chain Fab methodologies. <bold>B,</bold> Schematic of the IL-6Rα/IL-8RB/BS1 antibody-binding model kinetics. BS1 can bind to either IL-6Rα or IL-8RB, and, having done so, the BS1-receptor complex can then bind to the other receptor. k<sub>on,6R</sub> and k<sub>on,8R</sub> describe the association rates for the formation of binary antibody-receptor complexes, and k<sub>on,6R</sub> and k<sub>on,8R</sub> describe the association rates for the formation ternary receptor-antibody-receptor complexes. The same k<sub>off,6R</sub> and k<sub>off,8R</sub> rate constants are used for the dissociation of both the binary and the ternary complexes. Schematics for the two monoclonal antibodies, tocilizumab and 10H2, are included in the ##SUPPL##0##Supplemental Information## [##SUPPL##0##Figure S1##]. <bold>C,</bold> Simplified view of the schematic in <bold>B</bold> illustrates how the reactions form a thermodynamic cycle. The reactions can proceed in a clockwise or counter-clockwise manner to return back to the starting reactants, forming a cycle with a net free energy change of 0. This figure was created with <ext-link xlink:href=\"https://BioRender.com\" ext-link-type=\"uri\">BioRender.com</ext-link>.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2.</label><caption><title>Optimization of binding association and dissociation constants to experimental data.</title><p id=\"P92\">The cost function is calculated as the sum of the squared differences between the normalized model output and the normalized experimental data at each antibody concentration used. “All Norm” includes all of the optimized parameter sets from each of the different normalization options described in the <xref rid=\"S2\" ref-type=\"sec\">Methods</xref>, and “BS1 Norm” highlights the parameter sets where the model output was normalized against the bound concentration of BS1 at the initial concentrations used to normalize the experimental data, which was the best-performing normalization. Figures separated by normalization scheme and figures with a narrow range of parameter values are available in the ##SUPPL##0##Supplemental Information## [##SUPPL##0##Figures S2##–##SUPPL##0##S4##]. <bold>A,</bold> Relationship between initial guesses and optimized values for each binding reaction rate constant. k<sub>on,8R</sub> is not pictured because its initial and ‘optimized’ values were determined from the other parameters using the thermodynamic cycle relationship. <bold>B,</bold> Distribution of optimized parameter values across all optimizations performed. Marked points indicate the values of the lowest cost parameter set (values are listed in ##TAB##1##Table 2##). <bold>C,</bold> Relationship between optimized parameter values and the cost of the optimized parameter sets compared to experimental data, separated by parameter. Optimized points with the same value are grouped into a single point, with the point size indicating how many optimized parameter values are in the group.</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3.</label><caption><title>Model simulation results using the best-fit parameter set compared to the experimental data used to fit the model parameters.</title><p id=\"P93\">Simulations were performed under the same conditions as the experiment: 10<sup>5</sup> cells/well, receptor expression levels from the transduced cell lines [##TAB##0##Table 1##], and with a 2-hour initial association period followed by a 15-minute free antibody washout. The model simulation results (lines) are compared to the equivalent experimental data (dots). Simulations beyond the range of antibody concentrations used in the experimental data are indicated with dashed lines. Experimental data was not obtained for the combination of tocilizumab and 10H2, but simulations are presented here for comparison. Model output and experimental data are each normalized to the bound BS1 concentration from the initial antibody concentrations where binding reached saturation. The error bars depict the standard error from three experimental replicates; the experimental data was previously published [##REF##35841152##28##]. Simulation results with all obtained parameter sets are included in the ##SUPPL##0##Supplemental Information## [##SUPPL##0##Figure S7##].</p></caption></fig>", "<fig position=\"float\" id=\"F4\"><label>Figure 4.</label><caption><title>Simulations illustrate the dynamics of BS1 antibody binding to IL-6R and IL-8R over time.</title><p id=\"P94\">Initial BS1 concentration = 100 nM and 10<sup>5</sup> cells/well for all simulations. Free (unbound) BS1 concentration was set to 0 nM at 2 hours to simulate antibody washout from the system. The expression levels of IL-6R and IL-8R from the transduced experimental cell lines [##TAB##0##Table 1##] were used in the simulations. Simulation results for additional antibodies and antibody concentrations are included in the ##SUPPL##0##Supplemental Information## [##SUPPL##0##Figure S8##].</p></caption></fig>", "<fig position=\"float\" id=\"F5\"><label>Figure 5.</label><caption><title>Simulated Binary (Ab-R), Ternary (R-Ab-R), and Total Bound (Binary + Ternary) levels of bispecific BS1-Receptor complexes, over a range of antibody doses and receptor expression levels.</title><p id=\"P95\">In these simulations, IL-6R and IL-8R are present in a 1:1 ratio, and simulations were performed for 24 hours after antibody dosing. A-B, Fraction of total BS1 concentration in free (unbound) state for different levels of initial BS1 (<bold>A</bold>) and receptor (<bold>B</bold>). <bold>C-D,</bold> Fraction of total receptor (IL-6R + IL-8R) in different forms/complexes for different levels of initial BS1 (<bold>C</bold>) and receptor (<bold>D</bold>). <bold>E,</bold> Bound receptor fraction across different levels of initial BS1 and receptors. The color indicates the fraction of the total receptor (IL-6R + IL-8R) that is bound to BS1 in each antibody-receptor complex type. A similar heat map for different tocilizumab and 10H2 concentrations is included in the ##SUPPL##0##Supplemental Information## [##SUPPL##0##Figure S9##].</p></caption></fig>", "<fig position=\"float\" id=\"F6\"><label>Figure 6.</label><caption><title>Simulations of monovalent BS1 binding over varying initial antibody and receptor concentrations.</title><p id=\"P96\">In these simulations, although the cells express both receptors, the formation of ternary complexes was suppressed by setting k<sub>on,6R</sub> and k<sub>on,8R*</sub>to 0. IL-6R and IL-8R are present in a 1:1 ratio, and simulations were performed for 24 hours after antibody dosing. Similar results for the combination of the monospecific antibodies tocilizumab and 10H2 are included in the ##SUPPL##0##Supplemental Information## [##SUPPL##0##Figure S11##]. <bold>A,</bold> Fraction of total BS1 that is free (unbound) for different levels of receptor expression and initial BS1 concentration. <bold>B,</bold> Fraction of total receptor concentration (IL-6R + IL-8R) that is unbound (free) or bound (in binary antibody-receptor complexes) for different levels of receptor expression and initial BS1 concentration. The same results, but with antibody and receptor visualization reversed, are included in the ##SUPPL##0##Supplemental Information## [##SUPPL##0##Figure S10##]. <bold>C,</bold> Bound receptor fraction across different initial BS1 and receptor levels. The color indicates the fraction of the total receptor (IL-6R + IL-8R) that is bound to antibody. <bold>D,</bold> Comparison of monovalent and bivalent binding. The lines indicate the fraction of total receptor (IL-6R + IL-8R) that is bound in different complex types in the original simulations and the simulations restricted to monovalent binding only. Each panel represents a different receptor level (in # receptors/cell).</p></caption></fig>", "<fig position=\"float\" id=\"F7\"><label>Figure 7.</label><caption><title>Comparison of antibody-receptor complex formation: BS1 vs. combination of tocilizumab and 10H2.</title><p id=\"P97\">All simulations were performed for 24 hours after antibody dosing. <bold>A,</bold> Fraction of all receptors (IL-6R + IL-8R) that are bound in Binary and Ternary complexes, and Total Bound receptor (Binary + Ternary) across different IL-6R and IL-8R concentrations. The color indicates the fraction of all receptors (IL-6R + IL-8R) that are bound in each antibody-receptor complex type. Initial BS1 concentration = 10 nM; initial tocilizumab concentration = 5 nM and initial 10H2 concentration = 5 nM. Heat maps of additional total antibody concentrations are available in the ##SUPPL##0##Supplemental Information## [##SUPPL##0##Figure S12##]. <bold>B,</bold> The fractional occupancy of each receptor individually when one receptor (IL-8R) is in excess. IL-6R was fixed at 10<sup>3</sup> receptors/cell for these simulations, while IL-8R ranged from 10<sup>2</sup> to 10<sup>7</sup> receptors/cell. The fractional occupancy indicates the fraction of the specific receptor concentration (either IL-6R or IL-8R) that is bound to antibody (either BS1 or the combination of tocilizumab and 10H2). Results with IL-8R as the fixed receptor are included in the ##SUPPL##0##Supplemental Information## [##SUPPL##0##Figure S13##]. <bold>C,</bold> Comparison of receptor bound by BS1 (BsAb) or the combination of tocilizumab and 10H2 (mAbs) across different receptor concentrations. Relative binding is the ratio of fractional bound receptor (the fraction of total IL-6R + IL-8R bound to antibody) when BS1 is used compared to when the combination of mAbs is used. Similar heat maps for different total antibody concentrations are included in the ##SUPPL##0##Supplemental Information## [##SUPPL##0##Figure S14##].</p></caption></fig>", "<fig position=\"float\" id=\"F8\"><label>Figure 8.</label><caption><title>Local and global sensitivity of model output to association and dissociation rate constants and the initial antibody and receptor concentrations.</title><p id=\"P98\"><bold>A,</bold> Local sensitivity analysis of model output with varying rate constants and initial concentrations. 10 nM baseline BS1 concentration, [IL-6R] = [IL-8R] = 5 × 10<sup>4</sup> receptors/cell, and output at t = 2 hours for all simulations. Area Under the Curve (AUC) is calculated as the integration of the BS1-receptor complex concentration over time, determined for the ternary complexes and for the total bound receptor (IL-6R + IL-8R). Sensitivity is calculated as the percentage change in the output divided by the percentage change in the parameter (10% for these simulations). <bold>B,</bold> Global sensitivity of fractional bound receptor concentration over varying rate constant value. Each parameter was varied over two orders of magnitude below and above its optimized value. Fractional occupancy is determined as the fraction of total receptor (IL-6R + IL-8R) that is bound to BS1 in a particular complex type, separated for ternary complexes and total bound in binary or ternary complexes. Simulations were performed for 24 hours after antibody dosing, and [IL-6R] = [IL-8R] = 5 × 10<sup>4</sup> receptors/cell for all simulations.</p></caption></fig>" ]	[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1.</label><caption><p id=\"P99\">IL-6Rα and IL-8RB quantification on transduced HEK 293T cells (expressed in # receptors/cell).</p></caption><table frame=\"void\" rules=\"groups\"><colgroup span=\"1\"><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/></colgroup><thead><tr><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Cell Line</th><th align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">IL-6R Expression</th><th align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">IL-8R Expression</th></tr></thead><tbody><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">IL-6Ra+/IL-8RB<sup>−</sup></td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.08 × 10<sup>5</sup></td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">N/A</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">IL-6Ra<sup>−</sup>/IL-8RB<sup>+</sup></td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">N/A</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1.30 × 10<sup>6</sup></td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">IL-6Ra+/IL-8RB<sup>+</sup></td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3.16 × 10<sup>5</sup></td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">6.18 × 10<sup>5</sup></td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T2\" orientation=\"landscape\"><label>Table 2.</label><caption><p id=\"P100\">Binding rate constants estimated from IL-6Rα and IL-8RB HEK 293T cell surface binding assays. S.D. = standard deviation of log-transformed optimized values. α = 8.3 × 10<sup>−7</sup> nM/(# / cell) is used to convert the rate constants between nM and number of receptors per cell. The derivation of this value for the unit conversion is included in the ##SUPPL##0##Supplemental Information##.</p></caption><table frame=\"void\" rules=\"groups\"><colgroup span=\"1\"><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/></colgroup><thead><tr><th align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Parameter</th><th align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Initial Guess Range</th><th align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Optimization Bounds</th><th align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Best Fit Value</th><th align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">S.D.</th><th align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Units</th><th align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Reference</th></tr></thead><tbody><tr><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">[10<sup>−9</sup>,10<sup>−2</sup>]</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">[10<sup>−11</sup>,1]</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.92 × 10<sup>−6</sup></td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1.15</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Best fit to the data</td></tr><tr><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">[10<sup>−9</sup>,10<sup>−2</sup>]</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">[10<sup>−11</sup>,1]</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">9.03 × 10<sup>−6</sup></td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.953</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Best fit to the data</td></tr><tr><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">[10<sup>−13</sup>,10<sup>−5</sup>]</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">[10<sup>−15</sup>,10<sup>−3</sup>]</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">8.11 × 10<sup>−8</sup></td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.10</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Best fit to the data</td></tr><tr><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">[1.2 × 10<sup>−7</sup>,12]</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">[1.2 × 10<sup>−</sup>9,1.2 × 10<sup>3</sup>]</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">9.77 × 10<sup>−2</sup></td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.10</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\"> converted to </td></tr><tr><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">—</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">—</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1.24 × 10<sup>−7</sup></td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.25</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Dependent on other binding constants</td></tr><tr><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">—</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">—</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1.49 × 10<sup>−1</sup></td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.25</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\"> converted to </td></tr><tr><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">[10<sup>−10</sup>,10<sup>−7</sup>]</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">[10<sup>−9</sup>,1]</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.61 × 10<sup>−5</sup></td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1.08</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Best fit to the data</td></tr><tr><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">[10<sup>−7</sup>,10<sup>−2</sup>]</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">[10<sup>−9</sup>,1]</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">6.38 × 10<sup>−5</sup></td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1.36</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n\n</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Best fit to the data</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T3\"><label>Table 3.</label><caption><p id=\"P101\">Normalization schemes tested in the optimization of the binding rate constants against the flow cytometry binding assay data. The “BS1, Data” scheme was selected for the parameter optimization.</p></caption><table frame=\"void\" rules=\"groups\"><colgroup span=\"1\"><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/></colgroup><thead><tr><th align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Scheme</th><th align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Normalized Against</th><th align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Initial Concentrations</th></tr></thead><tbody><tr><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">BS1, Data</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">BS1</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Concentrations used for experimental data</td></tr><tr><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">BS1, Max</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">BS1</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Maximum antibody concentration</td></tr><tr><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Ab, Data</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Individual Ab</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Concentrations used for experimental data</td></tr><tr><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Ab, Max</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Individual Ab</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Maximum antibody concentration</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T4\"><label>Table 4.</label><caption><p id=\"P102\">Dissociation equilibrium constants for the initial binding to form binary complexes and the cross-linking to form ternary complexes. Values are calculated from the binding rate constants [##TAB##1##Table 2##] The antibodies share the same equilibrium constants due to the assumptions made in the parameter optimization.</p></caption><table frame=\"void\" rules=\"groups\"><colgroup span=\"1\"><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/></colgroup><thead><tr><th align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Equilibrium Constant</th><th align=\"left\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Description</th><th align=\"center\" valign=\"bottom\" rowspan=\"1\" colspan=\"1\">Value (nM)</th></tr></thead><tbody><tr><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Initial binding to IL-6R for tocilizumab and BS1</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">9.5</td></tr><tr><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Initial binding to IL-8R for 10H2 and BS1</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">7.1</td></tr><tr><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Cross-linking to IL-6R for tocilizumab-IL-6R and BS1-IL-8R</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.7 × 10<sup>−4</sup></td></tr><tr><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Cross-linking to IL-8R for 10H2-IL-8R and BS1-IL-6R</td><td align=\"center\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.3 × 10<sup>−4</sup></td></tr></tbody></table></table-wrap>" ]	[ "<disp-formula id=\"FD1\">\n<label>(1)</label>\n<mml:math id=\"M26\" display=\"block\"><mml:mrow><mml:mfrac><mml:mrow><mml:mi>d</mml:mi><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>⋅</mml:mo><mml:mi>A</mml:mi><mml:mi>b</mml:mi></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:mi>d</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:mfrac><mml:mo>=</mml:mo><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>o</mml:mi><mml:mi>n</mml:mi><mml:mo>,</mml:mo><mml:msub><mml:mi>R</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mrow></mml:msub><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mrow><mml:mo>]</mml:mo></mml:mrow><mml:mo stretchy=\"false\">[</mml:mo><mml:mi>A</mml:mi><mml:mi>b</mml:mi><mml:mo 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"<disp-formula id=\"FD2\">\n<label>(2)</label>\n<mml:math id=\"M27\" display=\"block\"><mml:mrow><mml:mfrac><mml:mrow><mml:mi>d</mml:mi><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>⋅</mml:mo><mml:mi>A</mml:mi><mml:mi>b</mml:mi><mml:mo>⋅</mml:mo><mml:msub><mml:mi>R</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:mi>d</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:mfrac><mml:mo>=</mml:mo><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>o</mml:mi><mml:mi>n</mml:mi><mml:mo>,</mml:mo><mml:msub><mml:mi>R</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo></mml:mo></mml:mrow></mml:msub><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mrow><mml:mo>]</mml:mo></mml:mrow><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mo>⋅</mml:mo><mml:mi>A</mml:mi><mml:mi>b</mml:mi></mml:mrow><mml:mo>]</mml:mo></mml:mrow><mml:mo>+</mml:mo><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>o</mml:mi><mml:mi>n</mml:mi><mml:mo>,</mml:mo><mml:msub><mml:mi>R</mml:mi><mml:mrow><mml:msup><mml:mn>2</mml:mn><mml:mo></mml:mo></mml:msup></mml:mrow></mml:msub></mml:mrow></mml:msub><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow><mml:mo>]</mml:mo></mml:mrow><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>⋅</mml:mo><mml:mi>A</mml:mi><mml:mi>b</mml:mi></mml:mrow><mml:mo>]</mml:mo></mml:mrow><mml:mo>−</mml:mo><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>o</mml:mi><mml:mi>f</mml:mi><mml:mi>f</mml:mi><mml:mo>,</mml:mo><mml:msub><mml:mi>R</mml:mi><mml:mrow><mml:msup><mml:mn>1</mml:mn><mml:mo></mml:mo></mml:msup></mml:mrow></mml:msub></mml:mrow></mml:msub><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>⋅</mml:mo><mml:mi>A</mml:mi><mml:mi>b</mml:mi><mml:mo>⋅</mml:mo><mml:msub><mml:mi>R</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow><mml:mo>]</mml:mo></mml:mrow><mml:mo>−</mml:mo><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>o</mml:mi><mml:mi>f</mml:mi><mml:mi>f</mml:mi><mml:mo>,</mml:mo><mml:msub><mml:mi>R</mml:mi><mml:mrow><mml:msup><mml:mn>2</mml:mn><mml:mo></mml:mo></mml:msup></mml:mrow></mml:msub></mml:mrow></mml:msub><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>⋅</mml:mo><mml:mi>A</mml:mi><mml:mi>b</mml:mi><mml:mo>⋅</mml:mo><mml:msub><mml:mi>R</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mrow></mml:math>\n</disp-formula>", "<disp-formula id=\"FD3\">\n<label>(Reaction 1)</label>\n<mml:math id=\"M28\" display=\"block\"><mml:mrow><mml:mtext>Ab</mml:mtext><mml:mo>+</mml:mo><mml:msub><mml:mtext>R</mml:mtext><mml:mn>1</mml:mn></mml:msub><mml:munder><mml:mrow><mml:mover><mml:mo>⇌</mml:mo><mml:mrow><mml:msub><mml:mtext>k</mml:mtext><mml:mrow><mml:mtext>on</mml:mtext><mml:mo>,</mml:mo><mml:msub><mml:mtext>R</mml:mtext><mml:mn>1</mml:mn></mml:msub></mml:mrow></mml:msub></mml:mrow></mml:mover></mml:mrow><mml:mrow><mml:msub><mml:mtext>k</mml:mtext><mml:mrow><mml:mtext>off</mml:mtext><mml:mo>,</mml:mo><mml:msub><mml:mtext>R</mml:mtext><mml:mn>1</mml:mn></mml:msub></mml:mrow></mml:msub></mml:mrow></mml:munder><mml:mtext>Ab</mml:mtext><mml:mo>⋅</mml:mo><mml:msub><mml:mtext>R</mml:mtext><mml:mn>1</mml:mn></mml:msub></mml:mrow></mml:math>\n</disp-formula>", "<disp-formula id=\"FD4\">\n<label>(Reaction 2)</label>\n<mml:math id=\"M29\" display=\"block\"><mml:mrow><mml:mtext>Ab</mml:mtext><mml:mo>+</mml:mo><mml:msub><mml:mtext>R</mml:mtext><mml:mn>2</mml:mn></mml:msub><mml:munder><mml:mrow><mml:mover><mml:mo>⇌</mml:mo><mml:mrow><mml:msub><mml:mtext>k</mml:mtext><mml:mrow><mml:mtext>on</mml:mtext><mml:mo>,</mml:mo><mml:msub><mml:mtext>R</mml:mtext><mml:mn>2</mml:mn></mml:msub></mml:mrow></mml:msub></mml:mrow></mml:mover></mml:mrow><mml:mrow><mml:msub><mml:mtext>k</mml:mtext><mml:mrow><mml:mtext>off</mml:mtext><mml:mo>,</mml:mo><mml:msub><mml:mtext>R</mml:mtext><mml:mn>2</mml:mn></mml:msub></mml:mrow></mml:msub></mml:mrow></mml:munder><mml:mtext>Ab</mml:mtext><mml:mo>⋅</mml:mo><mml:msub><mml:mtext>R</mml:mtext><mml:mn>2</mml:mn></mml:msub></mml:mrow></mml:math>\n</disp-formula>", "<disp-formula id=\"FD5\">\n<label>(Reaction 3)</label>\n<mml:math id=\"M30\" display=\"block\"><mml:mrow><mml:mtext>Ab</mml:mtext><mml:mo>⋅</mml:mo><mml:msub><mml:mtext>R</mml:mtext><mml:mn>2</mml:mn></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mtext>R</mml:mtext><mml:mn>1</mml:mn></mml:msub><mml:munder><mml:mrow><mml:mover><mml:mo>⇌</mml:mo><mml:mrow><mml:msub><mml:mtext>k</mml:mtext><mml:mrow><mml:mtext>on</mml:mtext><mml:mo>,</mml:mo><mml:msub><mml:mtext>R</mml:mtext><mml:mn>1</mml:mn></mml:msub></mml:mrow></mml:msub><mml:mo></mml:mo></mml:mrow></mml:mover></mml:mrow><mml:mrow><mml:msub><mml:mtext>k</mml:mtext><mml:mrow><mml:mtext>off</mml:mtext><mml:mo>,</mml:mo><mml:msub><mml:mtext>R</mml:mtext><mml:mn>1</mml:mn></mml:msub><mml:mo></mml:mo></mml:mrow></mml:msub></mml:mrow></mml:munder><mml:msub><mml:mtext>R</mml:mtext><mml:mn>1</mml:mn></mml:msub><mml:mo>⋅</mml:mo><mml:mtext>Ab</mml:mtext><mml:mo>⋅</mml:mo><mml:msub><mml:mtext>R</mml:mtext><mml:mn>2</mml:mn></mml:msub></mml:mrow></mml:math>\n</disp-formula>", "<disp-formula id=\"FD6\">\n<label>(Reaction 4)</label>\n<mml:math id=\"M31\" display=\"block\"><mml:mrow><mml:mtext>Ab</mml:mtext><mml:mo>⋅</mml:mo><mml:msub><mml:mtext>R</mml:mtext><mml:mn>1</mml:mn></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mtext>R</mml:mtext><mml:mn>2</mml:mn></mml:msub><mml:munder><mml:mrow><mml:mover><mml:mo>⇌</mml:mo><mml:mrow><mml:msub><mml:mtext>k</mml:mtext><mml:mrow><mml:mtext>on</mml:mtext><mml:mo>,</mml:mo><mml:msub><mml:mtext>R</mml:mtext><mml:mn>2</mml:mn></mml:msub><mml:mo></mml:mo></mml:mrow></mml:msub></mml:mrow></mml:mover></mml:mrow><mml:mrow><mml:msub><mml:mtext>k</mml:mtext><mml:mrow><mml:mtext>off</mml:mtext><mml:mo>,</mml:mo><mml:msub><mml:mtext>R</mml:mtext><mml:mn>2</mml:mn></mml:msub><mml:mo></mml:mo></mml:mrow></mml:msub></mml:mrow></mml:munder><mml:msub><mml:mtext>R</mml:mtext><mml:mn>1</mml:mn></mml:msub><mml:mo>⋅</mml:mo><mml:mtext>Ab</mml:mtext><mml:mo>⋅</mml:mo><mml:msub><mml:mtext>R</mml:mtext><mml:mn>2</mml:mn></mml:msub></mml:mrow></mml:math>\n</disp-formula>", "<disp-formula id=\"FD7\">\n<label>(3)</label>\n<mml:math id=\"M32\" display=\"block\"><mml:msub><mml:mi>K</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mrow><mml:mo stretchy=\"false\">[</mml:mo><mml:mi>A</mml:mi><mml:mi>b</mml:mi><mml:mo stretchy=\"false\">]</mml:mo></mml:mrow><mml:mrow><mml:mi>e</mml:mi><mml:mi>q</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:mi>e</mml:mi><mml:mi>q</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mi>A</mml:mi><mml:mi>b</mml:mi><mml:mo>⋅</mml:mo><mml:msub><mml:mi>R</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:mi>e</mml:mi><mml:mi>q</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfrac><mml:mtext> </mml:mtext><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>o</mml:mi><mml:mi>f</mml:mi><mml:mi>f</mml:mi><mml:mo>,</mml:mo><mml:msub><mml:mi>R</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>o</mml:mi><mml:mi>n</mml:mi><mml:mo>,</mml:mo><mml:msub><mml:mi>R</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mrow></mml:msub></mml:mrow></mml:mfrac><mml:mspace linebreak=\"newline\"/><mml:msub><mml:mi>K</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mrow><mml:mo stretchy=\"false\">[</mml:mo><mml:mi>A</mml:mi><mml:mi>b</mml:mi><mml:mo stretchy=\"false\">]</mml:mo></mml:mrow><mml:mrow><mml:mi>e</mml:mi><mml:mi>q</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:mi>e</mml:mi><mml:mi>q</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mi>A</mml:mi><mml:mi>b</mml:mi><mml:mo>⋅</mml:mo><mml:msub><mml:mi>R</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:mi>e</mml:mi><mml:mi>q</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfrac><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>o</mml:mi><mml:mi>f</mml:mi><mml:mi>f</mml:mi><mml:mo>,</mml:mo><mml:msub><mml:mi>R</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>o</mml:mi><mml:mi>n</mml:mi><mml:mo>,</mml:mo><mml:msub><mml:mi>R</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow></mml:msub></mml:mrow></mml:mfrac><mml:mspace linebreak=\"newline\"/><mml:msub><mml:mi>K</mml:mi><mml:mn>3</mml:mn></mml:msub><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mrow><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mi>A</mml:mi><mml:mi>b</mml:mi><mml:mo>⋅</mml:mo><mml:msub><mml:mi>R</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:mi>e</mml:mi><mml:mi>q</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:mi>e</mml:mi><mml:mi>q</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>⋅</mml:mo><mml:mi>A</mml:mi><mml:mi>b</mml:mi><mml:mo>⋅</mml:mo><mml:msub><mml:mi>R</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:mi>e</mml:mi><mml:mi>q</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfrac><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>o</mml:mi><mml:mi>f</mml:mi><mml:mi>f</mml:mi><mml:mo>,</mml:mo><mml:msub><mml:mi>R</mml:mi><mml:mrow><mml:msup><mml:mn>1</mml:mn><mml:mo></mml:mo></mml:msup></mml:mrow></mml:msub></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>o</mml:mi><mml:mi>n</mml:mi><mml:mo>,</mml:mo><mml:msub><mml:mi>R</mml:mi><mml:mrow><mml:msup><mml:mn>1</mml:mn><mml:mo></mml:mo></mml:msup></mml:mrow></mml:msub></mml:mrow></mml:msub></mml:mrow></mml:mfrac><mml:mspace linebreak=\"newline\"/><mml:msub><mml:mi>K</mml:mi><mml:mn>4</mml:mn></mml:msub><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mrow><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mi>A</mml:mi><mml:mi>b</mml:mi><mml:mo>⋅</mml:mo><mml:msub><mml:mi>R</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:mi>e</mml:mi><mml:mi>q</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:mi>e</mml:mi><mml:mi>q</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>⋅</mml:mo><mml:mi>A</mml:mi><mml:mi>b</mml:mi><mml:mo>⋅</mml:mo><mml:msub><mml:mi>R</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:mi>e</mml:mi><mml:mi>q</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfrac><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>o</mml:mi><mml:mi>f</mml:mi><mml:mi>f</mml:mi><mml:mo>,</mml:mo><mml:msub><mml:mi>R</mml:mi><mml:mrow><mml:msup><mml:mn>2</mml:mn><mml:mo></mml:mo></mml:msup></mml:mrow></mml:msub></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>o</mml:mi><mml:mi>n</mml:mi><mml:mo>,</mml:mo><mml:msub><mml:mi>R</mml:mi><mml:mrow><mml:msup><mml:mn>2</mml:mn><mml:mo></mml:mo></mml:msup></mml:mrow></mml:msub></mml:mrow></mml:msub></mml:mrow></mml:mfrac></mml:math>\n</disp-formula>", "<disp-formula id=\"FD8\">\n<label>(4)</label>\n<mml:math id=\"M33\" display=\"block\"><mml:mrow><mml:msub><mml:mi>K</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:msub><mml:mi>K</mml:mi><mml:mn>4</mml:mn></mml:msub><mml:msubsup><mml:mi>K</mml:mi><mml:mn>2</mml:mn><mml:mrow><mml:mo>−</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msubsup><mml:msubsup><mml:mi>K</mml:mi><mml:mn>3</mml:mn><mml:mrow><mml:mo>−</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>o</mml:mi><mml:mi>f</mml:mi><mml:mi>f</mml:mi><mml:mo>,</mml:mo><mml:msub><mml:mi>R</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mrow></mml:msub><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>o</mml:mi><mml:mi>f</mml:mi><mml:mi>f</mml:mi><mml:mo>,</mml:mo><mml:msub><mml:mi>R</mml:mi><mml:mrow><mml:msup><mml:mn>2</mml:mn><mml:mo></mml:mo></mml:msup></mml:mrow></mml:msub></mml:mrow></mml:msub><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>o</mml:mi><mml:mi>n</mml:mi><mml:mo>,</mml:mo><mml:msub><mml:mi>R</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow></mml:msub><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>o</mml:mi><mml:mi>n</mml:mi><mml:mo>,</mml:mo><mml:msub><mml:mi>R</mml:mi><mml:mrow><mml:msup><mml:mn>1</mml:mn><mml:mo></mml:mo></mml:msup></mml:mrow></mml:msub></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>o</mml:mi><mml:mi>n</mml:mi><mml:mo>,</mml:mo><mml:msub><mml:mi>R</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mrow></mml:msub><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>o</mml:mi><mml:mi>n</mml:mi><mml:mo>,</mml:mo><mml:msub><mml:mi>R</mml:mi><mml:mrow><mml:msup><mml:mn>2</mml:mn><mml:mo></mml:mo></mml:msup></mml:mrow></mml:msub></mml:mrow></mml:msub><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>o</mml:mi><mml:mi>f</mml:mi><mml:mi>f</mml:mi><mml:mo>,</mml:mo><mml:msub><mml:mi>R</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow></mml:msub><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>o</mml:mi><mml:mi>f</mml:mi><mml:mi>f</mml:mi><mml:mo>,</mml:mo><mml:msub><mml:mi>R</mml:mi><mml:mrow><mml:msup><mml:mn>1</mml:mn><mml:mo></mml:mo></mml:msup></mml:mrow></mml:msub></mml:mrow></mml:msub></mml:mrow></mml:mfrac><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mrow><mml:mo stretchy=\"false\">[</mml:mo><mml:mi>A</mml:mi><mml:mi>b</mml:mi><mml:mo stretchy=\"false\">]</mml:mo></mml:mrow><mml:mrow><mml:mi>e</mml:mi><mml:mi>q</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:mi>e</mml:mi><mml:mi>q</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mi>A</mml:mi><mml:mi>b</mml:mi><mml:mo>⋅</mml:mo><mml:msub><mml:mi>R</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:mi>e</mml:mi><mml:mi>q</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:mi>e</mml:mi><mml:mi>q</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>⋅</mml:mo><mml:mi>A</mml:mi><mml:mi>b</mml:mi><mml:mo>⋅</mml:mo><mml:msub><mml:mi>R</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:mi>e</mml:mi><mml:mi>q</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mi>A</mml:mi><mml:mi>b</mml:mi><mml:mo>⋅</mml:mo><mml:msub><mml:mi>R</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mrow><mml:mi>e</mml:mi></mml:msub></mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mi>A</mml:mi><mml:mi>b</mml:mi><mml:mo>⋅</mml:mo><mml:msub><mml:mi>R</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:mi>e</mml:mi><mml:mi>q</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>⋅</mml:mo><mml:mi>A</mml:mi><mml:mi>b</mml:mi><mml:mo>⋅</mml:mo><mml:msub><mml:mi>R</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:mi>e</mml:mi><mml:mi>q</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mo stretchy=\"false\">[</mml:mo><mml:mi>A</mml:mi><mml:mi>b</mml:mi><mml:mo stretchy=\"false\">]</mml:mo></mml:mrow><mml:mrow><mml:mi>e</mml:mi><mml:mi>q</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:mi>e</mml:mi><mml:mi>q</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mi>A</mml:mi><mml:mi>b</mml:mi><mml:mo>⋅</mml:mo><mml:msub><mml:mi>R</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:mi>e</mml:mi><mml:mi>q</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mrow><mml:mo>]</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:mi>e</mml:mi><mml:mi>q</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfrac><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:math>\n</disp-formula>", "<disp-formula id=\"FD9\">\n<label>(5)</label>\n<mml:math id=\"M34\" display=\"block\"><mml:mrow><mml:mfrac><mml:mrow><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>o</mml:mi><mml:mi>n</mml:mi><mml:mo>,</mml:mo><mml:msub><mml:mi>R</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo></mml:mo></mml:mrow></mml:msub><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>o</mml:mi><mml:mi>f</mml:mi><mml:mi>f</mml:mi><mml:mo>,</mml:mo><mml:msub><mml:mi>R</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>o</mml:mi><mml:mi>n</mml:mi><mml:mo>,</mml:mo><mml:msub><mml:mi>R</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mrow></mml:msub><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>o</mml:mi><mml:mi>f</mml:mi><mml:mi>f</mml:mi><mml:mo>,</mml:mo><mml:msub><mml:mi>R</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo></mml:mo></mml:mrow></mml:msub></mml:mrow></mml:mfrac><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>o</mml:mi><mml:mi>n</mml:mi><mml:mo>,</mml:mo><mml:msub><mml:mi>R</mml:mi><mml:mrow><mml:msup><mml:mn>2</mml:mn><mml:mo></mml:mo></mml:msup></mml:mrow></mml:msub></mml:mrow></mml:msub><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>o</mml:mi><mml:mi>f</mml:mi><mml:mi>f</mml:mi><mml:mo>,</mml:mo><mml:msub><mml:mi>R</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>o</mml:mi><mml:mi>n</mml:mi><mml:mo>,</mml:mo><mml:msub><mml:mi>R</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow></mml:msub><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>o</mml:mi><mml:mi>f</mml:mi><mml:mi>f</mml:mi><mml:mo>,</mml:mo><mml:msub><mml:mi>R</mml:mi><mml:mrow><mml:msup><mml:mn>2</mml:mn><mml:mo></mml:mo></mml:msup></mml:mrow></mml:msub></mml:mrow></mml:msub></mml:mrow></mml:mfrac></mml:mrow></mml:math>\n</disp-formula>", "<disp-formula id=\"FD10\">\n<label>(6)</label>\n<mml:math id=\"M35\" display=\"block\"><mml:mrow><mml:mfrac><mml:mrow><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>o</mml:mi><mml:mi>n</mml:mi><mml:mo>,</mml:mo><mml:msub><mml:mi>R</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo></mml:mo></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>o</mml:mi><mml:mi>n</mml:mi><mml:mo>,</mml:mo><mml:msub><mml:mi>R</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:mrow></mml:msub></mml:mrow></mml:mfrac><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>o</mml:mi><mml:mi>n</mml:mi><mml:mo>,</mml:mo><mml:msub><mml:mi>R</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mo></mml:mo></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>o</mml:mi><mml:mi>n</mml:mi><mml:mo>,</mml:mo><mml:msub><mml:mi>R</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow></mml:msub></mml:mrow></mml:mfrac></mml:mrow></mml:math>\n</disp-formula>", "<disp-formula id=\"FD11\">\n<label>(7)</label>\n<mml:math id=\"M36\" display=\"block\"><mml:mrow><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>o</mml:mi><mml:mi>n</mml:mi><mml:mo>,</mml:mo><mml:mn>6</mml:mn><mml:mi>R</mml:mi></mml:mrow></mml:msub><mml:mspace width=\"1pt\"/><mml:mo>=</mml:mo><mml:mspace width=\"1pt\"/><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>o</mml:mi><mml:mi>n</mml:mi><mml:mo>,</mml:mo><mml:mtext mathvariant=\"italic\">Toci</mml:mtext><mml:mo>−</mml:mo><mml:mn>6</mml:mn><mml:mi>R</mml:mi></mml:mrow></mml:msub><mml:mspace width=\"1pt\"/><mml:mo>=</mml:mo><mml:mspace width=\"1pt\"/><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>o</mml:mi><mml:mi>n</mml:mi><mml:mo>,</mml:mo><mml:mi>B</mml:mi><mml:mi>S</mml:mi><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:mn>6</mml:mn><mml:mi>R</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math>\n</disp-formula>", "<disp-formula id=\"FD12\">\n<label>(8)</label>\n<mml:math id=\"M37\" display=\"block\"><mml:mrow><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>o</mml:mi><mml:mi>n</mml:mi><mml:mo>,</mml:mo><mml:mn>8</mml:mn><mml:mi>R</mml:mi></mml:mrow></mml:msub><mml:mspace width=\"1pt\"/><mml:mo>=</mml:mo><mml:mspace width=\"1pt\"/><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>o</mml:mi><mml:mi>n</mml:mi><mml:mo>,</mml:mo><mml:mn>10</mml:mn><mml:mi>H</mml:mi><mml:mn>2</mml:mn><mml:mo>−</mml:mo><mml:mn>8</mml:mn><mml:mi>R</mml:mi></mml:mrow></mml:msub><mml:mspace width=\"1pt\"/><mml:mo>=</mml:mo><mml:mspace width=\"1pt\"/><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>o</mml:mi><mml:mi>n</mml:mi><mml:mo>,</mml:mo><mml:mi>B</mml:mi><mml:mi>S</mml:mi><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:mn>8</mml:mn><mml:mi>R</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math>\n</disp-formula>", "<disp-formula id=\"FD13\">\n<label>(9)</label>\n<mml:math id=\"M38\" display=\"block\"><mml:mrow><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>o</mml:mi><mml:mi>n</mml:mi><mml:mo>,</mml:mo><mml:mn>6</mml:mn><mml:msup><mml:mi>R</mml:mi><mml:mo></mml:mo></mml:msup></mml:mrow></mml:msub><mml:mspace width=\"1pt\"/><mml:mo>=</mml:mo><mml:mspace width=\"1pt\"/><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>o</mml:mi><mml:mi>n</mml:mi><mml:mo>,</mml:mo><mml:mtext mathvariant=\"italic\">Toci</mml:mtext><mml:mo>−</mml:mo><mml:mn>6</mml:mn><mml:msup><mml:mi>R</mml:mi><mml:mo></mml:mo></mml:msup></mml:mrow></mml:msub><mml:mspace width=\"1pt\"/><mml:mo>=</mml:mo><mml:mspace width=\"1pt\"/><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>o</mml:mi><mml:mi>n</mml:mi><mml:mo>,</mml:mo><mml:mi>B</mml:mi><mml:mi>S</mml:mi><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:mn>6</mml:mn><mml:msup><mml:mi>R</mml:mi><mml:mo></mml:mo></mml:msup></mml:mrow></mml:msub></mml:mrow></mml:math>\n</disp-formula>", "<disp-formula id=\"FD14\">\n<label>(10)</label>\n<mml:math id=\"M39\" 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"<inline-formula><mml:math id=\"M16\" display=\"inline\"><mml:mrow><mml:mi>n</mml:mi><mml:msup><mml:mi>M</mml:mi><mml:mrow><mml:mo>−</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msup><mml:msup><mml:mi>S</mml:mi><mml:mrow><mml:mo>−</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></inline-formula>", "<inline-formula>\n<mml:math id=\"M17\" display=\"inline\"><mml:mrow><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>o</mml:mi><mml:mi>f</mml:mi><mml:mi>f</mml:mi><mml:mo>,</mml:mo><mml:mn>6</mml:mn><mml:mi>R</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math>\n</inline-formula>", "<inline-formula>\n<mml:math id=\"M18\" display=\"inline\"><mml:mrow><mml:msup><mml:mi>S</mml:mi><mml:mrow><mml:mo>−</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math>\n</inline-formula>", "<inline-formula>\n<mml:math id=\"M19\" display=\"inline\"><mml:mrow><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mi>o</mml:mi><mml:mi>f</mml:mi><mml:mi>f</mml:mi><mml:mo>,</mml:mo><mml:mn>8</mml:mn><mml:mi>R</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math>\n</inline-formula>", "<inline-formula>\n<mml:math id=\"M20\" display=\"inline\"><mml:mrow><mml:msup><mml:mi>S</mml:mi><mml:mrow><mml:mo>−</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math>\n</inline-formula>", "<inline-formula><mml:math id=\"M21\" display=\"inline\"><mml:mrow><mml:mtext mathvariant=\"italic\">via</mml:mtext><mml:mspace width=\"1pt\"/><mml:msub><mml:mi>K</mml:mi><mml:mi>D</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mtext mathvariant=\"italic\">off</mml:mtext></mml:mrow></mml:msub><mml:mo>/</mml:mo><mml:msub><mml:mi>k</mml:mi><mml:mrow><mml:mtext mathvariant=\"italic\">on</mml:mtext></mml:mrow></mml:msub><mml:mo>.</mml:mo></mml:mrow></mml:math></inline-formula>", "<inline-formula>\n<mml:math id=\"M22\" display=\"inline\"><mml:mrow><mml:msub><mml:mi>K</mml:mi><mml:mrow><mml:mi>D</mml:mi><mml:mo>,</mml:mo><mml:mn>6</mml:mn><mml:mi>R</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math>\n</inline-formula>", "<inline-formula>\n<mml:math id=\"M23\" display=\"inline\"><mml:mrow><mml:msub><mml:mi>K</mml:mi><mml:mrow><mml:mi>D</mml:mi><mml:mo>,</mml:mo><mml:mn>8</mml:mn><mml:mi>R</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math>\n</inline-formula>", "<inline-formula>\n<mml:math id=\"M24\" display=\"inline\"><mml:mrow><mml:msub><mml:mi>K</mml:mi><mml:mrow><mml:mi>D</mml:mi><mml:mo>,</mml:mo><mml:mn>6</mml:mn><mml:msup><mml:mi>R</mml:mi><mml:mo></mml:mo></mml:msup></mml:mrow></mml:msub></mml:mrow></mml:math>\n</inline-formula>", "<inline-formula>\n<mml:math id=\"M25\" display=\"inline\"><mml:mrow><mml:msub><mml:mi>K</mml:mi><mml:mrow><mml:mi>D</mml:mi><mml:mo>,</mml:mo><mml:mn>8</mml:mn><mml:msup><mml:mi>R</mml:mi><mml:mo>*</mml:mo></mml:msup></mml:mrow></mml:msub></mml:mrow></mml:math>\n</inline-formula>" ]	[]	[]	[]	[]	[ "<supplementary-material id=\"SD1\" position=\"float\" content-type=\"local-data\"><label>Supplement 1</label></supplementary-material>" ]	[]	[ "<graphic xlink:href=\"nihpp-2023.12.18.570445v1-f0001\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-2023.12.18.570445v1-f0002\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-2023.12.18.570445v1-f0003\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-2023.12.18.570445v1-f0004\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-2023.12.18.570445v1-f0005\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-2023.12.18.570445v1-f0006\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-2023.12.18.570445v1-f0007\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-2023.12.18.570445v1-f0008\" position=\"float\"/>" ]	[ "<media xlink:href=\"media-1.pdf\" id=\"d64e3696\" position=\"anchor\"/>" ]	[{"label": ["4."], "surname": ["Petersen", "Flad", "Brandt"], "given-names": ["F", "HD", "E"], "article-title": ["Neutrophil-activating peptides NAP-2 and IL-8 bind to the same sites on neutrophils but interact in different ways. Discrepancies in binding affinities, receptor densities, and biologic effects"], "source": ["Journal of Immunology"], "comment": ["Internet", "Available from"], "year": ["1994"], "month": ["Mar"], "volume": ["152"], "issue": ["5"], "fpage": ["2467"], "lpage": ["78"], "ext-link": ["https://www.jimmunol.org/content/152/5/2467.short"]}, {"label": ["5."], "surname": ["Chuntharapai", "Kim"], "given-names": ["A", "KJ"], "article-title": ["Regulation of the expression of IL-8 receptor A/B by IL-8: possible functions of each receptor"], "source": ["Journal of Immunology"], "comment": ["Internet", "Available from"], "year": ["1995"], "month": ["Sep"], "volume": ["155"], "issue": ["5"], "fpage": ["2587"], "lpage": ["94"], "ext-link": ["https://www.jimmunol.org/content/155/5/2587.short"]}, {"label": ["12."], "surname": ["Jayatilaka", "Tyle", "Chen", "Kwak", "Ju", "Kim"], "given-names": ["H", "P", "JJ", "M", "J", "HJ"], "article-title": ["Synergistic IL-6 and IL-8 paracrine signalling pathway infers a strategy to inhibit tumour cell migration - 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PMC10769335	38187624	[ "<title>Introduction</title>", "<p id=\"P3\">Most neurons communicate with each other via both chemical and electrical forms of signaling. The relative timing between dopamine neurotransmitter release and action potentials of target neurons has been shown to regulate synaptic plasticity (##REF##31437453##Brzosko et al., 2019##; ##REF##11544526##Reynolds et al., 2001##; ##REF##29603470##Shindou et al., 2019##; ##REF##25258080##Yagishita et al., 2014##). Thus, measuring how chemical and electrical neural signals are coordinated during online behavior is central to studying brain function and the multi-modal mechanisms that shape how neural circuits are programmed to regulate behavior.</p>", "<p id=\"P4\">Nevertheless, most of our understanding of brain function comes from measurements of either chemical or electrical signals, but not both. Such single mode measurements preclude the ability to look at how molecules, such as dopamine neurotransmitters, modulate nearby neuronal spike activity, and vice-versa—how neuronal activity influences extracellular molecular signaling. Furthermore, there is a critical gap in our knowledge of how such signals are coordinated to mediate behavior. Thus, dual electrical and chemical measurements are imperative to dissect these interactions, especially in awake behaving animals.</p>", "<p id=\"P5\">Such dual recording may be achieved by combining standard electrophysiology (EPhys) with molecular recording techniques. Molecular measurements may involve electrochemical or optical methods that provide the spatiotemporal resolution to capture the millisecond dynamics of neurotransmitter release and clearance within microscale domains (##REF##21939738##Rice et al., 2011##). Optical methods have been introduced recently, and advanced immensely in use over the past few years (##REF##29853555##Patriarchi et al., 2018##; ##REF##30007419##Sun et al., 2018##). These methods require genetic modification of neurons to express synthetic fluorescent receptors (e.g., dLight and GRABDA), and/or introduction of exogenous compounds (e.g., fluorescent false neurotransmitters) (##REF##19423778##Gubernator et al., 2009##; ##REF##26900925##Pereira et al., 2016##) or cells (e.g., CNiFER) (##REF##20010818##Nguyen et al., 2010##). Fluorescent approaches demonstrate remarkable specificity for a variety of neurotransmitters and neuromodulators such as dopamine, serotonin, acetylcholine, and many more compounds (##REF##32989318##Jing et al., 2020##; ##REF##33333022##Unger et al., 2020##). Nevertheless, application in nonhuman primates has been limited, and such genetic modification is prohibited in humans due to ethical reasons.</p>", "<p id=\"P6\">This work uses electrochemical (EChem) methods, specifically, fast-scan cyclic voltammetry (FSCV), which has been established over several decades (##REF##20037591##Clark et al., 2010##; ##REF##8412305##Kawagoe et al., 1993##) to record current generated through reduction and oxidation (i.e., redox) of dopamine and other chemical compounds. Voltage is scanned at the implanted electrode to generate this redox current at analyte-specific voltages, and this current is proportional to the concentration of the analyte. Carbon fiber (CF) electrodes (CFEs) have been the mainstay for such sensors given their high electron transfer, biocompatibility, and adsorptive properties (##REF##18557655##McCreery, 2008##). Other innovative materials and structures have also shown great potential for enhancing measurement sensitivity and selectivity (##REF##33450612##Cuniberto et al., 2021##; ##REF##23941323##Schmidt et al., 2013##). Chemical specificity is determined by the redox voltages, and these voltages are also dependent on the sensor material and scan parameters. Parameters optimal for selective and sensitive dopamine detection have been established over several decades (##REF##21966586##Keithley and Wightman, 2011##; ##REF##28127962##Rodeberg et al., 2017##). These electrochemical techniques are also capable of providing readouts of multiple chemical entities by virtue of each compound’s distinct redox voltages (##UREF##1##Calhoun et al., 2018##; ##REF##32955048##E. Dunham and Jill Venton, 2020##; ##REF##26900429##Nguyen and Venton, 2014##). However, use of standard scan parameters (400 V/s) and materials (CF) does not allow distinction of molecules with similar chemical structures (e.g., dopamine and norepinephrine). Another limitation of FSCV is the inability to directly record non-electroactive compounds (e.g., acetylcholine or glutamate). On the other hand, there are ways to indirectly measure these compounds using enzyme coatings that convert these to current-generating H<sub>2</sub>O<sub>2</sub> (##REF##27722568##Asri et al., 2016##; ##REF##37962541##Kimble et al., 2023##). Finally, sensor “fouling” has been a challenge as it relates to the sensitivity degradation of the implanted sensor, and is caused by the semi-permanent adsorption of redox-reaction byproducts or other molecules in the parenchyma (but, see (##REF##32955048##E. Dunham and Jill Venton, 2020##) for methods to address such issues). Nevertheless, FSCV has demonstrated robust performance for chronic measurements in rodents (##REF##20037591##Clark et al., 2010##; ##REF##29809203##Schwerdt et al., 2018##) and monkeys (##REF##32978148##Schwerdt et al., 2020##, ##REF##29158415##2017##), and has even been applied in humans intra-operatively (##REF##26598677##Kishida et al., 2016##). Thus, the realization of its potential for clinical translation is imminent.</p>", "<p id=\"P7\">Concurrent measurement of molecular and electrical neuronal activity is performed by combining EChem and EPhys, respectively, in this work, and will be referred to hereon as ECP (i.e., electrochemical and electrophysiological) recording for simplicity (##FIG##1##Fig. 1##). ECP measurements are not straightforward due to the interference caused by FSCV (##FIG##1##Fig. 1B##). FSCV operates by applying a triangular time-varying voltage (−0.4 to 1.3 V) to the implanted sensor to induce voltage-dependent redox of molecules at its surface; This is usually done at a sampling rate of 10 Hz (i.e., scans are applied every 100 ms). These applied voltages may be directly picked up by the EPhys system in the form of voltage artifacts. Removing these artifacts from the analysis is imperative to compute accurate local field potential (LFP) and spike information in the EPhys recording. Scan artifacts can be erroneously labeled as spikes using standard thresholding and clustering-based spike sorting methods (##REF##15680687##Schmitzer-Torbert et al., 2005##; ##REF##14736863##Schmitzer-Torbert and Redish, 2004##). Consequent analytical measures, like spike firing rates, are then susceptible to error and misrepresentation of the actual neural environment. The level of interference depends on how these two measurements are combined; ECP may be performed on the same sensor, or on separate sensors inside the brain.</p>", "<p id=\"P8\">Recording EPhys and FSCV simultaneously from the same sensor usually requires actively switching between the recording modalities so that the applied voltage from FSCV does not overly saturate and/or damage the input amplifiers on the EPhys system. This temporal multiplexing is possible because standard FSCV parameters optimized for dopamine detection use only an 8.5 ms period of the 100 ms sampling interval for applying the redox-generating voltage scan; the remaining 91.5 ms interval is used to hold a negative potential (e.g., −0.4 V) to attract positively charged molecules such as dopamine to the CF (##REF##11140768##Bath et al., 2000##). However, applying a holding potential would effectively short the EPhys input in between scans when applied at the same sensor.</p>", "<p id=\"P9\">Instead, FSCV and EPhys may be applied at separate sensors; this decoupled ECP configuration permits applying a holding potential, and, in addition, may remove the need for supplementary circuits to switch between EPhys and FSCV operations since the applied voltage is attenuated as a function of distance to other implanted sensors. Nevertheless, the FSCV voltage scanning artifacts will still be transmitted to the EPhys recording sensors through the conductive tissue, with the voltage attenuating as a function of distance between the sensors.</p>", "<p id=\"P10\">Here, we developed a temporal interpolation algorithm to extract neuronal spike activity from EPhys recordings with concurrent FSCV, without the need for additional hardware. We leverage the periodicity of the FSCV scans to detect the timing of these signals and interpolate these artifacts away in the EPhys recording. The algorithm was extended for use to remove 60 Hz line noise along with its harmonics. Removing these artifacts is necessary as they frequently are indistinguishable from physiological spikes and may be falsely identified as such. We characterized the recovery rate of our spike extraction techniques by applying simulated FSCV scan artifacts onto isolated EPhys recordings, demonstrating the ability to extract a large percentage (84.5%) of the original spikes. These methods were further validated in task-performing monkeys, where we were able to capture behaviorally-relevant computations of synchronous dopamine and spike activity from functionally diverse neurons in the striatum. In summary, the techniques described here introduce a new way to decipher the interactive relationship between neuromodulator signaling and surrounding neuronal activity and how these coordinate activities influence and/or are shaped by ongoing behavior.</p>" ]	[ "<title>Materials and Methods</title>", "<title>Animal procedures</title>", "<p id=\"P11\">Measurements and experimental methods used in this study were obtained from one female Rhesus monkey (approximately 8.5 years old and weighing approximately 10 kg, at time of recordings). All experimental procedures were approved by the Committee on Animal Care of the Massachusetts Institute of Technology and are described in a previous report (##REF##32978148##Schwerdt et al., 2020##).</p>", "<title>Behavioral task</title>", "<p id=\"P12\">The monkey performed a visually guided reward-biased task as described previously (##REF##32978148##Schwerdt et al., 2020##). Briefly, each trial consisted of an initial central cue (C) that the animal had to saccade to and fixate on for 1.6 s. A peripheral target (T) cue appeared on the left or right of the screen after the C extinguished. The animal had to saccade and fixate on the T visual cue for 4 s to receive a small or large reward (RW), which consisted of 0.1 – 0.3 mL or 1.5 – 2.8 mL of liquid-food, respectively. The association between target direction (left or right) and reward size (small or large) was switched every 20–30 trials.</p>", "<title>ECP recording setup</title>", "<p id=\"P13\">Both EPhys and EChem-FSCV measurements were made from CF sensors, fabricated following previously published methods (##REF##29158415##Schwerdt et al., 2017##), implanted in the monkey striatum (##FIG##1##Fig. 1##). The setup is described in a previous publication (##REF##32978148##Schwerdt et al., 2020##) and a brief summary is provided here. CF electrodes consisted of either conventional silica tube-threaded electrodes (silica-CF) or parylene-encapsulated electrodes (py-CF). CF sensors were mounted onto microdrives atop a custom-designed chamber (Gray Matter Research) that was affixed to the monkey’s skull. The sensors were inserted into the brain through guide tubes (Connecticut Hypodermics, 27G XTW) and lowered to the striatum (i.e., caudate nucleus, CN, and putamen). Sensors were individually connected to either standard electrophysiological (EPhys) recording system (Neuralynx, HS-32) or an FSCV system (obtained from S.B. Ng-Evans at University of Washington) as selected on a day-by-day basis. EPhys signals were referenced to multiple tied stainless-steel wires inserted in the tissue above the dura mater (A-M Systems, 790700). Ag/AgCl electrodes were implanted in the epidural tissue and/or in a white matter brain region to serve as the FSCV reference.</p>", "<p id=\"P14\">Dopamine concentration changes were recorded from implanted sensors using a 4 channel FSCV system (from S. B. Ng-Evans at University of Washington). This consisted of a transimpedance amplifier headstage to convert and amplify electrochemical current to voltage, and a computer to control the applied voltage scans and record and store current. This system generated a triangular voltage ramping from −0.4 V up to 1.3 V and back to −0.4 V, with a scan rate of 400 V/s. This was applied at a sampling frequency of 10 Hz to the connected electrodes. A holding potential of −0.4V was maintained at the electrode between scans. Background-subtracted color plots were generated by plotting the relative current change as color, applied scan voltage on the y-axis (i.e., −0.4 to 1.3 to −0.4 V), and time (i.e., each scan at 100 ms intervals) on the x-axis.</p>", "<p id=\"P15\">EPhys recording was performed using a standard electrophysiology system (Neuralynx, HS-32). The recording settings were configured as follows: input range of ± 1 mV, sampling rate of 30 kHz, and band pass filter with a passband at 0.1 – 7,500 Hz. This system received the timestamps for behavioral task events as generated from the VCortex behavioral system. A digital messaging system (Neuralynx, NetCom Router) allowed transmitting of trial-start events from the EPhys system to the FSCV system to provide shared timestamps between the FSCV and EPhys systems.</p>", "<title>Dopamine concentration estimation</title>", "<p id=\"P16\">Dopamine concentration changes ([ΔDA]) were approximated using principal component analysis (PCA), as previously described (##REF##29158415##Schwerdt et al., 2017##). Briefly, each FSCV scan produces a cyclic voltammogram (CV) (i.e., current vs. voltage plot), which is background subtracted to remove the larger current contributions associated mainly with nonfaradaic processes and to distinguish the smaller current changes related to chemical redox. The background subtraction usually occurs at an arbitrary reference time (i.e., alignment event such as the T cue) to provide a uniform reference for all task-modulated signals on each trial. These background-subtracted currents are projected onto the principal components computed from standards of dopamine, pH, and movement artifact as previously established (##REF##29158415##Schwerdt et al., 2017##). CVs that produced an excessive variance (<italic toggle=\"yes\">Q</italic>) above a tolerance level (<italic toggle=\"yes\">Q</italic><sub><italic toggle=\"yes\">α</italic></sub>) could not be accounted as a physiological signal and were nulled automatically (assigned NaN values in MATLAB). Signals were also nulled where CVs were correlated to movement artifact standards (<italic toggle=\"yes\">r</italic> > 0.8). These strict procedures ensure that at least 90% of the signals are identified as dopamine, with a false negative rate of less than 30%, and 0% false positives.</p>", "<title>Spike sorting</title>", "<p id=\"P17\">Spike sorting was performed manually in commercial software (Plexon, Offline Sorter) following standard protocols (##REF##22170970##Feingold et al., 2012##). Raw EPhys data were first high pass filtered using a Butterworth filter with a cut-off frequency of 250 Hz (4-pole, forward only). A negative threshold was set to identify negative-going crossings of the action potential waveforms. Waveforms were extracted with a length of 1.6 ms (48 samples) using a prethreshold period of 0.267 ms (8 samples). These waveforms were then visualized in terms of their energy, non-linear energy, and their projections onto principal components (PC) space (e.g., PC1 vs PC2). Waveforms were first invalidated based on the prominence of energy and non-linear energy features, which enhances visualization of artifact-like signals such as large transient glitches created by electrostatic discharge as well as from the reward delivery peristaltic pump. Waveforms were then manually invalidated in the PC space if they looked like the artifacts mentioned previously and did not resemble physiological spike signals (e.g., high-frequency changes in voltage). PCs were recalculated after invalidating waveforms to distinguish potential clusters based on the variances calculated for valid waveforms. Clusters were manually drawn on the PC space when a distinct boundary was observed between the projected points. These clustered waveforms were then exported for plotting average waveforms and histograms in MATLAB (Mathworks, 2023b). Interspike interval (ISI) histograms were generated using 1 or 2 ms bin widths to visualize the relative distribution of spike firing intervals, which is used as a parameter to distinguish different cell-types (##REF##8207500##Aosaki et al., 1994##).</p>", "<title>Temporal interpolation methods to extract spike data</title>", "<p id=\"P18\">We developed a simple automated algorithm to reliably extract extracellular action potentials by performing linear interpolation over the FSCV artifacts embedded in the EPhys waveforms in the time-domain. These methods were implemented in MATLAB (Mathworks, 2023b). The artifact shape and amplitude depends on a number of factors, including the distance between the FSCV recording electrodes and the EPhys electrodes, as well as the characteristics of the EPhys electrodes (e.g., impedance, and other nonlinear electrode-tissue interface properties) (##UREF##2##Nag et al., 2015##). A flowchart of the algorithm is illustrated in ##FIG##2##Fig. 2## and details are as follows. 1) The first step in the algorithm is to get a first approximation of the timings of the artifacts. Signals from multiple EPhys electrodes are averaged to enhance the artifact peaks that are coherent across channels, and to reduce background spike and LFP fluctuations that may inadvertently trigger threshold crossings used in subsequent steps (##FIG##3##Fig. 3A## and ##FIG##3##B##). 2) The averaged signal is bandpass filtered (BPF) at 10 – 100 Hz, and then its absolute value is taken. This procedure further enhances the artifact peaks over surrounding signals. 3) The artifact peak timings are identified by first finding positive-going crossings over a threshold, a multiple (i.e., 1.75) of the standard deviation of the previously calculated signal. The local peak directly following each positive crossing is identified and stored. 4) Only peaks demonstrating periodicity according to the expected FSCV scan frequency (10 Hz) are retained to prevent unwanted removal of physiological signals. This is done by checking the periodicity of each peak relative to its prior peak. Any non-periodic peak is removed from the stored variable containing a list of threshold crossing peaks. Furthermore, missing peaks are added based on the periodicity of the peaks captured before or after the window in which peaks were not detected. This ensures that artifacts that exist below the initial detection threshold are captured and resolved to minimize erroneous physiological data. 5) Finally, linear interpolation is performed on the signal around each identified peak with a window of −5 to 7 ms. This window was empirically determined through incremental (0.5 ms) increases beyond the defined 8.5 ms scan window applied in FSCV. The wider window accounts for recovery time of the amplifier as well as low pass filtering and resulting broadening of the signal through the tissue. This interpolation removes the artifact, preventing it from being detected falsely as a spike during subsequent high pass filtering and thresholding steps used for spike sorting.</p>", "<p id=\"P19\">This algorithm was further extended to remove artifacts caused by line noise at 60 Hz and its harmonic frequencies (e.g., 120 Hz) (##FIG##0##Extended Data Fig. 3–1##). Such line noise interference was frequently present in our experimental setup, most likely due to the shared ground between EPhys and EChem systems. Removing this interference only required a simple modification to the previous algorithm, where the second filtering step was replaced by a high pass filter (300 Hz cutoff) as this accounted for the higher-frequency content of the noise, and that the threshold computation was performed by taking a multiple (i.e., 8) of the average of the filtered signal rather than its standard deviation.</p>", "<p id=\"P20\">These interpolation algorithms do not require a clock input to provide timings of the FSCV scans and instead relies on the periodicity of the signal and its appearance on multiple EPhys recording electrodes. The periodicity of the signal is a key input into the algorithm as simple thresholding methods used to remove large glitches or artifacts may inadvertently remove physiological signals. Template methods (##REF##31705936##Banaie Boroujeni et al., 2020##; ##REF##11772439##Hashimoto et al., 2002##; ##UREF##2##Nag et al., 2015##; ##REF##29265009##O’Shea and Shenoy, 2018##; ##REF##21745499##Wichmann and Devergnas, 2011##) frequently used for removing artifacts caused by electrical stimulation are not able to account for the range of variability in the shape of the FSCV artifact, which depends on the distance between the electrodes and the electrode properties. These algorithms are available at <ext-link xlink:href=\"http://github.com\" ext-link-type=\"uri\">github.com</ext-link> (<ext-link xlink:href=\"https://github.com/hschwerdt/extractFSCVspikes\" ext-link-type=\"uri\">https://github.com/hschwerdt/extractFSCVspikes</ext-link>).</p>" ]	[ "<title>Results</title>", "<title>Validation of spike extraction methods</title>", "<p id=\"P21\">Our algorithm was validated by adding synthetic FSCV artifacts onto EPhys recordings made without FSCV (i.e., EPhys-only) (##FIG##4##Fig. 4##). FSCV artifacts were emulated following methods previously established for validating LFP extraction algorithms in similar ECP measurement configurations (##REF##32978148##Schwerdt et al., 2020##). Three different types of artifacts were simulated: resistive (R), resistive-capacitive (RC), and rail (i.e., saturating). These differences reflect characteristics of the artifact duration and amplitude observed in EPhys recordings made during concurrent FSCV, and arise due to differences in distance between electrodes, as well as the electrode properties. Spike sorting was done on the EPhys-only recording, before adding artifacts, and then again after adding artifacts and implementing our interpolation algorithms. Spike recovery rate was defined as the percent of physiological spikes retained after interpolating the artifacts.</p>", "<p id=\"P22\">Simulation of each of the three types of artifacts was implemented individually on 5 recorded channels from 3 different sessions and the results are shown in ##TAB##0##Table 1##. We calculated the spike recovery rate for each type of artifact being interpolated with our algorithm. We found that the spike recovery rate was 84.5% as averaged for all artifact types. This is equivalent to a loss of 15.5%, which was a reasonable result given that the 8.5 ms FSCV scans make up 8.5% of the recording, and our interpolation occurs over a wider 12 ms and therefore 12% of the recording. The average rates for interpolating each type of artifact were 88%, 85.8%, and 79.6% for the R, RC, and rail type artifacts. Spike recovery rates were the worst for interpolating rail-type artifacts as expected given their broader width that encompasses the amplifier recovery time after its input saturates. This is important to consider when designing higher-density configurations of CF sensor arrays as minimizing the distance between sensors will increase the artifact amplitudes. Code and data used for simulating artifacts and validation are available at zenodo (<ext-link xlink:href=\"10.5281/zenodo.10396372\" ext-link-type=\"doi\">10.5281/zenodo.10396372</ext-link>).</p>", "<title>Measurements of cell selective spike activity</title>", "<p id=\"P23\">Extracted spike activity was analyzed as measured from implanted CF sensors in the CN and putamen during concurrent FSCV in a task-performing monkey (##FIG##5##Figs. 5## and ##FIG##6##6##). Striatal units have been shown to display specific waveform shapes and firing characteristics depending on the cell type classification (##REF##12473069##Apicella, 2002##; ##REF##2723720##Hikosaka et al., 1989##; ##REF##20547134##Thorn et al., 2010##; ##REF##15071097##Yamada et al., 2004##). Our units displayed distinct characteristics that largely resembled either putative medium spiny neurons (MSNs) or tonically active neurons (TANs), as classified in prior work. TANs display a longer after-hyperpolarization (i.e., broader shape) (##FIG##6##Fig. 6E##) in comparison to MSNs (##FIG##5##Fig. 5B##) (##REF##12473069##Apicella, 2002##; ##REF##2723720##Hikosaka et al., 1989##). Furthermore, MSNs are known to fire scarcely (< 1 Hz) and increase sharply (i.e., burst) in response to relevant behavioral events, which is noticed in the peak spike counts amongst the low interspike intervals (ISIs) in the plotted ISI histograms (##FIG##5##Fig. 5B## and ##FIG##6##Fig. 6B##). On the other hand, TANs maintain spontaneous firing rates of 2 – 12 Hz and show transient pauses in response to a variety of stimuli or events (##REF##7608768##Aosaki et al., 1995##, ##REF##8207500##1994##; ##REF##12473069##Apicella, 2002##; ##REF##2723720##Hikosaka et al., 1989##). TANs may also display up to two peaks in their ISI histograms (##REF##8207500##Aosaki et al., 1994##), which was also observed in our unit (##FIG##6##Fig. 6E##). These cell type distinctions may also be perceived in the spike rate histograms plotted in association with behavioral events (##FIG##5##Fig. 5C## and ##FIG##6##Fig. 6A## and ##FIG##6##D##), where the average spontaneous firing rates outside of trial bounds (i.e., before the central start cue and after the outcome) are higher for putative TANs (##FIG##6##Fig. 6D##) than for MSNs (##FIG##5##Fig. 5C## and ##FIG##6##Fig. 6A##). The event-related discharges and pauses of putative MSNs and TANs, respectively, may be observed in these plots. These results collectively demonstrate our ability to resolve cell-type specific spike activity as measured during concurrent FSCV electrochemical recording.</p>", "<title>Behaviorally relevant measurements of dopamine and spike co-activity</title>", "<p id=\"P24\">We analyzed how synchronously recorded dopamine and spike activity were modulated by behavioral task events related to reward size, eye movement direction, and visual cues. Multimodal measurements were made in a monkey performing a task where eye movements were made to left or right targets to receive liquid-food rewards. The size of the reward (i.e., large or small) depended on the target location (i.e., left or right) and this was switched every block to counterbalance reward size and movement direction variables. More details of the task are described above in “Behavioral task”.</p>", "<p id=\"P25\">Spike activity of a putative MSN recorded in the CN (c34) was shown to increase significantly in response to the appearance of the initial central start cue. This neuron also showed significant modulation by the size (large or small) of the upcoming reward (##FIG##5##Fig. 5C##). Such reward-size modulation is similar to that observed in prior experiments (##REF##12611937##Cromwell and Schultz, 2003##; ##REF##14523067##Kawagoe et al., 2004##). These reward-size activities were maintained for a sustained period throughout the period from the target cue onset to the reward delivery, possibly reflecting ongoing functions related to invigoration (##REF##12611937##Cromwell and Schultz, 2003##). Furthermore, pre-cue anticipatory activity is observed in this neuron, also seen in previous work (##REF##14523067##Kawagoe et al., 2004##), and has been linked to facilitating subsequent movements. We found that dopamine was also modulated by reward size anticipation in a neighboring site (c66) in the CN, but not in our putamen site (p15) (##FIG##5##Fig. 5D##). Dopamine signals were not modulated by the initial central cue in either site.</p>", "<p id=\"P26\">In another CN site (c33) and session, a putative MSN’s spike activity was found to be target direction sensitive, displaying higher firing rates during gazes to right (ipsilateral) targets in comparison to left (##FIG##6##Fig. 6A##). Unlike the previous unit, this neuron was not modulated by reward size. Such spatial selectivity has been observed of striatal units recorded in similar eye movement tasks (##REF##14523067##Kawagoe et al., 2004##). Synchronously recorded dopamine signals, on the other hand, were modulated by both reward size and target direction (##FIG##6##Fig. 6C##). However, dopamine was slightly higher for contralateral targets.</p>", "<p id=\"P27\">A putative TAN recorded in the putamen (p23), also in a separate session, displayed transient decreases in spike firing in response to displayed visual cues (i.e., initial central cue and peripheral target) followed by a rebound increase, replicating event-related pause activity frequently observed in TANs (##FIG##6##Fig. 6D##) (##REF##7608768##Aosaki et al., 1995##, ##REF##8207500##1994##; ##REF##12473069##Apicella, 2002##; ##REF##2723720##Hikosaka et al., 1989##). The neuron did not discriminate between reward size or movement direction. These patterns of observations are in line with prior work (##REF##12473069##Apicella, 2002##; ##REF##2723720##Hikosaka et al., 1989##; ##REF##20547134##Thorn et al., 2010##; ##REF##15071097##Yamada et al., 2004##), which have largely attributed these signals to representing the motivational value or arousing aspects of external stimuli. Striatal dopamine in the putamen and CN was modulated by reward and target cue direction (##FIG##6##Fig. 6F##), similar to our previous examples and other reports (##REF##32978148##Schwerdt et al., 2020##).</p>" ]	[ "<title>Discussion</title>", "<p id=\"P28\">Methods for recording and analyzing extracellular action potentials as measured concomitantly with FSCV-based dopamine signals were developed and validated in this study. Minimal additional hardware was required beyond standard EPhys and FSCV instrumentation to develop our ECP system. Spike extraction was carried out offline using simple custom-made temporal interpolation algorithms. These algorithms leveraged the periodicity of the FSCV voltage scans to remove the interfering FSCV voltage scan artifacts off the EPhys recordings. We validated the extraction technique using artificial artifacts to ensure that spikes were retained with high fidelity. ECP recording was performed on CF sensors implanted in the striatum of a monkey to uncover the co-active dopamine and spike signals underlying reward and movement behaviors. We further demonstrated the ability to distinguish different neuronal cell-types as well as behavioral functions of our recorded units.</p>", "<p id=\"P29\">Spike recovery rates of 84.5% were achieved using the temporal interpolation and spike extraction techniques developed in this study. This high recovery rate allowed us to distinguish behavioral correlates of multiple identified neural units, and to differentiate dopamine and neuronal functions. We found that unit activity and dopamine displayed very different patterns of signaling in response to behavioral events in our task. Striatal neurons are known to represent a multitude of parameters related to conflict decision-making (##UREF##0##Amemori et al., 2020##), learning (##REF##26291166##Desrochers et al., 2015##), and other behavioral functions (##REF##12473069##Apicella, 2002##; ##REF##2723720##Hikosaka et al., 1989##; ##REF##20547134##Thorn et al., 2010##; ##REF##15071097##Yamada et al., 2004##) (##REF##7608768##Aosaki et al., 1995##, ##REF##8207500##1994##; ##REF##12473069##Apicella, 2002##; ##REF##2723720##Hikosaka et al., 1989##). On the other hand, dopamine has been largely attributed to a role in reward valuation and prediction error signaling (##REF##9054347##Schultz et al., 1997##). Only recently has dopamine been shown to also provide a prolific representation of motor and sensory variables, outside of simple reward variables (##REF##36653450##Coddington et al., 2023##; ##REF##30177795##Menegas et al., 2018##; ##REF##32978148##Schwerdt et al., 2020##). A remaining question is how dopamine influences the activity of nearby neurons (##REF##36635111##Sippy and Tritsch, 2023##), such as in the form of plasticity (##REF##31437453##Brzosko et al., 2019##; ##REF##29603470##Shindou et al., 2019##), and how these interactions modify or are modified by behavior. The reverse of this, understanding how neuronal activity influences dopamine release (##REF##22794260##Threlfell et al., 2012##), also remains an unresolved question that may be addressed through multi-modal measurements such as those demonstrated in this work. Nevertheless, standard trial-averaged computations, as used here, may provide limited insight of such potential interactions. Furthermore, measurements of dopamine and neuronal activity should be performed at local sites given dopamine’s spatially heterogenous operations (##REF##33861952##Hamid et al., 2021##; ##REF##32978148##Schwerdt et al., 2020##). Higher recovery rates may be possible for non-saturating artifacts (i.e., R and RC type) using techniques that may potentially be applied to subtract out the artifact and recover spikes during the artifact (##REF##31705936##Banaie Boroujeni et al., 2020##; ##REF##11772439##Hashimoto et al., 2002##; ##UREF##2##Nag et al., 2015##; ##REF##29265009##O’Shea and Shenoy, 2018##; ##REF##21745499##Wichmann and Devergnas, 2011##).</p>", "<p id=\"P30\">One of the limitations of our current configuration is that dopamine and neuronal signals are measured from separate, and distal (> 1 mm), sensors. Single-sensor systems have been previously developed to combine FSCV transimpedance and EPhys voltage amplifiers with active switching circuitry to measure chemical and voltage signals from the same sensor and site.(##REF##21806203##Takmakov et al., 2011##). These have been applied in rodents to successfully measure electrical-stimulation evoked dopamine and spike activity (##REF##16380429##Cheer et al., 2005##). Nevertheless, as described in the “<xref rid=\"S1\" ref-type=\"sec\">Introduction</xref>”, a single-sensor configuration prevents a negative hold potential from being applied in between applied voltage scans, significantly restricting the sensitivity to measure dopamine and other positively charged molecules (##REF##11140768##Bath et al., 2000##). As far as we are aware, measurements of endogenous (i.e., not electrically stimulated) neurochemical signaling with this single-sensor configuration have not been demonstrated. On the other hand, separating the sensors for FSCV and EPhys enabled concurrent recording of naturally-occurring dopamine and voltage fluctuations during behavior in rodents (##REF##28211999##Parent et al., 2017##) and monkeys (##REF##32978148##Schwerdt et al., 2020##). This decoupled ECP configuration was therefore used in this work.</p>", "<p id=\"P31\">So far, two variations of a decoupled ECP system have been reported, as far as we are aware (##REF##28211999##Parent et al., 2017##; ##REF##32978148##Schwerdt et al., 2020##). The first system attempted to address or reduce the effects of the FSCV artifacts through several hardware and software modifications. This would allow successful measurements of stimulation and pharmacologically evoked dopamine signals in awake and mobile rodents. A relay circuit was used to isolate FSCV scan voltages from the EPhys recording system. In principle, this would be helpful to prevent large voltages from saturating the EPhys input amplifiers when the electrodes are close to each other. Nevertheless, the relay induced larger artifacts than FSCV scan voltages. A timing signal for the scan voltages was sent to both FSCV and EPhys systems, which allowed readily extracting EPhys recorded signals during the window in between the known scan periods, or interpolating away these periods. An additional window of interpolation (4.5 ms) was added around the scan period, similar to the current work, most likely also to remove the effects of capacitive discharge and amplifier recovery time. Furthermore, a lower scan frequency (5 Hz) was used to produce a wider artifact-free window in which lower frequency LFPs could be extracted. However, this limits the temporal resolution of the dopamine measurements. Similar to our current work, the second system utilized the standard 10 Hz scan frequency to maintain a higher temporal resolution to capture the fast millisecond dynamics of dopamine release and clearance, and did not require any clocked input for transmitting timings of the FSCV scans, or other specialized hardware (i.e., relay) (##REF##32978148##Schwerdt et al., 2020##). EPhys and FSCV recordings were synchronized through shared behavioral event codes. This system applied a custom-made algorithm to interpolate away these artifacts in the frequency domain, allowing reliable extrapolation of a broad frequency range (0.1 Hz – 1 kHz) of LFPs with a high correlation to original waveforms (R ~ 0.99), based on simulated artifacts. However, these algorithms were not capable of reliably extracting extracellular action potentials (i.e., spikes or units) due to the higher frequency content of these signals (up to ~8 kHz) (##REF##8985880##Fee et al., 1996##). Thus, in this work, time-domain interpolation is used instead of the frequency-domain to preserve the broad frequency content of the spike waveforms, which is imperative to identify individual units via standard spike-sorting methods.</p>", "<p id=\"P32\">Another potential advantage of our ECP system is the ability to record both unit activity and dopamine signals from the same CF sensor, albeit at different times. This would allow comparisons between spike and dopamine activity from the same site, as measured from different recording sessions. This could be useful for making associations between dopamine and neuronal activity during well-maintained and reproducible behaviors across sessions. Nevertheless, ideally, such measurements would occur at the same time in order to infer temporal correlations between recorded multi-modal signals. Measurements from juxtaposed electrode contacts may provide the needed focal site-specific metrics of interacting signals.</p>", "<p id=\"P33\">In summary, we developed a simple system combining standard EPhys and FSCV instrumentation for synchronous measurements of neuronal spike and dopamine signaling for use in nonhuman primates. The system could easily be adopted for use in other species, such as rodents (##REF##28211999##Parent et al., 2017##) and humans (##REF##26598677##Kishida et al., 2016##). Combinatorial methods such as those described here are needed in behavioral experiments to help resolve important questions related to the physiological mechanisms of plasticity and the interactions between neuromodulators and target neurons that regulate ongoing behavior.</p>" ]	[]	[ "<p id=\"P1\"><bold>Author Contributions</bold>: H.N.S. and U.A. designed and validated methods. H.N.S. performed <italic toggle=\"yes\">in vivo</italic> experiments. H.N.S, U.A., J.C., and R.M. analyzed data. H.N.S., D.J.G., and A.M.G. guided methods and experiments. H.N.S., U.A., and J.C. wrote manuscript with comments from all other authors.</p>", "<p id=\"P2\">Measuring the dynamic relationship between neuromodulators, such as dopamine, and neuronal action potentials is imperative to understand how these fundamental modes of neural signaling interact to mediate behavior. Here, we developed methods to measure concurrently dopamine and extracellular action potentials (i.e., spikes) and applied these in a monkey performing a behavioral task. Standard fast-scan cyclic voltammetric (FSCV) electrochemical (EChem) and electrophysiological (EPhys) recording systems are combined and used to collect spike and dopamine signals, respectively, from an array of carbon fiber (CF) sensors implanted in the monkey striatum. FSCV requires the application of small voltages at the implanted sensors to measure redox currents generated from target molecules, such as dopamine. These applied voltages create artifacts at neighboring EPhys-measurement sensors, producing signals that may falsely be classified as physiological spikes. Therefore, simple automated temporal interpolation algorithms were designed to remove these artifacts and enable accurate spike extraction. We validated these methods using simulated artifacts and demonstrated an average spike recovery rate of 84.5%. This spike extraction was performed on data collected from concurrent EChem and EPhys recordings made in a task-performing monkey to discriminate cell-type specific striatal units. These identified units were shown to correlate to specific behavioral task parameters related to reward size and eye-movement direction. Synchronous measures of spike and dopamine signals displayed contrasting relations to the behavioral task parameters, as taken from our small set of representative data, suggesting a complex relationship between these two modes of neural signaling. Future application of our methods will help advance our understanding of the interactions between neuromodulator signaling and neuronal activity, to elucidate more detailed mechanisms of neural circuitry and plasticity mediating behaviors in health and in disease.</p>" ]	[ "<title>Extended Data</title>" ]	[ "<title>Acknowledgment:</title>", "<p id=\"P35\">This work was supported by NIH NINDS (R00 NS107639 to H.N.S.), the Michael J. Fox Foundation for Parkinson’s Research (MJFF) and the Aligning Science Across Parkinson’s (ASAP) initiative (ASAP-020-519 to H.N.S.), NIH/NIMH (P50 MH119467 to A.M.G), the Army Research Office (W911NF-16-1-0474 to A.M.G.), and Mr. Robert Buxton (to A.M.G.). MJFF administers the ASAP-020-519 on behalf of ASAP and itself.</p>", "<title>Data Availability:</title>", "<p id=\"P36\">All source data are available at <ext-link xlink:href=\"http://zenodo.org\" ext-link-type=\"uri\">zenodo.org</ext-link> (<ext-link xlink:href=\"10.5281/zenodo.10397754\" ext-link-type=\"doi\">10.5281/zenodo.10397754</ext-link>, <ext-link xlink:href=\"10.5281/zenodo.10397773\" ext-link-type=\"doi\">10.5281/zenodo.10397773</ext-link>).</p>" ]	[ "<fig position=\"anchor\" id=\"F7\"><label>Extended Data Figure 3–1.</label><caption><p id=\"P34\">Same as ##FIG##3##Fig. 3##, but demonstrating an example with 60 Hz + harmonics noise and the additional steps to remove these signals. Bottom left plot shows the noise as enhanced after high pass filtering. Bottom right plot shows the signal after interpolating both 60 Hz harmonics noise and the FSCV artifacts.</p></caption></fig>", "<fig position=\"float\" id=\"F1\"><label>Figure 1.</label><caption><p id=\"P39\">(A) ECP recording setup for synchronous recording of dopamine and neuronal action potentials as recorded from separate CF electrodes implanted in the monkey striatum (colored purple and green) and connected to electrochemical (FSCV) and EPhys recording systems, respectively. (B) Example recordings from ECP system in a task-performing monkey. FSCV-recorded dopamine signals are plotted as a function of time as displayed on a color plot where current changes (color scale) are clearly observed at the redox potentials for dopamine (~−0.2 V and 0.6 V) and its PCA-extracted dopamine concentration change ([ΔDA]) below it. Task events for the display of the initial central cue (C), peripheral reward-predictive target (T), and reward delivery (RW) are displayed as vertical lines. Below this, the concurrent EPhys recording is shown for a small time window (black dashed rectangle) during the dopamine trace, showing the interfering FSCV scan artifacts. A close-up of the EPhys recording between two scan artifacts (green dashed rectangle) is shown to visualize clear spike action potentials (arrowheads on top right inset). The bottom panel shows the signal after high-pass filtering at 250 Hz using forward-only filters as would be applied for standard spike detection algorithms. This period includes a close-up of the scan artifact demonstrating its triggering of multiple threshold (dashed line)-crossings (circles). Three physiological units are also detected, but the first of these is largely distorted by the forward-filtering of the artifact.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2.</label><caption><p id=\"P40\">Flow chart of temporal interpolation algorithm used to extract spike activity from ECP recordings. Details may be found in the text.</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3.</label><caption><p id=\"P41\">Temporal interpolation process applied to an example ECP recording. Signals from 5 electrodes containing FSCV scan artifacts are plotted in the first panel (top-left) with electrode sites labeled in the legend (c denotes CN site and p denotes putamen site). All these signals are then averaged (step 1 in interpolation algorithm) (top-middle). The averaged signal is bandpass filtered (BPF) and a threshold is computed (1.75 x STD) to find the positive-crossings and local peaks for each of these (circles) (top-right). Only periodic peaks are retained (or added if they did not cross the threshold initially). Linear interpolation is performed around each of these identified peaks using a window of −3 – 7 ms for each electrode channel. An example is shown for site c34 in the bottom-left plot, as well as a close-up on the bottom-right, to visualize recorded unit spike activity.</p></caption></fig>", "<fig position=\"float\" id=\"F4\"><label>Figure 4.</label><caption><p id=\"P42\">(A) Validation setup showing 3 different simulated FSCV artifact types (RC, R, Rail) (green trace) added onto EPhys-only recording (black trace) (session 65B and site p32). (B) (Top) Spike waveforms projected onto PC space (top) with colored clusters. (Bottom) Average spike waveforms for drawn clusters from the PC space (top). Shading represents +/− SD.</p></caption></fig>", "<fig position=\"float\" id=\"F5\"><label>Figure 5.</label><caption><p id=\"P43\">Analysis of task modulated signaling from concurrent recordings of dopamine and spike activity from three sites in the CN and putamen measured from a single session (session 127). (A) (Top) FSCV color plot, (middle) PCA-computed dopamine concentration change ([ΔDA]), and (bottom) concurrent measurements of electrical neural activity high pass filtered (HPF) to visualize spike action potentials. Two windows (blue rectangles) are magnified to show the individual spike action potential waveforms (right). Task events are labeled following notation in ##FIG##1##Fig. 1##. (B) (Left) Average waveform of unit detected (shading represents +/− SD). (Right) ISI histogram of the detected unit. (C) (Top) Trial by trial raster plot of spike activity (dot) measured in the CN (c34) as aligned to the peripheral target display event (0 s). T<sub>on</sub> represents the average time from the peripheral target display event at which the monkey begins fixation on the peripheral target. (Bottom) Average spike rate for large and small reward trial conditions (shading represents +/− SE). Large and small reward trials are denoted by red and blue colors, respectively. (D) Dopamine concentration changes measured at neighboring sites in the CN (c66) and putamen (p15) as aligned to the same events as (C) for large and small reward conditions (shading represents +/− SE). Color coding is the same as in (C).</p></caption></fig>", "<fig position=\"float\" id=\"F6\"><label>Figure 6.</label><caption><p id=\"P44\">Dopamine and spike activity recorded concurrently from a single session show diverse responses to behavioral events related to reward size and spatial target direction. (A) Average waveform of a putative MSN (top) and its ISI histogram (bottom) as recorded in the CN (c33). (B) Raster plot of spike activity for the unit in (A) as plotted in ##FIG##5##Fig. 5C##, except for left (purple) and right (green) peripheral target conditions, demonstrating higher neural responses to gaze of the right peripheral targets than to left targets. (C) Dopamine concentration changes measured at a neighboring site in the CN (c54) displaying oppositive sensitivity to target direction (higher for left than for right target) in comparison to the unit in (B) (top), and stronger modulation by reward size (bottom). Color coding is the same as in (B) and ##FIG##5##Fig. 5C##. (D) Same as (A) except for another unit (putative TAN) recorded in a separate session (69) and site in the putamen (p23). (E) Same as (B) except for large (red) and small (blue) reward conditions. No distinction is observed in the cell firing for the reward size or target direction. (F) Dopamine concentration changes measured at neighboring sites in the CN (c62) and putamen (p13) where stronger modulation by reward size is observed in comparison to the unit response in (E). Color coding is the same as in (E).</p></caption></fig>" ]	[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1.</label><caption><p id=\"P45\">Validation results showing percent recovery of spikes extracted after interpolating simulated artifacts relative to spikes extracted from clean EPhys recording for the different types of artifacts (R, RC, and Rail).</p></caption><table frame=\"box\" rules=\"all\"><colgroup span=\"1\"><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/></colgroup><thead><tr><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Session</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Channel</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">R recovery</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">RC recovery</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Rail recovery</th></tr></thead><tbody><tr><td rowspan=\"2\" align=\"left\" valign=\"top\" colspan=\"1\">\n<bold>65B</bold>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">p23</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">87.13</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">86.39</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">79.55</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">p32</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">87.23</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">86.9</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">83.51</td></tr><tr><td rowspan=\"2\" align=\"left\" valign=\"top\" colspan=\"1\">\n<bold>109B</bold>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">p35</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">88.66</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">86.07</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">76.07</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">c34</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">91.78</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">84.48</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">81.02</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>161b</bold>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">p36</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">85.35</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">85.06</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">77.94</td></tr></tbody></table></table-wrap>" ]	[]	[ "<boxed-text id=\"BX1\" position=\"float\"><caption><title>Significance statement:</title></caption><p id=\"P46\">We present a simple method for recording synchronous molecular and neuronal spike signals. Conventional electrophysiological and electrochemical instruments are combined without the need for additional hardware. A custom-designed algorithm was made and validated for extracting neuronal action potential signals with high fidelity. We were able to compute cell-type specific spike activity along with molecular dopamine signals related to reward and movement behaviors from measurements made in the monkey striatum. Such combined measurements of neurochemical and extracellular action potentials may help pave the way to elucidating mechanisms of plasticity, and how neuromodulators and neurons are orchestrated to mediate behavior.</p></boxed-text>" ]	[]	[]	[]	[]	[ "<fn-group><fn fn-type=\"COI-statement\" id=\"FN2\"><p id=\"P37\"><bold>Competing Interests:</bold> All authors declare no financial or non-financial competing interests.</p></fn><fn id=\"FN3\"><p id=\"P38\"><bold>Code Availability</bold>: MATLAB code used to analyze data are available at GitHub (<ext-link xlink:href=\"https://github.com/hschwerdt/extractFSCVspikes\" ext-link-type=\"uri\">https://github.com/hschwerdt/extractFSCVspikes</ext-link>, <ext-link xlink:href=\"https://github.com/hschwerdt/fscvartifactcreation\" ext-link-type=\"uri\">https://github.com/hschwerdt/fscvartifactcreation</ext-link>).</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"nihpp-2023.12.23.573130v1-f0007\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-2023.12.23.573130v1-f0001\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-2023.12.23.573130v1-f0002\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-2023.12.23.573130v1-f0003\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-2023.12.23.573130v1-f0004\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-2023.12.23.573130v1-f0005\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-2023.12.23.573130v1-f0006\" position=\"float\"/>" ]	[]	[{"surname": ["Amemori", "Amemori", "Gibson", "Graybiel"], "given-names": ["K", "S", "DJ", "AM"], "year": ["2020"], "article-title": ["Striatal Beta Oscillation and Neuronal Activity in the Primate Caudate Nucleus Differentially Represent Valence and Arousal Under Approach-Avoidance Conflict"], "source": ["Front Neurosci"]}, {"surname": ["Calhoun", "Meunier", "Lee", "McCarty", "Sombers"], "given-names": ["SE", "CJ", "CA", "GS", "LA"], "year": ["2018"], "article-title": ["Characterization of a Multiple-Scan-Rate Voltammetric Waveform for Real-Time Detection of Met-Enkephalin"], "source": ["ACS Chem Neurosci"], "volume": ["10"], "fpage": ["2022"], "lpage": ["2032"]}, {"surname": ["Nag", "Sikdar", "Thakor", "Rao", "Sharma"], "given-names": ["S", "SK", "NV", "VR", "D"], "year": ["2015"], "article-title": ["Sensing of Stimulus Artifact Suppressed Signals From Electrode Interfaces"], "source": ["IEEE Sens J"], "volume": ["15"], "fpage": ["3734"], "lpage": ["3742"]}]	{ "acronym": [], "definition": [] }	57	CC BY	no	2024-01-13 00:14:49	bioRxiv. 2023 Dec 24;:2023.12.23.573130	oa_package/7c/27/PMC10769335.tar.gz
PMC10769351	38187530	[ "<title>Introduction</title>", "<p id=\"P2\">Neurons are polarized cells with a defined signaling directionality from dendrites to soma to axon <sup>##UREF##0##1##</sup>. To achieve this morphological and functional polarization, neurons sort protein material into specific subcellular compartments <sup>##REF##27511065##2##,##REF##33098763##3##</sup>. Voltage-gated Ca<sup>2+</sup> channels (Ca<sub>V</sub>s), which couple electrical activity to changes in intracellular Ca<sup>2+</sup> signaling, are a prototypical example of sorting specificity. They are a large protein family, and individual members localize to distinct subcellular domains in the dendrites, the soma and the axon <sup>##REF##21746798##4##,##REF##24698266##5##</sup>. However, Ca<sub>V</sub> subtypes exhibit limited differences in their sequences, and the molecular determinants that target Ca<sub>V</sub>s to specific subcellular compartments remain elusive.</p>", "<p id=\"P3\">Ca<sub>V</sub>s are defined by their pore-forming Ca<sub>V</sub>α1 subunit, and their expression, trafficking and function are modulated by Ca<sub>V</sub>β subunits and Ca<sub>V</sub>α2δ proteins <sup>##REF##21746798##4##–##REF##16382099##7##</sup>. Vertebrate Ca<sub>V</sub>α1 subunits are encoded by ten genes classified into Ca<sub>V</sub>1 (Ca<sub>V</sub>1.1–1.4, L-type), Ca<sub>V</sub>2 (Ca<sub>V</sub>2.1–2.3, P/Q-, N- and R-type), and Ca<sub>V</sub>3 (Ca<sub>V</sub>3.1–3.3, T-type) channels. Most Ca<sub>V</sub>s are abundantly co-expressed in central neurons. Ca<sub>V</sub>1.2 and Ca<sub>V</sub>1.3 have important roles in the somatodendritic compartment. There, Ca<sup>2+</sup> influx through Ca<sub>V</sub>1 channels activates effectors to induce gene transcription <sup>##REF##18817726##8##–##REF##8980227##11##</sup> and modulates neuronal firing directly and through Ca<sup>2+</sup>-activated K<sup>+</sup> channels <sup>##REF##24848473##12##–##REF##17068255##16##</sup>. In presynaptic nerve terminals, Ca<sub>V</sub>2.1 (P/Q-type) and Ca<sub>V</sub>2.2 (N-type) are the primary Ca<sup>2+</sup> sources for synaptic vesicle release <sup>##REF##7901765##17##–##REF##32616470##20##</sup>. They are recruited to a specialized release apparatus, the active zone, where they are tethered near fusion-competent vesicles <sup>##REF##25533484##21##–##UREF##2##25##</sup>. This organization couples action potential-induced Ca<sup>2+</sup> entry to vesicular release sites for the rapid and robust triggering of neurotransmitter exocytosis. Overall, Ca<sub>V</sub>s contribute to diverse cellular processes, and their functions are directly tied to their subcellular localization.</p>", "<p id=\"P4\">The mechanisms that distinguish Ca<sub>V</sub>1s from Ca<sub>V</sub>2s and sort them into the somatodendritic and axonal compartments, respectively, remain unclear. Starting from their primary site of synthesis in the soma, Ca<sub>V</sub>s likely undergo a series of interactions that target each subtype to its respective subcellular domain <sup>##REF##27511065##2##,##REF##32161339##26##</sup>. However, Ca<sub>V</sub>s are highly similar in structure <sup>##REF##24698266##5##,##REF##34234349##27##,##REF##27580036##28##</sup>, and notable overlap exists within the Ca<sub>V</sub>1 and Ca<sub>V</sub>2 interactome. For example, interactions with Ca<sub>V</sub>β, Ca<sub>V</sub>α2δ, and calmodulin have been implicated in Ca<sub>V</sub> trafficking <sup>##REF##10707982##29##–##REF##23664615##34##</sup>, but these proteins interact indiscriminately with Ca<sub>V</sub>1s and Ca<sub>V</sub>2s and are thus unlikely to encode specific sorting information. The intracellular Ca<sub>V</sub> C-termini might mediate targeting specificity. Ca<sub>V</sub> C-termini include a proximal segment with two EF hands and an IQ motif, and a distal segment containing binding sites for scaffolding proteins (##SUPPL##0##Figs. S1A##+##SUPPL##0##B##). The Ca<sub>V</sub>2 C-terminus binds to the PDZ domain of the active zone protein RIM, and it contains a proline-rich sequence (which is also present in Ca<sub>V</sub>1s) that binds to RIM-BP <sup>##REF##21241895##24##,##REF##11988172##35##,##REF##30661983##36##</sup>. Together, these interactions help tether Ca<sub>V</sub>2s to the presynaptic active zone <sup>##REF##32616470##20##,##REF##21241895##24##,##REF##27537484##37##–##REF##12177192##42##</sup>. Analogous sequences in Ca<sub>V</sub>1.3 bind to the postsynaptic scaffold Shank, and overall, Ca<sub>V</sub>1 C-termini support cell surface expression and the assembly of Ca<sub>V</sub>1 into dendritic clusters <sup>##REF##15689539##43##,##REF##10816591##44##</sup>. An additional poly-arginine motif specific to Ca<sub>V</sub>2.1 may also contribute to its localization <sup>##REF##32616470##20##,##UREF##3##45##</sup>. Sequences outside the C-terminus could also be involved. For example, binding of the Ca<sub>V</sub>2 cytoplasmic II-III loop to SNARE proteins <sup>##REF##7993624##46##–##REF##9254677##48##</sup> and Ca<sub>V</sub> interactions with material in the synaptic cleft may mediate anchoring at presynaptic sites <sup>##REF##15577901##49##,##REF##21228161##50##</sup>. Taken together, multiple interactions have been implicated in Ca<sub>V</sub> trafficking and targeting, but how these interactions direct Ca<sub>V</sub>1s and Ca<sub>V</sub>2s to opposing compartments has remained unclear.</p>", "<p id=\"P5\">Here, we found that the Ca<sub>V</sub> C-termini are the primary determinants of channel localization in hippocampal neurons. Swapping the Ca<sub>V</sub>2.1 C-terminus onto Ca<sub>V</sub>1.3 targets the channel to the presynaptic active zone in Ca<sub>V</sub>2 knockout neurons. This chimeric Ca<sub>V</sub>1.3 channel mediates Ca<sup>2+</sup> entry for neurotransmitter release and renders synaptic vesicle exocytosis sensitive to L-type Ca<sub>V</sub> blockers. In contrast, the inverse swap prevents active zone localization of Ca<sub>V</sub>2.1. Within the Ca<sub>V</sub>2.1 proximal C-terminus, an EF hand is required for presynaptic targeting, and its removal leads to loss of Ca<sub>V</sub>2.1 from the active zone. We conclude that the C-terminus specifies Ca<sub>V</sub> localization, and we identify the EF hand as an essential trafficking motif.</p>" ]	[ "<title>Materials and methods</title>", "<title>Mice</title>", "<p id=\"P23\">Ca<sub>V</sub>2 conditional triple homozygote floxed mice were described before <sup>##REF##32616470##20##</sup> and they contain homozygote floxed alleles for Ca<sub>V</sub>2.1 (<italic toggle=\"yes\">Cacna1a</italic>, <sup>##REF##17146767##68##</sup>), Ca<sub>V</sub>2.2 (<italic toggle=\"yes\">Cacna1b</italic>, <sup>##REF##32616470##20##</sup>), and Ca<sub>V</sub>2.3 (<italic toggle=\"yes\">Cacna1e</italic>, <sup>##REF##11923483##69##</sup>). Mice were housed as breeding pairs or separated by sex, and they were under a 12 h light-dark cycle with free access to food and water in a room set to 22 °C (range 20–24 °C) and 50% humidity (range 35–70%). Mice were genotyped either in the lab following established protocols <sup>##REF##32616470##20##</sup> or by Transnetyx. For <italic toggle=\"yes\">Cacna1a</italic>, the following oligonucleotide primer pair was used for in-lab genotyping: forward, ACCTACAGTCTGCCAGGAG; reverse, TGAAGCCCAGACATCCTTGG (expected band sizes, wild type: 393 bp, floxed: 543 bp); for <italic toggle=\"yes\">Cacna1b</italic>: forward, TGGTTGGTGTCCTGTTCTCC; reverse, TAAGGAGCAGGGAATCCTGG (expected band sizes, wild type: 219bp, floxed: 359 bp); for <italic toggle=\"yes\">Cacna1c</italic>: forward, GACAAGACCCCAATGTCTCG; reverse, TCCATGTTCCTTCTCACTCC (expected band sizes, wild type: 295 bp, floxed: 334 bp). Animal experiments were performed according to approved protocols at Harvard University.</p>", "<title>Primary neuronal cultures</title>", "<p id=\"P24\">Primary mouse hippocampal cultures were generated from newborn mice as described previously <sup>##REF##32616470##20##,##REF##27537483##38##,##REF##35176221##39##</sup>. Hippocampi were dissected out from newborn mice within 24 h after birth. Cells were dissociated and plated onto Matrigel-treated glass coverslips in plating medium composed of Minimum Essential Medium (MEM) with 0.5% glucose, 0.02% NaHCO3, 0.1 mg/mL transferrin, 10% Fetal Select bovine serum (Atlas Biologicals FS-0500-AD), 2 mM L-glutamine, and 25 μg/mL insulin. Cells from mice of both sexes were mixed. Cultures were maintained in a 37 °C-tissue culture incubator, and after ~24 h the plating medium was exchanged with growth medium composed of MEM with 0.5% glucose, 0.02% NaHCO3, 0.1 mg/mL transferrin, 5% Fetal Select bovine serum (Atlas Biologicals FS-0500-AD), 2% B-27 supplement (Thermo Fisher 17504044), and 0.5 mM L-glutamine. On day in vitro (DIV) 1 to 2, depending on growth, 50% or 75% of the medium was exchanged with growth medium supplemented with 4 μM Cytosine β-D-arabinofuranoside (AraC). Experiments and analyses were performed at DIV15 to 19, as described below.</p>", "<title>Cell lines</title>", "<p id=\"P25\">HEK293T cells, an immortalized cell line of female origin, were cultured as described before <sup>##REF##32616470##20##,##REF##27537483##38##,##REF##35176221##39##</sup>. They were purchased from ATCC (CRL-3216, RRID: CVCL_0063), expanded, and stored in liquid nitrogen until use. After thawing, the cells were grown in Dulbecco’s Modified Eagle Medium (DMEM) with 10% fetal bovine serum (Atlas Biologicals F-0500-D) and 1% penicillin-streptomycin. HEK293T cells were passaged every 1 to 3 d at a ratio of 1:3 to 1:10. HEK293T cell batches were typically replaced after 20 passages by thawing a fresh vial from the expanded stock.</p>", "<title>Lentiviruses</title>", "<p id=\"P26\">Lentiviruses used to transduce primary hippocampal neurons were produced in HEK293T cells. HEK293T cells were transfected with the Ca<sup>2+</sup> phosphate method with REV (p023), RRE (p024) and VSVG (p025), as well as a lentiviral plasmid encoding the protein of interest. For Ca<sub>V</sub> proteins of interest, these were plasmids p789, p947, p1077, p1078, p1079, p1080, p1083, and p1084. To produce lentiviruses expressing EGFP-tagged Cre recombinase (to generate Ca<sub>V</sub>2 cTKO neurons), pFSW EGFP-Cre (p009) was used. For lentiviruses expressing a truncated, enzymatically inactive EGFP-tagged Cre (to generate Ca<sub>V</sub>2 control neurons), pFSW EGFP-ΔCre (p010) was used. Plasmids were transfected at a 1:1:1:1 molar ratio and with a total amount of 6.7 μg DNA. Approximately 24 h after transfection, the medium was switched to neuronal growth medium (described above), and the HEK293T cell supernatant was harvested 24–36 h later by centrifugation at 700 × g. For expression of EGFP-Cre and EGFP-ΔCre, neurons were infected by adding HEK293T cell supernatant at DIV5. For expression of Ca<sub>V</sub>s, neurons were infected at DIV1. Ca<sub>V</sub>2 control neurons were additionally infected with a virus made using a pFSW plasmid (p008) lacking a cDNA in the multiple cloning site in place of an expression virus. Neuronal protein expression from these lentiviruses was driven by a human synapsin promoter <sup>##REF##27537483##38##,##REF##25209271##70##</sup>.</p>", "<title>Ca<sub>V</sub> expression constructs</title>", "<p id=\"P27\">For experiments in neurons, lentiviral backbones containing a human synapsin promoter were used (pFSW HA-Ca<sub>V</sub>2.1, p789; pFSW HA-Ca<sub>V</sub>1.3, p1077; pFSW HA-Ca<sub>V</sub>1.3<sup>2.1Ct</sup>, p1078; pFSW HA-Ca<sub>V</sub>2.1<sup>1.3Ct</sup>, p1079; pFSW HA-Ca<sub>V</sub>1.3<sup>ΔCt</sup>, p1080; pFSW HA-Ca<sub>V</sub>1.3<sup>2.1ProxCt</sup>, p1083; pFSW HA-Ca<sub>V</sub>1.3<sup>2.1DistCt</sup>, p1084; pFSW HA-Ca<sub>V</sub>2.1<sup>ΔEF1</sup>, p947). For experiments in HEK293T cells, expression vectors with a CMV promoter were used (pCMV HA-Ca<sub>V</sub>2.1, p771; pCMV HA-Ca<sub>V</sub>1.3, p1073; pCMV HA-Ca<sub>V</sub>1.3<sup>2.1Ct</sup>, p1074; pCMV HA-Ca<sub>V</sub>2.1<sup>1.3Ct</sup>, p1075; pCMV HA-Ca<sub>V</sub>1.3<sup>ΔCt</sup>, p1076; pCMV HA-Ca<sub>V</sub>1.3<sup>2.1ProxCt</sup>, p1081; pCMV HA-Ca<sub>V</sub>1.3<sup>2.1DistCt</sup>, p1082; pCMV HA-Ca<sub>V</sub>2.1<sup>ΔEF1</sup>, p939). For these constructs, the Ca<sub>V</sub> coding sequences were identical between corresponding pFSW and pCMV versions. The <underline>sequence of Ca<sub>V</sub>2.1</underline> was identical to GenBank Entry AY714490.1 (mouse) with the addition of an HA-tag after position V<sub>27</sub> flanked by short, exogenous linkers. The resulting cDNAs (p771 and p789) had the sequence M<sub>1</sub>ARF…GVVV<sub>27</sub>-AS-YPYDVPDYA-ACR-G<sub>28</sub>AAG…DDWC<sub>2369</sub>. The <underline>sequence of Ca<sub>V</sub>1.3</underline> was as follows: the pore region was identical to residues M<sub>1</sub>QHQ…FDYL<sub>1466</sub> from Ca<sub>V</sub>1.3e[8a,11,31b,Δ32,42a] (rat) and corresponds to residues M<sub>10</sub>QHQ…FDYL<sub>1475</sub> of GenBank Entry EDL89004.1. Ca<sub>V</sub>1.3e[8a,11,31b,Δ32,42a] was a gift from D. Lipscombe (Addgene Plasmid #49333; <ext-link xlink:href=\"http://n2t.net/addgene:49333\" ext-link-type=\"uri\">http://n2t.net/addgene:49333</ext-link>; RRID:Addgene_49333) <sup>##REF##11487617##71##</sup>. The intracellular C-terminal tail was identical to residues T<sub>7</sub> to L<sub>695</sub> from GenBank Entry AF370010.1 (a partial cDNA, rat); a Ca<sub>V</sub>1.3 plasmid containing this C-terminal tail was a gift from I. Bezprozvanny <sup>##REF##15689539##43##</sup>. An HA-tag was inserted after position G<sub>29</sub> (referring to the numbering of Addgene Plasmid #49333) and flanked by short, exogenous linkers. The resulting cDNAs (p1073 and p1077) had the sequence M<sub>1</sub>QHQ…SGEG<sub>29</sub>-AS-YPYDVPDYA-ACR-P<sub>30</sub>TSQ…FDYL<sub>1466</sub>-T<sub>1467</sub>RDW…ITTL<sub>2155</sub>, with M<sub>1</sub>QHQ-SGEG<sub>29</sub> and P<sub>30</sub>TSQ-FDYL<sub>1466</sub> derived from Addgene Plasmid #49333 <sup>##REF##11487617##71##</sup>, and with T<sub>1467</sub>RDW-ITTL<sub>2155</sub> derived from the plasmid obtained from I. Bezprozvanny <sup>##REF##15689539##43##</sup>. The <underline>sequence of Ca<sub>V</sub>1.3</underline><sup><underline>2.1Ct</underline></sup> (p1074 and p1078) contained the pore region (MQHQ…DWSI) from p1077 (Ca<sub>V</sub>1.3) followed by the C-terminus (LGPH…DDWC) from p789 (Ca<sub>V</sub>2.1, see ##SUPPL##0##Fig. S1B##). The <underline>sequence of Ca<sub>V</sub>2.1</underline><sup><underline>1.3Ct</underline></sup> (p1075 and p1079) contained the pore region (MARF…FEYL) from p789 (Ca<sub>V</sub>2.1) followed by the C-terminus (TRDW…ITTL) from p1077 (Ca<sub>V</sub>1.3, see ##SUPPL##0##Fig. S1B##). The <underline>sequence of Ca<sub>V</sub>1.3</underline><sup><underline>2.1ProxCt</underline></sup> (p1081 and 1083) contained the pore region (MQHQ…DWSI) from p1077 (Ca<sub>V</sub>1.3), followed by the proximal C-terminus (LGPH…QAMR) from p789 (Ca<sub>V</sub>2.1) and then by the distal C-terminus (GKYP…ITTL) from p1077 (Ca<sub>V</sub>1.3, see ##SUPPL##0##Fig. S1B##). The <underline>sequence of Ca<sub>V</sub>1.3</underline><sup><underline>2.1DistCt</underline></sup> (p1082 and 1084) contained the pore region and the proximal C-terminus (MQHQ…QGLV) from p1077 (Ca<sub>V</sub>1.3) followed by the distal C-terminus (EEQN…DDWC) from p789 (Ca<sub>V</sub>2.1, see ##SUPPL##0##Fig. S1B##). In the <underline>sequence of Ca<sub>V</sub>2.1</underline><sup><underline>ΔEF1</underline></sup> (p939 and p947), the first EF hand (EYVR…LLRVI) was replaced with residues EY in p789 (Ca<sub>V</sub>2.1, see ##SUPPL##0##Fig. S1B##). The <underline>sequence of Ca<sub>V</sub>1.3</underline><sup><underline>ΔCt</underline></sup> (p1076 and 1080) contained the pore region (MQHQ…DWSI) from p1077 (Ca<sub>V</sub>1.3) and did not contain a C-terminus (see ##SUPPL##0##Fig. S1B##).</p>", "<title>Confocal and STED microscopy of synapses</title>", "<p id=\"P28\">Confocal and STED microscopy and analyses were performed as described before <sup>##REF##32616470##20##,##UREF##2##25##,##REF##35176221##39##,##REF##34031393##53##,##REF##29439199##72##,##UREF##5##73##</sup>. Neurons cultured on 0.17 mm thick 12 mm diameter (#1.5) coverslips were washed two times with PBS warmed to 37 °C, and then fixed in 2% PFA + 4% sucrose (in PBS) at room temperature. After fixation, coverslips were rinsed three times in PBS + 50 mM glycine, then permeabilized in PBS + 0.1% Triton X-100 + 3% BSA (TBP) for 1 h at room temperature. Coverslips were stained with primary antibodies diluted in TBP for ~48 h at 4 °C. The following primary antibodies were used: mouse IgG1 anti-HA (1:500, RRID: AB_2565006, A12), rabbit anti-Ca<sub>V</sub>2.1 (1:200, RRID: AB_2619841, A46), guinea pig anti-PSD-95 (1:500, RRID: AB_2619800, A5), rabbit anti-synapsin (1:500, RRID: AB_2200097, A30), and mouse IgG1 anti-synapsin (1:500, RRID_2617071, A57). After primary antibody staining, coverslips were rinsed twice and washed three times for 5 min in PBS + 50 mM glycine at room temperature. Alexa Fluor 488 (to detect HA-tagged Ca<sub>V</sub>s or endogenous Ca<sub>V</sub>2.1; anti-mouse IgG1, RRID: AB_2535764, S7; or, anti-rabbit, RRID: AB_2576217, S5), Alexa Fluor 555 (to detect the postsynaptic marker PSD-95; anti-guinea pig, RRID: AB_2535856, S23), and Alexa Fluor 633 (to detect the synaptic vesicle cloud; anti-rabbit, RRID: AB_2535731, S33; or, anti-mouse IgG1, RRID: AB_2535768, S29) conjugated antibodies were diluted in TBP at 1:200 (for Alexa Fluor 488 and 555) or 1:500 (for Alexa Fluor 633), and coverslips were incubated with the secondary antibody solution for ~24 h at 4 °C. Coverslips were then rinsed twice with PBS + 50 mM glycine and once with deionized water, air-dried and mounted on glass slides in fluorescent mounting medium. Confocal and STED images were acquired on a Leica SP8 Confocal/STED 3X microscope with an oil immersion 100× 1.44 numerical aperture objective and gated detectors as described previously <sup>##REF##32616470##20##,##REF##29439199##72##</sup>. 58.14 × 58.14 μm<sup>2</sup> areas were acquired using 2x digital zoom (4096 × 4096 pixels, pixel size of 14.194 × 14.194 nm<sup>2</sup>). Alexa Fluor 633, Alexa Fluor 555, and Alexa Fluor 488 were excited at 633 nm, 555 nm and 488 nm using a white light laser at 1–10% of 1.5 mW laser power. The Alexa Fluor 633, Alexa Fluor 555, and Alexa Fluor 488 channels were acquired first in confocal mode. For the Alexa Fluor 555 and Alexa Fluor 488 channels, the same areas were then sequentially acquired in STED mode using 660 nm and 592 nm depletion lasers, respectively. Identical imaging and laser settings were applied to all conditions within a given biological repeat. For analyses of presynaptic Ca<sub>V</sub> distribution in STED images, synapses were selected in side-view. Side-view synapses were defined as synapses that contained a synaptic vesicle cluster labeled with synapsin and were associated with an elongated PSD-95 structure along the edge of the vesicle cluster as described previously <sup>##REF##32616470##20##,##REF##35176221##39##,##REF##32521280##52##,##REF##29439199##72##,##UREF##6##74##</sup>. For intensity profile analyses, a ~1000 nm long, 200 nm wide, rectangular ROI was drawn perpendicular and across the center of the PSD-95 structure, and the intensity profiles were obtained using this ROI for both the protein of interest and PSD-95. To align individual profiles, the PSD-95 signal only was smoothened using a rolling average of 5 pixels, and the smoothened signal was used to define the peak position of PSD-95. The profiles for the protein of interest (Ca<sub>V</sub> or HA) and smoothened PSD-95 were aligned to the PSD-95 peak position, averaged across synapses, and then plotted. Peak intensities were also analyzed by extracting the maximal value from the line profiles of the protein of interest (Ca<sub>V</sub> or HA) and smoothened PSD-95 within a 200 nm window around the PSD-95 peak. Peak intensity values were plotted for each synapse and averaged. For quantification of confocal images, a custom MATLAB program (<ext-link xlink:href=\"https://github.com/hmslcl/3D_SIM_analysis_HMS_Kaeser-lab_CL\" ext-link-type=\"uri\">https://github.com/hmslcl/3D_SIM_analysis_HMS_Kaeser-lab_CL</ext-link>) was used to generate masks of the presynaptic marker (synapsin), with the threshold determined by automatic two-dimensional segmentation (Otsu algorithm) <sup>##REF##29398114##75##</sup>. Regions of interest (ROIs) were defined as synapsin-positive areas formed by contiguous pixels of at least 0.05 μm<sup>2</sup> in size. Each image typically contained between 500 and 1500 synapsin ROIs. Levels of HA or Ca<sub>V</sub>2.1 within these ROIs were measured and the average intensity across all ROIs within an image was calculated and plotted. Representative images in figures were cropped, rotated with bi-linear interpolation, and then brightness and contrast adjusted to facilitate inspection. Brightness and contrast adjustments were made for display in figures and were done identically for images within an experiment, but image quantification was performed on raw images without these adjustments. The experimenter was blind to the condition/genotype for image acquisition and analyses for STED and confocal microscopic experiments.</p>", "<title>Confocal imaging of neuronal somata</title>", "<p id=\"P29\">Neurons cultured on 0.17 mm thick 12 mm diameter (#1.5) coverslips were washed with PBS warmed to 37 °C and fixed in 2% PFA + 4% sucrose for 10 min at room temperature. Coverslips were then rinsed three times in PBS + 50 mM glycine at room temperature, permeabilized in TBP for 1 h at room temperature, and incubated in primary antibodies at for ~48 h at 4 °C. The following primary antibodies were used: mouse IgG1 anti-HA (1:500, RRID: AB_2565006, A12) and mouse IgG2b anti-NeuN (1:500, RRID: AB_101711040, A254). After staining with primary antibodies, coverslips were rinsed twice and washed three times for 5 min in PBS + 50 mM glycine at room temperature. Alexa Fluor 555 (to detect HA; anti-mouse IgG1, RRID: 2535769, S19), and 633 (to detect neuronal somata; anti-mouse IgG2b, RRID: AB_1500899, S31) conjugated secondary antibodies were used at 1:500 dilution in TBP. Secondary antibody staining was carried out for ~24 h at 4 °C. Coverslips were rinsed twice in PBS + 50 mM glycine, once in deionized water, air-dried and then mounted on glass slides using fluorescent mounting medium. Confocal images of neuronal somata were acquired on a Leica Stellaris 5 microscope with a 63x oil-immersion objective. Single section, 92.65 × 92.65 μm<sup>2</sup> areas were acquired using 2x digital zoom (1024 × 1024 pixels, pixel size of 90.2 × 90.2 nm<sup>2</sup>). Imaging and laser settings were identical for all conditions within a given biological repeat. For analyses of somatic HA signals, the NeuN signal was used to mark the neuron somata, and EGFP-Cre or EGFP-ΔCre was used to define nuclei. Somatic ROIs were drawn as donut shapes by using the outer edge of the NeuN profile along the main somatic compartment not including neurites, and by excluding the EGFP-labeled nucleus. The average pixel intensity within the somatic ROI was then calculated for HA and plotted for each cell. Representative images in figures were cropped and adjusted for brightness and contrast to facilitate inspection. Brightness and contrast adjustments were made for display in figures and were done identically for images within an experiment, but image quantification was performed on raw images without these adjustments. The experimenter was blind to the condition/genotype for image acquisition and analyses.</p>", "<title>Electrophysiology</title>", "<p id=\"P30\">Electrophysiological recordings in cultured hippocampal neurons were performed as described previously <sup>##REF##32616470##20##,##REF##35176221##39##,##UREF##6##74##</sup> at DIV16 to 19. Glass pipettes were pulled at 2 to 5 MΩ and filled with intracellular solution containing (in mM) for EPSCs: 120 Cs-methanesulfonate, 2 MgCl2, 10 EGTA, 4 Na<sub>2</sub>-ATP, 1 Na-GTP, 4 QX314-Cl, 10 HEPES-CsOH (pH 7.4, ~300 mOsm) and for IPSCs: 40 CsCl, 90 K-gluconate, 1.8 NaCl, 1.7 MgCl<sub>2</sub>, 3.5 KCl, 0.05 EGTA, 2 Mg-ATP, 0.4 Na<sub>2</sub>-GTP, 10 phosphocreatine, 4 QX314-Cl, 10 HEPES-CsOH (pH 7.2, ~300 mOsm). Cells were held at +40 mV for NMDAR-EPSCs and at −70 mV for IPSCs. Access resistance was monitored during recordings and compensated to 2–3 MΩ, and cells were discarded if the uncompensated access exceeded 15 MΩ during the experiment. The extracellular solution contained (in mM): 140 NaCl, 5 KCl, 2 MgCl<sub>2</sub>, 1.5 CaCl<sub>2</sub>, 10 glucose, 10 HEPES-NaOH (pH 7.4, ~300 mOsm), and recordings were performed at room temperature (20–24 °C). For NMDAR-EPSCs, picrotoxin (PTX, 50 μM) and 6-Cyano-7-nitroquinoxaline-2,3-dione (CNQX, 20 μM) were present in the extracellular solution. IPSCs were recorded in the presence of D-2-amino-5-phosphonopentanoic acid (D-AP5, 50 μM) and CNQX (20 μM) in the extracellular solution. Action potentials were elicited with a bipolar focal stimulation electrode fabricated from nichrome wire. To evaluate the Ca<sub>V</sub> blocker sensitivity of synaptic transmission, ω-agatoxin IVA (to block Ca<sub>V</sub>2.1) or isradipine (to block Ca<sub>V</sub>1s) were used. Blockers were pipetted into the recording chamber as concentrated stocks diluted in extracellular solution for a final working concentration of 200 nM for ω-agatoxin IVA and 20 μM for isradipine. For wash-in, cells were incubated after blocker addition for 5 min. IPSCs were recorded first in the absence of Ca<sub>V</sub> blockers. Then, IPSCs were measured after wash-in of 200 nM ω-agatoxin IVA and again after wash-in of 200 nM ω-agatoxin IVA and 20 μM isradipine (##FIG##3##Fig. 4F##–##FIG##3##I##), or after wash-in of 20 μM isradipine (##SUPPL##0##Fig. S5##). Data were acquired at 5 kHz and lowpass filtered at 2 kHz with an Axon 700B Multiclamp amplifier and digitized with a Digidata 1440A digitizer. Data acquisition and analyses were done using pClamp10. For electrophysiological experiments, the experimenter was blind to the genotype throughout data acquisition and analyses.</p>", "<title>Western blotting</title>", "<p id=\"P31\">Lysates from transfected HEK293T cells were used for Western blotting. Ca<sub>V</sub>1 and Ca<sub>V</sub>2 constructs were co-transfected with Ca<sub>V</sub>β1b (p754; pMT2 Ca<sub>V</sub>β1b-GFP was a gift from A. Dolphin, Addgene plasmid # 89893; <ext-link xlink:href=\"http://n2t.net/addgene:89893\" ext-link-type=\"uri\">http://n2t.net/addgene:89893</ext-link>; RRID: Addgene_89893) <sup>##REF##27489103##76##</sup> and Ca<sub>V</sub>α2δ1 (p752; CaVα2δ1 was a gift from D. Lipscombe, Addgene plasmid # 26575; <ext-link xlink:href=\"http://n2t.net/addgene:26575\" ext-link-type=\"uri\">http://n2t.net/addgene:26575</ext-link>; RRID: Addgene_26575) <sup>##REF##15201306##77##</sup>. Plasmids were transfected with the Ca<sup>2+</sup> phosphate method at a 1:1:1 molar ratio with a total of 6.7 μg DNA. Around 48 h after transfection, HEK293T cells were harvested in 1 mL of standard 1x SDS buffer per flask. Homogenates were centrifuged at 16,200 × g for 10 min at room temperature, run on 6% (for Ca<sub>V</sub>s) or 12% (for β-actin) polyacrylamide gels, and transferred onto nitrocellulose membranes for 6.5 h at 4 °C in transfer buffer (containing per L, 200 mL methanol, 14 g glycine, 3 g Tris). Membranes were blocked in filtered 10% nonfat milk/5% goat serum in TBST (Tris-buffered saline with 0.1% Tween) for 1 h at room temperature and incubated with primary antibodies in 5% nonfat milk/2.5% goat serum in TBST overnight at 4 °C. The primary antibodies used were mouse IgG1 anti-HA (1:1000; RRID: AB_2565006, A12) and mouse IgG1 anti-β-actin (1:2000; RRID: AB_476692, A127). Membranes were washed five times for 3 min each at room temperature in TBST and then incubated with secondary antibodies in 5% nonfat milk/2.5% goat serum for 1 h at room temperature. The secondary antibodies used were peroxidase-conjugated goat anti-mouse IgG (1:10,000, RRID: AB_2334540, S52) and peroxidase-conjugated goat anti-rabbit IgG (1:10,000, RRID: AB_2334589, S53). Membranes were again washed five times for 3 min each at room temperature in TBST, then incubated in a chemiluminescent reagent for 30 s. Finally, the membranes were exposed to films, and films were developed and scanned. Corresponding western blots of Ca<sub>V</sub>s and β-actin were run simultaneously, on the same day, and on separate gels using the same samples. For illustration in figures, blots were rotated with bilinear interpolation and cropped for display.</p>", "<title>Quantification and statistical analyses</title>", "<p id=\"P32\">Data are displayed as mean ± SEM. Statistics were performed in GraphPad Prism 9, and significance is presented as p < 0.05, p < 0.01, and **p < 0.001. Sample sizes and statistical tests for each experiment are included in each figure legend. For electrophysiological experiments, the sample size used for statistical analyses was the number of recorded cells. For STED microscopic data, the sample size used for statistical analyses was the number of synapses. For confocal microscopic data, the sample size used for statistical analyses was the number of images for analyses of synapsin ROIs, or the number of neurons for analyses of somata. Single factor, multiple group comparisons were conducted using Kruskal-Wallis tests followed by Dunn’s multiple comparisons post-hoc tests for proteins of interest (HA or Ca<sub>V</sub>2.1) and for current amplitudes (EPSCs, IPSCs). To compare the efficacy of blockade of synaptic transmission by different pharmacological agents in ##FIG##3##Fig. 4H##, Friedman tests and Dunn’s multiple comparisons post-hoc tests were used. To compare the effects of Ca<sub>V</sub> blockers on synaptic transmission across genotypes in ##FIG##3##Fig. 4I##, two-way, repeated-measures ANOVA and Dunnett’s multiple comparisons post-hoc tests were used. In ##SUPPL##0##Fig. S5##, the Wilcoxon matched-pairs signed rank test was used.</p>" ]	[ "<title>Results</title>", "<title>Lentivirally expressed Ca<sub>V</sub>2.1, but not Ca<sub>V</sub>1.3, localizes to active zones and mediates neurotransmitter release after Ca<sub>V</sub>2 ablation</title>", "<p id=\"P6\">To determine the Ca<sub>V</sub> sequences important for active zone localization, we expressed various Ca<sub>V</sub>s using lentiviruses in cultured hippocampal neurons that lack Ca<sub>V</sub>2.1, Ca<sub>V</sub>2.2 and Ca<sub>V</sub>2.3. Specifically, we transduced neurons that contain “floxed” conditional knockout alleles for these three channels (##FIG##0##Fig. 1A##) with lentiviruses that express cre recombinase under a synapsin promoter to generate Ca<sub>V</sub>2 cTKO neurons <sup>##REF##32616470##20##</sup>. Control neurons (Ca<sub>V</sub>2 control) were identical except for transduction by a lentivirus expressing a truncated, recombination-deficient version of cre. In addition, we transduced Ca<sub>V</sub>2 cTKO neurons with either a lentivirus expressing HA-tagged Ca<sub>V</sub>2.1 or with a lentivirus expressing HA-tagged Ca<sub>V</sub>1.3. The tags were inserted near the Ca<sub>V</sub> N-terminus in a position shown previously to not interfere with the expression (##FIG##0##Figs. 1B##, ##SUPPL##0##S1A##–##SUPPL##0##1E##), targeting and function of Ca<sub>V</sub>2.1 <sup>##REF##32616470##20##,##REF##21411672##51##</sup>. We then used stimulated emission depletion (STED) microscopy (##FIG##0##Fig. 1C##–##FIG##0##H##), confocal microscopy (##SUPPL##0##Fig. S1F##–##SUPPL##0##I##), and electrophysiology (##FIG##0##Fig. 1I##–##FIG##0##L##) to assess Ca<sub>V</sub> localization and synaptic transmission.</p>", "<p id=\"P7\">For morphological analyses, neurons were stained with antibodies against Ca<sub>V</sub>2.1 or HA to detect Ca<sub>V</sub>s, PSD-95 to mark postsynaptic densities, and synapsin to label synaptic vesicle clusters. For STED analyses (##FIG##0##Fig. 1C##–##FIG##0##H##), we selected synapses in side-view through the presence of a vesicle cloud (imaged with confocal microscopy) and an elongated PSD-95 structure (STED) at one edge of the vesicle cloud, as established previously <sup>##REF##32616470##20##,##UREF##2##25##,##REF##27537483##38##,##REF##35176221##39##,##REF##32521280##52##</sup>. We assessed Ca<sub>V</sub> distribution and levels (STED) in these side-view synapses using line profiles drawn perpendicular to the PSD-95 structure, and we plotted the average line profiles (##FIG##0##Fig. 1D##+##FIG##0##G##) and peak intensities (##FIG##0##Fig. 1E##+##FIG##0##H##).</p>", "<p id=\"P8\">Endogenous and re-expressed Ca<sub>V</sub>2.1 formed elongated structures apposed to PSD-95 with a maximal intensity within tens of nanometers of the PSD-95 peak (##FIG##0##Fig. 1C##–##FIG##0##H##). We have established before that this distribution is characteristic of active zone localization <sup>##REF##32616470##20##,##UREF##2##25##,##REF##35176221##39##,##REF##34031393##53##</sup>. Furthermore, a strong PSD-95 peak was present in all conditions, matching our previous work that did not find morphological defects following Ca<sub>V</sub>2 triple knockout <sup>##REF##32616470##20##</sup>. Exogenously expressed Ca<sub>V</sub>1.3, monitored via the HA-tag, was not detected at the active zone (##FIG##0##Fig. 1F##–##FIG##0##H##). Consistent with the STED analyses, robust levels of Ca<sub>V</sub>2.1, but not Ca<sub>V</sub>1.3, were present in synaptic regions of interest (ROIs) defined by synapsin (##SUPPL##0##Fig. S1F##–##SUPPL##0##I##). Independent of their synaptic targeting, both Ca<sub>V</sub>2.1 and Ca<sub>V</sub>1.3 were effectively expressed in the somata of transduced Ca<sub>V</sub>2 cTKO neurons and in transfected HEK293T cells (##SUPPL##0##Fig. S1C##–##SUPPL##0##E##).</p>", "<p id=\"P9\">These morphological experiments were complemented with analyses of synaptic transmission in the same conditions (##FIG##0##Fig. 1I##–##FIG##0##L##). A focal stimulation electrode was used to evoke action potentials, and inhibitory or excitatory postsynaptic currents (IPSCs or EPSCs) were isolated pharmacologically. EPSCs were monitored via NMDA receptors because network excitation confounds the interpretation of EPSC amplitudes when AMPA receptors are not blocked. Ca<sub>V</sub>2 cTKO nearly abolished synaptic transmission, as characterized in detail before <sup>##REF##32616470##20##</sup>. Re-expression of Ca<sub>V</sub>2.1 restored EPSCs and IPSCs effectively, but exogenous expression of Ca<sub>V</sub>1.3 failed to produce any recovery (##FIG##0##Fig. 1I##–##FIG##0##L##), in agreement with the absence of Ca<sub>V</sub>1.3 from presynaptic sites (##FIG##0##Fig. 1F##–##FIG##0##H##). Taken together, these results establish that Ca<sub>V</sub>2.1, but not Ca<sub>V</sub>1.3, localizes to the active zone and gates neurotransmitter release when expressed in Ca<sub>V</sub>2 cTKO neurons.</p>", "<title>Ca<sub>V</sub>1.3 chimeras that contain the Ca<sub>V</sub>2.1 C-terminus localize to the active zone</title>", "<p id=\"P10\">Given the diverse interactions that converge within the Ca<sub>V</sub> C-termini (##SUPPL##0##Fig. S1A##+##SUPPL##0##B##) <sup>##REF##32616470##20##,##REF##12177192##42##,##REF##15689539##43##</sup>, we hypothesized that the C-terminal sequences contain sufficient information to instruct Ca<sub>V</sub> compartment specificity. To test this hypothesis, we generated two chimeric Ca<sub>V</sub>s: (1) in Ca<sub>V</sub>1.3, we replaced the entire intracellular C-terminus immediately after the last transmembrane segment with that of Ca<sub>V</sub>2.1, generating a channel we named Ca<sub>V</sub>1.3<sup>2.1Ct</sup>; and (2) we produced the inverse construct by replacing the Ca<sub>V</sub>2.1 C-terminus with that of Ca<sub>V</sub>1.3, generating Ca<sub>V</sub>2.1<sup>1.3Ct</sup> (##FIG##1##Figs. 2A##, ##SUPPL##0##S1A##). Both chimeric channels were efficiently expressed in transfected HEK293T cells (##SUPPL##0##Fig. S2A##) and were robustly detected in neuronal somata following lentiviral transduction of Ca<sub>V</sub>2 cTKO neurons (##SUPPL##0##Fig. S2B##+##SUPPL##0##C##).</p>", "<p id=\"P11\">We then assessed the localization of these chimeric channels in the experimental setup described above and compared them side-by-side with Ca<sub>V</sub>2.1 and Ca<sub>V</sub>1.3. Strikingly, translocating the Ca<sub>V</sub>2.1 C-terminus onto Ca<sub>V</sub>1.3 efficiently targeted the resulting chimeric Ca<sub>V</sub>1.3<sup>2.1Ct</sup> channel to the active zone in Ca<sub>V</sub>2 cTKO neurons, as assessed with STED microscopy (##FIG##1##Fig. 2B##–##FIG##1##D##). The distribution profile of Ca<sub>V</sub>1.3<sup>2.1Ct</sup> and its abundance at the active zone recapitulated those of re-expressed Ca<sub>V</sub>2.1 (##FIG##1##Fig. 2B##–##FIG##1##D##). In contrast, the inverse swap abolished active zone localization of Ca<sub>V</sub>2.1<sup>1.3Ct</sup> (##FIG##1##Fig. 2B##–##FIG##1##D##) despite effective somatic expression (##SUPPL##0##Fig. S2B##+##SUPPL##0##C##). Confocal microscopic analyses of Ca<sub>V</sub> levels in synaptic ROIs corroborated these findings by revealing robust synaptic localization of Ca<sub>V</sub>1.3<sup>2.1Ct</sup> but not of Ca<sub>V</sub>2.1<sup>1.3Ct</sup> (##FIG##1##Fig. 2E##+##FIG##1##F##).</p>", "<p id=\"P12\">These results establish that Ca<sub>V</sub>1.3 is targeted to the presynaptic active zone when its C-terminus is replaced with that of Ca<sub>V</sub>2.1. Conversely, Ca<sub>V</sub>2.1 loses its active zone localization following the reverse swap. We conclude that the Ca<sub>V</sub> C-termini contain sufficient information to define Ca<sub>V</sub> compartment specificity, and these and previous data lead to two predictions. First, because removing known scaffolding motifs in the distal C-terminus only partially impaired active zone localization <sup>##REF##32616470##20##,##UREF##3##45##</sup>, there must be essential targeting motifs in the Ca<sub>V</sub> C-terminus that have not yet been identified. Second, if the chimeric Ca<sub>V</sub>1.3<sup>2.1Ct</sup> channel is appropriately coupled to primed vesicles within the active zone, then Ca<sub>V</sub>1.3<sup>2.1Ct</sup> expression should restore synaptic transmission in Ca<sub>V</sub>2 cTKO neurons and render neurotransmitter release sensitive to L-type channel blockade. We next tested both predictions.</p>", "<title>An EF hand in the proximal C-terminus is necessary for Ca<sub>V</sub>2 active zone targeting</title>", "<p id=\"P13\">Removal of the known active zone scaffolding motifs in the Ca<sub>V</sub>2.1 C-terminus produces a partial defect in Ca<sub>V</sub>2.1 active zone targeting, but truncation of the entire C-terminus fully abolishes active zone localization <sup>##REF##32616470##20##</sup>. To define C-terminal sequences that contain unidentified targeting motifs, we segregated the Ca<sub>V</sub>2.1 C-terminus into a distal segment containing the active zone scaffolding motifs, and the complementary proximal segment (##SUPPL##0##Fig. S1A##+##SUPPL##0##B##). We generated two additional Ca<sub>V</sub>1.3 chimeras (##FIG##2##Fig. 3A##) by translocating either only the Ca<sub>V</sub>2.1 proximal C-terminus (Ca<sub>V</sub>1.3<sup>2.1ProxCt</sup>) or only the Ca<sub>V</sub>2.1 distal C-terminus (Ca<sub>V</sub>1.3<sup>2.1DistCt</sup>) onto Ca<sub>V</sub>1.3. Both Ca<sub>V</sub>1.3<sup>2.1ProxCt</sup> and Ca<sub>V</sub>1.3<sup>2.1DistCt</sup> were expressed efficiently in HEK293T cells after transfection (##SUPPL##0##Fig. S3A##) and in neuronal somata after lentiviral transduction (##SUPPL##0##Fig. S3B##+##SUPPL##0##C##). With STED microscopy, we detected Ca<sub>V</sub>1.3<sup>2.1ProxCt</sup> at the active zone (##FIG##2##Fig. 3B##–##FIG##2##D##) of Ca<sub>V</sub>2 cTKO neurons. Active zone levels of Ca<sub>V</sub>1.3<sup>2.1ProxCt</sup> were reduced compared to Ca<sub>V</sub>1.3<sup>2.1Ct</sup> and resembled those of a mutant Ca<sub>V</sub>2.1 that lacks the active zone scaffolding motifs in the distal C-terminus <sup>##REF##32616470##20##</sup>. Hence, active zone targeting of chimeric Ca<sub>V</sub>1.3s operates in part through these distal sequences. Accordingly, Ca<sub>V</sub>1.3<sup>2.1DistCt</sup> exhibited strong active zone localization in Ca<sub>V</sub>2 cTKO neurons and was indistinguishable from Ca<sub>V</sub>1.3<sup>2.1Ct</sup> (##FIG##2##Fig. 3B##–##FIG##2##D##). Confocal analyses of protein levels in synaptic ROIs matched these findings (##SUPPL##0##Fig. S3D##+##SUPPL##0##E##).</p>", "<p id=\"P14\">Ca<sub>V</sub>1.3<sup>2.1ProxCt</sup> demonstrates that translocation of the Ca<sub>V</sub>2.1 proximal C-terminus onto Ca<sub>V</sub>1.3 suffices to mediate some active zone localization (##FIG##2##Fig. 3B##–##FIG##2##D##) and indicates that the proximal C-terminal sequences are important for presynaptic trafficking. The Ca<sub>V</sub> proximal C-termini (##SUPPL##0##Fig. S1A##+##SUPPL##0##B##) contain two EF hands <sup>##UREF##4##54##,##REF##26680202##55##</sup>. The first EF hand has been implicated in calmodulin-dependent modulation of Ca<sub>V</sub> function <sup>##REF##23591884##56##–##REF##10733970##58##</sup>, though no evidence to date establishes a role in Ca<sub>V</sub> trafficking. We tested whether the first EF hand mediates active zone targeting by deleting the first EF hand from Ca<sub>V</sub>2.1 (Ca<sub>V</sub>2.1<sup>ΔEF1</sup>, ##FIG##2##Fig. 3E##). Ca<sub>V</sub>2.1<sup>ΔEF1</sup> was readily expressed in transfected HEK293T cells and detected in somata of lentivirally transduced neurons (##SUPPL##0##Fig. S3F##–##SUPPL##0##H##). However, deleting the first EF hand abolished Ca<sub>V</sub>2.1 active zone localization in STED microscopy (##FIG##2##Fig. 3F##–##FIG##2##H##) and rendered Ca<sub>V</sub>2.1<sup>ΔEF1</sup> undetectable at synapses in confocal microscopy (##SUPPL##0##Fig. S3I##+##SUPPL##0##J##).</p>", "<p id=\"P15\">In summary, the Ca<sub>V</sub>2.1 distal C-terminus needs to be paired with proximal C-terminal elements to effectively localize Ca<sub>V</sub>s to the active zone. Our data establish that the proximal EF hand is required for active zone targeting of Ca<sub>V</sub>2.1.</p>", "<title>Ca<sub>V</sub>1.3<sup>2.1Ct</sup> supports neurotransmitter release and confers L-type blocker sensitivity after Ca<sub>V</sub>2 ablation</title>", "<p id=\"P16\">Efficient neurotransmitter release requires that Ca<sub>V</sub>s are coupled to fusion-competent synaptic vesicles. Having demonstrated that translocation of the Ca<sub>V</sub>2.1 C-terminus directs Ca<sub>V</sub>1.3 to the active zone, we next asked whether the chimeric Ca<sub>V</sub>1.3<sup>2.1Ct</sup> channel provides Ca<sup>2+</sup> for action potential-triggered release (##FIG##3##Fig. 4A##). Ca<sub>V</sub>1.3<sup>2.1Ct</sup> expression in Ca<sub>V</sub>2 cTKO neurons indeed resulted in EPSCs (##FIG##3##Fig. 4B##+##FIG##3##C##) and IPSCs (##FIG##3##Fig. 4D##+##FIG##3##E##) that were indistinguishable from those measured from Ca<sub>V</sub>2 cTKO neurons with re-expressed Ca<sub>V</sub>2.1. In contrast, and consistent with the loss of active zone targeting (##FIG##1##Fig. 2B##–##FIG##1##D##), Ca<sub>V</sub>2.1<sup>1.3Ct</sup> failed to restore synaptic transmission (##FIG##3##Fig. 4B##–##FIG##3##E##).</p>", "<p id=\"P17\">It is possible that the presynaptic targeting and function of Ca<sub>V</sub>1.3<sup>2.1Ct</sup> results from removal of a dendritic targeting sequence rather than addition of an axonal targeting motif. To address this possibility, we generated a Ca<sub>V</sub>1.3 lacking the entire C-terminus (Ca<sub>V</sub>1.3<sup>ΔCt</sup>). Ca<sub>V</sub>1.3<sup>ΔCt</sup> was effectively expressed (##SUPPL##0##Fig. S4A##–##SUPPL##0##D##) but was not targeted to synapses (##SUPPL##0##Fig. S4E##+##SUPPL##0##F##) or active zones (##SUPPL##0##Fig. S4G##–##SUPPL##0##I##). Furthermore, Ca<sub>V</sub>1.3<sup>ΔCt</sup> did not mediate neurotransmitter release (##SUPPL##0##Fig. S4J##–##SUPPL##0##M##). We conclude that active zone targeting of Ca<sub>V</sub>1.3<sup>2.1Ct</sup> arises from an instructive role of the Ca<sub>V</sub>2.1 C-terminus.</p>", "<p id=\"P18\">At central synapses, neurotransmitter release is insensitive to L-type Ca<sub>V</sub> blockade (##SUPPL##0##Fig. S5##) <sup>##REF##7901765##17##</sup>. Given that we replaced presynaptic Ca<sub>V</sub>2s with an L-type-like Ca<sub>V</sub> (Ca<sub>V</sub>1.3<sup>2.1Ct</sup>), we finally tested whether we also altered the pharmacological sensitivity of synaptic transmission. We performed serial Ca<sub>V</sub> blockade (##FIG##3##Fig. 4F##) through sequential application of ω-agatoxin IVA (to block Ca<sub>V</sub>2.1) and isradipine (to block Ca<sub>V</sub>1s). In Ca<sub>V</sub>2 control neurons, ω-agatoxin IVA reduced IPSCs approximately by half (##FIG##3##Fig. 4G##–##FIG##3##I##), consistent with the reliance of neurotransmitter release on both Ca<sub>V</sub>2.1 and Ca<sub>V</sub>2.2 <sup>##REF##21241895##24##,##REF##15795222##59##</sup>. Isradipine had no effect in Ca<sub>V</sub>2 control neurons (##SUPPL##0##Fig. S5##). Naturally, ω-agatoxin IVA fully inhibited synaptic transmission in Ca<sub>V</sub>2 cTKO neurons rescued with Ca<sub>V</sub>2.1. However, for Ca<sub>V</sub>2 cTKO neurons that expressed Ca<sub>V</sub>1.3<sup>2.1Ct</sup>, synaptic transmission was resistant to ω-agatoxin IVA and instead wholly sensitive to the L-type channel blocker isradipine (##FIG##3##Fig. 4G##–##FIG##3##I##). Hence, Ca<sub>V</sub>1.3<sup>2.1Ct</sup> functionally replaces endogenous Ca<sub>V</sub>2s in Ca<sub>V</sub>2 cTKO neurons and renders neurotransmission fully dependent on L-type Ca<sub>V</sub> activity.</p>" ]	[ "<title>Discussion</title>", "<p id=\"P19\">Voltage-gated Ca<sup>2+</sup> channels are a prototypical protein family to illustrate neuronal polarization: distinct Ca<sub>V</sub>s are sorted effectively to dendritic, somatic and axonal compartments. Here, we establish that the Ca<sub>V</sub> C-termini contain the necessary and sufficient information to sort Ca<sub>V</sub>s into specific subcellular compartments. Within the C-terminus of Ca<sub>V</sub>2.1, the proximal EF hand is essential for presynaptic targeting and it operates in concert with distal scaffolding motifs. Together, the Ca<sub>V</sub>2.1 C-terminal sequences are sufficient to re-direct somatodendritic Ca<sub>V</sub>1 channels to the active zone. Conversely, the Ca<sub>V</sub>1.3 C-terminal sequences disrupt Ca<sub>V</sub>2.1 active zone localization. Our work establishes mechanisms for compartment-specific targeting of a protein family central to the polarized organization of neurons.</p>", "<p id=\"P20\">Multiple cargo selectivity filters converge within the endoplasmic reticulum, the Golgi apparatus, the axon initial segment, and the presynaptic bouton that together permit the targeting of a limited subset of proteins to the active zone while deflecting other cargo <sup>##REF##24320232##60##,##REF##29510294##61##</sup>. Sequence motifs within these proteins may dictate compartment sorting at two major checkpoints: (1) they may mediate protein recruitment into cargo vesicles that are directed to the axon, and (2) they may stabilize proteins at the active zone following their delivery <sup>##REF##27511065##2##,##REF##32403081##62##</sup>. Our work establishes that the Ca<sub>V</sub>2.1 C-terminus encodes necessary and sufficient information to navigate these two checkpoints and implies a cooperative relationship between the proximal and distal elements. The Ca<sub>V</sub>2.1 distal C-terminus efficiently localizes chimeric Ca<sub>V</sub>1.3s to the active zone, indicating that the distal C-terminal sequences permit both Ca<sub>V</sub> sorting into presynaptic cargo and Ca<sub>V</sub> tethering at the active zone, so long as a proximal EF hand is present. The distal motifs that bind to active zone proteins likely fulfill these roles as disrupting their interactions with RIM and RIM-BP leads to targeting defects <sup>##REF##32616470##20##,##REF##21241895##24##,##REF##30661983##36##,##REF##27537484##37##,##UREF##3##45##</sup> similar to those exhibited by chimeric Ca<sub>V</sub>1.3s with the Ca<sub>V</sub>2.1 proximal C-terminus and the Ca<sub>V</sub>1.3 distal C-terminus (##FIG##2##Fig. 3##).</p>", "<p id=\"P21\">The efficiency with which the chimeric Ca<sub>V</sub>1.3<sup>2.1Ct</sup> and Ca<sub>V</sub>1.3<sup>2.1DistCt</sup> channels are targeted to the active zone establishes that the proximal C-termini of both Ca<sub>V</sub>1.3 and Ca<sub>V</sub>2.1 contain necessary information for active zone Ca<sub>V</sub> delivery. This is in line with the high homology of the EF hands and IQ-motif across Ca<sub>V</sub> proximal C-termini and with the presence of these sequences in other voltage-gated channels <sup>##REF##26680202##55##,##REF##24949975##63##</sup>. The proximal C-terminus might include multiple instructive signals that together inform Ca<sub>V</sub> targeting. The EF hand binds to AP-1 and possibly Ca<sup>2+</sup>, which could provide for a trafficking control checkpoint <sup>##REF##26511252##64##,##REF##31577951##65##</sup>. Calmodulin binds to the IQ motif and might regulate channel trafficking and function <sup>##REF##11598293##10##,##REF##17715345##33##,##REF##23664615##34##,##REF##23591884##56##</sup>. Other unknown interactions with these sequences or with sequences elsewhere in the proximal C-terminus might be involved in targeting as well. Altogether, we posit that the proximal EF hand is necessary for passing a trafficking checkpoint that permits incorporation of these Ca<sub>V</sub>s into axon-bound cargo, but likely has no role in stabilizing Ca<sub>V</sub>s within the active zone.</p>", "<p id=\"P22\">Our work on Ca<sub>V</sub>s provides mechanistic insight into the polarized trafficking of protein material in neurons and raises multiple questions. First, some synapses depend on only a single Ca<sub>V</sub>2 subtype while others redundantly use multiple Ca<sub>V</sub>2s, and some synapses experience developmental switches in their Ca<sub>V</sub>2 usage <sup>##REF##9575291##66##,##REF##10627581##67##</sup>. Whether there are specific trafficking and anchoring mechanisms or whether these properties are determined wholly by switches in gene expression remains to be determined. Second, the proximal sequences we identified as important for targeting are also present in other ion channels that undergo polarized trafficking, for example in neuronal Na<sup>+</sup> channels <sup>##REF##26680202##55##,##REF##24949975##63##</sup>. It is possible that the mechanisms we describe for Ca<sub>V</sub>s are broadly employed across channel proteins. The example of Ca<sub>V</sub>s forms an ideal framework to build on and further define mechanisms that sort proteins into specific neuronal compartments.</p>" ]	[]	[ "<p id=\"P1\">To achieve the functional polarization that underlies brain computation, neurons sort protein material into distinct compartments. Ion channel composition, for example, differs between axons and dendrites, but the molecular determinants for their polarized trafficking remain obscure. Here, we identify the mechanisms that target voltage-gated Ca<sup>2+</sup> channels (Ca<sub>V</sub>s) to distinct subcellular compartments. In hippocampal neurons, Ca<sub>V</sub>2s trigger neurotransmitter release at the presynaptic active zone, and Ca<sub>V</sub>1s localize somatodendritically. After knockout of all three Ca<sub>V</sub>2s, expression of Ca<sub>V</sub>2.1, but not of Ca<sub>V</sub>1.3, restores neurotransmitter release. Chimeric Ca<sub>V</sub>1.3 channels with Ca<sub>V</sub>2.1 intracellular C-termini localize to the active zone, mediate synaptic vesicle exocytosis, and render release fully sensitive to blockade of Ca<sub>V</sub>1 channels. This dominant targeting function of the Ca<sub>V</sub>2.1 C-terminus requires an EF hand in its proximal segment, and replacement of the Ca<sub>V</sub>2.1 C-terminus with that of Ca<sub>V</sub>1.3 abolishes Ca<sub>V</sub>2.1 active zone localization. We conclude that the intracellular C-termini mediate compartment-specific Ca<sub>V</sub> targeting.</p>" ]	[ "<title>Supplementary Material</title>" ]	[ "<title>Acknowledgements</title>", "<p id=\"P33\">We thank members of the Kaeser laboratory for insightful discussions and comments, and we specifically acknowledge R. Held, H. Nyitrai, C. Tan, K. Ma, and A. Morabito for help and advice early in the project and/or feedback on the findings and the manuscript. We acknowledge J. Wang, C. Qiao, V. Charles and G. Handy for technical support. We thank A.M.J.M. van den Maagdenberg for providing Ca<sub>V</sub>2.1 floxed mice, and T. Schneider for Ca<sub>V</sub>2.3 floxed mice. This work was supported by the NIH (R01NS083898 to PSK, F31NS127399 to MC), by a Stuart H.Q. and Victoria Quan fellowship (to MC), and by Harvard Medical School. We acknowledge the Neurobiology Imaging Facility at Harvard Medical School for microscope availability and support.</p>" ]	[ "<fig position=\"float\" id=\"F1\"><label>Figure 1.</label><caption><title>Lentivirally expressed Ca<sub>V</sub>2.1, but not Ca<sub>V</sub>1.3, localizes to active zones and restores synaptic transmission in Ca<sub>V</sub>2 triple knockout neurons.</title><p id=\"P36\">(A) Strategy for Ca<sub>V</sub>2 triple knockout in cultured hippocampal neurons as described before <sup>##REF##32616470##20##</sup>. Transduction of neurons from triple-floxed mice with a lentivirus expressing Cre recombinase produced Ca<sub>V</sub>2 cTKO neurons, Ca<sub>V</sub>2 control neurons were identical except for the expression of a truncated, recombination-deficient Cre.</p><p id=\"P37\">(B) Schematic of the conditions for comparison (schematics similar to <sup>##REF##32616470##20##</sup>); HA-tagged (HA) Ca<sub>V</sub>s were expressed by lentiviral transduction.</p><p id=\"P38\">(C-E) Representative images (C) and summary plots of intensity profiles (D) and peak levels (E) of Ca<sub>V</sub>2.1 and PSD-95 at synapses in side-view, levels are shown in arbitrary units (a.u.). Neurons were stained with antibodies against Ca<sub>V</sub>2.1 (analyzed by STED microscopy), PSD-95 (STED), and synapsin (confocal). Dashed lines in D denote levels in Ca<sub>V</sub>2 cTKO (black) and Ca<sub>V</sub>2 control (grey); Ca<sub>V</sub>2 control, 195 synapses/3 independent cultures; Ca<sub>V</sub>2 cTKO, 202/3; Ca<sub>V</sub>2 cTKO + Ca<sub>V</sub>2.1, 205/3; Ca<sub>V</sub>2 cTKO + Ca<sub>V</sub>1.3, 201/3.</p><p id=\"P39\">(F-H) As in C to E, but for synapses stained with antibodies against HA (to detect lentivirally expressed Ca<sub>V</sub>s, STED), PSD-95 (STED), and synapsin (confocal). Dashed lines in G denote levels in Ca<sub>V</sub>2 cTKO (black) and Ca<sub>V</sub>2 cTKO + Ca<sub>V</sub>2.1 (orange); Ca<sub>V</sub>2 control, 208/3; Ca<sub>V</sub>2 cTKO, 222/3; Ca<sub>V</sub>2 cTKO + Ca<sub>V</sub>2.1, 227/3; Ca<sub>V</sub>2 cTKO + Ca<sub>V</sub>1.3, 214/3.</p><p id=\"P40\">(I+J) Representative traces (I) and quantification (J) of NMDAR-mediated EPSCs recorded in whole-cell configuration and evoked by focal electrical stimulation; 18 cells/3 independent cultures each.</p><p id=\"P41\">(K+L) As in I and J, but for IPSCs; 18/3 each.</p><p id=\"P42\">Data are mean ± SEM; *p < 0.001. Statistical significance compared to Ca<sub>V</sub>2 cTKO was determined with Kruskal-Wallis tests followed by Dunn’s multiple comparisons post-hoc tests for the proteins of interest or amplitudes in E, H, J and L. In H, the small but significant decreases in HA intensity in Ca<sub>V</sub>2 control and Ca<sub>V</sub>2 cTKO + Ca<sub>V</sub>1.3 compared to Ca<sub>V</sub>2 cTKO (which does not express an HA-tagged protein) are unlikely biologically meaningful. For C-terminal Ca<sub>V</sub> sequences, and Ca<sub>V</sub> expression analyses by Western blot and confocal microscopy, see ##SUPPL##0##Fig. S1##.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2.</label><caption><title>The Ca<sub>V</sub>2.1 C-terminus suffices to target Ca<sub>V</sub>1.3 to the presynaptic active zone.</title><p id=\"P43\">(A) Schematic of the conditions for comparison.</p><p id=\"P44\">(B-D) Representative images (B) and summary plots of intensity profiles (C) and peak levels (D) of HA and PSD-95 at side-view synapses stained for HA (STED), PSD-95 (STED), and synapsin (confocal). Dashed lines in C denote levels in Ca<sub>V</sub>2 cTKO (black) and Ca<sub>V</sub>2 cTKO + Ca<sub>V</sub>2.1 (orange); Ca<sub>V</sub>2 cTKO, 205 synapses/3 independent cultures; Ca<sub>V</sub>2 cTKO + Ca<sub>V</sub>2.1, 203/3; Ca<sub>V</sub>2 cTKO + Ca<sub>V</sub>1.3, 222/3; Ca<sub>V</sub>2 cTKO + Ca<sub>V</sub>1.3<sup>2.1Ct</sup>, 218/3; Ca<sub>V</sub>2 cTKO + Ca<sub>V</sub>2.1<sup>1.3Ct</sup>, 208/3.</p><p id=\"P45\">(E+F) Representative areas of confocal images (E) and quantification (F) of HA levels in synapsin regions of interest (ROIs). Identical areas (58.14 × 58.14 μm<sup>2</sup>) from the same cultures were imaged for confocal (E+F) and STED (B-D) analyses and whole images were quantified; 12 images/3 independent cultures each.</p><p id=\"P46\">Data are mean ± SEM; p < 0.01 and *p < 0.001. Statistical significance compared to Ca<sub>V</sub>2 cTKO was determined with Kruskal-Wallis tests followed by Dunn’s multiple comparisons post-hoc tests for the protein of interest in D and F. For Ca<sub>V</sub> expression analyses by Western blot and confocal microscopy, see ##SUPPL##0##Fig. S2##.</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3.</label><caption><title>An EF hand in the proximal C-terminus is essential for Ca<sub>V</sub>2 active zone targeting.</title><p id=\"P47\">(A) Schematic of the conditions for comparison in B-D.</p><p id=\"P48\">(B-D) Representative images (B) and summary plots of intensity profiles (C) and peak levels (D) of HA and PSD-95 at side-view synapses stained for HA (STED), PSD-95 (STED), and synapsin (confocal). Dashed lines in C denote levels in Ca<sub>V</sub>2 cTKO (black) and Ca<sub>V</sub>2 cTKO + Ca<sub>V</sub>1.3<sup>2.1Ct</sup> (purple); Ca<sub>V</sub>2 cTKO, 207 synapses/3 independent cultures; Ca<sub>V</sub>2 cTKO + Ca<sub>V</sub>1.3<sup>2.1Ct</sup>, 204/3; Ca<sub>V</sub>2 cTKO + Ca<sub>V</sub>1.3<sup>2.1ProxCt</sup>, 209/3; Ca<sub>V</sub>2 cTKO + Ca<sub>V</sub>1.3<sup>2.1DistCt</sup>, 210/3.</p><p id=\"P49\">(E) Schematic of the conditions for comparison in F-H.</p><p id=\"P50\">(F-H) Representative images (F) and summary plots of intensity profiles (G) and peak levels (H) of HA and PSD-95 at side-view synapses stained for HA (STED), PSD-95 (STED), and synapsin (confocal). Dashed lines in G denote levels in Ca<sub>V</sub>2 cTKO (black) and Ca<sub>V</sub>2 cTKO + Ca<sub>V</sub>2.1 (orange); Ca<sub>V</sub>2 cTKO, 200/3; Ca<sub>V</sub>2 cTKO + Ca<sub>V</sub>2.1, 180/3; Ca<sub>V</sub>2 cTKO + Ca<sub>V</sub>2.1<sup>ΔEF1</sup>, 203/3.</p><p id=\"P51\">Data are mean ± SEM; p < 0.001. Statistical significance compared to Ca<sub>V</sub>2 cTKO was determined with Kruskal-Wallis tests followed by Dunn’s multiple comparisons post-hoc tests for the protein of interest in D and H. For Ca<sub>V</sub> expression analyses by Western blot and confocal microscopy, see ##SUPPL##0##Fig. S3##.</p></caption></fig>", "<fig position=\"float\" id=\"F4\"><label>Figure 4.</label><caption><title>Ca<sub>V</sub>1.3<sup>2.1Ct</sup> channels mediate neurotransmitter release and render it L-type blocker sensitive.</title><p id=\"P52\">(A) Schematic of the conditions for comparison, as in ##FIG##1##Fig. 2##.</p><p id=\"P53\">(B+C) Representative traces (B) and quantification (C) of NMDAR-mediated EPSCs; 18 cells/3 independent cultures each.</p><p id=\"P54\">(C+E) As in B and C, but for IPSCs; 18/3 each.</p><p id=\"P55\">(F) Experimental strategy to evaluate blocker sensitivity of synaptic transmission. Evoked IPSCs were recorded before blocker application (before), after wash-in of 200 nM ω-agatoxin-IVA alone (+ ω-agatoxin, to block Ca<sub>V</sub>2.1), and after wash-in 200 nM ω-agatoxin-IVA and 20 μM isradipine (+ ω-agatoxin + isradipine, to block Ca<sub>V</sub>1s and Ca<sub>V</sub>2.1).</p><p id=\"P56\">(G+H) Representative traces (G) and quantification (H) of IPSCs recorded as outlined in F; 9 cells/3 independent cultures each.</p><p id=\"P57\">(I) Comparison of IPSCs normalized to “before” in each condition; 9/3 each.</p><p id=\"P58\">Data are mean ± SEM; p < 0.05, p < 0.01, and *p < 0.001. Statistical significance compared to Ca<sub>V</sub>2 cTKO in C and E was determined with Kruskal-Wallis tests followed by Dunn’s multiple comparisons post-hoc tests. Statistical significance compared to “before” in H was determined with Friedman tests followed by Dunn’s multiple comparisons post-hoc tests. Statistical significance compared to Ca<sub>V</sub>2 control in I was determined with two-way, repeated-measures ANOVA followed by Dunnett’s multiple comparisons post-hoc tests. For characterization of C-terminally truncated Ca<sub>V</sub>1.3, see ##SUPPL##0##Fig. S4##; for assessment of isradipine-sensitivity of synaptic transmission in Ca<sub>V</sub>2 control neurons, see ##SUPPL##0##Fig. S5##.</p></caption></fig>" ]	[]	[]	[]	[]	[]	[]	[ "<supplementary-material id=\"SD1\" position=\"float\" content-type=\"local-data\"><label>Supplement 1</label></supplementary-material>" ]	[ "<fn-group><fn fn-type=\"COI-statement\" id=\"FN2\"><p id=\"P34\">Declaration of interests</p><p id=\"P35\">The authors declare no conflicts of interest.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"nihpp-2023.12.23.573183v1-f0001\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-2023.12.23.573183v1-f0002\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-2023.12.23.573183v1-f0003\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-2023.12.23.573183v1-f0004\" position=\"float\"/>" ]	[ "<media xlink:href=\"NIHPP2023.12.23.573183v1-supplement-1.pdf\" id=\"d64e1918\" position=\"anchor\"/>" ]	[{"label": ["1."], "surname": ["y Cajal", "Sherrington"], "given-names": ["S.R.", "S.C.S."], "year": ["1891"], "source": ["Significaci\u00f3n fisiol\u00f3gica de las expansiones protoplasm\u00e1ticas y nerviosas de las c\u00e9lulas de la sustancia gris (Establecimento tipografico)"]}, {"label": ["6."], "surname": ["Dolphin"], "given-names": ["A.C."], "year": ["2012"], "article-title": ["Calcium channel auxiliary \u03b12\u03b4 and \u03b2 subunits: trafficking and one step beyond"], "source": ["Nat Rev Neurosci"], "volume": ["13"], "fpage": ["664"], "lpage": ["664"], "pub-id": ["10.1038/nrn3311"]}, {"label": ["25."], "surname": ["Emperador-Melero", "Andersen", "Metzbower", "Levy", "Dharmasri", "de Nola", "Blanpied", "Kaeser"], "given-names": ["J.", "J.W.", "S.R.", "A.D.", "P.A.", "G.", "T.A.", "P.S."], "year": ["2023"], "article-title": ["Molecular definition of distinct active zone protein machineries for Ca 2+ channel clustering and synaptic vesicle priming"], "source": ["bioRxiv"], "pub-id": ["10.1101/2023.10.27.564439"]}, {"label": ["45."], "surname": ["L\u00fcbbert", "Goral", "Satterfield", "Putzke", "van den Maagdenberg", "Kamasawa", "Young"], "given-names": ["M.", "R.O.", "R.", "T.", "A.M.", "N.", "S.M."], "year": ["2017"], "article-title": ["A novel region in the CaV2.1 \u03b11 subunit C-terminus regulates fast synaptic vesicle fusion and vesicle docking at the mammalian presynaptic active zone"], "source": ["Elife"], "volume": ["6"], "pub-id": ["10.7554/eLife.28412"]}, {"label": ["54."], "surname": ["Babitch"], "given-names": ["J."], "year": ["1990"], "article-title": ["Channel hands"], "source": ["Nature 1990 346"], "volume": ["6282"], "issue": ["346"], "fpage": ["321"], "lpage": ["322"], "pub-id": ["10.1038/346321B0"]}, {"label": ["73."], "surname": ["Emperador-Melero", "de Nola", "Kaeser"], "given-names": ["J.", "G.", "P.S."], "year": ["2021"], "article-title": ["Intact synapse structure and function after combined knockout of PTP\u03b4, PTP\u03c3 and LAR"], "source": ["Elife"], "fpage": ["2021.01.17.427005"], "pub-id": ["10.1101/2021.01.17.427005"]}, {"label": ["74."], "surname": ["Tan", "de Nola", "Qiao", "Imig", "Born", "Brose", "Kaeser"], "given-names": ["C.", "G.", "C.", "C.", "R.T.", "N.", "P.S."], "year": ["2022"], "article-title": ["Munc13 supports fusogenicity of non-docked vesicles at synapses with disrupted active zones"], "source": ["Elife"], "volume": ["11"], "fpage": ["2022.04.01.486686"], "pub-id": ["10.7554/eLife.79077"]}]	{ "acronym": [], "definition": [] }	77	CC BY	no	2024-01-13 00:14:49	bioRxiv. 2023 Dec 23;:2023.12.23.573183	oa_package/a4/43/PMC10769351.tar.gz
PMC10769383	38187666	[ "<title>Introduction</title>", "<p id=\"P2\">Nonribosomal peptides (NRPs) are natural products that possess a range of biological activities, such as antibiotic,<sup>##REF##9545426##1##</sup> anticancer,<sup>##REF##11908996##2##</sup> biosurfactant,<sup>##REF##14762003##3##</sup> and immunosuppressant.<sup>##REF##23818858##4##</sup> Their peptide scaffold is biosynthesized by nonribosomal peptide synthetases (NRPSs), multimodular enzymes that work like assembly lines.<sup>##REF##15700962##5##–##REF##31699907##7##</sup> A typical peptide elongation module consists of three domains: condensation (C), adenylation (A), and thiolation (T). A conserved serine of the T domain is posttranslationally modified by the addition of phosphopantetheine.<sup>##REF##10625633##8##,##REF##11851476##9##</sup> The A domain activates an amino acid by adenylation and loads it onto the phosphopantetheine arm of the T domain as an acyl-thioester intermediate. The C domain then catalyzes the formation of peptide bonds between intermediates bound to the T domain to extend the peptide chain (##FIG##0##Figure 1A##).</p>", "<p id=\"P3\">A subset of NRPSs are encoded in biosynthetic gene clusters (BGCs) with enzymes annotated as lantibiotic dehydratases <sup>##REF##25363770##10##,##REF##28135077##11##</sup> Lantibiotic dehydratases (protein family PF04738) generate the dehydroamino acids<sup>##UREF##0##12##</sup> of ribosomally synthesized and post-translationally modified peptides (RiPPs),<sup>##UREF##1##13##</sup> including lanthipeptides<sup>##REF##28135077##11##</sup> (called lantibiotics if they display antibiotic activity) and thiopeptides.<sup>##REF##26675417##14##</sup> The dehydration reaction involves the glutamylation of serine and threonine hydroxyl groups using glutamyl-tRNA<sup>Glu</sup> and subsequent elimination of glutamate to generate peptidyl dehydroamino acids<sup>##REF##25363770##10##,##REF##23589847##15##</sup> (##FIG##0##Figure 1B##). Other enzymes frequently mis-annotated as lantibiotic dehydratase are peptide aminoacyl-tRNA ligases (PEARLs).<sup>##REF##31320540##16##–##REF##34725492##18##</sup> PEARLs catalyze peptide bond formation at the C-terminus of a carrier peptide using adenosine-5’-triphosphate (ATP) and aminoacyl-tRNA<sup>##REF##31751505##17##</sup> (##FIG##0##Figure 1B##). The amino acid added by the PEARL will undergo enzymatic modifications and proteolysis to yield amino acid-derived natural products.<sup>##REF##21815669##19##–##REF##35787182##21##</sup> However, neither a RiPP precursor peptide nor a cognate PEARL carrier peptide can be identified in the NRPS BGCs, indicating the putative lantibiotic dehydratases serve a different function in the biosynthesis of NRPs.</p>", "<p id=\"P4\">In this study, we investigated a hybrid NRPS-PKS BGC<sup>##UREF##2##22##–##REF##19554239##24##</sup> from <italic toggle=\"yes\">Stackebrandtia nassauensis</italic> that contains a putative lantibiotic dehydratase (##FIG##0##Fig. 1C##). Heterologous expression, comparative metabolomics, and structural elucidation revealed a series of novel metabolites. The biosynthetic sequence was revealed by omitting select biosynthetic enzymes during heterologous expression. The NRPS SnaA links two arginine amine groups through a ureido group, leaving an inert carboxylate at the initiation position that cannot be further extended by the NRPS machinery. The putative lantibiotic dehydratase SnaE catalyzes peptide bond formation at this unactivated carboxylate of the terminal ureido group, achieving chain extension in the opposite direction to NRPS-PKS biosynthesis. The results show that the annotated lantibiotic dehydratases that colocalize with NRPS/PKSs likely biosynthesize amide bonds that are not amenable to thioester assembly line biochemistry.</p>", "<p id=\"P5\">Ureido group formation is one of the many versatile reactions during NRP biosynthesis.<sup>##REF##34676836##25##,##REF##28526268##26##</sup> In vitro studies of SylC in syringolin biosynthesis suggest that the ureido moiety likely originates from bicarbonate.<sup>##REF##19968303##27##</sup> This unusual head-to-head condensation reaction between two amino acids led us to hypothesize that the condensation domain of the NRPS SnaA is specialized for ureido group formation. Analysis of the condensation domain sequences associated with ureido-containing NRPs predicts that the active site signature of ureido-generating condensation (UreaC) domain is EHHXXHDG (X represents any amino acid) compared to the canonical XHHXXXDG motif for peptide bond formation.<sup>##REF##11851476##9##,##REF##26831698##28##</sup> Condensation domains that do not generate ureido groups in the Minimum Information about a Biosynthetic Gene cluster (MiBiG) database<sup>##REF##31612915##29##</sup> never have the EHHXXHDG motif, suggesting the extra conservation of glutamate and histidine residues in the active site of C domains marks the signature of ureido group formation.</p>" ]	[]	[ "<title>Results</title>", "<title>Products produced by the <italic toggle=\"yes\">sna</italic> BGC</title>", "<p id=\"P6\">Around two thousand NRPS/PKS BGCs contain enzymes annotated as lantibiotic dehydratase (NCBI, June 2023). Most of the annotated lantibiotic dehydratases are stand-alone enzymes, but some of them are fused to thioesterase or condensation domains. No lanthipeptide precursor peptides can be bioinformatically identified in these BGCs. This observation suggests that these putative lantibiotic dehydratases are involved in NRP or polyketide (PK) biosynthesis rather than lanthipeptide biosynthesis.</p>", "<p id=\"P7\">The BGCs in question are mostly from Actinobacteria. We chose one representative candidate gene cluster from <italic toggle=\"yes\">S. nassauensis</italic> (##FIG##0##Figure 1C##) for heterologous expression in <italic toggle=\"yes\">Streptomyces albus</italic> J1074.<sup>##REF##977549##30##</sup> Expression of a construct containing <italic toggle=\"yes\">snaABCDOET</italic><sub><italic toggle=\"yes\">1</italic></sub><italic toggle=\"yes\">T</italic><sub><italic toggle=\"yes\">2</italic></sub> under control of the SP44 constitutive promoter<sup>##REF##26374838##31##</sup> produced several new metabolites detected by liquid chromatography-mass spectrometry (LC-MS). Expression in two liters of media, purification, and characterization by nuclear magnetic resonance spectroscopy revealed the structures of three major metabolites (compounds A, B, and C, ##FIG##1##Figure 2##).</p>", "<p id=\"P8\">To investigate the enzymes required to produce these compounds, <italic toggle=\"yes\">snaA</italic> (NRPS), <italic toggle=\"yes\">snaO</italic> (dehydrogenase), and <italic toggle=\"yes\">snaE</italic> (putative lantibiotic dehydratase) were individually inactivated during heterologous expression (##FIG##1##Figure 2##). For <italic toggle=\"yes\">snaA</italic>, both serine codons of the T domains were mutated to alanine to yield an inactive mutant that cannot be converted to the holo form. In-frame deletions were used to inactivate <italic toggle=\"yes\">snaO</italic> and <italic toggle=\"yes\">snaE</italic> (##SUPPL##0##Supporting Information##). Production of compound A with two arginines required SnaA but not SnaO or SnaE (##FIG##1##Figure 2##). Compound B contains one more Thr than compound A, and its biosynthesis required SnaA and SnaE but not SnaO. Compound C has an additional alkene group compared to compound B, and required SnaA, SnaE, and SnaO for biosynthesis. These results suggest that compound A is an early-stage biosynthetic intermediate produced by the NRPS and PKS (vide infra), and compound C is likely a later intermediate or the final product. Structural comparison between A and B strongly suggests that the putative lantibiotic dehydratase SnaE catalyzes the formation of a peptide bond between a threonine donor and a motif made by the NRPS/PKS. Therefore, SnaE is a peptide bond-forming enzyme rather than a dehydratase.</p>", "<p id=\"P9\">In addition to compounds A-C, we observed three other products, compounds D-F. Compounds A and D, B and E, and C and F are always produced together, respectively (##FIG##1##Figure 2##, ##FIG##2##3A##). High-resolution mass spectrometry suggests the difference in molecular formulae of each pair is H<sub>2</sub>O. A spontaneous intramolecular dehydrative cyclization between the ureido NH and the ketone explains the formation of compounds A, B, and C from D, E, and F, respectively. Similar reactions of guanidino nitrogens spontaneously cyclizing onto an arginine ethyl ketone have been observed in the study of saxitoxin biosynthesis.<sup>##REF##24718696##32##–##UREF##4##34##</sup> Compounds D-F eluded spectroscopic characterization because of their high cyclization reactivity during purification efforts, but high-resolution MS/MS spectra (##SUPPL##0##Figure S1##–##SUPPL##0##3##) as well as observed non-enzymatic conversion of D to A, E to B, and F to C during purification strongly support the structural assignment.</p>", "<title>Proposed biosynthetic pathway</title>", "<p id=\"P10\">Knowing the required enzymes for the biosynthesis of each metabolite, we propose the following biosynthetic sequence (##FIG##2##Figure 3B##). The two adenylation domains of SnaA (NCBI ADD43706.1) both activate and load arginine onto the peptidyl carrier protein (PCP) as activated thioesters. The condensation domain is unusual from bioinformatic analysis (vide infra) and catalyzes the condensation between two amine groups of arginine to form a ureido group that is likely derived from bicarbonate (HCO<sub>3</sub><sup>−</sup>).<sup>##REF##19968303##27##</sup> The PKS SnaB (NCBI ADD43707.1) incorporates a propionate extension unit into the growing chain, as shown by isotope enrichment upon feeding 2-<sup>13</sup>C sodium propionate to the heterologous expression system (##SUPPL##0##Figure S4##). The SnaB-bound intermediate may be hydrolyzed by the thioesterase SnaD (NCBI ADD43709.1) to form compound D which upon cyclization gives compound A (##SUPPL##0##Figure S5A##). Threonine addition by SnaE (NCBI ADD43711.1) can occur on PCP/acyl carrier protein (ACP)-bound intermediates <bold>I</bold> or <bold>II</bold> (##FIG##2##Figure 3B##) or on the free molecules D or H (##SUPPL##0##Figure S5##). Based on precedence with the dehydrogenase EpnF,<sup>##REF##26999044##35##,##REF##26789439##36##</sup> compound F could be produced from compound G by SnaO (NCBI ADD43710.1) via a decarboxylation-dehydrogenation reaction sequence, but conversion of compound E to F by SnaO cannot be ruled out. As outlined in the <xref rid=\"S6\" ref-type=\"sec\">Discussion</xref> section, we consider compound F the final product of the pathway and term this compound threopeptin, whereas the formation of compounds A-E are proposed to be off-pathway via non-enzymatic cyclization and/or premature thioesterase activity (##SUPPL##0##Figure S5##).</p>", "<title>Biochemical and bioinformatic studies on ureido group formation</title>", "<p id=\"P11\">Ureido group formation is one of the many versatile reactions catalyzed by C domains during NRP biosynthesis.<sup>##REF##34676836##25##,##REF##28526268##26##</sup> Based on current understanding, we could not have predicted that the <italic toggle=\"yes\">sna</italic> BGC would produce a ureido structure. Therefore, we bioinformatically investigated whether the C domains associated with known ureido-containing natural products (UreaC domains) have a distinct active site amino acid signature. We compiled the UreaC domains in the MiBiG database based on collinearity to product structures (e.g. anabaenopeptins,<sup>##REF##20338518##37##</sup> bulbfieramide,<sup>##REF##37092875##38##</sup> chitinimide,<sup>##REF##34301933##39##</sup> and pseudovibriomide<sup>##UREF##5##40##</sup>) as well as examples with in vitro confirmation of the ureido formation enzyme activity (e.g. syringolin A,<sup>##REF##19968303##27##</sup> pacidamycin,<sup>##REF##20826445##41##</sup> antipain,<sup>##REF##34767710##42##</sup> and muraymycin<sup>##REF##34767710##42##</sup>). Multiple sequence alignment showed that UreaC domains have a conserved EHHXXHDG active site (##FIG##3##Figure 4A##) compared to the canonical XHHXXXDG active site of the amide bond-forming C domains.<sup>##REF##11851476##9##,##REF##26831698##28##</sup> Examining all C domain sequences in MiBiG showed that none of the C domains with other functions have an EHHXXHDG active site. Therefore, based on current examples, the EHHXXHDG signature of the C domain active site appears to be sufficient and necessary to indicate the ureido formation activity.</p>", "<p id=\"P12\">The ureido-forming activity of SnaA was confirmed in vitro using holo-SnaA hetereologously expressed in <italic toggle=\"yes\">E. coli</italic> BAP1.<sup>##REF##11230695##43##</sup> The PCP-bound products of SnaA were intercepted using cysteamine<sup>##REF##24041368##44##</sup> followed by chemical derivatization with fluorenylmethyloxycarbonyl chloride (Fmoc-Cl) and LC-MS analysis<sup>##UREF##6##45##</sup> (##FIG##3##Figure 4B##). The observed products confirmed that SnaA is responsible for formation of intermediate <bold>I</bold> (##FIG##2##Figure 3##). When the active site glutamate and histidine residues of the UreaC domain were individually mutated to alanine, the resulting mutants showed significantly decreased production of the arginine ureido dipeptide in vitro (##FIG##3##Figure 4C##), indicating that the conserved glutamate and histidine residues in the UreaC active site are important (but not essential) for the ureido bond-forming activity of SnaA.</p>" ]	[ "<title>Discussion</title>", "<p id=\"P13\">The formation of a ureido group during NRP biosynthesis is termed a chain-reversal event because it generates a carboxylate rather than the usual amine group at the initiation position. Proposed mechanisms of ureido group formation are presented in ##SUPPL##0##Figure S6##. For ureido-forming BGCs that contain two A domains, the specificity of the A domain of the loading A-T didomain usually corresponds to the amino acid at the terminal position of the ureido group.<sup>##REF##20338518##37##,##REF##37092875##38##,##UREF##5##40##,##REF##34767710##42##</sup> Similarly, the A domain specificity of the first extension module (UreaC-A-T) usually corresponds to the internal amino acid of the ureido group.<sup>##REF##20338518##37##,##REF##37092875##38##,##UREF##5##40##,##REF##34767710##42##</sup> After ureido bond formation, the amino acid at the internal position is still attached to the PCP as a thioester and can be further extended by NRPS/PKS biochemistry.<sup>##REF##34767710##42##</sup> However, the terminal amino acid is left with an unactivated carboxylate and can no longer be extended by the assembly line chemistry.<sup>##REF##34767710##42##</sup> This model explains why all isolated ureido-containing NRPs only have one side of the ureido moiety further extended by the NRPS/PKS (##SUPPL##0##Figure S7##). If chain extension of the terminal carboxylate is desired for biological activity, two possible solutions can be envisioned. Either the ureido forming process will need to take place using a T-domain bound polypeptide (rather than amino acid) that is activated by ATP (##SUPPL##0##Figure S6C##), a mechanism that has been ruled out in the case of SylC.<sup>##REF##19968303##27##</sup> Alternatively, the system needs a separate amidation machinery. The PEARL-like enzyme SnaE appears to have been recruited for this latter purpose. Based on its sequence homology to PEARLs, SnaE is likely to add threonine using a similar ATP- and aminoacyl-tRNA-dependent mechanism,<sup>##REF##31751505##17##</sup> in which ATP is used to phosphorylate the terminal carboxylate to form an activated acyl-phosphate intermediate, which is then attacked by Thr-tRNA<sup>Thr</sup> as the Thr donor in a condensation reaction. Hydrolysis of the tRNA as is observed in PEARLs would then provide the observed products.</p>", "<p id=\"P14\">The formation of the ethyl ketone in compound D and E follows a unique mechanism where the ethyl group originates from the decarboxylation of methylmalonate. Ethyl ketones are commonly observed motifs during PK/NRP biosynthesis, but the biosynthetic precursors of the ethyl group are usually <italic toggle=\"yes\">S</italic>-adenosyl methionine (SAM) and malonate. For instance, the ethyl ketone derivative of arginine is a biosynthetic intermediate of saxitoxin<sup>##REF##24718696##32##,##UREF##3##33##</sup> and is biosynthesized by a polyketide-like synthase SxtA.<sup>##REF##29390180##46##</sup> Malonyl-CoA is loaded onto the ACP and is then methylated by the methyltransferase domain of SxtA. The methylmalonyl-ACP is thought to be decarboxylated to propionyl-ACP, which is followed by a pyridoxal phosphate-dependent condensation between arginine and propionyl-ACP to yield the arginine ethyl ketone.<sup>##REF##29390180##46##</sup></p>", "<p id=\"P15\">In the case of epoxyketone proteasome inhibitors such as epoxomicin and eponemycin,<sup>##REF##24168704##47##–##REF##25831524##49##</sup> the ethyl groups of the epoxyketone warhead originate similarly from malonyl-CoA and on-ACP methylation(s) by a methyltransferase domain of the PKS EpxE/EpnH.<sup>##REF##26999044##35##,##REF##26477320##50##</sup> The epoxide is thought to be generated by a conserved acyl-CoA dehydrogenase-like enzyme EpxF/EpnF via a decarboxylation-dehydrogenation-epoxidation sequence after thioesterase-mediated release from the assembly lines.<sup>##REF##26999044##35##</sup> Given that the vinylketone in compounds C/F originates from methylmalonate, the acyl-CoA dehydrogenase-like enzyme SnaO may also use a decarboxylative dehydrogenation mechanism to install the α,β-unsaturated ketone. Interestingly, the reaction of SnaO seems to stop at dehydrogenation, because no epoxidation was observed during heterologous expression in <italic toggle=\"yes\">S. albus</italic>.</p>", "<p id=\"P16\">Different from the biosynthetic pathways of saxitoxin and epoxyketones, the methyltransferase domain for the methylation of malonate is absent in the <italic toggle=\"yes\">sna</italic> BGC. This absence is consistent with the PKS SnaB using methylmalonyl-CoA to produce the ethyl group of threopeptin. The ethyl group of the epoxyketone macyranone could also originate from methylmalonate since its biosynthetic PKS module lacks a methyltransferase domain.<sup>##REF##26050527##48##</sup></p>", "<p id=\"P17\">We hypothesize that compound F (threopeptin) is the final product of the <italic toggle=\"yes\">sna</italic> BGC because its biosynthesis depends on SnaA, SnaO, and SnaE, and it carries an α,β-unsaturated ketone that could function as an electrophilic warhead. The antipain group of protease inhibitors<sup>##UREF##7##51##</sup> structurally resembles threopeptin. The aldehyde of antipain covalently targets protease active site serine or cysteine residues and the vinyl ketone of threopeptin may similarly target a protease active site serine or cysteine residue via 1,4-conjugate addition. Although the instability of threopeptin prevented isolation and bioactivity testing, <italic toggle=\"yes\">S. nassauensis</italic> may produce the compound to inhibit proteases of competitor or predator organisms after secretion by SnaT<sub>1</sub> and T<sub>2</sub>.</p>" ]	[]	[ "<p id=\"P1\">A subset of nonribosomal peptide synthetases (NRPSs) and polyketide synthases (PKSs) are encoded in their biosynthetic gene clusters (BGCs) with enzymes annotated as lantibiotic dehydratases. The functions of these putative lantibiotic dehydratases remain unknown. Here, we characterize an NRPS-PKS BGC with a putative lantibiotic dehydratase from the bacterium <italic toggle=\"yes\">Stackebrandtia nassauensis</italic> (<italic toggle=\"yes\">sna</italic>). Heterologous expression revealed several metabolites produced by the BGC, and the omission of selected biosynthetic enzymes revealed the biosynthetic sequence towards these compounds. The putative lantibiotic dehydratase catalyzes peptide bond formation that extends the peptide scaffold opposite to the NRPS and PKS biosynthetic direction. The condensation domain of the NRPS catalyzes the formation of a ureido group, and bioinformatics analysis revealed distinct active site residues of ureido-generating condensation (UreaC) domains. This work demonstrates that the annotated lantibiotic dehydratase serves as a separate amide bond-forming machinery in addition to the NRPS, and that the lantibiotic dehydratase enzyme family possesses diverse catalytic activities in the biosynthesis of both ribosomal and non-ribosomal natural products.</p>" ]	[ "<title>Supplementary Material</title>" ]	[ "<title>Acknowledgments</title>", "<p id=\"P18\">We thank Dr. Lingyang Zhu in the School of Chemical Sciences NMR Lab for assistance in data interpretation and Professor Gregory Challis (University of Warwick) for helpful discussions.</p>", "<title>Funding</title>", "<p id=\"P19\">This study was supported by the National Institutes of Health (Grant R37 GM058822 to WAV), the Howard Hughes Medical Institute, a Barbara H. Weil fellowship (to YY), and a Seemon H. Pines fellowship (to YY).</p>", "<title>Data Availability Statement</title>", "<p id=\"P20\">The authors declare that the data supporting the findings of this study are available within the paper and its ##SUPPL##0##Supporting Information files##, and at Mendeley Data, V1, doi: <ext-link xlink:href=\"10.17632/rjytc5c3cr.1\" ext-link-type=\"doi\">10.17632/rjytc5c3cr.1</ext-link> as well as from the corresponding author upon reasonable request.</p>" ]	[ "<fig position=\"float\" id=\"F1\"><label>Figure 1.</label><caption><p id=\"P24\">(<italic toggle=\"yes\">A</italic>) The peptide bond formation chemistry of NRPSs. (<italic toggle=\"yes\">B</italic>) Known enzymatic activities of the lantibiotic dehydratase enzyme family. (<italic toggle=\"yes\">C</italic>) Schematic diagram of the <italic toggle=\"yes\">sna</italic> BGC from <italic toggle=\"yes\">Stackebrandtia nassauensis</italic>. KS: ketosynthase. Te: thioesterase. For the accession IDs for all proteins in the <italic toggle=\"yes\">sna</italic> BGC, see the ##SUPPL##0##Supporting Information##.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2.</label><caption><p id=\"P25\">Structures of metabolites produced from the <italic toggle=\"yes\">sna</italic> BGC using different heterologous expression constructs. EIC: extracted ion chromatogram. Only key NMR connectivities used to solve the structures are shown. For complete NMR data, see the ##SUPPL##0##Supporting Information##.</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3.</label><caption><p id=\"P26\">(<italic toggle=\"yes\">A</italic>) Proposed structures of compounds D, E, and F. Stereochemistry could not be determined because of the high cyclization reactivity of these compounds. (<italic toggle=\"yes\">B</italic>) The proposed biosynthetic sequence to generate compound F (threopeptin).</p></caption></fig>", "<fig position=\"float\" id=\"F4\"><label>Figure 4.</label><caption><p id=\"P27\">(<italic toggle=\"yes\">A</italic>) Multiple sequence alignment of UreaC domains. The ##SUPPL##0##Supporting Information Table S3## contains the MiBiG BGC repository identification numbers for the listed enzymes. (<italic toggle=\"yes\">B</italic>) Scheme of the derivatization of the bisarginine ureido structure generated by SnaA in vitro. (<italic toggle=\"yes\">C</italic>) EICs of Fmoc- and cysteamine-derivatized bisarginine ureido structures generated in vitro by wild type and mutant SnaA.</p></caption></fig>" ]	[]	[]	[]	[]	[]	[]	[ "<supplementary-material id=\"SD1\" position=\"float\" content-type=\"local-data\"><label>Supplement 1</label></supplementary-material>" ]	[ "<fn-group><fn fn-type=\"COI-statement\" id=\"FN1\"><p id=\"P21\">The authors declare no competing financial interest(s).</p></fn><fn id=\"FN2\"><p id=\"P22\">Supporting Information</p><p id=\"P23\">##SUPPL##0##Supporting Figures S1##–##SUPPL##0##S7##, ##SUPPL##0##Supporting Tables S1##–##SUPPL##0##S3##, NMR spectra of all new compounds, and accession numbers of all proteins discussed in this study.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"nihpp-2023.12.23.573212v1-f0001\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-2023.12.23.573212v1-f0002\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-2023.12.23.573212v1-f0003\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-2023.12.23.573212v1-f0004\" position=\"float\"/>" ]	[ "<media xlink:href=\"media-1.pdf\" id=\"d64e580\" position=\"anchor\"/>" ]	[{"label": ["(12)"], "surname": ["Wang", "Wu", "Tang", "Deng"], "given-names": ["S.", "K.", "Y. J.", "H."], "article-title": ["Dehydroamino acid residues in bioacve natural products"], "source": ["Nat. Prod. Rep"], "year": ["2023"], "pub-id": ["10.1039/d1033np00041a"]}, {"label": ["(13)"], "surname": ["Montalb\u00e1n-L\u00f3pez", "Scot", "Ramesh", "Rahman", "van Heel", "Viel", "Bandarian", "Ditmann", "Genilloud", "Goto", "Grande Burgos", "Hill", "Kim", "Koehnke", "Latham", "Link", "Marnez", "Nair", "Nicolet", "Rebuffat", "Sahl", "Sareen", "Schmidt", "Schmit", "Severinov", "S\u00fcssmuth", "Truman", "Wang", "Weng", "van Wezel", "Zhang", "Zhong", "Piel", "Mitchell", "Kuipers", "van der Donk"], "given-names": ["M.", "T. A.", "S.", "I. R.", "A. J.", "J. H.", "V.", "E.", "O.", "Y.", "M. J.", "C.", "S.", "J.", "J. A.", "A. J.", "B.", "S. K.", "Y.", "S.", "H.-G.", "D.", "E. W.", "L.", "K.", "R. D.", "A. W.", "H.", "J.-K.", "G. P.", "Q.", "J.", "J.", "D. A.", "O. P.", "W. A."], "article-title": ["New developments in RiPP discovery, enzymology and engineering"], "source": ["Nat. Prod. 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PMC10769429	38187688	[ "<title>Introduction</title>", "<p id=\"P2\">The diverse species that make up the human gut microbiome continually evolve throughout a host’s lifetime. Recent work has shown that rapid adaptation is a hallmark of evolution in the microbiome, as novel mutations often arise and sweep to high frequency within healthy hosts on timescales of days to months [##REF##30673701##1##, ##REF##31028005##2##, ##UREF##0##3##, ##UREF##1##4##, ##UREF##2##5##, ##REF##34301627##6##, ##REF##35545448##7##]. These evolutionary dynamics can have functional consequences for the host, as microbial genetic variants are associated with metabolic capacity, disease susceptibility, and many other host phenotypes [##REF##20376150##8##, ##UREF##3##9##, ##REF##23869020##10##, ##REF##29590046##11##].</p>", "<p id=\"P3\">A novel adaptation which appears initially in one host may spread across hosts through strain or phage transmission and subsequent horizontal gene transfer (HGT). The human gut is known to be a hotspot for HGT, allowing alleles which provide a fitness benefit within a host or facilitate the colonization of other hosts to be easily recombined onto new genetic backgrounds [##UREF##4##12##, ##REF##22037308##13##, ##REF##33794144##14##]. But beyond a handful of particular examples—such as the proliferation of antibiotic resistance genes [##REF##32143027##15##]—the extent to which HGT facilitates the spread of adaptive alleles across hosts is at present unclear, as the ability to systematically detect such adaptations is still emergent.</p>", "<p id=\"P4\">Should an adaptive allele spread horizontally between hosts in a “gene-specific” selective sweep, the same genomic sequence, or haplotype, will appear in many otherwise distantly related strains collected from many different hosts [##UREF##4##12##, ##REF##22491847##16##, ##REF##24630527##17##]. Such shared haplotypes will result in distinct signatures of locally elevated linkage disequilibrium (LD), or, correlations among variants that have “hitchhiked” to high frequency with the adaptive allele. However, while elevations in LD have long been leveraged as a signature of selection [##REF##8013910##18##, ##UREF##5##19##, ##REF##12397357##20##, ##REF##16494531##21##, ##REF##25706129##22##, ##REF##24554778##23##], other evolutionary forces, including demographic contractions and reduced recombination rates, also result in elevations in LD, confounding its use in the discovery of adaptation [##UREF##6##24##, ##REF##10835415##25##, ##REF##11410837##26##, ##UREF##7##27##].</p>", "<p id=\"P5\">One way to control for these non-selective forces is to compare LD among synonymous and non-synonymous variants. While both are subject to the same non-selective forces, synonymous variants are far more likely to be neutral. The vast majority of non-synonymous mutations, by contrast, are deleterious in any population [##UREF##8##28##], and are always found to be preferentially rare [##REF##24656563##29##, ##REF##29844134##30##]. Hitchhikers that are rare prior to the sweep will exhibit tight linkage with the adaptive mutation during the sweep as they will typically be found only on haplotypes bearing the adaptive mutation. Therefore, we expect non-synonymous variants to be more tightly linked during selective sweeps than synonymous variants in the vicinity of the adaptive locus (##FIG##0##Figure 1A##).</p>", "<p id=\"P6\">In this work, we first confirm our hypothesis that deleterious hitchhiking drives an increase of LD among non-synonymous relative to synonymous variants with simulations. We further find that this signal does not manifest under neutrality, as a result of purifying selection alone, or due to low recombination rates or demographic contractions. Next, in a panel of more than 20 prevalent and abundant gut microbiome species, we find that elevations of LD among non-synonymous variants are common at the whole genome level, suggesting that positive selection is widespread. Lastly, we develop a novel statistic leveraging these insights (iLDS, the integrated Linkage Disequilibrium Score) to detect specific loci under selection in these gut microbial species. Application of iLDS to human metagenomic data reveals an abundance of adaptive alleles that have spread across hosts.</p>" ]	[]	[ "<title>Results</title>", "<title>Positive selection generates elevated linkage disequilibrium among common non-synonymous variants compared to synonymous variants</title>", "<p id=\"P7\">We first test whether positive selection can drive an excess of LD among non-synonymous variants compared to synonymous variants when deleterious variants hitchhike with a positively selected variant. To do so, we performed forward population genetic simulations of selective sweeps in SLiM 4.0 [##UREF##9##31##] (##SUPPL##0##Supplementary Section 2##). While the beneficial variant and any hitchhikers may be expected to become common in the population, deleterious variants not linked to the adaptive variant should remain rare. Assuming all non-synonymous sites are either subject to purifying selection or are adaptive, we expect non-synonymous variants that become common to either be adaptive or to have hitchhiked with and therefore be tightly linked to an adaptive variant. As a result, we expect that will be elevated relative to specifically among common variants (##FIG##0##Figure 1A##).</p>", "<p id=\"P8\">To examine the potential effects of purifying and positive selection on patterns of LD, we analyzed LD among variants that are either rare (minor allele frequency MAF ≤ 0.05) or common (MAF ≥ 0.2) in the broader population, respectively. To quantify whether is significantly elevated over , we computed the difference in area under their respective distance decay curves (AUC) (##FIG##0##Figure 1C##). This test statistic, which we refer to as , allows us to assess differences in total levels of and in a manner that controls for genomic distance (and therefore effective recombination rates) between pairs of alleles (##SUPPL##0##Supplementary Section 1.3##).</p>", "<p id=\"P9\">Before assessing if selective sweeps generate excess LD among common non-synonymous versus synonymous variants, we first determined if this pattern can arise under scenarios of neutrality, purifying selection, or demographic contractions. As expected, under neutrality, we observed that was not significantly different from zero for either common or rare variants (##FIG##0##Figure 1B## and ##SUPPL##0##S1##–##SUPPL##0##S6##). Similarly, we found that in populations evolving under purifying selection, in which new non-synonymous mutations experienced purifying selection of strength varying from −10<sup>−5</sup> to −10<sup>−1</sup> (encompassing a value weaker than the effect of drift ) to very strong selection , common variants failed to produce . However, in these scenarios of purifying selection rare variants showed a depression in versus (##SUPPL##0##Figure S5##), consistent with both Hill-Robertson interference [##UREF##10##32##] or epistasis between deleterious variants, as previously observed by [##REF##35100407##33##, ##REF##28473589##34##, ##REF##34319975##35##, ##REF##35736370##36##]. Finally, given that demographic contractions are known to affect patterns of diversity and linkage in ways that closely resemble sweeps [##UREF##6##24##, ##REF##10835415##25##, ##REF##11410837##26##], we tested if a population bottleneck could lead to a stochastic increase in the frequency of haplotypes bearing particular combinations of linked deleterious variants, and therefore potentially to an elevation of versus among common variants. However, in two demographic scenarios tested, was not significantly different from zero (##SUPPL##0##Figures S1## – ##SUPPL##0##S3##).</p>", "<p id=\"P10\">Next, we tested whether selective sweeps could induce among common variants. To do so, we introduced a novel, beneficial mutation to a population already evolving under purifying selection, and allowed it to rise to intermediate (50%) frequency. The strength of beneficial selection ranged from nearly-neutral (10<sup>−5</sup>) to strongly beneficial (10<sup>−1</sup>). First, regardless of \n and among common variants generally increased monotonically with , reflecting the decrease in the expected time for the sweeping variant to reach intermediate frequency. Second, we found that selective sweeps can in fact produce ; however, this pattern only manifests under particular combinations of and . Specifically, the strength of purifying selection must exceed drift (i.e. ), and the strength of positive selection must exceed that of purifying selection (##FIG##0##Figures 1D## and ##SUPPL##0##S2##). Additionally, increased with the strength of and , as well as with the rate of recombination (##SUPPL##0##Figures S1## – ##SUPPL##0##S3##). Moreover, remained elevated over among rare variants during the selective sweep, provided purifying selection exceeded drift (##SUPPL##0##Figure S4## – ##SUPPL##0##S6##). Thus, when a population experiences both purifying and positive selection, we expect to see differences between synonymous and non-synonymous LD among both rare and common variants.</p>", "<title>Elevation of LD among non-synonymous variants in gut commensal species</title>", "<p id=\"P11\">Having established in simulations that LD between non-synonsymous variants can be elevated relative to synonymous variants primarily due to selective sweeps, we next quantified and across host metagenomes to assess if this signature of positive selection is observed at a genome-wide scale in gut microbiome species. To do so, we analyzed data from metagenomic samples of 693 individuals from North America, Europe, and China [##REF##28953883##37##, ##REF##27818083##38##, ##REF##23023125##39##, ##REF##29496731##40##]. To identify single nucleotide polymorphisms (SNPs) from these samples, we aligned shotgun reads to a database of reference genomes using MIDAS [##REF##27803195##41##] (##SUPPL##0##Supplementary Section 3##). We showed previously that samples in which a single dominant strain of a species is present can be confidently ‘quasi-phased’ such that pairs of alleles can be assigned to the same haplotype with low probability of error, and that subsequently LD can be computed between these pairs of alleles [##REF##30673701##1##]. With this quasi-phasing approach, we extracted 2641 haplotypes belonging to 30 species across the 693 individuals we examined. Some of the species examined exhibit considerable population structure, with strong gene flow boundaries between clades, so we focused our analyses only on haplotypes belonging to the largest clade of each species (##SUPPL##0##Supplementary Section 3.6##) [##REF##30673701##1##, ##UREF##4##12##].</p>", "<p id=\"P12\">Shown in ##FIG##1##Figure 2A## are examples of genome-wide and for the species <italic toggle=\"yes\">Ruminococcus bromii</italic> and <italic toggle=\"yes\">Prevotella copri</italic>. Among both rare and common variants, and decay with increasing distance between pairs of genomic loci, as expected for recombining species. The rate of decay differs among species; however, for all species, LD appears to eventually saturate to some roughly constant value. In <italic toggle=\"yes\">R. bromii</italic>, for instance, both rare and common variant LD appear to saturate around ~10Kb. In ##SUPPL##0##Supplementary Section 4.1##, we show how the initial decay and eventual saturation of LD can be related to an underlying model of recombination, which in turn can be used to infer the mean tract length of horizontally transferred segments for each species.</p>", "<p id=\"P13\">For both species in ##FIG##1##Figure 2A##, is significantly greater than zero among common variants and less than zero among rare variants. More broadly, across the 30 species analyzed in this study, is significantly greater than zero among common variants in 27/30 species. Among rare variants, was significantly less than zero for all but one species (##FIG##1##Figure 2B##). Together, these patterns of LD among synonymous and non-synonymous variants are consistent with widespread purifying and positive selection acting on non-synonymous sites in these species.</p>", "<p id=\"P14\">Finally, we more finely examined the dependence of on allele frequency. As purifying selection drives deleterious variants to low frequencies and positive selection tends to elevate allele frequencies, we expect to observe a generally positive relationship between allele frequency and if both purifying and positive selection affect these populations. In ##SUPPL##0##Figure S13##, we see that universally increases with allele frequency, as expected. Additionally, we see that flips from negative to positive at a frequency of approximately 0.05 in most species. It is possible that the majority of non-synonymous variants with allele frequencies below this threshold are deleterious, while those with allele frequencies above this threshold are more likely to be either beneficial themselves or tightly linked to a beneficial variant.</p>", "<title>Detecting recombinant selective sweeps with iLDS</title>", "<p id=\"P15\">Genome-wide patterns of LD among synonymous and non-synonymous variants indicate that selection—both positive and purifying—is pervasive at the nucleotide level in gut microbiome species. While only a minority of intermediate frequency non-synonymous sites are likely adaptive, positive selection at these sites is evidently strong enough to create highly significant genome-wide linkage patterns. To identify these specific adaptive loci, we developed a novel statistic—the integrated Linkage Disequilibrium Score (iLDS)—which detects genomic regions exhibiting both and elevated LD relative to the genomic background. By combining these sources of information, we identify regions which have elevated LD due to positive selection and not other non-selective forces.</p>", "<p id=\"P16\">To detect specific genomic regions under selection, iLDS is calculated in sliding windows across a genome. To calculate iLDS in a genomic window, we first determine among common SNVs (MAF ≥ 0.2) within the window. To augment our ability to detect selection, we also identify windows with outlier LD values, which is expected to be elevated in selective sweeps. To do so, we measure local elevation in overall LD in the window compared to the genomic background by computing the area under the LD curve between all intermediate frequency variants in the same window (i.e. ), irrespective of whether they are synonymous or non-synonymous, and then normalize this quantity by the average genome-wide LD over the distance defined by the window: . The statistic is then defined as:\n</p>", "<p id=\"P17\">In essence, quantifies the increase in total LD within the window relative to the expected level of across the whole genomic background for a region of the same size, while quantifies the local elevation in linkage among non-synonymous variants relative to synonymous variants. Both of these terms are expected to be elevated during a sweep; however, iLDS should not be elevated in regions where total linkage is high due to non-selective factors, as will remain near zero in such regions. LLDS is determined to be significant if both is significantly greater than , and is significantly greater than (##SUPPL##0##Supplementary Section 4.2##).</p>", "<p id=\"P18\">We first tested iLDS’s capacity to correctly detect simulated selective sweeps, as well as its potential for misclassifying genomic regions with elevated LD arising from demographic contractions as selective sweeps. To do so, we evaluated the true positive rate (TPR) and false positive rate (FPR) of the statistic in the simulations described above (##SUPPL##0##Supplementary Section 4.3##). Among positive selection scenarios, iLDS was frequently significant when and and had increasing accuracy for stronger sweeps. In particular, iLDS had a true positive rate of 85% when and , and 100% when and or −10<sup>−2</sup> (##SUPPL##0##Figure S7##). In the populations which had undergone demographic contractions, by contrast, iLDS was almost never significant regardless of the strength of purifying selection—the FPR was 0% for the vast majority of parameter combinations and only when recombination was very weak and purifying selection was weaker than drift did the FPR reach a maximum of ~1% (##SUPPL##0##Figure S8##). These simulation results indicate that overall, iLDS is capable of correctly identifying sweeps when is sufficiently strong and exceeds the strength of purifying selection and very rarely identifies non-sweeps as sweeps.</p>", "<title>iLDS reveals pervasive selective sweeps in gut bacteria</title>", "<p id=\"P19\">We next applied iLDS to gut bacteria. To do so, it is first necessary to define genomic windows to calculate iLDS in. The window size should ideally be large enough that genome-wide LD can be expected to fully decay by the edges of the window, but not so large that the footprint of the sweep is very small relative to the size of the window. To determine this species-specific window size in the bacteria examined here, we estimated a typical upper bound on the size of a horizontally transferred tract under an idealized model of HGT (##SUPPL##0##Supplementary Section 4.1##). LD should fully decay at approximately as linkage between fragments separated by greater than this distance is always broken by recombination, while variants which are closer may be transferred together horizontally, preserving linkage. By visual inspection, we found that the inferred value of did in fact correspond to the point at which LD fully decayed among common synonymous variants in the data (##SUPPL##0##Supplementary Section 4.1##, ##SUPPL##0##Figure S17##, ##SUPPL##1##Table S4##). To ensure that each window contains both an adequate and comparable number of synonymous and non-synonymous variants with which to calculate and curves, we employed a SNP based windowing approach as opposed to a base-pair defined window. Specifically, we defined each window to consist of the average number (for that species) of consecutive non-synonymous, intermediate frequency SNPs (MAF ≥ 0.2) spanning (##SUPPL##1##Table S3##).</p>", "<p id=\"P20\">Next, to assess the ability of iLDS to detect known instances of positive selection in a natural population, we applied iLDS to a set of 257 isolates of <italic toggle=\"yes\">Clostridiodes difficile</italic> (##FIG##2##Figure 3A##, ##SUPPL##0##Supplementary Section 6##), an enteric pathogen. <italic toggle=\"yes\">C. difficile</italic> has experienced recombination mediated selective sweeps at the <italic toggle=\"yes\">tcdB</italic> locus, which encodes the its chief virulence factor, toxin B [##REF##32620855##42##, ##REF##33370413##43##]. In the majority of windows, iLDS remains close to 0, as expected in the absence of positive selection. However, the value of the statistic peaks sharply in several regions across the genome, and these peaked regions contain large numbers of significant iLDS values. Since adjacent windows may belong to the same selective event, we clustered groups of significant windows into a peak if the SNPs they were centered around were both physically close and tightly linked to one another, as would be expected following a selective sweep (##SUPPL##0##Supplementary Section 4.4##). The second highest peak in the scan coincides with the <italic toggle=\"yes\">tcdB</italic> locus, confirming that our scan can indeed recover known instances of positive selection mediated by HGT. Other peaks may be candidates for selection as well, such as the leftmost peak, which is made up of genes in the flagellar operon <italic toggle=\"yes\">fli</italic> that have also been previously reported to be under positive selection in <italic toggle=\"yes\">C. difficile</italic> [##UREF##11##44##]. The locations, names, and annotations of all genes found within a peak for <italic toggle=\"yes\">C. difficile</italic> and all other species can be found in ##SUPPL##1##Supplementary Table S4##.</p>", "<p id=\"P21\">Finally, we applied the scan to 23 gut microbiome species shown in ##FIG##1##Figure 2B## whose reference genomes were not fragmented into a large number of contigs, which otherwise produced sparse, unreliable scans (##SUPPL##0##Supplementary Section 3.5##). Across these 23 species, we recovered 169 peaks, with a median of 5 peaks per species. All species exhibited at least 1 peak (##SUPPL##1##Table S4##, ##SUPPL##0##Figure S14##).</p>", "<p id=\"P22\">##FIG##2##Figure 3B## shows the iLDS scan for one of these species: <italic toggle=\"yes\">R. bromii</italic>, a common gut commensal species known to be critical for the digestion of resistant starches in the colon [##REF##22343308##45##]. We detected a total of 8 distinct peaks in this species, in line with the typical number of peaks observed per species in our dataset. The tallest peak coincides with the genes and , which are ATP-binding cassette transporters involved in metabolizing maltodextrin [##REF##32680872##46##]. <italic toggle=\"yes\">R. bromii</italic> was not alone in exhibiting peaks containing genes related to maltodextrin metabolism: 3 other species (<italic toggle=\"yes\">E. rectale</italic>, <italic toggle=\"yes\">E. siraeum</italic>, and <italic toggle=\"yes\">B. cellulosilyticus</italic>) also had peaks overlapping maltodextrin-related genes (##SUPPL##1##Table S4##). A gene enrichment analysis showed that maltodextrin-related genes were the only class of genes enriched for signatures of selection in our dataset, after controlling for false discovery (##SUPPL##0##Supplementary Section 5##). Several other classes of genes were found to be under selection in multiple species, including two-component sensor histidine kinases and SusC/SusD polysaccharide utilization loci that have been previously observed to evolve within hosts [##REF##31028005##2##, ##REF##35545448##7##]; however, they were not statistically overrepresented among selected genes relative to their overall prevalence in core genes.</p>", "<p id=\"P23\">To assess if the peaks in the <italic toggle=\"yes\">. bromii</italic> scan replicate in an independent dataset, we repeated our analysis in a collection of isolates collected from healthy adults [##UREF##12##47##]. Both of the highest peaks from HMP (including ) also appeared as peaks in the isolate dataset, though the remaining 6 smaller peaks did not (##SUPPL##0##Figure S16##, ##SUPPL##0##Supplementary Text 6##).</p>", "<p id=\"P24\">The putatively adaptive regions in <italic toggle=\"yes\">R. bromii</italic> show evidence of extensive, recent horizontal gene transfer. To illustrate this, ##FIG##2##Figure 3C## shows the genomic diversity of a ~10Kb window surrounding the ~3Kb locus (gold region), in which strains are grouped by haplotype identity at . At right we show genome-wide nucleotide divergence at fourfold synonymous sites (that is, sites where no nucleotide change induces a change in amino acid) among each group of strains which are identical across . By visual inspection, it is clear that the top half of the figure is dominated by 26 haplotypes (comprising 48/128 strains) which are much more similar one another than the remaining 50 haplotypes, whose of 1.73×10<sup>−2</sup>/bp is quite close to genome-wide average (##SUPPL##0##Figure S15##). However, haplotype identity among those lineages harboring identical haplotypes decays rapidly in the regions immediately flanking , with little apparent linkage between variants found on either end of the extended window. For each of the 26 haplotypes highlighted above, genome-wide pairwise divergence falls within a factor of 2 of the average for all pairs of strains (blue dots, ##FIG##2##Figure 3C##). This is in contrast to a set of lineages which are closely related genome-wide (, see ##SUPPL##0##Supplemental Section 3.6##) that are not only identical at but also maintain substantial haplotype identity in the flanking regions. Together, these patterns are highly consistent with the horizontal transfer of this segment in a recombinant sweep.</p>" ]	[ "<title>Discussion</title>", "<p id=\"P25\">In this paper, we perform the first comprehensive scan for sweeps that have spread across human microbiomes. Although previous work has found evidence of genomic fragments shared across hosts in a manner inconsistent with neutrality [##UREF##4##12##], we definitively establish here that many fragments are spreading via gene-specific selective sweeps. To do so, we develop a novel statistic, iLDS, that is robust to many evolutionary forces which traditionally confound selection scans, including purifying selection, demographic contractions, and reduced recombination rates [##UREF##6##24##, ##REF##10835415##25##, ##REF##11410837##26##], and show that the spread of adaptive fragments across hosts in gene-specific sweeps is a pervasive feature of the evolution of ~20 of the most prevalent and abundant commensal microbiome species in a cohort of healthy, Western adults.</p>", "<p id=\"P26\">The iLDS statistic that we develop to discover sweeps across host microbiomes is versatile enough to be applied to any recombining species. Demonstrating this, we found that iLDS is capable of recovering known selective sweeps not only in recombinant bacteria (like the pathogen <italic toggle=\"yes\">C. difficile</italic>), but also in <italic toggle=\"yes\">Drosophila melanogaster</italic> (##SUPPL##0##Figure S21##, ##SUPPL##0##Supplementary Section 7##). iLDS may have power in diverse systems because it exploits a common signature associated with selective sweeps: deleterious variants hitchhiking to high frequency with a beneficial variant. To our knowledge, the tight linkage of beneficial variants with hitchhiking deleterious variants, which has been shown to be a common feature of evolution both in theory and in numerous systems [##REF##23222656##48##, ##UREF##13##49##, ##REF##21901107##50##, ##UREF##14##51##, ##REF##21884046##52##], has not been explicitly incorporated into any selection scan statistic. By contrast, current LD-based methodology for detection of sweeps instead relies more generically on signatures of hitchhiking which are not unique to selective sweeps [##REF##8013910##18##, ##UREF##5##19##, ##REF##12397357##20##, ##REF##16494531##21##, ##REF##25706129##22##, ##REF##24554778##23##]. Building on these previous scans, iLDS quantifies not only the overall increase of LD but also the excess of LD among non-synonymous variants compared to synonymous variants, leveraging additional power that deleterious hitchhikers provide in sensitively detecting sweeps.</p>", "<p id=\"P27\">Others have also shown that elevated linkage among common non-synonymous variants relative to synonymous variants can be a signature of adaptation; however, the connection with deleterious hitchhiking had not previously been noted. Stolyarova <italic toggle=\"yes\">et al.</italic> [##REF##35532122##53##] found that sign epistasis generated elevated LD among non-synonymous variants in the highly polymorphic fungus <italic toggle=\"yes\">Schizophyllum commune</italic>, while Arnold <italic toggle=\"yes\">et al.</italic> [##REF##31589312##54##] concluded that epistatic interactions were not necessary to generate this signal in <italic toggle=\"yes\">Neisseria gonorrhoeae</italic>, and that adaptive inter-specific HGT of short genomic fragments bearing multiple positively selected non-synonymous alleles was the likely driving factor. We emphasize that our findings are fully consistent with those of Stolyarova <italic toggle=\"yes\">et al.</italic> and Arnold <italic toggle=\"yes\">et al</italic>. But crucially, our results suggest that elevated linkage among common non-synonymous variants is not by itself sufficient to establish that all such variants are adaptive. We find instead that it is highly likely some proportion of common non-synonymous polymorphisms will be deleterious hitchhikers in any adapting population, with this proportion growing, paradoxically, as the strength of positive selection increases. Establishing precisely what level of non-synonymous diversity is truly inconsistent with hitchhiking—a quantity which will at minimum depend on the distribution of deleterious fitness effects, the strength of positive selection, and the ratio of recombination to mutation rate—is an avenue of future research.</p>", "<p id=\"P28\">iLDS is powerful in detecting strong selective sweeps in the human gut microbiome, therefore is potentially biased towards the most extreme cases of adaptation. Specifically, we found that iLDS had power to detect sweeps when the strength of positive selection exceeds that of purifying selection , and when the strength of purifying selection exceeds drift . Both very weak sweeps and sweeps in regions of the genome in which purifying selection is relaxed are unlikely to be detected with iLDS. Indeed, absent purifying selection, iLDS will fail to detect even the very strongest sweeps. However, we note that purifying selection is a pervasive feature of bacterial genomes [##REF##30673701##1##, ##REF##23228887##55##, ##UREF##15##56##, ##REF##20838590##57##, ##REF##23222524##58##], and it seems unlikely that there would be large genomic regions devoid of variants that are under purifying selection.</p>", "<p id=\"P29\">Additionally, iLDS is best suited to detect recombinant, gene—specific selective sweeps. Genome-wide sweeps—for instance, a rapid clonal expansion of a single lineage in an asexually evolving population—should produce globally elevated LD among common variants linked on the selected haplotype, and consequently little variation in either or across windows. At the other extreme, parallel, recurrent adaptations which arise in different hosts and do not spread between hosts will also be undetectable with iLDS, as each adaptive sweeping variant is likely to carry private, minimally overlapping sets of hitchhikers in each distinct host. While both modes may contribute to adaptation in differing degrees in different species, we emphasize that iLDS is designed to detect gene-specific sweeps mediated by recombination. We note, however, that the selective sweeps we found need not have arisen from a single mutational origin: rather, these adaptations may have arisen multiple times independently in different hosts and subsequently spread simultaneously via migration and recombination.</p>", "<p id=\"P30\">But while iLDS may fail to detect all true positives, it is also robust to known sources of false positives that previous scans for selection may be sensitive to, including demographic contractions. We found that FDR values for iLDS are zero for most evolutionary scenarios, and it is only when the strength of purifying selection is smaller than the effect of drift and when recombination rates are extremely low that FDRs can achieve rates up to ~1%. These conditions are unlikely in the microbiome, as recombination is a ubiquitous and powerful force in the evolution of gut commensals and as stated above, it is unlikely that bacterial genomes experience such weak purifying selection. However, we acknowledge that finer resolution maps of purifying selection and recombination across these genomes will be informative for understanding the extent to which sweeps are mis-inferred due to these processes. Additionally, despite the extremely low rate of false positives observed in simulations, we also employed a conservative approach to identifying “peaks” (i.e. individual selective sweeps) in our scans, requiring that each peak be supported by multiple significant windows centered around variants with tight linkage to one another. iLDS is thus insulated from producing false positive inferences of selection at multiple levels.</p>", "<p id=\"P31\">Some of the candidate sweeps we found may be related to changing Western lifestyles. In particular, we saw that genes related to the metabolism maltodextrin—a synthetic starch which has increased dramatically in abundance in Western diets in recent years [##REF##9813736##59##, ##REF##25674937##60##]—appeared to be under selection in several species. As consumption of maltodextrin has been implicated experimentally in the onset of colitis [##REF##30765332##61##, ##REF##35359925##62##], these selective sweeps may have implications for human health. More broadly, future work in which systematic scans of adaptation are performed in a variety of cohorts may reveal genetic loci in the microbiome that are especially relevant to certain human conditions. By being able to detect adaptations in gut microbiomes in a high throughput manner, we can look for patterns that are common to one cohort and absent from another. In doing so, we may gain mechanistic insight into how microbiome genotypes confer disease, improving our ability to diagnose and treat such conditions, and potentially allowing us to deploy existing natural, adaptive variation in the design of rational probiotics.</p>" ]	[]	[ "<p id=\"P1\">The human gut microbiome is composed of a highly diverse consortia of species which are continually evolving within and across hosts. The ability to identify adaptations common to many host gut microbiomes would not only reveal shared selection pressures across hosts, but also key drivers of functional differentiation of the microbiome that may affect community structure and host traits. However, to date there has not been a systematic scan for adaptations that have spread across host microbiomes. Here, we develop a novel selection scan statistic, named the integrated linkage disequilibrium score (iLDS), that can detect the spread of adaptive haplotypes across host microbiomes via migration and horizontal gene transfer. Specifically, iLDS leverages signals of hitchhiking of deleterious variants with the beneficial variant, a common feature of adaptive evolution. We find that iLDS is capable of detecting simulated and known cases of selection, and moreover is robust to potential confounders that can also elevate LD. Application of the statistic to ~20 common commensal gut species from a large cohort of healthy, Western adults reveals pervasive spread of selected alleles across human microbiomes mediated by horizontal gene transfer. Among the candidate selective sweeps recovered by iLDS is an enrichment for genes involved in the metabolism of maltodextrin, a synthetic starch that has recently become a widespread component of Western diets. In summary, we demonstrate that selective sweeps across host microbiomes are a common feature of the evolution of the human gut microbiome.</p>" ]	[ "<title>Supplementary Material</title>" ]	[ "<title>Acknowledgments</title>", "<p id=\"P32\">This work was funded by NIGMS NIH award R35GM151023, NSF CAREER award (no. 2240098), and a Paul Allen Research Foundation grant to NRG, as well as a UCLA EEB departmental fellowship to RW. The authors would like to thank Dmitri Petrov for helpful conversations early in the project, Kirk Lohmueller for his help, as well as all members of the Garud lab and Emma Derrick for helpful discussions and feedback on the manuscript.</p>" ]	[ "<fig position=\"float\" id=\"F1\"><label>Figure 1:</label><caption><title>Linkage disequilibrium among common non-synonymous versus synonymous variants during a selective sweep.</title><p id=\"P35\"><bold>(A)</bold> Adaptive variant sweeping across host microbiomes. Each horizontal line represents a bacterial haplotype from a different host’s microbiome. The yellow region of each haplotype represents a fragment that bears an adaptive allele that has recombined onto different lineages’ backgrounds. <bold>(B)</bold>\n and among common variants under neutrality. <bold>(C)</bold>\n among common variants where and . <bold>(D)</bold>\n is expected to be greater than zero when and both and are stronger than the effects of drift (, dashed lines). In this schematic and in all simulations (prior to a demographic contraction), . See ##SUPPL##0##Figures S1## – ##SUPPL##0##S3## for common variant and across a comprehensive set of simulated evolutionary scenarios.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2:</label><caption><title> measured in prevalent commensal gut microbiota.</title><p id=\"P36\"><bold>(A)</bold> Decay in LD among common (MAF ≥ 0.2) (top) and rare (MAF ≤ 0.05) (bottom) variants for the species <italic toggle=\"yes\">Ruminococcus bromii</italic> and <italic toggle=\"yes\">Prevotella copri</italic>. Both species show significant differences between and for common and rare variants, as denoted by the orange star. <bold>(B)</bold>\n among rare (orange) and common (green) alleles for 30 gut commensal bacteria species. Among rare variants, is significantly negative for all but one species (yellow stars, at bottom). Among common variants, is significantly positive in 27/30 of species (yellow stars, at top).</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3:</label><caption><title>Recombinant selective sweeps in gut bacteria.</title><p id=\"P37\"><bold>(A)</bold> iLDS scan in <italic toggle=\"yes\">Clostridioides difficile</italic>. Each point corresponds to an iLDS value for a given genomic window centered around a single intermediate frequency non-synonymous SNP. Significant windows are colored orange, while nonsignificant windows are colored green. The locations of peaks are shown as colored bars below the scan. <bold>(B)</bold> iLDS scan in <italic toggle=\"yes\">R. bromii</italic>. <bold>(C)</bold> Haplotype plot of a ~10Kb region surrounding the sweep candidate genes. Non-synonymous variants are colored red, while synonymous variants are colored blue, and missing sites are colored white. Horizontal lines separate strains into haplotype groups that are genetically identical at . Haplotypes are ordered based on their genetic distance to the largest haplotype at the locus. To aid in visualization, intermediate frequency variants (MAF > 0.2) are colored based on the identity of the first strain in the first haplotype group (i.e. the top row), while the color of all other sites is determined by minor allele at that position. The alternate allele at each site assigned by this polarization scheme is colored gray. At right, mean genome-wide divergence within each haplotype group containing three or more strains is shown with blue dots, while the pink region shows the typical range of (within a factor of two of the mean), and the orange region denotes the region , indicative of close-relatedness among strains.</p></caption></fig>" ]	[]	[ "<inline-formula><mml:math id=\"M1\" display=\"inline\"><mml:mfenced separators=\"\|\"><mml:mrow><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:mrow></mml:mfenced></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M2\" display=\"inline\"><mml:mfenced separators=\"\|\"><mml:mrow><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:mrow></mml:mfenced></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M3\" display=\"inline\"><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M4\" display=\"inline\"><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M5\" display=\"inline\"><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M6\" display=\"inline\"><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M7\" display=\"inline\"><mml:mi mathvariant=\"normal\">A</mml:mi><mml:mi mathvariant=\"normal\">U</mml:mi><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mfenced separators=\"\|\"><mml:mrow><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup><mml:mo>−</mml:mo><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:mrow></mml:mfenced></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M8\" display=\"inline\"><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M9\" display=\"inline\"><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M10\" display=\"inline\"><mml:mi mathvariant=\"normal\">A</mml:mi><mml:mi mathvariant=\"normal\">U</mml:mi><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mfenced separators=\"\|\"><mml:mrow><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup><mml:mo>−</mml:mo><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:mrow></mml:mfenced></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M11\" display=\"inline\"><mml:mfenced separators=\"\|\"><mml:mrow><mml:msub><mml:mrow><mml:mi>s</mml:mi></mml:mrow><mml:mrow><mml:mi>D</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfenced></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M12\" display=\"inline\"><mml:mfenced close=\"\" separators=\"\|\"><mml:mrow><mml:msub><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mi>e</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mi>s</mml:mi></mml:mrow><mml:mrow><mml:mi>D</mml:mi></mml:mrow></mml:msub><mml:mo>≪</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:mfenced></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M13\" display=\"inline\"><mml:mfenced separators=\"\|\"><mml:mrow><mml:msub><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mi>e</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mi>s</mml:mi></mml:mrow><mml:mrow><mml:mi>D</mml:mi></mml:mrow></mml:msub><mml:mo>≫</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:mfenced></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M14\" display=\"inline\"><mml:mi mathvariant=\"normal\">A</mml:mi><mml:mi mathvariant=\"normal\">U</mml:mi><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mfenced separators=\"\|\"><mml:mrow><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup><mml:mo>−</mml:mo><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:mrow></mml:mfenced><mml:mo>></mml:mo><mml:mn>0</mml:mn></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M15\" display=\"inline\"><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M16\" display=\"inline\"><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M17\" display=\"inline\"><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M18\" display=\"inline\"><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M19\" display=\"inline\"><mml:mi mathvariant=\"normal\">A</mml:mi><mml:mi mathvariant=\"normal\">U</mml:mi><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mfenced separators=\"\|\"><mml:mrow><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup><mml:mo>−</mml:mo><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:mrow></mml:mfenced></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M20\" display=\"inline\"><mml:mi mathvariant=\"normal\">A</mml:mi><mml:mi mathvariant=\"normal\">U</mml:mi><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mfenced separators=\"\|\"><mml:mrow><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup><mml:mo>−</mml:mo><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:mrow></mml:mfenced><mml:mo>></mml:mo><mml:mn>0</mml:mn></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M21\" display=\"inline\"><mml:mfenced separators=\"\|\"><mml:mrow><mml:msub><mml:mrow><mml:mi>s</mml:mi></mml:mrow><mml:mrow><mml:mi>B</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfenced></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M22\" display=\"inline\"><mml:msub><mml:mrow><mml:mi>s</mml:mi></mml:mrow><mml:mrow><mml:mi>D</mml:mi></mml:mrow></mml:msub><mml:mo>,</mml:mo></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M23\" display=\"inline\"><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M24\" display=\"inline\"><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M25\" display=\"inline\"><mml:msub><mml:mrow><mml:mi>s</mml:mi></mml:mrow><mml:mrow><mml:mi>B</mml:mi></mml:mrow></mml:msub></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M26\" display=\"inline\"><mml:mi mathvariant=\"normal\">A</mml:mi><mml:mi mathvariant=\"normal\">U</mml:mi><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mfenced separators=\"\|\"><mml:mrow><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup><mml:mo>−</mml:mo><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:mrow></mml:mfenced><mml:mo>></mml:mo><mml:mn>0</mml:mn></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M27\" display=\"inline\"><mml:msub><mml:mrow><mml:mi>s</mml:mi></mml:mrow><mml:mrow><mml:mi>B</mml:mi></mml:mrow></mml:msub></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M28\" display=\"inline\"><mml:msub><mml:mrow><mml:mi>s</mml:mi></mml:mrow><mml:mrow><mml:mi>D</mml:mi></mml:mrow></mml:msub></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M29\" display=\"inline\"><mml:msub><mml:mrow><mml:mi>s</mml:mi></mml:mrow><mml:mrow><mml:mi>D</mml:mi></mml:mrow></mml:msub><mml:mo>></mml:mo><mml:mfrac><mml:mrow><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mi>e</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfrac></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M30\" display=\"inline\"><mml:mfenced separators=\"\|\"><mml:mrow><mml:msub><mml:mrow><mml:mi>s</mml:mi></mml:mrow><mml:mrow><mml:mi>B</mml:mi></mml:mrow></mml:msub><mml:mo>></mml:mo><mml:msub><mml:mrow><mml:mi>s</mml:mi></mml:mrow><mml:mrow><mml:mi>D</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfenced></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M31\" display=\"inline\"><mml:mi mathvariant=\"normal\">A</mml:mi><mml:mi mathvariant=\"normal\">U</mml:mi><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mfenced separators=\"\|\"><mml:mrow><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup><mml:mo>−</mml:mo><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:mrow></mml:mfenced></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M32\" display=\"inline\"><mml:msub><mml:mrow><mml:mi>s</mml:mi></mml:mrow><mml:mrow><mml:mi>B</mml:mi></mml:mrow></mml:msub></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M33\" display=\"inline\"><mml:msub><mml:mrow><mml:mi>s</mml:mi></mml:mrow><mml:mrow><mml:mi>D</mml:mi></mml:mrow></mml:msub></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M34\" display=\"inline\"><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M35\" display=\"inline\"><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M36\" display=\"inline\"><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M37\" display=\"inline\"><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M38\" display=\"inline\"><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M39\" display=\"inline\"><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M40\" display=\"inline\"><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M41\" display=\"inline\"><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M42\" display=\"inline\"><mml:mi mathvariant=\"normal\">A</mml:mi><mml:mi mathvariant=\"normal\">U</mml:mi><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mfenced separators=\"\|\"><mml:mrow><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup><mml:mo>−</mml:mo><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:mrow></mml:mfenced></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M43\" display=\"inline\"><mml:mi mathvariant=\"normal\">A</mml:mi><mml:mi mathvariant=\"normal\">U</mml:mi><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mfenced separators=\"\|\"><mml:mrow><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup><mml:mo>−</mml:mo><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:mrow></mml:mfenced></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M44\" display=\"inline\"><mml:mi mathvariant=\"normal\">A</mml:mi><mml:mi mathvariant=\"normal\">U</mml:mi><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mfenced separators=\"\|\"><mml:mrow><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup><mml:mo>−</mml:mo><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:mrow></mml:mfenced></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M45\" display=\"inline\"><mml:mi mathvariant=\"normal\">A</mml:mi><mml:mi mathvariant=\"normal\">U</mml:mi><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mfenced separators=\"\|\"><mml:mrow><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup><mml:mo>−</mml:mo><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:mrow></mml:mfenced></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M46\" display=\"inline\"><mml:mi mathvariant=\"normal\">A</mml:mi><mml:mi mathvariant=\"normal\">U</mml:mi><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mfenced separators=\"\|\"><mml:mrow><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup><mml:mo>−</mml:mo><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:mrow></mml:mfenced></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M47\" display=\"inline\"><mml:mi mathvariant=\"normal\">A</mml:mi><mml:mi mathvariant=\"normal\">U</mml:mi><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mfenced separators=\"\|\"><mml:mrow><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup><mml:mo>−</mml:mo><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:mrow></mml:mfenced></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M48\" display=\"inline\"><mml:mi mathvariant=\"normal\">A</mml:mi><mml:mi mathvariant=\"normal\">U</mml:mi><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mfenced separators=\"\|\"><mml:mrow><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup><mml:mo>−</mml:mo><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:mrow></mml:mfenced></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M49\" display=\"inline\"><mml:mi mathvariant=\"normal\">A</mml:mi><mml:mi mathvariant=\"normal\">U</mml:mi><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mfenced separators=\"\|\"><mml:mrow><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup><mml:mo>−</mml:mo><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:mrow></mml:mfenced><mml:mo>></mml:mo><mml:mn>0</mml:mn></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M50\" display=\"inline\"><mml:mi mathvariant=\"normal\">A</mml:mi><mml:mi mathvariant=\"normal\">U</mml:mi><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mfenced separators=\"\|\"><mml:mrow><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup><mml:mo>−</mml:mo><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:mrow></mml:mfenced></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M51\" display=\"inline\"><mml:mi mathvariant=\"normal\">A</mml:mi><mml:mi mathvariant=\"normal\">U</mml:mi><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mfenced separators=\"\|\"><mml:mrow><mml:msup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:mfenced></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M52\" display=\"inline\"><mml:mi mathvariant=\"normal\">A</mml:mi><mml:mi mathvariant=\"normal\">U</mml:mi><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mrow><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"italic\">genome</mml:mi><mml:mo>−</mml:mo><mml:mi mathvariant=\"italic\">wide</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:mrow><mml:mo stretchy=\"false\">)</mml:mo></mml:math></inline-formula>", "<disp-formula id=\"FD1\">\n<label>(1)</label>\n<mml:math id=\"M53\" display=\"block\"><mml:mi mathvariant=\"normal\">i</mml:mi><mml:mi mathvariant=\"normal\">L</mml:mi><mml:mi mathvariant=\"normal\">D</mml:mi><mml:mi mathvariant=\"normal\">S</mml:mi><mml:mo>=</mml:mo><mml:mi mathvariant=\"normal\">A</mml:mi><mml:mi mathvariant=\"normal\">U</mml:mi><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mrow><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup><mml:mo>−</mml:mo><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:mrow><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>×</mml:mo><mml:mo stretchy=\"false\">(</mml:mo><mml:mrow><mml:mi mathvariant=\"normal\">A</mml:mi><mml:mi mathvariant=\"normal\">U</mml:mi><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mrow><mml:msup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:mrow><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>/</mml:mo><mml:mi mathvariant=\"normal\">A</mml:mi><mml:mi mathvariant=\"normal\">U</mml:mi><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mrow><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"italic\">genome</mml:mi><mml:mo>−</mml:mo><mml:mi mathvariant=\"italic\">wide</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:mrow><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo stretchy=\"false\">)</mml:mo></mml:math>\n</disp-formula>", "<inline-formula><mml:math id=\"M54\" display=\"inline\"><mml:mi mathvariant=\"normal\">A</mml:mi><mml:mi mathvariant=\"normal\">U</mml:mi><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mrow><mml:msup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:mrow><mml:mo stretchy=\"false\">)</mml:mo><mml:mo>/</mml:mo><mml:mi mathvariant=\"normal\">A</mml:mi><mml:mi mathvariant=\"normal\">U</mml:mi><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mrow><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"italic\">genome</mml:mi><mml:mo>−</mml:mo><mml:mi mathvariant=\"italic\">wide</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:mrow><mml:mo stretchy=\"false\">)</mml:mo></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M55\" display=\"inline\"><mml:mi>L</mml:mi><mml:mi>D</mml:mi></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M56\" display=\"inline\"><mml:mi mathvariant=\"normal\">A</mml:mi><mml:mi mathvariant=\"normal\">U</mml:mi><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mfenced separators=\"\|\"><mml:mrow><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup><mml:mo>−</mml:mo><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:mrow></mml:mfenced></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M57\" display=\"inline\"><mml:mi mathvariant=\"normal\">A</mml:mi><mml:mi mathvariant=\"normal\">U</mml:mi><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mfenced separators=\"\|\"><mml:mrow><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup><mml:mo>−</mml:mo><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:mrow></mml:mfenced></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M58\" display=\"inline\"><mml:mi mathvariant=\"normal\">A</mml:mi><mml:mi mathvariant=\"normal\">U</mml:mi><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mfenced separators=\"\|\"><mml:mrow><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:mrow></mml:mfenced></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M59\" display=\"inline\"><mml:mi mathvariant=\"normal\">A</mml:mi><mml:mi mathvariant=\"normal\">U</mml:mi><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mrow><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:mrow><mml:mo stretchy=\"false\">)</mml:mo></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M60\" display=\"inline\"><mml:mi mathvariant=\"normal\">A</mml:mi><mml:mi mathvariant=\"normal\">U</mml:mi><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mrow><mml:msup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:mrow><mml:mo stretchy=\"false\">)</mml:mo></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M61\" display=\"inline\"><mml:mi mathvariant=\"normal\">A</mml:mi><mml:mi mathvariant=\"normal\">U</mml:mi><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mrow><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"italic\">genome</mml:mi><mml:mo>−</mml:mo><mml:mi mathvariant=\"italic\">wide</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:mrow><mml:mo stretchy=\"false\">)</mml:mo></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M62\" display=\"inline\"><mml:msub><mml:mrow><mml:mi>s</mml:mi></mml:mrow><mml:mrow><mml:mi>B</mml:mi></mml:mrow></mml:msub><mml:mo>></mml:mo><mml:msub><mml:mrow><mml:mi>s</mml:mi></mml:mrow><mml:mrow><mml:mi>D</mml:mi></mml:mrow></mml:msub></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M63\" display=\"inline\"><mml:msub><mml:mrow><mml:mi>s</mml:mi></mml:mrow><mml:mrow><mml:mi>D</mml:mi></mml:mrow></mml:msub><mml:mo>></mml:mo><mml:mfrac><mml:mrow><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mi>e</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfrac></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M64\" display=\"inline\"><mml:msub><mml:mrow><mml:mi>s</mml:mi></mml:mrow><mml:mrow><mml:mi>B</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:msup><mml:mrow><mml:mn>10</mml:mn></mml:mrow><mml:mrow><mml:mo>−</mml:mo><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M65\" display=\"inline\"><mml:msub><mml:mrow><mml:mi>s</mml:mi></mml:mrow><mml:mrow><mml:mi>D</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:msup><mml:mrow><mml:mn>10</mml:mn></mml:mrow><mml:mrow><mml:mo>−</mml:mo><mml:mn>3</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M66\" display=\"inline\"><mml:msub><mml:mrow><mml:mi>s</mml:mi></mml:mrow><mml:mrow><mml:mi>B</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:msup><mml:mrow><mml:mn>10</mml:mn></mml:mrow><mml:mrow><mml:mo>−</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M67\" display=\"inline\"><mml:msub><mml:mrow><mml:mi>s</mml:mi></mml:mrow><mml:mrow><mml:mi>D</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mo>−</mml:mo><mml:msup><mml:mrow><mml:mn>10</mml:mn></mml:mrow><mml:mrow><mml:mo>−</mml:mo><mml:mn>3</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M68\" display=\"inline\"><mml:msub><mml:mrow><mml:mi>s</mml:mi></mml:mrow><mml:mrow><mml:mi>B</mml:mi></mml:mrow></mml:msub></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M69\" display=\"inline\"><mml:mfenced separators=\"\|\"><mml:mrow><mml:msub><mml:mrow><mml:mi>l</mml:mi></mml:mrow><mml:mrow><mml:mi>D</mml:mi><mml:mi>D</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfenced></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M70\" display=\"inline\"><mml:msub><mml:mrow><mml:mi>l</mml:mi></mml:mrow><mml:mrow><mml:mi>D</mml:mi><mml:mi>D</mml:mi></mml:mrow></mml:msub></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M71\" display=\"inline\"><mml:msub><mml:mrow><mml:mi>l</mml:mi></mml:mrow><mml:mrow><mml:mi>D</mml:mi><mml:mi>D</mml:mi></mml:mrow></mml:msub></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M72\" display=\"inline\"><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M73\" display=\"inline\"><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M74\" display=\"inline\"><mml:msub><mml:mrow><mml:mi>l</mml:mi></mml:mrow><mml:mrow><mml:mi>D</mml:mi><mml:mi>D</mml:mi></mml:mrow></mml:msub></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M75\" display=\"inline\"><mml:mi>m</mml:mi><mml:mi>d</mml:mi><mml:mi>x</mml:mi><mml:mi>E</mml:mi><mml:mi>F</mml:mi></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M76\" display=\"inline\"><mml:mi>m</mml:mi><mml:mi>d</mml:mi><mml:mi>x</mml:mi><mml:mi>E</mml:mi><mml:mi>F</mml:mi></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M77\" display=\"inline\"><mml:mi>R</mml:mi></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M78\" display=\"inline\"><mml:mi>m</mml:mi><mml:mi>d</mml:mi><mml:mi>x</mml:mi><mml:mi>E</mml:mi><mml:mi>F</mml:mi></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M79\" display=\"inline\"><mml:mi>m</mml:mi><mml:mi>d</mml:mi><mml:mi>x</mml:mi><mml:mi>E</mml:mi><mml:mi>F</mml:mi></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M80\" display=\"inline\"><mml:mi>m</mml:mi><mml:mi>d</mml:mi><mml:mi>x</mml:mi><mml:mi>E</mml:mi><mml:mi>F</mml:mi></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M81\" display=\"inline\"><mml:mi>d</mml:mi></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M82\" display=\"inline\"><mml:mi>m</mml:mi><mml:mi>d</mml:mi><mml:mi>x</mml:mi><mml:mi>E</mml:mi><mml:mi>F</mml:mi></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M83\" display=\"inline\"><mml:mfenced separators=\"\|\"><mml:mrow><mml:mi>d</mml:mi><mml:mo>=</mml:mo><mml:mn>3.06</mml:mn><mml:mo>×</mml:mo><mml:msup><mml:mrow><mml:mn>10</mml:mn></mml:mrow><mml:mrow><mml:mo>−</mml:mo><mml:mn>3</mml:mn></mml:mrow></mml:msup><mml:mo>/</mml:mo><mml:mi mathvariant=\"normal\">b</mml:mi><mml:mi mathvariant=\"normal\">p</mml:mi></mml:mrow></mml:mfenced></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M84\" display=\"inline\"><mml:mi>d</mml:mi></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M85\" display=\"inline\"><mml:mi>d</mml:mi><mml:mo>=</mml:mo><mml:mn>1.69</mml:mn><mml:mo>×</mml:mo><mml:msup><mml:mrow><mml:mn>10</mml:mn></mml:mrow><mml:mrow><mml:mo>−</mml:mo><mml:mn>2</mml:mn></mml:mrow></mml:msup><mml:mo>/</mml:mo><mml:mi mathvariant=\"normal\">b</mml:mi><mml:mi mathvariant=\"normal\">p</mml:mi></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M86\" display=\"inline\"><mml:mi>m</mml:mi><mml:mi>d</mml:mi><mml:mi>x</mml:mi><mml:mi>E</mml:mi><mml:mi>F</mml:mi></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M87\" display=\"inline\"><mml:mi>m</mml:mi><mml:mi>d</mml:mi><mml:mi>x</mml:mi><mml:mi>E</mml:mi><mml:mi>F</mml:mi></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M88\" display=\"inline\"><mml:mi>d</mml:mi></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M89\" display=\"inline\"><mml:mi>d</mml:mi></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M90\" display=\"inline\"><mml:mi>d</mml:mi><mml:mo><</mml:mo><mml:mn>5</mml:mn><mml:mo>×</mml:mo><mml:msup><mml:mrow><mml:mn>10</mml:mn></mml:mrow><mml:mrow><mml:mo>−</mml:mo><mml:mn>4</mml:mn></mml:mrow></mml:msup><mml:mo>/</mml:mo><mml:mi mathvariant=\"normal\">b</mml:mi><mml:mi mathvariant=\"normal\">p</mml:mi></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M91\" display=\"inline\"><mml:mi>m</mml:mi><mml:mi>d</mml:mi><mml:mi>x</mml:mi><mml:mi>E</mml:mi><mml:mi>F</mml:mi></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M92\" display=\"inline\"><mml:mfenced separators=\"\|\"><mml:mrow><mml:msub><mml:mrow><mml:mi>s</mml:mi></mml:mrow><mml:mrow><mml:mi>B</mml:mi></mml:mrow></mml:msub><mml:mo>></mml:mo><mml:msub><mml:mrow><mml:mi>s</mml:mi></mml:mrow><mml:mrow><mml:mi>D</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfenced></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M93\" display=\"inline\"><mml:mo stretchy=\"false\">(</mml:mo><mml:mrow><mml:msub><mml:mrow><mml:mi>s</mml:mi></mml:mrow><mml:mrow><mml:mi>D</mml:mi></mml:mrow></mml:msub><mml:mo>></mml:mo><mml:mfrac><mml:mrow><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mi>e</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfrac></mml:mrow><mml:mo stretchy=\"false\">)</mml:mo></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M94\" display=\"inline\"><mml:mi mathvariant=\"normal\">A</mml:mi><mml:mi mathvariant=\"normal\">U</mml:mi><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mfenced separators=\"\|\"><mml:mrow><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup><mml:mo>−</mml:mo><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:mrow></mml:mfenced></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M95\" display=\"inline\"><mml:mi mathvariant=\"normal\">A</mml:mi><mml:mi mathvariant=\"normal\">U</mml:mi><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mfenced separators=\"\|\"><mml:mrow><mml:msup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:mfenced><mml:mo>/</mml:mo><mml:mi mathvariant=\"normal\">A</mml:mi><mml:mi mathvariant=\"normal\">U</mml:mi><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mrow><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"italic\">genome</mml:mi><mml:mo>−</mml:mo><mml:mi mathvariant=\"italic\">wide</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:mrow><mml:mo stretchy=\"false\">)</mml:mo></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M96\" display=\"inline\"><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M97\" display=\"inline\"><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M98\" display=\"inline\"><mml:mi mathvariant=\"normal\">A</mml:mi><mml:mi mathvariant=\"normal\">U</mml:mi><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mfenced separators=\"\|\"><mml:mrow><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup><mml:mo>−</mml:mo><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:mrow></mml:mfenced></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M99\" display=\"inline\"><mml:msub><mml:mrow><mml:mi>s</mml:mi></mml:mrow><mml:mrow><mml:mi>D</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mo>−</mml:mo><mml:msup><mml:mrow><mml:mn>10</mml:mn></mml:mrow><mml:mrow><mml:mo>−</mml:mo><mml:mn>3</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M100\" display=\"inline\"><mml:msub><mml:mrow><mml:mi>s</mml:mi></mml:mrow><mml:mrow><mml:mi>B</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mo>−</mml:mo><mml:msup><mml:mrow><mml:mn>10</mml:mn></mml:mrow><mml:mrow><mml:mo>−</mml:mo><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M101\" display=\"inline\"><mml:mi mathvariant=\"normal\">A</mml:mi><mml:mi mathvariant=\"normal\">U</mml:mi><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mfenced separators=\"\|\"><mml:mrow><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup><mml:mo>−</mml:mo><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:mrow></mml:mfenced></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M102\" display=\"inline\"><mml:msub><mml:mrow><mml:mi>s</mml:mi></mml:mrow><mml:mrow><mml:mi>B</mml:mi></mml:mrow></mml:msub><mml:mo>></mml:mo><mml:msub><mml:mrow><mml:mi>s</mml:mi></mml:mrow><mml:mrow><mml:mi>D</mml:mi></mml:mrow></mml:msub></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M103\" display=\"inline\"><mml:msub><mml:mrow><mml:mi>s</mml:mi></mml:mrow><mml:mrow><mml:mi>D</mml:mi></mml:mrow></mml:msub></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M104\" display=\"inline\"><mml:msub><mml:mrow><mml:mi>s</mml:mi></mml:mrow><mml:mrow><mml:mi>B</mml:mi></mml:mrow></mml:msub></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M105\" display=\"inline\"><mml:mfrac><mml:mrow><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mi>e</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfrac></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M106\" 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separators=\"\|\"><mml:mrow><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup><mml:mo>−</mml:mo><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:mrow></mml:mfenced></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M113\" display=\"inline\"><mml:mi mathvariant=\"normal\">A</mml:mi><mml:mi mathvariant=\"normal\">U</mml:mi><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mfenced separators=\"\|\"><mml:mrow><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup><mml:mo>−</mml:mo><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:mrow></mml:mfenced></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M114\" display=\"inline\"><mml:mi mathvariant=\"normal\">A</mml:mi><mml:mi mathvariant=\"normal\">U</mml:mi><mml:mi mathvariant=\"normal\">C</mml:mi><mml:mfenced separators=\"\|\"><mml:mrow><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup><mml:mo>−</mml:mo><mml:msubsup><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msubsup></mml:mrow></mml:mfenced></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M115\" display=\"inline\"><mml:mi>m</mml:mi><mml:mi>d</mml:mi><mml:mi>x</mml:mi><mml:mi>E</mml:mi><mml:mi>F</mml:mi></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M116\" display=\"inline\"><mml:mi>m</mml:mi><mml:mi>d</mml:mi><mml:mi>x</mml:mi><mml:mi>E</mml:mi><mml:mi>F</mml:mi></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M117\" display=\"inline\"><mml:mi>m</mml:mi><mml:mi>d</mml:mi><mml:mi>x</mml:mi><mml:mi>E</mml:mi><mml:mi>F</mml:mi></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M118\" display=\"inline\"><mml:mi>d</mml:mi></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M119\" display=\"inline\"><mml:mi>d</mml:mi></mml:math></inline-formula>", "<inline-formula><mml:math id=\"M120\" display=\"inline\"><mml:mi>d</mml:mi><mml:mo><</mml:mo><mml:mn>5</mml:mn><mml:mo>×</mml:mo><mml:msup><mml:mrow><mml:mn>10</mml:mn></mml:mrow><mml:mrow><mml:mo>−</mml:mo><mml:mn>4</mml:mn></mml:mrow></mml:msup><mml:mo>/</mml:mo><mml:mi mathvariant=\"normal\">b</mml:mi><mml:mi mathvariant=\"normal\">p</mml:mi></mml:math></inline-formula>" ]	[]	[]	[]	[]	[ "<supplementary-material id=\"SD1\" position=\"float\" content-type=\"local-data\"><label>Supplement 1</label></supplementary-material>", "<supplementary-material id=\"SD2\" position=\"float\" content-type=\"local-data\"><label>Supplement 2</label></supplementary-material>" ]	[ "<fn-group><fn fn-type=\"COI-statement\" id=\"FN1\"><p id=\"P33\">Competing interests</p><p id=\"P34\">The authors declare no competing interests.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"nihpp-2023.12.22.573162v1-f0001\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-2023.12.22.573162v1-f0002\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-2023.12.22.573162v1-f0003\" position=\"float\"/>" ]	[ "<media xlink:href=\"media-1.pdf\" id=\"d64e2615\" position=\"anchor\"/>", "<media xlink:href=\"media-2.zip\" id=\"d64e2618\" position=\"anchor\"/>" ]	[{"label": ["[3]"], "surname": ["Poyet"], "given-names": ["M."], "article-title": ["A library of human gut bacterial isolates paired with longitudinal multiomics data enables mechanistic microbiome research"], "source": ["Nature medicine"], "volume": ["25"], "issue": ["9"], "year": ["2019"], "fpage": ["1442"], "lpage": ["1452"]}, {"label": ["[4]"], "surname": ["Yaffe", "Relman"], 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A."], "article-title": ["Tracking microbial evolution in the human gut using Hi-C reveals extensive horizontal gene transfer, persistence and adaptation"], "source": ["Nature microbiology"], "volume": ["5"], "issue": ["2"], "year": ["2020"], "fpage": ["343"], "lpage": ["353"]}, {"label": ["[5]"], "surname": ["Zlitni"], "given-names": ["S."], "article-title": ["Strain-resolved microbiome sequencing reveals mobile elements that drive bacterial competition on a clinical timescale"], "source": ["Genome medicine"], "volume": ["12"], "year": ["2020"], "fpage": ["1"], "lpage": ["17"]}, {"label": ["[9]"], "surname": ["Zahavi"], "given-names": ["L."], "article-title": ["Bacterial SNPs in the human gut microbiome associate with host BMI"], "source": ["Nature Medicine"], "year": ["2023"], "fpage": ["1"], "lpage": ["8"]}, {"label": ["[12]"], "surname": ["Liu", "Good"], "given-names": ["Z.", "B. H."], "article-title": ["Dynamics of bacterial recombination in the human gut microbiome"], "source": ["bioRxiv"], "year": ["2022"], "fpage": ["2022"], "lpage": ["08"]}, {"label": ["[19]"], "surname": ["Vitti", "Grossman", "Sabeti"], "given-names": ["J. J.", "S. R.", "P. C."], "article-title": ["Detecting natural selection in genomic data"], "source": ["Annual review of genetics"], "volume": ["47"], "year": ["2013"], "fpage": ["97"], "lpage": ["120"]}, {"label": ["[24]"], "surname": ["Koropoulis", "Alachiotis", "Pavlidis"], "given-names": ["A.", "N.", "P."], "article-title": ["Detecting positive selection in populations using genetic data"], "source": ["Statistical population genomics"], "year": ["2020"], "fpage": ["87"], "lpage": ["123"]}, {"label": ["[27]"], "surname": ["Slatkin"], "given-names": ["M."], "article-title": ["Linkage disequilibrium\u2014understanding the evolutionary past and mapping the medical future"], "source": ["Nature Reviews Genetics"], "volume": ["9"], "issue": ["6"], "year": ["2008"], "fpage": ["477"], "lpage": ["485"]}, {"label": ["[28]"], "surname": ["Eyre-Walker", "Keightley"], "given-names": ["A.", "P. 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S."], "article-title": ["Homologous recombination in Clostridioides difficile mediates diversification of cell surface features and transport systems"], "source": ["Msphere"], "volume": ["5"], "issue": ["6"], "year": ["2020"], "fpage": ["10"], "lpage": ["1128"]}, {"label": ["[47]"], "surname": ["Almeida"], "given-names": ["A."], "article-title": ["A unified catalog of 204,938 reference genomes from the human gut microbiome"], "source": ["Nature Biotechnology"], "volume": ["39"], "issue": ["1"], "year": ["2021"], "fpage": ["105"], "lpage": ["114"]}, {"label": ["[49]"], "surname": ["Steux", "Szpiech"], "given-names": ["C.", "Z. A."], "article-title": ["The Maintenance of Deleterious Variation in Wild Chinese Rhesus Macaques"], "source": ["bioRxiv"], "year": ["2023"], "fpage": ["2023"], "lpage": ["10"]}, {"label": ["[51]"], "surname": ["Assaf", "Petrov", "Blundell"], "given-names": ["Z. J.", "D. A.", "J. 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PMC10769438	38187684	[ "<title>Introduction</title>", "<p id=\"P2\">Most biodiversity evolved during adaptive radiation, the process by which a single lineage colonizes myriad ecological niches and rapidly diversifies (##REF##31958131##Gillespie et al., 2020##; ##REF##36237480##Martin and Richards, 2019##; ##UREF##30##Simpson, 1944##; ##UREF##32##Stroud and Losos, 2016##). However, this process of rapid adaptation is also constrained by physical laws governing interactions between the organism and the essential tasks needed for survival and reproduction within its environment, known as the emerging field of ecomechanics (##UREF##11##Higham et al., 2016##; ##REF##34218955##Higham et al., 2021##; ##REF##33171080##Perevolotsky et al., 2020##). Understanding how functional traits interact in complex ways to achieve these essential life history tasks is fundamental for connecting population genomics to phenotypes to performance to fitness to adaptive radiation within new environments (##UREF##1##Arnold, 1983##; ##REF##33791533##Martin et al., 2019##). For example, estimating the performance landscape for a functional task or tasks can lead to deep insights about patterns of morphospace occupation and rates of morphological diversification across diverse taxa (##UREF##0##Armbruster, 1990##; ##UREF##2##Benkman, 1993##; ##REF##23815654##Figueirido et al., 2013##; ##UREF##12##Holzman et al., 2022##; ##UREF##13##Keren et al., 2018##; ##REF##32333778##Olsson et al., 2020##; ##UREF##25##Raup, 1966##; ##REF##33784869##Schultz et al., 2021##; ##REF##30820948##Stayton, 2019##; ##REF##29441363##Tseng and Flynn, 2018##). However, few systems have been investigated across these levels of biological organization.</p>", "<p id=\"P3\">Scale-biting, or lepidophagy, is a fascinating ecological niche that has evolved at least 20 times in fishes and within a diverse range of environments, from rift lakes (##REF##17807183##Hori, 1993##; ##REF##27178345##Raffini et al., 2017##; ##REF##20102595##Stewart and Albertson, 2010##; ##REF##17945014##Takahashi et al., 2007##) to tropical streams and rivers (##UREF##5##Evans et al., 2017##; ##REF##29191622##Gosavi et al., 2018##; ##REF##30952366##Gosavi et al., 2019##; ##UREF##10##Grubh et al., 2004##) to the mesopelagic zone (##UREF##20##Nakae and Sasaki, 2002##) and across ontogenetic stages (##REF##21235560##Davis et al., 2011##; ##UREF##16##MacLeod, 2020##; ##UREF##21##Novakowski et al., 2004##; ##UREF##34##Szelistowski, 1989##) in a diverse range of fish groups, including cichlidiformes, characiformes, siluriformes, and sharks (##REF##29410862##Kolmann et al., 2018##; ##REF##23976994##Martin and Wainwright, 2013c##; ##UREF##28##Sazima, 1984##). This trophic niche provides an excellent setting for investigating the functional traits underpinning performance and fitness because successful scale-eating appears to require both intensive prey capture behaviors and efficient energy use per strike (##UREF##28##Sazima, 1984##). In contrast to piscivory, all scale-eaters are generally smaller than their larger prey and numerous strikes must be performed efficiently due to the low energy payoff in calories per strike, resulting in an excellent laboratory and field model for observing repeated prey capture behaviors. Scale-eaters rarely or never consume whole prey (##REF##29191622##Gosavi et al., 2018##; ##UREF##14##Kovac et al., 2019##; ##REF##23976994##Martin and Wainwright, 2013c##; ##UREF##28##Sazima, 1984##; ##UREF##29##Sazima, 1986##).</p>", "<p id=\"P4\">The scale-biting pupfish, <italic toggle=\"yes\">Cyprinodon desquamator</italic>, is the youngest lepidophage so far discovered; this species evolved within the past 10–15 kya on San Salvador Island, Bahamas (##UREF##17##Martin and Wainwright, 2013a##; ##REF##33791533##Martin et al., 2019##). Scales comprise approximately 50% of its diet in addition to macroalgae and microinvertebrates in several hypersaline lakes where it is endemic (##REF##21790569##Martin and Wainwright, 2011##; ##REF##23976994##Martin and Wainwright, 2013c##; ##UREF##19##McLean and Lonzarich, 2017##). All other extant scale-eating lineages likely evolved at least 1 Mya (##REF##17383901##Koblmüller et al., 2007##), except for the extinct Lake Victorian lepidophagous cichlid <italic toggle=\"yes\">Haplochromis welcommei</italic> (##UREF##8##Greenwood, 1965##). In several interior hypersaline lakes on San Salvador Island (SSI), <italic toggle=\"yes\">C. desquamator</italic> occurs in sympatry at low frequencies in the same benthic macroalgae-dominated habitats as <italic toggle=\"yes\">C. variegatus</italic>, a widespread generalist species, <italic toggle=\"yes\">C. brontotheroides</italic>, an endemic oral-shelling molluscivore species, and <italic toggle=\"yes\">C.</italic> sp. ‘wide-mouth’, a newly discovered intermediate scale-eating ecomorph which is not included in this study (##REF##29161774##Hernandez et al., 2018##; ##REF##24393262##Martin and Feinstein, 2014##; ##REF##35611537##Richards and Martin, 2022##; ##REF##32278332##St John et al., 2020##).</p>", "<p id=\"P5\">The SSI adaptive radiation exhibits one of fastest rates of craniofacial diversification among any measured vertebrate group, up to 1,000 times faster than generalist populations on neighboring Bahamian islands for oral jaw length, due to rapid adaptation to the novel trophic niche of lepidophagy (##REF##27593215##Martin, 2016b##; ##REF##21790569##Martin and Wainwright, 2011##). Previous field experiments measuring the growth and survival of F2 and F5 hybrids among all three species placed in field enclosures within two hypersaline lakes on San Salvador Island estimated a two-peak fitness landscape and a large fitness valley isolating the hybrids with greatest phenotypic similarity to scale-eaters (##REF##23307743##Martin and Wainwright, 2013b##; ##UREF##23##Patton et al., 2022##). This two-peak landscape was stable over multiple years, lake environments, and phenotype-frequency manipulations, suggesting that biophysical constraints on the interaction between pupfish craniofacial traits and their bite performance on scales may ultimately shape the adaptive landscape in this system, rather than frequency-dependent competition (e.g. (##REF##17807183##Hori, 1993##; ##REF##22976018##Martin, 2012##)) or seasonal resource abundance (##REF##11976447##Grant and Grant, 2002##), which would predict a dynamically changing landscape (##REF##27130447##Martin, 2016a##; ##REF##33312688##Martin and Gould, 2020##). Furthermore, our genomic and developmental genetic analyses suggest that introgression of genetic variation for traits related to feeding behavior triggered adaptive radiation on SSI and led to the reassembly of Caribbean-wide genetic variation into a specialized scale-eater and molluscivore on a single island over several thousand years (##REF##28028132##McGirr and Martin, 2017##; ##REF##30283665##McGirr and Martin, 2018##; ##UREF##22##Palominos et al., 2023##; ##REF##28796803##Richards and Martin, 2017##; ##UREF##26##Richards et al., 2021##).</p>", "<p id=\"P6\">In our initial pilot study of scale-biting kinematics in this system, we found that scale-eaters behaviorally reduced their realized peak gape during feeding strikes by reducing their jaw opening angle (##REF##32029459##St. John et al., 2020##). However, we were unable to robustly quantify the performance landscape with limited strike and landmark data and did not measure F2 hybrids. Here we used all new feeding videos filmed at substantially improved resolution and developed a new automated machine-learning pipeline to quantify five landmarks on nearly every frame. We sampled extensive phenotypic diversity within the radiation using both purebred species and multiple F2 hybrid intercrosses and backcrosses to estimate the five-dimensional kinematic performance landscape for scale-biting.</p>" ]	[ "<title>Methods</title>", "<title>Collection and Husbandry</title>", "<p id=\"P7\">Using seine nets or hand nets, we collected molluscivore (<italic toggle=\"yes\">C. brontotheroides</italic>) and scale-eating (<italic toggle=\"yes\">C. desquamator</italic>) pupfish from Crescent Pond, Little Lake, and Osprey Lake on San Salvador Island, Bahamas, and generalist (<italic toggle=\"yes\">C. variegatus</italic>) pupfish from Lake Cunningham on New Providence Island, Bahamas and Fort Fisher estuary in North Carolina, United States in 2017 and 2018. Generalist pupfish from SSI could not be readily trained to feed on gelatin cubes during the period of this study; however, generalists from neighboring islands exhibit essentially identical craniofacial morphology and kinematics relative to SSI generalists (##REF##27593215##Martin, 2016b##; ##REF##32029459##St. John et al., 2020##). Wild-caught and lab-reared fish were maintained in 40–80 l aquaria at salinities of 2–8 ppt and 23–27°C and fed a diet of frozen bloodworms, frozen mysis shrimp, and commercial pellet foods daily. This study used only second-generation through fourth generation lab-reared pupfishes. All newly hatched fry were fed <italic toggle=\"yes\">Artemia</italic> nauplii for approximately one month. All SSI species can be readily crossed to produce viable and fertile hybrids (##UREF##18##Martin et al., 2017##; ##UREF##31##St. John et al., 2021##). F1 hybrid and F2 hybrid intercrosses were generated from molluscivore x scale-eater crosses from both Osprey Lake and Crescent Pond, generalist x scale-eater and generalist x molluscivore crosses from Little Lake, and generalist x <italic toggle=\"yes\">C</italic>. sp. ‘broadmouth’ F1 hybrids from Osprey Lake (##REF##35611537##Richards and Martin, 2022##). Prior to filming, pupfishes were fed exclusively Repashy Superfood gel diets for acclimation and training for at least one week before filming.</p>", "<title>High-speed filming and measurement of gelatin bites</title>", "<p id=\"P8\">We recorded pupfishes feeding on standardized gelatin cubes (dimensions: 1.5 cm × 1.5cm × 1.5 cm × 1.5 cm cube; Community Plus Omnivore Gel Premix, Repashy Specialty Pet Products). Gels were prepared in batches of 50 at precisely a 4:1 water:mix ratio in silicone molds following the manufacturer’s instructions and allowed to set overnight at 4° C. Gels were stored covered at 4° C for a maximum of two weeks before discarding. The gel cube retains its shape in water and therefore allows precise measurements of the dimensions and area removed by each bite.</p>", "<p id=\"P9\">Individuals were trained in group tanks to feed on gelatin cubes and then netted individually for filming. We filmed all strikes at 1,100 fps using a full-color Phantom VEO 440S (Vision Research Inc.) with a Canon EF-S 60 mm lens mounted on a standard tripod. Fish were filmed individually in 7.5 liter bare glass tanks with a solid matte background at salinities of 2–3 ppt and 21–23°C. Illumination was provided by two dimmable full spectrum LED lights placed on either side of the filming tank. Gelatin cubes were held with forceps with one edge facing in a horizontal direction toward the fish (##FIG##0##Fig. 1##). Trained fish usually attacked the gel almost immediately after placement in the filming tank. After each strike the cube was immediately removed and inspected to confirm a bite mark; missed strikes were confirmed from the video replay. New cubes were used for each feeding strike and never re-used. All videos were filmed in lateral view.</p>", "<p id=\"P10\">The length, width, and depth of each gelatin bite were measured using digital high-precision calipers (Mitutoyo) under a stereomicroscope for nearly all strikes (##SUPPL##0##Fig. S1##). Strikes were characterized as an edge, corner, scrape, or miss. Edge bites occurred along the edge but not the corner of the gelatin cubes and comprised the majority of strikes. Corner bites were removed from one of the corners, which may affect bite dimensions, so were distinguished from edge strikes. Scrapes were defined by bites in which the jaws did not completely close around the gel to remove a chunk of the material, but instead left two distinct indentations. Misses were defined as strikes in which the oral jaws of the fish contacted the gel but did not leave any marks. Strikes in which the jaws did not make visible contact with the gel were excluded. Most individuals were filmed consecutively over one or two filming periods for up to sixteen strikes. After each filming session for each individual, an image of a ruler was photographed in the filming tank at the same distance as the gelatin cubes for calibration of videos.</p>", "<title>Machine learning for quantifying kinematic landmarks on each frame</title>", "<p id=\"P11\">We used the SLEAP (Social LEAP Estimates Animal Poses) analysis pipeline to automatically detect and place morphometric landmarks on each frame (##UREF##24##Pereira et al., 2020##; ##REF##35379947##Pereira et al., 2022##). This software supports data input of raw videos and then provides an interactive GUI to create a labeled training dataset. Predictions from trained models can then be adjusted to enable a ‘human-in-the-loop’ workflow to efficiently and progressively obtain more accurate models and inferences of landmarks.</p>", "<p id=\"P12\">We manually placed five landmarks on 815 frames using the SLEAP GUI spanning 100 high-speed feeding videos including all three species and their hybrids. Frames were chosen for labeling both by eye and automatically by the software to span highly divergent scenes spread across the beginning, middle, and end of each strike. To train a model based on the labeled data, after exploring various configurations we achieved the best performance using the multi-animal bottom-up unet model with a receptive field of 156 pixels, max stride of 32 pixels, batch size of 3, input scaling of 0.75, and validation fraction of 0.1, resulting in a precision of 99% and mean distance between labeled and inferred landmarks of 5 pixels (##SUPPL##0##Fig. S2##). We trained this model on a laptop running a 16 Gb NVIDIA GeForce 3070 GPU, which completed training in less than twelve hours. We then used this trained model to infer landmarks on each frame of a larger set of strike videos using the flow cross-frame identity tracker, which shares information about landmark places across frames for each individual strike video. We culled to a single instance (i.e. one animal) per frame, given that fish were filmed individually, and connected single track breaks. We predicted landmarks on 114,000 frames from 227 .mp4 videos (batch converted from the original .cine files using Phantom Camera Control software) and spot-checked for accuracy (##FIG##0##Fig. 1##). Coordinate data from each frame were exported in .hdf5 format, imported into R (##UREF##4##Core Team, 2021##), and stored in any array using the rhdf5 package (##UREF##7##Fischer et al., 2017##).</p>", "<title>Kinematic variables</title>", "<p id=\"P13\">To quantify gel-biting strikes from coordinate data, we calculated five key kinematic variables per strike: 1) peak gape, the distance from the anterior tip of the premaxilla to the anterior tip of the dentary; 2) peak jaw protrusion, the distance from the center of the pupil to the anterior tip of the premaxilla; 3) peak lower jaw angle, the minimum angle between the lower jaw, the quadrate-articular point of jaw rotation, and the ventral surface of the fish beneath the suspensorium measured from an anteroventral landmark on the preopercle. This measures the maximum rotation of the oral jaws in a downward and outward direction toward the gel as defined in ##REF##32029459##St. John et al. (2020)##. Note that in Cyprinodontiformes oral jaw opening is decoupled from jaw protrusion by the maxillomandibular ligament such that peak gape does not necessarily occur simultaneously with peak jaw protrusion (##REF##19107942##Hernandez et al., 2009##). 4) Time to peak gape (TTPG) was the time in ms from 20% of peak gape to peak gape. 5) Ram speed (m/s) was calculated as the distance from 20% of peak gape to peak gape (mm) divided by the time to peak gape (ms). We calibrated each set of coordinates for each filming session using a ruler held at the same distance from the camera as the gelatin cube. Milliseconds were calculated by counting frames and multiplying by 0.909 to correct for the 1,100 frame rate per second. We then subsetted to only those frames from initial start position to maximum distance from the start position before the fish started to turn its head to the side post-bite so that kinematic variables were only calculated from the start of the strike to the time of impact with the gelatin cube (see Supplemental R code).</p>" ]	[ "<title>Results</title>", "<title>Scale-eaters displayed increased gape size and bite length</title>", "<p id=\"P19\">In mixed-effects models controlling for species, strike type, and repeated sampling of each individual, we found no effect of species on bite depth or width (effect of scale-eater factor level: <italic toggle=\"yes\">P</italic> = 0.062); however, there was a significant positive effect of scale-eater species identity on bite length (<italic toggle=\"yes\">P</italic> = 0.0060). Among the five kinematic variables, only increased peak gape was significantly associated with scale-eater strikes (<italic toggle=\"yes\">P</italic> = 0.040). No kinematic variables were significantly associated with any other species or hybrids across strike types (##FIG##1##Fig. 2##), even when comparing missed strikes to successful bites.</p>", "<title>Substantial similarity in strike kinematics among species and strike types</title>", "<p id=\"P20\">Similarly, we found substantial overlap in kinematic variation across species and strike types. Principal component analysis showed the strongest loadings of peak gape and peak jaw protrusion on PC1, explaining 36.7% of kinematic variance among strikes (##FIG##2##Fig. 3##).</p>", "<p id=\"P21\">Linear discriminant analysis by strike type successfully classified strikes at a rate of only 50.8%. The kinematic variable best separating misses from other strike types on discriminant axis one was peak gape. Linear discriminant analysis by species successfully classified species or hybrids based on their strike kinematics at a rate of only 32.3%. The kinematic variables best separating scale-eaters from other groups on discriminant axis one was again peak gape and peak jaw protrusion while TTPG had the weakest effect on classification of species by kinematic variables. Although plots of the first two principal components and discriminant axes indicate greater variation within scale-eater and hybrid strike kinematics, there was also clearly substantial overlap among species and hybrids (##FIG##2##Fig. 3##).</p>", "<title>Multi-peak performance landscape for gel-biting</title>", "<p id=\"P22\">We used generalized additive modeling to explore the relationship between kinematic variables and bite size dimensions. The best fit model (##TAB##0##Table 1##) included a two-dimensional thin-plate spline for peak gape and peak jaw protrusion along with fixed linear predictors for species, strike type, peak lower jaw angle, time to peak gape (TTPG), and ram speed. The nonlinear interaction between peak gape and peak jaw protrusion was significantly associated with both the bite length (edf = 10.82, <italic toggle=\"yes\">P</italic> = 9e<sup>−7</sup>) and the overall gel volume removed (edf = 8, <italic toggle=\"yes\">P</italic> = 0.0008) and displayed a bimodal surface with two isolated performance peaks (##FIG##3##Fig. 4##). The best fit model for bite length included additional significant linear effects of ram speed (<italic toggle=\"yes\">P</italic> = 0.008) and peak lower jaw angle (<italic toggle=\"yes\">P</italic> = 0.007), but not TTPG (<italic toggle=\"yes\">P</italic> = 0.440) in addition to significant factor levels of missed strikes (<italic toggle=\"yes\">P</italic> = 1.12e<sup>−10</sup>) and molluscivore species (<italic toggle=\"yes\">P</italic> = 0.012). Models without TTPG fit the data equally well (ΔAIC < 2).</p>", "<p id=\"P23\">Even after excluding missed strikes that made contact but left no mark on the gel and scraping bites in which the jaws did not fully occlude, the interaction between peak gape and jaw protrusion was still significantly associated with edge and corner bite length (edf = 10.34, <italic toggle=\"yes\">P</italic> = 9e<sup>−6</sup>) and volume (edf = 11.17, <italic toggle=\"yes\">P</italic> = 4.89e<sup>−5</sup>). Bite width was significantly linearly associated with the interaction between peak gape and peak jaw protrusion resulting in a flat performance landscape (edf = 1.759, <italic toggle=\"yes\">P</italic> = 0.0002), which is largely controlled by the morphological dimensions of the oral jaws of each fish, rather than kinematic variables. Bite depth was not significantly associated with peak gape and peak jaw protrusion nor any linear variable in this model except for the factor of scraping strikes as expected based on the shallow dimensions of these strikes (<italic toggle=\"yes\">P</italic> = 0.046).</p>" ]	[ "<title>Discussion</title>", "<p id=\"P24\">We estimated a surprisingly complex performance landscape for the unusual trophic niche of lepidophagy from high-speed videos of gelatin-removing bites. In contrast to studies of suction-feeding performance on evasive, attached, and strain-sensitive (e.g. zooplankton) prey (##REF##18840664##Holzman et al., 2008##; ##UREF##12##Holzman et al., 2022##; ##UREF##13##Keren et al., 2018##; ##REF##32333778##Olsson et al., 2020##), we found no effect of kinematic timing variables such as TTPG or even ram speed on the performance of scale-biting strikes, measured by the length, width, depth, and total volume of gelatin removed. Instead, successful scale-biting appears to require strike coordination between jaw opening (peak gape) and jaw protrusion and, surprisingly, this interaction resulted in two distinct performance optima: 1) individuals with small peak gapes removed the greatest amount of material per bite at small jaw protrusion distances; 2) individuals with large peak gapes removed the greatest volumes at large jaw protrusion distances; and 3) these two performance optima were surrounded by reduced bite performance in all directions including more extreme values and intermediate values of peak gape and protrusion. This resulted in two distinct performance peaks on the two-dimensional thin-plate spline for jaw protrusion and peak gape in the model best supported by the data (##FIG##3##Fig. 4##, ##TAB##0##Table 1##). Thus, the strikes with the largest peak gapes and jaw protrusion distances observed suffered a performance decline, in line with a few datapoints from our initial kinematic study of biting in this system (##REF##32029459##St. John et al., 2020##); similarly, the strikes with the smallest peak gapes and jaw protrusion distances also suffered a performance decline. Therefore, we unexpectedly found evidence of a multi-peak performance landscape for the relatively straightforward functional task of biting, a well-studied functional task which is often viewed as a simple mechanical system (##REF##18083736##Herrel et al., 2008##; ##UREF##35##Wainwright and Richard, 1995##; ##UREF##36##Westneat, 2005##).</p>", "<title>Two distinct performance optima for biting rather than a linear ridge</title>", "<p id=\"P25\">Surprisingly, there was no simple linear ridge for the interaction between peak gape and peak jaw protrusion in relation to bite volume or bite length; both extreme and intermediate values of these kinematic variables resulted in poor performance, i.e. reduced gelatin bite sizes (##FIG##3##Fig. 4##). Only strikes by scale-eating specialists and some hybrid strikes resided on the second performance peak with larger gapes and jaw protrusion while all generalists and molluscivores occurred on or near the first performance peak. This suggests that a performance valley isolates the recently evolved scale-eating specialist <italic toggle=\"yes\">C. desquamator</italic> from its generalist ancestor. Explanations for this performance landscape must also account for the poor performance of intermediate strike values observed, rather than just a simple performance ridge indicating that coordination between peak gape and peak jaw protrusion is important.</p>", "<p id=\"P26\">One possible explanation is a biomechanical tradeoff in precision and targeted bite area with the most adverse effects on gel-biting performance at intermediate values. Interactions between oral jaw scraping and biting with the gelatin surface may only be effective within two different kinematic regimes. Smaller peak gapes with less jaw protrusion may allow for precise targeting and higher mechanical advantage for removing more gelatin. Larger peak gapes with greater jaw protrusion may reduce precision and mechanical advantage of the bite, but cover a large area, resulting in more gelatin removed per bite. Intermediate values may suffer the costs of less precise biting and less area covered per bite. It is tempting to speculate that strike speed or lower jaw angle play a role in this precision/target area tradeoff. However, while ram speed and lower jaw angle with the suspensorium both had strong linear effects on bite performance, there was no evidence of any nonlinear interactions with peak gape or peak jaw protrusion distance (##TAB##0##Table 1##). Similarly, timing (TTPG) seems to play no role in bite performance, which would seem surprising if precision is important for gelatin removal since faster time to peak gape should reduce bite precision. However, biting strikes generally achieved peak gape substantially before contact with the gel, suggesting that the time to reach peak gape does not seem to be important as long as the jaw is open at some point before contact with the target. This is the consistent with the ‘plateau effect’ observed in the scale-eating piranha, the only other direct study of scale-biting kinematics in other systems (##REF##16326957##Janovetz, 2005##).</p>", "<p id=\"P27\">Alternatively, it is striking that none of the generalist or molluscivore species exhibited feeding strikes with jaw protrusion distances within range of the second performance peak (##FIG##3##Fig. 4##). Similarly, only hybrids with scale-eater ancestry were capable of producing feeding strikes with jaw protrusion distances in this range (> 6.5 mm; see supplemental raw data). Thus, there appears to be a genetic basis underlying the two performance peaks: only scale-eaters and hybrids with scale-eater ancestry can protrude their jaws sufficiently to reach the second performance optimum. This may be due to additional anatomical properties of their oral jaws that allow for greater extension during strikes, such as different ratios of muscle fiber types within the adductor mandibulae (##REF##30060635##Ono and Kaufman, 1983##; ##UREF##33##Summers and Long, 2005##), along with unmeasured aspects of their behavior or strike kinematics. Indeed, scale-eaters exhibit significant differences in their boldness and exploratory behaviors (##REF##32029459##St. John et al., 2020##). Genome-wide association scans for oral jaw length also identified collagen genes with fixed regulatory differences between scale-eaters and molluscivores, including collagen type XV alpha 1 (col15a1), suggesting that the elasticity of jaw opening may be under selection in this species (##REF##33791533##Martin et al., 2019##; ##REF##28028132##McGirr and Martin, 2017##). Greater peak gapes are possible due to the two-fold larger oral jaws of the scale-eater. However, scale-eaters still do not open their jaws as wide as possible during strikes (##REF##32029459##St. John et al., 2020##) or achieve 180° angles with their open jaws as in other scale-eating specialists such as the scale-eating piranha (##REF##16326957##Janovetz, 2005##), indicating adaptive behavioral compensation for their extreme oral anatomy during strikes.</p>", "<p id=\"P28\">Finally, we cannot rule out more esoteric explanations for the unexpected fitness valley between bite performance optima. Sensory perception during strikes may be limited at intermediate strike distances due to the blind spot caused by the positioning of the vertebrate optic nerve in front of the retina, although biomechanical implications of this in fishes are unknown (##UREF##9##Gregory and Cavanagh, 2011##). Alternatively, intermediate jaw protrusion may be an indirect effect of premature suspension of strike behavior or lack of motivation during the strike. However, excluding missed strikes did not alter the observed two-peak performance landscape (##FIG##3##Fig. 4##).</p>", "<title>Similarity between the performance and fitness landscapes</title>", "<p id=\"P29\">Both field measurements of fitness and laboratory measurements of scale-biting performance support a two-peak landscape. Repeated field experiments in this system measured the fitness landscape from the growth and survival of advanced generation hybrids placed within 3–4 m enclosures in their natural hypersaline lake environments for 3 – 11 months and estimated two fitness peaks separated by a fitness valley (##REF##23307743##Martin and Wainwright, 2013b##). Surprisingly, these landscapes remained relatively stable and exhibited a similar two-peak topography across years, lakes, and frequency-manipulations of hybrids (##REF##27130447##Martin, 2016a##; ##REF##33312688##Martin and Gould, 2020##). However, in all cases where it was detected, the second peak corresponded to the phenotype of molluscivores; whereas hybrids resembling the scale-eater survived and grew at the lowest rates across all fitness experiments. Thus, a single fitness peak corresponds to generalist morphology and kinematics for both fitness and performance, whereas the second scale-biting performance peak has no analog in field enclosures, perhaps due to a lack of hybrids with the necessary combination of scale-biting kinematics and morphology. However, these laboratory estimates do suggest that F2 hybrids with scale-eater ancestry display sufficient jaw protrusion distances during gel-biting strikes to occupy the second performance peak.</p>", "<p id=\"P30\">Ultimately, the goal of connecting morphological fitness landscapes to performance landscapes is difficult due to the many-to-one mapping of hybrid morphologies onto biting kinematics. However, better understanding of the genetic basis of kinematic variables, such as jaw protrusion distance, may enable connecting the genetic regulatory networks underlying morphological, kinematic, and behavioral traits through genotypic fitness networks informed by laboratory performance and field fitness experiments. Our initial study of genotypic fitness networks found rare but accessible pathways in genotype space (i.e. equal or increasing in fitness at each step) connecting scale-eaters to other species, but so far we have found no associations between fitness and any behavioral genes (##UREF##23##Patton et al., 2022##). Also see the role of <italic toggle=\"yes\">sox9b</italic> in cichlid foraging performance, in which correcting for genotype improves the form-function relationship (##REF##37422435##Matthews et al., 2023##).</p>" ]	[ "<title>Conclusion</title>", "<p id=\"P31\">Here we explore the biomechanics of a highly specialized trophic niche and demonstrate the power of machine-learning approaches to analyze kinematic data. We estimated a surprisingly complex two-peak performance landscape for biting that indicates that the highly protrusible jaws of scale-eating specialists may provide a performance benefit for scale-eating. This study provides a new framework for understanding bite mechanics in fishes – particularly scraping dynamics – and a foundation for dissecting the genetic basis of these predatory behaviors and their relationship to fitness landscapes driving rapid adaptive radiation in the wild.</p>" ]	[ "<p id=\"P1\">The physical interactions between organisms and their environment ultimately shape their rate of speciation and adaptive radiation, but the contributions of biomechanics to evolutionary divergence are frequently overlooked. Here we investigated an adaptive radiation of <italic toggle=\"yes\">Cyprinodon</italic> pupfishes to measure the relationship between feeding kinematics and performance during adaptation to a novel trophic niche, lepidophagy, in which a predator removes only the scales, mucus, and sometimes tissue from their prey using scraping and biting attacks. We used high-speed video to film scale-biting strikes on gelatin cubes by scale-eater, molluscivore, generalist, and hybrid pupfishes and subsequently measured the dimensions of each bite. We then trained the SLEAP machine-learning animal tracking model to measure kinematic landmarks and automatically scored over 100,000 frames from 227 recorded strikes. Scale-eaters exhibited increased peak gape and greater bite length; however, substantial within-individual kinematic variation resulted in poor discrimination of strikes by species or strike type. Nonetheless, a complex performance landscape with two distinct peaks best predicted gel-biting performance, corresponding to a significant nonlinear interaction between peak gape and peak jaw protrusion in which scale-eaters and their hybrids occupied a second performance peak requiring larger peak gape and greater jaw protrusion. A bite performance valley separating scale-eaters from other species may have contributed to their rapid evolution and is consistent with multiple estimates of a multi-peak fitness landscape in the wild. We thus present an efficient deep-learning automated pipeline for kinematic analyses of feeding strikes and a new biomechanical model for understanding the performance and rapid evolution of a rare trophic niche.</p>" ]	[ "<title>Statistical analyses</title>", "<title>Mixed-effect modeling</title>", "<p id=\"P14\">Due to our repeated measures design of multiple strikes per fish, we used mixed-effects models to compare kinematic variables and bite dimensions across species groups. We used the lme4 and lmerTest packages in R to fit linear mixed-effects models for each kinematic response variable and bite metric with independent fixed effects for strike type and species (scale-eater, molluscivore, generalist, or hybrid) plus the random intercept effect of individual ID. We measured up to 16 strikes per individual fish. P-values were assessed for each factor level using Satterthwaite’s method (##UREF##15##Kuznetsova et al., 2017##). We used AIC to compare additional models with random slopes and interactions among the fixed effects (##UREF##3##Burnham et al., 2011##).</p>", "<p id=\"P15\">Finally, due to the failure of this radiation to fit a tree-like model of evolution due to extensive secondary gene flow (##UREF##26##Richards et al., 2021##), in addition to our inclusion of several hybrid crosses, we did not correct for phylogenetic signal in our analyses. However, we note that both generalist outgroup populations are more distantly related to each other than the scale-eater, molluscivores, and hybrids on SSI (##REF##24393262##Martin and Feinstein, 2014##), indicating that kinematic variables and bite dimensions exhibit minimal phylogenetic signal (##REF##18673385##Losos, 2008##).</p>", "<title>Multivariate analyses of kinematic variation</title>", "<p id=\"P16\">We calculated principal component analysis (princomp) from the correlation matrix of kinematic variables. We also used linear discriminant analysis from the MASS package in R (##UREF##27##Ripley et al., 2013##) to explore overall kinematic variation among species and strike type. We further calculated classification accuracy using species or strike type as the grouping variable using all five kinematic variables.</p>", "<title>Generalized additive modeling</title>", "<p id=\"P17\">We used generalized additive semi-parametric models (GAMs) to test for nonlinear terms relationships and interactions between kinematic variables and bite dimensions (##UREF##39##Wood and Augustin, 2002##). Because we were interested in directly predicting bite performance from the kinematic data for each strike, we treated strike as our unit of replication, not fish, and did not control for individual in our statistical models. However, within-individual variation often exceeded between-species variation and we were not directly interested in species kinematic differences using this modeling framework, which we previously addressed explicitly using mixed-effects models controlling for individual. Moreover, methods for fitting mixed-effects GAM models in R are currently limited.</p>", "<p id=\"P18\">We fit GAM models using the mgcv package in R (##UREF##38##Wood, 2012##; ##UREF##40##Wood and Wood, 2015##) with the response variables of bite length, width, depth, or volume and independent covariates of species and strike type, and independent continuous kinematic variables of peak gape, peak jaw protrusion, peak lower jaw angle, time to peak gape, and ram speed. We used the REML method for calculating smoothness of splines and Gaussian distributions for all models. We compared the fit of each kinematic variable modeled as both a linear term or a smoothing spline using model selection with the AIC criterion in R. We further allowed for shrinkage of each smoothing spline within the full model to determine which kinematic variables were best modeled as spline terms. We then systematically compared models with both two-way thin-plate splines or all one-way splines to explore whether there were any nonlinear interactions between kinematic variables. Model fits were visualized with the mgViz (##UREF##6##Fasiolo et al., 2020##) and ggplot2 packages (##UREF##37##Wickham et al., 2016##) in R. All coding was assisted by suggestions from ChatGPT 3.5 and 4.0 (OpenAI, Inc.).</p>", "<title>Supplementary Material</title>" ]	[ "<title>Acknowledgments</title>", "<p id=\"P32\">We thank Jack Tseng and the Martin and Holzman labs for valuable comments and discussion of the results. We also thank the Gerace Research Centre and Troy Day for logistical support and the government of the Bahamas for permission to collect and export breeding colonies in 2017 and 2018. This research was funded by the Binational Science Foundation 2016136 to RH and CHM, and the National Science Foundation DEB CAREER grant #1749764, National Institutes of Health grant 5R01DE027052<sup>‐</sup>02, the University of North Carolina at Chapel Hill, and the University of California, Berkley to CHM and a Discovery for All grant for discovery-based learning to AT.</p>" ]	[ "<fig position=\"float\" id=\"F1\"><label>Fig. 1</label><caption><title>High-speed video of gel-biting strikes with automated landmarks.</title><p id=\"P33\">Strike sequences for a scraping bite by a scale-eater (a-c), a miss by a hybrid (d-f), and an edge bite by a molluscivore (g-i). Videos were filmed at 1,100 fps on a Phantom VEO 440S camera. Frames illustrate approximately 20% of peak gape (first column), peak gape (second column), and jaw adduction immediately after the bite (third column). Five yellow landmarks on each frame were placed automatically using our SLEAP inference model and are illustrated as small yellow dots to emphasize the accuracy of these inferred landmarks.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Fig. 2</label><caption><title>Scale-eaters only differ in increased peak gape during gel-biting strikes.</title><p id=\"P34\">Boxplots overlaid with raw data show five kinematic variables measured during gel-biting strikes measured from automated landmarking of 227 videos. Species or hybrid cross is indicated by color and strike type is indicated by shape. TTPG: time to peak gape. Lower jaw angle is the minimum angle between the lower jaw and suspensorium from 20% to peak gape. Ram speed was calculated from the distance traveled between 20% and peak gape.</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Fig. 3</label><caption><title>Principal component and linear discriminate analyses illustrate substantial overlap by strike type and species.</title><p id=\"P35\">Species or hybrid is indicated by color and strike type is indicated by shape. Multivariate analyses were based on five kinematic variables: peak gape, peak jaw protrusion, minimum lower jaw angle with the suspensorium, time to peak gape (TTPG), and ram speed.</p></caption></fig>", "<fig position=\"float\" id=\"F4\"><label>Fig. 4</label><caption><title>Generalized additive modeling supports a nonlinear interaction between peak gape and peak protrusion for bite performance.</title><p id=\"P36\">Thin-plate splines from the best-fitting GAM model (##TAB##0##Table 1##) for the response variable of bite length (first column: <bold>a,c</bold>) or total bite size (gel volume removed: second column: <bold>b,d</bold>). First row includes all strike types and second row excludes scraping and missed strikes. <bold>e.</bold> Performance landscape for bite length in 3D perspective view. Species or hybrid cross is indicated by color and strike type is indicated by shape for 130 filmed strikes with data for gel bite dimensions. Gel volume removed was calculated from length x width x depth of gelatin bite measured with a digital caliper under a stereomicroscope.</p></caption></fig>" ]	[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1.</label><caption><p id=\"P37\">Model selection of generalized additive models (<italic toggle=\"yes\">n</italic> = 130 strikes with complete kinematic and gel bite data) predicting bite length or total bite volume removed from Repashy gelatin cubes by a single strike. sp: species; g: peak gape, jp: peak jaw protrusion; ja: peak lower jaw angle with the suspensorium; TTPG: time to peak gape; rs: ram speed. Strike type included full bites from the gelatin edge, corner, scraping bites, and misses in which the jaws made contact with the gelatin but left no impression. Significant terms within each GAM model (<italic toggle=\"yes\">P</italic> < 0.05) are highlighted in bold.</p></caption><table frame=\"above\" rules=\"none\"><colgroup span=\"1\"><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/></colgroup><thead><tr><th align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">model</th><th align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">deviance explained</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">df</th><th align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AIC</th><th align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ΔAIC</th></tr><tr><th colspan=\"5\" align=\"left\" valign=\"top\" rowspan=\"1\">\n<hr/>\n</th></tr></thead><tbody><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">length ~ <bold>species + strike type + s(g, jp) + ja + rs +</bold> TTPG</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">79.2%</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">23.1</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">322.7</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">--</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">length ~ <bold>sp. + strike type + s(g, jp) + ja + rs</bold></td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">79.2%</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">22.3</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">321.2</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">--</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">length ~ <bold>sp. + strike type + s(g) + s(jp) + ja</bold> + TTPG + <bold>rs</bold></td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">77.4%</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">19.7</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">327.0</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">6</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">length ~ <bold>sp. + strike type + s(g)</bold> + s(jp) + s(<bold>ja</bold>) + s(TTPG) + s(<bold>rs</bold>)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">74.7%</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">14.9</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">331.6</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">10</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">length ~ <bold>sp. + strike type + g</bold> + jp + <bold>ja</bold> + TTPG + <bold>rs</bold></td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">70.7%</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">13</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">347.1</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">16</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">length ~ <bold>sp. + s(g, jp) + ja</bold> + TTPG + rs</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">50.3%</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">17.4</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">424.5</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">103</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">length ~ <bold>strike type + s(g, jp)</bold> + ja + TTPG + <bold>rs</bold></td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">72.3%</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">18.6</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">351.0</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">30</td></tr><tr><td colspan=\"5\" align=\"left\" valign=\"top\" rowspan=\"1\">\n<hr/>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">volume ~ species <bold>+ strike type + s(g, jp)</bold> + ja + TTPG + rs</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">41.5</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">22.2</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">669.8</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">--</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">volume ~ species + <bold>strike type + s(g, jp)</bold> + ja + rs</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">42.7</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">22.2</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">667.6</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">--</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">volume ~ sp. + <bold>strike type + s(g)</bold> + s(jp) + ja + TTPG + rs</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">28.9</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">12.1</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">674.8</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">7</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">volume ~ <bold>sp. + strike type + s(g)</bold> + s(jp) + s(<bold>ja</bold>) + s(TTPG) + s(<bold>rs</bold>)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">28.6</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">9.9</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">671.1</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">14</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">volume ~ sp. <bold>+ strike type + g</bold> + jp + ja + TTPG + rs</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">28.9</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">13</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">676.7</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">19</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">volume ~ sp. + <bold>s(g, jp)</bold> + ja + TTPG + rs</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">13.7</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">9.7</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">695.5</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">28</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">volume ~ strike type + <bold>s(g, jp)</bold> + ja + TTPG + rs</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">24.5</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">9.7</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">677.9</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">10</td></tr></tbody></table></table-wrap>" ]	[]	[]	[]	[]	[]	[ "<supplementary-material id=\"SD1\" position=\"float\" content-type=\"local-data\"><label>Supplement 1</label></supplementary-material>" ]	[]	[ "<graphic xlink:href=\"nihpp-2023.12.22.573139v1-f0001\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-2023.12.22.573139v1-f0002\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-2023.12.22.573139v1-f0003\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-2023.12.22.573139v1-f0004\" position=\"float\"/>" ]	[ "<media xlink:href=\"NIHPP2023.12.22.573139v1-supplement-1.pdf\" id=\"d64e725\" position=\"anchor\"/>" ]	[{"surname": ["Armbruster"], "given-names": ["W. 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M."], "year": ["1966"], "article-title": ["Geometric Analysis of Shell Coiling: General Problems"], "source": ["J. Paleontol"], "volume": ["40"], "fpage": ["1178"], "lpage": ["1190"]}, {"surname": ["Richards", "McGirr", "Wang", "St John", "Poelstra", "Solano", "O\u2019Connell", "Turner", "Martin"], "given-names": ["E. J.", "J. A.", "J. R.", "M. E.", "J. W.", "M. J.", "D. C.", "B. J.", "C. H."], "year": ["2021"], "article-title": ["A vertebrate adaptive radiation is assembled from an ancient and disjunct spatiotemporal landscape"], "source": ["Proc. Natl. Acad. Sci. U. S. A"], "volume": ["118"]}, {"surname": ["Ripley", "Venables", "Bates", "Hornik", "Gebhardt", "Firth", "Ripley"], "given-names": ["B.", "B.", "D. M.", "K.", "A.", "D.", "M. B."], "year": ["2013"], "article-title": ["Package \u2018mass.\u2019"], "source": ["Cran r"], "volume": ["538"], "fpage": ["113"], "lpage": ["120"]}, {"surname": ["Sazima", "Zaret"], "given-names": ["I.", "T. M."], "year": ["1984"], "source": ["Scale-eating in characoids and other fishes"], "conf-name": ["In Evolutionary ecology of neotropical freshwater fishes: Proceedings of the 1st international symposium on systematics and evolutionary ecology of neotropical freshwater fishes, held at DeKalb, Illinois, U.S.A"], "conf-date": ["June 14\u201318, 1982"], "fpage": ["9"], "lpage": ["23"], "publisher-loc": ["Dordrecht"], "publisher-name": ["Springer Netherlands"]}, {"surname": ["Sazima"], "given-names": ["I."], "year": ["1986"], "article-title": ["Similarities in feeding behaviour between some marine and freshwater fishes in two tropical communities"], "source": ["J. Fish Biol"], "volume": ["29"], "fpage": ["53"], "lpage": ["65"]}, {"surname": ["Simpson"], "given-names": ["G. G."], "year": ["1944"], "source": ["The Major Features of Evolution"], "publisher-name": ["Columbia University Press"]}, {"surname": ["St. John", "Dunker", "Richards", "Romero", "Martin"], "given-names": ["M. E.", "J. C.", "E. J.", "S.", "C. H"], "year": ["2021"], "article-title": ["Parallel genetic changes underlie integrated craniofacial traits in an adaptive radiation of trophic specialist pupfishes"], "source": ["bioRxiv"], "fpage": ["2021.07.01.450661"]}, {"surname": ["Stroud", "Losos"], "given-names": ["J. T.", "J. B."], "year": ["2016"], "article-title": ["Ecological Opportunity and Adaptive Radiation"], "source": ["Annu. Rev. Ecol. Evol. Syst"], "volume": ["47"], "fpage": ["507"], "lpage": ["532"]}, {"surname": ["Summers", "Long"], "given-names": ["A. P.", "J. H."], "year": ["2005"], "part-title": ["Skin and Bones, Sinew and Gristle: the Mechanical Behavior of Fish Skeletal Tissues"], "source": ["Fish Physiology"], "fpage": ["141"], "lpage": ["177"], "publisher-name": ["Academic Press"]}, {"surname": ["Szelistowski"], "given-names": ["W. A."], "year": ["1989"], "article-title": ["Scale-Feeding in Juvenile Marine Catfishes (Pisces: Ariidae)"], "source": ["Copeia"], "volume": ["1989"], "fpage": ["517"], "lpage": ["519"]}, {"surname": ["Wainwright", "Richard"], "given-names": ["P. C.", "B. A."], "year": ["1995"], "article-title": ["Predicting patterns of prey use from morphology of fishes"], "source": ["Environ. Biol. Fishes"]}, {"surname": ["Westneat"], "given-names": ["M. W."], "year": ["2005"], "part-title": ["Skull Biomechanics and Suction Feeding in Fishes"], "source": ["Fish Physiology"], "fpage": ["29"], "lpage": ["75"], "publisher-name": ["Academic Press"]}, {"surname": ["Wickham", "Chang", "Wickham"], "given-names": ["H.", "W.", "M. H."], "year": ["2016"], "article-title": ["Package \u2018ggplot2.\u2019 Create elegant data visualisations using the grammar of graphics"], "source": ["Version"], "volume": ["2"], "fpage": ["1"], "lpage": ["189"]}, {"surname": ["Wood"], "given-names": ["S."], "year": ["2012"], "source": ["mgcv: Mixed GAM Computation Vehicle with GCV/AIC/REML smoothness estimation"]}, {"surname": ["Wood", "Augustin"], "given-names": ["S. N.", "N. H."], "year": ["2002"], "article-title": ["GAMs with integrated model selection using penalized regression splines and applications to environmental modelling"], "source": ["Ecol. Modell"], "volume": ["157"], "fpage": ["157"], "lpage": ["177"]}, {"surname": ["Wood", "Wood"], "given-names": ["S.", "M. S."], "year": ["2015"], "article-title": ["Package \u2018mgcv."], "source": ["R package version"], "volume": ["1"], "fpage": ["729"]}]	{ "acronym": [], "definition": [] }	84	CC BY	no	2024-01-13 00:14:49	bioRxiv. 2023 Dec 23;:2023.12.22.573139	oa_package/33/b3/PMC10769438.tar.gz
PMC10769451	38187621	[ "<title>Introduction</title>", "<p id=\"P5\">Zoonotic diseases represent a major threat to human health, with hundreds of thousands of deaths and millions of infections occurring annually (##REF##21175957##Cascio et al., 2011##). Of the many zoonoses that impact humans, hantaviruses are a growing threat due to their increasing global incidence and high mortality rate (##REF##20375360##Jonsson et al., 2010##). Particularily, the Old World hantaviruses are responsible for more than 100,000 infections per year (##UREF##0##Avšič-Županc et al., 2019##), and disproportionately affect low-income, rural areas (##REF##20375360##Jonsson et al., 2010##). Of the Old World strains, Seoul <italic toggle=\"yes\">Orthohantavirus</italic> has caused more than 1.6 million infections in China since the 1950’s (##REF##20375360##Jonsson et al., 2010##).</p>", "<p id=\"P6\">Humans are primarily exposed to hantaviruses through aerosolized excrement of infected rodents, which is broadly associated with activities that bring humans into contact with these animals and where rodent densities are relatively high (##REF##23607444##Watson et al., 2014##). In the Old World hantaviruses, the disease associated with infection in humans is hemorrhagic fever with renal syndrome and has a mortality rate of 15% (##REF##9204290##Schmaljohn and Hjelle, 1997##). Antiviral drugs and vaccines are not yet available, with control instead relying on exposure prevention (Brocato and Hooper, 2019). Thus, understanding the enzootic ecology of these viruses, factors facilitating spillover into non-reservoir hosts, as well as viral evolution, are important aspects to prevent human infections (##REF##20375360##Jonsson et al., 2010##).</p>", "<p id=\"P7\"><italic toggle=\"yes\">Orthohantavirus</italic> species from the Hantaviridae family form a diverse genus of RNA viruses with a vast set of enzootic reservoir host Old World hantaviruses are primarily harbored by Muridae (rodents) and <italic toggle=\"yes\">Myodes</italic> (voles) (##REF##20375360##Jonsson et al., 2010##). Other reservoirs include shrews (Soricidae) in Guinea (##REF##23408889##Guo et al., 2013##; Klempa et al., 2007), China (##REF##23408889##Guo et al., 2013##), Korea (Lee et al., 2017), Vietnam (Song et al., 2007), and Hungary (##REF##19394994##Kang et al., 2009b##) and bats in China (##REF##23408889##Guo et al., 2013##), Côte d’Ivoire (##REF##22281072##Sumibcay et al., 2012##) and Sierra Leone (##REF##22261176##Weiss et al., 2012##). Phylogenetic studies reveal co-divergence, reassortment, and host-switching between members of the Cricetid rodents, particularily <italic toggle=\"yes\">Lemmus sibirica</italic> and <italic toggle=\"yes\">Microtus fortis</italic> (##REF##10364307##Vapalahti et al., 1999##) and between the Talpidae and Soricidae families (##REF##19394994##Kang et al., 2009##), all of which account for the high diversity and worldwide distribution of hantaviruses.</p>", "<p id=\"P8\">Climatic factors such as fluctuations in temperature and rainfall in combination with local human modifications in landscape structure affect reservoir abundance [reviewed in (##REF##28620680##Prist et al., 2017##)], which in turn changes the dynamics of hantavirus prevalence and human exposure risk (##REF##19604276##Klempa, 2009##). Abundance of reservoir hosts and infection prevalence varies seasonally with food availability (##REF##24763320##Nsoesie et al., 2014##; ##REF##28620680##Prist et al., 2017##). Habitat availability (##REF##19120179##Suzán et al., 2008##) and fragmentation (##UREF##3##Fialho et al., 2019##; ##UREF##5##Ganzhorn, 2003##; ##UREF##6##Goodin et al., 2006##) also impact community composition, with fragmented areas and agricultural fields often having denser populations of introduced commensal species such as mice (<italic toggle=\"yes\">Mus musculus)</italic> and rats <italic toggle=\"yes\">(Rattus norvegicus and R.rattus.)</italic> (##UREF##12##Umetsu and Pardini, 2007##). Deforestation also benefits invasive generalist species such as introduced rodents, which in turn tends to decrease the diversity of endemic and native species in the local mammal communities (##REF##21060870##Pardini et al., 2010##).</p>", "<p id=\"P9\">Madagascar provides a valuable context to investigate the effect of anthropogenic disturbance on hantavirus prevalence in small mammals as the island is experiencing rapid deforestation and habitat fragmentation (Vieilledent et al., 2018). Madagascar’s exceptional biodiversity and levels of endemism are due to isolation in deep geological time (Antonelli et al., 2022). This landmass is home to a unique native small mammal fauna of Madagascar, including 46 species of bats (80% endemic), 31 species of endemic tenrecs (Geogalinae, Oryzorictinae and Tenrecidae), and 28 species of endemic rodents (subfamily Nesomyinae) (##UREF##7##Goodman, 2022##). Non-native Muridae rodents (<italic toggle=\"yes\">Rattus</italic> and <italic toggle=\"yes\">Mus</italic>) and Soricidae shrews (<italic toggle=\"yes\">Suncus</italic>) also inhabit the island. This isolation and diversity may explain the evolution of unique hantavirus lineages described from the island such as Anjozorobe virus (ANJZV), a variant of Thailand virus (<italic toggle=\"yes\">Orthohantavirus thailandense</italic>), which was found in introduced <italic toggle=\"yes\">Rattus rattus</italic> and endemic <italic toggle=\"yes\">Eliurus majori</italic> on Madagascar (##UREF##4##Filippone et al., 2016##; ##REF##34372549##Kikuchi et al., 2021##; ##REF##29743115##Raharinosy et al., 2018a##; ##REF##24575755##Reynes et al., 2014##). Human exposure to ANJZV in Madagascar is estimated at 2.7% nationally, with higher seroprevalence (7.2%) in communities adjacent to forests (##REF##32091377##Rabemananjara et al., 2020##). A closely related hantavirus lineage, called <italic toggle=\"yes\">MayoV</italic>, has also been described in <italic toggle=\"yes\">R. rattus</italic> on the neighboring island of Mayotte (##UREF##4##Filippone et al., 2016##).</p>", "<p id=\"P10\">Here, we investigated the ecology of hantavirus infection in rural northeastern Madagascar in relation to mammalian community composition and human land use patterns. We surveyed a wide range of terrestrial small mammals and bats for hantavirus over a 5-year period along gradients of land use in and around Marojejy National Park in northeastern Madagascar. We focused on land-use gradients spanning three villages in an area of mixed human activities, including swidden agriculture that results in the clearing of forests and a fragmented landscape. Remnant forests are largely restricted to protected areas, and secondary forests are embedded in a matrix of brushy regrowth and agricultural fields (e.g. rice fields, vanilla agroforests). Previous work showed that these habitat modifications have resulted in higher abundances of commensal small mammals, specifically non-native rodents and shrews around one of our study villages, as compared to the nearby protected forest (Herrera et al., 2020). These factors are anticipated to influence hantavirus ecology by modifying abundance and diversity of mammal hosts and by increasing the risk of contact with hantavirus-infected substrates and materials [reviewed in (Tian and Stenseth, 2019)].</p>", "<p id=\"P11\">As found in other tropical settings, we hypothesized that hantavirus infections would be more common during the rainy season, driven by increased population densities of small mammal hosts at this time of year (Scobie et al., 2023). We also hypothesized that viral prevalence in small mammals would be higher in large, adult males, as observed in other studies, which attribute this observation to increased direct competition for resources (##REF##20375360##Jonsson et al., 2010##). We expected that viral prevalence would increase with anthropogenic disturbance and agricultural use, as a result of increased <italic toggle=\"yes\">R.rattus</italic> abundance. Lastly, we used the variability in hantavirus prevalence across a range of lande use types to better understand the findings of ##REF##29743115##Raharinosy et al. (2018)##. reporting lower viral prevalence within houses than outside of them, on Madagascar.</p>" ]	[ "<title>Material and methods</title>", "<title>Ethics statement and sample collection</title>", "<p id=\"P12\">Lung tissue samples from wild small mammals were collected between 2017 and 2021 in the vicinity of three villages adjacent to Marojejy National Park in the SAVA Region in northeastern Madagascar, following a standard grid-trapping protocol detailed below. The village of Mandena (14.477049° S, 49.8147° E) and its surroundings were sampled during the dry season (September-December) in 2017 and 2019, during the wet season (March-May) in 2020, and in the transitional dry-wet season (June-August) in 2018 and 2020. A second village, Manantenina (14.497213° S, 49.821347° E), was sampled only in the transitional season (2019) while a third village, Sarahandrano (14.607567° S, 49.647759° E), was sampled during the dry (2020) and wet (2021) seasons. Bats were trapped along flyways located on or adjacent to the trap grids and similar habitat types using harp traps and mist nets. Bats were also captured from caves in the area using butterfly nets. Bat trapping occurred during September-November 2019 in Mandena (dry season) and March-May 2021 in Sarahandrano (wet season).</p>", "<p id=\"P13\">Small mammal traps were installed in different land-use types, including semi-intact forest within the national park, as well as secondary forest and agroforest outside the park, and agricultural fields, flooded rice fields, and brushy regrowth (fallow areas after swidden cultivation) around villages. Traps were also placed in houses within villages adjacent to the trap grids. Sampling methods varied by location and trap grid; trap grids prior to September 2019 were 90 m<sup>2</sup> and after that date were 100 m<sup>2</sup> and consisted of metal (Sherman, model LFAm Tallahassee, Florida, USA) and mesh (Tomahawk, model 201, Hazelhurst, Wisconsin, USA) live traps placed 10 m apart. At each sampling site, two pitfall lines of 100 m in length, composed of 15 L buckets placed 10 m apart and the line bisected by a vertical plastic drift fence partially buried. The pitfall lines were located 20 to 50 m from the trap grid. Most grids were sampled for six consecutive nights, but where abundance was low, longer trapping periods were needed (up to 15 nights), and trapping within houses was limited to five nights. All non-native animals and a subset of native animals were collected. Tissue samples were stored in 70% ethanol and placed in long term storage for 1–4 months at −20°C until the molecular analyses were conducted.</p>", "<p id=\"P14\">All animals were processed using the same methods and samples were stored in the same conditions until further laboratory analysis. All procedures were approved by IACUC at Duke University (protocol number A002–17-01, 2017–2019, A262–19-12 2019–2021) and by Malagasy authorities (No. 289/17, 146/18, 280/19, 57/20, 191/20, 307/21—MEEF/SG/DGF/DSAP/SCB).</p>", "<title>Nucleic acids extraction</title>", "<p id=\"P15\">Lung samples were rehydrated overnight at 4°C in 1.5 mL of autoclaved milliQ water. Then 25–50 mg of rehydrated tissue was cut into small pieces with a sterile disposable scalpel blade and transferred into individual 2 ml Eppendorf tubes containing 180 μL of ATL and 20 μL of proteinase K provided by the IndiSpin<sup>®</sup> QIAcube<sup>®</sup> HT Pathogen Kit (Qiagen, Courtaboeuf, France). Tubes were incubated between 6 and 12 h at 56°C until full proteolysis was achieved. Extraction was then performed in 96 well plates using the Qiagen Cador pathogen kit and QiaCube XT robot following the manufacturer’s instructions. Nucleic acids were collected in a 200 μL elution buffer and stored at −80°C.</p>", "<title>Hantavirus detection</title>", "<p id=\"P16\">Reverse transcription was conducted with 10 μL of eluted nucleic acids using the ProtoScript<sup>®</sup> II Reverse Transcriptase (New England Biolabs, Massachusetts, USA). For each sample, RNA was denatured at 70°C for 5 min. in a mix containing 1 μL of DNTP, 1 μL of Rnase free water and 0.5 μL of random primers and thawed in a cold block. Then, a second mix was added, containing 4 μL of protoscript buffer, 2 μL of 10X DTT, 1 μL of the reverse transcription enzyme, and 0.5 μL of RNAsin (New England Biolabs, Massachusetts, USA), resulting in a total volume of 20 μL. The mix was incubated at 25°C for 10 min., 42°C for 50 min. and 65°C for 20 min.</p>", "<p id=\"P17\">cDNAs were used as a template in a previously published nested-PCR targeting the L-segment coding for the RNA-polymerase RNA-dependent (##REF##16704849##Klempa et al., 2006##). The mix contained 12.5 μL of GoTaq<sup>®</sup> G2 Hot Start Polymerase (Promega,, Wisconsin, USA), 1 μL of degenerated primers at 10 μM (HAN-L-F1 and HAN-L-R1 for primary PCR, HAN-L-F2 and HAN-L-R2 for secondary PCR). 2 μL of cDNA was used for the primary PCR’s template. The secondary PCR was prepared using 0.5 μL of the primary PCR product as DNA template. Thermal cycling conditions were identical for both primary and secondary PCR and were as follows: nucleic acids were denatured at 95°C for 5 min. followed by 2 cycles at 94°C for 45 sec., 46°C for 45 sec. and 72°C for 60 sec., 2 cycles at 94°C for 45 sec., 44°C for 45 sec. and 72°C for 60 sec., and then 30 cycles at 94°C for 45 sec., 42°C for 45 sec. and 72°C for 60 sec. before finalizing the PCR with 72°C for 10 min. DNA was visualized using a 1.8% TBE agarose gel stained with Gel Red (Biotium, Fremont, California, USA).</p>", "<title>PCR targeting hantavirus in endemic hosts.</title>", "<p id=\"P18\">Additional primers were developed to detect hantavirus RNA that might be hosted by endemic Malagasy small mammals. Primers were either referenced from the literature (Table S1) or newly designed using available reference sequences of ThaiV (MZ343362.1), MayoV (KU587796.1) and ANJZV (LC553724.1, NC_034556.1, KC490924.1, KC490923.1 and KC490922.1). Three semi-nested PCR schemes were used to screen endemic mammals and their associated nucleotide sequences are presented in Table S1. The lack of positive hantavirus detection in endemic mammals resulted in the need to test newly designed primers on positive <italic toggle=\"yes\">R. rattus</italic> found in this study, through a nested PCR protocol. Primer pairs that did not successfully amplify these <italic toggle=\"yes\">R. rattus</italic> hantaviruses were excluded from subsequent analyses, resulting in three semi-nested PCR systems, which were all used following these PCR conditions: initial denaturation at 95°C for 5 min. was followed by 35 cycles of 45 sec. at 95°C, 45 sec. annealing at 50°C and 1 min. elongation at 72°C. PCR ended with a final elongation step of 10 min. at 72°C. PCR products were visualized on a 2% agarose gel and gel bands with the expected size were gel purified (QIAquick Gel Extraction Kit) and used for Sanger sequencing (Genoscreen, Lille, France).</p>", "<title>Phylogenetic analyses</title>", "<p id=\"P19\">Positive nested PCR samples were Sanger sequenced on both DNA strands at Genoscreen (Lille, France). Chromatograms were manually edited using Geneious 9. Sequences of the partial L segment, 347 bp, are available on GenBank under accession numbers OP328829-OP328903 (Table S5). We included in the analyses sequences from reference HantaV strains belonging to lineages directly related to ThaiV viruses, and using Sangassou virus as an outgroup. The final tree was based on a set of 85 sequences of 347 bp composed of 11 external reference sequences related to ThaiV hantavirus, and 74 sequences generated in the context of this study. We imported our dataset in Datamonkey Adaptive Evolution server to identify the presence of recombinant strains (<ext-link xlink:href=\"https://www.datamonkey.org/\" ext-link-type=\"uri\">https://www.datamonkey.org/</ext-link>). The most appropriate phylogenetic model was determined using PhyML3. Phylogenetic signal was checked for with DAMBEE and the tree was built on 20,000,000 iterations. MEGA10 was used to select the appropriate evolution model. MrBayes package on Geneious 9.1 was fed with all aforementioned settings while other settings were set to default values. Reference sequences were trimmed to 347pb and all sequences were manually aligned using the “Maft alignment” plugin on Geneious 9.</p>", "<title>Statistical Analyses</title>", "<p id=\"P20\">All analyses were performed in R version 4.3.0 (##UREF##10##R Core Team, 2023##). All pairwise comparisons were made using a significant level of 0.05. Only <italic toggle=\"yes\">R. rattus</italic> were considered in the analyses as hantaviruses were only detected in this species. We used generalized linear mixed-effects models (GLMMs) with a binomial error structure to investigate the effect of environmental and individual variables on hantavirus infection. To examine the random effect of trap grid identity, a unique identifier given to each trap grid installation was included in all models to control for the non-independance of animals captured in the same grid. We considered the fixed environmental effects of season (dry, wet, and transitional), village, direct trap distance to the village, and habitat type (semi-intact forest, secondary forest, agroforest, brushy regrowth, agriculture, flooded rice fields and village). Individual-level fixed effects considered were sex, age based on tooth eruption and wear (sub-adult and adult), mass (g), head-body length (mm), body condition score, and the interaction term between mass and head-body length. The body condition was calculated using mass per length cubed (Fulton’s index) and served as an indicator for an individual’s health. A final fixed effect, the number of <italic toggle=\"yes\">R. rattus</italic> captured during each trap grid installation (e.g. flooded rice field in Mandena during the dry season 2019), was also used as an approximation for animal density. To assess whether variation in hantavirus prevalence between habitat types could be explained by individual-level traits, environmental traits, or rat density, we used a model containing the combinations of those traits (environmental + demographic, environmental + number of <italic toggle=\"yes\">R. rattus</italic>, and environmental + demographic + number of <italic toggle=\"yes\">R. rattus</italic>). The data used in all models was subset to include observations containing all representative metadata. Furthermore, trapping effort and sampling methods in villages differed substantially from all other sites. Thus, these variables were excluded in the models that included animal density. All models were also rerun with the complete data set available for covariates included to verify the robustness of the results.</p>", "<p id=\"P21\">Global models were fitted using the glmmTMB package, version 1.1.7 (##UREF##2##Brooks et al., 2017##) and diagnostic tests were performed using the DHARMa package, version 0.4.6 ((Hartig, 2020). We used the MuMIn package, version 1.47.5 (##UREF##1##Barton 2023##) ‘dredge’ function to identify the best models based on the corrected Akaike information criterion (AICc), then retained all models in the 95% cumulative sum weight confidence set to find the cumulative sum AICc weights (importance) for each predictor and calculate model-averaged parameter estimates. We used the full averaged model to perform Tukey-adjusted post-hoc pairwise comparisons using the emmeans package, version 1.8.6 (##UREF##9##Lenth 2023##). The proportion of variance explained by the marginal (fixed) effects of best model (pseudo R2) was found using the ‘r.squaredGLMM’ function in the MuMIn package (##UREF##1##Barton, 2023##).</p>" ]	[ "<title>Results</title>", "<title>Animal composition and hantavirus prevalence</title>", "<p id=\"P22\">We trapped 1681 small mammals, of which 1663 were tested for hantavirus infection. The tested samples consisted of 72.94% (N=1213) nonnative and 27.1% (N=450) endemic mammals (Table S2). Introduced <italic toggle=\"yes\">R. rattus</italic> represented 47.8% (N=794) of the tested animals and the only species in which hantavirus was detected. We thus limited the statistical analyses to this species.</p>", "<p id=\"P23\">Hantavirus prevalence in <italic toggle=\"yes\">R. rattus</italic> was 9.5% (75/794, 95% CI: 7.5–11.7%) and varied with environmental and demographic factors as well as animal density (##FIG##0##Fig. 1##, Table S3). The age composition varied significantly by habitat type (Fisher’s exact test, p<0.001; ##FIG##0##Fig. 1a##). The average body mass of infected rats (124.97 g) was significantly greater than uninfected individuals (81.70 g; ##FIG##0##Fig. 1b##; Welch’s two-sample t-tests, p<0.001). The average mass of <italic toggle=\"yes\">R. rattus</italic> varied significantly by habitat type (##FIG##0##Fig.1c##; Kruskal-Wallis, p<0.001) and the infected individuals in each habitat type were mostly of above average mass. The same trends displayed in (##FIG##0##Fig. 1b##) and (##FIG##0##Fig. 1c##) were observed for head-body length (mm) but not body condition (g/mm3); see Fig. S1 and Fig. S2.</p>", "<title>Complementary screening of endemic mammals</title>", "<p id=\"P24\">All endemic species of small mammals and bats tested negative through nested PCR (##REF##16704849##Klempa et al., 2006##). To test for possible false negative PCRs resulting from infections with distinct hantaviruses, we tested most endemic mammals, as well as introduced shrews, with alternative PCR schemes. For this, we first validated alternative PCR schemes with known positive <italic toggle=\"yes\">R. rattus</italic> from this study. Seven out of 16 primers produced amplicons of the expected size and were further tested on subsequent endemic rodents. Samples from 214 endemic mammals representing 12.8% of all trapped mammals including <italic toggle=\"yes\">Microgale brevicaudata</italic> (N=176), <italic toggle=\"yes\">Eliurus</italic> spp. (N=9), <italic toggle=\"yes\">Setifer setosus</italic> (N=29), <italic toggle=\"yes\">Suncus murinus</italic> (N=55), and native bats (N=141, see Table S1) were tested. None of the samples tested positive using these alternative end-point PCR schemes.</p>" ]	[ "<title>Discussion</title>", "<p id=\"P32\">Our extensive investigation of hantavirus in small mammals from northeastern Madagascar only detected infections in <italic toggle=\"yes\">R. rattus</italic> (9.5%) despite testing over 20 species of terrestrial mammals, composed of 1663 individuals (including 450 endemic specimens) and 227 native bats. The detected hantavirus is closely related to ANJZV/MayoV, which were previously described on Madagascar and Mayotte (##UREF##4##Filippone et al., 2016##; ##REF##32091377##Rabemananjara et al., 2020##; ##REF##24575755##Reynes et al., 2014##). Infection prevalence was highest in animals captured in agricultural land use areas. This effect weakened, however, when the variability in animal body size between land-use types was taken into account. The findings involving body size highlight the importance of demographic factors in reservoir populations for understanding prevalence and spillover risk, while also identifying potential mechanisms that relate land use change to zoonotic disease risk.</p>", "<p id=\"P33\">The absence of hantavirus detection in any of the endemic or native species may be due to extremely low prevalence or the presence of a distant viral lineage that could not be detected with our PCR scheme. We screened 269 of the 450 trapped endemic terrestrial mammals with three alternative semi-nested PCR schemes, which still led to negative results (Supplementary Table S1). ANJZV has been previously reported from the endemic rodent <italic toggle=\"yes\">Eliurus majori</italic> (n tested = 15) in the Central Highlands of Madagascar with the same PCR scheme used herein (##REF##24575755##Reynes et al., 2014##). Since hantaviruses are notorious for host switching (##REF##23408889##Guo et al., 2013##), we can hypothesize that the absence of detectable virus in endemic animals mirrors a recent introduction to Madagascar, where adaptation to the endemic species is in an early stage. However, further investigations are needed to date the introduction of the virus.</p>", "<p id=\"P34\">The viral sequences were distinct but genetically nested within the clade containing ANJZV, MayoV, and ThaiV sequences. The viral lineage found in <italic toggle=\"yes\">R. rattus</italic> from our samples in the Marojejy National Park area in northeast Madagascar show less than 92% identity with ANJZV from central eastern Madagascar, located 480 km southwest of the study site, and MayoV from Mayotte Island, located 530 km northwest of the study site. However, longer sequences are needed to robustly establish the genetic relationships of the virus occurring in the Marojejy area and the previously reported ANJZV and MayoV.</p>", "<p id=\"P35\">Prevalence of hantavirus infection in our study was similar to other studies of the virus on Madagascar (##REF##29743115##Raharinosy et al., 2018##; ##REF##24575755##Reynes et al., 2014##). We found an infection prevalence of 9.5% in <italic toggle=\"yes\">R. rattus</italic>, which is not significantly different from a previous study reporting an infection prevalence in <italic toggle=\"yes\">R. rattus</italic> of 12.4% across the island (χ<sup>2</sup>=2.7, p=0.10) and 5.8% in Sambava (χ<sup>2</sup>=0.69, p=0.41), in close proximity to our study site (##REF##29743115##Raharinosy et al., 2018##). Notably, both of these studies predicted higher prevalence in the more mesic regions of Madagascar (##REF##29743115##Raharinosy et al., 2018##), such as where the present study took place, which we did not observe (##REF##29743115##Raharinosy et al., 2018##). Our findings which did not demonstrate strong seasonal trends in hantavirus prevalence, as predicted by ##REF##29743115##Raharinosy et al. (2018)##, align with the mild seasonal patterns of this region of Madagascar.</p>", "<p id=\"P36\">Our sampling schema across a matrix of land-use types allowed us to highlight determinants of infection at a finer scale than previous studies (##REF##29743115##Raharinosy et al., 2018##; ##REF##24575755##Reynes et al., 2014##). We found that agricultural land-use types (agroforest, brushy regrowth, agriculture, and flooded rice fields) displayed higher prevalence than the most disturbed (village) and least disturbed (semi-intact and secondary forest) habitats. Of note, not a single <italic toggle=\"yes\">R. rattus</italic> captured in the semi-intact forest (n=52) tested positive. The low prevalence in the village is in accordance with previous findings from Madagascar (##REF##29743115##Raharinosy et al., 2018##). The overall effect of habitat type on infection probability appears to be due to differences in individual traits, particularly the size (mass and length) and sex of <italic toggle=\"yes\">R. rattus</italic> captured in each habitat type. In the Manantenina study area, statistically significant differences in body size of <italic toggle=\"yes\">R. rattus</italic>, based on cranio-dental measurements, have been identified between native forest and anthropogenic habitats (##REF##37041752##Ranaivoson et al., 2022##). The niche breadth of animals living in natural forest was greater than in anthropogenic habitats (##REF##28222708##Dammhahn et al., 2017##), presumably indicating a more stable diet during periods of seasonal variation and driving higher life expectancy. Hence, the longer-lived animals have a greater chance to come in contact with the virus. Alternatively, we cannot exclude that older males tend to leave the villages or are more at risk to be killed by domestic animals and people, hence contributing to lower overall infection prevalence.</p>", "<p id=\"P37\">Based on these findings, we propose that differences in hantavirus prevalence in <italic toggle=\"yes\">R. rattus</italic> across our habitat gradient is due to a succession of synergetic biotic and abiotic factors, including resource availability, animal density, life expectancy, and level of habitat disturbance. Together these factors impacted population demography, which appears to drive infection prevalence in this system. Disturbances that alter the population demography to favor larger-bodied and presumably older individuals may lead to increases in prevalence and thus human exposure risk. Based on the results that prevalence was highest in agricultural fields, we also expect that human exposure risk is highest when conducting farm-based activities that aerosolize excreta or bring people into contact with <italic toggle=\"yes\">R. rattus.</italic></p>" ]	[ "<title>Conclusions</title>", "<p id=\"P38\"><italic toggle=\"yes\">Rattus rattus</italic> is a remarkably adaptable species, and appears to be the principal reservoir of hantavirus in the area surrounding Marojejy National Park in northeastern Madagascar. The positiuve individuals from our study formed a subclade of MayoV and Anjozorobe strains of Thailand hantaviruses, both of which have previously been described on Madagascar. Infection prevalence in <italic toggle=\"yes\">R. rattus</italic> varied across the land-use matrix and was higher in agricultural areas than in forests and villages. Highly disturbed habitats had higher abundances of <italic toggle=\"yes\">R. rattus</italic>. However, the larger body size of <italic toggle=\"yes\">R. rattus</italic> living in agricultural land-use types compared forests and villages likely explained the increased viral prevalence. Differences in body size likely indicate a longer lifespan and increased odds of viral exposure. As invasive rat populations and interactions between endemic and introduced species continue to grow due to the ongoing conversion of forest into agricultural land, we expect hantavirus exposure risk to humans to increase, particularly when these changes positively alter <italic toggle=\"yes\">R. rattus</italic> demography.</p>" ]	[ "<p id=\"P1\">Equal participation</p>", "<p id=\"P2\">Authors contributions:</p>", "<p id=\"P3\">The project was conceptualized by PT, CLN, JPH, SMG, and VS. Funding Acquisition was done by PT, CLN, SMG and JPH. Field investigations were carried out by TMR and JPH. Laboratory investigators were JD and PT. Data Curation was done by KMK, JPH, and TMR. Formal analysis and writing of the original draft was done by JD and KMK. All authors reviewed and edited the work.</p>", "<p id=\"P4\">Hantaviruses are globally distributed zoonotic pathogens capable of causing fatal disease in humans. Rodents and other small mammals are the typical reservoirs of hantaviruses, though the particular host varies regionally. Addressing the risk of hantavirus spillover from animal reservoirs to humans requires identifying the local mammal reservoirs and the predictors of infection in those animals, such as their population density and habitat characteristics. We screened native and non-native small mammals and bats in northeastern Madagascar for hantavirus infection to investigate the influence of habitat, including effects of human land use on viral prevalence. We trapped 227 bats and 1663 small mammals over 5 successive years in and around Marojejy National Park across a range of habitat types including villages, agricultural fields, regrowth areas, and secondary and semi-intact forests. Animals sampled included endemic tenrecs (Tenrecidae), rodents (Nesomyidae) and bats (6 families), along with non-native rodents (Muridae) and shrews (Soricidae). A hantavirus closely related to the previously described Anjozorobe virus infected 9.5% of <italic toggle=\"yes\">Rattus rattus</italic> sampled. We did not detect hantaviruses in any other species. Habitat degradation had a complex impact on hantavirus prevalence in our study system: more intensive land use increase the abundance of <italic toggle=\"yes\">R. rattus</italic>. The average body size of individuals varied between agricultural and nonagricultural land-use types, which in turn affected infection prevalence. Smaller <italic toggle=\"yes\">R.rattus</italic> had lower probability of infection and were captured more commonly in villages and forests. Thus, infection prevalence was highest in agricultural areas. These findings provide new insights to the gradients of hantavirus exposure risk for humans in areas undergoing rapid land use transformations associated with agricultural practices.</p>" ]	[ "<title>Genetic diversity of the newly found hantavirus</title>", "<p id=\"P25\">All amplicons matching the expected size were sequenced, resulting in partial L segments of 347bp. Genetic identity with previously reported hantaviruses from Madagascar (##REF##34372549##Kikuchi et al., 2021##; ##REF##29743115##Raharinosy et al., 2018a##; ##REF##24575755##Reynes et al., 2014b##) ranged between 85.0% and 91.6%. Identity restricted to our study cohort only (75 sequences) ranged from 89.6% to 100%. Forty one of the 75 obtained sequences were unique, indicating a fairly high level of diversity. Our sequences did not form a phylogenetic cluster with hantaviruses known from nearby islands (##FIG##1##Fig. 2##).</p>", "<title>Relationship between environmental factors and hantavirus prevalence</title>", "<p id=\"P26\">Infected <italic toggle=\"yes\">R. rattus</italic> were present in all habitat types with the exception of semi-intact forests within the park where all 52 individuals tested negative. Across the other habitat types, prevalence differed significantly (p=0.001, Fisher’s exact test, ##FIG##0##Fig. 1##). The prevalence was also significantly different by season (p=0.01, Fisher’s exact test) and study site (p<0.001, Fisher’s exact test) but did not differ by distance to village (p=0.595, Wilcoxon test). Hantavirus prevalence in a grid correlated positively with the number of <italic toggle=\"yes\">R. rattus</italic> captured on that grid (p<0.001, ρ=0.49, Spearman’s rank correlation). Because the trap grid design was altered to include 21 additional traps starting in September 2019, we verified that no significant differences occurred in the average number of <italic toggle=\"yes\">R. rattus</italic> captured per grid before and after this change (Wilcoxon test, p=0.866). Across trap grid installations (omitting trapping done in homes in the village), the average number of <italic toggle=\"yes\">R. rattus</italic> captured per grid was 16.3 ± 11.2 (range 1 to 42).</p>", "<p id=\"P27\">The best-models subset for the environmental covariates (habitat type, village, season, and distance to village) contained a model with habitat type, season, and village (AICc weight 0.501); a model with habitat type and village (AICc weight 0.310); and the global model with all variables (AICc weight 0.189). The most important predictors were habitat type and village, which were present in all models (AICc weights 1.00). The season and distance to the village had respective weights of 0.69 and 0.19. The full model-averaged estimates are shown in ##FIG##2##Fig. 3## and Table S4. Post-hoc comparisons between habitat types (##FIG##2##Fig. 3##) revealed that the probability of infection was highest in flooded rice fields (0.1607) and agroforests (0.1277), and lowest in secondary forests (0.0403) and villages (0.0262). The prevalence between flood rice fields and village habitat types was significantly different (p=0.030). Semi-intact forests were excluded from this analysis because there were no positive animals. The probability of infection in Sarahandrano (0.1821) was significantly higher than in both Mandena (0.0501, p<0.001) and Manantenina (0.0444, p=0.033). By contrast, there was no significant difference in the probability of infection between the three seasons (p>0.05).</p>", "<title>Relationship between <italic toggle=\"yes\">Rattus rattus</italic> demography and hantavirus prevalence</title>", "<p id=\"P28\">Hantavirus was not detected in any of the sub-adult <italic toggle=\"yes\">R. rattus</italic> (N=137, ##FIG##0##Fig. 1##). The mass of <italic toggle=\"yes\">R. rattus</italic> in which we detected hantavirus was 50% heavier (mean = 125.0 ± 28.1g) than those testing negative (81.7 ± 41.3g; Welch’s two sample t-test; p<0.001; ##FIG##0##Fig. 1b##). The infected <italic toggle=\"yes\">R. rattus</italic> also had significantly greater head-body length (166.2 ± 22.8 mm vs 139.0 ± 34.0 mm; p <0.001; Welch’s two sample t-test), but not significantly different body condition scores (p=0.470, Welch’s two sample t-test). Prevalence in males (12.3%, 51/416) was significantly higher than in females (6.1%, 23/375; p=0.005, χ<sup>2</sup>=8.02).</p>", "<p id=\"P29\">The best model subset of the 12 possible models of individual-level predictors of infection included a model with the interaction between age and mass, and the lower order terms (AICc weight 0.435) and four other models with AICc weights <0.3 (Fig. S3 and Table S4). Mass and head-body length were the most important predictors and were present in all models (cumulative sum weight = 1) followed by their interaction (cumulative sum weight =0.97). Sex and body condition score were both in two models, with sum weights of 0.37 and 0.26 respectively.</p>", "<title>Covariance between habitat type and demography</title>", "<p id=\"P30\">The morphometric measurements of trapped <italic toggle=\"yes\">R. rattus</italic> covaried with the environmental predictors of infection, namely habitat types, seasons and villages. Body mass and head-body length were significantly different across all three variables (p<0.001, Kruskal-Wallis) while body condition was also significantly different across habitat types and seasons (p=0.047, Kruskal-Wallis), but not villages (p=0.64, Kruskal-Wallis). Age was significantly different across habitat types (p<0.001), seasons (p=0.005) and villages (p<0.001, Fisher’s exact test). By contrast, sex ratio was not significantly different across habitat types (p=0.56), seasons (p=0.90), or villages (p=0.01, Fisher’s exact test). Distance to village was not strongly correlated with mass or body condition and was not significantly different between ages or sexes (p=0.21 and 0.08; respectively, Wilcoxon test). Full-model averaged coefficients and the importance of predictors is provided in Table S4.</p>", "<title>Individual and environmental predictors of infection</title>", "<p id=\"P31\">To address whether the observed differences in the probability of infection between habitat types were due to demographic variability, rat density, or a combination of these effects, three additional GLMM averaged results were considered. The best fit GLMM combined environmental (habitat types, seasons, and villages) and demographic (mass, head-body length, their interaction, and sex) effects. Full model averaged coefficients are shown in ##FIG##1##Fig. 2##, Fig. S3, and Table S4. The most important predictors across the 16 models in the 95% cumulative sum weight subset were head-body length and mass (sum weights of 1), followed by their interaction term (14 models; sum weight = 0.98). The next important effects were season (10 models, sum weight = 0.90), habitat type (6 models, sum weight = 0.57), village (7 models, sum weight = 0.46) and sex (7 models, sum weight = 0.44). Post-hoc comparisons of the probability of infection between habitat types show that the effect of habitat type is minimized when demographic effects are included in the model (##FIG##2##Fig. 3##). The descriptions and estimated full-model averaged coefficients of the GLMMs containing (i) environmental effects along with the number of <italic toggle=\"yes\">R. rattus</italic> captured and (ii) all predictors except body condition and distance to village can be found in Table S4, Fig S3, and Fig S4.</p>" ]	[ "<title>Acknowledgement</title>", "<p id=\"P39\">This research was funded by the joint NIH-NSF-NIFA Ecology and Evolution of Infectious Disease award R01-TW011493, the Duke Global Health Institute, the Bass Connections Program at Duke University, and a Duke University Provost’s Collaboratory grant. In addition, the collaboration between Duke University and the Universite de La Réunion was supported by the Thomas Jefferson Fellowship from the FACE Foundation; <ext-link xlink:href=\"http://facefoundation.org/thomas-jefferson-fund/\" ext-link-type=\"uri\">http://facefoundation.org/thomas-jefferson-fund/</ext-link>. We are grateful to the Mention Zoologie et Biodiversite Animale, Université d’Antananarivo; Madagascar National Parks; and the Direction des Aires Protégées, des Ressources Naturelles Renouvelables et des Ecosystèmes for administrative aid and issuing research permits. We thank the PIMIT laboratory and all the technical assistance given by Magalie Turpin. The Duke Lemur Center’s SAVA Conservation Initiative provided logistical and other support in the SAVA Region. We also thank those who have hosted us in their communities over the years of trapping that occurred, including the many field assistants from the villages where research took place.</p>" ]	[ "<fig position=\"float\" id=\"F1\"><label>Figure 1:</label><caption><title>Hantavirus prevalence by (a) age class, (b) body mass, and (c) mass and habitat type.</title><p id=\"P40\">Orange represents animals in which hantavirus was detected and green not detected. The average (μ) mass (g) is displayed on (b) and (c) and indicated with a red diamond. Individuals with missing age information (n=62) were omitted from plot (a) and individuals with missing mass information (n=4) were omitted from the plot (b) and (c).</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2.</label><caption><title>Bayesian phylogeny of sequence hantavirus samples.</title><p id=\"P41\">Bayesian phylogeny showing relatedness of hantaviruses from within and near the Marojejy National Park area to ThaiV and its descendants. The outgroup (black) is a Sangassou virus. Support level shows posterior probability. Nodes with a posterior probability >0.75 are represented with black squares while red squares represent nods with probabilities >0.95. Specimens from Mandena are in yellow, Manantenina in red, and Sarahandrano in blue .and all are from Rattus rattus. Accession numbers are listed in supplementary Table S5. Satellite images were produced using Google SNES/Airbus Maxar Technolgies Data SIO, NOAA, U.S. Navy, NGA, GEBCO and the north indicated.</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3.</label><caption><title>Estimated coefficients of the GLMMs.</title><p id=\"P42\">Full model-averaged coefficients for the GLMM of models containing environmental (green squares) and environmental as well as demographic (orange circles) predictors. The coefficients (shapes) are shown with 95% confidence intervals. Fig S3. Shows comparisons of all four models considered.</p></caption></fig>", "<fig position=\"float\" id=\"F4\"><label>Figure 4.</label><caption><title>Predicted probability of hantavirus infection in R. rattus across habitat types.</title><p id=\"P43\">Estimated marginal means probability of hantavirus infection across the different habitat types after accounting for variability in prevalence due to other variables in the respective models, with a Tukey adjustment for multiple comparisons. The post hoc estimate made by the environment model (green square) and environment + demographic model (orange circle) and shaded areas of their corresponding 95% confidence intervals are displayed. Fig S4 shows comparisons of all 4 models considered and other categorical predictors.</p></caption></fig>" ]	[]	[]	[]	[]	[]	[]	[]	[]	[ "<graphic xlink:href=\"nihpp-2023.12.24.573235v1-f0001\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-2023.12.24.573235v1-f0002\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-2023.12.24.573235v1-f0003\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-2023.12.24.573235v1-f0004\" position=\"float\"/>" ]	[]	[{"surname": ["Av\u0161i\u010d-\u017dupanc", "Saksida", "Korva"], "given-names": ["T.", "A.", "M."], "year": ["2019"], "article-title": ["hantavirus infections"], "source": ["Clin. Microbiol. Infect"], "volume": ["21"], "fpage": ["e6"], "lpage": ["e16"]}, {"surname": ["Barto\u0144"], "given-names": ["K"], "year": ["2023"], "article-title": ["MuMIn: Multi-Model Inference"], "source": ["R package version 1.47.5"], "ext-link": ["https://CRAN.R-project.org/package=MuMIn"]}, {"surname": ["Brooks", "Kristensen", "Van Benthem", "Magnusson", "Berg", "Nielsen", "Skaug", "Machler", "Bolker"], "given-names": ["M.E.", "K.", "K.J.", "A.", "C.W.", "A.", "H.J.", "M.", "B.M."], "year": ["2017"], "article-title": ["glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling"], "source": ["R J"], "volume": ["9"], "fpage": ["378"], "lpage": ["400"]}, {"surname": ["Fialho", "Cerboncini", "Passamani"], "given-names": ["M.Y.G.", "R.A.S.", "M."], "year": ["2019"], "article-title": ["Linear forest patches and the conservation of small mammals in human-altered landscapes"], "source": ["Mamm. Biol"], "volume": ["96"], "fpage": ["87"], "lpage": ["92"], "pub-id": ["10.1016/j.mambio.2018.11.002"]}, {"surname": ["Filippone", "Castel", "Murri", "Beaulieux", "Ermonval", "Jallet", "Wise", "Ellis", "Marston", "McElhinney", "Fooks", "Desvars", "Halos", "Vourc\u2019h", "Marianneau", "Tordo"], "given-names": ["C.", "G.", "S.", "F.", "M.", "C.", "E.L.", "R.J.", "D.A.", "L.M.", "A.R.", "A.", "L.", "G.", "P.", "N."], "year": ["2016"], "article-title": ["Discovery of hantavirus circulating among Rattus rattus in French Mayotte Island, Indian Ocean"], "source": ["J. Gen. Virol"], "pub-id": ["10.1099/jgv.0.000440"]}, {"surname": ["Ganzhorn"], "given-names": ["J.U."], "year": ["2003"], "article-title": ["Effects of introduced Rattus rattus on endemic small mammals in dry deciduous forest fragments of western Madagascar"], "source": ["Anim. Conserv"], "volume": ["6"], "fpage": ["147"], "lpage": ["157"], "pub-id": ["10.1017/S1367943003003196"]}, {"surname": ["Goodin", "Koch", "Owen", "Chu", "Hutchinson", "Jonsson"], "given-names": ["D.G.", "D.E.", "R.D.", "Y.-K.", "J.M.S.", "C.B."], "year": ["2006"], "article-title": ["Land cover associated with hantavirus presence in Paraguay: Land cover associated with hantavirus presence"], "source": ["Glob. Ecol. Biogeogr"], "volume": ["15"], "fpage": ["519"], "lpage": ["527"], "pub-id": ["10.1111/j.1466822X.2006.00244.x"]}, {"surname": ["Goodman"], "given-names": ["S.M."], "year": ["2022"], "source": ["The new natural history of Madagascar"], "publisher-name": ["Princeton University Press"]}, {"surname": ["Hartig"], "given-names": ["F."], "year": ["2022"], "article-title": ["DHARMa: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models"], "source": ["R package version 0.4.6"]}, {"surname": ["Lenth"], "given-names": ["R"], "year": ["2023"], "article-title": ["emmeans: Estimated Marginal Means, aka Least-Squares Means"], "source": ["R package version 1.8.6"]}, {"surname": ["R Core Team"], "given-names": ["R."], "year": ["2023"], "source": ["R: A language and environment for statistical computing"], "publisher-name": ["R Foundation for Statistical Computing"], "publisher-loc": ["Vienna, Austria"], "ext-link": ["https://www.R-project.org/"]}, {"surname": ["Scobie", "Rahelinirina", "Soarimalala", "Andriamiarimanana", "Rahaingosoamamitiana", "Randriamoria", "Rahajandraibe", "Lambin", "Rajerison", "\u2121fer"], "given-names": ["K.", "S.", "V.", "F.M.", "C.", "T.", "S.", "X.", "M.", "S."], "article-title": ["Reproductive ecology of the black rat (Rattus rattus) in Madagascar: the influence of density-dependent and -independent effects"], "source": ["Integr. Zool"], "pub-id": ["10.1111/1749-4877.12750"]}, {"surname": ["Umetsu", "Pardini"], "given-names": ["F.", "R."], "year": ["2007"], "article-title": ["Small mammals in a mosaic of forest remnants and anthropogenic habitats\u2014evaluating matrix quality in an Atlantic forest landscape"], "source": ["Landsc. Ecol"], "volume": ["22"], "fpage": ["517"], "lpage": ["530"], "pub-id": ["10.1007/s10980-006-9041-y"]}]	{ "acronym": [], "definition": [] }	35	CC BY	no	2024-01-13 23:49:38	bioRxiv. 2023 Dec 24;:2023.12.24.573235	oa_package/26/e5/PMC10769451.tar.gz
PMC10775373	38196586	[ "<title>Introduction</title>", "<title>Sex differences in anxiety and mood disorders and the neurobiology of stress response</title>", "<p id=\"P8\">The US National Institute of Mental Health reports a 60% higher lifetime prevalence of anxiety disorders in women compared to men, and highlights sex differences in the onset, severity, clinical course, and treatment response in anxiety disorders [##REF##36373774##1##–##UREF##0##3##]. Women seem to experience more severe and longer-lasting symptoms of anxiety than men [##REF##33008537##4##, ##REF##25376429##5##]. In a sample of over 20,000 adults, the lifetime and 12-months male to female prevalence ratios of anxiety disorder were 1:1.7 and 1:1.8, respectively, with women having higher rates of lifetime diagnosis of most anxiety disorders [##REF##21439576##6##]. Further, women with a lifetime diagnosis of an anxiety disorder were more likely than men to be also diagnosed with another anxiety disorder and major depressive disorder [##REF##21439576##6##] .</p>", "<p id=\"P9\">Both preclinical and human studies have examined the neurobiological mechanisms underlying sex differences in anxiety-like behavior. For instance, activation of the endocannabinoid 2-arachidonoyl glycerol, a key regulator of neurotransmitter release, via the cannabinoid receptor (CB1) resulted in more frequent freezing behavior in male rats, but less freezing and more frequent darting (active avoidance) in female rats [##REF##34545241##7##]. In female rats and humans, fluctuations in estradiol levels can impact limbic circuit activity and fear extinction [##UREF##1##8##, ##REF##26581193##9##]. Individuals with mood disorders often exhibit hypersecretion of corticotropin releasing factor (CRF), which stimulates noradrenaline release from the locus coeruleus (LC), leading to higher levels of alertness and anxiety symptoms [##REF##34545241##7##]. Importantly, animal studies showed that LC neurons are more sensitive to CRF in females than in males [##UREF##2##10##, ##UREF##3##11##]. Following exposure to social stress, a single dose of intranasal oxytocin reduced distress in men but elevates distress and anger in women [##REF##22387929##12##]. In animal models of social distress, blocking oxytocin receptors in the bed nucleus of the stria terminalis reduces anxiety-like behavior in female but not male mice; in contrast, oxytocin receptor blocking enhanced social-avoidance like behavior in unstressed males [##REF##29066224##13##]. Together, ample evidence suggests that stress response is not only mediated through distinct neurobiological pathways but also manifested differently in behaviors between sexes.</p>", "<title>Sex differences in neural processing of negative emotion</title>", "<p id=\"P10\">Many human imaging studies have reported differences in regional activities in viewing negative emotional vs. neutral pictures, with the amygdala, thalamus, dorsal/ventral visual cortex, parietal cortex, inferior frontal gyrus, insula, orbitofrontal and medial frontal cortices, amongst others, showing higher activity during exposure to negative emotions [##REF##35001394##14##–##REF##16488159##16##]. Earlier reviews and meta-analyses indicated that women generally show stronger neural responses to negative emotions, whereas men exhibit greater responses to positive emotions, in behavioral paradigms aimed to elicit emotional experiences [##REF##21600956##17##, ##REF##22450197##18##]. The amygdala, thalamus, caudate, putamen, superior/middle frontal gyri, and orbitofrontal gyrus showed higher responses to negative emotions in women vs. men, whereas the amygdala, inferior frontal gyrus, and fusiform gyrus showed higher responses to positive emotions in men vs. women [##REF##21600956##17##, ##REF##22450197##18##]. However, in a later meta-analysis, no differences between men and women was noted during negative vs neutral emotion processing [##REF##27168344##19##]. David and colleagues identified no significant increase in the number of regional foci with larger sample sizes, suggesting the presence of excess “significance bias,” i.e., reporting bias, in the neuroimaging literature on sex differences [##REF##29666377##20##]. Further, a recent meta-analysis did not observe significant effects of sex in meta-regression of negative vs neutral face processing [##REF##37857413##21##]. Thus, we need more studies of large sample size to revisit sex differences in negative emotion processing.</p>", "<p id=\"P11\">Another dimension of sex differences concerns the correlates of individual variation. A few studies noted no sex differences in overall brain activity but significant differences in the neural correlates of individual variation in subjective experiences, including arousal [##REF##23596188##22##], anxiety [##REF##27870417##23##], and mood [##REF##27246897##24##] ratings during negative emotion processing. These findings highlight a critical dimension of sex differences that have not been thoroughly explored. Further, previous imaging studies have either employed a paradigm that required no explicit behavioral response or have not examined sex differences in neural correlates of behavioral performance. This contrasts with animal studies where anxiety-like behavior can be objectively quantified, as reviewed earlier. Characterizing how negative emotions may interfere with target identification in the Hariri task (valenced/neutral picture matching task [##REF##12482086##25##]), for instance, would offer a behavioral measure of individual variation in anxiety and a venue to investigate sex differences in the impact of anxiety on negative emotion processing.</p>", "<title>Anxiety and negative emotion processing</title>", "<p id=\"P12\">Emotional states can alter how we process affective stimuli, as noted in many studies of people with mood disorders. For instance, compared to healthy controls, individuals with social anxiety disorder exhibited higher bilateral amygdala and insula activity during identification of negative vs. neutral images [##REF##19568481##26##]. Another study noted greater left amygdala and inferior frontal gyrus activation in individuals with generalized anxiety disorder, as compared to healthy participants, viewing emotionally negative vs. neutral pictures [##REF##28501740##27##]. A meta-analysis of individuals with social anxiety, post-traumatic stress disorder, and specific phobia showed hyperactive amygdala and insula during passive viewing or identification of negative vs. positive or neutral emotional images or vs. a resting baseline [##REF##17898336##28##]. Individuals with anxiety disorders relative to neurotypical people showed higher right anterior insula activation and connectivity with frontoparietal regions during anticipatory anxiety [##REF##21181800##29##]. Individuals with anxiety and mood disorders exhibited higher amygdala and visual cortical responses to passively viewing negative, emotionally arousing scenes, such as those involving violence or contamination, as compared to neutral scenes [##REF##30861143##30##]. Furthermore, lower reactivity in these regions while viewing emotional as opposed to neutral scenes was correlated with higher trauma scores, suggesting blunted neural activities in response to more severe and oftentimes repeated exposure to trauma [##REF##30861143##30##].</p>", "<p id=\"P13\">Apart from mood disorders, individual variation in anxiety can influence how emotional stimuli are processed in neurotypical populations. Individuals with higher elevation in cortisol levels (greater stress response) showed lower OFC activity during negative vs neutral emotion processing [##REF##28688266##31##]. In another study, ventromedial prefrontal cortical activity during threat vs. safe condition increased with greater individual state of anxiety [##REF##29981291##32##]. A few studies reported the findings in women or men alone or specifically noted sex differences in the findings of individual variation. For instance, the severity of dysphoric mood, as assessed through the Profile of Mood States and State-Trait Anxiety Inventory, was associated with heightened hypothalamic activity during the processing of negative vs. neutral images [##REF##27246897##24##]. The latter study also reported elevated amygdala activity in positive correlation with dysphoric mood in women but not in men [##REF##27246897##24##]. In contrast, a more recent work noted retro-splenial cortex and precuneus activity during negative emotional face vs neutral shape identification in negative correlation with NIH Toolbox anger- and fear-affect scores in men but not in women [##REF##32682098##33##]. Thus, these studies indicate that anxiety’s impact on negative emotions may manifest in a sex-specific manner, emphasizing the need for further exploration in this direction.</p>", "<p id=\"P14\">Together, earlier studies demonstrate the impact of individual differences in mood and anxiety, whether meriting a clinical diagnosis or not, on the neural activities of negative emotion processing. Here, we aimed to study how such an impact of individual differences in mood and anxiety may vary between men and women.</p>", "<title>The present study</title>", "<p id=\"P15\">We recruited 119 healthy adults, evaluated their anxiety state with the State-Trait Anxiety Inventory, and tested their brain responses to negative emotion in a Hariri picture matching task [##REF##12482086##25##] using International Affective Picture System (IAPS, a database of pictures for studying emotion) negative and neutral pictures. A widely used paradigm to query brain activation to negative emotional stimuli, the Hariri task reliably engages corticolimbic structures [##REF##12482086##25##, ##REF##21148006##34##, ##REF##30728379##35##].</p>", "<p id=\"P16\">We have two distinct aims. First, we revisited sex differences in regional brain activations during negative emotion processing. As the latest meta-analyses suggested no sex differences in the overall brain responses, we hypothesized no sex differences between men and women in their regional responses to matching of pictures of negative vs. neural emotional content. Second, we examined sex differences in the influences of individual anxiety state on both the behavioral performance and neural responses to negative emotion processing. Accurate and expedient matching in the Hariri task would require participants to divert their attention away from their natural emotional reactions and concentrate on generating a motor response. Thus, a faster reaction time (RT) would indicate better emotion regulation and less reactivity [##REF##37263454##36##]. We posited that individuals with higher levels of anxiety would be more sensitive to the interference by negative emotional stimuli on cognitive motor processing and demonstrate prolonged RT and diminished activities in the emotion regulatory circuit in matching negative vs. neutral pictures. Further, this effect would be more prominent in women than in men. Finally, we performed mediation analyses to characterize the inter-relationship of individual anxiety, regional brain activities, and RT.</p>" ]	[ "<title>Methods</title>", "<title>Participants and clinical assessments</title>", "<p id=\"P17\">One hundred and nineteen healthy adults (59 women) 19 to 85 years of age volunteered to participate in the study. Candidates were recruited from the greater New Haven, Connecticut, area. All participants were physically healthy, cognitively intact (Mini Mental State Examination Score > 27) with no major medical conditions. Those with current use of prescription medications or with a history of head injury or neurological illness were excluded. Other exclusion criteria included current or history of Axis I disorders according to the Structured Clinical Interview for DSM-IV [##UREF##4##37##]. Candidates who reported current use of illicit substances or tested positive for cocaine, methamphetamine, opioids, marijuana, barbiturates, or benzodiazepines were not invited to participate. All participants were assessed with the State-Trait Anxiety Inventory (STAI). The STAI State score ranged from 20 to 63 with a mean ± SD of 32.24 ± 10.41 in the current sample. The Human Investigation Committee at Yale School of Medicine approved the study procedures. All participants signed an informed consent prior to the study.</p>", "<title>MRI protocol and behavioral task</title>", "<p id=\"P18\">Brain images were collected using multiband imaging with a 3-Tesla MR scanner (Siemens Trio, Erlangen, Germany). Conventional T1-weighted spin echo sagittal anatomical images were acquired for slice localization. Anatomical 3D MPRAGE image were next obtained with spin echo imaging in the axial plane parallel to the AC–PC line with TR = 1900 ms, TE = 2.52 ms, bandwidth = 170 Hz/pixel, field of view = 250 × 250 mm, matrix = 256 × 256, 176 slices with slice thickness = 1 mm and no gap. Functional, blood oxygen level-dependent (BOLD) signals were acquired with a single-shot gradient echoplanar imaging sequence. Fifty-one axial slices parallel to the AC–PC line covering the whole brain were acquired with TR = 1000 ms, TE = 30 ms, bandwidth = 2290 Hz/pixel, flip angle = 62°, field of view = 210 × 210 mm, matrix = 84 × 84, 51 slices with slice thickness = 2.5 mm and no gap, 392 volumes, and multiband acceleration factor = 3. Images from the first ten TRs at the beginning of each scan were discarded to ensure that only BOLD signals in steady-state equilibrium between RF pulsing and relaxation were included in data analyses.</p>", "<p id=\"P19\">In the Hariri picture matching task, 24 different images were used, with 12 each of negative and neutral emotional IAPS pictures, in a block design. The target picture was shown on the top and two pictures either matching or not matching the target were shown at the bottom. Participants were asked to match one of two simultaneously presented pictures with the target picture by pressing a left or right buttons on their right or dominant hand (##FIG##0##Fig. 1A##). A session comprised 10s of dummy scans, followed by the task instruction to “choose one to match the picture at the top” for 2s and 4 picture blocks in the sequence: one neutral block ◊ two negative blocks ◊ one neutral block. Each block started with a fixation period of 2s, followed by 6 stimuli each lasting 6s. The 6 stimuli were presented consecutively without inter-stimuli gap. The blocks last approximately 152s (2.5 minutes). During imaging, subjects responded by pressing one of two buttons, allowing for the determination of accuracy and reaction time (RT). Subjects were told that the stimuli would be presented long enough for them to make an accurate match but were not explicitly instructed to respond as fast as possible. This allowed us to assess the natural preferences in emotion processing across subjects [##REF##18234477##38##]. Please note that this task is a component of a larger task, and we focused on the picture matching blocks in the current manuscript.</p>", "<title>Imaging data processing and modeling</title>", "<p id=\"P20\">Data were analyzed with Statistical Parametric Mapping (SPM12, Welcome Department of Imaging Neuroscience, University College London, U.K.), following our published routines [##REF##37263454##36##]. Images of each individual subject were first realigned (motion corrected) and corrected for slice timing. A mean functional image volume was constructed for each subject per run from the realigned image volumes. These mean images were co-registered with the high-resolution structural image and segmented for normalization with affine registration followed by nonlinear transformation. The normalization parameters determined for the structure volume were then applied to the corresponding functional image volumes for each subject. The resampled voxel size is 2.5 × 2.5 × 2.5 mm<sup>3</sup>. Finally, the images were smoothed with a Gaussian kernel of 8 mm at Full Width at Half Maximum.</p>", "<p id=\"P21\">A statistical analytical block design was constructed for each individual subject using a general linear model (GLM) by convolving the canonical hemodynamic response function (HRF) with the boxcar function in SPM, separately for negative and neutral images. Realignment parameters in all six dimensions were also entered in the model. The GLM estimated the component of variance that could be explained by each of the regressors.</p>", "<title>Statistical analyses of imaging data</title>", "<p id=\"P22\">In the first-level analysis, we constructed for each individual subject a contrast of negative vs. neutral picture blocks (Neg-Neu) to evaluate differences in regional responses to matching these images. The contrast images (.<italic toggle=\"yes\">con</italic>) of the first-level analysis were used for group statistics. In random effects analyses, we conducted a full-factorial analysis on all subjects’ .<italic toggle=\"yes\">con</italic>, with sex as a two-level factor, STAI score as a covariate with interaction effects involving sex, and age as a covariate of no interest (SPM design matrix shown in <bold>Supplementary Figure S1</bold>). The model factored the STAI score based on sex and enabled us to evaluate differences in the regression slope of (Neg-Neu) activity against STAI score between men and women, controlling for the overall effect of age [##REF##31057331##39##]. We assessed the model for: (1) BOLD activity during (Neg-Neg) in men, women and all participants and differences in BOLD activity between men and women (men > women, women > men) (2) regression slope differences in BOLD activity during (Neg-Neu) against STAI score between men and women, as well as regression separately in men and women, using T-contrasts. Following current reporting standards [##REF##37263454##36##], all results were evaluated with voxel p < 0.001, uncorrected, in combination with cluster p < 0.05, FWE corrected, on the basis of Gaussian random field theory as implemented in SPM.</p>", "<p id=\"P23\">We used MarsBaR (<ext-link xlink:href=\"http://marsbar.sourceforge.net/\" ext-link-type=\"uri\">http://marsbar.sourceforge.net/</ext-link>) to derive for each individual subject the parameter estimates (β’s) of the functional ROIs identified from full factorial analysis and assessed the correlation between β’s and behavioral data. In addition to whole-brain analyses of a directional contrast of men and women in STAI score regression, we performed slopes tests to examine sex differences in the regression of β’s identified of men or women alone vs. STAI score. As a threshold was imposed in whole-brain regressions and those findings identified in, say, women, might have just missed the threshold in men, and vice versa. Thus, a slope test was needed to confirm sex differences, an analysis that should not be considered as “double-dipping.”</p>", "<title>Connectivity analysis: Psychophysiological interaction (PPI)</title>", "<p id=\"P24\">We conducted a generalized gPPI analysis with significant clusters identified from whole-brain correlates of STAI score (See <xref rid=\"S17\" ref-type=\"sec\">Results</xref>) to explore anxiety-related changes in functional connectivity during emotion processing. Following published methods [##REF##37263454##36##], we created a PPI model for each subject with three regressors: the physiological variable that represents temporally filtered, mean-corrected and deconvolved time series of the seed region, the psychological variable that represents the task contrast (negative vs. neutral), and a PPI variable that was computed as element-by-element product of deconvolved time series of the seed and contrast, followed by re-convolution with the HRF. The PPI images of each subject were used in random effect analyses – including whole-brain regression against STAI score and RT (Neg-Neu).</p>", "<p id=\"P25\">With MarsBaR, we extracted the average functional connectivity (FC β) between the seed and clusters (if any) identified from regression analysis and assessed the correlations between the FC β’s and behavioral data.</p>", "<title>Mediation analyses</title>", "<p id=\"P26\">For the clusters with activity and/or connectivity (FC) β’s correlated both with STAI score and RT, we performed mediation analyses, with ‘age’ as covariate to characterize the inter-relationships of these clinical, behavioral, and neural metrics (see <xref rid=\"S17\" ref-type=\"sec\">Results</xref>), following our previous study [##REF##37252876##40##] and as described in the Supplement. We specifically focused on the model: [anxiety → β/FC β → RT] to test the hypotheses that the neural correlates mediated the effects of anxiety on behavioral performance.</p>" ]	[ "<title>Results</title>", "<title>Behavioral results:</title>", "<p id=\"P27\">Across negative and neutral trials, the mean RTs ranged from 0.82 to 3.16 s and the mean accuracy rates ranged from 71 to 100% across subjects (##FIG##0##Fig. 1B##). A 2 (stimulus: negative vs. neutral) × 2 (sex: men vs. women) ANOVA with age as a covariate did not show any significant main or interaction effects for accuracy rate: main stimulus effect (F<sub>1,117</sub> = 0.00, p = 0.997), main sex effect (F<sub>1,117</sub> = 2.45, p = 0.120), stimulus × sex (F<sub>1,117</sub> = 0.14, p = 0.708); or for RT: main stimulus effect (F<sub>1,117</sub> = 0.68, p = 0.411), main sex effect (F<sub>1,117</sub> = 0.01, p = 0.910), stimulus × sex (F<sub>1,117</sub> = 3.65, p = 0.058).</p>", "<p id=\"P28\">Controlling for age, men and women did not differ in the STAI score (men: 30.37 ± 10.22, women: 34.15 ± 10.34, p = 0.137). Neither accuracy rate (Neg – Neu) or RT (Neg – Neu) showed a significant correlation with the STAI score in Pearson regression with age as a covariate: accuracy rate (r = 0.06, p = 0.506) and RT (r = 0.15, p = 0.095) for all subjects; accuracy rate (r = 0.11, p = 0.385) and RT (r = −0.08, p = 0.517)) for men. In women, RT (Neg-Neu) but not the accuracy rate (Neg – Neu) showed a significant correlation with STAI score (r = 0.48, p < 0.001 and r = −0.05, p = 0.699, respectively). Slope test revealed significant differences in regression slope of RT vs. STAI score (t = 3.20, p = 0.002) but not of accuracy rate vs. STAI score (t = −0.66, p = 0.509). These findings are shown in ##FIG##0##Fig. 1C## and ##FIG##0##1D##. Thus, although the behavioral performance in matching negative vs. neutral pictures did not vary between men and women, anxiety significantly affected performance in women but not in men.</p>", "<title>Imaging results:</title>", "<title>Neural responses to matching of negative vs. neutral pictures</title>", "<p id=\"P29\">Across all subjects, bilateral inferior occipital gyrus, superior frontal gyrus, middle/inferior frontal gyrus, left amygdala, and left thalamus/caudate showed higher activation during matching of negative vs. neutral pictures (<bold>Supplementary Fig. 2A</bold>). This pattern of activation was consistent in men (<bold>Supplementary Fig. 2B</bold>) and women (<bold>Supplementary Fig. 2C</bold>). Although women appeared to show greater regional activations than men, the differences were not significant in a direct contrast.</p>", "<title>Neural correlates of anxiety</title>", "<p id=\"P30\">In whole-brain regression of (Neg-Neu) activity against STAI score with age as a covariate, a single cluster in the lingual gyrus (LG, x= −10, y= −64, z= −7, voxel Z = 4.50, 139 voxels) showed activity in positive correlation with STAI score across all subjects (##FIG##1##Fig. 2A##). The analyses in men alone did not reveal any significant clusters (##FIG##1##Fig. 2B##). In women alone, a cluster in the LG (x= −10, y= −61, z= −7, voxel Z = 4.88, 150 voxels) showed activity in positive correlation with STAI score, and three clusters each in the medial prefrontal cortex (mPFC, in pregenual and subgenual anterior cingulate gyrus; x = −8, y = 36, z = 3, voxel Z= −5.11, 295 voxels), right superior frontal gyrus (SFG, x = 15, y = 46, z = 28, voxel Z=−4.77, 262 voxels), and left SFG (x= −15, y = 42, z = 28, voxel Z= −4.61, 354 voxels) showed activity in negative correlation with STAI score (##FIG##1##Fig. 2C##). We did not observe any clusters showing significant sex differences in the regression of (Neg-Neu) activity against STAI score in whole-brain analysis.</p>", "<p id=\"P31\">We extracted the β estimates of (Neg-Neu) of the LG cluster identified from the regression across all subjects. The β’s were correlated significantly with the STAI score (r = 0.37, p < 0.001), as expected, and also significantly with the RT (Neg-Neu) but not accuracy rate (Neg-Neu), with age as covariate (r = 0.32, p < 0.001 and r= −0.11, p = 0.247, respectively). In a slope test, men and women did not differ significantly in regression slope of LG vs. STAI score (t= −1.47, p = 0.144) or vs. RT (Neg-Neu), with age as covariate (t = 1.73, p = 0.086).</p>", "<p id=\"P32\">We also extracted the β’s of “Neg-Neu” of the LG, mPFC, and SFG clusters identified in women. With age as a covariate, the clusters showed β’s in significant correlation with the STAI score in women, as expected: LG (r = 0.45, p < 0.001), mPFC (r = −0.45, p < 0.001), right SFG (r = −0.51, p < 0.001), and left SFG (r = −0.49, p < 0.001). In slope tests with age as a covariate, men and women showed significant differences in regression slope of the β’s vs. STAI score for the mPFC (t = −3.17, p = 0.002), right SFG (t = −2.76, p = 0.007), left SFG (t = −3.11, p = 0.002), and marginally for the LG (t = 2.13, p = 0.035).</p>", "<p id=\"P33\">We evaluated the relationship of these β’s and RT (Neg-Neu) and accuracy rate (Neg-Neu) in women. The β’s of the LG (r = 0.43, p < 0.008) and mPFC (r = −0.29, p = 0.026), but not the right SFG (r = −0.23, p = 0.083) or left SFG (r = −0.18, p = 0.172) were significantly correlated with RT (Neg-Neu), with age as covariate. In slope tests of β’s vs. RT (Neg-Neu), the mPFC (t = −2.50, p = 0.014) but not the LG β (t = 1.95, p = 0.054) showed significant sex differences in the regression slope. None of the β’s was significantly correlated with accuracy rate (Neg-Neu) (−0.06 < r’s < 0.04, 0.676 < p’s < 0.991).</p>", "<p id=\"P34\">To summarize, for all of the clusters identified from whole-brain regression against STAI score across all subjects or in women alone, only the mPFC cluster identified from women showed a significant correlation of the β’s with RT (Neg-Neu) as well as a significant sex difference in slope in the regression of the β’s vs. STAI score and of the β’s vs. RT (Neg-Neu).</p>", "<title>Functional connectivity</title>", "<p id=\"P35\">The mPFC cluster identified from women showed a significant correlation of the β’s with RT (Neg-Neu) as well as a significant sex difference in slope in the regression of the β’s vs. STAI score and of the β’s vs. RT (Neg-Neu). Thus, we focused on the mPFC cluster as a seed region and conducted a gPPI analysis. The results showed (Neg-Neu) gPPI correlates of STAI score in the right superior frontal gyrus (SFG) and inferior frontal gyrus (IFG) and left parahippocampal gyrus (PHG). The extracted gPPI β’s of these clusters (##TAB##0##Table 1##, ##FIG##2##Fig. 3A##) as well as the average gPPI β (r = 0.49, p < 0.001) correlated significantly with RT (Neg - Neu). In a separate regression, we identified gPPI correlates of RT (Neg - Neu) in the PHG, and IFG. The extracted gPPI β’s of these clusters (##TAB##0##Table 1##, ##FIG##2##Fig. 3B##) and the average β (r = 0.47, p < 0.001) correlated with STAI score.</p>", "<title>Mediation analyses</title>", "<p id=\"P36\">We performed mediation analysis to assess the mediating effects of mPFC β and mPFC FC β (average of all clusters identified in gPPI regression) on the association between anxiety and RT. Thus, we tested the model with anxiety and RT each as the independent and outcome variable and β as the mediating variable, with ‘age’ as covariate. We tested the model separately for men and women.</p>", "<p id=\"P37\">The model with mPFC β was not significant either in men or in women; however, the model with mPFC FC β was significant in women but not in men (##FIG##3##Fig. 4##, <bold>Supplementary Table S1</bold>). Thus, mPFC connectivity, but not the mPFC activity mediated the association between anxiety and RT (Neg-Neu) in women. In men, neither mPFC activity nor connectivity mediated the association between anxiety and RT (Neg-Neu).</p>" ]	[ "<title>discussion</title>", "<p id=\"P38\">Men and women did not demonstrate significant differences in behavioral performance in the Hariri task. However, women but not men showed a significant correlation between STAI score and RT (Neg – Neu), and the sex difference was confirmed by a slope test. Men and women also did not demonstrate significant differences in regional activities during matching negative vs. neutral images, consistent with the findings of the latest meta-analysis [##REF##27168344##19##]. However, women but not men showed a significant correlation between mPFC activity and STAI score, with the sex difference confirmed by slope test. Functional connectivity revealed by gPPI analysis with the mPFC cluster as seed identified the right inferior frontal gyrus, right superior frontal gyrus and left parahippocampal gyrus with gPPI in positive correlation both with STAI score and RT (Neg – Neu). Mediation analysis described a significant model whereby STAI score influenced mPFC connectivities and in turn the RT. Together, the findings suggest sex differences in the neural and behavioral processes underlying individual differences in anxiety. Studies with other task paradigms are needed to investigate how the behavioral and neural processes of anxiety may manifest in men.</p>", "<title>Behavioral correlates of anxiety</title>", "<p id=\"P39\">We did not observe significant differences in RT or accuracy rate (Neg-Neu) between men and women, consistent with earlier findings of no sex differences in an emotional Stroop task [##REF##19863758##41##]. Similarly, a review article highlighted the lack of a clear pattern of sex differences in RT across different emotion processing tasks [##REF##22245006##42##]. Note that the current findings should be considered specific to non-clinical samples, where the interference caused by emotional content may not significantly impact performance. Furthermore, although anxiety scores and RT (Neg-Neu) were both comparable between men and women, anxiety showed a positive correlation with RT (Neg-Neu) in women but not in men. This suggests that women’s response to negative emotion is more sensitive to their state of anxiety, such that higher anxiety slows the motor response, possibly due to greater attention to negative emotional content hindering task performance [##REF##37263454##36##]. These findings not only characterize a behavioral correlate of anxiety in women but also suggest the importance of examining the data of men and women separately in investigating individual differences in emotion processing.</p>", "<title>Neural Correlates: mPFC activity</title>", "<p id=\"P40\">Negative vs. neutral emotional picture processing reliably activated cotico-limbic regions in all, men, and women, with men and women showing statistically indistinguishable patterns of activations, consistent with a previous meta-analysis [##REF##27168344##19##]. In women and in all subjects, we observed a positive association between anxiety and LG activity, and in women, a negative association between anxiety and mPFC and SFG activity, during negative vs. neutral processing.</p>", "<p id=\"P41\">A higher-order visual area, the LG is involved in processing emotional stimuli and experience [##REF##11027916##43##–##REF##26892030##45##]. In the present study, LG showed a trend-level decrease in activity during matching of negative vs neutral pictures (<bold>Supplementary Figure S3</bold>), consistent with earlier reports of reduced LG activity during negative vs neutral face/picture processing [##REF##18798977##44##, ##REF##23196633##46##, ##REF##32199954##47##] and greater activity during happy vs neutral face processing [##REF##25140051##48##]. Across all subjects and in women, LG activity correlated positively with anxiety, suggesting that LG activity elevates in participants who focus more on the negative emotional content of the pictures. Hence, we also noted longer RT with higher LG activity during matching of negative vs. neutral images, an effect that did not appear to be sex different. These findings also suggest that visual processing can be significantly affected by anxiety.</p>", "<p id=\"P42\">In women, we observed a negative correlation between anxiety and frontal cortical (mPFC and SFG) activation during negative vs neutral picture processing. Frontal cortical activation is noted widely across studies of emotion picture/scene processing [##REF##35001394##14##–##REF##16488159##16##]. Whereas the broad mPFC responds to reward and self-referential evaluation [##REF##33479323##49##] as well as appraisal, regulation, and expression of emotion [##REF##21167765##50##], the pregenual and subgenual anterior cingulate cortex (pgACC and sgACC) appears most critical in emotion regulation [##REF##21167765##50##]. However, studies of people with anxiety disorders have reported mixed findings, with hyperactivity [##REF##27761400##51##, ##REF##32878626##52##], hypoactivity [##REF##20716396##53##, ##REF##21557888##54##] or no differences in activity [##REF##18483136##55##–##REF##25171782##57##] of the mPFC all been reported in individuals with general anxiety disorder (GAD) vs. controls during exposure to negative emotions. In a meta-analysis of regional responses to negative emotions, hypoactive dorsal/rostral ACC and ventromedial PFC were observed in individuals with posttraumatic stress disorder (PTSD) but not those with social anxiety disorder or specific phobia, or in healthy participants during fear conditioning [##REF##17898336##28##]. Further, in an emotional Stroop task, Etkin and colleagues noted higher pgACC activity during incongruent vs. congruent trials in healthy participants but a trend of reduced activity in people with GAD [##REF##20123913##58##]. Thus, literature suggests a complex pattern of anxiety-related mPFC activities during negative emotion processing that may vary with behavioral tasks and the content of anxiety. Activities of the SFG appeared to vary across behavioral tasks of emotion processing, with emotion regulation but not passive exposure eliciting higher SFG response [##REF##31863185##59##–##REF##32659287##61##].</p>", "<p id=\"P43\">A neurocognitive model posits a key role of selective attention to threat and regulation by the PFC in manifesting the effects of anxiety [##REF##18591476##62##]. Here, although we did not observe significant differences in mPFC or SFG activity during negative vs. neutral picture matching (<bold>Supplementary Figure S3</bold>), the activity correlated negatively with state of anxiety, suggesting less emotion regulation in women with higher levels of anxiety.</p>", "<title>Neural Correlates: mPFC connectivity</title>", "<p id=\"P44\">In women, the FC of mPFC, a component of the default mode network (DMN), showed enhanced connectivity with the SFG, IFG, and parahippocampal gyrus (PHG) in link with higher individual anxiety. The DMN comprises a set of interconnected brain regions where activities tend to increase in synchrony during unfocused or internally-directed mental states, when people are at rest, recollecting the past, or contemplating the future, but decrease during goal-directed tasks [##REF##18400922##63##]. Dispositional self-focus may be more significantly elevated during negative emotional scene exposure along with higher frontal cortical interconnectivity in individuals with higher levels of anxiety [##REF##34341966##64##]. Mostly noted for autobiographical memory retrieval or self-directed thought during emotion processing [##REF##31427147##65##], the PHG is part of a broadly defined DMN, connecting the DMN with the memory system of the medial temporal cortex [##REF##23404748##66##]. A previous study reported reduced frontal-PHG connectivity during negative emotion processing in patients with major depressive disorder and discussed the finding as a marker of impaired emotion regulation [##REF##33403894##67##]. Dynamic resting connectivity between the frontal cortex and PHG was also reduced in individuals with PTSD [##UREF##5##68##]. Thus, here, enhanced mPFC-PHG connectivity in individuals with higher levels of anxiety may indicate greater emotion regulation demands in neurotypical populations, although this regulatory mechanism may come apart in people with anxiety disorders.</p>", "<p id=\"P45\">It’s worth noting that these FCs also exhibited significant correlations with prolonged RT (Neg-Neu), indicating their behavioral relevance. Interestingly, mPFC functional connectivity, rather than activity, completely mediated the relationship between anxiety and RT (Neg-Neu). This suggests mPFC’s role in emotion regulation but only an indirect role in manifesting the behavioral outcome of anxiety. Indeed, the SFG/IFG has been implicated in both emotion [##REF##24574991##69##] and cognitive motor [##UREF##6##70##, ##REF##22084637##71##] processing. For instance, in an emotional Stroop task, negative vs. neutral RT correlated with activity within a cluster that included the medial and superior frontal gyri during negative vs. neutral trials [##REF##22210673##72##]. Exposure to sad versus neutral stimuli was linked to delayed stop signal reaction time, suggesting interference with motor inhibition, accompanied by heightened activation of the SFG in an emotional stop signal task [##REF##32903296##73##]. In another study, greater IFG activation along with prolonged RT was noted for negative vs neutral distractors in affective Stroop task [##REF##17239620##74##]. Other studies noted higher PHG activity when individuals were presented with previously encountered negatively arousing vs. neutral events during a mental navigation task, possibly as an adaptive mechanism of avoidance as shown by a faster RT [##REF##23984944##75##]. In another study, imitation of emotional vs non-emotional facial expression activated the PHG as well as motor cortex, amygdala, and insula [##REF##23990890##76##]. Thus, broadly consistent with these previous studies, we observed the effects of anxiety on behavioral motor response through mPFC connectivites. Notably, the findings of connectivity rather activity support the mediating effects were reported in previous studies of dopamine receptor availability and working memory [##UREF##7##77##] as well as mindfulness and implicit learning [##REF##27121302##78##]. Functional connectivity as revealed by gPPI may represent neural markers of individual differences that warrant more studies.</p>", "<title>Limitations and conclusion</title>", "<p id=\"P46\">We discussed a few limitations of the study. First, we considered the effects of individual variation in natural mood rather than experimentally modulated the state of anxiety. While this approach is valuable for assessing participants’ inherent emotional tendencies, future research is required to ascertain whether these findings apply to controlled experimental conditions. Second, our participants scored from 20 to 60 out of a range of 20 to 80 in STAI score. Thus, individuals with higher STAI score may be needed to fully understand the effects of anxiety on the behavioral and neural responses to negative emotions. Third, previous studies showed that the neural correlates of negative emotion processing may depend on the stimuli, e.g., face vs. non-face, and behavioral task, e.g., whether working memory is involved [##REF##23196633##46##, ##REF##33919024##79##]. Therefore, the current findings should be considered as specific to matching of emotional scenes. Finally, behavioral contingencies that distinguish passive emotional exposure and active regulation of emotions within subjects are needed in future studies to better identify regulatory activities and investigate the effects of anxiety on the circuit activity.</p>", "<p id=\"P47\">In conclusion, women appear to be more sensitive to anxiety when processing negative information, an effect that manifests in prolonged RT in matching negative vs. neural pictures in the Hariri task. This heightened sensitivity may be mediated by dysregulated negative emotion processing in the mPFC and other brain regions connected with the mPFC. These sex-specific findings offer insights into a behavioral and neural mechanism of susceptibility of women to mood disorders.</p>" ]	[ "<title>Limitations and conclusion</title>", "<p id=\"P46\">We discussed a few limitations of the study. First, we considered the effects of individual variation in natural mood rather than experimentally modulated the state of anxiety. While this approach is valuable for assessing participants’ inherent emotional tendencies, future research is required to ascertain whether these findings apply to controlled experimental conditions. Second, our participants scored from 20 to 60 out of a range of 20 to 80 in STAI score. Thus, individuals with higher STAI score may be needed to fully understand the effects of anxiety on the behavioral and neural responses to negative emotions. Third, previous studies showed that the neural correlates of negative emotion processing may depend on the stimuli, e.g., face vs. non-face, and behavioral task, e.g., whether working memory is involved [##REF##23196633##46##, ##REF##33919024##79##]. Therefore, the current findings should be considered as specific to matching of emotional scenes. Finally, behavioral contingencies that distinguish passive emotional exposure and active regulation of emotions within subjects are needed in future studies to better identify regulatory activities and investigate the effects of anxiety on the circuit activity.</p>", "<p id=\"P47\">In conclusion, women appear to be more sensitive to anxiety when processing negative information, an effect that manifests in prolonged RT in matching negative vs. neural pictures in the Hariri task. This heightened sensitivity may be mediated by dysregulated negative emotion processing in the mPFC and other brain regions connected with the mPFC. These sex-specific findings offer insights into a behavioral and neural mechanism of susceptibility of women to mood disorders.</p>" ]	[ "<p id=\"P1\">Authors’ Contribution</p>", "<p id=\"P2\">SC and CSL conceptualized the study; SC and SZ conducted the study, analyzed the data, and drafted the first manuscript, HKW and YC assisted in data analyses, all authors participated in revision and finalization of the manuscript.</p>", "<title>Background</title>", "<p id=\"P3\">Men and women are known to show differences in the incidence and clinical manifestations of mood and anxiety disorders. Many imaging studies have investigated the neural correlates of sex differences in emotion processing. However, it remains unclear how anxiety might impact emotion processing differently in men and women.</p>", "<title>Method</title>", "<p id=\"P4\">We recruited 119 healthy adults and assessed their levels of anxiety using State-Trait Anxiety Inventory (STAI) State score. With functional magnetic resonance imaging (fMRI), we examined regional responses to negative vs. neutral (Neg-Neu) picture matching in the Hariri task. Behavioral data were analyzed using regression and repeated-measures analysis of covariance with age as a covariate, and fMRI data were analyzed using a full-factorial model with sex as a factor and age as a covariate.</p>", "<title>Results</title>", "<p id=\"P5\">Men and women did not differ in STAI score, or accuracy rate or reaction time (RT) (Neg-Neu). However, STAI scores correlated positively with RT (Neg-Neu) in women but not in men. Additionally, in women, STAI score correlated positively with lingual gyrus (LG) and negatively with medial prefrontal cortex (mPFC) and superior frontal gyrus (SFG) activity during Neg vs. Neu trials. The parameter estimates (β’s) of mPFC also correlated with RT (Neg-Neu) in women but not in men. Generalized psychophysiological interaction (gPPI) analysis in women revealed mPFC connectivity with the right inferior frontal gyrus, right SFG, and left parahippocampal gyrus during Neg vs. Neu trials in positive correlation with both STAI score and RT (Neg-Neu). In a mediation analysis, mPFC gPPI but not mPFC activity fully mediated the association between STAI scores and RT (Neg-Neu).</p>", "<title>Conclusion</title>", "<p id=\"P6\">With anxiety affecting the behavioral and neural responses to negative emotions in women but not in men and considering the known roles of the mPFC in emotion regulation, we discussed heightened sensitivity and regulatory demands during negative emotion processing as neurobehavioral markers of anxiety in women.</p>", "<title>Plain Language Summary</title>", "<p id=\"P7\">Men and women often experience and express their emotional problems in different ways. In this study, we investigated how anxiety affects negative emotion processing in men and women. By understanding these differences, we hope to elucidate how men and women differ in the perception and processing of negative emotions in association with individual differences in anxiety. To this end, we recruited 60 men and 59 women from the community. We evaluated participants’ anxiety state using a validated instrument and their brain responses to negative emotional and neutral pictures in picture matching task using functional brain imaging. The results showed that individual levels of anxiety were positively correlated with the speed of matching negative vs. neutral pictures, suggesting interference of negative emotions with cognitive motor processing, in women, but not in men. Thus, women with more severe anxiety may be more sensitive to distraction by negative emotional stimuli. In brain imaging data, the activities of the medial prefrontal cortex, a region that supports emotion regulation, during negative vs. neutral emotion processing were negatively correlated with anxiety in women, and this effect was not seen in men. Further, the medial prefrontal cortex showed connectivities with other brain regions and these functional connectivities mediated the effects of anxiety on matching speed in women. These findings suggest that heightened sensitivity to negative emotions in anxious women are possibly due to emotion dysregulation within the medial prefrontal cortex. These findings may help us better understand why women are more vulnerable to emotional problems and develop more personalized treatments for anxiety and mood disorders.</p>" ]	[]	[ "<title>Funding</title>", "<p id=\"P48\">The current study was supported by NIH grants R21AG067024 (Li) and R01AG072893 (Li). The NIH is otherwise not responsible for the design of the study or data analyses and interpretation or in the decision to publish these findings.</p>", "<title>Availability of data and materials</title>", "<p id=\"P49\">Data sets are available from the corresponding author upon reasonable request.</p>" ]	[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p id=\"P56\">Behavioral task and performance. (A) Example images used in the matching task. (B) Accuracy rate and reaction time (RT) plotted separately for men and women. (C) Correlation of difference in accuracy rate and of RT between negative and neutral blocks with anxiety scores. Note: Data points representing men and women are shown in blue and red, respectively.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p id=\"P57\">Whole-brain regression of the contrast (Neg – Neu) against STAI score with age as a covariate in (A) all subjects, (B) men, and (C) women, evaluated at p<0.001, uncorrected. Color bars show voxel T values, with warm and cool color each for positive and negative correlation. LG: lingual gyrus; mPFC: medial prefrontal cortex; SFG: superior frontal gyrus. The inset in (C) showed the mPFC cluster in sagittal sections.</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3</label><caption><p id=\"P58\">Whole-brain mPFC gPPI regression on (A) STAI score and (B) “neg-neu” RT in women. The gPPI seeds are shown in ‘Red’; IFG: inferior frontal gyrus, SFG: superior frontal gyrus, PHG: parahippocampal gyrus. Color bars show voxel T values, with warm and cool color each for positive and negative correlations.</p></caption></fig>", "<fig position=\"float\" id=\"F4\"><label>Figure 4</label><caption><p id=\"P59\">Mediation models of mPFC β/mPFC FC β, anxiety, RT (Neg-Neu), with age as covariate. Note: the path statistics represent the coefficient and p value; mPFC: middle prefrontal cortex, FC: functional connectivity, β: parameter estimate, RT: reaction time.</p></caption></fig>" ]	[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p id=\"P60\">Whole-brain mPFC gPPI regression on STAI score and RT (Neg - Neu) in women.</p></caption><table frame=\"box\" rules=\"rows\"><colgroup span=\"1\"><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/></colgroup><thead><tr><th rowspan=\"2\" align=\"left\" valign=\"top\" colspan=\"1\">volume (voxels)</th><th rowspan=\"2\" align=\"left\" valign=\"top\" colspan=\"1\">peak voxel (Z)</th><th colspan=\"3\" align=\"left\" valign=\"top\" rowspan=\"1\">MNI coordinates (mm)</th><th rowspan=\"2\" align=\"left\" valign=\"top\" colspan=\"1\">side</th><th rowspan=\"2\" align=\"left\" valign=\"top\" colspan=\"1\">identified brain region</th><th rowspan=\"2\" align=\"left\" valign=\"top\" colspan=\"1\">Pearson r, p-value (age as covariate)</th></tr><tr><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">x</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">y</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">z</th></tr></thead><tbody><tr><td colspan=\"5\" align=\"left\" valign=\"top\" rowspan=\"1\">\n<italic toggle=\"yes\">Regression vs. STAI score (Positive)</italic>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">Correlation with RT (Neg-Neu)</italic>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">144</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.73</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">30</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">12</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">13</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">R</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">IFG</td><td rowspan=\"2\" align=\"left\" valign=\"top\" colspan=\"1\">0.43, < 0.001</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.25</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">45</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">17</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">16</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">114</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.17</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">59</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">18</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">R</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">SFG</td><td rowspan=\"3\" align=\"left\" valign=\"top\" colspan=\"1\">0.31, 0.017</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3.83</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">13</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">44</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">28</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3.61</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">8</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">49</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">33</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">104</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.12</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">−15</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">−46</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">6</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">L</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">PHG</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.47, <0.001</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3.43</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">−20</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">−39</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">−5</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td colspan=\"8\" align=\"left\" valign=\"top\" rowspan=\"1\">\n<italic toggle=\"yes\">Regression vs. STAI score (Negative)</italic>\n</td></tr><tr><td colspan=\"8\" align=\"left\" valign=\"top\" rowspan=\"1\">None</td></tr><tr><td colspan=\"5\" align=\"left\" valign=\"top\" rowspan=\"1\">\n<italic toggle=\"yes\">Regression vs. RT (Neg - Neu) (Positive)</italic>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">Correlation with STAI score</italic>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">204</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.29</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">−18</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">−51</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">L</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">PHG</td><td rowspan=\"3\" align=\"left\" valign=\"top\" colspan=\"1\">0.41, 0.001</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.08</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">−8</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">−69</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">−17</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3.69</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">−13</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">−61</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">−15</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">169</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.12</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">38</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">17</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">23</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">R</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">IFG</td><td rowspan=\"3\" align=\"left\" valign=\"top\" colspan=\"1\">0.42, < 0.001</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3.83</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">40</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">24</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">18</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3.82</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">38</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">14</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">13</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td colspan=\"5\" align=\"left\" valign=\"top\" rowspan=\"1\">\n<italic toggle=\"yes\">Regression vs. RT (Neg - Neu) (Negative)</italic>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td colspan=\"5\" align=\"left\" valign=\"top\" rowspan=\"1\">None</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/></tr></tbody></table></table-wrap>" ]	[]	[ "<boxed-text id=\"BX1\" position=\"float\"><caption><title>Highlights</title></caption><list list-type=\"bullet\" id=\"L1\"><list-item><p id=\"P62\">Men and women did not differ in accuracy or RT during matching of negative vs. neutral images in Hariri picture matching task.</p></list-item><list-item><p id=\"P63\">In women, but not in men, anxiety correlated positively with negative vs. neutral RT.</p></list-item><list-item><p id=\"P64\">Negative vs. neutral image matching engaged corticolimbic regions comparably in men and women.</p></list-item><list-item><p id=\"P65\">In women but not in men, activity of the mPFC during negative vs. neutral image matching correlated negatively with anxiety and with negative vs. neutral RT.</p></list-item><list-item><p id=\"P66\">In women, mPFC connectivity with the frontal cortex and parahippocampus mediated the association between anxiety and negative vs. neutral RT.</p></list-item><list-item><p id=\"P67\">MPFC dysfunction and heightened sensitivity to negative emotions may explain higher susceptibility of women to mood and anxiety disorders.</p></list-item></list></boxed-text>", "<boxed-text id=\"BX3\" position=\"float\"><caption><title>Perspectives and significance</title></caption><p id=\"P68\">Our finding suggests that state of anxiety modulates negative emotion processing mainly through reduced activity of regulatory brain regions in women, but not in men.</p></boxed-text>" ]	[]	[]	[]	[]	[ "<fn-group><fn fn-type=\"COI-statement\" id=\"FN2\"><p id=\"P50\">Competing Interests</p><p id=\"P51\">The authors declare they have no competing financial or other interests to report.</p></fn><fn id=\"FN3\"><p id=\"P52\">Ethics approval and consent to participate</p><p id=\"P53\">The Human Investigation Committee at Yale School of Medicine approved the study procedures. All participants signed an informed consent prior to the study.</p></fn><fn id=\"FN4\"><p id=\"P54\">Consent for publication</p><p id=\"P55\">Not applicable.</p></fn></fn-group>", "<table-wrap-foot><fn id=\"TFN1\"><p id=\"P61\">Note: IFG: inferior frontal gyrus, SFG: superior frontal gyrus, PHG: parahippocampal gyrus</p></fn></table-wrap-foot>" ]	[ "<graphic xlink:href=\"nihpp-rs3701951v1-f0001\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-rs3701951v1-f0002\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-rs3701951v1-f0003\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-rs3701951v1-f0004\" position=\"float\"/>" ]	[]	[{"label": ["3."], "surname": ["Jalnapurkar", "Allen", "Pigott"], "given-names": ["I", "M", "T"], "article-title": ["Sex Differences in Anxiety Disorders: A Review"], "source": ["J Psychiatry Depress Anxiety"], "year": ["2018"], "volume": ["4"], "fpage": ["3"], "lpage": ["16"]}, {"label": ["8."], "surname": ["Rey", "Lipps", "Shansky"], "given-names": ["CD", "J", "RM"], "article-title": ["Dopamine D1 receptor activation rescues extinction impairments in low-estrogen female rats and induces cortical layer-specific activation changes in prefrontal-amygdala circuits"], "source": ["Neuropsychopharmacol Off Publ Am Coll Neuropsychopharmacol"], "year": ["2014"], "volume": ["39"], "fpage": ["1282"], "lpage": ["9"]}, {"label": ["10."], "surname": ["Curtis", "Bethea", "Valentino"], "given-names": ["AL", "T", "RJ"], "article-title": ["Sexually dimorphic responses of the brain norepinephrine system to stress and corticotropin-releasing factor"], "source": ["Neuropsychopharmacol Off Publ Am Coll Neuropsychopharmacol"], "year": ["2006"], "volume": ["31"], "fpage": ["544"], "lpage": ["54"]}, {"label": ["11."], "surname": ["Bangasser", "Curtis", "Reyes", "Bethea", "Parastatidis", "Ischiropoulos"], "given-names": ["DA", "A", "BAS", "TT", "I", "H"], "article-title": ["Sex differences in corticotropin-releasing factor receptor signaling and trafficking: potential role in female vulnerability to stress-related psychopathology"], "source": ["Mol Psychiatry"], "year": ["2010"], "volume": ["15"], "fpage": ["896"], "lpage": ["904"]}, {"label": ["37."], "surname": ["First", "Spitzer", "Gibbon", "Williams"], "given-names": ["MB", "R", "M", "J"], "source": ["Structured clinical interview for DSM-IV axis I disorders"], "publisher-loc": ["Washington, DC"], "publisher-name": ["American Psychiatric Press"], "year": ["1996"]}, {"label": ["68."], "surname": ["Chen", "Qi", "Ke", "Qiu", "Xu", "Zhang"], "given-names": ["HJ", "R", "J", "J", "Q", "Z"], "article-title": ["Altered dynamic parahippocampus functional connectivity in patients with post-traumatic stress disorder"], "source": ["world J Biol psychiatry Off J World Fed Soc Biol Psychiatry"], "year": ["2021"], "volume": ["22"], "fpage": ["236"], "lpage": ["45"]}, {"label": ["70."], "surname": ["Hu", "Ide", "Zhang", "Li"], "given-names": ["S", "JS", "S", "C-SR"], "article-title": ["The Right Superior Frontal Gyrus and Individual Variation in Proactive Control of Impulsive Response"], "source": ["J Neurosci Off J Soc Neurosci"], "year": ["2016"], "volume": ["36"], "fpage": ["12688"], "lpage": ["96"]}, {"label": ["77."], "surname": ["Nour", "Dahoun", "McCutcheon", "Adams", "Wall", "Howes"], "given-names": ["MM", "T", "RA", "RA", "MB", "OD"], "article-title": ["Task-induced functional brain connectivity mediates the relationship between striatal D2/3 receptors and working memory"], "source": ["Elife"], "year": ["2019"], "volume": ["8"]}]	{ "acronym": [], "definition": [] }	79	CC BY	no	2024-01-13 23:36:45	Res Sq. 2023 Dec 19;:rs.3.rs-3701951	oa_package/66/60/PMC10775373.tar.gz
PMC10775377	38196632	[ "<title>Introduction</title>", "<p id=\"P2\">Necroptosis is widely viewed as an inflammatory form of cell death due to the release of DAMPs from ruptured plasma membrane. Receptor interacting protein kinase 3 (RIPK3), the essential serine/threonine kinase in necroptosis, can be activated by one of three distinct upstream activators: the related kinase RIPK1, the toll-like receptor 3 (TLR3) and TLR4 adaptor TIR domain containing adaptor molecule 1 (TRIF), and the viral RNA sensor Z-DNA binding protein 1 (ZBP1). RIPK3 oligomerizes and forms cytosolic signaling complexes via its RIP homotypic interaction motif (RHIM), which allows it to phosphorylate the necroptosis effector molecule Mixed Lineage Kinase domain-Like (MLKL). Phosphorylation causes MLKL to oligomerize and translocate to the plasma membrane to induce membrane rupture, leading to the leakage of damage associated molecular patterns (DAMPs) and inflammatory responses (##REF##33462412##1##, ##REF##32732131##2##). In addition to promoting necroptosis, RIPK3 can also participate in apoptosis in certain situations. For instance, in the absence of MLKL or when the kinase activity of RIPK3 is inhibited, RIPK3 can stimulate formation of an alternate apoptosis-inducing complex with caspase-8, RIPK1, and FAS-associated death domain protein (FADD) (##REF##26786097##3##). Moreover, RIPK3 can also stimulate inflammatory gene expression in a RHIM-dependent but cell death-independent manner (##REF##25367573##4##–##REF##25567679##7##). In this context, the RHIM serves as a scaffold to stimulate NF-κB activation (##REF##25367573##4##, ##REF##28273458##8##, ##REF##29703889##9##).</p>", "<p id=\"P3\">RIPK3 expression is downregulated in many tumor types, suggesting that RIPK3 has important functions in tumor suppression (##REF##30157175##10##, ##UREF##0##11##). In support of its anti-tumor role, lower RIPK3 expression correlates with worsened patient survival in lung cancer (##REF##33889504##12##), chronic lymphocytic leukemia (##REF##31142279##13##), colon cancer (##REF##30904034##14##), malignant mesothelioma (##REF##33203643##15##), and breast cancer (##REF##33634619##16##). In contrast, tumor RIPK3 expression appears to be beneficial for tumor immune surveillance. For instance, expression of RIPK3 and other necroptotic adaptors in tumor cells was associated with improvement in CD8<sup>+</sup> T cell infiltration in hepatocellular carcinoma (##UREF##1##17##), cholangiocarcinoma (##REF##34083572##18##), and prostate cancer (##REF##33282964##19##).</p>", "<p id=\"P4\">The current gold standard approach for studying the immunogenicity of cell death <italic toggle=\"yes\">in vivo</italic> is to use dead or dying cells as a tumor vaccine to immunize syngeneic mice (##UREF##2##20##). Previous studies have demonstrated that RIPK3-induced necroptosis promotes dendritic cell (DC) cross-priming of tumor-specific CD8<sup>+</sup> T cells to achieve control of tumor growth. However, necroptosis was accompanied by strong NF-κB-dependent cytokine expression in these studies. Thus, it was not possible to distinguish the direct contribution of necroptosis-induced DAMPs release in the induction of anti-tumor immunity (##UREF##3##21##–##UREF##4##24##). Further, cell-intrinsic NF-κB signaling during necroptosis can also promote carcinogenesis (##REF##37329888##25##). Consequently, there remains a need to clarify the consequences of these distinct aspects of tumor RIPK3 signaling in the antitumor response.</p>", "<p id=\"P5\">We previously showed that in a doxycycline (DOX)-inducible system, RIPK3 expression accompanied by proteasome inhibition was sufficient to drive necroptosis in 3T3 fibroblasts. Here, we adopted this DOX-inducible system in tumor cells. In contrast to other models in which RIPK3 activation was achieved using chemical-induced dimerization of synthetic RIPK3 chimeric cassettes (##REF##26786097##3##), RIPK3 activation in our system was independent of RIPK1 and did not induce strong NFκB activation. By selectively restricting cell death to either RIPK3-dependent apoptosis or necroptosis, we found that immunization with necroptotic cells, but not apoptotic cells showed marked protection to subsequent tumor challenge. Surprisingly, immunization with necroptotic cells stimulated an anti-tumor CD4<sup>+</sup> T cell response while CD8<sup>+</sup> T cells were dispensable for tumor protection. The protection conferred by necroptotic cell immunization was observed with tumors from different tissue origin. Mechanistically, we showed that interferon beta (IFNβ) was specifically induced in necroptosis but not in apoptosis, and blocking cell death effectively eliminated this type I IFN response. Furthermore, the protective effect of necroptosis immunization was abrogated when host IFN signaling was inhibited by IFNAR deficiency. These data suggest that necroptosis in the absence of NF-κB dependent cytokine expression drives anti-tumor immunity through a distinct type I IFN and CD4<sup>+</sup> T cell dependent mechanism.</p>" ]	[ "<title>Methods</title>", "<title>Cell Lines</title>", "<p id=\"P20\">The LLC-OVA murine lung carcinoma cell line was generated as previously described (##REF##17942937##40##). LLC-OVA, B16-F1, and human embryonic kidney (HEK) 293T cells were maintained in Dulbecco’s Modified Eagle Medium (DMEM) supplemented with 10% fetal bovine serum (FBS), 2 mM L-glutamine, and 1% penicillin-streptomycin (complete DMEM). Transduced LLC-OVA cells were maintained in complete DMEM with 2 μg/ml puromycin. All cells were cultured at 37°C with 5% CO<sub>2</sub>.</p>", "<title>Lentiviral transduction and CRISPR-Cas9 gene editing</title>", "<p id=\"P21\">Lentivirus was generated through transfection of HEK 293 T Cells with packaging plasmids (pMD2.G and psPAX2 vectors) and previously described plasmids containing mouse wild type <italic toggle=\"yes\">Ripk3</italic> on a modified lentiviral tet-on pTRIPZ/Puro vector using the TransIT<sup>®</sup>-Lenti transfection reagent (Mirus Bio, Madison, WI, USA) (##REF##26786097##3##). After 48 hours, the culture supernatant was filtered with 0.45 μm cellulose acetate filters (VWR, Radnor, PA, USA) to collect RIPK3-encoding lentivirus. LLC-OVA cells were incubated with lentivirus in complete DMEM containing polybrene (8 μg/mL) for an additional 48 hours. Transduced cells were selected with 2 μg/ml puromycin.</p>", "<p id=\"P22\">For generating RIPK1, CASP8 and MLKL-deficient cell lines, the following guide RNA (gRNA) sequences were cloned into LentiCRISPRv2-Blast lentiviral vector [a gift from Mohan Babu; Addgene plasmid # 83480]: 5’- CAGACTGAGACACAGTCGAG-3’ (murine <italic toggle=\"yes\">Ripk1</italic> gRNA #1), 5’- TGTGAAAGTCACGATCAACG-3’ (murine <italic toggle=\"yes\">Ripk1</italic> gRNA #2), 5’-AGACGACACCCTTGTCACCG-3’ (murine <italic toggle=\"yes\">Casp8</italic> gRNA #1), 5’- AGATGTCAGGTCATAGATGG-3’ (murine <italic toggle=\"yes\">Casp8</italic> gRNA #2), 5’-CAAAGTATTCAACAACCCCC-3’ (murine <italic toggle=\"yes\">Mlkl</italic> gRNA #1), 5’- AGGAACATCTTGGACCTCCG-3’ (murine <italic toggle=\"yes\">Mlkl</italic> gRNA #2). Constructs were transduced into LLC OVA cells as described above and selected in Blasticidin (8 μg/mL).</p>", "<title>Mice</title>", "<p id=\"P23\">Age- and sex-matched mice of C57BL/6J background were used for these experiments unless otherwise specified. C57BL/6J (Stock No: 000664), OT-II (B6.Cg-Tg(TcraTcrb)425Cbn/J, Stock No: 004194), CD45.1 (B6.SJL-<italic toggle=\"yes\">Ptprc<sup>a</sup>Pepc<sup>b</sup></italic>/BoyJ, Stock No: 002014), and IFN-αR<sup>−/−</sup> (B6(Cg)-Ifnar1<sup>tm1.2Ees/</sup>J, Stock No: 028288), mice were purchased from The Jackson Laboratory (Bar Harbor, ME, USA). <italic toggle=\"yes\">Batf3</italic><sup>−/−</sup> mice (B6.129S(C)-<italic toggle=\"yes\">Batf3<sup>tm1Kmm</sup></italic>/J) were kindly provided by Dr. Dee Gunn (Stock No: 013755). All mice were housed in a specific pathogen-free (SPF) facility at Duke University and maintained according to protocols approved by the Duke University Institutional Animal Care and Use Committee.</p>", "<title>In vivo prophylactic dying tumor cell immunization</title>", "<p id=\"P24\">Tumor cells were seeded on 15-cm tissue culture dishes and cell death was induced <italic toggle=\"yes\">in vitro</italic> by treating cells with DOX (1 μg/ml) for 9 hours followed by MG132 (4 μM, APExBio, Houston, TX, USA) for 4.5 hours. Dying tumor cells were then collected, washed twice in PBS (Thermo Fisher Scientific, Waltham, MA, USA), then re-suspended at 7.5 × 10<sup>6</sup> cells/mL in PBS. Mice were immunized subcutaneously with 7.5 × 10<sup>5</sup> cells (100 μl) in the right flank. On day 8 after vaccination, mice were challenged subcutaneously on the left flank with 5 × 10<sup>5</sup> live tumor cells suspended in serum free-DMEM mixed 1:1 with Matrigel (Matrigel<sup>®</sup> Basement Membrane Matrix, LDEV-free, Corning Life Sciences, Tewksbury, MA, USA). Tumor growth on the challenge site was evaluated using calipers. Tumor volume was calculated using the formula: 0.5 × long axis × short axis<sup>2</sup>. Mice were euthanized if tumors exceeded 2000 mm<sup>3</sup>.</p>", "<title>In vivo antibody administration</title>", "<p id=\"P25\">For INFAR1 blockade, 1 mg of anti-IFNAR-1 antibody (clone MAR1–5A3, Bio X Cell, Lebanon, NH, USA) or isotype control was administered to mice intravenously via retroorbital injection the day prior to dying cell immunization. For T cell depletion, 350 μg of anti-CD8 (clone YTS 169.4, Bio X Cell), anti-CD4 (clone GK1.5, Bio X Cell), or Isotype control was administered to mice intravenously prior to dying cell immunization. Where indicated, an additional 150 μg of anti-CD8, anti-CD4, or Isotype control was administered on the day prior to dying cell immunization.</p>", "<title>OT-II adoptive transfer</title>", "<p id=\"P26\">For OT-II adoptive transfer experiments, spleens were collected from congenic OT-II mice, mechanically homogenized and filtered through a 70 μM cell strainer. Erythrocytes were then lysed using ACK Lysis Buffer (150 mM NH<sub>4</sub>Cl, 10 mM KHCO<sub>3</sub>, 0.1 mM Na<sub>2</sub>EDTA). Splenocytes were subsequently counted and the percentage of CD4<sup>+</sup> T cells was determined by flow cytometry. Splenocytes were resuspended in RPMI at 10 × 10<sup>6</sup> CD4<sup>+</sup> T cells/mL and 100 μl was administered to mice intravenously via retroorbital injection.</p>", "<title>Splenocyte co-culture with necroptotic cells</title>", "<p id=\"P27\">For expansion of endogenous myeloid populations <italic toggle=\"yes\">in vivo</italic>, mice were implanted subcutaneously on the flank with 2.5 × 10<sup>5</sup> cells Flt3L expressing B16 cells (B16-Flt3L) (##REF##14712275##41##). On day 14 post-tumor implantation, spleens were collected, minced and digested in HBSS with Ca and Mg (Thermo Fisher Scientific) + 5% FBS + 10mM HEPES + 2 mg/mL Type IV Collagenase (Sigma, St. Louis, MO, USA C-5138) + 10 IU/ml DNase I (Sigma D4263–1VL) for 30 minutes at 37°C. Spleens were then homogenized and filtered through a 70 μM cell strainer. Erythrocytes were then lysed using ACK Lysis Buffer (150 mM NH<sub>4</sub>Cl, 10 mM KHCO<sub>3</sub>, 0.1 mM Na<sub>2</sub>EDTA).</p>", "<p id=\"P28\">Cell death was induced in tumor cells with DOX (1 μg/ml) for 9 hours followed by MG132 (4 μM, APExBio) for 4.5 hours. Dying cells were collected for co-culture. Dying tumor cells and splenocytes were co-cultured at a 10:1 ratio in a 24 well plate with 0.5 mL of RPMI 1640 with 10% fetal bovine serum (FBS), 1% Non-essential Amino Acids, 1% sodium pyruvate, 2 mM L-glutamine, and 1% penicillin-streptomycin. Cells were harvested 8 and 24 hours later for flow cytometry analysis.</p>", "<title>Flow cytometry</title>", "<p id=\"P29\">Single cell suspensions were obtained from tumors by digesting minced tumor tissue in complete RPMI containing type IV collagenase (1 mg/ml, Sigma C-5138) and deoxyribonuclease I (20 IU/ml, Sigma D4263–1VL) at 37°C with gentle agitation for 30 minutes followed by tissue homogenization. Cell suspension was then passed through a 70 μM cell strainer. Erythrocytes were lysed using ACK Lysis Buffer. Two million cells were stained with LIVE/DEAD fixable aqua dead cell stain kit (Thermo Fisher Scientific) for 30 mins at 4°C. Cells were incubated with Fc-blocking antibody (clone 2.4G2) for 15 mins prior to incubation with fluorochrome-conjugated antibodies in 1x PBS, 2% FBS, and 2 mM EDTA at 4°C for 30 mins. Flow cytometry was performed on a BD Fortessa instrument. Analysis of flow cytometry data was done using FlowJo Treestar) software (version 10.8.1).</p>", "<p id=\"P30\">Cells were stained with the following antibodies: NK1.1 (PK136, FITC), CD11b (M1/70, PerCP-Cy5.5), CD11b (M1/70, PE-Cy7), CD19 (6D5, PE-Cy7), CD3 (17A2, APC), CD3 (17A2, FITC), I-A/I-E (M5/114.15.2, AlexaFluor 700), CD8β (YTS156.7.7, APC-Cy7), CD45–2 (104, Pacific Blue, CD45–2 (30-F11, BV605), CD45–1 (A20, FITC), Ly6C (HK1.4, BV605), B220 (RA3–6B2, BV650), XCR1 (ZET, BV785), CD11c (N418, PE), CD4 (GK1.5, PE-Cy5), Ly6G (1A8, PE-Dazzle594), F4/80 (BM8, PE-Cy7, Sirpα (P84, APC), CD80 (16–10A1, PE-Dazzle594), CD44 IM7, BV711), CD62L (MEL-14, PE), PD-1 (29F.1A12, PE-Cy7) from Biolegend (San Diego, CA, USA) and TCR-β (H57–597, APC) from eBiosciences (San Diego, CA, USA).</p>", "<title>NanoString RNA analysis and qRT-PCR</title>", "<p id=\"P31\">To assess tumor cell cytokine production, cell death was induced in tumor cells using DOX and MG132. Total RNA was isolated using the Qiagen RNeasy Mini Kit (Qiagen). For Nanostring analysis, RNA was run on a NanoString nCounter Pro Analysis System using an nCounter Mouse Tumor Signaling 360 Panel (Nanostring, Seattle, WA, USA). Data were normalized and analyzed using ROSALIND software (NanoString). We thank the Duke University School of Medicine for the use of the Microbiome Core Facility, which provided NanoString Gene Expression service. For qPCR, cDNA was synthesized using the iScripts cDNA synthesis kit (Bio-Rad 170–8891). Thermal cycling reaction was then performed using iQ<sup>™</sup> SYBR<sup>®</sup> Green Supermix (Bio-Rad, Hercules, CA, USA 170–8882) and a CFX Connect Real-Time PCR Detection System (Bio-Rad). Cycle threshold (CT) values for target genes were normalized to CT values of the housekeeping gene <italic toggle=\"yes\">Tbp1</italic> (ΔCT = CT(Target) − CT(Tbp1)) and subsequently normalized to baseline control values (ΔΔCT = ΔCT(Experimental) − ΔCT(Control)).</p>", "<title>Primers:</title>", "<title>RNAseq</title>", "<p id=\"P32\">Total RNA was extracted from single cell suspensions from tumor tissues. A mRNA library was prepared using the DNBSEQ platform by BGI with data filtering using the SOAPnuke software (##UREF##7##42##). HISAT2 was selected to map the filtered sequenced reads to the reference genome. BAM files containing mapping results were counted using the featureCounts function using Python. Counting was performed using the mouse genome for comparison. Downstream analyses were performed using iDEP.96 web interface (##REF##30567491##43##). DEG analysis was then performed using DESeq2 considering all genes with FDR ≤ 0.1 and 1 ≤ Log2FC ≤ −1. Functional analysis of genes with FDR ≤ 0.1, regardless of Log2FC, comprised of GO and GSEA (Gene Set Enrichment Analysis) analyses. For GSEA, gene sets used in this assessment included curated gene sets, known pathways (KEGG), and gene ontology terms (Biological Process & Molecular Function).</p>", "<title>Incucyte Cell Death Assays</title>", "<p id=\"P33\">Cells were seeded in a 96-well plate with 10,000 cells per well in 200 μl complete growth medium. Eight hours prior to cell death initiation, medium was exchanged for Complete DMEM with DOX (1 μg/ml) or DMSO. Cells were subsequently treated with MG132 (4 μM, APExBio). Imaging was subsequently performed using the IncuCyte S3 (Sartorius, Göttingen, Germany; version 2021C). Nine images per well were captured, analyzed, and averaged. Cell death was assessed through measuring uptake of YoYo-1 (50 nM, Thermo Fisher Scientific) and expressed as the area of YoYo-1<sup>+</sup> cells as a percentage of the total phase area. In experiments where zVAD-fmk and GSK’843 were used, zVAD-fmk (20 μM, APExBio) was administered 30 minutes prior to treatment with GSK’843 (20 μM, Sigma) and MG132.</p>", "<title>Western blot</title>", "<p id=\"P34\">Cell lysates were prepared in RIPA buffer containing 0.15 M NaCl, 0.05 M Tris (pH 8.0), 0.1% SDS, 0.5% Sodium deoxycholate, and 1% Nonidet P-40 supplemented with Protease (Roche, Basel, Switzerland11836145001) and Phosphatase inhibitor cocktails (Sigma P5726). Protein concentration was determined using a BCA Protein Assay (Thermo Fisher Scientific). The proteins were separated by SDS-PAGE and transferred to nitrocellulose membranes. Primary antibodies used were anti-MLKL phospho-S345 (Cell Signaling Technology, Danvers, MA, USA 37333), anti-MLKL (Cell Signaling Technology, 37705), anti-RIPK3 phospho-S232 (Abcam, Cambridge, United Kingdom, ab195117), anti-RIPK3 (Genentech, San Francisco, CA, USA, PUR135347), anti-RIPK3 (Prosci, Poway, CA, USA, 2283), anti-RIPK1 (BD Biosciences, Franklin Lakes, NJ, USA, 610459), anti-cleaved caspase-3 (Cell Signaling Technology, 9664), anti-caspase-8 (Enzo, Farmingdale, NY, USA, ALX-804-447-C100), anti-p65 phospho-S536 (Cell Signaling Technology, 3033), anti-p65 (Santa Cruz Biotechnology, Dallas, TX, USA sc-8008), anti-IκBα phospho-S32/36 (Cell Signaling Technology, 9246), anti-IκBα (Cell Signaling Technology, 4814), anti-Actin (Cell Signaling Technology, 3700). HRP-conjugated goat anti-rabbit immunoglobulin G (IgG) (111-035-144), rabbit anti-mouse IgG (315-035-008) or goat anti-rat IgG (112-035-175) were obtained from Jackson ImmunoResearch Laboratories Inc (West Grove, PA, USA). After incubation with the appropriate secondary antibodies, membranes were incubated with Clarity ECL western blotting substrate (Bio-Rad, 170–5061) or Clarity Max ECL (Bio-Rad, 170–5062).</p>", "<title>Statistics</title>", "<p id=\"P35\">Statistical analysis was performed in GraphPad Prism (version 9). Unpaired two-tailed Student’s t-test was used to compare two independent groups. Tukey’s multiple comparison test, or one-way Analysis of variance (ANOVA) or two-way ANOVA were used to compare multiple (> 2) groups with one or two independent variables, respectively; with multiple comparisons tests as indicated. P values > 0.05 were considered statistically non-significant. p value < 0.05, p value < 0.01, p value < 0.001, **p value < 0.0001.</p>" ]	[ "<title>Results</title>", "<title>Expression of RIPK3 accompanied by proteasome inhibition drives tumor cell necroptosis</title>", "<p id=\"P6\">RIPK3 expression is frequently inhibited in tumor cells through promoter hypermethylation (##REF##16435073##26##). To explore whether re-expression of RIPK3 might enhance anti-tumor immune surveillance, we first attempted to restore expression of endogenous RIPK3 in Lewis Lung Carcinoma cells expressing chicken ovalbumin (LLC-OVA) using the DNA methyltransferase inhibitor 5-AZA-dC. Although 5-AZA-dC successfully restored RIPK3 expression, its toxicity prevented further exploration of cell death responses (##FIG##0##Fig. 1a##\n<bold>and data not shown)</bold>. We therefore opted to utilize a doxycycline (DOX)-inducible system to restore RIPK3 expression in LLC-OVA cells. Consistent with previous report (##REF##26786097##3##), DOX-induced expression of RIPK3 was not sufficient to cause cell death of the LLC-OVA cells. However, addition of the proteasome inhibitor MG132 to prevent RIPK3 degradation led to strong cell death in DOX-induced LLC-OVA cells (##FIG##0##Fig. 1b##). This cell death was dependent on RIPK3 expression, since MG132 alone did not compromise cell survival. Increased phosphorylation of MLKL (pMLKL) was observed in DOX- and MG132-treated cells, indicating that necroptosis was the dominant form of cell death (##FIG##0##Fig. 1c##). The RIPK3 kinase inhibitor GSK’843 did not inhibit cell death (##FIG##0##Fig. 1d##), although it effectively inhibited pMLKL (##FIG##0##Fig. 1e##). Rather, GSK’843 increased caspase-3 cleavage (##FIG##0##Fig. 1e##), suggesting a switch from necroptosis to apoptosis (##REF##25459880##6##). Indeed, co-treatment with GSK’843 and the pan-caspase inhibitor zVAD-fmk largely abrogated pMLKL, caspase-3 cleavage and cell death (##FIG##0##Fig. 1d##–##FIG##0##e##). Since pMLKL and caspase-3 cleavage required DOX-induced RIPK3 expression (##FIG##0##Fig. 1e##), these results indicate that RIPK3 has the capacity to promote necroptosis as well as apoptosis.</p>", "<title>Selective induction of RIPK3-dependent tumor cell necroptosis or apoptosis</title>", "<p id=\"P7\">The switch from necroptosis to apoptosis with RIPK3 kinase inhibitor revealed a possible method to manipulate RIPK3-dependent cell death. To circumvent the off-target effects of chemical inhibitors, we utilized the CRISPR/Cas9 system to inactivate either <italic toggle=\"yes\">caspase-8</italic> (Casp8) or <italic toggle=\"yes\">Mlkl</italic> in the DOX-inducible LLC-OVA cells (WT). Casp8-KO and MLKL-KO cells underwent DOX- and MG132-induced cell death with similar kinetics and magnitude when compared to WT cells (##FIG##0##Fig. 1f##). As in the case of RIPK3 kinase inhibition, MLKL-KO cells exhibited increased caspase-3 cleavage, indicating a switch from necroptosis to apoptosis (##FIG##0##Fig. 1g##). By contrast, Casp8-KO retained pMLKL but did not exhibit any caspase-3 cleavage (##FIG##0##Fig. 1g##). Knockout of both <italic toggle=\"yes\">Mlkl</italic> and <italic toggle=\"yes\">Casp8</italic> completely abrogated cell death (##FIG##0##Fig. 1h##–##FIG##0##i##). Hence, selective activation of RIPK3-dependent necroptosis and apoptosis was achieved by genetic inactivation of <italic toggle=\"yes\">Casp8</italic> and <italic toggle=\"yes\">Mlkl</italic> respectively (##FIG##0##Fig. 1j##).</p>", "<title>RIPK3-induced tumor cell death does not elicit strong cytokine response</title>", "<p id=\"P8\">Several studies have shown that chemical-induced dimerization of RIPK3 concomitantly led to necroptosis and strong RIPK1-mediated, NFκB-dependent cytokine expression (##REF##26405229##22##, ##REF##27050509##23##). In contrast to these studies, Nanostring analysis revealed that cytokine expression was largely undetectable in DOX- and MG132-treated WT, Casp8-KO, and MLKL-KO LLC-OVA cells (##FIG##1##Fig. 2a##–##FIG##1##b##). While <italic toggle=\"yes\">Ccl2</italic> was the only detectable cytokine in the Nanostring panel (##FIG##1##Fig. 2a##), its expression was not enhanced by RIPK3 expression nor cell death (##FIG##1##Fig. 2b##; <bold>Supplemental Fig. 1)</bold>. In fact, qPCR analysis revealed that MG132 modestly reduced <italic toggle=\"yes\">Ccl2</italic> expression (##FIG##1##Fig. 2c##).</p>", "<p id=\"P9\">Yatim and colleagues found that chemical-induced RIPK3 dimerization led to RHIM-dependent recruitment and activation of RIPK1 and cytokine expression (##REF##26405229##22##). However, NF-κB activation as determined by IκBα phosphorylation (p-IκBα) and IκBα degradation was minimal and independent of RIPK3 expression (##FIG##1##Fig. 2d##). CRISPR/Cas9 knockout of <italic toggle=\"yes\">Ripk1</italic> (RIPK1-KO) did not affect DOX- and MG132-induced cell death (##FIG##1##Fig. 2e##–##FIG##1##f##), indicating that necroptosis in our LLC-OVA cells was RIPK1-independent. Taken together, these data indicate that RIPK1- and NF-κB-dependent cytokine production was absent in our necroptosis induction system.</p>", "<title>Immunization with necroptotic cells protects against tumor challenge</title>", "<p id=\"P10\">Immunogenic cell death (ICD) such as necroptosis was widely thought to stimulate immune responses through the release of DAMPs. We tested this premise using our LLC-OVA cell lines. Casp8-KO or MLKL-KO LLC-OVA cells were treated with DOX and MG132 to induce necroptosis and apoptosis respectively, and co-cultured with splenic DCs from Flt3L-treated mice. We found that necroptotic cells (NEC) bolstered expression of the costimulatory molecule CD80 on multiple DC subsets and monocytes compared to either apoptotic cells (APOP) or untreated controls (##FIG##2##Fig. 3a##–##FIG##2##b##, <bold>Supplemental Fig. 2)</bold>, consistent with the notion that necroptosis is more immunogenic than apoptosis.</p>", "<p id=\"P11\">We next immunized mice with NEC or APOP subcutaneously followed by challenge with live LLC-OVA cells on the opposite flank eight days post-immunization (##FIG##2##Fig. 3c##). Importantly, when compared to PBS or APOP-immunized groups, tumor growth was significantly blunted by NEC immunization (##FIG##2##Fig. 3d##). NEC immunization similarly protected the hosts from subsequent challenge with B16-F1 melanoma (##FIG##2##Fig. 3e##). Hence, NEC immunization provides protection against tumors of different tissue origin. NEC immunization increased overall T cell infiltration in the tumor (##FIG##2##Fig. 3f##). In contrast to previous studies in which necroptosis was shown to be superior in promoting CD8<sup>+</sup> T cell responses (##UREF##3##21##–##REF##27050509##23##), effector/memory CD8<sup>+</sup> T cell (CD8<sup>+</sup>CD44<sup>hi</sup>CD62L<sup>−</sup>) infiltration was similar between NEC and APOP immunization groups (##FIG##2##Fig. 3g##). By contrast, overall CD4<sup>+</sup> T cell and effector CD4<sup>+</sup>CD44<sup>hi</sup>CD62L<sup>−</sup> T cell infiltration was significantly elevated in NEC-immunized mice compared to APOP-immunized or PBS-treated mice (##FIG##2##Fig. 3h##).</p>", "<title>Necroptosis immunization stimulates CD4<sup>+</sup> T cell-dependent anti-tumor immunity</title>", "<p id=\"P12\">Increase in CD8<sup>+</sup> and CD4<sup>+</sup> T cell infiltration in NEC-immunized mice in comparison to PBS control group was already evident on day 5 post-implantation when tumors were first palpable (##FIG##3##Fig. 4a##–##FIG##3##b##). This is in contrast to tumor myeloid populations, which were comparable at this timepoint <bold>(Supplemental Fig. 3)</bold>. To test the contribution of CD4<sup>+</sup> and CD8<sup>+</sup> T cells in the protective effect conferred by NEC immunization, we used antibodies to deplete these populations. Importantly, only the depletion of CD4<sup>+</sup> cells, but not CD8<sup>+</sup> cells prior to immunization was able to abrogate the tumor protection by NEC immunization (##FIG##3##Fig. 4c##). Consistent with the dispensable role of CD8<sup>+</sup> T cell in this process, disrupting cDC1-dependent cross-priming of CD8<sup>+</sup> T cells using <italic toggle=\"yes\">Batf3</italic>-deficient hosts did not affect the tumor suppressive effect of NEC immunization (##FIG##3##Fig. 4d##). In aggregate, these data suggest that NEC immunization confers anti-tumor restriction through CD4<sup>+</sup> T cells and independent of CD8<sup>+</sup> T cells.</p>", "<title>Full CD4<sup>+</sup> T cell priming requires secondary challenge with live tumor cells</title>", "<p id=\"P13\">To test whether CD4<sup>+</sup> T cell priming occurs during the immunization phase, we adoptively transferred CD4<sup>+</sup> T cells from OT-II mice and found that NEC immunization did not enhance OT-II CD4<sup>+</sup> T cells accumulation in the draining lymph node (<bold>Supplemental Fig. 4a)</bold>, even after a second immunization with NEC cells <bold>(Supplemental Fig. 4b-c)</bold>. In contrast, increased CD4<sup>+</sup> OT-II T cells accumulation in response to NEC immunization was detected in the draining lymph nodes (##FIG##4##Fig. 5b##–##FIG##4##c##) and tumor (##FIG##4##Fig. 5d##–##FIG##4##f##) when OT-II T cells were adoptively transferred the day prior to tumor challenge (##FIG##4##Fig. 5a##). This effect was evident whether the host received a single or multiple doses of NEC immunization <bold>(Supplemental Fig. 4d-e)</bold>.</p>", "<title>Type I interferon mediates protection conferred by necroptosis immunization</title>", "<p id=\"P14\">To interrogate the mechanism by which NEC immunization conferred protection against tumor challenge, we performed bulk RNA sequencing on the tumor tissues. We found that the gene expression largely clustered based on the different immunization regimen (##FIG##5##Fig. 6a##). In comparing the top differentially expressed genes between the NEC- and APOP-immunized groups, we found that the majority were interferon stimulated genes (ISGs) (##FIG##5##Fig. 6b##). Gene Set Enrichment Analysis (GSEA) further confirmed that NEC-immunized tumors showed enrichment for genes involved in response to interferon-beta (IFN-β) (##FIG##5##Fig. 6c##–##FIG##5##d##). Increased expression of several ISGs in tumors from NEC-immunized mice compared to those from APOP-immunized mice was further validated by qPCR (##FIG##5##Fig. 6e##).</p>", "<p id=\"P15\">We next sought to determine whether this IFN response might have originated from the dying tumor cells. Strikingly, we detected production of IFN-β and ISGs in necroptotic cells, but not apoptotic or DKO cells that lack Casp8 and MLKL. (##FIG##6##Fig. 7a##). Moreover, NEC immunization in <italic toggle=\"yes\">Ifnar1</italic><sup>−/−</sup> mice failed to improve tumor control (##FIG##6##Fig. 7b##). Furthermore, IFNAR neutralizing antibody also abrogated the NEC immunization-mediated protection (##FIG##6##Fig. 7c##). Consistent with the notion that CD4<sup>+</sup> T cells are critical for NEC immunization-mediated tumor control, CD4<sup>+</sup> T cell infiltration was reduced in <italic toggle=\"yes\">Ifnar1</italic><sup>−/−</sup> mice compared to WT controls (##FIG##6##Fig. 7d##). By contrast, NEC-induced tumor suppression and CD4<sup>+</sup> T cell infiltration was comparable between WT and <italic toggle=\"yes\">Ifngr</italic><sup>−/−</sup> mice <bold>(Supplemental Fig. 5)</bold>. These data suggest that necroptosis stimulates cell–intrinsic IFN-β production to initiate a cascade of reaction that triggers host type I IFN signaling to bolster anti-tumor CD4<sup>+</sup> T cell responses.</p>" ]	[ "<title>Discussion</title>", "<p id=\"P16\">RIPK3 signaling can stimulate necroptosis, apoptosis, and death-independent inflammatory cytokine production. To further complicate matters, DAMPs release from dying cells can also promote inflammatory gene expression. The difficulty in separating these diverse signaling events has led to conflicting reports on the role of RIPK3 signaling in anti-tumor immunity. Here, we utilized a system to drive RIPK3-dependent necroptosis or apoptosis without strong death-independent cytokine expression to interrogate the impact of necroptosis-associated DAMPs release in tumor immunity. Using this system, we observed that prophylactic immunization with necroptotic cells was sufficient to drive protective antitumor CD4<sup>+</sup> T cell responses. Although both CD8<sup>+</sup> and CD4<sup>+</sup> T cell infiltration was enhanced, depletion experiments revealed that only CD4<sup>+</sup> T cells were indispensable for this protective effect. The enhanced recruitment of CD4<sup>+</sup> T cells is dependent on host type I interferon signaling, consistent with the well-known role of interferon in anti-tumor response.</p>", "<p id=\"P17\">Our results differ from several previous studies in which chemical-induced dimerization of chimeric RIPK3 fusion proteins led to necroptosis and concomitant RIPK1-dependent cytokine expression (##UREF##3##21##–##REF##27050509##23##). The lack of RIPK1 engagement and cytokine expression in our system might be due to the use of native RIPK3 rather than RIPK3 fusion cassettes, which causes a lower level of RIPK3 nucleation. Due to the natural turnover of RIPK3, proteasome inhibition is required to elicit cell death and to unleash full protective antitumor immune responses. Since our system does not require expression of a foreign dimerization cassette, it also avoids issues such as immune reaction against exogenously introduced protein antigens.</p>", "<p id=\"P18\">Our results differ from the traditional view that ICD mainly stimulates DC cross-priming of CD8<sup>+</sup> T cells to promote tumor protection (##UREF##5##27##, ##REF##31646070##28##). CD4<sup>+</sup> T cells have been described to contribute to anti-tumor immunity through a variety of mechanisms including direct killing of tumor cells, augmenting the tumor microenvironment through local secretion of effector cytokines, and providing help to CD8<sup>+</sup> T cells (##REF##20156971##29##–##REF##37402168##33##). Our study complement prior reports that necroptotic signaling in tumors (##REF##32923163##34##) and cardiac allografts (##REF##29633982##35##) can bolster effector CD4<sup>+</sup> T cell responses. Thus, different methods of ICD induction can elicit distinct mechanisms to confer anti-tumor immunity.</p>", "<p id=\"P19\">Consistent with the importance of type I IFN in anti-tumor immunity (##UREF##4##24##, ##REF##36449659##36##, ##REF##34613770##37##), type I IFN signaling was also critical for the protection conferred by necroptosis immunization. This type I IFN response likely originates from the necroptotic cells in a cell death-dependent manner, since the modest induction of IFNβ and ISGs was abrogated when cell death was inhibited by <italic toggle=\"yes\">Mlkl</italic> inactivation. How might necroptosis promote this interferon response? Recent reports have shown that mitochondrial DNA (mtDNA) accumulates in the cytosol when necroptosis was induced in tumors in response to irradiation and Cisplatin (##REF##36449659##36##, ##REF##34613770##37##). The release of cytosolic mtDNA instigated tumor-intrinsic production of IFNβ and ISGs via the cGAS/STING pathway (##REF##36449659##36##, ##REF##34613770##37##). In this regard, it is noteworthy that MLKL can translocate to the nuclear and mitochondrial membranes during necroptosis (##REF##32200799##38##, ##REF##29358703##39##). It is tempting to speculate that MLKL-dependent pore formation may facilitate release of mtDNA to stimulate cGAS/STING, which in turn induces the first wave of IFN within the necroptotic tumor cells. However, the precise role of MLKL in mediating these effects remains to be elucidated. Further, since type I IFN signaling in the host is also required for protection mediated by NEC immunization, our data support a model in which the initial wave of IFN signal continues to propagate in the host after clearance of dying necroptotic cells to achieve optimal anti-tumor effects. However, the cellular mediators of this host response remain an open question. Nevertheless, our data suggests that, cell-intrinsic type I interferon signaling plays a key role in promoting the immunologic consequences of necroptosis.</p>" ]	[]	[ "<p id=\"P1\">Necroptosis is an inflammatory form of cell suicide that critically depends on the kinase activity of Receptor Interacting Protein Kinase 3 (RIPK3). Previous studies showed that immunization with necroptotic cells conferred protection against subsequent tumor challenge. Since RIPK3 can also promote apoptosis and NF-κB-dependent inflammation, it remains difficult to determine the contribution of necroptosis-associated release of damage-associated molecular patterns (DAMPs) in anti-tumor immunity. Here, we describe a system that allows us to selectively induce RIPK3-dependent necroptosis or apoptosis with minimal NF-κB-dependent inflammatory cytokine expression. In a syngeneic tumor challenge model, immunization with necroptotic cells conferred superior protection against subsequent tumor challenge. Surprisingly, this protective effect required CD4<sup>+</sup> T cells rather than CD8<sup>+</sup> T cells and is dependent on host type I interferon signaling. Our results provide evidence that death-dependent type I interferon production following necroptosis is sufficient to elicit protective anti-tumor immunity.</p>" ]	[]	[ "<title>Acknowledgement</title>", "<p id=\"P36\">This work is supported by departmental startup fund from the Department of Immunology, Duke University School of Medicine. AJR is supported by a National Institutes of Health (NIH) T32 grant 5T32AI141342 in immunobiology; CP was supported by NIH grant AI007349; EAM is partially supported by R01NS121067 and R01AI148302. FKM is supported by grants from the National Science Foundation of China (32350710189) and Department of Science and Technology of Zhejiang Province (2023ZY1015).</p>" ]	[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><title>RIPK3-expressing tumor cells undergo necroptosis or RIPK3-dependent apoptosis upon proteasome inhibition.</title><p id=\"P38\"><bold>(a)</bold>LLC-OVA cells were treated with DMSO or 5-AZA-dC (30 mM) for 72 hours prior to lysing cells for western blot. Data is representative of two independent experiments. <bold>(b-c)</bold> Transduced LLC-OVA cells (WT) were treated with DMSO or DOX (1 mg/mL) for 8 hours prior to treatment with either DMSO or MG132 (4 mM). <bold>(b).</bold> Cell death was measured by tracking YoYo1 (50 nM) uptake in cells via Incucyte. <bold>(c)</bold> Cell lysates were collected 4 hours after treatment with MG132 for western blot. For <bold>b-c</bold>, data is representative of greater than three independent experiments. <bold>(d-e)</bold> WT cells were treated with DMSO or DOX (1 mg/mL) for 7 hours, cells were pre-treated with ZVAD (20 mM) 30 mins prior to GSK’843 (20 mM) then MG132 (4 mM) after another 30 mins. <bold>(d)</bold> Cell death was monitored via Incucyte. <bold>(e)</bold> Cell lysates were collected 4 hours after MG132 treatment for western blot. For <bold>d-e</bold>, data is representative of two independent experiments. <bold>(f-g)</bold> WT, Casp8-KO, and MLKL-KO LLC-OVA cells were treated for 8 hours with DOX (1 mg/mL) followed by treatment with MG132 (4 mM). <bold>(f)</bold>Cell death measured via Incucyte. <bold>(g)</bold> Cell lysates were collected at 4 hours following treatment with MG132 for western blot. For <bold>f-g</bold>, data is representative of two independent experiments. <bold>(h-i)</bold> WT or MLKL- and Casp8-KO (DKO) cells were treated for 8 hours with DOX (1 mg/mL) followed by treatment with MG132 (4 mM). <bold>(h)</bold> Cell death measured via Incucyte. <bold>(i)</bold> Cell lysates were collected at 4 hours following treatment with MG132 for western blot. For <bold>h-i</bold>, data is from a single experiment. <bold>(j)</bold> Graphical summary of RIPK3-dependent cell death in tumor cells following proteasome inhibition. Image was created with <ext-link xlink:href=\"https://www.biorender.com/\" ext-link-type=\"uri\">BioRender.com</ext-link>.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><title>Cell death following proteasome inhibition occurs in the absence of RIPK1 and NFkB-dependent cytokine production.</title><p id=\"P39\"><bold>(a-b)</bold> WT, Casp8-KO, and MLKL-KO LLC-OVA cells were treated for 8 hours with DOX (1 mg/mL) followed by treatment with MG132 (4 mM). Four hours after treatment with MG132, RNA was prepped using tumor cell lysates. Tumor cytokine expression was measure via Nanostring using the mouse tumor 360 signaling panel. <bold>(a)</bold> Cytokine production in tumor cells with DOX + MG132. <bold>(b)</bold> Tumor cytokine production in WT cells with DMSO, MG132, DOX + DMSO, or DOX + MG132. For <bold>a-b</bold>, data is from a single experiment. The red solid line indicates the mean read count for the negative controls and the dashed black line indicates the mean read count for the lowest positive control. <bold>(c-d)</bold> WT cells were treated in a similar fashion to <bold>a-b</bold>. Four hours after treatment with MG132, RNA was prepared from tumor cell lysates for qPCR. <bold>(c)</bold> Gene expression for <italic toggle=\"yes\">Ccl2</italic> and <italic toggle=\"yes\">Cxcl1</italic> following treatments as indicated. Each point represents an average of technical replicates from an individual experiment. <bold>(d)</bold> WT cells were treated with DMSO or DOX (1 mg/mL) 8 hours prior to treatment with MG132 (4 mM). Cell lysates were collected immediately, 30 mins, 1 hour, 2 hours, and 6 hours after treatment with MG132 for western blot. Data is representative of two independent experiments. <bold>(e-f)</bold> RIPK1 knockout in WT cells was performed using the CRISPR/Cas9 system. RIPK1 KO and WT cells were then treated with MG132 (4 mM) following an 8-hour induction with DOX (1 mg/mL). <bold>(e)</bold> Lysates were collected 4 hours following treatment with MG132 for western blot. Data is a from a single experiment. <bold>(f)</bold> Cell death was measured using Incucyte. Data is representative of two independent experiments. For <bold>c</bold>, treatment groups were compared using two-way ANOVA. P < 0.05, P < 0.01, P < 0.001, and *P < 0.0001.</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3</label><caption><title>Necroptotic dying cells stimulate superior protection against tumor challenge than apoptotic dying cells.</title><p id=\"P40\"><bold>(a-b)</bold> Splenocytes from B16-Flt3L tumor bearing mice were co-cultured with dying cells. CD80 Mean Fluorescence Intensity (MFI) was then assessed on splenic myeloid cells by flow cytometry at 8 hours <bold>(a)</bold> and 24 hours <bold>(b)</bold> post-initiation of co-culture. Data is representative of two independent experiments (n = 3 per experiment). <bold>(c)</bold> Schematic of experimental model for assessing anti-tumor immune response following dying cell immunization. Image was created with <ext-link xlink:href=\"https://www.biorender.com/\" ext-link-type=\"uri\">BioRender.com</ext-link>. <bold>(d-g)</bold> NEC, APOP or PBS was injected into the right flank of mice 8 days prior to challenge with live LLC-OVA cells. <bold>(d)</bold> Tumor volume was assessed. Plot represents aggregated data from 4 independent experiments (n = 4 – 5 per treatment group per experiment). <bold>(e)</bold> Mice were immunized with NEC LLC-OVA or PBS followed by challenge with live LLC-OVA or B16-F1 melanoma as indicated. <bold>(f-h)</bold> Tumors were harvested at day 10 post-tumor challenge to assess for tumor infiltration by total <bold>(f)</bold> T cells, <bold>(g)</bold> CD8<sup>+</sup> T cells and Teff cells (CD44<sup>Hi</sup>CD62L<sup>−</sup>), and <bold>(h)</bold> CD4<sup>+</sup> T cells and Teff cells per gram of tumor via flow cytometry. Data aggregated from two independent experiments (n = 4 – 5 per treatment group per experiment). For <bold>a-b and e-h</bold>, treatment groups were compared using one-way ANOVA. For <bold>d,</bold> treatment groups were compared using two-way ANOVA. P < 0.05, P < 0.01, P < 0.001, and **P < 0.0001.</p></caption></fig>", "<fig position=\"float\" id=\"F4\"><label>Figure 4</label><caption><title>Tumor restriction with necroptotic dying cell immunization is CD4+ T cell dependent.</title><p id=\"P41\"><bold>(a-b)</bold> Quantification of total numbers of <bold>(a)</bold> CD8<sup>+</sup> T cells and <bold>(b)</bold> CD4<sup>+</sup> T cells per gram of tumor from tumors of NEC or PBS immunized mice at day 5 post tumor challenge. Treatment groups were compared using unpaired Student’s t-test. Data is aggregated from two independent experiments (n = 4 – 5 per treatment group per experiment). <bold>(c)</bold> Mice received anti-CD8a, anti-CD4, or Isotype control antibody prior to immunization with NEC or PBS followed 8 days later by challenge with live tumor cells. Data is aggregated from two independent experiments (n = 4 – 5 per treatment group per experiment). <bold>(d)</bold> NEC immunization and subsequent live tumor challenge was performed in WT or <italic toggle=\"yes\">Batf3</italic><sup>−/−</sup> mice with tumor volume assessment at the indicated timepoints. Data is aggregated from two independent experiments (n = 5 – 6 per treatment group per experiment). For <bold>c-d</bold>, treatment groups were compared using two-way ANOVA. For <bold>f</bold>, treatment groups were compared using one-way ANOVA. P < 0.05, P < 0.01, P < 0.001, and **P < 0.0001</p></caption></fig>", "<fig position=\"float\" id=\"F5\"><label>Figure 5</label><caption><title>Necroptotic dying cell immunization results in dying cell antigen-independent tumor suppression.</title><p id=\"P42\"><bold>(a)</bold> Mice were immunized with NEC and boosted with a second immunization two days later (2x NEC). Mice subsequently received OT-II cells five days later followed by challenge with live tumor challenge the next day. At day 9 post-tumor implant the tumor and draining inguinal lymph nodes from the tumor (tdLN) and immunization site (immLN) were collected for flow cytometry. Image was created with <ext-link xlink:href=\"https://www.biorender.com/\" ext-link-type=\"uri\">BioRender.com</ext-link>. <bold>(b)</bold> Total count and <bold>(c)</bold> % of CD44<sup>Hi</sup> for OT-II CD4<sup>+</sup> T cells found in the lymph nodes as indicated. Treatment groups were compared using two-way ANOVA. <bold>(d)</bold> Total count per gram of tumor, <bold>(e)</bold> % of CD44<sup>Hi</sup> and <bold>(f)</bold> % of PD-1<sup>Hi</sup> for tumor OT-II CD4<sup>+</sup> T cells. Treatment groups were compared using unpaired Student’s t-test. For <bold>a-f</bold>, data is representative of two independent experiments (n = 3 – 4 per treatment group per experiment). Plot represents aggregated data from two independent experiments (n = 4 – 5 per treatment group per experiment). Treatment groups were compared using two-way ANOVA. P < 0.05, P < 0.01, P < 0.001, and **P < 0.0001.</p></caption></fig>", "<fig position=\"float\" id=\"F6\"><label>Figure 6</label><caption><title>Immunization with necroptotic dying cells induces a type I interferon gene signature in the tumor.</title><p id=\"P43\"><bold>(a-d)</bold>Mice were immunized with NEC, APOP, or PBS followed 8 days later by challenge with live tumor cells. At day 14 post-tumor challenge, tumors were harvested, and RNA was prepared from tumor single cell suspensions. Bulk RNA-seq was subsequently performed. <bold>(a)</bold> Principal component analysis of tumor samples. <bold>(b)</bold> Top differentially expressed genes for NEC- vs. APOP-immunized tumors. <bold>(c)</bold> GSEA pathway analysis for NEC- vs. APOP-immunized tumors. <bold>(d)</bold> Heatmap for “Response to interferon beta” GO term in NEC- and APOP-immunized tumor samples. <bold>(e)</bold> Tumor RNA was used for qPCR using primers for the indicated ISGs. Data is from a single experiment (n = 4 per treatment group). Treatment groups were compared using one-way ANOVA. P < 0.05, P < 0.01, P < 0.001, and **P < 0.0001.</p></caption></fig>", "<fig position=\"float\" id=\"F7\"><label>Figure 7</label><caption><title>Anti-tumor immunity induced by necroptotic dying cells is abrogated with loss of type I interferon signaling.</title><p id=\"P44\"><bold>(a)</bold> WT, Casp8-KO, MLKL-KO, and DKO LLC-OVA cells were treated for 8 hours with DOX (1 mg/mL) followed by treatment with MG132 (4 mM). Four hours after treatment with MG132, RNA was prepped using tumor cell lysates. Gene expression for <italic toggle=\"yes\">Ifnb</italic>, <italic toggle=\"yes\">Ifi44</italic>, and <italic toggle=\"yes\">Ifit1</italic> was assessed by qPCR as indicated Each point represents an average of technical replicates from an individual experiment. <bold>(b)</bold> Dying cell immunization and subsequent live tumor challenge was performed in WT or <italic toggle=\"yes\">Ifnar1</italic><sup>−/−</sup> mice. Data is aggregated from two independent experiments (n = 4 – 5 per treatment group per experiment). <bold>(c)</bold> Anti-IFNAR1 antibody or Isotype control was administered the day prior to necroptotic cell immunization. Live tumor challenge was performed 8 days post-immunization. Data is representative of three independent experiments (n = 3 – 5 per treatment group per experiment). <bold>(d)</bold> T cell infiltrate was assessed in tumors at day 14 post-tumor challenge in in WT or <italic toggle=\"yes\">Ifnar1</italic><sup>−/−</sup> mice. Data is aggregated from two independent experiments (n = 4 – 5 per treatment group per experiment). For <bold>a & d,</bold> treatment groups were compared using one-way ANOVA. For <bold>b & c,</bold> treatment groups were compared using two-way ANOVA. P < 0.05, P < 0.01, P < 0.001, and ***P < 0.0001.</p></caption></fig>" ]	[ "<table-wrap position=\"anchor\" id=\"T1\"><table frame=\"box\" rules=\"rows\"><colgroup span=\"1\"><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/></colgroup><tbody><tr><td rowspan=\"2\" align=\"left\" valign=\"middle\" colspan=\"1\">\n<italic toggle=\"yes\">Mtbp</italic>\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Forward</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CAAACCCAGAATTGTTCTCCTT</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Reverse</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ATGTGGTCTTCCTGAATCCCT</td></tr><tr><td rowspan=\"2\" align=\"left\" valign=\"middle\" colspan=\"1\">\n<italic toggle=\"yes\">Tnf</italic>\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Forward</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CCCACTCTGACCCCTTTACT</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Reverse</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">TTTGAGTCCTTGATGGTGGT</td></tr><tr><td rowspan=\"2\" align=\"left\" valign=\"middle\" colspan=\"1\">\n<italic toggle=\"yes\">Ccl2</italic>\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Forward</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AGGTGTCCCAAAGAAGCTGTA</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Reverse</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">ATGTCTGGACCCATTCCTTCT</td></tr><tr><td rowspan=\"2\" align=\"left\" valign=\"middle\" colspan=\"1\">\n<italic toggle=\"yes\">Cxcl1</italic>\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Forward</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CGAAGTCATAGCCACACTCAA</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Reverse</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GAGCAGTCTGTCTTCTTTCTCC</td></tr><tr><td rowspan=\"2\" align=\"left\" valign=\"middle\" colspan=\"1\">\n<italic toggle=\"yes\">Ifnb1</italic>\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Forward</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AATTTCTCCAGCACTGGGTG</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Reverse</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">AGTTGAGGACATCTCCCACG</td></tr><tr><td rowspan=\"2\" align=\"left\" valign=\"middle\" colspan=\"1\">\n<italic toggle=\"yes\">Ifit1</italic>\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Forward</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CACCAGTATGAAGAAGCAGAGAG</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Reverse</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GCCATAGCGGAGGTGAATATC</td></tr><tr><td rowspan=\"2\" align=\"left\" valign=\"middle\" colspan=\"1\">\n<italic toggle=\"yes\">Ifi44</italic>\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Forward</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GGGCTGTGATGAAGATGGAA</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Reverse</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CCCAGTGAGTCACACAGAATAA</td></tr><tr><td rowspan=\"2\" align=\"left\" valign=\"middle\" colspan=\"1\">\n<italic toggle=\"yes\">Ifi208</italic>\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Forward</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GCACAGAGAAGAGAAGGAGAAA</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Reverse</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CTGTTGTCTGTGGTGGAGATAG</td></tr><tr><td rowspan=\"2\" align=\"left\" valign=\"middle\" colspan=\"1\">\n<italic toggle=\"yes\">Ifi213</italic>\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Forward</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GATGGAAGCTTGGGAAGTAGAA</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Reverse</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GAGAGAACGAGCTTAGTGGATG</td></tr><tr><td rowspan=\"2\" align=\"left\" valign=\"middle\" colspan=\"1\">\n<italic toggle=\"yes\">Tgtp1</italic>\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Forward</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CTTCCCAAAGCTGGAAACTAAAC</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Reverse</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GTTAATGGTGGCCTCAGTAAGA</td></tr><tr><td rowspan=\"2\" align=\"left\" valign=\"middle\" colspan=\"1\">\n<italic toggle=\"yes\">Tgpt2</italic>\n</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Forward</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">CTTCCCAAAGCTGGAAACTAAAC</td></tr><tr><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">Reverse</td><td align=\"left\" valign=\"middle\" rowspan=\"1\" colspan=\"1\">GTTAATGGTGGCCTCAGTAAGA</td></tr></tbody></table></table-wrap>" ]	[]	[]	[]	[]	[]	[]	[ "<fn-group><fn id=\"FN1\" fn-type=\"COI-statement\"><p id=\"P37\"><bold>Additional Declarations:</bold> There is no duality of interests</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"nihpp-rs3713558v1-f0001\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-rs3713558v1-f0002\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-rs3713558v1-f0003\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-rs3713558v1-f0004\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-rs3713558v1-f0005\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-rs3713558v1-f0006\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-rs3713558v1-f0007\" position=\"float\"/>" ]	[]	[{"label": ["11."], "surname": ["Moriwaki", "Bertin", "Gough", "Orlowski", "Chan"], "given-names": ["K", "J", "P", "G", "F"], "article-title": ["Differential roles of RIPK1 and RIPK3 in TNF-induced necroptosis and chemotherapeutic agent-induced cell death"], "source": ["Cell Death and Disease"], "year": ["2015"], "volume": ["6"], "fpage": ["1"], "lpage": ["11"]}, {"label": ["17."], "surname": ["Nicole", "Sanavia", "Cappellesso", "Maffeis", "Akiba", "Kawahara"], "given-names": ["L", "T", "R", "V", "J", "A"], "article-title": ["Necroptosis-driving genes RIPK1, RIPK3 and MLKL-p are associated with intratumoral CD3(+) and CD8(+) T cell density and predict prognosis in hepatocellular carcinoma"], "source": ["J Immunother Cancer"], "year": ["2022"], "volume": ["10"], "issue": ["3"]}, {"label": ["20."], "surname": ["Galluzzi", "Vitale", "Warren", "Adjemian", "Agostinis", "Martinez"], "given-names": ["L", "I", "S", "S", "P", "AB"], "article-title": ["Consensus guidelines for the definition, detection and interpretation of immunogenic cell death"], "source": ["J Immunother Cancer"], "year": ["2020"], "volume": ["8"], "issue": ["1"]}, {"label": ["21."], "surname": ["Snyder", "Hubbard", "Messmer", "Kofman", "Hagan", "Orozco"], "given-names": ["AG", "NW", "MN", "SB", "CE", "SL"], "article-title": ["Intratumoral activation of the necroptotic pathway components RIPK1 and RIPK3 potentiates antitumor immunity"], "source": ["Sci Immunol"], "year": ["2019"], "volume": ["4"], "issue": ["36"]}, {"label": ["24."], "surname": ["Van Hoecke", "Van Lint", "Roose", "Van Parys", "Vandenabeele", "Grooten"], "given-names": ["L", "S", "K", "A", "P", "J"], "article-title": ["Treatment with mRNA coding for the necroptosis mediator MLKL induces antitumor immunity directed against neo-epitopes"], "source": ["Nature Communications"], "year": ["2018"], "volume": ["9"], "issue": ["1"]}, {"label": ["27."], "surname": ["Huang", "Wang", "Dai", "Lin", "Sun", "Zhang"], "given-names": ["FY", "JY", "SZ", "YY", "Y", "L"], "article-title": ["A recombinant oncolytic Newcastle virus expressing MIP-3alpha promotes systemic antitumor immunity"], "source": ["J Immunother Cancer"], "year": ["2020"], "volume": ["8"], "issue": ["2"]}, {"label": ["32."], "surname": ["Dolina", "Lee", "Brightman", "McArdle", "Hall", "Thota"], "given-names": ["JS", "J", "SE", "S", "SM", "RR"], "article-title": ["Linked CD4+/CD8+ T cell neoantigen vaccination overcomes immune checkpoint blockade resistance and enables tumor regression"], "source": ["Journal of Clinical Investigation"], "year": ["2023"], "volume": ["133"], "issue": ["17"]}, {"label": ["42."], "surname": ["Chen", "Chen", "Shi", "Huang", "Zhang", "Li"], "given-names": ["Y", "Y", "C", "Z", "Y", "S"], "article-title": ["SOAPnuke: a MapReduce acceleration-supported software for integrated quality control and preprocessing of high-throughput sequencing data"], "source": ["Gigascience"], "year": ["2018"], "volume": ["7"], "issue": ["1"], "fpage": ["1"], "lpage": ["6"]}]	{ "acronym": [], "definition": [] }	43	CC BY	no	2024-01-13 23:36:45	Res Sq. 2023 Dec 19;:rs.3.rs-3713558	oa_package/d3/4d/PMC10775377.tar.gz
PMC10775390	38196606	[ "<title>Introduction</title>", "<p id=\"P4\">Natural killer (NK) cells are innate lymphoid cells that influence many physiologic and pathologic conditions through their effector and regulatory functions<sup>##REF##18425107##1##</sup>. NK cells are canonically known to recognize and kill aberrant cells, such as virus–infected or malignant cells, using a complex detection system comprised of multiple inhibitory and activating receptors<sup>##REF##18425107##1##</sup>. Beyond their roles as effector cells, NK cells regulate the functions of other cell types, including dendritic cells, T cells, B cells and endothelial cells, through the release of immunomodulating cytokines<sup>##REF##18178824##2##–##REF##31183653##6##</sup>. Due to their central role in the immune system and disease etiologies, efforts to manipulate NK cell activity have long been sought and developed to improve patient outcomes across many medical fields.</p>", "<p id=\"P5\">In cancer, patients with high tumoral NK cell content and activation have improved survival<sup>##REF##31088844##7##,##UREF##0##8##</sup> and response to immunotherapy<sup>##REF##30713793##9##–##REF##29942093##11##</sup>. Thus, NK cells are emerging as major targets to promote cancer immunotherapy<sup>##UREF##2##12##</sup>. Current NK-focused immunotherapy approaches include autologous or allogeneic NK cell transfer<sup>##REF##26303618##13##</sup>, chimeric antigen receptor-engineered (CAR) NK cells<sup>##REF##32023374##14##</sup>, NK cell immune checkpoint inhibitors<sup>##UREF##3##15##</sup>, bi-or tri-specific killer engagers (BiKEs and TriKES)<sup>##REF##29941459##16##</sup>, and cytokine super-agonists<sup>##REF##28236454##17##</sup>. An impediment to these therapies is inadequate NK cell homing to and/or infiltration into solid tumors.</p>", "<p id=\"P6\">Strategies that increase NK cell infiltration into tumors represent plausible ways to enhance NK cell-related antitumor immunotherapies. Such work has focused almost entirely on modulating NK chemokine receptors and chemoattractants<sup>##REF##25344904##18##,##REF##28923105##19##</sup>. However, lymphocyte migration depends on more than just chemotaxis. For NK cells to successfully infiltrate any tissue, including solid tumors, they must traverse complex microenvironments (e.g., extravasation from blood vessels and navigation through dense extracellular matrices)<sup>##REF##32690238##20##</sup>. Beyond the chemokine/chemoattractant system, little is known about the mechanisms NK cells employ to physically migrate through these tissues.</p>", "<p id=\"P7\">Here we describe for the first time that human NK cells express fibroblast activation protein (FAP). FAP is a transmembrane serine protease primarily expressed on activated fibroblasts during wound healing or pathological conditions such as fibrosis, arthritis, and cancer<sup>##REF##32601975##21##</sup>. FAP is primarily known for its extracellular matrix (ECM) remodeling capabilities due to its collagenase activity. Since FAP is overexpressed in diseased tissue, yet mostly absent from healthy tissue<sup>##REF##32601975##21##</sup>, it is a promising therapeutic target in conditions like cardiac fibrosis<sup>##REF##31511695##22##</sup> and cancer<sup>##REF##29772538##23##</sup>.</p>", "<p id=\"P8\">After identifying FAP expression by human NK cells, we used computational approaches to elucidate the function of FAP in NK cells and validated these computational findings <italic toggle=\"yes\">in vitro</italic> using 2D assays. We then explored the impact of genetic manipulation and pharmacologic inhibition of FAP on NK cell migratory properties, extravasation, and tumor infiltration. We found that pharmacologic inhibition or deletion of FAP restrict NK cell migration, extravasation, and invasion through matrix. Conversely, forced overexpression of FAP significantly promotes NK cell invasion through matrix in both transwell and tumor spheroid assays, ultimately enhancing tumor cell lysis. Additionally, FAP-overexpressing cells showed a significantly enhanced ability to infiltrate tumors <italic toggle=\"yes\">in vivo</italic>. These findings demonstrate the necessity of proteolytic migration in NK cell function and provide an entirely new way to enhance the anti-tumor activity of NK cells. The elucidation of FAP’s role in enhancing NK cell migration and tumor infiltration presents a promising avenue for the development of novel immunotherapeutic strategies in cancer treatment, potentially improving the efficacy of NK-cell based therapies.</p>" ]	[ "<title>Materials and Methods</title>", "<title>Cell pellets, lines and culture</title>", "<p id=\"P33\">Primary human PSCs (ScienCell, cat#3830) were maintained on plastic and passaged every 1–3 days in stellate cell medium (ScienCell, cat#5301). For all experiments, PSC passage 5–9 was used. All human NK cell lines (NK92, NKL, YT and KHYG-1) and murine NK cell lines (LNK) were kindly provided by Dr. Kerry S. Campbell (Fox Chase Cancer Center, Philadelphia, PA). The NK92-GFP expressed GFP due to nucleofection with pmaxGFP according to manufacturer’s protocol (Lonza, cat#VVCA-1001). All NK cell lines were cultured as previously described<sup>##UREF##9##57##</sup>, tested for mycoplasma every 3–6 months and fingerprinted annually. (NKL could not be fingerprinted because it has no published profile). PANC-1 cells were cultured in 10%FBS in DMEM. Phoenix amphotropic cells were provided by Dr. Kerry S. Campbell (Fox Chase Cancer Center, Philadelphia, PA) and were cultured in 10% FBS in RPMI. The cell pellets of cell lines tested for FAP expression by Western blot (Jurkat, HuT 78, CCRF-CEM, Ramos, Namwala, IM-9, mono-mac 6, THP-1, U-937, Swiss3T3, RAW264.7, JAWSII, P815, BW5147.3, EL4 and A-20) were obtained from the Georgetown Lombardi Comprehensive Cancer Center Tissue Culture and Biobanking Shared Resource.</p>", "<title>Healthy donor derived cells</title>", "<p id=\"P34\">Fresh healthy donor NK cells were purchased from AllCells with either CD56 positive selection or CD56 negative selection (Allcells, cat#PB012-P or PB012-N). For 2D migration experiments, NK cells were enriched from peripheral blood using RosetteSep (StemCell Technologies) from healthy adult donors. T cells, B cells and monocytes were isolated from PBMCs (AllCells) using Mojosort magnetic cell separation system from Biolegend via CD3 positivity (Biolegend, cat#480133), CD19 positivity (Biolegend, cat#480105), CD14 positivity (Biolegend, cat#480093). PBMC purity was assessed using flow cytometry: CD3-APC (Biolegend, cat#300411), CD14-BV421 (Biolegend, cat#325627), CD45-FITC (BD Bioscience, cat#347463), CD56-PE (BD Bioscience, cat#555516), CD20-PE (BD Bioscience, cat#555623). For donor NK cell lysis of PANC-1 clusters, primary donor NK cells were purchased from AllCells then expanded using irradiated K562–4-1BBL-mbIL-21 (names “CSTX002”) cells kindly provided by Dr. Dean Lee according to his protocol<sup>##UREF##10##58##</sup>.</p>", "<title>Generation of FAP Overexpressing Cells</title>", "<p id=\"P35\">Overexpression of FAP was induced in NK92 by retroviral transduction. Phoenix amphotropic cells were transfected with the pBMN plasmid containing the FAP gene (received from vectorbuilder) using Lipofectamine and Plus reagent (Life Technologies) as previously described<sup>##REF##14500636##59##</sup>. Supernatants were collected from these cells after growing for 48 hours in Opti-MEM media (Life Technologies). The supernatant was mixed with Lipofectamine and Plus reagent and added to 2×10<sup>6</sup> NK92 cells in a 6-well plate. These cells were centrifuged for 45 min at 2000×g. This process was repeated two consecutive times and cells were flow sorted for GFP positivity three days after the final transduction.</p>", "<title>Generation of NK92 FAP Knockout Cells</title>", "<p id=\"P36\">Knockout of FAP in NK92 cells was accomplished by CRISPR using nucleofection, as previously described<sup>##UREF##11##60##</sup>. 2μL of CAS9 RNP (Horizon Discovery, cat#CAS12205) and 2μL FAP sgRNA (Horizon Discovery, cat#SQ-003829–01-0002) were incubated together for 15 minutes at room temperature. The sgRNA complexes were then added to 1×10<sup>6</sup> NK92 cells resuspended in 16uL of P3 nucleofection buffer (Lonza). The nucleofection mixture was transferred to a 16-well strip for nucleofection in the Lonza 4D Nucleofector using the pulse code CM-138. The 20μL nucleofection mixture was then added directly to 1mL of pre-warmed NK media. This process was repeated an additional time and cells were used 72 hours later.</p>", "<title>FAP Activity Assay</title>", "<p id=\"P37\">One day prior to assay, 5,000 PSCs/well were added to 96 well flat clear bottom white polystyrene TC-treated microplates (Corning, cat#3610). The following day, PSC media was aspirated off and 50 μL of NK92 cells (lacking GFP) were added to each well containing PSCs at a 4:1 E:T ratio and incubated overnight at 37°C. 100 mM stock of dipeptidylpeptidase substrate (Acetyl-Aka-Gly-Pro-AFC) (Anaspec, CatAS-24126) was made by resuspending lyophilized substrate in DMSO. On the day of the assay, DMSO stock was then diluted 1:1000 in FAP activity assay buffer (50 mM Tris-BCl, 1 M NaCl, 1 mg/mL BSA, pH 7.5). A standard curve was generated using rFAP (R&D systems, 3715-SE-010). 50 μL of rFAP standard ranging in concentration from 0.03125–2ug/mL was added to wells in triplicate. 50 μL of substrate was added to each well and the plate was incubated for 5 minutes at 37°C. The plate was read on a PerkinElmer EnVision Multimode Plate Reader with 390–400 nm excitation and 580 – 510 nm emission wavelengths. The final concentration of FAP per well was calculated using the standard curve. Data were compiled and assessed for statistical significance using GraphPad Prism 9.</p>", "<title>PSC-NK92 Coculture Assay</title>", "<p id=\"P38\">PSCs were plated one day prior to assay at 100,000 cells/well in a 6 well collagen coated plate. NK92 cells were added at 1:1 or 4:1 effector to target (E:T) ratios and cocultured for 3–4 hours. Each well contained 50% v/v NK and PSC media and 1% v/v IL-2. Following incubation, nonadherent cells were collected. Adherent cells were washed 2X with PBS and then trypsinized with 0.05% trypsin. After detachment trypsin was quenched with equal volume PSC media and cells were collected, pelleted and washed 2X with PBS then resuspended in 600 μL of 1% BSA. Cells were immediately sent for nonsterile flow sorting of GFP + from GFP− using the BD FACS Aria Ilu cell sorter in the Georgetown Lombardi Comprehensive Cancer Center Flow Cytometry and Cell Sorting Shared Resource (FCSR).</p>", "<title>Annexin V NK cell lysis study</title>", "<p id=\"P39\">One day prior to assay, PSCs or PANC1 cells were stained with DiI. If donor NK cells were used, they were stained with DiO prior to the experiment. Cells were then plated as described for the PSC-NK92 coculture assay. Following incubation period of 4 hours, all cells from a single well were collected and washed 2X with PBS. Samples were then processed by the FCSR using the Alexa Fluor 647 Annexin V and Sytox Blue staining (Biolegend). Flow data were analyzed using FloJo (v10.4.1) and statistics was performed using GraphPad Prism 9.</p>", "<title>RNA Isolation and rt-qPCR</title>", "<p id=\"P40\">RNA was isolated using the PureLink RNA Mini Kit (Ambion, cat#12183020). The RNA concentration was measured using NanoDrop 8000 (Thermo Fisher Scientific). cDNA was generated from 20–100 ng of RNA using the GoTaq 2-step RT-qPCR System (Promega, cat# A6110). qPCR was performed with SYBR Green on a StepOnePlus real-time PCR system (Applied Biosystems). Gene expression was normalized to HPRT and analyzed using 1/ΔCt method.</p>", "<title>Primers sequences:</title>", "<p id=\"P41\"><italic toggle=\"yes\">FAP</italic>: (F: ATGAGCTTCCTCGTCCAATTCA; R: AGACCACCAGAGAGCATATTTTG)</p>", "<p id=\"P42\"><italic toggle=\"yes\">HPRT</italic>: (F: GATTAGCGATGATGAACCAGGTT; R: CCTCCCATCTCCTTCATGACA)</p>", "<title>Western Blot</title>", "<p id=\"P43\">Western blots were performed as previously described<sup>##UREF##9##57##</sup>. Western blots were conducted using anti-FAP (ab207178, abcam) at concentrations of 1:1000 diluted in 5% milk in PBST. Secondary antibody was anti-rabbit IgG, HRP linked (Cell Signaling, cat# 7074S) at 1:1000. Antibody was validated with additional anti-FAP antibodies (MyBiosource, cat#MBS303414 and abcam, cat#ab53066). GAPDH antibody (Cell Signaling, cat#5174S) was used at 1:10,000. The secondary antibody was anti-rabbit IgG, HRP linked (Cell Signaling) used at 1:5000. Chemiluminescent substrate (Pierce, cat#32109 or cat#34094) was used for visualization.</p>", "<title>Immunofluoresence</title>", "<p id=\"P44\">5×10<sup>5</sup> cells were plated on coverslips for 2 hours in a 12-well plate. Cells were fixed for 15 minutes at room temperature with 4% paraformaldehyde, washed with PBS, and permeabilized with 0.5% Triton X-100 for 15 minutes at room temperature. The cells were washed with PBS and then blocked with 1% BSA for 30 minutes. These cells were then incubated with primary antibody for 1 hour at room temperature. Immunofluorescence was conducted using anti-FAP antibody (Santa Cruz, sc-65398) at a concentration of 1:500. The cells were washed three times with PBS. They were then incubated with Alexa Fluor 647 anti-mouse secondary antibody (Thermofisher, A21236) at a concentration of 1:1000. The cells were again washed three times with PBS. They were then incubated with DAPI diluted 1:1000 for 20 minutes at room temperature. They were again washed three times and then mounted on a slide using ProLong Antifade Mountant (Invitrogen, cat#10144). Antibody was validated with additional anti-FAP antibody (ab207178, abcam). These slides were then imaged on a Leica SP8 AOBS microscope in the LCCC Microscopy and Imaging Shared Resource (MISR).</p>", "<title>Cell Surface Biotinylation</title>", "<p id=\"P45\">Cell surface biotinylation of NK92, NKL, YT and KHYG-1 cells was performed with the Pierce Cell Surface Protein Isolation kit (Thermo Scientific, cat#89881) according to the manufacturer’s protocol. In brief, 4×10<sup>8</sup> cells were pelleted and washed with cold PBS then incubated with EZ-LINK Sulfo-NHS-SS-biotin for 30 min at 4°C followed by the addition of a quenching solution. Another 1×10<sup>6</sup> cells were collected and saved for total cell western blotting. Cells were lysed with lysis buffer (500 μL) containing the complete protease inhibitor cocktail (Roche, cat#11697498001). The biotinylated surface proteins were excluded with NeutrAvidin agarose gel (Pierce, 39001). Samples were diluted 50 ug in ultrapure water supplemented with 50 mM DTT. Lysates were subjected to Western blotting with the anti-FAP antibody described above.</p>", "<title>Gene expression analyses of NK cell lines</title>", "<p id=\"P46\">NK lymphoma and cell line gene expression was downloaded from GEO (GEO accession GSE19067)<sup>##REF##21052088##25##</sup> using R version 3.6.2 and read using affy in Bioconductor<sup>##REF##14960456##61##</sup>. Non-NK cell samples were excluded from analysis. Heatmap was created using ComplexHeatMap version 2.1.1<sup>##REF##27207943##62##</sup>. Correlation analysis was performed using limma in Bioconductor<sup>##REF##25605792##63##</sup>. Gene set enrichment analysis was performed using GO enrichment<sup>##REF##23868073##64##</sup>.</p>", "<title>2D cell migration studies</title>", "<p id=\"P47\">2D migration studies were done as previously reported<sup>##REF##29021341##31##,##REF##32392635##32##,##UREF##12##65##</sup>. In brief, EL08.1D2 stromal cells were grown to a confluent monolayer on flat-bottomed 96 well ImageLock plates (Essen Bioscience) pre-coated with 0.1% gelatin (Stemcell Technologies). For imaging primary cells, 10 μM of Cpd60 in RPMI media was added to the chamber 15 min before imaging. Freshly isolated human NK cells or 5,000 NK92 cells (NK92, FAP KO, FAP OE) were imaged in 96-well plates on the IncuCyte ZOOM Live-Cell Analysis System (Essen Bioscience) at 37°C in the phase-contrast mode (10× objective). Cells were allowed to settle for 30 min prior to beginning imaging every 2 minutes for 1–3 hours in an Incucyte Zoom using brightfield settings.</p>", "<title>Automated cell tracking and analysis</title>", "<p id=\"P48\">Exported TIFF stacks from Incucyte images were segmented using the Cyto2 trained network provided by Cellpose {Stringer, 2021 #3} using a classification object diameter of 7. btrack {Ulicna, 2021 #11} was used to track segmented cells between frames. Data was analysed using cellPLATO<sup>##UREF##13##66##</sup>. A HDF5 file containing segmented masks and tracks for each cell was generated for each TIFF stack and saved. Custom Python functions were used to make 29 separate shape, migration, and clustering measurements per timepoint per cell. Cell tracks were filtered by area (40–300 μm<sup>2</sup>) and by the number of timepoints a cell is segmented (5 to 1800).</p>", "<title>Manual cell tracking and analysis</title>", "<p id=\"P49\">Manual tracking of live cells was done using the manual tracking feature in Fiji<sup>##REF##22743772##67##</sup>. Tracks were plotted using the Chemotaxis plugin of Fiji. Cells that were in the field of imaging for fewer than two frames were discarded, as were cells which were non-adherent or floating. EL08.1D2 cells were used as de facto fiducial markers to ensure that neither they nor the microscope stage was drifting and causing apparent NK cell movement. Length and displacement measurements were derived directly from tracked cells and graphed using GraphPad software. Velocity data was obtained by dividing the total track length by the time of imaging.</p>", "<title>Transwell assay</title>", "<p id=\"P50\">Matrigel was diluted 1:4 in NK media. 50μL of this mixture was plated on the underside of a 5μm pore transwell insert (Corning, cat#CLS421). This was allowed to solidify for 20 minutes at room temperature. 2×10<sup>5</sup> cells in 200μL media were plated in the top well of the plate. 100ng/mL CXCL9 (R&D systems, cat# 392-MG) was added to 400μL media plated in the lower well of the plate. The cells were allowed to migrate for 24 hours and the number of cells in the bottom well was counted using a hemocytometer.</p>", "<title>Droplet assay</title>", "<p id=\"P51\">Cells were stained with DiO prior to the experiment. 2,000 cells were resuspended in 1μL of ECM mixture and plated on one end of a well on a 4 well Labtek plate (Thermo Scientific, cat#154917). 0.8ng of CXCL9 (R&D systems, cat# 392-MG) was resuspended in 2μL of ECM and plated on the other end of a well. The ECM mixture consisted of 20% growth factor reduced Matrigel (Corning, 10–12 mg/ml stock concentration, #354230) and 80% rat tail collagen type I at 3mg/mL (Gibco, A1048301). The two droplets were then covered in 75μL of ECM and was allowed to solidify for 45 minutes at 37°C. 800μL of NK media was then added to the well and the cells were allowed to migrate for 24 hours. The entire slide was then imaged on the Olympus IX-71 Inverted Epifluorescent Microscope at 5X in the LCCC Microscopy Shared Resource and the number of cells that had migrated beyond the initial droplet were counted using FIJI.</p>", "<title>Zebrafish studies</title>", "<p id=\"P52\">Zebrafish studies were conducted in accordance with NIH guidelines for the care and use of laboratory animals and were approved by the Georgetown University Institutional Animal Care and Use Committee. Zebrafish husbandry, injections, and mounting was performed by the Georgetown-Lombardi Animal Shared Resource. Two day post fertilization stage <italic toggle=\"yes\">Tg(kdrl:mCherry-CAAX)</italic> embryos were anesthetized with 0.016% tricaine (Sigma-Aldrich, St. Louis, MO, USA) in fish water (0.3g/L Sea Salt, Instant Ocean, Blacksburg, VA) and were injected with 100–200 NK92-GFP cells into the precardiac sinus using an air driven Picospritzer II microinjector (General Valve/Parker Hannifin) under a stereoscope. Following injection, embryos with cells in the caudal hematopoietic tissue were selected for analysis and mounted in 1.5% agarose plus 0.011% tricaine in fish water. Fish were maintained at 33°C until imaging. Confocal imaging was performed on a Leica SP8 AOBS microscope in the Georgetown-Lombardi Microscopy Shared Resource. Widefield fluorescent imaging was performed on a Keyence BZ-X inverted microscope. Images were taken at 10X across multiple z-stacks. Z-stack images were compressed using full focus and haze reduction in Keyence BZ-X software. NK extravasation quantification was performed by counting the number of GFP cells outside red vasculature. NK extravasation quantification was performed blinded to the treatment conditions. Graphs of resulting data and statistical analysis was generated using Graphpad Prism 9.</p>", "<title>3D cluster studies</title>", "<p id=\"P53\">3D clusters were generated, embedded and stained as previously described<sup>##UREF##14##68##,##REF##32661136##69##</sup>. In brief, clusters were generated by plating 1,000 cells per well into 96-well Nunclon Sphera low adhesion plates (Thermo Scientific, cat#174925) and incubated overnight at 37°C. The following day, 6 clusters were embedded into an ECM containing 2,000 NK cells and were plated into one well of a Nunc Lab-Tek II 8-well chamber slide (ThermoScientific, cat#154534PK). The ECM mixture consisted of 20% growth factor reduced Matrigel (Corning, 10–12 mg/ml stock concentration, #354230) and 80% rat tail collagen type I at 3mg/mL (Gibco, A1048301). Cells were either imaged for the following 24 hours every 30 minutes using a Zeiss LSM800 scanning confocal microscope enclosed in a heated chamber supplemented with CO<sub>2</sub> or allowed to incubate overnight at 37°C. After 24 hours, cells in matrix were fixed with 5.4% formalin for 1 hour, permeabilized with 0.5% Triton-X and blocked using goat serum. For invasion assays, NK-92-GFP cells were stained with anti-GFP (ThermoFisher, cat#A-11122). For the cell lysis assays, clusters were stained using anti-cleaved caspase-3 (Cell Signaling, cat#9661). Hoechst 33342, phalloidin, and secondary antibodies labeled with Alexa Fluor 488 nm, 546 nm, 647 nm, or 680 nm (Invitrogen) were used.</p>", "<title>Animal studies</title>", "<p id=\"P54\">10 NSG (NOD.Cg-Prkdc Il2rg /SzJ) mice were divided into 3 groups of 3 with one kept as a negative control. We inoculated animals with 1×10<sup>6</sup> human PANC-1 cells by subcutaneous injection. When tumors were < 100mm, three mice were injected with 1×10<sup>7</sup> NK92 cells and 4,000 IU IL-2, three were inject with 1×10<sup>7</sup> FAP OE NK92 cells and 4,000 IU IL-2, three were inject with 1×10<sup>7</sup> FAP KO NK92 cells and 4,000 IU IL-2, and one was injected with saline by IV tail vein as a negative control for staining. 24 hours after injection, mice were euthanized and the tumor, lung, liver, and spleen were excised. The tumors and organs were submitted to the histopathology and tissue shared resource core at Georgetown. Slides were stained using an anti CD-56 antibody (Abcam, ab133345) at 1:800. Slides were then blinded and NK cells were manually counted using Qupath.</p>" ]	[ "<title>Results</title>", "<title>Human natural killer cells express fibroblast activation protein (FAP)</title>", "<p id=\"P9\">NK cells were not previously known to produce FAP; however, we detected FAP expression at the protein level in NK92 cells and three additional human NK cell lines: NKL, YT and KHYG-1 (##FIG##0##Fig. 1A## and Fig. S1 C and D). To exclude the possibility that FAP expression was specific to NK cell malignancies, we assessed FAP expression in NK cells isolated from PBMCs of five different healthy human donors and found robust FAP expression in all donor NK cells (##FIG##0##Fig. 1B## and Fig. S1E). To determine if additional human immune cell types express FAP, we assessed multiple different human B, T and monocyte cell lines for FAP expression by Western blot and found heterogeneous protein expression (##FIG##0##Fig. 1C##). This cell-line specific FAP protein expression was consistent with FAP mRNA expression as determined by analysis of RNAseq data derived from the cancer cell line encyclopedia<sup>##REF##22460905##24##</sup> (Fig. S1F). While we saw heterogeneous expression of FAP in B, T and monocyte cell lines, we did not detect FAP expression in healthy donor PBMC-derived B cells (CD19<sup>+</sup>), T cells (CD3<sup>+</sup>), and macrophages (CD14<sup>+</sup>) (##FIG##0##Fig. 1D## and Fig. S1G). Thus, FAP expression in non-NK cell lines is likely driven by their malignant biology, since FAP can be upregulated during the process of malignant transformation<sup>##REF##32601975##21##</sup>. To further support our Western blot data, we confirmed FAP protein expression was detected on NK92 as well as normal healthy donor NK cells by immunofluorescence (##FIG##0##Fig. 1F##).</p>", "<p id=\"P10\">Canonically, FAP is surface-expressed, so we examined FAP expression on the NK cell surface. In order to assess this, we biotinylated cell surface proteins, and then excluded them from the cell lysate via magnetic separation. We then determined that FAP is present in total cell lysate but absent from the intracellular protein lysate (##FIG##0##Fig. 1E##), demonstrating that FAP is indeed expressed on the NK cell surface.</p>", "<title>In NK cells, FAP gene expression correlates with extracellular matrix and migration regulating genes</title>", "<p id=\"P11\">We leveraged transcriptional analysis to further determine FAP’s function in human natural killer cells. In 2011, Iqbal et al. performed a gene expression array on multiple NK cell lymphoma samples and NK cell lines<sup>##REF##21052088##25##</sup>. Using these data, we assessed FAP expression in 22 NK cell lymphomas and 11 NK cell lines (##FIG##1##Fig. 2A##) and performed a correlation analysis to assess the genes that were most positively and negatively correlated with FAP expression (##FIG##1##Fig. 2B##). The top 19 genes that were most positively correlated with FAP expression are shown in ##FIG##1##Fig. 2C##. We then performed GO enrichment analysis of these genes and determined that the pathways most positively correlated with FAP expression were related to ECM remodeling and cellular migration (##FIG##1##Fig. 2D##). This is consistent with the current understanding of FAP function, which is to cleave ECM components such as collagen and enhance cellular migration/invasion<sup>##REF##32601975##21##</sup>. It is also interesting that matrix metalloproteases (MMPs) were among the top 19 genes positively correlated with FAP expression. MMPs regulate rat, mouse and human NK cell migration into collagen or Matrigel <italic toggle=\"yes\">in vitro</italic><sup>##REF##16877347##26##–##UREF##4##28##</sup>. These data suggest that FAP may also regulate NK cell migration.</p>", "<title>Manipulation of FAP regulates human NK cell migration on matrix</title>", "<p id=\"P12\">Based on the computational analysis, we hypothesized that FAP was expressed by human NK cells to enhance migration. To test this hypothesis, we initially compared primary NK cell migration <italic toggle=\"yes\">ex vivo</italic> in the presence and absence of an FAP-specific inhibitor (Cpd60)<sup>##REF##24617858##29##</sup> that inhibited FAP but not FAP’s most closely related protein, DPPIV (##FIG##2##Fig. 3A##) or the other members of the prolyl oligopeptidase family S9<sup>##REF##24617858##29##</sup>. Cpd60 had no effect on NK cell viability (Fig. S2A). We then cocultured primary NK cells with EL08.1D2 cells, which have previously been shown to support spontaneous NK cell migration <sup>##UREF##5##30##,##REF##29021341##31##</sup> and produce ECM<sup>##REF##32392635##32##</sup>, and live imaged them for 24 h capturing photos every 2 minutes (##FIG##2##Fig. 3B##). From these time-lapse videos we manually tracked NK cell migratory paths (Movie S1 and S2). These experiments were repeated with NK cells from three different donors, with similar results. We found that FAP inhibition with Cpd60 significantly reduced NK cell velocity (##FIG##2##Fig. 3E##) and the accumulated distance traveled by NK cells (##FIG##2##Fig. 3F##).</p>", "<p id=\"P13\">These findings were confirmed using an FAP knockout NK92 cell line. FAP was knocked out (FAP KO) in NK92 cells via a CRISPR-Cas9 system. This knockout was confirmed by Western blot and rt-qPCR (Fig. S3A and B). Similar to primary cells, NK92 cells were incubated on a confluent layer of EL08.1D2 stromal cells and imaged at 2 min intervals for 1–3 hours. Instead of manual tracking, cells were segmented and tracked using automated detection and tracking as described in Methods. Because of this, we were able to get data from 800–1800 cells per condition. We found that FAP KO cells displayed significantly longer arrest coefficients, defined as the frequency of time cells were found in arrest, (##FIG##2##Fig. 3E##), slower speed (##FIG##2##Fig. 3F##), and lower accumulated distance (##FIG##2##Fig. 3G##).</p>", "<p id=\"P14\">We hypothesized that if FAP KO reduced NK cell migration then FAP overexpression would increase migration. We generated a FAP-overexpressing NK92 cells line (FAP OE) using retroviral transfection. These cells were selected via the GFP expression conferred by the plasmid (Fig. S4A). This upregulation was confirmed by Western blot and RT-qPCR (Fig. S4B and C). As hypothesized, FAP OE NK92 cells displayed significantly shorter arrest coefficients (##FIG##2##Fig. 3E##), faster speed (##FIG##2##Fig. 3F##), and longer accumulated distance (##FIG##2##Fig. 3G##).</p>", "<title>FAP manipulation regulates the invasion of human NK cells through matrix.</title>", "<p id=\"P15\">We then examined the impact of FAP on NK cell invasion through matrix. To analyze this, NK92 cells (NK92, FAP KO, FAP OE) were plated in the top well of a transwell chamber. CXCL9, a known NK cell chemoattractant<sup>##UREF##6##33##</sup>, was placed in the lower well to stimulate NK cell invasion. NK cells were allowed to invade through the membrane coated with a Matrigel barrier for 24 hours (##FIG##3##Fig. 4A##). Notably, FAP OE in NK92 cells resulted in an almost three-fold increase in invasion through Matrigel. (##FIG##3##Fig. 4C##). Additionally, knockout as well as inhibition of FAP enzymatic activity with Cpd60, an FAP specific inhibitor, resulted in a significant decrease in invasion through the Matrigel (##FIG##3##Fig. 4B##). No additional decrease was seen following treatment of FAP KO cells with Cpd60, suggesting that the decrease seen in response to Cpd60 is due to the inhibition of FAP (Fig. S5).</p>", "<p id=\"P16\">To verify these findings, we performed a droplet assay. 2,000 NK cells (NK92, FAP KO, and FAP OE) were plated on one side of a four well LabTek plate with CXCL9 plated on the other side of the well. They were then covered in ECM and allowed to invade for 24 hours (##FIG##3##Fig. 4D##). FAP KO in NK92 cells significantly reduced invasion through matrix while FAP OE in NK92 cells significantly increased invasion through matrix (##FIG##3##Fig. 4E##).</p>", "<title>FAP inhibition reduces NK cell extravasation in vivo</title>", "<p id=\"P17\">We next set out to determine if FAP altered NK cell migratory behaviors <italic toggle=\"yes\">in vivo</italic>. Since we could not detect FAP expression in murine NK cells (Fig. S1H) we opted to use zebrafish—a novel <italic toggle=\"yes\">in vivo</italic> model that allows us to monitor human NK cell migratory behaviors in real-time. We injected NK92-GFP cells into the precardiac sinus of <italic toggle=\"yes\">Tg(kdrl:mCherry-CAAX)y171</italic> zebrafish embryos that express endothelial membrane-targeted mCherry (##FIG##4##Fig. 5A##). Immediately after injection, NK cells migrated via the circulation to the caudal hematopoietic tissue (##FIG##4##Fig. 5B##) gradually disseminating throughout the rest of the zebrafish vasculature. Using confocal live-imaging, with images taken approximately every 3 minutes, we captured an NK cell crawling along the inside of the blood vessel, searching for an appropriately sized pore just prior to extravasation (##FIG##4##Fig. 5C## and Movie S3, Fig. S6). After confirming that human NK cells could migrate throughout and extravasate from zebrafish vasculature, we tested the effects of FAP inhibition on NK cell extravasation. Since fluorescent microscopy is amenable to imaging multiple fish simultaneously, we used fluorescent microscopy to quantify the effects of the FAP inhibitor Cpd60 on NK cell extravasation. We confirmed that the fluorescent microscope was capable of detecting NK cell extravasation (##FIG##4##Fig. 5D##), and then imaged 20 fish injected with NK92-GFP cells, 10 of which were bathed in 10 μM of Cpd60, and 10 fish that were bathed in vehicle. We found that FAP inhibition significantly reduced NK cell extravasation from the blood vessels (##FIG##4##Fig. 5E## and ##FIG##4##F##).</p>", "<title>FAP manipulation regulates NK cell infiltration and lysis of PANC-1 cell clusters embedded in matrix</title>", "<p id=\"P18\">NK cells regulate tumor growth and viability, yet relatively little is known about the mechanisms NK cells employ to invade through dense tumor-related extracellular matrices. To determine if FAP activity affects NK cell infiltration into tumors, we assessed the effect of FAP inhibition on NK cell infiltration into PANC-1 clusters embedded in matrix. To accomplish this, we plated 1,000 PANC-1 cells in low-adhesion U-bottom plates and allowed them to form clusters for 24 hours. We then embedded the clusters in matrix that consisted of 80% collagen/20% Matrigel and NK92-GFP cells, and added either 10 μM Cpd60 or vehicle to the media. We live imaged the cocultures for 24 hours, capturing images every 30 minutes. Then we fixed the slides and stained for GFP by immunofluorescence to quantify the amount of NK cell infiltration into the clusters (##FIG##5##Fig. 6A##). FAP inhibition had no effect on cluster size (Fig. S7A). FAP inhibition significantly reduced NK92-GFP cell infiltration into PANC-1 clusters embedded in matrix (##FIG##5##Fig. 6B##, Movies S4–7). These experiments were repeated using PSCs with similar results (Fig. S8).</p>", "<p id=\"P19\">To determine if the reduced NK cell infiltration was accompanied by reduced tumor cell lysis, we repeated the PANC-1 and NK92 coculture experiment and stained the cells for actin using phalloidin and cleaved caspase-3 to identify apoptotic cells. Using the phalloidin stain we outlined the PANC-1 cell cluster, and then transposed the outline onto the cleaved caspase-3 images and quantified the intensity of cleaved caspase-3 within PANC-1 cell clusters (##FIG##5##Fig. 6D## and ##FIG##5##F##). We found that FAP inhibition significantly reduced the amount of PANC-1 cell apoptosis (##FIG##5##Fig. 6E##) in 3D cultures, despite having no effect on PANC-1 cell apoptosis in 2D cell cocultures (Fig. S7B). To determine if FAP inhibition also reduced donor NK cell migration and tumor lysis, we repeated these experiments with NK cells from two donors. Since the range of PANC-1 cluster areas in the donor NK cell experiment was much wider than the range in the NK92 experiment (10–208 versus 12–70) we normalized the intensities in the donor NK cell experiment to the area of the cluster. In agreement with the NK92 cell experiments, FAP inhibition reduced donor NK cell lysis of PANC-1 cells in 3D (##FIG##5##Fig. 6D##) but not 2D (Fig. S7B). This demonstrates that FAP inhibition does not alter target cell lysis through direct impacts on NK cell cytotoxicity but rather via modulation of NK cell migration through matrix.</p>", "<p id=\"P20\">To determine whether FAP overexpression enhances NK cell invasion into these tumor spheroids, we repeated these experiments with NK92 cells and FAP OE NK92 cells. FAP OE NK92 cells showed significantly increased invasion into tumor spheroids and significantly increased cleaved caspase-3 expression (##FIG##5##Fig. 6G##). No increase in cytotoxicity in FAP OE NK92 cells was seen in 2D systems, suggesting the increase in apoptosis is due to an increase in NK cell invasion (Fig. S9). This suggests that FAP overexpression could be a method to enhance tumor infiltration by NK cells.</p>", "<title>FAP overexpression enhances NK cell infiltration into tumors in vivo</title>", "<p id=\"P21\">As a proof of concept experiment, we set out to determine if NK cells engineered to overexpress FAP displayed enhanced infiltration into in a human tumor murine model. To test this, we injected NK92, FAP KO NK92, and FAP OE NK92 cells intravenously into mice bearing PANC-1 pancreatic tumors. Specifically, we injected 1×10<sup>6</sup> PANC-1 cells subcutaneously into NSG mice then waited until tumors were at least 100 mm<sup>##REF##15933055##3##</sup> in size before injecting 1×10<sup>7</sup> NK92, FAP OE NK92, or FAP KO NK92 cells into the tail vein (##FIG##6##Fig. 7A##). The mice were euthanized after 24 hours and tumors were collected, fixed and stained with an anti-CD56 antibody. Tumors from mice injected with FAP OE NK92 cells had more than three times as many NK cells when compared to tumors from mice injected with NK92 cells (##FIG##6##Fig. 7B##,##FIG##6##C##).</p>", "<p id=\"P22\">Interestingly, there was no difference in invasion between the NK92 and FAP KO NK92 cells. Potentially because an effect size is too small to detect with our limited sample size. Alternatively, other known drivers of NK cell migration, such as MMPs, could potentially compensate for the loss of FAP<sup>##REF##16877347##26##,##REF##9574526##27##,##UREF##4##28##</sup>.</p>", "<p id=\"P23\">To assess NK cell trafficking to non-tumor locations, we collected and stained spleen, liver, and lung samples from each of the treated mice. There was no significant difference in NK content in any of the examined organs (Fig. S10). This suggests that under these experimental conditions NK92 cells preferentially invade into tumors rather than other organs; further supporting the notion that this technology could be implemented therapeutically.</p>" ]	[ "<title>Discussion</title>", "<p id=\"P24\">Here we show that human NK cells express FAP, which is a key regulator of NK cell migration, invasion, extravasation and tumor infiltration. This novel finding significantly broadens the existing understanding of NK cell migration and tissue infiltration, and illustrates a mechanism for NK cell extravasation from blood vessels. Our findings reveal that both knockout and inhibition of FAP restrict NK cell tumor infiltration, and attenuate NK cell-mediated tumor cell lysis, underscoring the critical role of FAP-mediated migratory mechanisms in the anti-cancer activity of NK cells. Importantly, FAP overexpression enhances NK cell invasion through matrix, promoting tumor infiltration both <italic toggle=\"yes\">in vitro</italic> and <italic toggle=\"yes\">in vivo</italic>. Therefore, this work reveals novel insights into FAP biology and NK cell biology, with important implications for emerging NK cell-focused therapeutic strategies.</p>", "<p id=\"P25\">For extravasation or tissue invasion, cells must penetrate the basement membrane and interstitial tissue. During this process they are confronted by a 3D ECM that provides a substrate for adhesion and traction, as well as biomechanical resistance. For cells to traffic effectively through the ECM, which can offer narrow or non-existent pores for passage, leukocytes must adopt contracted shapes. Excessive cellular deformation can result in nuclear rupture that causes genomic damage, long-term genomic alterations and limited cellular survival. To circumvent nuclear damage, some cells employ proteolytic digestion to widen pores in the ECM<sup>##REF##32690238##20##</sup>. Although proteolytic migration is considered less common in leukocytes versus other cell types, it has been documented. Zebrafish neutrophils and macrophages use proteolytic digestion for basement membrane transmigration<sup>##REF##31167131##34##</sup>. Human neutrophils secrete elastase, a serine protease, to facilitate their endothelial transmigration<sup>##REF##27701149##35##</sup>.</p>", "<p id=\"P26\">Unlike other immune cell types, there are few studies investigating the physical mechanisms driving NK cell migration. Decades-old research demonstrated that mouse and rat NK cell migration through Matrigel was dependent on MMPs<sup>##REF##9574526##27##,##REF##10820269##36##,##REF##8679543##37##</sup>. More recent studies have used physiologic models. Putz et al. showed that heparinase regulated mouse NK cell infiltration into murine tumors<sup>##REF##28581441##38##</sup>. Prakash et al. showed that granzyme B released from murine cytotoxic lymphocytes, including NK cells, enhanced lymphocyte extravasation via ECM remodeling, although it did not affect interstitial migration. They confirmed that a granzyme B inhibitor reduced human donor T cell transmigration through a Matrigel coated semi-permeable membrane (i.e., Boyden chamber assay)<sup>##REF##25526309##39##</sup>. Although these authors did not assess changes in human donor NK cell migration in response to a granzyme B inhibitor, it is reasonable to assume it would be similar to that of T cell migration, since both cell types express and release granzyme B. However, our finding that FAP is expressed in human NK cells, but not in all murine NK cells or other human immune cell types (##FIG##0##Fig. 1##), suggests that some migratory mechanisms can be cell-type and species-specific. Unlike these previous studies that investigated either extravasation or tumor infiltration, we investigated both and found that NK cells use the same proteolytic migration strategy for basement membrane degradation/extravasation as well as tumor tissue infiltration. We further demonstrate that defects in proteolytic migration directly impair the ability of NK cells to infiltrate and lyse tumor cells.</p>", "<p id=\"P27\">FAP is a well-studied protein. Although once thought to be restricted to activated fibroblasts, FAP expression has been found in additional cell types such as epithelial tumor cells<sup>##REF##12963128##40##–##REF##15713998##42##</sup>, melanocytes<sup>##REF##7923219##43##</sup> and macrophages<sup>##REF##24778275##44##,##REF##24074532##45##</sup>. In non-immune cells, FAP enhances cellular invasion<sup>##REF##7923219##43##,##REF##16651416##46##–##REF##29026005##49##</sup>. The role of FAP in macrophages is less clear. Arnold et al. showed that in murine tumors there is a FAP<sup>+</sup> minor sub-population of immunosuppressive F4/80<sup>hi</sup>/CCR2<sup>+</sup>/CD206<sup>+</sup> M2 macrophages. While this study highlighted how FAP<sup>+</sup> macrophages affect tumor growth, FAP’s function in these macrophages was not described<sup>##REF##24778275##44##</sup>. Tchou et al. identified FAP<sup>+</sup>CD45<sup>+</sup> cells in human breast tumors by immunofluorescence. They then used flow cytometry to demonstrate that a portion of these FAP<sup>+</sup>CD45<sup>+</sup> cells were CD11b<sup>+</sup>CD14<sup>+</sup>MHC<sup>−</sup>II + tumor associated macrophages. Since the flow cytometry panel used to categorize these FAP<sup>+</sup>CD45<sup>+</sup> cells consisted of only macrophage markers, those data do not exclude the possibility that some of the FAP<sup>+</sup>CD45<sup>+</sup> tumor cells were NK cells. In contrast to that study, we did not identify FAP expression in human macrophages (CD14<sup>+</sup> cells) (##FIG##0##Fig. 1F##). However, we examined circulating cells, as opposed to cells in the tumor microenvironment. Future studies are needed to further categorize FAP expression in tumor immune cell populations, potentially using multicolor immunofluorescent staining. Additionally, more studies are needed to determine if the function of FAP in FAP<sup>+</sup> tumor macrophages is the same as we have described here in NK cells.</p>", "<p id=\"P28\">The finding that human NK cells express FAP (##FIG##0##Fig. 1D##) has several clinical implications for existing FAP-targeted therapies. For example, an anti-FAP/IL-2 fusion protein has been utilized in clinical trials though the results are not yet published (<ext-link xlink:href=\"https://clinicaltrials.gov/ct2/show/NCT02627274\" ext-link-type=\"uri\">NCT02627274</ext-link>). The proposed mechanism of action of this drug is that it targets IL-2 to FAP expressing tumor stroma, thereby limiting on-target, off-site toxicities associated with IL-2 cytokine therapy. Our findings that FAP is expressed on the NK cell surface suggests that anti-FAP/IL-2 fusion protein may also target IL-2 directly to NK cells, enhancing NK cell activation and potentially tumor clearance.</p>", "<p id=\"P29\">Anti-FAP CAR therapies are also in development to treat conditions such as cardiac fibrosis<sup>##REF##34990237##50##,##REF##31511695##22##</sup>, malignant pleural mesothelioma<sup>##REF##23281771##51##</sup>, lung adenocarcinoma<sup>##REF##23732988##52##</sup> and other cancers<sup>##REF##37272107##53##,##REF##19920354##54##</sup>. Our data suggest that anti-FAP CAR cells may also be useful in NK cell malignancies such as aggressive NK-cell leukemia. There are potential caveats to the clinical use of anti-FAP CAR T cells. It is plausible that an anti-FAP CAR T cell could induce NK cell lysis, resulting in NK cell leukopenia in humans, this toxicity might be missed in preclinical murine models. For cancer immunotherapy, an ideal anti-FAP CAR would be engineered to target FAP expression by fibroblasts while sparing NK cells. It should be noted that Gulati et al. performed the first-in-human trial of an anti-FAP CAR T cell therapy and demonstrated that a FAP CAR T cell therapy induced stable disease for 1 year in a patient with malignant pleural mesothelioma without any treatment-terminating toxicities<sup>##REF##23281771##51##</sup>.</p>", "<p id=\"P30\">Our finding that FAP regulates NK cell tissue infiltration (##FIG##5##Figs. 6## and##FIG##6##7##) has clinical implications as well. These results imply the potential value of NK cells engineered to overexpress FAP in enhancing tumor infiltration and cell lysis.</p>", "<p id=\"P31\">Existing strategies aimed at enhancing NK cell infiltration into tumors rely on manipulating chemokine/receptor pathways. For example, Wennerberg et al. demonstrated that <italic toggle=\"yes\">ex vivo</italic> expanded NK cells express higher levels of chemokine receptor CXCR3 than unexpanded NK cells which then demonstrated increased migration towards CXCL10 expressing melanomas<sup>##REF##25344904##18##</sup>. Another approach that has been utilized is engineering NK cells to overexpress CXCR2, a chemokine receptor. This study showed that CXCR2 overexpressing NK cells had enhanced trafficking towards and lysis of renal cell carcinoma cells in vitro<sup>##REF##28923105##19##</sup>. These findings suggest that these strategies to enhance NK cell migration are feasible, however, chemokine pathway-altering strategies require not only elevated expression of the chemokine receptor on NK cells, but also secretion and maintenance of chemoattractants by the tumor. Additionally, many chemoattractants recruit multiple immune cell types, including immunosuppressive cells. For example, CXCL10 is a chemoattractant for cytotoxic T lymphocytes and NK cells, but also for regulatory T cells<sup>##UREF##8##56##</sup>. We postulate that the ideal migration-altering therapeutic approach would increase cytotoxic immune cell infiltration in tumor masses, without influencing or even reducing immunosuppressive immune cell content in the TME. Since overexpressing FAP enhances NK92 cell tumor infiltration and lysis <italic toggle=\"yes\">in vitro</italic> and <italic toggle=\"yes\">in vivo</italic> (##FIG##5##Figs. 6## and##FIG##6##7##), we speculate that engineering NK cells to overexpress FAP, either in autologous NK cell or NK CAR-NK therapies, could increase NK cell tumor infiltration and lysis. This approach is independent of tumor-associated factors, such as chemoattractant secretion, and would not be expected to induce the infiltration or expansion of immunosuppressive cell populations into the tumor microenvironment. Since proteolytic migration is required for NK cell killing of malignant cells (##FIG##5##Fig. 6##), the ability to alter protease expression or activity to enhance NK cell tumor infiltration represents a potentially promising approach to altering NK cell anti-tumor activity. Future studies are needed to explore the benefit of FAP-overexpressing NK cells in preclinical models and in clinical studies, and to determine what, if any, toxicities they induce.</p>", "<p id=\"P32\">In this study we have demonstrated that human NK cells express FAP and that FAP directly affects NK cell migration, extravasation and tumor infiltration. These findings further the understanding of both FAP and NK cell biology. Importantly, FAP overexpression promotes the infiltration of NK92 cells into human tumor xenografts, suggesting a role for manipulating FAP expression to promote NK cell therapeutics. Future studies will determine if these novel findings have meaningful implications for NK cell-based therapy strategies currently in development.</p>" ]	[]	[ "<p id=\"P1\">Author contributions:</p>", "<p id=\"P2\">A.A.F., R.E.M. and L.M.W. conceived the idea, designed the study and obtained funding. A.A.F. and R.E.M. wrote the manuscript, conducted experiments and analyzed the data. E.F.M. and E.J.F. performed the computational analysis and E.F.M generated the computational figures. A.N. and G.P. assisted in designing and performing 3D migration experiments. E.G. assisted in conducting the zebrafish studies. S.A.J. assisted with experiment design. P.V. provided FAP inhibitor Cpd60. E.M.M. performed and analyzed the 2D migration studies and assisted with study design.</p>", "<p id=\"P3\">Natural killer (NK) cells play a critical role in physiologic and pathologic conditions such as pregnancy, infection, autoimmune disease and cancer. In cancer, numerous strategies have been designed to exploit the cytolytic properties of NK cells, with variable success. A major hurdle to NK-cell focused therapies is NK cell recruitment and infiltration into tumors. While the chemotaxis pathways regulating NK recruitment to different tissues are well delineated, the mechanisms human NK cells employ to physically migrate are ill-defined. We show for the first time that human NK cells express fibroblast activation protein (FAP), a cell surface protease previously thought to be primarily expressed by activated fibroblasts. FAP degrades the extracellular matrix to facilitate cell migration and tissue remodeling. We used novel <italic toggle=\"yes\">in vivo</italic> zebrafish and <italic toggle=\"yes\">in vitro</italic> 3D culture models to demonstrate that FAP knock out and pharmacologic inhibition restrict NK cell migration, extravasation, and invasion through tissue matrix. Notably, forced overexpression of FAP promotes NK cell invasion through matrix in both transwell and tumor spheroid assays, ultimately increasing tumor cell lysis. Additionally, FAP overexpression enhances NK cells invasion into a human tumor in immunodeficient mice. These findings demonstrate the necessity of FAP in NK cell migration and present a new approach to modulate NK cell trafficking and enhance cell-based therapy in solid tumors.</p>" ]	[]	[ "<title>Acknowledgments:</title>", "<p id=\"P55\">We would like to thank Dr. Kerry Campbell for providing NK cell lines, the pBMN plasmid, and continuous intellectual and technical support; Dr. Dean Lee for providing the NK feeder cells; the Georgetown Lombardi Comprehensive Cancer Center Tissue Culture and Biobanking, Flow Cytometry and Cell Sorting, Genomics and Epigenomics, Histopathology and Tissue, and Microscopy and Imaging Shared Resources. Graphical abstract and schematics were created with <ext-link xlink:href=\"https://www.biorender.com/\" ext-link-type=\"uri\">BioRender.com</ext-link></p>", "<title>Funding:</title>", "<p id=\"P56\">This work is supported by grants from the National Institute of Health National Cancer Institute F30 CA239441(A.A.F.), R01 CA50633 (L.M.W.), P30 CA51008 (L.M.W), and NIH-NIAID R01AI137073 to E.M.M</p>", "<title>Data availability statement:</title>", "<p id=\"P57\">The public datasets analyzed in this paper are available at GEO accession GSE19067, doi: 10.1038/leu.2010.255. The authors declare that all other data supporting the findings of this study are available within the paper or its supplementary information files. All other relevant data are available from the corresponding author upon request.</p>" ]	[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><p id=\"P64\">Human natural killer cells express fibroblast activation protein. (<bold>a</bold>) Western blot showing four distinct human NK cell lines express FAP. (<bold>b</bold>) Western blot showing primary NK cells isolated from PBMCs from three different healthy human donors express FAP. (<bold>c</bold>) Western blot showing heterogenous FAP expression in multiple human immune cell lines. (<bold>d</bold>) Western blot showing FAP is only expressed in donor human NK cells and not in donor human T (CD3<sup>+</sup>), B (CD19<sup>+</sup>) or monocyte (CD14<sup>+</sup>) cells isolated from PBMC. NK92 cell line included as a positive control and PANC-1 cell line included as a negative control. Representative of results with two different donors. (<bold>e</bold>) Western blot of total protein (T) and intracellular (IC) protein isolated from human NK cell lines using cell surface protein biotinylation for exclusion of surface proteins. (<bold>f</bold>) Immunofluorescence images showing FAP expression in NK92 and human donor NK cells. Pancreatic stellate cells (PSCs) were included as positive controls as well as Jurkat cells as negative controls. Images are representative of three separate experiments. <italic toggle=\"yes\">P</italic> value was calculated using unpaired two-tailed t-test. *<italic toggle=\"yes\">P</italic><0.001, *<italic toggle=\"yes\">P</italic><0.0001. All bar plots represent mean ± SD.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><p id=\"P65\">In NK cells, FAP gene expression correlates with extracellular matrix and migration-regulating genes. (<bold>a</bold>) Level of FAP expression in NK cell lymphomas (n=22) and NK cell lines (n=11) as determined by Affymetrix gene expression array. (<bold>b</bold>) Heatmap of gene expression array data. Data are shown as z-score scaled values. (<bold>c</bold>) Top 19 genes that are significantly correlated with FAP expression. (<bold>d</bold>) Top GO pathways that significantly correlate with FAP expression.</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3</label><caption><p id=\"P66\">Manipulation of FAP regulates human NK cell migration on matrix (<bold>a</bold>) Fluorescent peptide dipeptidyl peptidase activity assay demonstrating FAP inhibitor (Cpd60) inhibits FAP but not DPPIV. (<bold>b</bold>) Schematic of live imaging of primary human NK cell migration on stromal cells. (<bold>c</bold>) The average velocity and (<bold>d</bold>) accumulated distance traveled by primary NK cells treated with either vehicle or 10μM Cpd60. Each point represents a single NK cell. Each condition contains 90 NK cells with 30 NK cells from three separate donors. Violin plot horizontal line represents the mean. (<bold>e</bold>) Arrest coefficient, defined as the frequency of time cells were found in arrest, (<bold>f</bold>) mean cell speed, and (<bold>g</bold>) cumulative track length of FAP KO NK92, FAP OE NK92, and NK92 cells. Data are shown as Plots of Difference with the wildtype condition as the reference. n=800–1800 cells per condition from 4 technical replicates.</p></caption></fig>", "<fig position=\"float\" id=\"F4\"><label>Figure 4</label><caption><p id=\"P67\">FAP manipulation regulates invasion of human NK cells through matrix. (<bold>a</bold>) Schematic representation of transwell assay. (<bold>b</bold>) Quantification of the percent of NK92 and FAP KO NK92 cells with and without Cpd60 that have invaded to the lower portion of transwell chambers. Data are from three different experiments. (<bold>c</bold>) Quantification of the percent of NK92 and FAP OE NK92 cells with and without Cpd60 that have invaded to the lower portion of transwell chambers. Data are from three separate experiments (<bold>d</bold>) Schematic representation of droplet assay. (<bold>e</bold>) Quantification of NK92, FAP KO NK92 and FAP OE NK92 cells migrated beyond the initial droplet. Data are from three separate experiments. p<0.05, p<0.01, p<0.001 as determined by unpaired two-tailed t-test. All bar plots represent mean ± SD.</p></caption></fig>", "<fig position=\"float\" id=\"F5\"><label>Figure 5</label><caption><p id=\"P68\">FAP inhibition reduces NK cell extravasation from zebrafish blood vessels. (<bold>a</bold>) Schematic representation (top) of zebrafish injections. Fluorescent and brightfield overlay image of <italic toggle=\"yes\">Tg(kdrl:mCherry-CAAX)y171</italic> zebrafish embryos expressing endothelial membrane targeted mCherry (bottom). (<bold>b</bold>) Representative images of caudal hematopoietic tissue immediately after NK92-GFP injection into the precardiac sinus. (<bold>c</bold>) Still image taken from confocal time-lapse video demonstrating NK92-GFP extravasation from mCherry labeled vasculature. (<bold>d</bold>) Representative fluorescent microcopy images demonstrating NK92-GFP extravasation. Extravascular image was taken approximately 5 minutes after the intravascular image. Images were taken at 20X. (<bold>e</bold>) Representative fluorescent microscopy images of NK92-GFP injected zebrafish in 10 μM FAP inhibitor (Cpd60) or vehicle showing NK92-GFP cell intravascular or extravascular localization 1 hour after injection. Images were taken at 10X. (<bold>f</bold>) Quantification of extravascular NK92-GFP cells in zebrafish injected with NK92-GFP cells 1 hour prior to imaging. p<0.05 analyzed by unpaired two-tailed t-test. Data are aggregated from two independent experiments, each with 10 fish per treatment condition and quantification was done blinded to treatment conditions. Bar plot represents mean ± SD.</p></caption></fig>", "<fig position=\"float\" id=\"F6\"><label>Figure 6</label><caption><p id=\"P69\">FAP manipulation regulates NK cell infiltration and lysis of PANC-1 cell clusters embedded in 3D cell matrix. (<bold>a</bold>) Schematic representation of experimental design. (<bold>b</bold>) Representative immunofluorescence images and quantification of NK92-GFP cell infiltration into PANC-1 after 24-hour coculture with vehicle or 10 μM Cpd60. PANC-1+vehicle n = 29; PANC-1+Cpd60 n=45. PANC-1 data aggregated from two independent experiments. (<bold>c</bold>) Representative immunofluorescence images of phalloidin and cleaved caspase-3 staining in PANC-1 cell clusters cocultured with NK92 and vehicle or 10 μM Cpd60. (<bold>d</bold>) Quantification of cleaved caspase-3 intensity staining in PANC-1 cell clusters cocultured with NK92 cells or donor NK cells. Three outliers were removed from the vehicle group and one outlier was removed from the Cpd60 group. Outliers were determined by Rout method where Q = 1%. PANC-1+NK92+vehicle n = 18; PANC-1+NK92+Cpd60 n = 9; PANC-1+Donor NK+vehicle n=25, PANC-1+Donor NK+Cpd60 n =12. Donor NK cell data is aggregated data from two independent experiments that used different donors. (<bold>e</bold>) Representative immunofluorescence images and quantification of GFP positive NK92 cells (NK92 or FAP OE) infiltration into PANC-1 after 24-hour. PANC-1+NK92 n = 32; PANC-1+FAP OE NK92 n = 45. (<bold>f</bold>) Representative immunofluorescence images of phalloidin and cleaved caspase-3 staining in PANC-1 cell clusters cocultured without NK cells, with NK92, or FAP OE NK92 cells. (<bold>g</bold>) Quantification of cleaved caspase-3 intensity staining in PANC-1 cell clusters cocultured without NK cells, with NK92 cells, or FAP OE NK92 cells. PANC-1 without NK cells n=18; PANC-1+NK92 n = 32; PANC-1+FAP OE NK92 n = 45. Data are aggregated from two independent experiments. p<0.05, p<0.01, *p<0.001 as determined by unpaired two-tailed t-test. All bar plots represent mean ± SD.</p></caption></fig>", "<fig position=\"float\" id=\"F7\"><label>Figure 7</label><caption><p id=\"P70\">FAP overexpression enhances the infiltration of intravenously administered NK92 cells into tumors. (<bold>a</bold>) Schematic representation of experimental design. (<bold>b</bold>) Quantification of NK92, FAP OE NK92, and FAP KO NK92 cells per mm<sup>2</sup> in PANC-1 tumors. (<bold>c</bold>) Representative hematoxylin and eosin stained images of PANC-1 tumors injected with NK92, FAP OE NK92, and FAP KO NK92 cells. Yellow indicates the border of the tumor and individual NK cells are indicated in red. n=3 mice. p<0.05 as determined by unpaired two-tailed t-test. All bar plots represent mean ± SD.</p></caption></fig>" ]	[]	[]	[]	[]	[]	[]	[]	[ "<fn-group><fn fn-type=\"COI-statement\" id=\"FN2\"><p id=\"P58\">Competing interests:</p><p id=\"P59\">The authors have no competing interests to declare.</p></fn><fn id=\"FN3\"><p id=\"P60\">Code availability statement:</p><p id=\"P61\">The authors declare that there is no custom code in this manuscript.</p></fn><fn id=\"FN4\"><p id=\"P62\">Ethics Statements:</p><p id=\"P63\">All NSG animal studies were reviewed and approved by the Georgetown University Institutional Animal Care and Use Committee (GU IACUC).</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"nihpp-rs3706465v1-f0001\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-rs3706465v1-f0002\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-rs3706465v1-f0003\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-rs3706465v1-f0004\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-rs3706465v1-f0005\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-rs3706465v1-f0006\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-rs3706465v1-f0007\" position=\"float\"/>" ]	[]	[{"label": ["8."], "surname": ["Li", "Jiang", "Li", "Fisher", "Li"], "given-names": ["B.", "Y.", "G.", "G. 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D."], "article-title": ["The Emergence of Natural Killer Cells as a Major Target in Cancer Immunotherapy"], "source": ["Trends Immunol"], "volume": ["40"], "year": ["2019"]}, {"label": ["15."], "surname": ["Fayette"], "given-names": ["J."], "article-title": ["Results of a phase II study evaluating monalizumab in combination with cetuximab in previously treated recurrent or metastatic squamous cell carcinoma of the head and neck (R/M SCCHN)"], "source": ["Ann. Oncol"], "volume": ["29"], "fpage": ["viii374"], "year": ["2018"]}, {"label": ["28."], "surname": ["Goldfarb Nannmark"], "given-names": ["R. H."], "article-title": ["Cells and Their Inhibitors Metalloproteinases of IL-2-Activated NK Secreted and Membrane-Associated Matrix"], "source": ["J Immunol Ref"], "volume": ["164"], "fpage": ["5883"], "lpage": ["5889"], "year": ["2000"]}, {"label": ["30."], "surname": ["Mace", "Gunesch", "Dixon", "Orange"], "given-names": ["E. M.", "J. T.", "A.", "J. 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Immunol"], "volume": ["11"], "year": ["2020"]}, {"label": ["65."], "surname": ["Martinez"], "given-names": ["A. L."], "article-title": ["Quantifying human natural killer cell migration by imaging and image analysis"], "source": ["Methods Mol Biol"], "volume": ["2453"], "fpage": ["129"], "lpage": ["151"], "year": ["2022"]}, {"label": ["66."], "surname": ["Shannon"], "given-names": ["M. J."], "article-title": ["Modulation of NK cell migration by Il-15 defined using a new tool for data-driven clustering of heterogeneous cell behaviour trajectories"], "source": ["bioRxiv. [preprint]"], "year": ["2023"]}, {"label": ["68."], "surname": ["Lin"], "given-names": ["Y. N."], "article-title": ["Monitoring cancer cell invasion and t-cell cytotoxicity in 3d culture"], "source": ["J. Vis. Exp."], "volume": ["2020"], "fpage": ["1"], "lpage": ["19"], "year": ["2020"]}]	{ "acronym": [], "definition": [] }	69	CC BY	no	2024-01-13 23:36:46	Res Sq. 2023 Dec 19;:rs.3.rs-3706465	oa_package/e1/17/PMC10775390.tar.gz
PMC10775391	38196609	[ "<title>Introduction</title>", "<p id=\"P19\">Coronary artery disease (CAD) is a highly heritable and leading cause of death worldwide. Genome-wide approaches estimate its heritability at 40%, with a complex underlying genetic architecture<sup>##UREF##0##1##</sup>. Genetic predisposition to CAD and its contributing risk factors can be summarized as polygenic risk scores (PRSs), which quantify an individual’s underlying genetic predisposition as a weighted sum across risk loci<sup>##REF##34811547##2##,##REF##29789686##3##</sup>. High polygenic risk for CAD has been independently and repeatedly associated with enhanced benefit from lipid-lowering therapy<sup>##UREF##1##4##–##REF##33433237##9##</sup>. Despite the ability to identify individuals with enhanced benefit from standard prevention strategies, demonstrations that communication of CAD PRS information can enhance adherence to clinical guidelines<sup>##UREF##3##10##–##REF##36147540##14##</sup>, and calls from thought leaders for the incorporation of CAD PRSs into clinical guidelines<sup>##UREF##4##11##,##REF##35130028##12##</sup>, there remains significant debate about the utility of PRSs, especially for risk stratification<sup>##REF##31367172##15##–##REF##30926966##18##</sup>.</p>", "<p id=\"P20\">Typically, studies that demonstrate no benefit of CAD PRSs in risk stratification focus on older populations with existing disease – performing cross-sectional rather than prospective prediction<sup>##REF##37219552##19##–##REF##31184202##21##</sup>. The conception of these studies is sub-optimal, given that the most appropriate scenario for the utility of a risk factor present at birth is the early detection and intervention of future risk, rather than the detection of late, ongoing disease, which is likely better captured by biomarkers downstream of genetics. In fact, studies that have focused on younger cohorts have consistently demonstrated a benefit of CAD PRS in risk stratification<sup>##UREF##5##22##–##REF##32273609##26##</sup>. Further, due to the heterogeneity of CAD as a disease and the genetic complexity of CAD predisposition, simple linear models are destined to fail to produce generalizable risk estimates and connections to multiple personalized interventions. The use of PRSs either needs to be contextualized to specific populations / interventions where linear models are useful, or, for multi-dimensional clinical decision-making scenarios, PRSs must be incorporated into more complex frameworks that allow for the interaction between multiple contributing genetic and non-genetic risk factors, a pre-requisite for the detection of personalized interventions.</p>", "<p id=\"P21\">Several strategies to incorporate multiple PRSs and clinical risk factors into unified CAD risk prediction frameworks have been pursued previously. Relatively straightforward attempts to combine single CAD PRSs and traditional risk factors linearly have resulted in marginal but potentially useful improvements in risk stratification<sup>##REF##32068817##20##,##REF##30104762##27##–##UREF##6##33##</sup>. Layering on some complexity to incorporate CAD risk factor associated variants (i.e. cholesterol and hypertension associations, etc.) into a single CAD-PRS improves prediction further, though any possibility of understanding the degree of contribution from individual risk factors and their interaction with measured risk factors is lost<sup>##REF##30309464##34##–##REF##30602773##36##</sup>. Similarly, combining individual CAD and risk factor PRSs with a wide range of candidate features in a linear manner allows for the identification of key genetic and clinical risk contributors; however, this brute-force strategy again leads to marginal improvements in risk prediction accuracy and fails to identify interactions required for personalized risk reduction recommendations<sup>##UREF##7##37##–##UREF##9##40##</sup>. Incorporation of PRSs into ML models introduces the possibility of complex interactions, but while there have been calls for multi-modal ML models with this utility<sup>##REF##28841416##41##–##UREF##10##43##</sup>, there have been no convincing demonstrations of ML modeling in genetically-informed and actionable risk estimation<sup>##REF##31922285##44##–##REF##35576555##48##</sup>.</p>", "<p id=\"P22\">Relatively simple approaches, from a genetic risk perspective, have been attempted for the integration of genetic and clinical risk factors in ML models. Some of these attempts have involved complex modeling, but ultimately produced cross-sectional predictions which, while useful and interesting in some contexts, are not useful for prevention<sup>##UREF##12##49##</sup>. Attempts at prospective prediction have also focused on manually selected CAD-specific PRSs, ignoring the genetic susceptibility of contributing risk factors and diagnoses, and leading to marginal improvements relative to clinical scores<sup>##REF##35331410##50##</sup>. For example, Steinfeldt et al. developed a prospective CAD risk prediction model using 5 manually selected CAD PRSs (and 1 stroke PRS), improving prospective prediction relative to standard clinical scores but lacking any demonstration of personalized prevention. Other attempts have failed to perform feature selection in the context of model performance, again leading to minimal improvement relative to standard clinical scores<sup>##UREF##13##51##,##REF##36713604##52##</sup>. For example, You et al. developed an ML model for 10-year incident CAD prediction using 645 candidate variables prioritized on the basis of multicollinearity with one another rather than contribution to predictive performance<sup>##UREF##14##53##</sup>. None of these prior approaches contemplate meta-prediction, which as we will demonstrate, results in the generation of the most important predictive features, which capture hidden unmodifiable risk status not necessarily expressed in biometrics and diagnoses at baseline, and critically, resulting in the identification of at-risk sub-groups with differing risk reduction benefit from standard clinical interventions.</p>", "<p id=\"P23\">Thus, our omnigenic<sup>##REF##28622505##54##–##REF##34331855##56##</sup>, integrative, meta-prediction framework is differentiated from this prior work by: 1) demonstrating prospective prediction, 2) adopting an omnigenic hypothesis by incorporating numerous PRSs in a unified risk prediction framework, 3) incorporating those PRSs in an ML framework which enables the detection of interactions between risk factors and interventions allowing for the detection of personalized interventions (i.e. CAD PRS interaction with changes in lipid levels leading to greater benefit in high PRS individuals), and 4) utilizing a meta-prediction framework which integrates predictions for numerous contributing risk factors and diagnoses at baseline and in the future to make an ultimate prediction about CAD event risk in the future. Our meta-prediction framework further incorporates unmodifiable risk predictions (based on age, sex, and genetic factors) as well as modifiable risk predictions (which incorporate measured biomarkers) which further aids in the separation of inherited vs acquired sources of risk. Each of these distinguishing factors contributes to the superior predictive performance we observed relative to prior prospective risk prediction work and allows us to derive personalized interventions directly from model predictions.</p>" ]	[ "<title>Methods</title>", "<title>Study population</title>", "<p id=\"P24\">We defined two primary cohorts (##FIG##0##Fig. 1a##), aged 40 to 69 at enrollment, from the UK Biobank to power our meta-prediction approach; 1) a prevalent disease cohort of 16,301 individuals with pre-existing CAD at the time of enrollment, and 2) an incident disease risk cohort composed of 15,809 individuals who developed CAD at any time up to 10 years after enrollment. All remaining individuals were considered as possible controls, with exclusion of individuals with insufficient EHR data to confirm lack of CAD.</p>", "<p id=\"P25\">To arrive at the primary control cohorts, all 470,304 remaining UK Biobank participants were considered as possible controls<sup>##REF##30305743##57##</sup>. 15,207 individuals were excluded due to withdrawal and/or a lack of genotype data. 147,817 individuals were excluded due to lack of sufficient EHR data, defined as those individuals with less than 1 year of follow-up by EHR or less than 3 EHR entries. And 4,054 individuals were excluded due to the development of CAD beyond 10 years of follow-up. After this filtration, 155,995 and 151,285 controls were randomly assigned to the prevalent disease and incident disease cohorts respectively, resulting in a balanced case rate in the two cohorts. The UK Biobank approved the use of the data under application number 41999. This study (IRB-17–7005) was approved by Scripps IRB review board.</p>", "<title>Definition of medical diagnoses and procedures</title>", "<p id=\"P26\">CAD, the primary outcome, is defined by the appearance of any of the following diagnostic or procedure codes in a subjects EHR, including for heart attack or myocardial infarction from International Classification of Diseases version 10 (ICD-10) codes I21-I24, I25.2 or I46, ICD-9 codes 410–412, 414.2 or 427.5, as well as revascularization and other surgical interventions from Office of Population Censuses and Surveys Classification of Interventions and Procedures version 4 (OPCS-4) codes K40-K46, K47.1, K49, K50, or K75. The date of the event is assigned to the date of the earliest qualifying code. CAD may also be defined by self-report, including responses to the following survey questions: “Vascular/heart problems diagnosed by doctor” response of “1: Heart attack”, “Non-cancer illness code, self-reported” response of “1075: heart attack/myocardial infarction”, “Operation code” response of “1070: coronary angioplasty (ptca) +/− stent”, “1095: coronary artery bypass grafts (cabg)” or “1523: triple heart bypass”. Age of self-report was used to determine the date of the event. Age of CAD was additionally used to further sub-divide the prevalent CAD cohort into an early onset (< 55 years old) and late onset (≥ 55 years old) cohort, allowing for the development of baseline risk models for CAD at any time, as well as early onset vs late onset CAD.</p>", "<p id=\"P27\">The definition of all 31 other contributing medical diagnoses and procedures used in this work are provided in <bold>Supplementary Table 1</bold>. These additional contributing diagnoses are used in three different ways: 1) they are used as outcomes to generate baseline prevalent risk models and predictions, 2) they are used to make prospective risk models and predictions, including those for secondary CAD diagnoses occurring in the prevalent CAD cohort, and 3) the actual occurrence and duration of diagnoses prior to baseline were used directly as predictive features. To generate baseline and incident risk prediction models for these diagnoses, the prevalent CAD cohort was also stratified into case-control cohorts by these additional diagnoses. Case-control numbers for each of these contributing diagnoses can be found in <bold>Supplementary Table 2</bold>. Controls for these cohorts were filtered to remove individuals with no new EHR entries after the first visit or who died during follow-up due to other reasons.</p>", "<title>Modifiable predictive feature prioritization and pre-processing</title>", "<p id=\"P28\">The initial set of predictive features, not including genetic features, considered for model development was based on established primary prevention risk models<sup>##REF##34247495##58##–##UREF##16##60##</sup> and input from experienced physicians and domain experts on the study team (<bold>Supplementary Table 3</bold>). 36 socio-demographic, 37 family history, 24 self-reported medication use, and 31 lifestyle features were included, collected through a questionnaire administered at the time of recruitment. 22 physical measurements and 63 laboratory tests also collected at the time of recruitment were included. Categorical features were first encoded using the CatBoost Encoding<sup>##UREF##17##61##</sup>. Repeated measures were aggregated by taking the mean on continuous measures and mode on ordinal measures. Data fields either missing in up to 20% of entries or recorded as “Do not know” or “Prefer not to answer” were subject to imputation using the multiple imputation by chained equations (MICE), incorporating all available variables including those with complete data, using miceRanger package with 5 iterations<sup>##UREF##18##62##</sup>. 12 features known to be important to CAD risk with greater than 20% missingness, for example smoking features, were also imputed. The preservation of the distribution of variable fields before and after imputation is demonstrated in <bold>Extended Data Fig. 1</bold>. A total of 21 medication variables were created by matching drug name keywords found in both verbal-interview data (<bold>Supplementary Table 4</bold>) and general practitioner (GP) primary care records (<bold>Supplementary Table 5</bold>). Medication variables were excluded from the imputation process.</p>", "<p id=\"P29\">In addition to the previously described EHR based phenotypes, another 28 synthetic features based on combinations of individual fields were defined and determined post-imputation (<bold>Supplementary Table 3</bold>). We also calculated 2 conventional CAD risk scores; the Pooled Cohort Equations (PCEs) and QResearch Cardiovascular disease Risk Algorithm (QRISK) using modified open-source modules: PCE (<ext-link xlink:href=\"https://github.com/Articulus-Tech/ascvd\" ext-link-type=\"uri\">https://github.com/Articulus-Tech/ascvd</ext-link>) and QRISK3 (<ext-link xlink:href=\"https://f1000research.com/articles/8-2139\" ext-link-type=\"uri\">https://f1000research.com/articles/8-2139</ext-link>).</p>", "<title>Unmodifiable predictive features</title>", "<p id=\"P30\">PRSs were generated using the array-based genotyping data from the UK Biobank after imputation using the standard weighted sum of allele effect and standardization approach<sup>##UREF##19##63##</sup>. Genotype imputation was performed using the Haplotype Reference Consortium (HRC) reference panel<sup>##REF##27548312##64##</sup> and following standard procedures<sup>##UREF##19##63##</sup>, resulting in 37,995,438 autosomal variants for analysis. We selected 17 PRSs not present in the PGS catalog for cardiometabolic traits based on GWAS summary statistics from the latest large-scale GWASs<sup>##UREF##19##63##,##REF##30061737##65##–##REF##30297969##71##</sup>. Furthermore, we calculated all 3,664 PRSs defined in the PGS Catalog as of 01 June 2023<sup>##REF##33692568##72##</sup>. To prevent potential overfitting, 2,588 PRSs exclusively derived using UK-Biobank data were excluded. A total of 1,093 standardized scores were retained for ML model training (<bold>Supplementary Table 6</bold>).</p>", "<p id=\"P31\">Genetic ancestry derived from 5 continental populations was estimated using the ADMIXTURE software using 67,047 ancestry informative markers and the 1000 Genomes reference<sup>##REF##19648217##73##</sup>. Sex and family history was determined by self-report. Age, sex, ancestry, family history, and polygenic risk comprise the set of 1,100 unmodifiable risk factors.</p>", "<title>Feature selection and development of the ML pipelines</title>", "<p id=\"P32\">Tree-based machine learning models, including XGBoost, LightGBM and CatBoost, were considered for all prediction tasks. Other ML models were considered initially but discarded after exploratory analyses demonstrated consistently superior results from XGBoost, LightGBM and CatBoost. Each cohort was divided into an 80:20 train-test split to assess model performance. The train set was further divided during cross-validation for hyperparameter selection as described below.</p>", "<p id=\"P33\">For feature selection, to accommodate the large number of initial predictive features, we performed a round of pre-selection applying the Saabas’s approximate algorithm to identify the top 200 important features by the average absolute SHapley Additive exPlanations (SHAP) values<sup>##UREF##20##74##</sup>. We then used our “zoish” wrapper package, which incorporates the v2 algorithm of the SHAP Tree Explainer from the fasttreeshap package to conduct extensive training trials testing various feature number upper bounds to identify minimal models achieving high predictive accuracy (<bold>Supplementary Table 7</bold>). Hyperparameter tuning was automated using our lohrasb module, which incorporates the Tree-structured Parzen Estimator (TPE) sampler along with the Hyperband pruner provided by optuna.</p>", "<p id=\"P34\">This structure was used to search for the best hyperparameter configuration within pre-defined bounds (<bold>Supplementary Table 8</bold>). For classification tasks, where possible we included sample weights and stratified sampling approaches to account for case-control class imbalance. For each model, we performed 100 trials to identify the best set of hyperparameters, with their performance evaluated using 10-fold cross-validation within the train set (<bold>Supplementary Table 7</bold>). Models were prioritized by F1 score for classification or R<sup>2</sup> score for regression. Our wrapper pipelines were implemented within the scikit-learn framework in Python 3.8.3 and are available via Github.</p>", "<title>CAD meta-prediction</title>", "<p id=\"P35\">Baseline prediction models trained in the prevalent CAD cohort included (##FIG##0##Fig. 1b##): 1) regression models predicting the baseline values of modifiable risk factors, 2) classification models predicting baseline CAD and CAD component diagnoses as well as contributing diagnoses, and 3) classification models predicting future contributing diagnosis. This body of prediction tasks results in 287 meta-features used in the final incident CAD prediction model.</p>", "<p id=\"P36\">Meta-features representing baseline predicted values of modifiable risk factors were generated in two ways (<bold>Supplementary Table 9</bold>): 1) by predicting their values using unmodifiable features alone, or 2) by predicting their baseline values using both unmodifiable and modifiable features.</p>", "<p id=\"P37\">In contrast, meta-features for baseline CAD, CAD component, and contributing diagnosis were generated using only unmodifiable features (in order to avoid reverse causation from modifiable risk factors measured at the first recruitment visit after past diagnoses had occurred). These diagnostic outcomes were modeled in three ways (<bold>Supplementary Table 9</bold>): 1) early onset (< 55 years old), 2) late onset (≥ 55 years old), and 3) onset at any age prior to the first UK Biobank recruitment visit.</p>", "<p id=\"P38\">To predict future contributing diagnoses, we developed two different but overlapping incident risk models per diagnosis (<bold>Supplementary Table 9</bold>): 1) incident disease onset within 10-years of baseline, and 2) within 20-years of baseline. Since reverse causation is not a concern here, both unmodifiable and modifiable predictive features were included in these models.</p>", "<p id=\"P39\">Inference in the incident CAD risk cohort was performed using these models trained on the prevalent CAD risk cohort. These predictions were stacked with their measured baseline values to produce a final ensemble model for the prediction of incident CAD risk over 10-years. 10 years risk is the standard risk interval used in clinical decision-making especially around the initiation of lipid lowering therapy.</p>", "<p id=\"P40\">The full set of baseline features and meta-features were then evaluated using multiple ML algorithms and parameters described previously, with the final XGBoost model selected due to a combination of high accuracy with lower complexity from a feature count and tree depth perspective (<bold>Supplementary Table 10</bold> – final model and parameters is bolded).</p>", "<title>Sub-group identification and therapeutic prioritization</title>", "<p id=\"P41\">Individual level SHAP model explanation values derived from the final meta-prediction model were used to cluster the prospective case cohort into risk sub-groups. This was achieved with agglomerative hierarchical clustering using Ward’s linkage method and Euclidean distance, resulting in sub-groups with individuals sharing similar overall risk profiles. The number of sub-groups was defined by fixed-height tree cutting of the resultant dendrogram. We then conducted one-way ANOVA across all predictive features and used the resultant η<sup>2</sup> value to prioritize the predictive features that most strongly distinguish the incident case sub-groups.</p>", "<p id=\"P42\">To assign control individuals to these incident risk sub-groups, we trained a multi-classification XGBoost model using prospective CAD cases and the same features selected for the meta-prediction. Control individuals were assigned to case sub-groups if they achieved an assignment probability of greater than 0.5. At-risk controls were then defined by elevation of individual risk factors (<bold>Supplementary Table 11</bold>).</p>", "<p id=\"P43\">The impact of preventive interventions was then calculated by modulating: 1) LDL alongside total cholesterol, 2) HbA1c alongside glucose, and 3) systolic blood pressure (SBP) to standard clinical targets (<bold>Supplementary Table 11</bold>). These modulated clinical risk factors were re-entered into the meta-prediction framework to produce an absolute risk estimate post-intervention. The difference between the individual’s initial and modulated risk prediction was then calculated as their absolute degree of risk reduction.</p>", "<title>Reporting</title>", "<p id=\"P44\">We followed the MI-CLAIM and TRIPOD checklists for reporting results (<bold>Supplementary Tables 12 and 13</bold>). All statistical analyses were performed in R v4.0.0 or Python v3.8.3. All accuracy metrics are reported on the hold-out test cohort after train:test splitting. Risk sub-groups and their differential risk reduction profiles are reported using the entire incident risk cohort. Comparative analysis to prior published PRS models and standard clinical risk scores was performed by training logistic regression models including PRSs, clinical scores, and/or age and sex as covariates, in the same train:test populations used in our ML model training.</p>" ]	[ "<title>Results</title>", "<title>Cohort definition</title>", "<p id=\"P45\">The final study population after quality filtration and case-control assignment included 339,390 participants aged between 40 and 69 who enrolled in the UK Biobank (UKB) from 2006 to 2010. 44.95% were male and 55.05% were female, with 9.46% having a diagnosis of coronary artery disease (CAD). 172,296 of these participants formed the prevalent CAD cohort, with 9.46% of these participants diagnosed with CAD prior to enrollment. For incident CAD prediction, we assembled a cohort of 167,094 participants, 9.46% being diagnosed with CAD after baseline over the 10-year follow-up period (##FIG##0##Fig. 1a##). Additional demographic and clinical characteristics of the prevalent and incident risk cohorts are available in ##TAB##0##Table 1##. While the size of these cohorts leads to statistically significant differences across all parameters, it can be observed that the absolute magnitude of all baseline values is similar across the prevalent and incident CAD cohorts, with the exception of medication usage. The full set of predictive features and their measures in the prevalent and incident risk cohorts are provided in <bold>Supplementary Table 3</bold> in addition to genetic predictors in <bold>Supplementary Table 6.</bold></p>", "<title>Meta-feature generation and feature selection</title>", "<p id=\"P46\">1,465 measured features were used to generate 287 additional meta-features by performing training in the prevalent CAD cohort and inference in the incident CAD cohort (##FIG##0##Fig. 1b##). <bold>Supplementary Table 14</bold> details the outcomes, whether modifiable or unmodifiable predictive features were used, and the test accuracy of each of these 287 predictive models. By design, models predicting diagnoses prior to baseline tend to include a larger number of PRSs as predictive features, whereas future diagnosis models included multiple PRSs alongside several, relevant, measured risk factors.</p>", "<p id=\"P47\">After meta-feature generation, a total of 1,752 features were considered for inclusion in the final 10-year incident CAD risk ML model via feature selection. Initial approximate feature selection resulted in 200 top ranked predictive features (<bold>Supplementary Table 15</bold>) of which the 60 most important features were selected for final model inclusion via final model training and SHAP-based feature selection. The final 60 prioritized features included 13 directly measured features, 12 PRSs and 35 meta-features (<bold>Extended Data Fig. 2</bold>). Included among the directly measured predictive features, there are 5 lipid measurements, 2 diabetes-related biomarkers, 3 physical measurements, 1 immune function factor, and 2 smoking factors – all of which have established clinical significance in mediating CAD. The genetic risk features selected for the final model included 5 CAD-PRSs (including one CHD-PRS), 1 lipoprotein A-PRS, 2 type 2 diabetes (T2D)-PRSs, 1 alcohol consumption-PRS, 1 major depressive disorder (MDD)-PRS, 1 coeliac disease-PRS, and 1 lung cancer-PRS. The derived meta-features selected for the final model included 2 predicted modifiable factors, 16 predicted future diagnoses, and 17 predicted baseline diagnoses. These predicted diagnoses included a battery of different cardiovascular events, diabetes, white blood cell leukocyte count and waist-hip ratio. These 35 meta-features effectively resulted in the addition of 97 embedded predictive features, 9 of which were also included as stand-alone features in the final model, while 88 of which were selected for inclusion solely via being embedded in a derived meta-feature. Thus, the final model was composed of 114 unique measured features included either as stand-alone features in the final model or embedded within derived meta-features (<bold>Supplementary Table 16</bold>). Further detail of the predictive features is provided after characterizing the accuracy of our final meta-prediction model.</p>", "<title>Meta-prediction performance for 10-year incident CAD risk</title>", "<p id=\"P48\">The final meta-prediction model (<bold>Supplementary Table 10</bold> – final model and its performance is bolded) effectively stratified the hold-out test set of the incident CAD cohort (n = 33,419) into distinct risk trajectories. By 6-years of follow-up from baseline, over 50.0% of subjects within the highest percentile bin of predicted risk developed CAD (##FIG##0##Fig. 1c##). At 10-years of follow-up, cumulative CAD incidence rates spanned from 0.3% in the lowest percentile to 63.0% in the highest (##FIG##0##Fig. 1c## and ##FIG##0##1d##). Altogether, the model had an AUROC of 0.81 (95% CI 0.80–0.82) (##FIG##0##Fig. 1e##) and AUPRC of 0.35 (95% CI 0.33–36) (##FIG##0##Fig. 1f##). The final model had a true positive rate of 54% (95% CI 52%–56%), true negative rate of 87% (95% CI 87%–88%), false positive rate of 13% (95% CI 12–13%), and false negative rate of 46% (95% CI 44%–48%). This equates to an accuracy of 84% (95% CI 84%–85%), a sensitivity of 54% (95% CI 52%–56%), and a specificity of 87% (95% CI 87%–88%), with an F1 score of 0.40 and a macro-averaged F1 score of 0.65. No individual feature category was able to achieve the performance of the final meta-prediction model, including a model composed solely of meta-features, demonstrating the importance of incorporating directly measured and predicted features for final model performance. Specifically, the 13 measured features achieved an AUROC of 0.69 and AUPRC of 0.19, 12 PRSs with an AUROC of 0.65 and AUPRC of 0.18, and the 35 meta-features with an AUROC of 0.78 and AUPRC of 0.30 (<bold>Extended Data Fig. 3</bold>).</p>", "<title>Comparative Evaluation of Clinical Validity between Meta-Prediction and Contemporary Risk Assessment Tools</title>", "<p id=\"P49\">The accuracy of our final meta-prediction model significantly and substantially exceeded the accuracy of conventional clinical risk scores and previous polygenic risk score benchmarks. Our model produced improved risk stratification across all percentiles of predicted risk across the 10-year follow-up period with an average 2-fold enrichment of CAD events at 10-years among the top percentile bin (<bold>Extended Data Fig. 4</bold>). The AUROC of our meta-prediction model was on average 10% higher than existing approaches (0.81 vs 0.73–0.74), with AUPRCs 63% higher on average (0.35 vs 0.21–0.22) (##FIG##0##Fig. 1e##–##FIG##0##f##). Similarly, a survival-based XGBoost estimator (Debiased BCE algorithm from XGBSE) using the same feature set achieved a similar concordance index (C-index) of 0.80. This C-index exceeded that achieved by applying Cox-PH regression to PCE, QRISK3, GPS<sub>CAD</sub><sup>##REF##30104762##27##</sup>, and metaGRS<sub>CAD</sub><sup>##REF##30309464##34##</sup>, in agreement with the performance metrics reported in the original publications for these scores, and again leading to a 10% improvement relative to all prior existing methods (0.80 vs 0.72–0.73) (<bold>Supplementary Table 17</bold>). This improved performance translates to a re-classification index of 0.6 (continuous net re-classification index - NRI), 0.3 (absolute integrated discrimination improvement - IDI), and relative IDI of 5.0 on average (<bold>Supplementary Table 18</bold>).</p>", "<p id=\"P50\">From a binary classification perspective, our meta-prediction approach led to substantial reclassification improvements relative to existing risk scores. The optimal cutoff point for classification was determined using Youden’s index of the AUROC curve, resulting in an optimal meta-prediction classification cutoff as 0.24. When compared to standard clinical score cutoffs of 7.5% for PCE and 10% for QRISK3, our meta-predictor achieved an NRI of 0.13 over PCE, 0.17 over QRISK3. The optimal cutoff for prior polygenic risk models was similarly determined using Youden’s index, leading to an NRI of 0.22 over GPS<sub>CAD</sub> and 0.23 over metaGRS<sub>CAD</sub> (##TAB##1##Table 2##).</p>", "<title>Robustness of meta-prediction across sub-populations</title>", "<p id=\"P51\">To ensure model robustness across various CAD risk sub-populations and determine which risk sub-populations may be more effectively captured by our ML model vs prior approaches, we stratified the incident risk cohort by various standard risk factors; PCE, QRISK3, age, sex, CAD-PRS, T2D-PRS, low-density lipoprotein (LDL), LDL-PRS, triglycerides (TGs), TGs-PRS, glycated hemoglobin (HbA1c), HbA1c-PRS, systolic blood pressure (SBP), SBP-PRS, waist-hip ratio (WHR), WHR-PRS, body mass index (BMI), BMI-PRS. Our meta-prediction approach resulted in superior performance across all strata explored, with an average 2-fold improvement in CAD event enrichment in the top percentile of CAD risk, an average 10% improvement in AUROC, and an average 68% improvement in AUPRC per strata, as observed for the overall cohort (##FIG##1##Fig. 2##\n<bold>and Supplementary Table 19)</bold>. Our meta-prediction approach achieved the greatest gains in performance relative to prior methods for individuals with low-PCE (< 7.5%), low-QRISK3 (< 10%), and younger individuals (< 55-year-old), as these groups exhibited enhancements 30–70% improvements in AUC relative to existing approaches (##FIG##1##Fig. 2##\n<bold>and Supplementary Table 20).</bold> These observations are concordant with the known deficiencies in existing risk stratification approaches for capturing at-risk individuals among the typically “low risk.” Although genetic factors are expected to be the primary drivers of risk detection in traditionally low risk individuals, the improved performance among low risk subpopulations included improvements beyond existing PRS-based prediction models, suggesting our improvements are derived from more than just the inclusion of genetic factors.</p>", "<title>Meta-prediction model explanation</title>", "<p id=\"P52\">To examine the interpretability of our meta-prediction approach, we now describe the measured features that compose the meta-features by examining their importance to those meta-features. Notably, 33 of the 35 meta-features represented predictions of baseline (52%) or future (48%) cardiometabolic or contributing diagnoses, including predictions of abdominal aortic aneurysm, angina, atherosclerotic cardiovascular disease, atrial fibrillation, non-stroke (pre-)cerebral disease, CAD, diabetes, dilated cardiomyopathy, heart failure, myocardial infarction, nonischemic cardiomyopathy, peripheral artery disease, revascularization and stroke risk. The two remaining meta-features were predictions of leukocyte counts and WHR at baseline.</p>", "<p id=\"P53\">Past diagnosis predictions were 41% early-onset, 12% late-onset and 47% any-onset, made using only unmodifiable predictive features. These predictors include several PRSs for cardiovascular conditions, lipid indicators, diabetic elements, immune system features, and a key coagulation activator (protease-activated receptor 1, PAR1), as well as family history. Future diagnosis predictions were 62.5% over 10- and 37.5% over 20-years (##FIG##2##Fig. 3##\n<bold>and Extended Data Fig. 2</bold>). SHAP plots for the top three predictive meta-features are provided on the left side of ##FIG##2##Fig. 3##. The predictive features contributing to these baseline and future risks were largely composed of PRSs, medication use, biomarker measurements, and long-term lifestyle choices. Example SHAP plots for non-CAD or non-CAD component meta-features are provided on the right side of ##FIG##2##Fig. 3##.</p>", "<p id=\"P54\">The SHAP plots for all meta-features included in the final model are provided in <bold>Extended Data Fig. 5</bold>. Among the 97 features used in these meta-features, we found that age, sex, and 3 CAD-PRSs were the most frequently used predictive factors (<bold>Supplementary Table 16</bold>). 27 meta-feature models contained a non-CAD PRS, including PRSs for contributing diagnoses, related diagnoses, and biomarker levels.</p>", "<title>CAD risk sub-group identification and characterization</title>", "<p id=\"P55\">Next, we set out to determine whether our model was able to capture sub-groups of CAD risk that respond differentially to standard clinical risk reducing interventions. As a first step, we identified risk sub-groups in an unbiased manner by clustering individuals by the SHAP values contributing to their overall CAD risk prediction. This approach effectively groups individuals who arrived at their final risk prediction due to similar risk factor profiles. Clustering of the incident cohort CAD cases revealed five distinct subgroups (##FIG##3##Fig. 4a##). Control individuals were assigned to these sub-groups as described in <xref rid=\"S2\" ref-type=\"sec\">Methods</xref>. A comprehensive summary of the baseline characteristics of each sub-group can be found in ##TAB##2##Table 3##. 67 predictive features were significantly stratified across these subgroups as measured by the ANOVA effect size η<sup>2</sup> > 0.01. 19 predictive features were differentiated across these sub-groups with a large effect size (η<sup>2</sup> ≥ 0.14), 9 with moderate effects (0.14 > η<sup>2</sup> ≥ 0.06), and the remainder with small effects (##FIG##3##Fig. 4b##). Among the features with large effect size, 15 (79%) were meta-features predicting baseline diagnoses, predicted by unmodifiable risk factors only. 33% of these meta-features were predictions of early-onset events, 13% predicted late-onset events, and 53% predicted events any-time prior to baseline. Notably the three CAD-PRSs (from PGS000337, PGS003446, and PGS003356) and the meta-feature representing baseline prediction of CAD onset using only unmodifiable risk factors showed the greatest degree of stratification across the risk sub-groups. These features differentiated the sub-groups to a greater extent than age or sex as measured by η<sup>2</sup> (##FIG##3##Fig. 4c##). Further delineation of sub-groups by further sub-division of the 5 major sub-groups differentiated by standard clinical risk factors including glycated hemoglobin, LDL, and SBP. In other words, measured biometric values defined sub-groups within the major sub-groups primarily defined by their unmodifiable risk profile (<bold>Extended Data Fig. 6).</bold></p>", "<title>Genetic risk stratification and differential response to clinical interventions</title>", "<p id=\"P56\">Upon identifying CAD risk sub-groups, we then set out to determine whether individuals within these sub-groups were predicted to respond differentially to standard clinical risk reducing interventions. In particular, we attempted to confirm and demonstrate that the degree of risk reduction achieved through standard preventive interventions was related to underlying genetic risk. Using our trained models, we simulated the influence of risk reducing interventions on the target analyte and performed model inference to determine the change in predicted risk. The clinical interventions, thresholds used to identify at risk individuals, the perturbed biomarker, and their clinical targets described in <bold>Supplementary Table 11</bold>. The degree of absolute CAD risk reduction achieved in the cohort overall by meeting clinical targets for LDL, HbA1c, and SBP by degree of the associated genetic risk is presented in ##FIG##4##Fig. 5a##–##FIG##4##c##. Similarly, the degree of absolute and relative risk reduction achieved in each CAD sub-group by meeting these targets is presented in ##FIG##4##Fig. 5d##–##FIG##4##f## and ##FIG##4##Fig. 5g##–##FIG##4##i##.</p>", "<p id=\"P57\">Across the population overall, for individuals with a PCE ≥ 7.5%, a widely accepted threshold for the initiation of cholesterol-lowering therapy, the degree of absolute CAD risk reduction increases with increasing CAD genetic risk (##FIG##4##Fig. 5a##). This is true across all LDL target levels tested. Notably, the degree of risk reduction achieved varied substantially across genetic risk and LDL targets, with the magnitude of risk reduction in high vs low genetic risk individuals ranging from 2.91% at an LDL target of 100 mg/dL to 11.13% at an LDL target of 35 mg/dL. This suggests that low LDL targets are substantially more beneficial for individuals at high genetic risk. Furthermore, when the degree of risk reduction is broken down by risk sub-groups, it can be observed that those individuals in the high CAD genetic risk sub-groups achieved substantially more absolute and relative risk reduction than other risk sub-groups, particularly for the low LDL target of 35 mg/dL (##FIG##4##Fig. 5d##,##FIG##4##g##). An absolute risk reduction difference of more than 8% can be achieved in these high genetic risk sub-groups going from an LDL of 100 mg/dL to 35 mg/dL (##FIG##4##Fig. 5d##). In contrast, the CAD risk sub-groups with non-high CAD-PRS achieved much more modest reductions in absolute and relative risk when going from an LDL of 100 mg/dL to 35 mg/dL (##FIG##4##Fig. 5g##).</p>", "<p id=\"P58\">For patients with HbA1c ≥ 6%, a diabetic or pre-diabetic threshold depending upon the national standard, the degree of absolute risk reduction achieved by HbA1c lowering increased with increasing T2D genetic risk (##FIG##4##Fig. 5b##). In this case there was little difference observed in the degree of risk reduction achieved at HbA1c targets of 6% (2.81% absolute risk reduction) vs a HbA1c target of 5.6% (2.89% absolute risk reduction). However, again we observed sub-groups achieving greater absolute and relative risk reduction levels through HbA1C lowering. Notably, two sub-groups (blue and purple) achieved similar and increased benefit from HbA1c lowering, with increasing benefit across HbA1c targets. These two sub-groups are enriched with diabetic individuals relative to other sub-groups except for the highest risk sub-groups.</p>", "<p id=\"P59\">For patients with SBP ≥ 140 mmHg, a similar trend of increasing absolute risk reduction with increasing SBP PRS is observed, though with the uncertainty in the SBP-PRS apparent in the risk trajectories, with high genetic risk individuals achieving 1.11% absolute risk reduction at an SBP target of 120 mmHg and 1.23% at an SBP target of 110 mmHg (##FIG##4##Fig. 5c##). Markedly different trajectories in risk reduction achieved by SBP lowering can be observed across the CAD risk subgroups (##FIG##4##Fig. 5f##,##FIG##4##i##). Notably, the highest CAD genetic risk sub-group (red) achieves the least benefit from SBP lowering, whereas the second highest CAD genetic risk sub-group (orange) achieves the greatest degree of absolute and relative risk reduction from SBP lowering. The optimal SBP target for this SBP responsive sub-group appears to be lower (100 mmHg) than the optimal SBP target for other sub-groups (110 mmHg). All sub-groups display increasing risk when SBP is overly lowered (≤ 90mmHg) and increasing risk above 140 mmHg (##FIG##4##Fig. 5f##,##FIG##4##i##).</p>" ]	[ "<title>Discussion</title>", "<p id=\"P60\">Here we demonstrate that our meta-prediction framework, which appropriately integrates genetic and non-genetic risk factors into an ensemble prospective risk prediction model, produces superior and more generalizable risk predictions relative to the current clinical standard as well as existing linear / percentile-based genetic risk stratification approaches. Individualized risk reduction profiles are captured natively by this model and are most strongly linked to differences in genetic risk background across individuals, without any engineering of the model to produce such differential risk reduction profiles. This work serves as a clear demonstration of the importance and utility of incorporating genetic risk into modern risk stratification approaches for clinical prevention, both for risk detection and for clinical-decision support.</p>", "<p id=\"P61\">One important strength of our approach is the comprehensive dissection of CAD risk factors into past, present, and future measures and projections of risk, handling modifiable and unmodifiable risk factors in a manner that allows for the separate and combined characterization of inborn vs acquired sources of risk. The substantially greater importance of meta-features incorporating only unmodifiable sources of risk further underscores the importance of genetic risk in producing superior and actionable risk profiles. Genetic risk for CAD, CAD components, other late cardiovascular outcomes like cardiomyopathy, contributing diagnoses like T2D and CKD, biomarkers linked to CAD risk including lipid levels, waist-hip ratio, and coagulation factors, and other diagnoses like depression and lung cancer all contribute to the genetic landscape driving CAD risk and personalized avenues for intervention. While we only project the benefits achieved by modulating standard clinical risk factors (LDL, glucose, blood pressure), it is likely that the plethora of polygenic risk scores contributing to this prediction underlie further opportunities for personalized health interventions. Furthermore, our approach to transforming basic unmodifiable risk factors like age and sex into more complex, risk-factor specific, unmodifiable risk profiles highlight the importance of personalizing even the most basic of risk factors. While age and sex are the major components of PCE risk, leading to highly non-personalized projections of risk, age and sex are not used as standalone predictors in our model. This ultimately results in the detection of risk among those individuals typically considered low risk under current standards.</p>", "<p id=\"P62\">In fact, most patients diagnosed with premature myocardial infarction (< 55 years old) are not identified as at risk before their event by the guideline standard PCE 10-year ASCVD risk estimator<sup>##REF##32762899##75##</sup>. There has also been concern regarding the significant overestimation of events by PCE and QRISK3<sup>##REF##27436865##76##,##REF##34100008##77##</sup>. Our model is able to overcome some of the flaws of these scores, particularly by identifying younger individuals who are at risk, identifying individuals at risk despite low PCE / QRISK, as well as identifying people not at risk despite elevated traditional risk factors. 20% of individuals without an event over the 10-year follow-up period were identified as at risk by PCE > 7.5% but not at risk by our meta-prediction model (##TAB##1##Table 2##). Similarly, > 10% of the population with an event over the 10-year follow-up period were identified as low risk by PCE / QRISK but captured as high risk by our model. These results suggest that our framework has the potential to overcome the key limitations of the current clinical standard.</p>", "<p id=\"P63\">A weakness of our work is the lack of independent replication. However, through careful train, validation, and test splits of the UK Biobank dataset, we were able to derive a robust model that recapitulates observations and clinical standards derived from external datasets.</p>", "<p id=\"P64\">For example, Mega et al. initially reported 1% – 8% absolute risk reduction by LDL lowering in high genetic risk individuals vs 0% – 2% absolute risk reduction by LDL lowering in low genetic risk individuals across several primary and secondary prevention trials<sup>##UREF##2##6##</sup> – these values correspond to the absolute risk reduction profiles produced by our independent ML model. Current cholesterol control guidelines include intensive-cholesterol lowering (target < 70mg/dL) therapy for individuals at high CAD risk due to traditional CAD risk factors<sup>##REF##30586774##78##,##REF##31504418##79##</sup>. Our model supports this conclusion and suggests that genetic risk profiles can help identify at-risk individuals who may benefit from intensive cholesterol-lowering therapy, further supporting targets of < 35 mg/dL proposed by some clinical guidelines for high-risk individuals<sup>##REF##32190133##80##</sup>. At the same time, our results support the conclusion that there is little benefit to reducing LDL below 70–100 mg/dL for individuals not at high risk, in line with current European guidelines (##FIG##4##Fig. 5d##)<sup>##REF##31504418##79##</sup>.</p>", "<p id=\"P65\">The 2017 American BP guidelines state that a SBP < 130 mmHg may be a reasonable target for hypertensive adults without additional markers of increased cardiovascular risk while the 2023 European guidelines support SBP < 130 mmHg for hypertensive individuals<sup>##REF##29133356##81##,##UREF##21##82##</sup>. In general, there is increasingly compelling evidence supporting the universal SBP goal of < 130 mmHg<sup>##REF##26551272##83##–##REF##34491661##86##</sup>. Our results support this, where model inference results in the majority of risk reduction being achieved at an SBP of 130 mmHg. However, our study also supports optimal SBP targets between 100 and 110 mmHg. A recent meta-analysis supports this finding by showing a strong and continuous dose-response between SBP levels and CAD risk from 100 to 200 mmHg, with optimal BP under 120 mmHg<sup>##REF##36216934##87##</sup>. With excessive SBP lowering, we observe slightly increased risk of CAD, which has been previously observed in clinical studies (##FIG##4##Fig. 5i##)<sup>##REF##27590090##88##,##REF##15837826##89##</sup>.</p>", "<p id=\"P66\">Similarly, clinical trials targeting HbA1c to near-normal levels using intensive glucose-lowering therapy have failed to demonstrate any benefit despite the fact that T2D is a major established risk factor for CAD<sup>##REF##18539916##90##–##REF##18539917##92##</sup>. The small degree of absolute risk reduction observed via glucose lowering is consistent with our findings (##FIG##4##Fig. 5e##). A recent study showed that intensive glucose-lowering therapy was beneficial in preventing incident CAD only in a subset of diabetic patients defined by haptoglobin type, implying that genotypic and/or phenotypic differences can mediate the cardioprotective effects of intensive glucose-lowering therapy<sup>##REF##32029134##93##</sup>. Our results support this possibility as the additional CAD risk reduction observed at the HbA1c level below 6%, the usual target value of intensive glucose-lowering therapy, was confined to two of the five subgroups classified according to genetic and phenotypic features (##FIG##4##Fig. 5e##).</p>", "<p id=\"P67\">Collectively, these findings suggest that meta-prediction framework can be used as a tool to plan and prioritize clinical intervention strategies and to tailor the therapeutic targets of each intervention to individual subjects, which may promote improved personalized care for primary CAD prevention. While our results are promising, we recognize the need for further research and external validation. Our findings must be tested across diverse populations and in various clinical settings to solidify the reliability and applicability of our framework. The pursuit of such confirmatory studies will be critical in refining our predictive tool and in ensuring that it can serve as a robust platform for the personalized prevention of CAD.</p>", "<p id=\"P68\">Improving the meta-prediction framework’s performance could involve several enhancements. Longitudinal biomarker and lifestyle data could add valuable temporal dimensions to risk profiling, capturing the evolving risk profile of an individual. Similarly, improvements in the capture of detailed EHR data would enable a more precise assessment of the duration of contributing diagnoses and a more accurate interpretation of those contributing risk factors. Furthermore, the integration of comprehensive ‘omics’ data—beyond genomics to include epigenomics, transcriptomics, proteomics, and metabolomics - could provide a means to measure the outcome of gene by environment interactions longitudinally, though multiple ‘omics assessments are likely not practical in real-world scenarios. In addition, widening the dataset to include a more diverse population, in terms of both ages and ancestry, could help to ensure that the model’s predictions are universally applicable. Although our study encompasses social determinants, enhancing the granularity and quality of this data could offer deeper insights into how these factors interact with biomarkers to influence individual health trajectories.</p>", "<p id=\"P69\">In summary, we present a fully integrative meta-prediction framework significantly outperforming current research and clinical standards for prospective prediction, especially in sub-groups of individuals traditionally considered low risk. This novel framework produces actionable predictions with magnitude of risk reduction inline with known guideline scenarios, and perhaps provides a basis for reconciling different risk factor targets across various national guidelines by identifying sub-groups differentially responsive to the more aggressive analyte thresholds proposed across those various guidelines. Finally, the genetic risk profile of individuals is the major factor driving the separation of these sub-groups, mediating differential benefit of standard interventions. Our work is a demonstration of the power of appropriately applying genetic risk early during risk stratification and in a manner that accounts for the heterogeneity of risk profiles apparent in a real-world population.</p>" ]	[]	[ "<p id=\"P1\">Author information</p>", "<p id=\"P2\">Authors and Affiliations</p>", "<p id=\"P3\">Scripps Research Translational Institute, La Jolla, CA, 92037, USA</p>", "<p id=\"P4\">Shang-Fu Chen, Hossein Javedani Sadaei, Ahmed Khattab, Corneliu Henegar, Nathan E. Wineinger, Evan D. Muse & Ali Torkamani</p>", "<p id=\"P5\">Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, CA, 92037, USA</p>", "<p id=\"P6\">Shang-Fu Chen, Hossein Javedani Sadaei, Ahmed Khattab, Corneliu Henegar, Nathan E. Wineinger, Evan D. Muse & Ali Torkamani</p>", "<p id=\"P7\">Department of Cardiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea</p>", "<p id=\"P8\">Sang Eun Lee</p>", "<p id=\"P9\">Department of Internal Medicine, Seoul National University Hospital and Seoul National University College of Medicine, Seoul, Korea</p>", "<p id=\"P10\">Jun-Bean Park</p>", "<p id=\"P11\">Cardiovascular Center, Seoul National University Hospital, Seoul, Republic of Korea</p>", "<p id=\"P12\">Jun-Bean Park</p>", "<p id=\"P13\">Scripps Clinic, La Jolla, CA, 92037, USA</p>", "<p id=\"P14\">Evan D. Muse</p>", "<p id=\"P15\">Contributions</p>", "<p id=\"P16\">Concept and design: A.T. Acquisition, analysis, or interpretation of data: A.T., S.F.C., S.E.L., H.J.S., C.H., and N.E.W. Drafting of the manuscript: A.T., S.F.C., J.B.P., and A.K. Critical revision of the manuscript for important intellectual content: A.T., J.B.P., and E.D.M.</p>", "<p id=\"P17\">Coronary artery disease (CAD) remains the leading cause of mortality and morbidity worldwide. Recent advances in large-scale genome-wide association studies have highlighted the potential of genetic risk, captured as polygenic risk scores (PRS), in clinical prevention. However, the current clinical utility of PRS models is limited to identifying high-risk populations based on the top percentiles of genetic susceptibility. While some studies have attempted integrative prediction using genetic and non-genetic factors, many of these studies have been cross-sectional and focused solely on risk stratification. Our primary objective in this study was to integrate unmodifiable (age / genetics) and modifiable (clinical / biometric) risk factors into a prospective prediction framework which also produces actionable and personalized risk estimates for the purpose of CAD prevention in a heterogenous adult population.</p>", "<p id=\"P18\">Thus, we present an integrative, omnigenic, meta-prediction framework that effectively captures CAD risk subgroups, primarily distinguished by degree and nature of genetic risk, with distinct risk reduction profiles predicted from standard clinical interventions. Initial model development considered ~ 2,000 predictive features, including demographic data, lifestyle factors, physical measurements, laboratory tests, medication usage, diagnoses, and genetics. To power our meta-prediction approach, we stratified the UK Biobank into two primary cohorts: 1) a prevalent CAD cohort used to train baseline and prospective predictive models for contributing risk factors and diagnoses, and 2) an incident CAD cohort used to train the final CAD incident risk prediction model. The resultant 10-year incident CAD risk model is composed of 35 derived meta-features from models trained on the prevalent risk cohort, most of which are predicted baseline diagnoses with multiple embedded PRSs. This model achieved an AUC of 0.81 and macro-averaged F1-score of 0.65, outperforming standard clinical scores and prior integrative models. We further demonstrate that individualized risk reduction profiles can be derived from this model, with genetic risk mediating the degree of risk reduction achieved by standard clinical interventions.</p>" ]	[]	[ "<title>Acknowledgments</title>", "<p id=\"P70\">We would like to thank J.C. Ducom, Lisa Dong, and the Scripps High Performance Computing service for their support. Thanks to Dr. Eric Topol for his comments on this manuscript. This work is supported by R01HG010881 to AT as well as grant UM1TR004407. We recognize that some of the factors labeled unmodifiable in this manuscript may be modifiable in some circumstances.</p>", "<title>Data availability</title>", "<p id=\"P71\">All data are made available from the UK Biobank (<ext-link xlink:href=\"https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access\" ext-link-type=\"uri\">https://www.ukbiobank.ac.uk/enable-your-research/apply-for-access</ext-link>) to researchers from universities and other institutions with genuine research inquiries following institutional review board and UK Biobank approval. This research has been conducted using the UK Biobank Resource under Application Number 41999.</p>" ]	[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><title>Overview of cohort construction, model development, performance assessment, and inference characterization for 10-year incident coronary artery disease (CAD) risk meta-prediction.</title><p id=\"P76\"><bold>a.</bold> Depiction of primary case cohorts (16,301 prevalent cases with a CAD diagnosis at baseline and 15,809 incident cases developing CAD within 10-years after baseline) derived from the UK Biobank. Controls were filtered to exclude individuals with insufficient EHR data and/or follow-up. <bold>b</bold>. High-level overview of the 10-year CAD risk meta-prediction process, integrating unmodifiable and modifiable risk factors to make predictions about baseline diagnosis, baseline predicted risk factor values, and predicted future diagnoses, which are then all combined to make the final 10-year incident CAD risk meta-prediction. <bold>c</bold>. Cumulative risk curve of CAD (%) development over the 10-year follow-up period stratified by percentile of predicted risk. <bold>d</bold>. Incidence rates of CAD observed across the test cohort, stratified by percentile of predicted risk. <bold>e</bold>. and <bold>f.</bold> Comparative test accuracy (n = 33,419) for our meta-prediction model (AUROC = 0.81; AUPRC = 0.35) versus other standard clinical and research risk scores, including PCE (AUROC = 0.73; AUPRC = 0.21), QRISK3 (AUROC = 0.74; AUPRC = 0.22), GPS<sub>CAD</sub> (AUROC = 0.73; AUPRC = 0.21) and metaGRS<sub>CAD</sub> (AUROC = 0.73; AUPRC = 0.21). Abbreviations; <bold>AUC</bold>: Area under curve; <bold>CAD</bold>: coronary artery disease; <bold>EHR</bold>: electric health records; <bold>PCE</bold>: pool cohort equations; <bold>UKB</bold>: UK Biobank.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><title>Comparative performance of meta-prediction stratified by standard risk factors.</title><p id=\"P77\">Three-tiered bar charts detailing the meta-prediction’s performance when evaluated across sub-populations stratified by standard clinical risk factors. Model performance is compared with other standard clinical and research risk scores; PCE, QRISK3, GPS<sub>CAD</sub>, and metaGRS<sub>CAD</sub>. The upper bar charts display CAD incidence (%) in the top percentile, the middle bar charts show AUROC values, and the lower bar charts presents AUPRC values. The average fold change in AUPRC of meta-prediction vs other scores is annotated for the three factors showing the greatest advantage of meta-prediction over prior approaches (bottom left bar charts). The bubbles depict the relative difference of these AUPRC-fold change values within each risk factor strata, highlighting those strata where the fold-change in improved performance differs across sub-groups, identifying those risk factors where more than average improvements in performance are achieved for a sub-group. These sub-groups with the greatest gains in performance relative to prior methods include typically low-risk populations (low PCE, low QRISK3, or younger individuals). Abbreviations; <bold>BMI</bold>: body-mass index; <bold>CAD</bold>: coronary artery disease; <bold>PCE</bold>: pool cohort equations; <bold>SBP</bold>: systolic blood pressure; <bold>TGs</bold>: triglycerides; <bold>WHR</bold>: waist-hip ratio.</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3</label><caption><title>SHAP summary plot of the top 30 features in the meta-prediction framework.</title><p id=\"P78\">This plot displays the top 30 of 60 total features contributing to meta-prediction. The vertical axis orders each feature by its overall importance to risk prediction. Each point represents a participant and is color-coded according to the feature’s direction of contribution to the individuals risk prediction (red increased risk, blue decreased risk). The value associated with each point on the x-axis represents the magnitude of its contribution to the individuals risk prediction. The sub-plots on the left and right provide SHAP plots for selected meta-features, top 3 meta-features on the left, and selected non-CAD future diagnoses on the right. Cerebral artery disease refers to cerebral and pre-cerebral disease other than stroke. Abbreviations; <bold>AAA</bold>: Abdominal aortic aneurysm: <bold>ASCVD</bold>: atherosclerotic cardiovascular disease; <bold>AF</bold>: atrial fibrillation; <bold>AID</bold>: auto immune disease; <bold>FH</bold>: family history; <bold>HCM</bold>: hypertrophic cardiomyopathy; <bold>HLR</bold>: high light scatter reticulocyte; <bold>MDD</bold>: major depressive disorder; <bold>NICM</bold>: nonischemic cardiomyopathy; <bold>PAR1</bold>: protease-activated receptor 1; <bold>PD</bold>: post duration; <bold>Qst</bold>: questionnaire response; <bold>VI</bold>: verbal interview; <bold>WBC</bold>: white blood cell.</p></caption></fig>", "<fig position=\"float\" id=\"F4\"><label>Figure 4</label><caption><title>Identification of CAD risk sub-groups and distinguishing features.</title><p id=\"P79\"><bold>a.</bold> A heatmap illustrating the outcome of hierarchical clustering on the SHAP value correlation matrix for all predictors, demarcating five case subgroups in the incident CAD cohort. Each subgroup is assigned a color used in other panels respectively. <bold>b</bold>. A line chart highlighting 57 features with η<sup>2</sup> values exceeding 0.01 among the five subgroups. Horizontal lines indicate thresholds for moderate (η<sup>2</sup> ≥ 0.06) and large (η<sup>2</sup> ≥ 0.14) effects. <bold>c</bold>. Visualization of the distribution of CAD-PRS<sub>PGS003356</sub> and meta-feature (baseline diagnosis of any-onset CAD predicted by unmodifiable factors) within the 5 subgroups, color-matched to <bold>a</bold>. Abbreviation: <bold>AAA</bold>: Abdominal aortic aneurysm: <bold>ASCVD</bold>: atherosclerotic cardiovascular disease; FH: family history; <bold>FEV1</bold>: forced expiratory volume in 1st second; FVC: forced vital capacity; <bold>NICM</bold>: nonischemic cardiomyopathy; <bold>TGs</bold>: triglycerides <bold>Qst</bold>: questionnaire response; <bold>VI</bold>: verbal interview; <bold>WBC</bold>: white blood cell; <bold>WHR</bold>: waist-hip ratio.</p></caption></fig>", "<fig position=\"float\" id=\"F5\"><label>Figure 5</label><caption><title>Benefit of clinical interventions by genetic risk and risk sub-groups.</title><p id=\"P80\">Upper panels (<bold>a-c</bold>) relate absolute risk reduction achieved with standard clinical interventions with degree of relevant genetic risk in at-risk individuals. Values are moving averages computed using a rolling window encompassing ±5 percentile bins, with error represented by SEM. Annotated values indicate the maximal benefit achieved per biomarker target: <bold>a.</bold> Absolute risk reduction achieved by LDL-lowering targets of 35, 55, 70, and 100 mg/dL vs standardized CAD-PRS<sub>PGS003356</sub>; <bold>b.</bold> Absolute risk-reduction achieved by HbA1c-lowering targets of 5.6% and 6%/ vs standardized T2D-PRS<sub>PGS000330</sub>; <bold>c.</bold> Absolute risk reduction achieved by SBP-lowering targets of 110 and 120 mmHg by standardized SBP-PRS<sub>PGS002257</sub>. Middle panels present the absolute risk reduction and lower panels present the relative risk change across risk sub-groups. Risk sub-groups are colored according to their assignments in ##FIG##3##Fig 4##. <bold>d.</bold> Absolute risk reduction and <bold>g.</bold> relative risk reduction achieved by LDL lowering targets of 35, 55, 70 and 100 mg/dL. <bold>e.</bold> Absolute risk reduction and <bold>h.</bold> relative risk reduction achieved by HbA1c lowering targets of 5, 5.6, 6, 6.5 and 7%. <bold>f.</bold> Absolute risk reduction and <bold>i.</bold> relative risk reduction achieved by SBP lowering targets of 80, 90, 100, 110, 120, 130, 140, 150, and 160 mmHg. Each data point represents the median, with error bars representing the standard error.</p></caption></fig>" ]	[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><p id=\"P81\">Baseline characteristics of the UK Biobank participants in the study (n = 339,390)</p></caption><table frame=\"box\" rules=\"rows\"><colgroup span=\"1\"><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/></colgroup><thead><tr><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Variable</th><th colspan=\"2\" align=\"left\" valign=\"top\" rowspan=\"1\">Count (%) or mean (± SD)</th></tr><tr><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Prevalent CAD cohort (N = 172,296)</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Incident CAD cohort (N = 167,094)</th></tr></thead><tbody><tr><td colspan=\"3\" align=\"left\" valign=\"top\" rowspan=\"1\">\n<bold>Demographic indicator</bold>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Age (year)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">57.76 (± 8.0)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">57.21 (±8.0)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Sex (male)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">92,703 (53.8%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">94,145 (56.34%)</td></tr><tr><td colspan=\"3\" align=\"left\" valign=\"top\" rowspan=\"1\">\n<bold>Diagnosis</bold>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Coronary artery disease</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">16,301 (9.46%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0 (0.0%)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Myocardial infarction</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">11,860 (6.88%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0 (0.0%)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Revascularization</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">10,269 (5.96%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0 (0.0%)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Angina</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">17,271 (10.02%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5,281 (3.16%)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Atherosclerotic cardiovascular disease</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">20,572 (11.94%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5,031 (3.01%)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Abdominal aortic aneurysm</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">346 (0.2%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">152 (0.09%)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Peripheral artery disease</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1,850 (1.07%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">870 (0.52%)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Non-stroke (pre-)cerebral disease</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">731 (0.42%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">237 (0.14%)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Type 1 diabetes</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1,247 (0.72%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">960 (0.57%)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Type2 diabetes</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5,990 (3.48%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4,223 (2.53%)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Chronic kidney disease</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">582 (0.34%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">472 (0.28%)</td></tr><tr><td colspan=\"3\" align=\"left\" valign=\"top\" rowspan=\"1\">\n<bold>Lifestyle</bold>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Smoking status (previous/current)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">64,695/19,667 (37.55%/11.41%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">59,702/18,261 (35.73%/10.93%)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Alcohol drinker (previous/current)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">7,296/156,862 (4.23%/91.04%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">6,623/152,626 (3.96%/91.34%)</td></tr><tr><td colspan=\"3\" align=\"left\" valign=\"top\" rowspan=\"1\">\n<bold>Medication (self-reported or verbal interview)</bold>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Cholesterol lowering</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">48,183 (27.97%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">36,547 (21.87%)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Blood pressure</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">46,277 (26.86%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">37,574 (22.49%)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Insulin</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2,652 (1.54%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2,216 (1.33%)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Antiplatelet</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">35,992 (20.89%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">24,161 (14.46%)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Hormone replacement therapy</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">16,648 (9.66%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">16,124 (9.65%)</td></tr><tr><td colspan=\"3\" align=\"left\" valign=\"top\" rowspan=\"1\">\n<bold>Family history</bold>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Heart disease</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">83,647 (48.55%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">79,339 (47.48%)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Stroke</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">50,292 (29.19%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">48,767 (29.19%)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">High blood pressure</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">87,445 (50.75%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">85,733 (51.31%)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Diabetes</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">41,053 (23.83%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">39,829 (23.84%)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>Body composition</bold>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">BMI</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">27.78 (± 4.96)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">27.70 (± 4.9)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Waist-hip ratio</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.88 (±0.09)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.87 (±0.09)</td></tr><tr><td colspan=\"3\" align=\"left\" valign=\"top\" rowspan=\"1\">\n<bold>Cardiovascular metric</bold>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Systolic blood pressure (mmHg)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">138.14 (±18.76)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">138.41 (±18.72)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Diastolic blood pressure (mmHg)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">81.98 (±10.21)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">82.47 (±10.13)</td></tr><tr><td colspan=\"3\" align=\"left\" valign=\"top\" rowspan=\"1\">\n<bold>Biomarker</bold>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Albumin (g/L)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">45.02 (± 2.65)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">45.09 (± 2.62)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Cholesterol (mmol/L)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.58 (±1.19)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.71 (±1.15)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Creatinine (umol/L)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">72.88 (± 20.12)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">72.01 (±19.86)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">C-reactive protein (mg/L)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.85 (± 4.72)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.78 (± 4.54)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Cystatin C (mg/L)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.93 (± 0.2)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.91 (±0.18)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Glucose (mmol/L)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.18 (±1.34)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.14 (±1.27)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Glycated hemoglobin (HbA1c) (mmol/mol)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">36.69 (± 7.35)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">36.36 (± 6.92)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">HDL cholesterol (mmol/L)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1.43 (±0.39)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1.45 (±0.38)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">LDL cholesterol (mmolL)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3.48 (± 0.9)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3.57 (±0.87)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Total bilirubin (umol/L)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">9.1 (±4.46)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">9.01 (±4.38)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Triglycerides (mmol/L)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1.78 (± 1.04)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1.78 (± 1.04)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">White blood cell leukocyte count (10<sup>9</sup> cells/L)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">7.01 (± 2.21)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">6.95 (± 2.12)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Hemoglobin concentration (g/dL)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">14.14 (± 1.27)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">14.14 (± 1.26)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Cholesterol HDL ratio</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.10 (± 1.13)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.15 (± 1.14)</td></tr><tr><td colspan=\"3\" align=\"left\" valign=\"top\" rowspan=\"1\">\n<bold>Clinical risk score</bold>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">PCE</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">9.04 (± 7.72)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">8.48 (± 7.36)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">QRISK3</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">15.81 (±11.42)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">14.85 (±10.66)</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T2\"><label>Table 2</label><caption><p id=\"P82\">Reclassification table</p></caption><table frame=\"box\" rules=\"rows\"><colgroup span=\"1\"><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/></colgroup><thead><tr><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><th colspan=\"2\" align=\"left\" valign=\"top\" rowspan=\"1\">No event (n = 30,257)</th><th colspan=\"2\" align=\"left\" valign=\"top\" rowspan=\"1\">Events(n = 3, 162)</th><th rowspan=\"2\" align=\"left\" valign=\"top\" colspan=\"1\">Net reclassification index (NRI)</th></tr><tr><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Meta-prediction</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Negative (< 0.24)</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Positive (≥ 0.24)</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Negative (< 0.24)</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Positive (≥ 0.24)</th></tr></thead><tbody><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">PCE</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td rowspan=\"5\" align=\"left\" valign=\"top\" colspan=\"1\">(19.57% + 1.72%) – (2.86% + 3.16%) + (4.97% + 6.33%) – (11.80% + 2.02) = 0.13</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Low (< 5%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">41.47%</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.86%</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">8.76%</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.97%</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Borderline (5–7.5%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">11.68%</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3.16%</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.71%</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">6.33%</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Intermediate (7.5–20%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">19.57%</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">14.68%</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">11.80%</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">40.73%</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">High (≥ 20%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1.72%</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.85%</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.02%</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">20.68%</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">QRISK3</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td rowspan=\"4\" align=\"left\" valign=\"top\" colspan=\"1\">(26.03% + 8.94%) – (2.41%) + (4.02%) – (11.86% + 7.62%) = 0.17</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Low (< 10%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">39.47%</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.41%</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">7.81%</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.02%</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Moderate (10–20%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">26.03%</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">9.22%</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">11.86%</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">21.06%</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">High (≥ 20%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">8.94%</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">13.93%</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">7.62%</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">47.63%</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">GPS<sub>CAD</sub></td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td rowspan=\"3\" align=\"left\" valign=\"top\" colspan=\"1\">(15.16%) – (8.21%) + (15.28%) – (11.01%) = 0.11</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Negative (< 0.09)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">59.28%</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">8..21%</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">16.29%</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">15.28%</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Positive (≥ 0.09)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">15.16%</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">17.35%</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">11.01%</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">57.43%</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">metaGRS<sub>CAD</sub></td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><td rowspan=\"3\" align=\"left\" valign=\"top\" colspan=\"1\">(16.75%) – (7.50%) + (14.04%) – (11.80%) = 0.11</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Negative (0.09)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">57.67%</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">7.50%</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">15.50%</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">14.04%</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Positive (≥ 0.09)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">16.78%</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">18.05%</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">11.80%</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">58.67%</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"T3\"><label>Table 3</label><caption><p id=\"P84\">Characteristics of the risk sub-groups in the study</p></caption><table frame=\"box\" rules=\"rows\"><colgroup span=\"1\"><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/><col align=\"left\" valign=\"middle\" span=\"1\"/></colgroup><thead><tr><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Variable</th><th colspan=\"5\" align=\"left\" valign=\"top\" rowspan=\"1\">Count (%) or mean (± SD)</th></tr><tr><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"/><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">highest risk sub-group (N = 11,772)</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">higher risk sub-group (N = 33,028)</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">medium risk sub-group (N = 21,511)</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">lower risk sub-group (N = 15,082)</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">lowest risk sub-group (N = 50,224)</th></tr></thead><tbody><tr><td colspan=\"6\" align=\"left\" valign=\"top\" rowspan=\"1\">\n<bold>Demographic indicator</bold>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Age (year)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">61.31 (± 6.09)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">59.69 (± 6.83)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">55.99 (± 8.18)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">58.12 (± 7.4)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">55.64 (± 8.45)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Sex (male)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">10,150 (86.22%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">18,993 (57.51%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">9,566 (44.47%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5,948 (39.44%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">16,845 (33.54%)</td></tr><tr><td colspan=\"6\" align=\"left\" valign=\"top\" rowspan=\"1\">\n<bold>Diagnosis</bold>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Angina</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">944 (8.02%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1,128 (3.42%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">782 (3.64%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">608 (4.03%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1,446 (2.88%)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Atherosclerotic cardiovascular disease</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">835 (7.09%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1,046 (3.17%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">827 (3.84%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">575 (3.81%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1,317 (2.62%)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Abdominal aortic aneurysm</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">28 (0.24%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">48 (0.15%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">26 (0.12%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">13 (0.09%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">29 (0.06%)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Peripheral artery disease</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">121 (1.03%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">197 (0.6%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">120 (0.56%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">78 (0.52%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">250 (0.5%)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Non-stroke (pre-) cerebral disease</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">54 (0.46%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">49 (0.15%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">38 (0.18%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">25 (0.17%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">50 (0.1%)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Type 1 diabetes</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">56 (0.48%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">143 (0.43%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">23 (0.11%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">207 (1.37%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">500 (1.0%)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Type 2 diabetes</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">538 (4.57%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">736 (2.23%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">353 (1.64%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">706 (4.68%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1,665 (3.32%)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Chronic kidney disease</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">38 (0.32%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">94 (0.28%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">116 (0.54%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">66 (0.44%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">108 (0.22%)</td></tr><tr><td colspan=\"6\" align=\"left\" valign=\"top\" rowspan=\"1\">\n<bold>Lifestyle</bold>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Smoking status (previous/current)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5,168/1,234 (43.9%/10.48%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">12,956/2,174 (39.23%/6.58%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">6,549/5,568 (30.44%/25.88%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5,777/1,904 (38.3%/12.62%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">16,524/4,850 (32.9%/9.66%)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Alcohol drinker (previous/current)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">511/10,812 (4.34%/91.85%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1,224/30,415 (3.71%/92.09%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1,043/19,422 (4.85%/90.29%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">734/13,593 (4.87%/90.13%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1876/45,700 (3.74%/90.99%)</td></tr><tr><td colspan=\"6\" align=\"left\" valign=\"top\" rowspan=\"1\">\n<bold>Medication (self-reported or verbal interview)</bold>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Cholesterol lowering</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3,989 (33.89%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">6,350 (19.23%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5,495 (25.55%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3,342 (22.16%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">6,922 (13.78%)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Blood pressure</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4,410 (37.46%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">8,259 (25.01%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5,507 (25.6%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3,831 (25.4%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">9,966 (19.84%)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Insulin</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">182 (1.55%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">329 (1.0%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">33 (0.15%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">486 (3.22%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1,124 (2.24%)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Antiplatelet</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3,063 (26.02%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4,925 (14.91%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4,857 (22.58%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2,554 (16.93%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5,709 (11.37%)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Hormone replacement therapy</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1,132 (9.62%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3,378 (10.23%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2,137 (9.93%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1,377 (9.13%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4,621 (9.2%)</td></tr><tr><td colspan=\"6\" align=\"left\" valign=\"top\" rowspan=\"1\">\n<bold>Family history</bold>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Heart disease</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">8,436 (71.66%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">19,020 (57.59%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">9,249 (43.0%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">7,812 (51.8%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">18,733 (37.3%)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Stroke</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3,714 (31.55%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">10,376 (31.42%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">6,301 (29.29%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4,575 (30.33%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">13,796 (27.47%)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">High blood pressure</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5,847 (49.67%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">16,719 (50.62%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">11,109 (51.64%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">7,751 (51.39%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">26,020 (51.81%)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Diabetes</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2,841 (24.13%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">7,707 (23.33%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5,217 (24.25%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3,835 (25.43%)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">12,286 (24.46%)</td></tr><tr><td colspan=\"6\" align=\"left\" valign=\"top\" rowspan=\"1\">\n<bold>Body composition</bold>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">BMI</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">28.34 (± 4.44)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">27.53 (± 4.41)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">28.0 (± 5.24)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">28.48 (± 5.11)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">27.78 (± 5.19)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Waist-hip ratio</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.93 (± 0.08)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.89 (± 0.09)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.88 (± 0.08)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.88 (± 0.09)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.86 (± 0.09)</td></tr><tr><td colspan=\"6\" align=\"left\" valign=\"top\" rowspan=\"1\">\n<bold>Cardiovascular metric</bold>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Systolic blood pressure (mmHg)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">145.63 (± 18.24)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">142.39 (± 18.23)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">135.5 (± 16.1)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">137.98 (± 18.81)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">137.77 (± 20.08)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Diastolic blood pressure (mmHg)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">84.69 (± 10.1)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">83.6 (± 9.9)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">81.95 (± 9.67)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">82.19 (± 10.19)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">82.32 (± 10.54)</td></tr><tr><td colspan=\"6\" align=\"left\" valign=\"top\" rowspan=\"1\">\n<bold>Biomarker</bold>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Albumin (g/L)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">45.14 (± 2.61)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">45.14 (± 2.57)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">45.01 (± 2.71)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">44.9 (± 2.62)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">45.12 (± 2.64)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Cholesterol (mmol/L)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.52 (± 1.18)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.78 (± 1.16)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.55 (± 1.15)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.74 (± 1.2)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.76 (± 1.16)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Creatinine (umol/L)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">79.72 (± 17.33)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">74.24 (± 19.53)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">73.29 (± 24.01)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">72.03 (± 22.93)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">69.9 (± 19.53)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">C-reactive protein (mg/L)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.96 (± 4.93)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.58 (± 4.33)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3.23 (± 5.17)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3.04 (± 4.67)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.82 (± 4.57)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Cystatin C (mg/L)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.97 (± 0.18)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.92 (± 0.17)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.95 (± 0.2)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.94 (± 0.21)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.9 (± 0.17)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Glucose (mmol/L)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.3 (± 1.44)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.13 (± 1.13)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.01 (± 0.81)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.36 (± 1.79)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">5.22 (±1.51)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Glycated hemoglobin (HbA1c) (mmol/mol)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">37.74 (± 7.87)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">36.23 (± 6.29)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">35.95 (± 4.64)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">37.94 (± 9.41)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">36.71 (± 8.15)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">HDL cholesterol (mmol/L)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1.28 (± 0.33)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1.43 (± 0.39)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1.38 (± 0.35)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1.39 (± 0.37)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1.49 (± 0.39)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">LDL cholesterol (mmolL)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3.5 (± 0.89)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3.63 (± 0.88)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3.48 (± 0.88)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3.62 (± 0.91)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">3.58 (± 0.88)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Total bilirubin (umol/L)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">9.88 (± 4.72)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">9.36 (± 4.5)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">8.77 (± 4.41)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">8.78 (± 4.17)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">8.81 (± 4.23)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Triglycerides (mmol/L)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">2.07 (± 1.18)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1.83 (± 1.05)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1.84 (± 1.03)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1.92 (± 1.09)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1.72 (± 1.04)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">White blood cell leukocyte count (10<sup>9</sup> cells/L)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">7.14 (± 2.06)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">6.81 (± 1.99)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">7.47 (± 2.07)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">6.96 (± 1.88)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">6.95 (± 2.32)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Hemoglobin concentration (g/dL)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">14.78 (± 1.16)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">14.36 (± 1.23)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">14.23 (± 1.25)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">14.09 (± 1.26)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">13.97 (± 1.26)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">Cholesterol HDL ratio</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.49 (± 1.17)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.25 (± 1.16)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.21 (± 1.14)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.34 (± 1.15)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">4.06 (± 1.15)</td></tr><tr><td colspan=\"6\" align=\"left\" valign=\"top\" rowspan=\"1\">\n<bold>Clinical risk score</bold>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">PCE</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">15.12 (± 8.14)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">10.66 (± 7.64)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">7.94 (± 6.46)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">8.81 (± 6.91)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">7.23 (± 7.26)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">QRISK3</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">24.44 (± 11.03)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">17.93 (± 10.56)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">14.02 (± 9.28)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">16.25 (± 10.77)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">12.91 (± 10.87)</td></tr><tr><td colspan=\"6\" align=\"left\" valign=\"top\" rowspan=\"1\">\n<bold>Polygenic risk score</bold>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">CAD-PRS (PGS003356)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">1.3152 (± 0.7885)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.4992 (± 0.8523)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">−0.0867 (± 0.8616)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">−0.1145 (± 0.8823)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">−0.3585 (± 0.9127)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">T2D-PRS (PGS000330)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.2049 (± 0.8777)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.0214 (± 0.9132)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">−0.0101 (± 0.993)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.0345 (± 0.9779)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.0162 (± 1.0842)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">SBP-PRS (PGS000301)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.2447 (± 0.9854)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.1007 (± 0.992)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">−0.0234 (± 0.9994)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">−0.0318 (± 0.9971)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">−0.0633 (± 1.0001)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">TGs-PRS (PGS002287)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.1108 (± 0.9105)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.0442 (± 0.9571)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.0118 (± 1.0029)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.0145 (± 0.9952)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">−0.0446 (± 1.0482)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">HbA1c-PRS (PGS001352)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.0949 (± 1.0029)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.0255 (± 0.9897)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.001 (± 1.001)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.0041 (± 0.9872)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">−0.0181 (± 0.9972)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">WHR-PRS (PGS000299)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.098 (± 0.9968)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.0307 (± 1.0067)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.026 (± 1.0068)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.0067 (± 0.998)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">−0.0256 (± 0.9974)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">BMI-PRS (PGS000910)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.2033 (± 0.9676)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.0561 (± 0.9707)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.048 (± 1.0109)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.0861 (± 0.9936)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.0162 (± 1.0208)</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<bold>Meta-prediction</bold>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.75 (± 0.27)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.33 (± 0.32)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.18 (± 0.25)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.17 (± 0.23)</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">0.15 (± 0.25)</td></tr></tbody></table></table-wrap>" ]	[]	[]	[]	[]	[]	[]	[ "<fn-group><fn id=\"FN3\"><p id=\"P72\">Code availability</p><p id=\"P73\">The machine learning code used to generate our meta-predictions can be found at: <ext-link xlink:href=\"http://github.com/TorkamaniLab/CAD_meta_prediction\" ext-link-type=\"uri\">http://github.com/TorkamaniLab/CAD_meta_prediction</ext-link></p></fn><fn id=\"FN4\"><p id=\"P74\">Ethic declarations</p><p id=\"P75\">AT declares he is co-founder and equity share holder of GeneXwell Inc. AT is advisor to InsideTracker. The remaining authors declare no competing interests.</p></fn></fn-group>", "<table-wrap-foot><fn id=\"TFN1\"><p id=\"P83\">Percentage of participants in test set of incident CAD cohort (n = 33,419) classified in each risk tier of PCE and QRISK3, as well as GPS<sub>CAD</sub> and metaGRS<sub>CAD</sub> vs. meta-prediction at the optimal risk threshold by maximizing Youden’s index.</p></fn></table-wrap-foot>" ]	[ "<graphic xlink:href=\"nihpp-rs3694374v1-f0001\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-rs3694374v1-f0002\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-rs3694374v1-f0003\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-rs3694374v1-f0004\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-rs3694374v1-f0005\" position=\"float\"/>" ]	[]	[{"label": ["1."], "surname": ["Muse", "Chen", "Torkamani"], "given-names": ["E. D.", "S. F.", "A."], "article-title": ["Monogenic and Polygenic Models of Coronary Artery Disease"], "source": ["Curr Cardiol Rep"], "volume": ["23"], "fpage": ["1"], "lpage": ["12"], "year": ["2021"]}, {"label": ["4."], "surname": ["Damask"], "given-names": ["A."], "article-title": ["Patients with High Genome-Wide Polygenic Risk Scores for Coronary Artery Disease May Receive Greater Clinical Benefit from Alirocumab Treatment in the ODYSSEY OUTCOMES Trial"], "source": ["Circulation"], "fpage": ["624"], "lpage": ["636"], "year": ["2020"], "pub-id": ["10.1161/CIRCULATIONAHA.119.044434"]}, {"label": ["6."], "surname": ["Mega"], "given-names": ["J. L."], "article-title": ["Genetic risk, coronary heart disease events, and the clinical benefit of statin therapy: An analysis of primary and secondary prevention trials"], "source": ["The Lancet"], "volume": ["385"], "fpage": ["2264"], "lpage": ["2271"], "year": ["2015"]}, {"label": ["10."], "surname": ["Muse"], "given-names": ["E. D."], "article-title": ["Impact of polygenic risk communication: an observational mobile application-based coronary artery disease study"], "source": ["NPJ Digit Med"], "volume": ["5"], "year": ["2022"]}, {"label": ["11."], "surname": ["Hollands"], "given-names": ["G. J."], "article-title": ["The impact of communicating genetic risks of disease on riskreducing health behaviour: Systematic review with meta-analysis"], "source": ["BMJ (Online)"], "volume": ["352"], "year": ["2016"]}, {"label": ["22."], "surname": ["Murthy"], "given-names": ["V. 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PMC10775394	38196657	[]	[ "<title>METHODS</title>", "<p id=\"P19\">See ##SUPPL##0##Supplemental Material##.</p>" ]	[ "<title>RESULTS</title>", "<title>T cell antigen experience prior to transduction with a CAR directs <italic toggle=\"yes\">in vitro</italic> proliferative and effector capacities of CD8+ CAR T cells.</title>", "<p id=\"P6\">Memory T cells demonstrate superior antigen sensitivity compared to naïve T cells in some contexts<sup>##REF##21903423##11##,##REF##24493801##12##</sup>. Thus, we hypothesized that CAR T cells derived from a memory T cell population would exhibit enhanced responsiveness to low antigen density leukemias compared to naïve-derived CAR T cells. T cells expressing a CAR containing an anti-mouse CD19 scFv incorporating a FLAG sequence and a CD28 costimulatory domain fused to mouse CD3zeta, followed by a 2A sequence and a truncated EGFR <sup>##REF##21653320##13##</sup> (##SUPPL##0##Figure S1A##) were used to target a murine leukemia driven by the E2A-PBX1 fusion protein (E2A-PBX)<sup>##REF##30209120##14##, ##REF##27460500##15##, ##UREF##2##16##</sup>. FLAG-specific antibody detection of the CAR correlated strongly with EGFR expression, allowing for use of EGFR as a marker for long term tracking of CAR+ cells <italic toggle=\"yes\">in vivo</italic> (##SUPPL##0##Figure S1B##). We expanded this model by generating a set of clones of E2A-PBX which express differing CD19 densities (##FIG##0##Figure 1A##, ##SUPPL##0##S1C##). Memory OT-I T cells generated using a well-characterized ovalbumin vaccination model <sup>##REF##37516968##17##, ##REF##34433030##18##, ##UREF##3##19##</sup> (##FIG##0##Figure 1B##). were used to produce memory-derived CD8+ CAR T cells (CAR8<sub>MD</sub>) for comparison to naïve-derived CD8+ OT-I CAR T cells (CAR8<sub>ND</sub>). As no difference was seen in leukemia control by memory or naïve-derived control T cells (##SUPPL##0##Figure S1D##), we used naïve-derived (EGFR8) in all subsequent experiments. A functional duality began to emerge upon <italic toggle=\"yes\">in vitro</italic> testing. As predicted, a greater proportion of CAR8<sub>MD</sub> cells had a polyfunctional effector profile, producing both TNFa and IFNg, or degranulating (as measured by CD107a), most pronounced in response to low target antigen (##FIG##0##Figure 1C##–##FIG##0##H##; ##SUPPL##0##S1E##–##SUPPL##0##G##). Interestingly, while the proportion of IFNg+ cells was greater in CAR8<sub>MD</sub>, the proportion of TNFa+ cells was slightly increased in CAR8<sub>ND</sub>, suggesting a predisposition toward either IFNg or TNFa (##FIG##0##Figure 1C## & ##FIG##0##F##). However, CAR8<sub>ND</sub> outperformed CAR8<sub>MD</sub> in cell cycle entry (Ki67 expression; ##FIG##0##Figure 1I##, ##SUPPL##0##S1H##) and extended proliferative capacity (##FIG##0##Figure 1J##, ##SUPPL##0##S1I##) across antigen densities. To compare polyclonal antigen-experienced and naïve T cells more analogous to human CAR T cells, we generated pathogen-elicited polyclonal T cells by infecting WT C57BL/6 mice with the common acute viral infection model LCMV-Armstrong. Memory (CD8+/CD44+/CD49d<sup>Hi</sup>) and naïve (CD8+/CD44−/CD49d<sup>Lo</sup>/CD62L+) T cell populations were FACS-sorted from the same mice 28 days after LCMV infection and used for CAR T cell manufacturing (##SUPPL##0##Figure S2A##). Polyclonal pathogen-elicited T cells behaved similarly <italic toggle=\"yes\">in vitro</italic> to memory and naïve OT-I cells: CAR8<sub>MD</sub> demonstrated superior effector function (increased proportions of cells producing IFNg) and CAR8<sub>ND</sub> demonstrated superior proliferative capacity (##SUPPL##0##Figure S2B##–##SUPPL##0##E##). Thus, CD8+ T cell antigen experience prior to transduction with a CAR promotes effector functions at the expense of proliferative capacity.</p>", "<title>Treatment of leukemia-bearing mice with a high CAR+ cell dose reveals enhanced cytotoxic profile and clearance of antigen-low leukemia by memory-derived CAR8.</title>", "<p id=\"P7\">Given the opposing functional profiles of naïve and memory-derived CAR8, we next compared the ability of these two populations to mediate tumor clearance <italic toggle=\"yes\">in vivo</italic>. Mice were engrafted with WT (35,000 antigens per cell), CD19<sup>Lo</sup> (10,000 antigens per cell) or CD19<sup>Neg</sup> leukemia followed 3 days later by a dose of 1e6 CAR T cells. The CD19<sup>Lo</sup> clone antigen density was chosen based on differential <italic toggle=\"yes\">in vitro</italic> responses and, although higher than antigen density reported for CAR relapses post-CD22 CAR treatment<sup>##REF##29155426##9##</sup>, is consistent with the drop-off in CAR sensitivity against other antigens<sup>##REF##34971569##8##, ##REF##32193224##10##</sup>. <italic toggle=\"yes\">Rag1</italic>-deficient hosts enabled CAR T cell expansion without irradiation and limited CAR T cell antigen exposure to CD19 densities expressed on leukemia rather than endogenous B cells. While we did not observe differences in proportions of CAR T cells in the marrow at peak expansion on day 4 (##FIG##1##Figure 2A##), post-contraction (day 11) CAR8<sub>ND</sub> had increased proportions and total counts of CAR T cells in mice bearing WT and CD19<sup>Lo</sup> leukemia (##FIG##1##Figure 2B##–##FIG##1##C##, ##SUPPL##0##Figure S3A##–##SUPPL##0##B##). Both CAR groups mediated robust clearance of WT leukemia by day 11. Although there was no significant difference in clearance of CD19<sup>Lo</sup> leukemia, 4/10 mice treated by CAR8<sub>ND</sub> had detectable leukemia at >15% of live bone marrow cells while all 10 mice treated with CAR8<sub>MD</sub> had minimal leukemic burdens (<5%) (##FIG##1##Figure 2D##). We next tested whether the enhanced clearance of CD19<sup>Lo</sup> leukemia was associated with maintenance of the superior cytotoxic capacity of CAR8<sub>MD</sub> observed <italic toggle=\"yes\">in vitro</italic>. Upon <italic toggle=\"yes\">ex vivo</italic> restimulation of CAR8 in the bone marrow, we found that, while IFNg production was highly variable, GZMB production was markedly greater in CAR8<sub>MD</sub> (##FIG##1##Figure 2E##–##FIG##1##F##). CAR8<sub>MD</sub> had significantly higher proportions of cells falling into short-lived effector cell (SLEC, IL7Ra−/KLRG1+) and effector memory precursor (EMP, CD27+/CD62L−) phenotypes, fewer cells in the central memory precursor phenotype (CMP, CD27+/CD62L+), and no change in memory precursor effector cell (MPEC, IL7Ra+/KLRG1−) populations (##SUPPL##0##Figure S4B##–##SUPPL##0##E##). Additionally, early expression of effector-associated TFs IRF4, T-bet and EOMES was greater in CAR8<sub>MD</sub> (##FIG##1##Figure 2G##–##FIG##1##I##). Finally, while mice bearing WT high-antigen leukemia showed no survival difference after treatment with CAR8<sub>MD</sub> versus CAR8<sub>ND</sub>, mice bearing CD19<sup>Lo</sup> leukemia treated with CAR8<sub>MD</sub> showed a significant survival benefit, with 20% of mice surviving to the 80 day experimental endpoint (##FIG##1##Figure 2J##). Together, these data show that CAR8<sub>MD</sub> mediate superior clearance of CD19<sup>Lo</sup> leukemia relative to CAR8<sub>ND,</sub> associated with maintenance of effector function and expression of effector-associated markers.</p>", "<title>Treatment of leukemia-bearing mice with a low CAR+ cell dose reveals enhanced proliferative capacity and clearance of WT leukemia by naïve-derived CAR8.</title>", "<p id=\"P8\">We next hypothesized that the benefit of enhanced proliferative capacity of naïve-derived CAR8 would emerge at a lower CAR+ cell dose (3e5). As anticipated, CAR8<sub>ND</sub> expanded to significantly higher numbers in the bone marrow by day 4 regardless of leukemia antigen density, mirroring <italic toggle=\"yes\">in vitro</italic> proliferative assays (##FIG##2##Figure 3A##–##FIG##2##B##, ##SUPPL##0##S3C##, ##FIG##0##1I##–##FIG##0##J##). While CAR8<sub>ND</sub> mediated enhanced clearance and survival in mice bearing WT leukemia, there was no improvement in leukemia clearance or survival of mice bearing CD19<sup>Lo</sup> leukemia (##FIG##2##Figure 3C##, ##FIG##2##3I##, ##SUPPL##0##S3D##), potentially due to reduced potency. Indeed, <italic toggle=\"yes\">ex vivo</italic> IFNg production was greater in CAR8<sub>MD</sub>, although there was no difference in GZMB production or expression of IRF4, T-bet or EOMES (##FIG##2##Figure 3D##–##FIG##2##H##). CAR8<sub>MD</sub> consistently demonstrated significantly higher proportions of SLECs at the early timepoint consistent with high CAR doses, but these differences disappeared by day 11 and no differences were seen in the MPEC population (##SUPPL##0##Figure S5A##,##SUPPL##0##B##). While EMP and CMP patterns mimicked high dose experiments, the differences were much less pronounced, indicating that naïve-derived cells largely became more “effector-like” with greater proliferative drive (##SUPPL##0##Figure S5C##,##SUPPL##0##D##), consistent with effector-polarization in the setting of low numbers of antigen-specific precursor populations<sup>##REF##17555991##20##, ##REF##16025119##21##</sup>. However, these changes, combined with the strong expansion, did not mediate survival benefit against CD19<sup>Lo</sup> leukemia (##FIG##2##Figure 3I##). Finally, we predicted that at this lower cell dose, T cell dysfunction could emerge. Indeed, CAR8<sub>MD</sub> expressed higher levels of exhaustion-associated markers against WT leukemia with failure of CAR8<sub>MD</sub> to control leukemia (##SUPPL##0##Figure S5E##–##SUPPL##0##F##, ##SUPPL##0##I##–##SUPPL##0##J##). Interestingly, we found that CD19<sup>Lo</sup> leukemia drove similar proportions of exhaustion phenotypes in both CAR8 populations, demonstrating that chronic, uncleared antigen exposure, even at low antigen density, can drive dysfunction (##SUPPL##0##Figure S5G##–##SUPPL##0##H##, ##SUPPL##0##K##–##SUPPL##0##L##). These findings highlight the importance of proliferative capacity and resistance to dysfunction afforded by CAR8<sub>ND</sub> at limiting cell dose.</p>", "<title>Epigenetic profiling of naïve and memory-derived CAR8 shows differential chromatin accessibility at binding sites for bZIP, Tcf, Runx and other TF families.</title>", "<p id=\"P9\">We predicted that functional traits were a product of distinct epigenetic states, given that functional distinctions of naïve and memory-derived CAR8 were dictated by status prior to CAR transduction. To test this, we performed bulk ATAC-seq on naïve and memory-derived cells at three timepoints: <italic toggle=\"yes\">ex vivo</italic> prior to CAR transduction (Day -5, “PreCAR”), <italic toggle=\"yes\">in vitro</italic> after CAR transduction (Day 0, “PostCAR”), and after reinfusion into mice bearing CD19<sup>Lo</sup> leukemia (Day 4, “Tumor”) (##FIG##3##Figure 4A##). Comparison of experimental replicates showed tight concordance of chromatin accessibility at each condition and timepoint (##SUPPL##0##Figure S6A##). Broadly, the data showed several thousand differentially accessible regions between either cell type compared to itself across timepoints, and between naïve and memory-derived cells at each timepoint (##SUPPL##0##Figure S6B##). We found predictable patterns of ATAC-seq signal at genetic loci involved in T cell activation or effector function, including higher accessibility in CAR8<sub>MD</sub> at <italic toggle=\"yes\">Gzmb</italic>, <italic toggle=\"yes\">Gzmc</italic>, and the <italic toggle=\"yes\">Pdcd1</italic> loci encoding for the PD1 protein. Concurrently, we found greater accessibility in CAR8<sub>ND</sub> at the <italic toggle=\"yes\">Tcf7</italic> loci encoding TCF1, a TF important for maintaining self-renewal capacity (##FIG##3##Figure 4B##).</p>", "<p id=\"P10\">We used ChromVAR<sup>##REF##28825706##22##</sup>, to associate these changes in chromatin accessibility to previously defined datasets and potential TF activities. Based on relative chromatin accessibility at regions that were differentially accessible in a published comparison of effector and memory CD8+ T cells after acute viral infection with LCMV-Armstrong<sup>##REF##27939672##23##</sup>, memory-derived CAR8 acquired effector-associated changes in chromatin accessibility during CAR generation in culture that were maintained after transfer into tumor bearing mice. CAR8 generated from memory T cells also had reduced chromatin accessibility at features associated with memory T cells. By comparison, naïve-derived CAR8 maintained chromatin accessibility patterns at regions associated with memory T cells and showed minimal skewing toward an effector-like profile<sup>##REF##27939672##23##</sup> (##FIG##3##Figure 4C##). To associate these changes with specific TF activities, we used ChromVAR to compare chromatin accessibility at regions containing DNA sequence motifs bound by different TFs (##FIG##3##Figure 4D##). Classifying this data using a kmeans clustering strategy, we found that there were distinct patterns of motif-associated chromatin accessibility between conditions and across each of the timepoints (##FIG##3##Figure 4E##). While motifs for bZIP and Irf family TFs broadly looked similar at the PreCAR timepoint, and became progressively enriched in memory cells, Tcf family motifs started similar and became enriched in naïve cells at the latter timepoints, while E2A family motifs started highly enriched in naïve and progressively converged. Uniquely, motifs for Runx family members were always more accessible in memory-derived cells and did not converge or diverge (##FIG##3##Figure 4D##–##FIG##3##E##, ##SUPPL##0##S6C##). Overall, these data show epigenetic features imprinted in the starting CD8+ T cell population are maintained through CAR engineering.</p>", "<title>Prior antigen experience directs distinct transcriptomic patterns of naïve and memory-derived CAR8.</title>", "<p id=\"P11\">To test whether the epigenetic states of naïve and memory-derived CAR8 resulted in concurrent transcriptomic changes, we performed bulk RNA-seq at the same timepoints as for ATAC-seq (##FIG##3##Figure 4A##). We found predictable differential gene expression at each timepoint, with genes associated with self-renewal and proliferative capacity (<italic toggle=\"yes\">Lef1, Sell, Id3, Tcf7, Slamf6, Il7r)</italic> upregulated in the naïve-derived cells and genes associated with effector capacity and activation (<italic toggle=\"yes\">Prf1, Ifng, Klrg1, Gzmb, Prdm1, Id2, Pdcd1, Tbx21)</italic> upregulated in the memory-derived cells (##FIG##4##Figure 5A##). Gene set enrichment analysis (GSEA) showed progressive bias by normalized enrichment score (NES) toward effector-like in memory-derived CAR8, and toward memory-like in naïve-derived CAR8<sup>##REF##17950003##24##, ##REF##16492737##25##, ##REF##16199517##26##</sup> (##FIG##4##Figure 5B##–##FIG##4##C##). Analysis with gene sets comparing memory and naïve T cells showed progressive decrease in the normalized enrichment score of memory or naïve-derived CAR8 toward the derivative cell population of each, suggesting the effector/memory gene set enrichment axis as the more accurate indicator of cell fate over time<sup>##REF##17950003##24##, ##REF##16492737##25##</sup> (##SUPPL##0##Figure S7A##). Looking at the top differentially-expressed TFs between the populations at the PreCAR timepoint, we found many expected hits, including <italic toggle=\"yes\">Bhlhe40</italic>, <italic toggle=\"yes\">Klf4</italic>, <italic toggle=\"yes\">Tbx21</italic>, <italic toggle=\"yes\">Id2</italic> and many bZIP family members (<italic toggle=\"yes\">Jun</italic>, <italic toggle=\"yes\">JunB</italic>, <italic toggle=\"yes\">Fos</italic>, <italic toggle=\"yes\">Cebpb</italic>) represented in the memory-derived group, while <italic toggle=\"yes\">Zeb1</italic>, <italic toggle=\"yes\">Myb</italic> and <italic toggle=\"yes\">Lef1</italic>, encoding TFs associated with self-renewal, were upregulated in the naïve-derived cells<sup>##REF##27939672##23##, ##REF##35263570##27##</sup> (##SUPPL##0##Figure S7D##). Notably, among the Runx family, which showed uniquely stable differential motif accessibility between naïve and memory cells (##FIG##3##Figure 4D##), <italic toggle=\"yes\">Runx2</italic> was among the most differentially expressed TF genes with marked overexpression in memory derived cells (##SUPPL##0##Figure S7D##). Ingenuity Pathway Analysis of global transcriptional profile implicated similar TF drivers<sup>##REF##24336805##28##</sup>(##SUPPL##0##Figure S7B##) with numerous distinct patterns of differential TF expression between memory and naïve-derived T cells. However, a very common pattern among ChromVAR-implicated TFs was high initial expression in memory cells at the PreCAR timepoint, followed by a convergence in expression between memory and naïve-derived CAR T cells at the PostCAR and Tumor timepoints, as seen with bZIP family members <italic toggle=\"yes\">Jun</italic>, <italic toggle=\"yes\">Fos</italic> and <italic toggle=\"yes\">Atf3</italic>, along with the gene <italic toggle=\"yes\">Tbx21</italic>, encoding canonical effector TF T-bet (##FIG##4##Figure 5D##). Among the Runx family, <italic toggle=\"yes\">Runx1</italic> and <italic toggle=\"yes\">Runx3</italic> gene expression tracked relatively closely between memory and naïve-derived cells at each timepoint, while <italic toggle=\"yes\">Runx2</italic> followed the “high in memory, then converging” pattern which was commonly found among other TF families (##FIG##4##Figure 5E##). In summary, naïve and memory-derived T cells show differential gene expression and gene set association with self-renewal or memory-associated genes and activation or effector-associated genes, respectively. Many relevant TF genes show a pattern of high initial expression in memory cells at the PreCAR timepoint which converges between the cell derivations upon transduction with a CAR and reinfusion into tumor-bearing hosts.</p>", "<title>RUNX2 overexpression boosts leukemia clearance, CAR T cell potency and CAR proportions in bone marrow.</title>", "<p id=\"P12\">To validate the epigenetic and transcriptomic data, we overexpressed two TFs from the ChromVAR-implicated bZIP family, BATF and c-Jun, both of which have been previously reported to impact CAR T cell function (##FIG##5##Figure 6A##–##FIG##5##B##)<sup>##REF##36240777##29##, ##REF##34282330##30##, ##REF##31802004##31##</sup>. Although neither TF increased cytokine production or proliferation <italic toggle=\"yes\">in vitro</italic> (##SUPPL##0##Figure S8C##–##SUPPL##0##E##), overexpression of either TF enhanced leukemia clearance by memory and naïve-derived CAR T cells (##FIG##5##Figure 6C##–##FIG##5##D##). There was no difference between BATF-CAR8 or JUN-CAR8 and control CAR8 in the PD1+ proportion (##SUPPL##0##Figure S9C##,##SUPPL##0##E##), co-expression of PD1 with markers of exhaustion (PD1+/CD39+ and PD1+/TOX+), or in the terminally exhausted Tcf1−/Tim3+ population (##SUPPL##0##Figure S9D##,##SUPPL##0##F##–##SUPPL##0##H##).</p>", "<p id=\"P13\">Due to the memory-like state of CAR8<sub>ND</sub>, we anticipated that comparison of factors enriched in memory cells over naïve cells could reveal important drivers of memory cell function that were not fully induced in naïve cells during the synthetic engineering process. Given the unique profile of chromatin accessibility for Runx-family binding motifs coupled with the pattern of <italic toggle=\"yes\">Runx2</italic> transcript expression which was high in PreCAR memory CD8+ T cells and then lost upon CAR transduction, we hypothesized that establishing RUNX2 expression in CAR8<sub>ND</sub> could enhance the existing memory-like profile of these T cells and boost T cell potency and anti-leukemia response. Murine RUNX2 was introduced into the pMSCV-IRES-eGFP (pMIG) backbone, containing a GFP reporter gene for long-term tracking of RUNX2-transduced T cell populations (RUNX2). Co-transduction of naïve CD8+ T cells with CAR-EGFR reporter and RUNX2-GFP reporter resulted in a large proportion of cells expressing both EGFR and GFP (##FIG##5##Figure 6A##). Upon intracellular staining for the RUNX2 protein, we found that the EGFR+ population in the RUNX2-transduced group showed approximately a 10-fold increase in RUNX2 expression relative to empty pMIG-transduced cells (##FIG##5##Figure 6B##). Co-culture of RUNX2-CAR8 and leukemia with a range of antigen densities revealed similar cytokine production and proliferation relative to pMIG-CAR8 (##FIG##5##Figure 6C##–##FIG##5##D##). To stress the ability of RUNX2-CAR8 to clear WT leukemia, we used an ultra-low CAR+ dose (1e5), against which both CAR8<sub>ND</sub> and CAR8<sub>MD</sub> exhibit markers of exhaustion and fail to control leukemia (##SUPPL##0##Figure S7A##–##SUPPL##0##C##). RUNX2 overexpression in CAR8<sub>ND</sub> strongly enhanced leukemia clearance and increased CAR proportions and absolute numbers in the marrow at 11 days post-CAR infusion (##FIG##5##Figure 6E##–##FIG##5##F##). While there was no difference in the PD1+ proportion, consistent with similar activation, mice treated with RUNX2-CAR8<sub>ND</sub> exhibited dramatically reduced proportion of PD1+/TOX+ cells, a lower proportion of PD1+/CD39+ cells and reduced proportions of TCF1−/TIM3+ cells, suggesting that RUNX2 overexpression counteracts the differentiation trajectory toward terminal exhaustion (##FIG##5##Figure 6L##, ##SUPPL##0##S9M##–##SUPPL##0##N##,##SUPPL##0##P##)<sup>##REF##35263570##27##, ##REF##36350991##32##</sup>. CAR8<sub>MD</sub> showed less of an increase in RUNX2 following transduction with RUNX2-eGFP (##SUPPL##0##Figure S7F##) potentially due to higher RUNX2 at baseline (##FIG##4##Figure 5E##). Nonetheless, RUNX2-overexpression resulted in a significant reduction in the PD1+/CD39+ exhaustion phenotype of RUNX2-CAR8<sub>MD</sub> responding to WT leukemia and reduction in leukemia counts in marrow (##SUPPL##0##Figure S9I##) but no difference in other exhaustion phenotypes, CAR proportions or CAR counts (##FIG##5##Figure 6K##, ##SUPPL##0##S9K##,##SUPPL##0##L##,##SUPPL##0##O##). We demonstrate that Runx2 overexpression in naïve-derived T cells enhances maintenance of CAR T cells in the marrow, boosts leukemia clearance and mediates a favorable exhaustion profile at a highly sub-curative CAR T cell dose with less impact in memory-derived CAR T cells, demonstrating that TF overexpression has a differential impact depending on starting T cell state.</p>" ]	[ "<title>DISCUSSION</title>", "<p id=\"P14\">Factors underlying tumor relapse after CAR T cell therapy are a central focus of study in the field of cell therapies for leukemia. Advances have been made in understanding and engineering solutions to prevent tumor cell escape via antigen modulation, T cell dysfunction, and poor T cell trafficking/persistence<sup>##REF##36813894##1##</sup>. However, defining <italic toggle=\"yes\">in vitro</italic> and <italic toggle=\"yes\">in vivo</italic> functional strengths and cellular profiles associated with different starting T cell populations may be an opportunity to specifically identify approaches to arm CAR T cells to overcome different tumor escape modalities. Importantly, refining qualities of the starting cell population will likely be a large contributor to efficacy of cellular therapeutics derived from healthy allogeneic donors or induced pluripotent stem cells, or in the case of <italic toggle=\"yes\">in vivo</italic> transduction platforms targeting genetic payloads to specific cell populations. Recent work has sought to use targeted modulation of TFs to enhance CAR T cell function or prevent dysfunction, with several publications focusing on the bZIP TF family, including forced expression of BATF and c-Jun, or genetic deletion of the Nr4a family of nuclear receptors<sup>##REF##36240777##29##, ##REF##34282330##30##, ##REF##31802004##31##, ##REF##30814732##33##, ##REF##36350986##34##</sup>. However, the impact of modulation of the bZIP family has been variable. Therefore, we set out to characterize functional attributes programmed by prior T cell antigen experience, with the prediction that these would be tied to epigenetic traits. We anticipated that downstream modulation of TFs implicated by this framework might have divergent functional outcomes depending on starting cell population.</p>", "<p id=\"P15\">In this study, we use a syngeneic murine model with anti-mouse CD19 CAR T cells targeting murine pre-B cell leukemia enabling more natural T cell differentiation trajectories without xenogeneic TCR stimulation. We also used a well-defined vaccine model for precise control of the antigen experience history of CAR T cells with a clonotypic TCR, with confirmation in a polyclonal memory response. With limiting T cell dose or low target antigen density as “stressors,” we report that antigen experience dictates multiple functional outputs of CAR T cells. Memory-derived CAR T cells exhibited stronger cytotoxic function across target antigen densities, while naïve-derived CAR T cells show greater proliferative capacity and more rapid cell cycle entry. This was associated with enhanced activity against low-antigen density leukemia by memory derived CAR T cells and enhanced activity of naïve-derived cells at limiting cell dose, a setting that drove phenotypic exhaustion and dysfunction of memory-derived cells.</p>", "<p id=\"P16\">T cell differentiation is a product of epigenetic and transcriptomic state<sup>##REF##27939672##23##, ##REF##35263570##27##</sup> and while CAR T cells have been extensively profiled post-manufacturing, little work has been done to characterize effects of prior T cell state on post-transduction CAR T cell profiles<sup>##UREF##1##5##</sup>. We demonstrate that features of these states are maintained through CAR manufacturing and associate with differences in functional profiles. Specifically, we find significant differences in bZIP family transcription factors, which have been previously implicated in CAR T cell function<sup>##REF##36240777##29##, ##REF##34282330##30##, ##REF##31802004##31##</sup>. BATF or JUN mediated enhanced leukemia clearance in our model independent of starting cell state, indicating that these TFs may derive most of their early <italic toggle=\"yes\">in vivo</italic> activity via binding to NFAT-AP1 composite motifs, which show high accessibility in both cell types. Surprisingly, there was no difference in phenotypic exhaustion in BATF or JUN-overexpressing CAR T cells relative to control, indicating preservation of function in an exhausted state rather than prevention of exhaustion.</p>", "<p id=\"P17\">As a novel finding, we use epigenomic and transcriptomic assays and implicate modulation of Runx-family TFs, particularly Runx2, as having a likelihood for higher impact in naive-derived cells compared to memory. Ectopic RUNX2 expression in naïve-derived CAR T cells resulted in superior clearance of leukemia, higher proportions of cells in the marrow, and reduced proportions of cells displaying terminally exhausted phenotypes relative to control. Our data suggest that RUNX2 overexpression, in contrast to overexpression of bZIP family members, can enhance functional potency of naïve-derived CD8+ CAR T cells while preventing entry into the exhaustion differentiation trajectory.</p>", "<p id=\"P18\">In addition to their activity as transcriptional activators, Runx family members have been shown to recruit chromatin remodeling factors to Runx binding sites to open these sites and allow for transcriptional activation. In other model systems, RUNX2 has been shown to interact with SWI/SNF complexes, histone acetyltransferases (MOZ, p300), histone deacetylases (HDAC3, HDAC4, HDAC6) and histone methyltransferases (SUV39H1), along with all three TET family enzymes, indicating a plausibility for the ability for RUNX2 to recruit enzymes which participate in chromatin remodeling at RUNX binding motifs<sup>##REF##22537242##35##, ##REF##36070691##36##, ##REF##35953487##37##, ##REF##34421907##38##</sup>. These features could help explain the contribution of RUNX2 overexpression to the enhanced functionality and exhaustion resistance of CAR8<sub>ND</sub> seen in our experiments. Additional studies will be necessary to fully elucidate the effects of RUNX2 in CAR T cells, and to confirm our findings in human CAR T cells. Nonetheless, using a model in which antigen history can be precisely controlled, we show that RUNX2 overexpression enhances <italic toggle=\"yes\">in vivo</italic> CAR T cell function dependent on the starting T cell. Finally, we have generated a framework for the role of antigen experience on function of a CAR T cell in stress situations of limiting T cell dose or target antigen density and highlight the importance of considering this framework when assessing the impact of approaches to apply synthetic immunology to manipulate therapeutic immune effector cell functions.</p>" ]	[]	[ "<p id=\"P1\">AUTHOR CONTRIBUTIONS</p>", "<p id=\"P2\">K.R.D. Conceptualized the studies, performed experiments and data analysis, and wrote the manuscript. E.D. Performed data analysis and provided expertise related to ATAC/RNA sequencing. M.D. Performed experiments. J.C. Conceptualized the studies and provided expertise related to the murine CAR and leukemia models. M.Y. Designed and generated DNA constructs. R.M.K. Provided expertise related to the vaccine model. M.E.K. Conceptualized the studies and provided expertise related to the murine CAR and leukemia models. J.P.S-B. Performed data analysis and provided expertise related to ATAC/RNA sequencing. T.J.F. Conceptualized, supervised and provided funding for the studies, and wrote the manuscript. All authors contributed to the article and approved the submitted version.</p>", "<p id=\"P3\">Chimeric antigen receptor T cells are an effective therapy for B-lineage malignancies. However, many patients relapse and this therapeutic has yet to show strong efficacy in other hematologic or solid tumors. One opportunity for improvement lies in the ability to generate T cells with desirable functional characteristics. Here, we dissect the biology of CD8+ CAR T cells (CAR8) by controlling whether the T cell has encountered cognate TCR antigen prior to CAR generation. We find that prior antigen experience influences multiple aspects of <italic toggle=\"yes\">in vitro</italic> and <italic toggle=\"yes\">in vivo</italic> CAR8 functionality, resulting in superior effector function and leukemia clearance in the setting of limiting target antigen density compared to antigen-inexperienced T cells. However, this comes at the expense of inferior proliferative capacity, susceptibility to phenotypic exhaustion and dysfunction, and inability to clear wildtype leukemia in the setting of limiting CAR+ cell dose. Epigenomic and transcriptomic comparisons of these cell populations identified overexpression of the Runx2 transcription factor as a novel strategy to enhance CAR8 function, with a differential impact depending on prior cell state. Collectively, our data demonstrate that prior antigen experience determines functional attributes of a CAR T cell, as well as amenability to functional enhancement by transcription factor modulation.</p>" ]	[ "<p id=\"P4\">Adoptive transfer of T cells expressing chimeric antigen receptors (CARs) has been highly successful for treating relapsed and treatment-refractory B-lineage hematologic malignancies. However, many patients do not achieve complete remission, or relapse. Poor response or lack of remission durability results from cancer cell resistance or suboptimal CAR T cell function<sup>##REF##36813894##1##</sup>. Thus, further studies into the immunobiology of these engineered cells are warranted to enhance remissions and expand therapeutic potential to other hematologic and solid tumors. CAR T cells are commonly generated from a heterogeneous population of peripheral blood T cells that varies between patients, likely impacting the quality of a CAR T cell product<sup>##REF##26369987##2##</sup>. Although it has been difficult to track cell fate through the manufacturing process and into patients, previous reports have shown differential function of CAR T cell products generated from memory versus naïve T cells sorted by surface marker phenotypes, which are not always an accurate representation of cellular differentiation state<sup>##REF##26369987##2##,##UREF##0##3##,##REF##36255386##4##</sup>. Emerging studies have demonstrated that phenotypic, transcriptomic and epigenomic attributes of the CAR product can influence patient outcomes<sup>##UREF##1##5##</sup>. During acute infections, naïve CD8+ T cells become activated through the T cell antigen receptor (TCR) by antigen presenting cells displaying cognate antigen and co-stimulatory ligands, and subsequently enter a highly regulated differentiation trajectory. A phase of rapid expansion and differentiation into effector cells is followed by contraction and formation of long-lived memory cells that rapidly respond to future exposures. However, if the pathogen is not cleared, antigen-specific T cell populations will receive recurring antigen stimulation. In this setting, rather than forming functional memory, T cells differentiate down a trajectory characterized by progressive dysfunction, preventing immune-mediated pathology, but simultaneously failing to clear the challenge. A growing body of work demonstrates that these differentiation trajectories (and resulting functional characteristics imbued on T cells) are controlled epigenetically in traditional T cell responses to viral infections and tumors. These programs are defined by progressive changes to the epigenome, associated with DNA methylation and histone modifications which are driven by a variety of transcription factors (TFs) and modulated by antigen receptor signaling<sup>##REF##33644866##6##</sup>. These molecular modifications alter chromatin accessibility and transcriptional profiles which characterize cellular differentiation state and functional capacity. Epigenetic modulation of T cells via stimulation through the physiologic TCR has a well-established role in impacting the differentiation program and functional capacity of a pool of antigen-experienced T cells<sup>##REF##26205583##7##</sup>. Emerging data also highlight the importance of epigenetic remodeling in CAR T cell responses to tumors<sup>##UREF##1##5##</sup>.</p>", "<p id=\"P5\">Here, we carefully examine and compare the biology of CAR-transduced CD8+ T cells that differ as to whether cognate antigen has been encountered through the TCR prior to transduction with a CAR. We hypothesize that 1) T cells exhibit functional characteristics after CAR transduction that are dictated by prior antigen experience via the TCR 2)the functional characteristics of CAR8 derived from naïve or memory cells are the result of epigenetic attributes maintained through CAR transduction and reinfusion, and that 3) TF modulation as a modality to enhance CAR8 function may be dependent on the epigenetic and transcriptomic contexts determined by prior antigen experience status. Prior work has shown dose-dependent effects in the anti-tumor responses of adoptively-transferred T cells<sup>##REF##26369987##2##</sup> and CAR T cells have been shown to elicit poor responses to tumors with low antigen density<sup>##REF##36813894##1##, ##REF##34971569##8##, ##REF##29155426##9##, ##REF##32193224##10##</sup>. Using limiting target antigen density or limiting T cell dose as stressors, we show that prior T cell antigen experience influences <italic toggle=\"yes\">in vitro</italic> and <italic toggle=\"yes\">in vivo</italic> functional characteristics of T cells stimulated through a CAR. Comparison of the epigenetic and transcriptomic states of CAR8 stratified by prior antigen-experience status revealed differential chromatin accessibility and transcriptional programming. We pinpoint divergent RUNX2 activity within the two populations as a potential driver of differential function and show that ectopic expression of RUNX2 enhances the anti-leukemia response and mediates exhaustion resistance in CAR T cells in a manner dependent on prior T cell antigen experience status.</p>", "<title>SUPPLEMENTAL METHODS</title>", "<title>Mouse Strains</title>", "<p id=\"P20\">B6.129S6-Rag2tm1Fwa Tg(TcraTcrb)1100Mjb (“OT-I,” Model #: 2334-F) mice were obtained from Taconic Biosciences. B6.SJL-Ptprca Pepcb/BoyJ (“PepBoy,” Strain #:002014), B6.129S7-Rag1tm1Mom/J (“<italic toggle=\"yes\">Rag1</italic><sup>−/−</sup>,” Strain #:002216), C57BL/6J mice (“B6,” Strain #:000664) were obtained from The Jackson Laboratory. Female mice were used for all experiments with B6 background mice. All mice were bred and/or maintained in the animal facility at University of Colorado Anschutz Medical Campus. All experiments were performed in compliance with the study protocol approved by University of Colorado Anschutz Medical Campus Institutional Animal Care and Use Committee (IACUC).</p>", "<title>Mouse CAR Constructs</title>", "<p id=\"P21\">The basic construction of the murine 1928z CAR was previously described<sup>##REF##20631379##39##</sup>. The murine anti-CD19 scFv was Flag-tagged to enable CAR detection, and all ITAMs in the CD3zeta domain were kept intact. A truncated human EGFR reporter protein was incorporated following a 2A skip sequence to provide an additional method for detection of CAR-transduced cells<sup>##REF##21653320##13##</sup>. The DNA was codon optimized, ordered from ThermoFisher GeneArt, and cloned into the MSCV-IRES-GFP backbone, a gift from Tannishtha Reya (Addgene plasmid # 20672 ; <ext-link xlink:href=\"http://n2t.net/addgene:20672\" ext-link-type=\"uri\">http://n2t.net/addgene:20672</ext-link> ; RRID:Addgene_20672), using XhoI and ClaI enzyme sites. A control plasmid with just the truncated EGFR reporter in the MSCV backbone was generated using similar methods.</p>", "<title>Cell lines and media</title>", "<p id=\"P22\">E2A-PBX pre-B cell acute lymphoblastic leukemia was developed in the laboratory as previously described<sup>##REF##30209120##14##, ##REF##27460500##15##, ##UREF##2##16##</sup>. Murine T cells and leukemia were cultured in Complete Mouse Media (CMM), consisting of RPMI 1640 medium (Gibco) with 10% heat-inactivated fetal calf serum (Omega Bio), 1% nonessential amino acids (Gibco), 1% sodium pyruvate (Gibco), 1% penicillin/streptomycin (Gibco), 1% L-glutamine (Gibco), 1% HEPES buffer (Gibco) and 50uM 2-mercaptoethanol (Sigma-Aldrich).</p>", "<title>Mouse CAR Transduction</title>", "<p id=\"P23\">CAR transduction was performed as previously described<sup>##REF##30209120##14##, ##REF##27460500##15##, ##UREF##2##16##</sup>. Briefly, spleens from 6–10 week old donor mice were harvested and CD8+ T cells were isolated using EasySep Mouse CD8+ T cell Isolation Kit from STEMCell Technologies or bulk T cells were isolated using the Mouse CD3+ T Cell Enrichment Column Kit (R&D Biosciences, Cat No. MTCC-25). On day 1, T cells were activated on anti-CD3/anti-CD28 Mouse T cell Activator DynaBeads (Invitrogen) at a 1:1 cell:bead ratio and cultured at 1e6/mL in CMM in the presence of rhIL-2 (40IU/mL) and rhIL-7(10ng/mL) from R&D Systems. On days 2 and 3, retroviral supernatant was added to Retronectin-coated (Takara Biosciences) 6 well plates and spun at 2000×g and 32°C for 2–3 hours. Supernatant was then removed and activated T cells were added to the wells at 1.67mL/well. On day 4, beads were removed and T cells were resuspended at 1e6/mL in fresh media with cytokines. CAR transduction was determined post-debeading by analyzing T cells by flow cytometry for a FLAG/EGFR double-positive population (or EGFR single-positive for control T cells), and T cells were used in assays or infused into mice on day 5 or 6.</p>", "<title>Vaccine Model</title>", "<p id=\"P24\">The ovalbumin vaccine consists of 100ug whole ovalbumin protein (InvivoGen, Cat. code: vac-pova-100), 40ug anti-mouse CD40 (BioXCell, Catalog #BE0016–2) and 40ug Polyinosinic:polycytidylic acid [Poly (I:C)] (InvivoGen, Cat. code: tlrl-pic-5) per mouse, resuspended to 200uL total volume in PBS<sup>##REF##37516968##17##, ##REF##34433030##18##, ##UREF##3##19##</sup>. CD8+ T cells were isolated from naïve 6 to 8 week old OT-I mouse splenocytes using the Mouse CD3+ T Cell Enrichment Column Kit (R&D Biosciences, Cat No. MTCC-25). PepBoy mice were given 5e3 OT-I T cells retro-orbitally and concurrently vaccinated intravenously. 3–4 weeks later, spleens from 5–20 vaccinated mice were pooled and CD45.2+ OT-I memory T cells were isolated using the EasySep Mouse CD8+ T cell Isolation Kit, followed by column isolation using biotinylated anti-mouse CD45.2 (BioLegend, Cat # 109804), LS Columns (Miltenyi Biotec, Order No. 130-042-401), and anti-Biotin MicroBeads (Miltenyi Biotec, Order No. 130-;090-485). Naïve T cells from 1–5 naïve OT-I donors were isolated in parallel. T cells were then activated and transduced as described for downstream experiments.</p>", "<title>Generation of CD19<sup>Lo</sup> E2A-PBX leukemia cell lines</title>", "<p id=\"P25\">The E2A-PBX murine leukemia was generated in our lab as previously described <sup>##REF##30209120##14##</sup>. CD19 knockout leukemia was produced using CRISPR/Cas9. A previously-validated murine CD19-targeting sgRNA<sup>##REF##27460500##15##</sup> from Integrated DNA Technologies was incubated with recombinant Cas9 from TakaraBio (Cat# 632641) to create an RNP complex. RNP was then electroporated into E2A-PBX using the Lonza 4D-Nucleofector X with nucleofector solution SG and pulse program CM-147. Electroporated cells were allowed to recover for 48 hours and then FACS-sorted twice to obtain a pure CD19 knockout cell line. This cell population was additionally single cell cloned to create a CD19 knockout single cell clone prior to transduction with murine CD19. A truncated/non-signaling murine CD19 was cloned into the pLV.SP146.gp91.GP91.cHS4 plasmid, a gift from Didier Trono (Addgene plasmid # 30480 ; <ext-link xlink:href=\"http://n2t.net/addgene:30480\" ext-link-type=\"uri\">http://n2t.net/addgene:30480</ext-link> ; RRID:Addgene_30480). Backbones were generated with the hEF1a promoter (pLV.hEF1a.cHS4) or the hUbC promoter (pLV.hUbC.cHS4) from the pLenti6/UbC/mSlc7a1 plasmid, a gift from Shinya Yamanaka (Addgene plasmid # 17224 ; <ext-link xlink:href=\"http://n2t.net/addgene:17224\" ext-link-type=\"uri\">http://n2t.net/addgene:17224</ext-link> ; RRID:Addgene_17224). VSV-G pseudotyped lentivirus was generated as described and E2A-PBX CD19KO underwent a single round of transduction using standard protocols, followed by single cell cloning to obtain clonally-derived lines expressing defined levels of CD19 target antigen.</p>", "<title>Flow Cytometry</title>", "<p id=\"P26\">Flow cytometry analysis was performed using an LSR-Fortessa X-20 flow cytometer (BD Biosciences) and analyzed using FlowJo (BD Biosciences). Monoclonal antibodies used in staining are listed in the <xref rid=\"S10\" ref-type=\"sec\">supplemental methods</xref>. Intracellular flow cytometry staining was performed using the TrueNuclear Transcription Factor Buffer Set (BioLegend) for <italic toggle=\"yes\">ex vivo</italic> staining of transcription factors, Cytofix/Cytoperm Fixation/Permeablization Kit (BD Biosciences) for intracellular cytokine staining, and Mouse Foxp3 Buffer Set (BD Biosciences) for intracellular staining of Ki67 and Runx2.</p>", "<title>CD107a Degranulation, Intracellular Cytokine Staining (ICCS), Ki67 and CellTrace Dilution <italic toggle=\"yes\">In Vitro</italic> Assays</title>", "<p id=\"P27\"><italic toggle=\"yes\">In vitro</italic> assays were performed using a 1:1 effector to target cell ratio with 1e5 of each cell type in a 96-well round-bottom plate followed by analysis by flow cytometry at the indicated timepoints. Degranulation assays were performed by incubation for 4 hours in the presence of 2uM monensin and 1uL of CD107a antibody. ICCS was performed by incubation for 6 hours, with 1uM monensin and 2.5uM Brefeldin A added at 1 hour in. Ki67 was performed by incubation for 18 hours, followed by intracellular staining for Ki67. CellTrace dilution assays were performed by staining T cells with CellTrace Violet (Thermo Fisher Scientific) per manufacturer protocols followed by incubation with target cells for 72 hours.</p>", "<title>LCMV infection and T cell isolation</title>", "<p id=\"P28\">6 week old female C57BL/6 mice were injected retro-orbitally with 2e5 PFU of LCMV-Armstrong. 4 weeks later, CD8+ T cells were isolated from 5 pooled spleens using the EasySep Mouse CD8+ T cell Isolation Kit from STEMCell Technologies and then FACS-sorted to obtain Memory (CD8+/CD44+/CD49d<sup>hi</sup>) and Naïve (CD8+/CD44−/CD49d<sup>lo</sup>/CD62L+) populations from the same mice. T cells were then transduced using the standard transduction protocol as described.</p>", "<title><italic toggle=\"yes\">In vivo</italic> experiments in <italic toggle=\"yes\">Rag1</italic><sup>−/−</sup> hosts</title>", "<p id=\"P29\">Experiments were carried out using a timeline previously optimized in the lab<sup>##REF##30209120##14##</sup>. Briefly, <italic toggle=\"yes\">Rag1</italic><sup>−/−</sup> hosts were inoculated with 1e6 E2A-PBX by tail vein I.V. injection on day −3 followed by CAR T cells via retroorbital injection at either 1e5, 3e5 or 1e6 CAR+ cell dose on day 0. Bone marrow was harvested and analyzed by flow cytometry on day 4 or 11 post-CAR infusion, or mice were euthanized at humane endpoints for survival experiments. <italic toggle=\"yes\">Ex vivo</italic> stimulation for cytokine production was performed using 1e6 E2A-PBX WT to stimulate approximately 1.5e6 whole bone marrow cells from each individual mouse, with pooled bone marrow from each n=5 experimental group stimulated by E2A-PBX CD19<sup>Neg</sup> as a negative control. Cells were co-cultured for 6 hours, with 1uM monensin and 2.5uM Brefeldin A added at 1 hour in and then analyzed by flow for cytokine production.</p>", "<title>Bulk ATAC and RNA sequencing experimental setup and workflows</title>", "<p id=\"P30\">OT-I CD8+ T cells were isolated from vaccinated or naïve donors and CARs were transduced into T cells as described above. CAR8 <italic toggle=\"yes\">Rag1</italic><sup>−<italic toggle=\"yes\">/</italic>−</sup> hosts were inoculated with 1e6 E2A-PBX CD19<sup>10,000</sup> followed by 1e6 CAR8<sub>MD</sub> or CAR8<sub>ND</sub> on the timeline described above. At day 4 post-CAR infusion, bone marrow from 10 mice per CAR group was harvested and pooled. At each of 3 timepoints, CD8+ cells were isolated using the EasySep Mouse CD8+ T cell Isolation Kit from STEMCell Technologies and then FACS-sorted to obtain 50,000 cells per condition. ATAC-seq and RNA-seq were performed in triplicate on separate sorted aliquots of 50,000 cells at “Pre-CAR/Day −5” (<italic toggle=\"yes\">ex vivo,</italic> directly after isolation of memory or naïve CD8+ T cells from donor mice), “Post-CAR/Day 0” (<italic toggle=\"yes\">in vitro</italic>, after CAR manufacturing) and “Tumor/Day 4” (<italic toggle=\"yes\">ex vivo</italic>, after reinfusion into leukemia bearing mice). Experimental analyses were performed on the first technical replicate from 2 separate experimental replicates. For RNA-seq, cells were homogenized in QIAzol Lysis Reagent (Qiagen, Cat. No. 79306) and then frozen at −80C for processing within 2 weeks. Samples were thawed and processed using the miRNeasy Micro Kit (Qiagen, Mat. No. 1071023), with on-column DNase treatment (RNase-Free DNase Set, Qiagen, Cat. No. 79254), both according to manufacturer protocols. RNA purity, quantity and integrity was determined with NanoDrop (ThermoFisher Scientific) and TapeStation 4200 (Agilent) analysis prior to RNA-seq library preparation. The Universal Plus mRNA-Seq library preparation kit with NuQuant was used (Tecan) with an input of 200ng of total RNA to generate RNA-seq libraries. Paired-end sequencing reads of 150bp were generated on NovaSeq 6000 (Illumina) sequencer at a target depth of 40 million clusters/80 million paired-end reads per sample. Raw sequencing reads were de-multiplexed using bcl2fastq. For ATAC-seq, cells were immediately processed using the Omni-ATAC protocol as previously described<sup><xref rid=\"R40\" ref-type=\"bibr\">40</xref></sup>. Briefly, sorted cells were washed once in 1X PBS, lysed, washed once in Wash Buffer and then the transposition reaction was carried out at 32°C for 30 minutes on a thermomixer set to 1000 rpm. Transposed chromatin was then purified using the Zymo Clean and Concentrator 5 Kit (Zymo Research, Cat # D4013) using manufacturer protocols. DNA was then ran on PCR for 12 total cycles with matched barcoding primers<sup><xref rid=\"R41\" ref-type=\"bibr\">41</xref></sup>. PCR reactions were then size-selected using AMPure XP beads (Beckman Coulter Life Sciences, Product No: A63880) and checked for quality and size distribution using TapeStation 4200 with D5000 reagents (Agilent). Libraries were pooled at equimolar ratios for sequencing and paired-end sequencing reads of 150bp for the first replicate and 50bp for the second replicate were generated on NovaSeq 6000 (Illumina) sequencer at a target depth of 40 million clusters/80 million paired-end reads per sample. Raw sequencing reads for replicate 1 were shortened to match the read lengths for replicate 2 using trimmomatic function CROP. Raw sequencing reads were de-multiplexed using bcl2fastq.</p>", "<title>RNA-seq Data Analysis</title>", "<p id=\"P31\">Quality of fastq files was accessed using FastQC (v.0.11.8) (<ext-link xlink:href=\"http://www.bioinformatics.babraham.ac.uk/projects/fastqc\" ext-link-type=\"uri\">http://www.bioinformatics.babraham.ac.uk/projects/fastqc</ext-link>), FastQ Screen (v.0.13.0)<sup><xref rid=\"R42\" ref-type=\"bibr\">42</xref></sup> and MultiQC (v.1.8)<sup><xref rid=\"R43\" ref-type=\"bibr\">43</xref></sup>. Illumina adapters and low-quality reads were filtered out using BBDuk (v. 38.87) (<ext-link xlink:href=\"http://jgi.doe.gov/data-and-tools/bb-tools\" ext-link-type=\"uri\">http://jgi.doe.gov/data-and-tools/bb-tools</ext-link>). Trimmed fastqc files were aligned to the mm10 murine reference genome and aligned counts per gene were quantified using STAR (v.2.7.9a) <sup><xref rid=\"R44\" ref-type=\"bibr\">44</xref></sup>. Differential gene expression analysis was performed using the DESeq2 package<sup><xref rid=\"R45\" ref-type=\"bibr\">45</xref></sup>. Pathway enrichment analysis was performed using GSEA (UC San Diego/Broad Institute)<sup>##REF##16199517##26##, <xref rid=\"R46\" ref-type=\"bibr\">46</xref></sup>, Metascape<sup><xref rid=\"R47\" ref-type=\"bibr\">47</xref></sup> for gene mapping and IPA (Qiagen)<sup>##REF##24336805##28##, <xref rid=\"R48\" ref-type=\"bibr\">48</xref></sup>. Differential gene expression was plotted using GraphPad Prism or ggplot2 (R package). RNA-seq differential gene expression statistics were run using the DESeq2 R package, with filtering threshold at 10 with greater than 2-fold change and adjusted p value < 0.05.</p>", "<title>ATAC-seq Data Analysis</title>", "<p id=\"P32\">Fastq files were used to map to the mm10 genome using the ENCODE ATAC-seq pipeline (<ext-link xlink:href=\"https://www.encodeproject.org/atac-seq/\" ext-link-type=\"uri\">https://www.encodeproject.org/atac-seq/</ext-link>), with default parameters, except bam files used for peak calling were randomly downsampled to a maximum of 50 million mapped reads. Peaks with a MACS2(<ext-link xlink:href=\"https://pypi.org/project/MACS2/\" ext-link-type=\"uri\">https://pypi.org/project/MACS2/</ext-link>) computed q value of less than 1e-6 and a signalValue of more than 4 in at least one replicate were merged with bedtools<sup><xref rid=\"R49\" ref-type=\"bibr\">49</xref></sup> function intersect and processed to uniform peaks with the functions getPeaks and resize from R package ChromVAR<sup>##REF##28825706##22##</sup>. Reads overlapping peaks were enumerated with getCounts function from ChromVAR and normalized and log2-transformed with voom from R package limma<sup><xref rid=\"R50\" ref-type=\"bibr\">50</xref></sup>. Peaks with 3 or more normalized counts per million mapped reads at least one replicate were included to define a global peak set of 82,410 peaks. Pairwise Euclidean distances were computed between all samples using log2-transformed counts per million mapped reads among the global peak set. Differentially accessible peaks were identified in pairwise comparisons based on fdr adjusted p values of less than 0.01, fold change of at least 4 and with an average of 3 normalized counts per million mapped reads using R package limma. Motif associated variability in ATAC-seq signal was computed with R package ChromVAR. Genome-wide visualization of ATAC-seq coverage was computed with deeptools<sup><xref rid=\"R51\" ref-type=\"bibr\">51</xref></sup> function coveragebam, using manually computed scale factors based on the number of reads within the total peak set.</p>", "<title>Statistics</title>", "<p id=\"P33\">Statistical tests for all experiments except sequencing analyses were performed using GraphPad Prism v9.0 for Macintosh (GraphPad Software). Comparisons between three groups were made with ordinary one-way ANOVA with Holm-Sidak’s multiple comparisons test, Brown-Forsythe and Welch one-way ANOVA with Dunnett’s T3 multiple comparisons test, or Kruskal-Wallis non-parametric test with Dunn’s multiple comparisons test were used depending on variance in standard deviations. Two-way ANOVA or mixed effects analysis with Tukey’s multiple comparisons test was used for <italic toggle=\"yes\">in vitro</italic> experimental comparisons with multiple antigen densities and <italic toggle=\"yes\">in vivo</italic> CAR expansion data. Two-tailed ordinary t test, Welch’s t test or Mann-Whitney test were performed for comparisons with two groups depending on normality of distributions. For multiple comparisons of two groups, multiple unpaired t tests or multiple Welch’s t tests, both with Holm-Sidak’s multiple comparisons test, were performed when appropriate depending on variance in standard deviations. Log-rank (Mantel-Cox) test was used for survival curve comparisons. All data represented as mean +/− standard deviation. * p<0.05, p<0.01, * p<0.001, **** p<0.0001. Technical and experimental replicates in each dataset are indicated in figure legends.</p>", "<title>Supplementary Material</title>" ]	[ "<title>ACKNOWLEDGEMENTS</title>", "<p id=\"P34\">We thank Lillie Leach for laboratory management, Amanda Novak for animal colony management, Garrett Hedlund and Henry Chu at the CU Anschutz Clinical Immunology Flow Core for their assistance in cell sorting, the CU Cancer Center Genomics Shared Resource (RRID: SCR_021984) for their help with sequencing/genomics, and the CU Anschutz OLAR and the animal facility for their support. This work was funded in part by Department of Defense W81XWH-19-1-0196 and partly supported by the National Institutes of Health P30CA046934 Bioinformatics and Biostatistics Shared Resource Core (RRID: SCR_021983). Some figures were created with <ext-link xlink:href=\"https://www.biorender.com/\" ext-link-type=\"uri\">BioRender.com</ext-link>.</p>", "<title>Data and Materials Availability</title>", "<p id=\"P35\">All data is readily available from authors upon request or accessible at Gene Expression Omnibus (<bold>GEO Accession Number will be provided before paper acceptance</bold>). All materials are either commercially available as described or available from authors upon request.</p>", "<title>METHODS REFERENCES</title>" ]	[ "<fig position=\"float\" id=\"F1\"><label>Figure 1:</label><caption><title>Antigen experience history directs multiple aspects of <italic toggle=\"yes\">in vitro</italic> functional capacity of murine CD8+ CAR T cells.</title><p id=\"P37\"><bold>1A:</bold> E2A-PBX murine leukemia was engineered to knockout CD19, followed by reintroduction of CD19 at different levels to generate a range of antigen density clones. <bold>1B:</bold> Schematic: Vaccine model for generating memory CD8+ OT-I T cells. 5e3 OT-I T cells were transferred into congenically distinct hosts which were concurrently vaccinated with antigen and adjuvants. 3–5 weeks later, CAR T cells were manufactured from memory OT-I’s (CAR8<sub>MD,</sub> memory-derived) or naïve OT-I’s (CAR8<sub>ND</sub>, naïve-derived) <bold>1C:</bold> Intracellular cytokine staining of IFNg and TNFa after 6 hour co-culture assay. <bold>1D:</bold> Degranulation as measured by CD107a expression after 4 hour co-culture assay. <bold>1E-G:</bold> Quantification of cytokine data, % positive cells for indicated cytokine. <bold>1H:</bold> Quantification of CD107a data, % positive cells. <bold>1I:</bold> Cell-cycle entry as measured by Ki-67 staining after 18 hour co-culture assay. <bold>1J:</bold> Proliferation as measured by dilution of CellTrace Violet dye after 72 hour co-culture assay. All <italic toggle=\"yes\">in vitro</italic> assays were performed with n=3 technical replicates, and are representative of 2 independent experiments. Data represent mean +/− SD. * p<0.05, p<0.01, * p<0.001, **** p<0.0001.</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2:</label><caption><title>CAR8<sub>MD</sub> exhibit enhanced cytotoxicity and clearance of CD19<sup>Lo</sup> leukemia <italic toggle=\"yes\">in vivo</italic> (high CAR dose).</title><p id=\"P38\"><bold>2A:</bold> Schematic: Timeline for <italic toggle=\"yes\">in vivo</italic> experiments. <italic toggle=\"yes\">Rag1</italic><sup>−/−</sup> mice were injected with 1e6 E2A-PBX1 leukemia on day −3, followed by 1e6 OT-I CD8+/EGFR+ T cells from indicated T cell condition on day 0. Bone marrow was analyzed by flow cytometry on day +4 or day +11. T cell populations were isolated memory-derived CAR T cells (CAR8<sub>MD</sub>), isolated naïve-derived CAR T cells (CAR8<sub>ND</sub>) or EGFR control T cells (EGFR8). Leukemia populations were CD19<sup>Neg</sup>, CD19<sup>Lo</sup>(10,000 antigens/cell), or WT (35,000 antigens/cell). <bold>2B-C:</bold> Early T cell expansion (day +4) or persistence (day +11) after infusion of transduced T cells against WT leukemia <bold>(B)</bold> and CD19<sup>Lo</sup> leukemia <bold>(C)</bold>. Transduced T cell populations measured by coexpression of CD8a+/TCRbeta+/EGFR+. <bold>2D:</bold> Clearance of WT and CD19<sup>Lo</sup> leukemia at day +11 after CAR infusion. E2A-PBX measured by coexpression of B220+/CD22+. <bold>2E-F:</bold> Intracellular cytokine staining of interferon gamma <bold>(E)</bold> or granzyme B <bold>(F)</bold> in CAR T cells from whole bone marrow restimulated <italic toggle=\"yes\">ex vivo</italic> with leukemia. Data represent mean +/− SD. <bold>2G-I:</bold> Intranuclear transcription factor staining of IRF4 <bold>(G)</bold>, EOMES <bold>(H)</bold>, or T-bet <bold>(I)</bold> on CAR+ T cells from mice bearing the indicated leukemia at day +4 after CAR infusion. Violin plot data represent median with quartiles. Data are from 2 pooled, independent experiments with n=10 mice per condition. * p<0.05, p<0.01, * p<0.001, **** p<0.0001. <bold>2J:</bold> Survival of mice after treatment with 1e6 EGFR+ CAR or control T cells. Survival statistics were performed using log-rank (Mantel-Cox) test * p<0.05, p<0.01, * p<0.001, **** p<0.0001. Data is from 2 independent pooled experiments, total n=10 mice per group.</p></caption></fig>", "<fig position=\"float\" id=\"F3\"><label>Figure 3:</label><caption><title>CAR8<sub>ND</sub> exhibit enhanced expansion capacity and clearance of WT leukemia <italic toggle=\"yes\">in vivo</italic> (low CAR dose).</title><p id=\"P39\"><bold>3A-B:</bold> Early T cell expansion (day +4) or persistence (day +11) after infusion of transduced T cells against WT leukemia <bold>(A)</bold> and CD19<sup>Lo</sup> leukemia <bold>(B)</bold>. Transduced T cell populations measured by coexpression of CD8a+/TCRbeta+/EGFR+. <bold>3C:</bold> Clearance of WT and CD19<sup>Lo</sup> leukemia at day +11 after CAR infusion. E2A-PBX measured by coexpression of B220+/CD22+. <bold>3D-E:</bold> Intracellular cytokine staining of interferon gamma <bold>(D)</bold> or granzyme B <bold>(E)</bold> in CAR T cells from whole bone marrow restimulated <italic toggle=\"yes\">ex vivo</italic> with leukemia. Data represent mean +/− SD. <bold>2F-H:</bold> Intranuclear transcription factor staining of IRF4 <bold>(F)</bold>, EOMES <bold>(G)</bold>, or T-bet <bold>(H)</bold> on CAR+ T cells from mice bearing the indicated leukemia at day +4 after CAR infusion. Violin plot data represent median with quartiles. Data are from 2 pooled, independent experiments with n=10 mice per condition. * p<0.05, p<0.01, * p<0.001, **** p<0.0001. <bold>2I:</bold> Survival of mice after treatment with 1e6 EGFR+ CAR or control T cells. * p<0.05, p<0.01, * p<0.001, **** p<0.0001. Data are from 2 independent pooled experiments, total n=10 mice per group.</p></caption></fig>", "<fig position=\"float\" id=\"F4\"><label>Figure 4:</label><caption><title>Prior antigen experience imprints chromatin accessibility states which follow unique patterns during CAR transduction and reinfusion.</title><p id=\"P40\"><bold>4A:</bold> Schematic: Layout for paired ATAC-seq/RNA-seq experiments. Memory-derived or naïve derived OT-I CD8+ T cells were sorted at three sequential timepoints: <italic toggle=\"yes\">Ex vivo</italic> from donor mice before CAR transduction (“PreCAR”), <italic toggle=\"yes\">in vitro</italic> after CAR transduction (“PostCAR”), and <italic toggle=\"yes\">ex vivo</italic> after reinfusion into CD19<sup>Lo</sup> leukemia-bearing <italic toggle=\"yes\">Rag1</italic><sup><italic toggle=\"yes\">−/−</italic></sup> mice (“Tumor”). <bold>4B:</bold> Chromatin accessibility at <italic toggle=\"yes\">Gzmb, Gzmc, Ifng, Tcf7 and Pdcd1</italic> gene loci for naïve and memory-derived T cells at each timepoint. <bold>4C:</bold> ChromVAR deviation z-scores between indicated populations at differentially accessible regions between Effector and Memory T cells after LCMV-Armstrong infection<sup>##REF##27939672##23##</sup>. Data are mean +/− range of two biological replicates. <bold>4D:</bold> Motif-associated ChromVAR deviation z-scores between indicated populations. Data are mean +/− range of two biological replicates <bold>4E:</bold> K-means clustering of relative ATAC-seq signal at differentially accessible regions (top, data from two biological replicates are shown) and motif enrichment in each cluster vs all regions (bottom).</p></caption></fig>", "<fig position=\"float\" id=\"F5\"><label>Figure 5:</label><caption><title>Prior antigen experience drives differential CAR8 transcriptomic states which follow unique patterns during CAR transduction and reinfusion.</title><p id=\"P41\">RNA-seq analysis was run on the timepoints/conditions indicated in the previous figure. <bold>5A:</bold> Volcano plots of significant differentially expressed genes between naïve and memory-derived cells at each of the three timepoints. <bold>5B:</bold> Normalized enrichment scores from gene set enrichment analysis (GSEA) of differentially enriched genesets between indicated CD8+ T cell subsets after LCMV-Armstrong acute viral infection<sup>##REF##17950003##24##</sup>\n<bold>5C:</bold> GSEA plots at each timepoint. <bold>5D:</bold> Top differentially expressed transcription factors at the “PreCAR” timepoint, generated using Ingenuity Pathway Analysis (IPA). <bold>5E:</bold> DESeq2-normalized counts of indicated transcription factors at each timepoint for naïve and memory-derived cells. <bold>5F:</bold> DESeq2-normalized counts of Runx family transcription factors at each timepoint for naïve and memory-derived cells. All statistics performed using DESeq2 with filtering threshold at 10, log2foldchange >2 and padj < 0.05.</p></caption></fig>", "<fig position=\"float\" id=\"F6\"><label>Figure 6:</label><caption><title>Runx2 overexpression as a novel strategy for enhancement of naïve-derived CD8+ CAR T cell potency and resistance to dysfunction.</title><p id=\"P42\"><bold>6A-B:</bold> Cotransduction of memory (A) or naïve (B) CD8+ T cells with CAR and pMIG-Empty, pMIG-BATF, or pMIG-JUN. For <bold>6C-F & I-L</bold>, <italic toggle=\"yes\">Rag1</italic><sup><italic toggle=\"yes\">−/−</italic></sup> mice were given leukemia on day −3, followed by 1e5 pMIG-Runx2 or pMIG-Empty co-transduced CAR8 on day 0. Bone marrow was analyzed by flow cytometry on day 11 post-CAR. <bold>6C & D:</bold> CAR T cell and leukemia proportions for naïve (C) and memory-derived (D) CAR T cells cotransduced with BATF, JUN or pMIG control. <bold>6E & F:</bold> Proportion of CAR T cells displaying PD1+/TOX+ phenotype. <bold>6G-H:</bold> Cotransduction of memory (G) or naïve (H) CD8+ T cells with CAR and pMIG-Empty or pMIG-Runx2 <bold>and</bold> intracellular staining for Runx2. <bold>6I & J:</bold> CAR T cell and leukemia proportions for naïve (C) and memory-derived (D) CAR T cells cotransduced with RUNX2 or pMIG control. <bold>6K & L:</bold> Proportion of CAR T cells displaying PD1+/TOX+ phenotype. Data in 6A,B,G & H are representative of 3–4 independent experiments. Data in 6C-F are from 1 experiment with n=5 mice per condition. Data in 6I-L are from 2 pooled, independent experiments with n=9 mice per condition. Data represent mean +/− SD. * p<0.05, p<0.01, * p<0.001, **** p<0.0001.</p></caption></fig>" ]	[]	[]	[]	[]	[]	[]	[ "<supplementary-material id=\"SD1\" position=\"float\" content-type=\"local-data\"><label>Supplement 1</label></supplementary-material>" ]	[ "<fn-group><fn fn-type=\"COI-statement\" id=\"FN2\"><p id=\"P36\"><bold>Additional Declarations: Yes</bold> there is potential Competing Interest. Patent applicant (whether author or institution): University of Colorado Anschutz Medical Campus Name of inventor(s): Kole R DeGolier, James P Scott-Browne, Terry J Fry Application number: U.S. Provisional Application No. 63/595,612 Status of application: Pending Aspect covered: Methods of enhancing potency of engineered immune cells via Runx2 modulation</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"nihpp-rs3712137v1-f0001\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-rs3712137v1-f0002\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-rs3712137v1-f0003\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-rs3712137v1-f0004\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-rs3712137v1-f0005\" position=\"float\"/>", "<graphic xlink:href=\"nihpp-rs3712137v1-f0006\" position=\"float\"/>" ]	[ "<media xlink:href=\"NIHPPrs3712137v1-supplement-1.pdf\" id=\"d64e1291\" position=\"anchor\"/>" ]	[{"label": ["3."], "surname": ["Qin"], "given-names": ["H."], "article-title": ["CAR T cells targeting BAFF-R can overcome CD19 antigen loss in B cell malignancies"], "source": ["Sci Transl Med"], "volume": ["11"], "year": ["2019"]}, {"label": ["5."], "surname": ["Alvanou"], "given-names": ["M."], "article-title": ["Empowering the Potential of CAR-T Cell Immunotherapies by Epigenetic Reprogramming"], "source": ["Cancers (Basel)"], "volume": ["15"], "year": ["2023"]}, {"label": ["16."], "surname": ["Yang"], "given-names": ["Y."], "article-title": ["TCR engagement negatively affects CD8 but not CD4 CAR T cell expansion and leukemic clearance"], "source": ["Sci Transl Med"], "volume": ["9"], "year": ["2017"]}, {"label": ["19."], "surname": ["Klarquist"], "given-names": ["J."], "article-title": ["Clonal expansion of vaccine-elicited T cells is independent of aerobic glycolysis"], "source": ["Sci Immunol"], "volume": ["3"], "year": ["2018"]}]	{ "acronym": [], "definition": [] }	39	CC BY	no	2024-01-13 23:36:46	Res Sq. 2023 Dec 21;:rs.3.rs-3712137	oa_package/80/de/PMC10775394.tar.gz
PMC10781595	38213380	[ "<title>Introduction</title>", "<p>Annually, over 356,000 cardiac arrests occur in the United States outside of a hospital setting [##REF##33894661##1##]. Following an out-of-hospital cardiac arrest (OHCA), rapid administration of cardiopulmonary resuscitation (CPR) can significantly increase patient survival. Data reported in 2019 found that adults suffering an OHCA who received CPR from a layperson demonstrated a reduction in hospital mortality as well as an increase in the probability of survival to discharge, and good neurologic outcomes on discharge compared to adult OHCA victims who did not receive CPR from a layperson [##UREF##0##2##].</p>", "<p>The onset of the coronavirus disease 2019 (COVID-19) pandemic marked an increased incidence of OHCAs. These effects were likely due to direct effects of COVID-19 infection as well as delayed access to healthcare and reluctance to seek medical attention for the fear of increased COVID-19 exposure [##REF##33894661##1##,##REF##32558876##3##,##UREF##1##4##]. Despite the increase in OHCAs, a survey performed by Grunau et al. indicated a decreased willingness to perform CPR in 2020 compared to prior years [##UREF##2##5##,##REF##33403365##6##]. Bystander CPR, which here signifies CPR administered prior to the arrival of the EMS, may be discouraged by a reduction in CPR training, or risk of exposure to potentially infectious respiratory secretions [##UREF##0##2##,##UREF##2##5##]. To date, several studies have outlined changes in CPR characteristics and public willingness to perform CPR following the COVID-19 outbreak in the United States [##UREF##0##2##,##UREF##1##4##,##REF##33403365##6##]. To our knowledge, no studies have assessed changes in the frequency of bystander CPR at a national level for greater than six months [##UREF##0##2##,##UREF##1##4##,##REF##33403365##6##,##UREF##3##7##]. Our study seeks to determine if the frequency of bystander-initiated CPR encounters in the United States was significantly reduced by the onset of the COVID-19 pandemic.</p>" ]	[ "<title>Materials and methods</title>", "<p>Study design</p>", "<p>A retrospective observational study design was implemented to evaluate the impact of the COVID-19 pandemic on the frequency of bystander-initiated CPR provided to OHCA victims before the arrival of EMS.</p>", "<p>Data source and description</p>", "<p>The National Emergency Medical Services Information System (NEMSIS) database was utilized in our study. The NEMSIS database is a federally funded national repository that collates standardized EMS activation details from over 10,000 EMS agencies throughout the United States. It offers a comprehensive perspective on pre-hospital care trends and practices.</p>", "<p>Data collection protocols</p>", "<p>Our team accessed data from the NEMSIS database for two calendar years: 2019 (pre-COVID-19) and 2020 (post-COVID-19) [##UREF##3##7##,##UREF##4##8##]. We followed all necessary protocols and secured approvals from the California University of Science and Medicine (CUSM) institutional review board to ensure ethical standards were met during data extraction and analysis.</p>", "<p>Participant selection</p>", "<p>Inclusion/Exclusion Criteria</p>", "<p>We established specific criteria to filter relevant cases for our analysis as outlined in Figure ##FIG##0##1##. The primary focus was on EMS activations where the primary medical emergency was identified as an OHCA and information about CPR administration prior to EMS arrival (bystander CPR) was explicitly documented. Cases with missing data or where the status of CPR administration before EMS arrival was ambiguous were excluded from the analysis.</p>", "<p>Dataset definition</p>", "<p>The data from 2019 served as our control group, representing the period before the declaration of the COVID-19 pandemic. In contrast, the 2020 data acted as our study group, reflecting the period after the pandemic's declaration. Calendar years were selected as the defining bounds of the COVID-19 pandemic to capture as many possible instances where the administration of bystander CPR could have been impacted by the COVID-19 pandemic. January 2020 was used as the starting month of the COVID-19 pandemic for our study for several reasons. First, this was the month where COVID-19 was initially identified in the United States [##REF##32004427##9##]. Second, previous research on news articles suggests widespread media coverage of the COVID-19 pandemic prior to the lockdowns that occurred in March of 2020 [##REF##32902813##10##]. Finally, we used the month of January as the starting month because this is the month when the Secretary of the Department of Health and Human Services of the United States declared the 2019 novel coronavirus outbreak a public health emergency [##UREF##5##11##].</p>", "<p>Statistical analysis</p>", "<p>All statistical computations were performed using the SPSS Statistics software suite (IBM Corp., Armonk, NY), known for its robust data-processing capabilities for biomedical research. We employed a chi-square test, which is suitable for analyzing categorical data and determining if significant differences exist between expected and observed frequencies in one or more categories. Specifically, we compared the observed frequencies of OHCA patients in 2019 and 2020 who received both bystander CPR and standard EMS interventions versus those who received only standard EMS interventions. To quantify the likelihood of an event occurring in one group compared to another, we calculated the odds ratio. In our study, this meant determining the odds that CPR would be initiated by a bystander in 2020 compared to 2019. An odds ratio of 1 implies that the event is equally likely in both groups, above 1 indicates an increased likelihood, and below 1 denotes a decreased likelihood.</p>" ]	[ "<title>Results</title>", "<p>A total of 577,011 cases from the NEMSIS database met study inclusion criteria. In 2019, 53.7% of patients (122,647 patients) received bystander CPR in addition to standard EMS interventions, compared to 46.3% of patients (105,612 patients) who received standard EMS interventions only (Table ##TAB##0##1##). In 2020, 52.5% (183,077 patients) received bystander CPR and standard EMS interventions, compared to 47.5% (165,675 patients) who received standard EMS interventions only. Therefore, there was a 1.2% decrease in the frequency of OHCA patients receiving bystander CPR in 2020 relative to 2019, with an odds ratio of 0.952 times chance that a victim would receive bystander CPR in 2020 compared to 2019 (Table ##TAB##1##2##).</p>", "<p>Pearson chi-square value, continuity correction, and likelihood ratio for cardiac arrests with CPR administered by bystanders and standard EMS interventions versus standard EMS interventions only were also calculated. The Pearson chi-square analysis yielded a value of 84.691, continuity correction yielded a value of 84.641, and the likelihood ratio test revealed a value of 84.711, each with a two-sided asymptotic significance of <0.001 (Table ##TAB##2##3##).</p>" ]	[ "<title>Discussion</title>", "<p>We hypothesized that the COVID-19 pandemic was associated with a significant decrease in the frequency of bystander CPR. The Pearson chi-square test, with an asymptotic significance of <0.001, allows for the rejection of the null hypothesis, and acceptance that there is a statistically significant difference in the frequency of bystander CPR administration between the years 2019 and 2020. This is further supported by the data from the continuity correction and likelihood ratio tests. The results therefore suggest that CPR administration by bystanders decreased during the COVID-19 pandemic. Furthermore, the odds ratio indicates that there was 0.952 times chance that CPR would be initiated by bystanders in 2020 compared to 2019. This suggests that there was a small reduction in the likelihood of CPR being performed by bystanders during the COVID-19 pandemic.</p>", "<p>Identifying decreases in bystander-administered CPR during the COVID-19 pandemic is integral to understanding potential reasons for this behavior change and finding solutions to combat it. Detecting declines in bystander-performed CPR can help public health programs create strategies to ensure the public feels comfortable administering CPR in out-of-hospital cardiac arrest cases. A previous research by Chan et al. demonstrated a decrease in rates of return of spontaneous circulation (ROSC) in OCHA during the COVID-19 pandemic and this finding correlates with the outcome of our study [##REF##33188678##12##]. A decline in ROSC could be partially explained by a decrease in bystander-performed CPR. Increased bystander-performed CPR is known to lead to increased cases of ROSC in OHCA [##REF##15950357##13##]. Therefore, a decrease in bystander-performed CPR as shown by our research could partially explain the observed decrease in return of spontaneous circulation that occurred during the COVID-19 pandemic. Our research further expands upon the previous research by Shekhar et al. that looked at the first six months of the COVID-19 pandemic and established declines in bystander-performed CPR through monthly comparisons [##REF##33838167##14##]. Our research expands upon their work by extending the time period of analysis and calculating more comparative statistics to better evaluate for potential pre- and post-COVID-19 differences.</p>", "<p>While our study focuses on OHCA, studies have shown that decreasing rates of CPR during the pandemic also negatively impacted adults with in-hospital cardiac arrest (IHCA) [##UREF##6##15##]. Additionally, research has shown that patients with active COVID-19 infection who developed IHCA and consequently received CPR had poor outcomes. In a retrospective study of patients with COVID-19 who suffered from IHCA and received CPR, only 13.2% achieved ROSC and only 2.9% survived beyond 30 days [##REF##32283117##16##]. Poor outcomes among COVID-19 patients who received IHCA CPR would imply that OHCA bystander CPR would have less of an effect on patients who were infected with COVID-19. Similarly, a cross-sectional study demonstrated a ROSC of 9% and only 2% surviving to discharge [##UREF##7##17##]. In a case series of 54 patients, zero survived to discharge [##UREF##8##18##]. Further research may explore whether the consequences of decreased CPR administration had negative outcomes for both patients with and without COVID-19 infection, or if patients without COVID-19 infection were disproportionately affected considering COVID-19 patients may have reduced survival regardless of CPR administration. In a study including only patients without COVID-19 infection, Tong et al. found that fewer patients received CPR by first-responders and that arrival to the scene was delayed, both leading to decreased ROSC [##REF##34507622##19##]. While our focus is on the overall impact of COVID-19 on the administration of CPR for OHCA, the consequences are important to consider in all settings and all patients.</p>", "<p>Limitations</p>", "<p>While the results of this study demonstrate a significant reduction in the frequency of bystander CPR performed during the COVID-19 pandemic, the study design and choice of database limit the ability to make further interpretations.</p>", "<p>First, because COVID-19 lockdowns did not occur until March of 2020, it is likely that many of the negative public health effects of the pandemic were not felt during the early months of 2020. Defining pre- and post-pandemic years as 2019 and 2020, respectively, likely reduced our calculated odds ratio because fewer lay people were probably aware of the COVID-19 cases that were occurring during this time.</p>", "<p>Second, our retrospective observational study cannot single out the COVID-19 pandemic as the causal factor in the decline. Appreciating how regional and temporal differences of the COVID-19 pandemic affect bystander CPR would help to establish a causal link between the COVID-19 pandemic and the decrease in bystander-initiated CPR and may help to elucidate specific aspects of the pandemic that led to the decline. Additionally, while the NEMSIS database is one of the most comprehensive tools available to evaluate overall trends in prehospital care, EMS agencies are not required to submit patient cases to the database. As such, the database is a convenience sample and does not necessarily reflect more detailed regional or temporal differences in bystander-administered CPR.</p>", "<p>Third, the NEMSIS database fails to capture and isolate tiered-level EMS responses. Therefore, there is likely an overcount of the number of cardiac arrest cases reported by the NEMSIS database. The inability to account for tiered EMS responses should however not affect the outcome of this study because it is unlikely that tiered responses occur in a biased fashion with respect to the bystander CPR status.</p>" ]	[ "<title>Conclusions</title>", "<p>In conclusion, bystanders are often the first to administer CPR following a cardiac arrest. The onset of the COVID-19 pandemic was associated with a small but significant decrease in the frequency of cardiac arrest victims who received bystander CPR prior to EMS arrival. Further examination of factors driving this change may help establish useful targets for public messaging and education regarding CPR administration.</p>" ]	[ "<p>Aim: Rapid administration of cardiopulmonary resuscitation (CPR) can significantly increase patient survival following an out-of-hospital cardiac arrest (OHCA). Through this study, we aimed to determine if the onset of the coronavirus disease 2019 (COVID-19) pandemic affected the likelihood of OHCA victims receiving bystander-initiated CPR prior to EMS arrival.</p>", "<p>Methods: We used data collected by the National Emergency Medical Services Information System (NEMSIS) for years 2019 and 2020. Data was filtered to include only cases of OHCA where the status of bystander CPR was listed. We used a chi-square analysis to compare frequencies of patients receiving both bystander CPR and standard EMS interventions versus patients receiving only standard EMS interventions for the years before and during the COVID-19 pandemic declaration (2019 and 2020, respectively).</p>", "<p>Results: Of the 577,011 cases that met our inclusion criteria, 228,259 occurred in 2019 and 348,752 occurred in 2020. The frequency of OHCA cases that reported bystander-initiated CPR prior to EMS arrival significantly decreased from 2019 to 2020 (53.7% vs. 52.5%, P<.001).</p>", "<p>Conclusion: Bystanders are often the first to administer CPR following a cardiac arrest. It was found that the likelihood of an OHCA victim receiving bystander CPR decreased from 2019 to 2020.</p>" ]	[]	[ "<p>The authors are grateful to the California University of Science and Medicine faculty and staff. The data that support the findings of this study are available from the National Emergency Medical Services Information System (NEMSIS) Home Office in Salt Lake City, Utah. Restrictions apply to the availability of these data, which were used with permission for this study. Data are available by request from the National Emergency Medical Services Information System (NEMSIS) website https://nemsis.org/using-ems-data/request-research-data/</p>" ]	[ "<fig position=\"anchor\" fig-type=\"figure\" id=\"FIG1\"><label>Figure 1</label><caption><title>NEMSIS selection criteria</title><p>NEMSIS, National Emergency Medical Services Information System; CPR, cardiopulmonary resuscitation</p></caption></fig>" ]	[ "<table-wrap position=\"float\" id=\"TAB1\"><label>Table 1</label><caption><title>CPR administration provider frequency counts and percentages for years 2019 and 2020</title><p>CPR, cardiopulmonary resuscitation; EMS, Emergency Medical Services</p></caption><table frame=\"hsides\" rules=\"groups\"><tbody><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">CPR status</td><td rowspan=\"1\" colspan=\"1\">Frequency counts</td><td rowspan=\"1\" colspan=\"1\">2020 (during COVID-19)</td><td rowspan=\"1\" colspan=\"1\">2019 (pre-COVID-19)</td><td rowspan=\"1\" colspan=\"1\">Total</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Bystander CPR and standard EMS interventions</td><td rowspan=\"1\" colspan=\"1\">Observed count (percent)</td><td rowspan=\"1\" colspan=\"1\">183077 (52.5)</td><td rowspan=\"1\" colspan=\"1\">122647 (53.7)</td><td rowspan=\"1\" colspan=\"1\">305,724</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\"> </td><td rowspan=\"1\" colspan=\"1\">Expected count (percent)</td><td rowspan=\"1\" colspan=\"1\">184783.1 (52.98)</td><td rowspan=\"1\" colspan=\"1\">120940.9 (52.98)</td><td rowspan=\"1\" colspan=\"1\">305,724</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Standard EMS interventions only</td><td rowspan=\"1\" colspan=\"1\">Observed count (percent)</td><td rowspan=\"1\" colspan=\"1\">165675 (47.5)</td><td rowspan=\"1\" colspan=\"1\">105612 (46.3)</td><td rowspan=\"1\" colspan=\"1\">271,287</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\"> </td><td rowspan=\"1\" colspan=\"1\">Expected count (percent)</td><td rowspan=\"1\" colspan=\"1\">163968.9 (47.02)</td><td rowspan=\"1\" colspan=\"1\">107318.1 (47.02)</td><td rowspan=\"1\" colspan=\"1\">271,287</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Total</td><td rowspan=\"1\" colspan=\"1\">Observed count (percent)</td><td rowspan=\"1\" colspan=\"1\">348752 (100)</td><td rowspan=\"1\" colspan=\"1\">228259 (100)</td><td rowspan=\"1\" colspan=\"1\">577,011</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\"> </td><td rowspan=\"1\" colspan=\"1\">Expected count (percent)</td><td rowspan=\"1\" colspan=\"1\">348752 (100)</td><td rowspan=\"1\" colspan=\"1\">228259 (100)</td><td rowspan=\"1\" colspan=\"1\">577,011</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"TAB2\"><label>Table 2</label><caption><title>Calculated odds ratio</title><p>CPR, cardiopulmonary resuscitation</p></caption><table frame=\"hsides\" rules=\"groups\"><tbody><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Statistical analysis</td><td rowspan=\"1\" colspan=\"1\">Value</td><td rowspan=\"1\" colspan=\"1\">Lower 95% CI</td><td rowspan=\"1\" colspan=\"1\">Upper 95% CI</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Odds ratio (bystander CPR after the pandemic/bystander CPR before the pandemic)</td><td rowspan=\"1\" colspan=\"1\">0.952</td><td rowspan=\"1\" colspan=\"1\">0.942</td><td rowspan=\"1\" colspan=\"1\">0.962</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"TAB3\"><label>Table 3</label><caption><title>Calculated chi-square results</title></caption><table frame=\"hsides\" rules=\"groups\"><tbody><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Statistical analysis</td><td rowspan=\"1\" colspan=\"1\">Value</td><td rowspan=\"1\" colspan=\"1\">df</td><td rowspan=\"1\" colspan=\"1\">Asymptotic significance (two-sided)</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Pearson chi-square</td><td rowspan=\"1\" colspan=\"1\">84.691</td><td rowspan=\"1\" colspan=\"1\">1</td><td rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Continuity correction</td><td rowspan=\"1\" colspan=\"1\">84.641</td><td rowspan=\"1\" colspan=\"1\">1</td><td rowspan=\"1\" colspan=\"1\"><0.001</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Likelihood ratio</td><td rowspan=\"1\" colspan=\"1\">84.711</td><td rowspan=\"1\" colspan=\"1\">1</td><td rowspan=\"1\" colspan=\"1\"><0.001</td></tr></tbody></table></table-wrap>" ]	[]	[]	[]	[]	[]	[]	[ "<fn-group content-type=\"other\"><title>Author Contributions</title><fn fn-type=\"other\"><p><bold>Concept and design:</bold> Simon P. Alarcon, Jordan L. Pace, James McDermott, Shazia Sheikh</p><p><bold>Acquisition, analysis, or interpretation of data:</bold> Simon P. Alarcon, Sam MacDowell, Shazia Sheikh</p><p><bold>Drafting of the manuscript:</bold> Simon P. Alarcon, Jordan L. Pace, James McDermott, Shazia Sheikh</p><p><bold>Critical review of the manuscript for important intellectual content:</bold> Simon P. Alarcon, Jordan L. Pace, James McDermott, Sam MacDowell, Shazia Sheikh</p><p><bold>Supervision:</bold> Shazia Sheikh</p></fn></fn-group>", "<fn-group content-type=\"other\"><title>Human Ethics</title><fn fn-type=\"other\"><p>Consent was obtained or waived by all participants in this study. California University of Science and Medicine Institutional Review Board (IRB) issued approval HS-2023-06. The IRB Committee determined that this research project is under Exempt Category 4, and the exemption was approved.</p></fn></fn-group>", "<fn-group content-type=\"other\"><title>Animal Ethics</title><fn fn-type=\"other\"><p><bold>Animal subjects:</bold> All authors have confirmed that this study did not involve animal subjects or tissue.</p></fn></fn-group>", "<fn-group content-type=\"competing-interests\"><fn fn-type=\"COI-statement\"><p>The authors have declared that no competing interests exist.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"cureus-0015-00000050353-i01\" position=\"float\"/>" ]	[]	[{"label": ["2"], "article-title": ["Heart Disease and Stroke Statistics\u20142021 Update: a report from the American Heart Association"], "source": ["Circulation"], "person-group": ["\n"], "surname": ["Virani", "Alonso", "Aparicio"], "given-names": ["SS", "A", "HJ"], "fpage": ["0"], "lpage": ["743"], "volume": ["143"], "year": ["2021"]}, {"label": ["4"], "article-title": ["Out of hospital cardiac arrest and bystander CPR during the COVID-19 pandemic: an early review"], "date-in-citation": ["\n"], "month": ["4"], "year": ["2022", "2021"], "uri": ["https://www.acep.org/criticalcare/newsroom/newsroom-articles/april2021/out-of-hospital-cardiac-arrest-and-bystander-cpr-during-the-covid-19-pandemic-an-early-review/."]}, {"label": ["5"], "article-title": ["First responders. Centers for Disease Control and Prevention"], "fpage": ["2022"], "volume": ["11"], "year": ["2020"], "uri": ["https://www.cdc.gov/coronavirus/2019-ncov/hcp/guidance-for-ems.html"]}, {"label": ["7"], "article-title": ["NEMSIS Research Dataset 2019"], "volume": ["3"], "uri": ["https://nemsis.org/using-ems-data/request-research-data/"]}, {"label": ["8"], "article-title": ["NEMSIS Research Dataset 2020"], "volume": ["3"], "uri": ["https://nemsis.org/using-ems-data/request-research-data/"]}, {"label": ["11"], "article-title": ["CDC museum COVID-19 timeline"], "date-in-citation": ["\n"], "month": ["10"], "year": ["2023", "2023"], "uri": ["https://www.cdc.gov/museum/timeline/covid19.html"]}, {"label": ["15"], "article-title": ["Characteristics and outcomes of in-hospital cardiac arrest events during the COVID-19 pandemic: a single-center experience from a New York City public hospital"], "source": ["Circ Cardiovasc Qual Outcomes"], "person-group": ["\n"], "surname": ["Miles", "Mejia", "Rios"], "given-names": ["JA", "M", "S"], "fpage": ["0"], "volume": ["13"], "year": ["2020"]}, {"label": ["17"], "article-title": ["Cardiopulmonary resuscitation outcomes of patients with COVID-19; a one-year survey"], "source": ["Arch Acad Emerg Med"], "person-group": ["\n"], "surname": ["Goodarzi", "Khodaveisi", "Abdi", "Salimi", "Oshvandi"], "given-names": ["A", "M", "A", "R", "K"], "fpage": ["70"], "lpage": ["10"], "volume": ["4"], "year": ["2021"]}, {"label": ["18"], "article-title": ["Clinical outcomes of in-hospital cardiac arrest in COVID-19"], "source": ["JAMA Intern Med"], "person-group": ["\n"], "surname": ["Thapa", "Kakar", "Mayer", "Khanal"], "given-names": ["SB", "TS", "C", "D"], "fpage": ["279"], "lpage": ["281"], "volume": ["1"], "year": ["2021"]}]	{ "acronym": [], "definition": [] }	19	CC BY	no	2024-01-13 00:02:18	Cureus.; 15(12):e50353	oa_package/4a/46/PMC10781595.tar.gz
PMC10781596	38213362	[ "<title>Introduction</title>", "<p>Acute generalized exanthematous pustulosis (AGEP) is classified as a rare type IV hypersensitivity reaction of non-follicular pustules on an erythematous edematous base, occasionally involving mucosal sites. Although 90% of AGEP cases are drug-induced, some cases have described virologic and bacterial etiologies [##REF##34409363##1##,##REF##27472323##2##]. AGEP is common among multi-medicated patients and may range from localized and mild to severe, life-threatening reactions [##REF##34409363##1##].</p>", "<p>AGEP is often misdiagnosed in the clinical setting as Staphylococcal scalded skin syndrome (SSSS) and pustular psoriasis. Skin biopsy of these patients should be performed to confirm diagnosis due to their similar clinical presentation [##REF##27472323##2##]. AGEP should be considered in patients with recent antibiotic administration and on multiple medications.</p>" ]	[]	[]	[ "<title>Discussion</title>", "<p>The differential diagnosis upon presentation was pustular psoriasis, bullous impetigo, SSSS, and AGEP. Pustular psoriasis was not favored, given the presence of dermal eosinophils. Numerous reports have compared the similarities in clinical presentation between pustular psoriasis and AGEP, with many categorizing AGEP as a subdivision of pustular psoriasis [##REF##11168761##3##]. Bullous impetigo was also possible, but bacterial forms were not identified in the biopsy (Figures ##FIG##2##3##-##FIG##3##4##). A periodic acid-Schiff stain was used to assess for the presence of fungi, and it was negative. SSSS was initially thought to be the diagnosis due to her septic presentation. There have been reported pediatric cases of SSSS coexisting with AGEP; however, there have been no clear reports in the elderly population [##UREF##0##4##]. The treatment of SSSS requires antibiotics, while the treatment of AGEP is supportive due to its self-limiting nature. The treatment of SSSS could exacerbate the symptoms of AGEP, as it is a drug-induced reaction.</p>", "<p>It is unclear which medication precipitated this reaction. Vancomycin is implicated as the offending agent in three previous reports, and it could be the most likely causal agent in our patient [##REF##34409363##1##,##REF##26561525##5##]. Acetaminophen-induced AGEP has also been described in literature more commonly in the pediatric population [##REF##25845401##6##]. Most recurrences are due to the administration of the same offending agent. However, there have been reports of relapsing AGEP with different agents, and it has been hypothesized to be a consequence of cytokine dysregulation [##UREF##1##7##].</p>", "<p>AGEP is a rare adverse drug reaction induced by a type IV hypersensitivity T-cell mediated neutrophilic response causing erythematous-based sterile pustules [##REF##27472323##2##]. While the disease course is usually self-limited, reports have shown that patients can be acutely at an increased risk of morbidity and systemic spread [##REF##34409363##1##]. More severe cases warrant systemic corticosteroids; however, our patient was not administered steroids because of a presumed infection. Blood cultures were negative, and systemic antibiotics were discontinued. Literature has shown that there is no definitive treatment for AGEP despite the self-limiting course of the disease [##REF##34409363##1##]. Once the diagnosis of AGEP was made in our patient, we recommended avoiding vancomycin and acetaminophen in the future. In addition, the patient was advised to use moisturizing agents and expect some post-inflammatory skin pigmentation. </p>" ]	[ "<title>Conclusions</title>", "<p>AGEP is a rare cutaneous disorder and should be considered in the differential diagnosis of patients presenting with severe cutaneous eruptions with systemic involvement. Although we suspected that two agents had been implicated in causing AGEP in our patient, the specific offending agent was not identified. This highlights the limitations of identifying the causative agent, as they may be difficult to isolate. Most cases have shown to be drug-induced, and both acetaminophen and vancomycin were presumed to be the offending agents in this patient. Literature has reported cases of vancomycin-induced AGEP and acetaminophen-induced AGEP, although both are perceived to be uncommon causes of the disease. </p>" ]	[ "<p>Acute generalized exanthematous pustulosis (AGEP) is an uncommon skin condition that should be considered when evaluating patients with severe skin eruptions accompanied by systemic symptoms. We present a woman in her 70s with end-stage renal disease on hemodialysis who developed a generalized pruritic rash seven days after the administration of pre-procedure vancomycin and acetaminophen. Our patient underwent a biopsy with findings consistent with AGEP. This report highlights the need to consider AGEP in patients with severe cutaneous eruptions and systemic involvement. Prompt biopsy and blood cultures are essential to prevent misdiagnosis and treatment delays.</p>" ]	[ "<title>Case presentation</title>", "<p>A woman in her 70s with a prior history of hypertension, gout, congestive heart failure, atrial fibrillation, and end-stage renal disease on hemodialysis presented to the emergency department with a generalized rash. The rash was pruritic and diffuse, involving the face, chest, torso, back, bilateral upper extremities, and proximal bilateral lower extremities. The rash developed seven days after the administration of vancomycin for surgical prophylaxis of a left arm AV fistula placement. At the same time, the patient took acetaminophen for pain.</p>", "<p>The patient's medications were allopurinol, atorvastatin, carvedilol, clopidogrel and sodium bicarbonate. No other medication was recently introduced. On the physical exam, the vital signs were a temperature of 36.6° C, heart rate of 68 beats/minute, respiratory rate of 18 breaths/minute, blood pressure of 93/42 mmHg, and oxygen saturation of 98% in ambient air. The respiratory, cardiovascular, and abdominal examinations were normal. Examination of the skin revealed diffuse fine pustules and areas of exfoliating skin sheets with superficial epidermal sloughing of the face, chest, torso, back, bilateral upper extremities, and proximal bilateral lower extremities (Figures ##FIG##0##1##-##FIG##1##2##). There was also oral mucosal involvement. Laboratory findings are presented in Table ##TAB##0##1##.</p>", "<p>The patient was initially treated for presumed SSSS, given the skin findings, hypotension, and leukocytosis. Broad-spectrum antibiotics were administered, including linezolid 600 mg oral twice daily, cefepime 1 g intravenous (IV), and clindamycin 600 mg IV daily. Steroids were not administered due to the concern of infection. Vancomycin and acetaminophen were avoided since these were the possible offending agents. The patient underwent a right thigh skin biopsy. The biopsy revealed a sub-corneal collection of neutrophils with adjacent neutrophils, eosinophils, and rare dyskeratotic keratinocytes in the papillary dermis consistent with the diagnosis of acute generalized exanthematous pustulosis (AGEP).</p>" ]	[]	[ "<fig position=\"anchor\" fig-type=\"figure\" id=\"FIG1\"><label>Figure 1</label><caption><title>Left upper extremity with areas of exfoliating skin</title></caption></fig>", "<fig position=\"anchor\" fig-type=\"figure\" id=\"FIG2\"><label>Figure 2</label><caption><title>Diffuse fine pustules and areas of exfoliating skin sheets with superficial epidermal sloughing of the upper abdomen and left upper extremity</title></caption></fig>", "<fig position=\"anchor\" fig-type=\"figure\" id=\"FIG3\"><label>Figure 3</label><caption><title>Skin with subcorneal pustule containing neutrophils</title><p>Hematoxylin and eosin (20x)</p></caption></fig>", "<fig position=\"anchor\" fig-type=\"figure\" id=\"FIG4\"><label>Figure 4</label><caption><title>Negative PAS special stain for detecting fungal presence</title><p>Periodic acid-Schiff (PAS; 4x)</p></caption></fig>" ]	[ "<table-wrap position=\"float\" id=\"TAB1\"><label>Table 1</label><caption><title>Relevant initial laboratory values with reference values</title><p>WBC - white blood count; BUN - blood urea nitrogen</p></caption><table frame=\"hsides\" rules=\"groups\"><tbody><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Laboratory test</td><td rowspan=\"1\" colspan=\"1\">Patient laboratory value</td><td rowspan=\"1\" colspan=\"1\">Normal laboratory range</td></tr><tr><td rowspan=\"1\" colspan=\"1\">WBC</td><td rowspan=\"1\" colspan=\"1\">19.4 x 10<sup>3</sup>/mcL</td><td rowspan=\"1\" colspan=\"1\">3.8-10.8 x 10<sup>3</sup>/mcL</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Hemoglobin</td><td rowspan=\"1\" colspan=\"1\">10.4 g/dL</td><td rowspan=\"1\" colspan=\"1\">11.7-15.5 g/dL</td></tr><tr><td rowspan=\"1\" colspan=\"1\">BUN</td><td rowspan=\"1\" colspan=\"1\">63 mg/dL</td><td rowspan=\"1\" colspan=\"1\">7-30 mg/dL</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Creatinine</td><td rowspan=\"1\" colspan=\"1\">6.4 mg/dL</td><td rowspan=\"1\" colspan=\"1\">0.7-1.2 mg/dL</td></tr></tbody></table></table-wrap>" ]	[]	[]	[]	[]	[]	[]	[ "<fn-group content-type=\"other\"><title>Author Contributions</title><fn fn-type=\"other\"><p><bold>Concept and design:</bold> J'Moi Saunders Corea, Kaley B. Schwartz, Ilya Fonarov, Damian Casadesus</p><p><bold>Acquisition, analysis, or interpretation of data:</bold> J'Moi Saunders Corea, Kaley B. Schwartz, Ilya Fonarov, Damian Casadesus</p><p><bold>Drafting of the manuscript:</bold> J'Moi Saunders Corea, Kaley B. Schwartz, Ilya Fonarov, Damian Casadesus</p><p><bold>Critical review of the manuscript for important intellectual content:</bold> J'Moi Saunders Corea, Kaley B. Schwartz, Ilya Fonarov, Damian Casadesus</p><p><bold>Supervision:</bold> Ilya Fonarov, Damian Casadesus</p></fn></fn-group>", "<fn-group content-type=\"other\"><title>Human Ethics</title><fn fn-type=\"other\"><p>Consent was obtained or waived by all participants in this study</p></fn></fn-group>", "<fn-group content-type=\"competing-interests\"><fn fn-type=\"COI-statement\"><p>The authors have declared that no competing interests exist.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"cureus-0015-00000050354-i01\" position=\"float\"/>", "<graphic xlink:href=\"cureus-0015-00000050354-i02\" position=\"float\"/>", "<graphic xlink:href=\"cureus-0015-00000050354-i03\" position=\"float\"/>", "<graphic xlink:href=\"cureus-0015-00000050354-i04\" position=\"float\"/>" ]	[]	[{"label": ["4"], "article-title": ["Coexisting staphylococcal scalded skin syndrome and acute generalized exanthematous pustulosis"], "source": ["Dermatologica Sinica"], "person-group": ["\n"], "surname": ["Chu", "Wang", "Chi", "Hsiao", "Su"], "given-names": ["TW", "SH", "CC", "CH", "LH"], "fpage": ["113"], "lpage": ["114"], "volume": ["32"], "year": ["2013"]}, {"label": ["7"], "article-title": ["Recurrent acute generalized exanthematous pustulosis to two different drugs: oxacillin and dextromethorphan confirmed by patch test"], "source": ["Dermatol Pract Concept"], "person-group": ["\n"], "surname": ["Kenani", "Gammoudi", "Fathallah"], "given-names": ["Z", "R", "N"], "fpage": ["0"], "volume": ["12"], "year": ["2022"]}]	{ "acronym": [], "definition": [] }	7	CC BY	no	2024-01-13 00:02:18	Cureus.; 15(12):e50354	oa_package/cd/f9/PMC10781596.tar.gz
PMC10781609	38213388	[ "<title>Introduction</title>", "<p>Polyhydramnios, characterised by excessive amniotic fluid accumulation during pregnancy, requires thorough investigation and management.<sup>##UREF##0##1##</sup> It affects approximately 0.5%–1.5% of pregnancies.<sup>##UREF##1##2##</sup> While most cases are idiopathic, congenital anomalies contribute to 21.3% of cases, especially those with moderate-to-severe degrees, with gastrointestinal malformations being the commonest.<sup>##UREF##0##1##, ####UREF##1##2##, ##REF##28930371##3####28930371##3##</sup> Among these cases, congenital diaphragmatic hernia (CDH) is a marked anomaly detected in 3.2% of patients.<sup>##REF##28930371##3##</sup> Additionally, trisomy 18 is the most frequently diagnosed genetic syndrome in patients with polyhydramnios and CDH.<sup>##REF##28930371##3##,##REF##17436298##4##</sup> This article presents a case of moderate-to-severe polyhydramnios in a pregnant woman with a foetus diagnosed with CDH and underlying Edwards syndrome. Some challenges encountered by primary care professionals in detecting such anomalies relate to professional expertise, scan mode, transducer frequency, small defects in early gestation and late development of the underlying disease.<sup>##REF##29753526##5##, ####UREF##2##6##, ##REF##24771905##7####24771905##7##</sup> While primary care medical officers may not be required to conduct detailed scans, it is essential for them to recognise major anomalies diligently. This study aims to raise awareness among primary care professionals about comprehensive evaluation and diligent consideration and ruling out of other potential causes of polyhydramnios beyond suboptimal gestational diabetes mellitus (GDM) for timely and accurate management, benefitting maternal and foetal outcomes.</p>" ]	[]	[]	[ "<title>Discussion</title>", "<p>Polyhydramnios, characterised by an excessive accumulation of amniotic fluid during pregnancy, is a condition that can present various challenges and implications for both the mother and foetus. It is classified as mild with a DVP of 8–11 cm (AFI of 25–30 cm), moderate with a DVP of 12–15 cm (AFI of 30.1–35 cm) or severe with a DVP above 16 cm (AFI of ≥35.1 cm).<sup>##REF##27625160##8##</sup> With mild polyhydramnios, an underlying disease is identified in only 17% of cases, while with moderate-to-severe polyhydramnios, an underlying disease is detected in 91%.<sup>##REF##27625160##8##</sup> In a separate study on patients with moderate-to-severe polyhydramnios, the prevalence of foetal malformations was notably high, reaching almost 80%.<sup>##REF##28930371##3##</sup></p>", "<p>In pregnancies affected by polyhydramnios, approximately 20% of cases are due to congenital anomalies, including gastrointestinal anomalies, central nervous system defects, musculoskeletal anomalies, airway malformations and CDH, while 60%–70% remain idiopathic with no discernible cause.<sup>##REF##27625160##8##</sup> Other foetal causes include intrapartum infections (i.e. TORCH infections), chromosomal abnormalities and urogenital disorders.<sup>##REF##27625160##8##</sup> Common maternal causes of polyhydramnios include GDM and alloimmunisation secondary to maternal antibodies resulting in foetal haemolytic anaemia.<sup>##UREF##0##1##,##REF##26720631##9##</sup> In the present case, the patient was diagnosed with GDM at 18 weeks of gestation and polyhydramnios during a routine prenatal visit at 28 weeks of gestation. Polyhydramnios was the first suspicious sign the primary care medical officer noticed. Therefore, the presence of polyhydramnios must not deter primary care medical officers from investigating other potential causes apart from GDM alone.</p>", "<p>At the primary care level, the approach to polyhydramnios should focus on a comprehensive assessment to identify potential causes and associated anomalies. While primary care medical officers may not be required to conduct detailed scans, they play a vital role in recognising gross foetal abnormalities. In this case, apart from moderate polyhydramnios, certain ultrasound findings such as a small AC, the absence of a stomach bubble and the presence of a stomach bubble in the thoracic cavity, raised the medical officer’s suspicion, as these are considered obvious signs of congenital abnormalities. The quality of scans in primary care clinics can be influenced by ultrasound skill and experience, transducer frequency, limited advanced ultrasound machines, small defects in early gestation and late development of the underlying disease.<sup>##REF##29753526##5##, ####UREF##2##6##, ##REF##24771905##7####24771905##7##</sup> This limitation could impact the accuracy of detecting complex anomalies, such as CDH. Therefore, advocating for local guidelines, strengthening resources and providing enhanced training in primary care settings can contribute to early detection and appropriate management of polyhydramnios-related conditions.</p>", "<p>CDH is a diaphragmatic deformity caused by failed closure of the pleuroperitoneal canal at approximately 10 weeks of gestation, resulting in herniation of abdominal contents into the thoracic cavity and compression of lung tissues.<sup>##UREF##3##10##</sup> Approximately 60% of cases are detected prenatally either on routine ultrasound (mean gestational age of 24.2 weeks) or as part of a workup for maternal polyhydramnios.<sup>##REF##31335403##11##</sup> The mortality rate of CDH continues to be high, ranging from 20% to 60%. It is particularly high when the condition is associated with a chromosome abnormality, and most affected babies do not survive beyond the first few weeks or months of life.<sup>##REF##27986977##12##</sup> The most common abnormality is trisomy 18, which affects 10% of all individuals with CDH.<sup>##REF##17436298##4##</sup> In our case, the foetus was diagnosed with CDH and underlying trisomy 18 at 28 weeks of gestation, resulting in a fresh stillbirth delivery at 34 weeks of gestation.</p>", "<p>Early detection is important for the benefit of both parents and the baby, as it allows for timely and informed decision-making, access to specialised care and interventions and the opportunity to plan for appropriate medical management and support.<sup>##REF##31335403##11##</sup> This goal can be achieved in multiple ways such as comprehensive prenatal ultrasound screening, especially during the second trimester; training and education of primary care medical officers in recognising common ultrasound findings associated with foetal anomalies; and genetic counselling and screening, especially for those at a higher risk of chromosomal abnormalities. However, CDH can be missed during early pregnancy owing to the smaller diaphragmatic defect and late development of CDH in a substantial proportion of patients.<sup>##UREF##4##13##</sup></p>", "<p>Early detection and prenatal diagnosis enable parental counselling, referral to a tertiary care centre and intervention for high-risk foetuses.<sup>##REF##31335403##11##</sup> Detailed ultrasound, foetal karyotyping and microarray analysis are needed to confirm the diagnosis and to identify related abnormalities and chromosomal abnormalities, as in our case, which is consistent with Edwards syndrome. After discussing the prognosis, they decided to opt for expectant management and comfort care, as the survival rate was low. Parents should be informed about this beforehand so they can psychologically and emotionally prepare for any circumstance that may arise. Any parents who find it difficult to handle their emotional stress can be referred to a counsellor or clinical psychologist. Counselling and support play a pivotal role in helping parents navigate their emotions and empower them to make informed decisions. Primary care professionals should be aware of the significance of counselling to guide and support parents effectively because parents who are unaware of their child’s well-being during pregnancy are bound to be taken aback by an unexpected stillbirth or infant death.</p>" ]	[ "<title>Conclusion</title>", "<p>This case report emphasises the significance of considering alternative differential diagnoses when encountering polyhydramnios in patients with GDM, as other more severe diseases may be the cause. Therefore, it is imperative that patients at a high risk of congenital abnormalities undergo a thorough assessment by an experienced primary care physician especially when routine ultrasound findings are abnormal. The present findings may be used as a basis to educate primary healthcare practitioners on managing polyhydramnios while focusing on CDH as one of the devastating causes of polyhydramnios that must not be missed. Early detection and referral to other multidisciplinary teams can guide the management and prognosis and prepare parents for any circumstances that may arise.</p>" ]	[ "<title>Abstract</title>", "<p>Polyhydramnios is defined as an increase in the amount of amniotic fluid during pregnancy. This article presents the case of a 35-year-old G4P3 lady at 28 weeks of gestation with suboptimised gestational diabetes Mellitus (GDM). Routine transabdominal ultrasound showed the presence of polyhydramnios, initially thought to be due to suboptimal glucose control. Further evaluation revealed a congenital diaphragmatic hernia with multiple soft markers. Identifying the underlying causes of polyhydramnios can be challenging in primary care settings, which can be attributed to various factors. Although primary care medical officers may not be required to perform detailed scans, they have a crucial role in identifying gross foetal abnormalities. This study highlights the potential for missed diagnoses in primary care settings and the importance of comprehensive prenatal assessments to ensure early detection and appropriate management of polyhydramnios-related conditions in women with GDM.</p>", "<title>Keywords</title>" ]	[ "<title>Case presentation</title>", "<p>A 35-year-old G4P3 lady at 28 weeks of gestation came for a routine antenatal checkup. She was diagnosed with GDM at 18 weeks of gestation and was on oral glucose-lowering drugs, as her blood sugar profile monitoring was suboptimised. She had no history of infection, osmotic diuresis or hypoglycaemic symptoms throughout her pregnancy. Her marriage was non-consanguineous, and both her and her husband’s families had no history of congenital anomalies or syndromes. There was also no history of supplement or traditional medication consumption. All her previous pregnancies were uneventful and all her children were healthy.</p>", "<p>On physical examination, the symphysial fundal height was 31 cm and the foetal parts were difficult to palpate. Transabdominal ultrasound revealed polyhydramnios with a deepest vertical pocket (DVP) of 12 cm, suggesting a ‘uterus larger than date’. This finding raised the operator’s suspicion to identify other causes of polyhydramnios apart from suboptimal GDM. Assessment of the growth parameters showed that the foetal abdominal circumference (AC) was much smaller than the other parameters, corresponding to 25 weeks of gestation, while the other parameters corresponded to 28–30 weeks of gestation. Furthermore, a stomach bubble was absent. The stomach bubble was noted in the thoracic cavity (##FIG##0##Figure 1##) and the heart was pushed to the right side (##FIG##1##Figure 2##). Other soft markers were found, such as cleft lip and abnormal hands. Her previous antenatal scan was unremarkable. The patient was referred to a maternal–foetal medicine (MFM) consultant to rule out CDH with multiple soft markers.</p>", "<p>A detailed scan by an MFM consultant revealed that the foetus had multiple anomalies such as left-side cleft lip, claw hand, rocker bottom feet, stomach bubble in the thoracic cavity with a lung-to-head ratio of 0.58 and polyhydramnios with a DVP of 12 cm which consistent with CDH. Other organs and structures, including the brain, spine, heart, abdominal wall, bladder and kidneys were normal. The estimated foetal weight was 1182 grams. An amniocentesis was conducted and sent for karyotyping confirming a male (XY) genotype affected with trisomy 18 with normal copy numbers of chromosomes 13 and 21, which suggested Edwards syndrome.</p>", "<p>After the karyotyping result was reviewed, the parents were informed regarding the diagnosis and prognosis. They were given the option of expectant management or early delivery where the baby will receive comfort care and expectant management in the event of delivery. The possibility of foetal demise, which could happen prenatally, intranatally or postnatally, was also being discussed. During subsequent follow-up, the patient had symptomatic polyhydramnios with an amniotic fluid index (AFI) of 43 cm and an amnioreduction was performed. The patient delivered via spontaneous vertex delivery at 34 weeks of gestation, with the foetus having no signs of life.</p>" ]	[ "<title>Acknowledgments</title>", "<p>None.</p>", "<title>Conflicts of interest</title>", "<p>There are no conflicts of interest to declare.</p>", "<title>Author Contributions</title>", "<p>Dr. Syamimi Nadiah A Wahab was responsible for attending to and managing this patient as well as writing the manuscript.</p>", "<p>Asst. Prof. Dr. Abdul Hadi Said was the main supervisor and contributed in editing the manuscript.</p>", "<p>Dr. Wan Hasmawati Wan Ismail has contributed in the consultation of the case by the corresponding author.</p>", "<title>Patient’s consent for the use of images and content for publication</title>", "<p>Informed consent was obtained before the preparation of the case report.</p>" ]	[ "<fig position=\"float\" id=\"f1\"><label>Figure 1</label><caption><title>Sagittal view of the foetus. The heart (1) and stomach bubble (2) are seen together in the thoracic cavity.</title></caption></fig>", "<fig position=\"float\" id=\"f2\"><label>Figure 2</label><caption><title>Axial view of the foetus. The heart (1) and stomach bubble (2) are seen together on the same axis. The heart is pushed towards the right side. Polyhydramnios (3) is also present.</title></caption></fig>" ]	[]	[]	[ "<boxed-text position=\"anchor\"><p>\n<bold>What is new in this case report compared to the previous literature?</bold>\n</p><list list-type=\"bullet\"><list-item><p>This case report raises awareness among family physicians and medical officers about the necessity of excluding other potential differential diagnoses related to polyhydramnios in patients with gestational diabetes mellitus.</p></list-item><list-item><p>This case report also emphasises the significance of good ultrasound techniques to avoid overlooking gross congenital defects at the primary care level, indicating the need for further multidisciplinary referral to confirm the diagnosis and plan for further management.</p></list-item></list><p>\n<bold>What is the implication to patients?</bold>\n</p><p>The presence of moderate-to-severe polyhydramnios may indicate the presence of congenital anomalies, such as CDH and underlying chromosomal abnormalities. Primary care professionals should conduct thorough evaluations, provide informed counselling and discuss appropriate management options with parents to ensure well-informed decisions regarding the pregnancy and potential outcomes for both the mother and baby.</p></boxed-text>" ]	[]	[]	[]	[]	[]	[ "<graphic xlink:href=\"MFP-18-70-g1\" position=\"float\"/>", "<graphic xlink:href=\"MFP-18-70-g2\" position=\"float\"/>" ]	[]	[{"label": ["1."], "surname": ["Hwang", "Mahdy"], "given-names": ["DS", "H"], "source": ["Polyhydramnios."], "year": ["2023", "2023"], "month": ["Feb", "Jan"], "day": ["20"], "part-title": ["StatPearls [Internet]"], "publisher-loc": ["Treasure Island (FL)"], "publisher-name": ["StatPearls Publishing"], "ext-link": ["https://www.ncbi.nlm.nih.gov/books/NBK562l40/"]}, {"label": ["2."], "surname": ["Magann", "Chauhan", "Doherty", "Lutgendorf", "Magann", "Morrison"], "given-names": ["EF", "SP", "DA", "MA", "MI", "JC"], "article-title": ["Polyhydramnios: causes, diagnosis and therapy."], "source": ["Ultrasound Obstet Gynecol."], "year": ["2015"], "volume": ["45"], "issue": ["6"], "fpage": ["601"], "lpage": ["611"]}, {"label": ["6."], "surname": ["Buijtendijk", "Shah", "Lugthart"], "given-names": ["M", "H", "MA"], "etal": ["et al"], "article-title": ["Diagnostic accuracy of ultrasound screening for fetal structural abnormalities during the first and second trimester of pregnancy in low-risk and unselected populations."], "source": ["Cochrane Database Syst Rev."], "year": ["2021"], "volume": ["2021"], "issue": ["7"], "pub-id": ["10.1002/14651858.CD014715"]}, {"label": ["10."], "surname": ["Shah", "Vaghela", "Patel", "Acharya"], "given-names": ["D", "PK", "S", "P"], "article-title": ["Antenatal diagnosis of congenital diaphragmatic hernia and successful outcome after postnatal surgery."], "source": ["J Fetal Med."], "year": ["2017"], "volume": ["4"], "issue": ["3"], "fpage": ["153"], "lpage": ["155"], "pub-id": ["10.1007/s40556-017-0131-5"]}, {"label": ["13."], "surname": ["Bentur", "Gur", "Pollak", "Masarweh", "Soit", "Bronshtein"], "given-names": ["L", "M", "M", "K", "I", "M"], "article-title": ["Early prenatal ultrasound diagnosis of congenital thoracic malformations."], "source": ["J Matern Fetal Neonatal Med."], "year": ["2018"], "fpage": ["1"], "lpage": ["6"], "pub-id": ["10.1080/14767058.2018.1465"]}]	{ "acronym": [], "definition": [] }	13	CC BY	no	2024-01-13 00:02:19	Malays Fam Physician. 2023 Dec 21; 18:70	oa_package/89/37/PMC10781609.tar.gz
PMC10781610	38213387	[ "<title>Introduction</title>", "<p>Gout is a common inflammatory arthritis caused by defective metabolism of uric acid and is predominantly managed in primary care settings.<sup>##REF##23024028##1##,##REF##17113492##2##</sup> Its prevalence differs according to geographic location, age and ethnicity. For example, the prevalence is lower in developing countries, with Malaysia, China, India, Bangladesh, Pakistan, the Philippines, Thailand and Vietnam having a prevalence of <0.5%.<sup>##REF##26150127##3##</sup> In Malaysia, multi-ethnic groups exist, including Malays, Chinese and Indians, who comprise 68%, 23% and 7% of the national population, respectively.<sup>##UREF##0##4##</sup> Malays show the highest prevalence of gout, followed by Chinese and Indians.<sup>##REF##28585370##5##</sup> Each ethnic group has its own religious beliefs, traditions, festivals and food cultures.</p>", "<p>Since gout is a form of arthritis, patients tend to seek a diverse range of complementary medicine, among which dietary supplements and vitamins are frequently used.<sup>##REF##24356480##6##</sup> Nevertheless, the choice of complementary medicine practices is shown to be influenced by geographical, cultural and social factors, with Asian patients being more likely to use herbal therapy and acupuncture.<sup>##REF##24356480##6##</sup> In the National Health and Morbidity Survey 2015, the prevalence of traditional medicine practices among Malays, Chinese and Indians was 31%, 33% and 18%, respectively. The types of traditional medicine practised were cupping and massages among Malays, acupuncture among Chinese and Ayurvedic medicine among Indians.<sup>##UREF##1##7##</sup></p>", "<p>Patients with gout are substantially affected by their symptoms, including not only their mobility during acute gout attacks but also their ability to work and socialise.<sup>##REF##28915838##8##</sup> As gout is a chronic disease that can present with multiple comorbidities, patients experience a sense of feeling ill that worsens their quality of life.<sup>##REF##28915838##8##,##REF##10261981##9##</sup> The loss of bodily functions can affect their performance and lead to social isolation, contributing to a progressive loss of self.<sup>##REF##10261981##9##</sup> Self-management plays an important part in gout management. Adhering to medical management, adapting lifestyle changes for behavioural management and reducing stress for emotional management all contribute to improving gout.<sup>##UREF##2##10##</sup></p>", "<p>Studies among patients with gout have shown that self-management is influenced by knowledge, cultures and beliefs. Most studies have been conducted in countries with a high prevalence of gout and cultural and sociodemographic profiles different from those in Malaysia. Diet control has been reported to benefit patients with gout relative to their overall outcome. It is known how ethnic cultural beliefs influence the perception and self-management of gout. To date, the overall self-management among patients with gout has not been fully studied. Over the years of the researcher’s practice in managing patients with gout, she has found it difficult to advise patients about their self-management practices, especially those from diflerent ethnic groups, as their cultures, religious beliefs and food habits differ. Therefore, this study aimed to explore the perceptions and practices of short- and longterm self-management of symptoms among patients with gout from diflerent ethnic groups in Malaysia. The findings are expected to help physicians understand patients’ point of view and consequently provide better patient-centred care.</p>" ]	[ "<title>Methods</title>", "<title>Design</title>", "<p>A qualitative study was conducted via semistructured in-depth individual interviews to explore the perceptions and practices of selfmanagement among adult patients with gout.</p>", "<title>Setting, participants, sampling and sample size</title>", "<p>Purposive sampling was used to recruit patients who were attending the primary care clinic of University Malaya Medical Centre, were aged more than 21 years, had a confirmed diagnosis of gout by a physician or medical officer based on their symptoms with at least 6 months of duration and were either Malays, Chinese or Indians. Further, patients who were either receiving or not receiving urate-lowering treatments and were able to communicate in either Malay or English language were included. Conversely, patients who were unable to provide consent owing to cognitive impairment were excluded. The sample comprised 20 participants, determined via data saturation, wherein the interviews were concluded when no new themes emerged.</p>", "<p>The health belief model was used as the conceptual framework of the study. The topic guide was developed based on the model, which addressed aspects of self-management of gout (perceived susceptibility and severity). The exploration extended to understanding the patients’ lifestyle changes in relation to the cultural practices among their multi-ethnic groups (perceived benefits, barriers and cues to action).</p>", "<title>Data collection</title>", "<p>The interviews were conducted by one trained researcher in the preferred language of the participants (either English or Malay), audiorecorded and transcribed verbatim. Field notes, including observed non-verbal cues, were taken during the interviews. Care was taken to minimise potential participant response bias by ensuring that, whenever possible, the participants were interviewed by the researcher’s supervisor.</p>", "<title>Data management and analysis</title>", "<p>The data analysis was conducted concurrently with the data collection, reaching completion at the end of a few transcripts. Eight of the interviews were transcribed verbatim by the researcher, and each transcript was checked for accuracy by the researcher’s supervisor. The participants were oflered the opportunity to verify the accuracy of the transcripts, but none of them accepted it. Twelve other interviews were transcribed by an independent transcriber and checked for accuracy by the researcher. Each interview was transcribed using a coding system to ensure anonymity of each participant. The transcripts were not translated, as the researcher was fluent in both Malay and English languages. The transcripts were analysed using QDA Miner Lite software by PROVALIS Research, Canada. The data were analysed using a thematic approach based on Strauss and Corbin’s method involving open, axial and selective coding.<sup>##UREF##3##11##</sup> Two of the transcripts were coded independently and compared with the codes obtained by the researcher’s supervisor, and any discrepancy with the codes was discussed until consensus on the list of codes and possible groups of codes was reached to form axial codes.<sup>##REF##24047204##12##</sup> All remaining transcripts were coded accordingly. The group of codes was later categorised under themes according to the objectives of the study, and irrelevant codes were removed.</p>" ]	[ "<title>Results</title>", "<p>A total of 20 patients participated in the indepth interviews. Among them, 18 were men, and two were women. Three patients refused to be interviewed owing to language barrier (n=1) and lack of interest (n=2). The participants were from the three major ethnic groups in Malaysia: Malays (n=9), Chinese (n=7) and Indians (n=4). The participants’ age ranged from 29 to 81 years. The gout duration ranged from 1 to 30 years. Regarding educational level, the majority of the participants received secondary education, followed by those with a diploma, degree and master’s qualification. One participant received only primary education.</p>", "<p>Three themes emerged from the interviews. Together with their subthemes, these themes are summarised in ##TAB##0##Table 1##</p>", "<title>Experiences with gout</title>", "<p>The participants were asked regarding their experiences with gout. In terms of onset, the participants could remember the onset of their gout as being the most painful and severe attack they ever had. Most participants thought that the symptoms were due to a sprain or twist even though they had no previous injury. The majority found that their gout was caused by their poor lifestyle and food habits, especially consumption of foods that can elevate uric acid levels. Some participants also identified that consumption of foods with excess amounts of protein either in the form of meat or vegetables caused their gout. Most participants reported frequent recurrence of acute gout attacks, which were likened to going to war. The participants said that gout substantially affected their quality of life in several ways, including reduction in their mobility and decline in their social and work lives. Regarding the impact of gout on diet, some participants found it difficult to control their diet, as the foods that they were supposed to control or avoid were their favourite. Some participants also experienced worsening of their symptoms over time, including the formation of tophi, causing joint deformities and complications such as renal calculi.</p>", "<p>\n<italic toggle=\"yes\">“Teruk tu maksudnya saya tak boleh berjalan langsung masa tu. Masa tu anak saya bantu saya, saya dipapahlah. ”</italic>\n</p>", "<p>P2, 60, Female, Malay, one year of gout</p>", "<p>\n<italic toggle=\"yes\">“Overall, it’s <bold>like going to war</bold>. Every little while, you get shot here and there. ”</italic>\n</p>", "<p>P15, 71, Male, Malay, 19 years of gout</p>", "<p>\n<italic toggle=\"yes\">“<bold>Quality of life</bold>. Like this gout happened three times when I was overseas you know, so not healthy. You know, when you’re here, when <bold>you have an attack, people said you want to go here, you can’t even join them</bold>. I have been <bold>pushed on a wheelchair at shopping mall so many times</bold> you know. It felt so helpless... You know like being a trouble to people. ”</italic>\n</p>", "<p>P16, 36, Male, Chinese, 14 years of gout</p>", "<title>Types of self-management of gout</title>", "<p>Each participant reported varying types of self-management. Diet was the main selfmanagement practice among the participants. The majority of the participants found that managing their diet, especially foods high in purine, could control their gout. Most participants experienced attacks after eating seafood, beef and mutton. Conversely, a few reported that they had attacks after eating vegetables high in protein such as beans, peanuts, pickled vegetables, cabbages, cauliflowers and broccolis. Apart from identifying foods that affected them, the participants also determined which among their favourite foods or drinks they could still consume without limitations. This gradual elimination of foods that triggered reactions was accomplished using the trial-and-error method. Further, some participants discovered that another way to control their gout was to limit the quantity of foods consumed, especially their favourite foods. Apart from diet, usage of pain killers was another main self-management practice reported, especially during acute gout attacks, which are generally substantially painful. Regarding traditional medicine practices, many participants were believers. An example of traditional medicine practised was the consumption of natural products such as celery juice, apple cider vinegar, papaya and pineapple juice. Exercise and stress-reducing activities were among the other types of selfmanagement practices.</p>", "<p>\n<italic toggle=\"yes\">“It’s a very <bold>difficult feeling</bold>.... If the <bold>mutton</bold> is there, mutton is my favourite. I cannot eat... <bold>Prawn</bold> is my favourite, <bold>crab is my favourite</bold>, <bold>cannot eat</bold>, because immediate attack. ”</italic>\n</p>", "<p>P14, 29, Male, Indian, four of years of gout</p>", "<p>\n<italic toggle=\"yes\">“.. Pain killer memang cepat... <bold>Kalau I travel, ada satu pouch. Tapi still, especially travel oversea, I memang bawa la. Standby</bold>. ”</italic>\n</p>", "<p>P7, 43. Male, Malay, 16 years of gout</p>", "<p>\n<italic toggle=\"yes\">“Once I started taking my <bold>pineapple and papaya</bold>, I was able to go back to my normal meal. ”</italic>\n</p>", "<p>P18, 60, Male, Malay, 2 years of gout</p>", "<title>Factors influencing self-management of gout</title>", "<p>Malay culture has changed over the years with urbanisation. For example, eating ‘tomyam’, especially seafood tomyam, was reported by the participants to be a practice, especially at urban places. Apart from food, a few Malay participants believed that having gout is a reminder from God not to over eat and to eat healthily. Other participants believed that having gout has no relation to God but is associated with their own doing. When asked about the reasons for not taking allopurinol, some participants mentioned that practising traditional medicine and not relying on modern medicine are part of Malay culture. One participant noticed that he had acute gout attacks after eating ice cream and another participant after drinking iced cube drinks. Accordingly, the relationship of hot and cold foods with gout was explored. The participants believed that eating hot foods was better, as it yielded therapeutic benefits. Although the Chinese participants usually ate hot foods, one participant said that the difference in the temperature of foods triggered no reactions. Among the Indian participants, ‘dhal’ was typically consumed; these participants identified that dhal caused a lot of ‘angin’ (wind), which could be the cause of their gout apart from foods rich in purine. The Indian participants also believed that being overweight would result in lifelong struggle, with no cure for their gout. In addition to cultural factors, family members played major influencing roles, especially those with children or relatives who were healthcare practitioners. Having friends with gout was also another influencing factor reported by the participants. They sought insights from their friends regarding their experiences with gout, comparing notes on factors such as the types of food that aflected them.</p>", "<p>\n<italic toggle=\"yes\">“Most Chinese will take hot food. They like to go for supper, eat mee, “wantan mee”, all hot. I believe that taking hot food has better therapeutic benefits. ”</italic>\n</p>", "<p>P20, 65, Male, Malay, five years of gout</p>", "<p>\n<italic toggle=\"yes\">“Dhal causes pain, maybe because of “angin”. Will eat also, little bit. ”</italic>\n</p>", "<p>P9, 59, Male, Indian, 30 years of gout</p>", "<p>\n<italic toggle=\"yes\">“Well, I <bold>consult with friends</bold> who had this thing and they just recommend to take those medicine la. ”</italic>\n</p>", "<p>P17, 67, Male, Chinese, five years of gout</p>" ]	[ "<title>Discussion</title>", "<p>Contrary to historical descriptions of gout dating back to 2600 BC by the Egyptians, the condition is not viewed as a “rich man’s disease” or ‘disease of kings’.<sup>##REF##16820040##13##,##REF##22737564##14##</sup> In the present study, the participants reported that although diet was the main contributing factor of gout, there were other causes, such as genetics and medication. According to the literature, gout is seen commonly in patients more than 20 years of age, and its severity increases with age, plateauing by 70 years of age.<sup>##REF##23024028##1##</sup> Some patients develop gout much earlier at around 16–17 years of age. Gout with an early onset is known as familial gout. This type of gout is recognised as a familial disorder, characterised by a family history of gout. Male members of such families usually develop gout earlier in life, about 7.5 years earlier than those with non-familial gout.<sup>##REF##24961941##15##</sup></p>", "<p>Herein, the participants likened their experience with gout to going to war, facing recurring attacks. The pain was inevitable to all participants and delineated the onset of gout. Their experiences were in accordance to the typical presentation of gout: severe pain waking them up at night or in the morning, commonly at the first metatarsophalangeal joint.<sup>##REF##26369796##16##</sup> The impact of gout was significant on the patients’ quality of life, social life and work life, consistent with other reports.<sup>##REF##21169857##17##,##REF##26185426##18##</sup></p>", "<p>Diet control was the main self-management practised by most participants. The commonly used ways of diet control were identification of triggering foods via the trial-and-error method and eventual avoidance or limitation of the amount of such foods. The trial-and-error method is an acceptable way of identifying which food affects patients, as each of them reacts differently to different types of food. Evidence shows that certain diet and amount consumed affect the clearance of uric acid from the kidneys.<sup>##REF##17570471##19##</sup> In this study, the participants reported being empowered by the decision of taking medication, as advised by their physicians. Regarding traditional medicine practices, there were believers and non-believers among the participants. The reasons for practising traditional medicine were to search for ways to control or cure their gout to avoid dependence on modern medications. Eating pineapple and papaya was found to improve their gout symptoms compared with the other practices used. This finding is consistent with a report showing that the leaf extracts of papaya yielded xanthine oxidase-inhibitory effects compared with other parts of papaya.<sup>##REF##26462366##20##</sup></p>", "<p>Among Malay groups, certain food cultural practices have changed over the years owing to urbanisation. Most Malay foods at stalls and restaurants are hybrid forms influenced by Thai, Chinese and French cuisines to suit the taste of locals and foreigners.<sup>##REF##31517558##21##,##REF##27562728##22##</sup> Among Chinese groups, food is used to establish relationships among people, such as making new friends or business partners. For example, their dinners usually consist of 4–10 dishes depending on the occasion and are shared together in a round table to unite people together.<sup>##UREF##4##23##,##UREF##5##24##</sup> Among Indian groups, eating dhal is part of their staple food with rice. In the present study, consumption of ‘chapati’ or ‘thosai’ was reported to cause acute gout attacks among the Indian participants. Some participants were even advised to avoid eating dhal, as it causes wind and can aggravate their gout symptoms. This finding is in contrast to a report showing that dhal has anti-inflammatory effects, and as a legume, this vegetable protein has less effect on gout than animal protein.<sup>##UREF##6##25##</sup> Patients with diseases that are considered cold, such as arthritis, rheumatic arthritis and neuralgia, are advised to restrict foods that are also considered cold.<sup>##UREF##7##26##</sup> Cold foods such as leafy vegetables and most fruits have higher water content and lower fat, protein and carbohydrate contents.<sup>##REF##23132168##27##</sup> Herein, some participants reported that cold foods including ice cubes and ice cream triggered attacks. Nevertheless, further studies are needed to delve into the association of cold foods with gout attacks.</p>", "<p>One strength of this study is the use of in-depth interviews; this approach allowed the researcher to explore the participants’ perceptions and cultural practices of self-management of their gout. Another strength of this study is that the participants had different demographic profiles, allowing the exploration of a wider range of experiences of self-management practices. In contrast, the limitation of this study is that it was conducted at a primary care clinic in an urban setting, making it difficult to explore cultural practices in greater detail. Nevertheless, the findings suggest that patients can be advised by healthcare practitioners about using a food diary, where they can individualise their own diet using the trial-and-error method. Further research must explore the facilitators and barriers to counselling by healthcare practitioners in primary care settings relative to self-management among patients with gout.</p>", "<p>In conclusion, diet control is the main selfmanagement practice of patients with gout. Traditional medicine practices include the use of natural methods. Each ethnicity has its own unique beliefs and food cultures. Therefore, by understanding the self-management practices of patients from different ethnicities, healthcare practitioners can personalise the management of gout.</p>" ]	[]	[ "<title>Abstract</title>", "<title>Introduction:</title>", "<p>Gout is a chronic disease commonly associated with other comorbidities. Patients’ perceived quality of life empowers them in managing their health. Self-management is imparted as part of management among patients with chronic disease. This study aimed to explore the perceptions and practices of self-management among patients with gout from different ethnic groups in Malaysia.</p>", "<title>Methods:</title>", "<p>A qualitative study was conducted among Malay, Chinese and Indian patients with gout via semi-structured in-depth interviews at the primary care clinic of University Malaya Medical Centre in either English or Malay language. All participants had a gout duration of more than 6 months and were either taking urate-lowering drugs or not using them at all.</p>", "<title>Results:</title>", "<p>A total of 20 participants were successfully recruited for the study. Among the participants, 18 were men, while two were women. Further, nine were Malays; six, Chinese; and four, Indians. The age ranged from 29 to 81 years, while the gout duration ranged from 1 to 30 years. From the interviews, three themes emerged: experiences with gout, types of self-management of gout and factors influencing self-management of gout.</p>", "<title>Conclusion:</title>", "<p>Diet control is the main self-management practice of patients with gout. Traditional medicine practices include natural methods such as consumption of different types of vegetable juices, pineapple and papaya. Each ethnicity has its own unique beliefs and food cultures. By understanding the self-management practices of patients from diverse ethnic backgrounds, healthcare practitioners can tailor the treatment of gout to individual needs.</p>", "<title>Keywords</title>" ]	[]	[ "<title>Acknowledgments</title>", "<p>The author would like to thank all her professors, supervisors, co-supervisors and colleagues for their constant support.</p>", "<title>Author Contributions</title>", "<p>SS conducted this research as part of her master’s programme to fulfil the requirements for graduation. This research was fully supervised by NSHH.</p>", "<title>Ethical approval</title>", "<p>Ethical approval was obtained from the University Malaya Medical Centre Committee on 8 July 2019 (MEC ref. no.: 201978-7628). Written informed consent was obtained from all participants. No ethical problem was encountered during the study.</p>", "<title>Conflicts of interest</title>", "<p>There are no conflicts of interest to disclose.</p>", "<title>Funding</title>", "<p>This study was fully self-funded by the author.</p>", "<title>Data sharing statement</title>", "<p>The data that support the findings of this study are available on request from the corresponding author. These data are not publicly available since they contain information that could compromise the privacy of the participants.</p>" ]	[]	[ "<table-wrap position=\"float\" id=\"t1\"><label>Table 1</label><caption><title>Themes and subthemes.</title></caption><table frame=\"box\" rules=\"groups\"><colgroup span=\"1\"><col align=\"left\" span=\"1\"/><col align=\"left\" span=\"1\"/></colgroup><thead valign=\"bottom\"><tr><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Themes</p>\n</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Subthemes</p>\n</th></tr></thead><tbody valign=\"top\"><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Experiences with gout</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Help-seeking behaviour</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Causes of gout</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Inevitable pain</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Frequency of gout flares</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Impact of gout</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Types of self-management of gout</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Diet control</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Usage of pain killers</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Allopurinol medication</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Other types</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Factors influencing self-management of gout</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Cultural influences</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Other factors</p>\n</td></tr></tbody></table></table-wrap>" ]	[]	[ "<boxed-text position=\"anchor\"><p>\n<bold>How does this paper make a difference in general practice?</bold>\n</p><list list-type=\"order\"><list-item><p>This paper can benefit those practicing in the field of traditional medicine.</p></list-item><list-item><p>This paper would help future researchers in their literature review for future research.</p></list-item><list-item><p>This paper could help future researcher in coming out with ideas of future research in the field of gout.</p></list-item><list-item><p>This paper can also be a guide to dietitian in improving their consultations to patients with gout who are referred to them.</p></list-item><list-item><p>This paper can be a guide to healthcare practitioners in improving the self-management of patients with gout in primary care.</p></list-item></list></boxed-text>" ]	[]	[]	[]	[]	[]	[]	[]	[{"label": ["4."], "person-group": ["\n"], "collab": ["Department of Statistics Malaysia."], "source": ["Current Population Estimates, Malaysia, 2014-2016."]}, {"label": ["7."], "person-group": ["\n"], "collab": ["National Institutes of Health, Ministry of Malaysia."], "source": ["Traditional and Complementary Medicine National Health and Morbidity Survey 2015."], "volume": ["Vol. IV."], "publisher-name": ["National Institutes of Health, Ministry of Malaysia"], "year": ["2015"]}, {"label": ["10."], "surname": ["Corbin", "Strauss"], "given-names": ["J", "A"], "article-title": ["Managing chronic illness at home: three lines of work."], "source": ["Qual Sociol."], "year": ["1985"], "volume": ["8"], "issue": ["3"], "fpage": ["224"], "lpage": ["247"], "pub-id": ["10.1007/BF00989485"]}, {"label": ["11."], "surname": ["Corbin", "Strauss"], "given-names": ["JM", "A"], "article-title": ["Grounded theory research: procedures, canons, and evaluative criteria."], "source": ["Qual Sociol."], "year": ["1990"], "volume": ["13"], "issue": ["1"], "fpage": ["3"], "lpage": ["21"], "pub-id": ["10.1007/BF00988593"]}, {"label": ["23."], "surname": ["Aziz", "Pawi"], "given-names": ["AR", "AA"], "article-title": ["Malay Food: Innovate or Perish."], "source": ["Journal of Tourism, Hospitality & Culinary Arts."], "year": ["2017"], "volume": ["9"], "issue": ["1"], "fpage": ["50"], "lpage": ["63"]}, {"label": ["24."], "surname": ["Raji", "Karim", "Ishak", "Arshad"], "given-names": ["MNA", "S", "FAC", "MM"], "article-title": ["Past and present practices of the Malay food heritage and culture in Malaysia."], "source": ["J Ethn Foods."], "year": ["2017"], "volume": ["4"], "issue": ["4"], "fpage": ["221"], "lpage": ["231"], "pub-id": ["10.1016/j.jef.2017.11.001"]}, {"label": ["25."], "person-group": ["\n"], "surname": ["Ma"], "given-names": ["G"], "article-title": ["Food, eating behavior, and culture in Chinese society."], "source": ["J Ethn Foods."], "year": ["2015"], "volume": ["2"], "issue": ["4"], "fpage": ["195"], "lpage": ["199"], "pub-id": ["10.1016/j.jef.2015.11.004"]}, {"label": ["26."], "surname": ["Yang", "Lattimore", "Lai"], "given-names": ["CL", "C", "MY"], "article-title": ["Eat to live or live to eat? Mapping food and eating perception of Malaysian Chinese."], "source": ["JHosp Mark Manag."], "year": ["2014"], "volume": ["23"], "issue": ["6"], "fpage": ["579"], "lpage": ["600"], "pub-id": ["10.1080/19368623.2013.813887"]}]	{ "acronym": [], "definition": [] }	27	CC BY	no	2024-01-13 00:02:19	Malays Fam Physician. 2023 Dec 18; 18:72	oa_package/62/d3/PMC10781610.tar.gz
PMC10781611	38213386	[ "<title>Introduction</title>", "<p>Diabetes mellitus substantially impacts the quality of life (QoL) of patients by affecting their physical, mental and social well-being.<sup>##UREF##0##1##</sup> Patients with type 2 diabetes mellitus (T2DM) may experience physical symptoms such as acute or chronic pain, fatigue or neuropathy and psychological symptoms such as depression, sleep disturbances or emotional disability.<sup>##REF##22854982##2##</sup></p>", "<p>T2DM-related symptoms can be as burdensome as the disease itself. The prevalence of these symptoms ranges from 3% to 48%.<sup>##REF##22854982##2##</sup> Patients with T2DM have been reported to have a poorer QoL than their healthy counterparts.<sup>##REF##22698420##3##</sup></p>", "<p>Despite their impact on QoL, diabetes mellitus-related symptoms are often not reported by patients. Most studies are focused on hypoglycaemia among patients with T2DM most likely because it is the most burdensome symptom.<sup>##REF##22698420##3##</sup> In contrast, limited studies have investigated the burden of other diabetes mellitus-related symptoms among patients with T2DM.</p>", "<p>In Malaysia, the prevalence of T2DM is high.<sup>##UREF##0##1##</sup> Accordingly, it is important to study the burden of diabetes mellitus-related symptoms and whether these symptoms are addressed by doctors, especially among patients with a high symptom burden, to consequently improve their QoL.</p>", "<p>Patients with T2DM have a poorer health-related QoL (HRQoL) than their healthy counterparts. Sociodemographic characteristics, disease control and symptoms are determinants of the HRQoL of patients with T2DM.<sup>##REF##21707235##4##</sup> A substantial number of patients report a poorer HRQoL owing to pain/discomfort, mobility problems, anxiety/depression, reduced activity performance and impaired ability for self-care.<sup>##UREF##1##5##</sup> Some patients with T2DM also develop complications such as ischaemic heart disease, stroke and neuropathy, further worsening their HRQoL.<sup>##REF##20132542##6##</sup> Older patients with T2DM tend to have a higher symptom burden than their younger counterparts. One study showed that patient-reported symptoms in older patients were risk factors for hospitalisation and emergency department visits.<sup>##REF##26029477##7##</sup></p>", "<p>A study conducted by the American Diabetes Association found that 56% of patients with T2DM experienced at least one diabetes mellitus-related symptom in the past 12 months.<sup>##REF##17712027##8##</sup> Patients with T2DM may also have emotional and psychological needs that must be addressed.<sup>##REF##30294583##9##</sup> Accordingly, the symptom burden is a patient concern.<sup>##REF##7955978##10##</sup> Addressing diabetes mellitus-related symptoms may improve patients’ emotional and psychological wellbeing.</p>", "<p>It is also essential to know whether doctors address patients’ symptoms because such symptoms are as important as their concerns. Two-thirds of patients have been shown to worry that their symptoms might represent a serious illness. Accordingly, identifying and addressing patients’ concerns are a crucial part of the patient-centred approach.<sup>##REF##29368281##11##</sup></p>", "<p>Current clinical practice focuses on the control of HbAlc levels and prevention of complications of T2DM rather than control of symptoms. Patients with T2DM may have symptoms, which may burden them. This study sought to conduct a proper clinical assessment and provide symptom relief to patients. Symptoms can be an indicator of disease progress or a complication of the disease, such as atherosclerosis.<sup>##REF##15534311##12##</sup> Diabetes mellitus-specific symptoms are important predictors that facilitate a patient-centred approach. This study then aimed to identify the prevalence and burden of diabetes mellitus-related symptoms among patients with T2DM and the degree of symptom management by primary care doctors.</p>" ]	[ "<title>Methods</title>", "<title>Design</title>", "<p>A prospective cross-sectional study was conducted from 1 October 2019 to 30 November 2019 at the Department of Primary Care Medicine in University Malaya Medical Centre, a tertiary hospital located in Kuala Lumpur, Malaysia.</p>", "<title>Participants</title>", "<p>Patients with T2DM who were aged >18 years and able to understand either English or Malay language were included in the study. Those who were cognitively impaired were excluded from the study.</p>", "<title>Instrument</title>", "<p>A self-administered questionnaire adapted from the Diabetes Symptom Checklist-Revised (DSC-R), with an additional section assessing participant demographics, was used. Its English version was translated to Malay language by two independent translators who were proficient in both languages. The Malay version was reviewed by an expert panel. This version underwent backward translation to English by two other independent translators who were also proficient in both languages. The questionnaire had two sections: preconsultation and post-consultation. The preconsultation section assessed the participants’ sociodemographic and clinical characteristics and diabetes mellitus-related symptoms. The modified DSC-R consisted of 34 diabetes mellitus-related symptoms, requiring the participants to respond either ‘yes’ or ‘no’ if they had any of those symptoms in the past 4 weeks. Those who responded ‘yes’ for each symptom were required to rate their symptom burden on a Likert scale ranging from 1 (‘not at all troublesome’) to 5 (‘extremely troublesome’). For the post-consultation section, the participants indicated their feedback regarding their consultation.</p>", "<title>Pilot study</title>", "<p>A pilot study was conducted among 30 participants prior to the actual data collection to identify any issues with the questionnaire and the recruitment process. These participants were able to understand the questionnaire and, on average, took about 20 min to complete it. No changes were made to the questionnaire after the pilot study.</p>", "<p>Subsequently, patients with T2DM were recruited via systematic random sampling. Eligible participants received the preconsultation questionnaire before their consultation and the post-consultation questionnaire after their consultation with their doctors.</p>", "<title>Main study</title>", "<p>A total of 1602 patients with T2DM were identified at the triage counter of the hospital within 1 month. These patients were randomly and systematically recruited, with one selected for every three. A total of 534 patients were selected. Among them, 54 were unable to read or understand English or Malay; 11 refused to participate; and nine had a cognitive impairment. Consequently, 460 patients remained and agreed to participate; they were given the Patient information sheet to read, and the consent form to sign. Once the consent form was signed, the participants were asked to complete the questionnaire. A total of 418 participants completed and returned the questionnaires. The primary outcome of this study was the prevalence of symptoms during the past 4 weeks. The secondary outcome was the symptom burden, which was assessed using a Likert scale. Other outcomes included symptoms reported to doctors and whether doctors addressed such symptoms.</p>", "<title>Data analysis</title>", "<p>Data were analysed using SPSS version 23.0 by IBM, Chicago, United States of America. Descriptive statistics were used to describe the sociodemographic and clinical data of the participants. Categorical variables were presented as percentages and frequencies and continuous variables as means with standard deviations (SDs). The independent variables were the sociodemographic and clinical characteristics, while the dependent variables were the symptom score, subscale score and post-consultation feedback. The association of the sociodemographic and clinical characteristics with the prevalence of symptoms and the symptom score was also evaluated.</p>", "<title>Ethical considerations</title>", "<p>Ethical approval was obtained from the University Malaya Medical Ethics Committee before commencement of the study (MREC ref. no.: 201973-7602). Written informed consent was obtained from all participants.</p>" ]	[ "<title>Results</title>", "<p>A total of 471 patients were eligible for inclusion, of whom 11 refused to participate, and 42 who consented did not return the questionnaires. This yielded a response rate of 88.7%. The participants had a mean age of 63 years. Approximately 55.5% were women, and 41.8% were Malays. Most participants (80.6%) had T2DM for more than 5 years, with a mean HbA1c level of 7.98% (##TAB##0##Table 1##).</p>", "<title>Prevalence and burden of diabetes mellitus-related symptoms</title>", "<p>The prevalence of diabetes mellitus-related symptoms ranged from 4.1% to 48.1%. The most commonly reported symptoms were frequent need to empty the bladder (48.1%), numbness of the hands (43.5%), lack of energy (42.6%) and numbness of the feet (40.9%).</p>", "<p>Most symptoms were reported to be slightly to moderately troublesome (mean score=2.00–2.58). The most troublesome symptoms were related to hyperglycaemia: polyuria (mean score=2.58), thirst (mean score=2.45) and lack of energy (mean score=2.44) (##TAB##1##Table 2##). Similarly, among the subscale symptoms, those related to hyperglycaemia scored the highest (0.91), followed by symptoms related to psychology–fatigue (0.64) and neurology–sensory (0.52) (##TAB##2##Table 3##).</p>", "<title>Management of symptoms by doctors</title>", "<p>Approximately 83.5% of the participants had previously consulted their attending doctors. Among them, 38% discussed their symptoms with their doctors. Approximately 88.1% (n=l40) reported one to three symptoms, with a mean number of symptoms of 2.26 (SD=1.6) (##TAB##4##Table 5##). Nearly all participants (97.5%) indicated that their symptoms were addressed by their doctors; most were satisfied (89.3%) with how their symptoms were addressed and were confident (78.0%) in coping with their symptoms.</p>" ]	[ "<title>Discussion</title>", "<p>This study showed that the overall prevalence of diabetes mellitus-related symptoms and the prevalence of each symptom among the patients with T2DM were quite low and below 50%, respectively. The participants in this study were generally old with a long disease duration, but the prevalence of symptoms was lower than expected. The most commonly reported symptoms were related to hyperglycaemia, possibly reflecting disease control. Most symptoms were acknowledged and addressed satisfactorily by the doctors of the participants.</p>", "<p>The prevalence of symptoms ranged from 4.1% to 48%, while the symptom burden score ranged from 1.76 to 2.58, reflecting a low symptom burden. These findings differ from the report by García et al., wherein the prevalence ranged from 14.1% to 67.6%.<sup>##REF##30853074##13##</sup> The authors found that a stronger perception of disease severity was associated with a higher symptom burden.<sup>##REF##30853074##13##</sup></p>", "<p>In the present study, the low prevalence of symptoms among the participants could be attributed to the relatively average HbA1c level of 7.9%. Müller et al. showed that most patients with T2DM with hyperglycaemic symptoms had an HbA1c level above 8.9%.<sup>##REF##34100271##14##</sup> Similar to the present findings, the hyperglycaemic symptoms that were most prevalent were frequent urination and tiredness.<sup>##REF##34100271##14##</sup> Further, higher HbA1c levels were associated with a higher symptom burden.</p>", "<p>Other complications of T2DM may also affect patients’ symptoms. Patients with cardiovascular or ophthalmological complications may have a higher symptom burden. Symptoms of hypoglycaemia may be more prevalent in patients on insulin; however, in this study, the score for this subscale was not significant. The patients with a longer T2DM duration tended to have a higher symptom. (##TAB##4##Table 5##)</p>", "<p>Among the participants who discussed their symptoms with their doctors, 88.1% reported a total of one to three symptoms. Most of them (97.5%) indicated that their doctors addressed their symptoms. Notably, this study was conducted at a primary care clinic in a tertiary hospital in Malaysia, where patients’ expectations tend to be higher. Providing adequate time during consultations contributes to patient satisfaction.<sup>##REF##35950009##15##</sup></p>", "<title>Strengths and limitations</title>", "<p>The strength of this study is that the findings are applicable to clinical practice. Patients with an advanced age and a higher HbA1c level may have a higher symptom burden.</p>", "<p>There are several limitations noted in this study. This study was conducted in a single setting, limiting the generalisation of the findings to other settings in Malaysia. Further, the questionnaire was translated only to Malay. Many patients who were unable to read or understand English or Malay were then excluded from this study. Some patients with visual impairment or stroke would require assistance in completing the questionnaire. Another limitation is that the questionnaire was tested for its face and content validities only; it was not validated with other questionnaires such as the Diabetes Distress Scale and SF-36. Moreover, the symptoms were evaluated retrospectively for the past 4 weeks, so there was a possibility of recall bias. The post-consultation section mainly evaluated the symptoms based on consultation with the doctors. However, the symptoms may not be the primary concern of some patients. The data were also susceptible to recall bias.</p>", "<p>Another limitation of this study is that the symptoms reported by the patients with T2DM may be attributed to diseases other than T2DM such as benign prostate hyperplasia (BPH) or overactive bladder syndrome (OAB). Liu et al. reported a high prevalence of OAB among patients with T2DM.<sup>##REF##21958505##16##</sup> In other studies, T2DM was found to be associated with BPH in men and bladder dysfunction in women. Berger et al. concluded that diabetic vascular damage may cause hypoxia, which may be involved in the pathogenesis of BPH.<sup>##REF##15756540##17##,##REF##25143916##18##</sup> The association of T2DM with prostate or bladder disorder could explain the high prevalence of urinary symptoms among patients with T2DM.</p>" ]	[ "<title>Conclusion</title>", "<p>The prevalence and burden of diabetes mellitus-related symptoms among patients with T2DM are low. Optimisation of glycaemic control is important in reducing the symptom burden. A lower symptom burden results in fewer discussions of symptoms with doctors. Generally, patients with T2DM are satisfied with the management of their symptoms by their doctors.</p>" ]	[ "<title>Abstract</title>", "<title>Introduction:</title>", "<p>Type 2 diabetes mellitus (T2DM) is a significant non-communicable disease in Malaysia, with a prevalence of 18.1%, per the National Health and Morbidity Survey. This study aimed to determine the prevalence and burden of diabetes mellitus-related symptoms and whether these symptoms were addressed by primary care doctors.</p>", "<title>Methods:</title>", "<p>This 1-month cross-sectional study was conducted at an urban hospital-based primary care clinic in Malaysia. Patients with T2DM were recruited using systematic random sampling. Participants answered a self-administered questionnaire adapted from the Diabetes Symptom Checklist-Revised, which evaluated the sociodemographic characteristics, burden of diabetes mellitus-related symptoms in the past month and post-consultation feedback about symptoms. Data were analysed using SPSS.</p>", "<title>Results:</title>", "<p>Four hundred eighteen participants were included, yielding a response rate of 97.7%. Hyperglycaemia was the most prevalent symptom, with 48.1% of the participants reporting a frequent need to empty their bladder. Most participants experienced a low symptom burden, so 56.7% did not report their symptoms to their doctors. The participants who reported their symptoms had a higher symptom burden. Among them, 97.5% indicated that their doctors addressed their symptoms. Approximately 78% reported satisfaction and good coping skills when their symptoms were addressed.</p>", "<title>Conclusion:</title>", "<p>Hyperglycaemia was the most prevalent diabetes mellitus-related symptom among the patients with T2DM. The symptom burden was generally low, so most patients did not report their symptoms to their doctors. Those who reported their symptoms had a higher symptom burden. Further studies must explore why patients do not report their symptoms and how doctors address patients’ symptoms.</p>", "<title>Keywords</title>" ]	[]	[ "<title>Acknowledgments</title>", "<p>The authors would like to thank all their professors, supervisors, co-supervisors and colleagues for their constant support.</p>", "<title>Author Contributions</title>", "<p>\n<bold>Author 1 - Tan JYH</bold>\n</p>", "<p>Conceived and designed the analysis Collected the data Contributed data analysis tool Performed the analysis Wrote the paper</p>", "<p>\n<bold>Author 2 –Ng CJ</bold>\n</p>", "<p>Conceived and designed the analysis Contributed data analysis tool Wrote the paper</p>", "<title>Ethical approval</title>", "<p>Ethical approval was obtained from the University Malaya Medical Centre Committee on 3 July 2019 (MREC ref. no.: 2019737602). Written informed consent was obtained from all participants. No ethical problem was encountered during the study.</p>", "<title>Conflicts of interest</title>", "<p>All authors have no conflicts of interest to disclose.</p>", "<title>Funding</title>", "<p>This study was fully self-funded by the main author.</p>", "<title>Data sharing statement</title>", "<p>The data that support the findings of this study are available on request from the corresponding author. These data are not publicly available since they contain information that could compromise the privacy of the participants.</p>" ]	[ "<fig position=\"float\" id=\"f1\"><label>Figure 1</label><caption><title>Flowchart of the study and data collection process.</title></caption></fig>" ]	[ "<table-wrap position=\"float\" id=\"t1\"><label>Table 1</label><caption><title>Sociodemographic and clinical variables of the respondents (N=418).</title></caption><table frame=\"box\" rules=\"groups\"><colgroup span=\"1\"><col align=\"left\" span=\"1\"/><col align=\"left\" span=\"1\"/></colgroup><thead valign=\"bottom\"><tr><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Sociodemographic and clinical variables</p>\n</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>n (%)</p>\n</th></tr></thead><tbody valign=\"top\"><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>\n<bold>Sex (n=4l8)</bold>\n</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Male</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>186 (44.5)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Female</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>232 (55.5)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>\n<bold>Age (year) (n=4l8)</bold>\n</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>30 and below</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>2 (0.5)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>31-40</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>16 (3.8)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>41-50</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>34 (8.1)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>51-60</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>86 (20.6)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>61-70</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>179 (42.8)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>71-80</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>89 (21.3)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>81 and above</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>12 (2.9)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>\n<bold>Ethnicity (n=4l8)</bold>\n</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Malay</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>174 (41.6)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Chinese</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>100 (24.0)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Indian</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>138 (33.0)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Others</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>6 (1.4)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>\n<bold>Highest educational qualification (n=4l8)</bold>\n</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Primary school and below</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>60 (14.4)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Secondary school</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>280 (67.0)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Pre-university</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>49(11.7)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Tertiary</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>29 (6.9)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>\n<bold>Duration of T2DM (year) (n=4l8)</bold>\n</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0-5</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>81 (19.4)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>6-10</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>148 (35.4)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>11-15</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>95 (22.7)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>16-20</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>71 (17.0)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>21-25</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>10 (2.4)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>26-30</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>8 (1.9)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>31 and above</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>5 (1.2)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>\n<bold>Other disease (n=4l8)</bold>\n</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Hypertension</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>317 (75.8)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>High cholesterol</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>283 (67.7)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Heart disease</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>44 (10.5)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Stroke</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>13 (3.1)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Kidney disease</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>18 (4.3)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>\n<bold>Medications for DM (n=4l8)</bold>\n</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>None</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>3 (0.7)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Insulin</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>97 (23.2)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Oral medications</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>318 (76.1)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>\n<bold>HbAlc level (%) (n=378)</bold>\n</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>6.5 and below</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>92 (24.3)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>6.6-8.0</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>134 (35.5)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>8.1 and above</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>152 (40.2)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>\n<bold>eGFR (n=389)</bold>\n</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Normal (90mL/min/1.73m<sup>2</sup>. and above)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>196 (50.4)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Stage 2 CKD</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>130 (33.4)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Stage 3 CKD</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>44(11.3)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Stage 4 CKD</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>8(2.1)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Stage 5 CKD</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>11 (2.8)</p>\n</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"t2\"><label>Table 2</label><caption><title>Prevalence and burden of diabetes mellitus-related symptoms.</title></caption><table frame=\"box\" rules=\"groups\"><colgroup span=\"1\"><col align=\"left\" span=\"1\"/><col align=\"left\" span=\"1\"/><col align=\"left\" span=\"1\"/></colgroup><thead valign=\"bottom\"><tr><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Occurring symptom</p>\n</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>n (%)</p>\n</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Mean (SD)</p>\n</th></tr></thead><tbody valign=\"top\"><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Frequent need to empty the bladder</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>201 (48.1)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>2.58 (0.95)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Numbness of the hands</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>182 (43.5)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>2.30 (0.86)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Lack of energy</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>178 (42.6)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>2.44 (0.80)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Numbness of the feet</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>171 (40.9)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>2.42 (0.85)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Very thirsty</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>150 (35.9)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>2.45 (0.86)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Dry mouth</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>146 (34.9)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>2.12 (0.79)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Overall sense of fatigue</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>133 (31.8)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>2.22 (0.86)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Drinking a lot</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>133 (31.8)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>2.51 (0.96)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Sleepiness or drowsiness</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>119 (28.5)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>2.12 (0.79)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Aching calves when walking</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>112 (26.8)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>2.24 (0.84)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Increasing fatigue during the course of the day</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>103 (24.6)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>2.00 (0.79)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Persistently blurred vision</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>98 (23.4)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>2.00 (0.91)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Tingling sensations in the limbs at night</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>91 (21.8)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>2.24 (0.69)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Difficulty concentrating</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>82 (19.6)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>2.00 (0.77)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Shooting pains in the leg</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>66 (15.8)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>2.23 (0.84)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Easily irritated or annoyed</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>65 (15.6)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>2.22 (1.08)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Fatigue in the morning when getting up</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>59 (14.1)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>2.19 (1.01)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Tingling or prickling sensations in the hands or fingers</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>54 (12.9)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>2.04 (0.73)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Deteriorating vision</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>52 (12.4)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>2.21 (0.67)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Difficulty paying attention</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>51 (12.2)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>2.06 (0.61)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Shortness of breath during physical exertion</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>49 (11.7)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>2.35 (1.01)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Tingling or prickling sensations in the lower legs</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>49 (11.7)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>2.27 (0.73)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Moodiness</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>48 (11.5)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>2.19 (0.87)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Burning pain in the calves at night</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>47 (11.2)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>2.11 (0.81)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Fuzzy feeling in the head</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>46 (11.0)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>1.76 (0.92)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Alternating clear and blurred vision</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>37 (8.9)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>2.11 (0.61)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Palpitation or pounding in the heart region</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>36 (8.6)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>1.78 (0.68)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Flashes or black spots in the field of vision</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>35 (8.4)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>1.89 (0.80)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Pain in the chest or heart region</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>34 (8.1)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>1.76 (0.82)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Odd feeling in the leg or feet when touched</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>30 (7.2)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>2.03 (0.76)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Shortness of breath at night</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>27 (6.5)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>1.85 (0.72)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Irritability just before a meal</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>25 (6.0)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>1.92 (1.15)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Burning pains in the legs during the day</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>23 (5.5)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>1.83 (0.94)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Sudden deterioration of vision</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>17 (4.1)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>2.24 (1.09)</p>\n</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"t3\"><label>Table 3</label><caption><title>Subscale scores.</title></caption><table frame=\"box\" rules=\"groups\"><colgroup span=\"1\"><col align=\"left\" span=\"1\"/><col align=\"left\" span=\"1\"/></colgroup><thead valign=\"bottom\"><tr><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Subscale (N=418)</p>\n</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Mean (SD)</p>\n</th></tr></thead><tbody valign=\"top\"><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Hyperglycaemia</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.91 (0.96)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Psychology-fatigue</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.64 (0.77)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Neurology-sensory</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.52 (0.57)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Psychology-cognitive</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.36 (0.60)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Neurology-pain</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.32 (0.57)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Hypoglycaemia</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.24 (0.59)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Ophthalmology</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.24 (0.51)</p>\n</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"t4\"><label>Table 4</label><caption><title>Association of the mean subscale score with age, HbAlc level and DM duration.</title></caption><table frame=\"box\" rules=\"groups\"><colgroup span=\"1\"><col align=\"left\" span=\"1\"/><col align=\"left\" span=\"1\"/><col align=\"left\" span=\"1\"/><col align=\"left\" span=\"1\"/><col align=\"left\" span=\"1\"/><col align=\"left\" span=\"1\"/><col align=\"left\" span=\"1\"/><col align=\"left\" span=\"1\"/><col align=\"left\" span=\"1\"/></colgroup><thead valign=\"bottom\"><tr><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Subscale</p>\n</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Psychology – fatigue</p>\n</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Psychology – cognitive</p>\n</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Neurology – pain</p>\n</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Neurology – sensory</p>\n</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Cardiology</p>\n</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Ophthalmology</p>\n</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Hypoglycaemia</p>\n</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Hyperglycaemia</p>\n</th></tr></thead><tbody valign=\"top\"><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Age (n=4l8)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Pearson</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.62</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.009</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.006</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.008</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>-0.088</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>-0.065</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>-0.020</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.028</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>correlation</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.203</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.857</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.898</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.100</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.074</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.187</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.681</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.568</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>P-value</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>HbAlc level (n=378)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>\n<bold>0.102</bold>\n</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.058</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.075</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.066</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>\n<bold>0.103</bold>\n</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.083</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.082</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.054</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Pearson correlation</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>\n<bold>0.047</bold>\n</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.257</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.145</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.203</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>\n<bold>0.045</bold>\n</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.106</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.113</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.294</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>P-value</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>DM duration (n=4l8)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>\n<bold>0.133</bold>\n</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>\n<bold>0.168</bold>\n</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>\n<bold>0.134</bold>\n</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Pearson correlation</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>\n<bold>0.006</bold>\n</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.064</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>-0.011</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>\n<bold>0.000</bold>\n</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>-0.029</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>\n<bold>0.006</bold>\n</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.035</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.093</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.192</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.825</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.558</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.479</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.057</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>P-value</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"t5\"><label>Table 5</label><caption><title>Post-consultation review.</title></caption><table frame=\"box\" rules=\"groups\"><colgroup span=\"1\"><col align=\"left\" span=\"1\"/><col align=\"left\" span=\"1\"/></colgroup><thead valign=\"bottom\"><tr><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Post-consultation feedback</p>\n</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>n (%)</p>\n</th></tr></thead><tbody valign=\"top\"><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"><p>Is this the first time you are seeing this doctor? (n=418)</p>\n<p>Yes</p>\n<p>No</p></td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"><p> </p>\n<p>69 (16.5)</p>\n<p>349 (83.5)</p></td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Did you discuss your symptom with doctor? (n=418)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Yes</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>159 (38.0)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>No</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>237 (56.7)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Do not have symptom</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>22 (5.3)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>How many symptoms did you discuss with the doctor? (n=159)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>1-3</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>140 (88.1)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>4-6</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>15 (9.4)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>7 and above</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>4 (2.5)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Did the doctor address your symptom(s)? (n=159)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Yes</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>155 (97.5)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>No</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>1 (0.6)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Not sure</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>3 (1.9)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Were you satisfied with how the doctor addressed your symptom(s)? (n=159)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Not satisfied at all</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>2 (1.3)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Not satisfied</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>2 (1.3)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Neutral</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>13 (8.2)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Somewhat satisfied</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>68 (42.8)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Very satisfied</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>74 (46.4)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"><p>After consultation with the doctor, how confident are you in coping with your symptom(s)? (n=159)</p>\n<p>Not confident at all</p>\n<p>Not confident</p>\n<p>Neutral Confident</p>\n<p>Very confident</p></td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\"><p> </p>\n<p>1 (0.6)</p>\n<p>2 (1.3)</p>\n<p>32 (20.1)</p>\n<p>83 (52.2)</p>\n<p>41 (25.8)</p></td></tr></tbody></table></table-wrap>" ]	[]	[ "<boxed-text position=\"anchor\"><p>\n<bold>How does this paper make a difference in general practice?</bold>\n</p><list list-type=\"bullet\"><list-item><p>The findings can benefit individuals practising in the field of family medicine, especially in areas where non-communicable diseases are prevalent.</p></list-item><list-item><p>The study can assist future researchers in conducting literature reviews.</p></list-item><list-item><p>The study can help future researchers in generating ideas for research in the field of non-communicable diseases.</p></list-item><list-item><p>The findings emphasise the importance of reporting symptoms to doctors.</p></list-item><list-item><p>The findings can be used as a guide by healthcare practitioners in addressing patients’ symptoms.</p></list-item></list></boxed-text>" ]	[]	[]	[]	[]	[ "<table-wrap-foot><fn id=\"t1n1\"><p>*eGFR = Estimated gromerular filtration rate</p></fn></table-wrap-foot>" ]	[ "<graphic xlink:href=\"MFP-18-71-g1\" position=\"float\"/>" ]	[]	[{"label": ["1."], "surname": ["Chandran", "Abdullah", "Abdul"], "given-names": ["A", "M", "F"], "article-title": ["National Diabetes Registry Report 2013-2019."], "source": ["MOH Malaysia."], "year": ["2019"], "fpage": ["1"], "lpage": ["34"]}, {"label": ["5."], "surname": ["Basir", "Juni", "Hejar", "Azman", "Abd Rahman"], "given-names": ["NF", "MH", "A", "AZF", "Z"], "article-title": ["Health-related quality of life of adult with type 2 diabetes mellitus in rural area."], "source": ["IJPHCS."], "year": ["2016"]}]	{ "acronym": [], "definition": [] }	18	CC BY	no	2024-01-13 00:02:19	Malays Fam Physician. 2023 Dec 5; 18:71	oa_package/e3/b1/PMC10781611.tar.gz
PMC10781612	38213389	[ "<title>Introduction</title>", "<p>Cardiovascular diseases (CVDs) are considered a leading cause of death in both developed and developing countries.<sup>##REF##9142060##1##</sup> Individuals with hyperglycaemia have a two- to four-fold increased risk of CVDs. Diabetic dyslipidaemia plays a critical role in the acceleration of macrovascular atherosclerosis and contributes to an increased risk of CVDs among patients with diabetes.<sup>##REF##2338751##2##</sup> Non-diabetic levels of hyperglycaemia, observed as impaired fasting glucose and impaired glucose tolerance, are also significantly associated with CVD morbidity and premature mortality.<sup>##REF##17576864##3##</sup> Epidemiological studies have shown that blood glucose levels in the prediabetic range are modestly correlated with many CVD risk factors including general and central obesity and increased blood pressure, triglyceride (TG), and lipoprotein levels.<sup>##REF##22683128##4##,##REF##11176766##5##</sup> A prospective study with an 8-year follow-up of Mexican Americans without diabetes also documented higher levels of low-density lipoprotein cholesterol (LDL-C), TG, total cholesterol (TC), and blood pressure and lower levels of high-density lipoprotein cholesterol (HDL-C) among patients who subsequently developed diabetes than among individuals without diabetes.<sup>##REF##2338751##2##</sup> These findings confirm the presence of lipid abnormalities in the prediabetic state and indicate that patients with prediabetes have atherogenic patterns of CVD risk factors possibly owing to obesity, hyperglycaemia, and more importantly, insulin resistance. These atherogenic patterns are known to be present for many years and increase the risk of macrovascular complications as much as the duration of clinically defined diabetes itself.<sup>##REF##2338751##2##</sup> Thus, early recognition, screening, and management of dyslipidaemia among subjects with borderline diabetes are important to halt disease progression as well as prevent the development of atherogenic CVD events.</p>", "<p>Although prediabetes has been associated with an increased risk of CVD events, the association observed is somewhat less than that with frank diabetes.<sup>##REF##11176766##5##,##REF##12610023##6##</sup> Differences in the peak plasma level of glucose/insulin and/or in the lipid profile could also be a factor. A priorly published cohort study conducted among Chinese patients with hyperglycaemia also documented differences in the patterns of dyslipidaemia between subjects with borderline diabetes and with diabetes.<sup>##REF##30325950##7##</sup> The prevalence of low HDL-C levels was not substantially high, but the prevalence of more atherogenic LDL-C and TG was higher among subjects with borderline diabetes than among diabetic subjects.<sup>##REF##30325950##7##</sup> This pattern of dyslipidaemia is also quite commonly found among South Asian populations.<sup>##UREF##0##8##</sup> However, no relevant studies have been conducted in Malaysia despite prediabetes affecting approximately 22.1% of adults aged ≥18 years in the country, with a high proportion (65.7%) of them having comorbid dyslipidaemia.<sup>##REF##21498788##9##</sup> The prevalence of dyslipidaemia among subjects with borderline diabetes is known to be high; however, available data regarding the patterns and associated factors of dyslipidaemia among Malaysian patients with borderline diabetes remain scarce. The limited knowledge about the status and patterns of dyslipidaemia may delay the implementation of effective treatment approaches for the prevention of lipid abnormalities and difficulties in the estimation of future CVD risks among patients with borderline diabetes.<sup>##REF##30641707##10##</sup> Accordingly, this retrospective study aimed to identify the patterns and associated factors of dyslipidaemia among adult Malaysian patients with borderline diabetes.</p>" ]	[ "<title>Methods</title>", "<title>Study design andpopulation</title>", "<p>A cross-sectional study via a retrospective review of medical records of patients with borderline diabetes was conducted at a primary healthcare centre at Universiti Sains Malaysia (USM). Ethical approval was obtained before the commencement of the study (reference no.: USM/JEPeM/18040197). The estimated sample size required for this study was calculated using the single-proportion formula,<sup>##UREF##1##11##</sup> assuming a prevalence of dyslipidaemia of 65.7% among Malaysian patients with borderline diabetes<sup>##REF##21498788##9##</sup> after considering a 90% confidence interval (CI). Given the retrospective nature of the study and a targeted population meeting the inclusion criteria of a smaller size, 90% CI was used in this study to reach the targeted population with acceptable findings, accounting for an attrition rate of 10% and a precision of ±0.05. Based on the calculation, 244 patients were estimated to be required for the study.</p>", "<p>A total of 250 patients who were aged ≥18 years, were diagnosed with borderline diabetes by a physician (plasma glucose levels above the cutoff values as recommended in the American Diabetes Association guidelines: fasting plasma glucose [FPG] level of 5.6–6.9 mmol/L and/or post-load plasma glucose level of 7.8–11.1 mmol/L and/or glycated haemoglobin level of 5.7–6.4%)<sup>##REF##30559228##12##</sup> and visited the study site from January 2017 to December 2018 were included in this study. Conversely, patients who were aged below 18 years; had type 1, type 2, gestational or steroid-induced diabetes; and had other causes of secondary dyslipidaemia such as hypothyroidism and other serious ailments such as myocardial infarction or stroke were excluded from this study.</p>", "<title>Data collection</title>", "<p>Convenience sampling was used to recruit patients with borderline diabetes who satisfied the eligibility criteria. The registration records of 1892 patients receiving medical care from January 2017 to December 2018 were screened. The records of those diagnosed with borderline diabetes were further evaluated to retrieve relevant sociodemographic, clinical, and laboratory parameters. Data on patients’ sex, age, ethnicity, occupation, body mass index (BMI), FPG level, blood pressure and lipid profiles including TC, HDL-C, LDL-C and TG levels were extracted. Repeated inclusion of the same patients was avoided by using a filter based on their unique record of clinic number and full name. Dyslipidaemia was defined as TC, TG, LDL-C, and HDL-C levels above the cutoff values as recommended in the Malaysian guidelines for dyslipidaemia management; the optimal levels of TC, TG, and LDL-C are ≥5.2 mmol/L, ≥1.7 mmol/L and ≥2.6 mmol/L, respectively, while the optimal level of HDL-C is ≤1.0 mmol/L in men and ≤1.2 mmol/L in women.<sup>##UREF##2##13##</sup> According to the patterns of dyslipidaemia, the patients were categorised into three types: 1) isolated dyslipidaemia, wherein any one of the lipid fractions is beyond the target level; 2) combined dyslipidaemia, wherein two lipid fractions are beyond the target level (i.e. high TG and LDL-C levels, high TG and low HDL-C levels and high LDL-C and low HDL-C levels); and 3) mixed dyslipidaemia, wherein more than two lipid fractions are beyond the target level (i.e. TG level of ≥1.7 mmol/L, LDL-C level of ≥2.6 mmol/L and HDL-C level of ≤1.0 mmol/L in men and ≤1.2 mmol/L in women). Conversely, the patients were considered to have hypertension when their recorded blood pressure was beyond the recommended range (i.e. systolic blood pressure of ≥140 mmHg and/or diastolic blood pressure [DBP] of ≥90 mmHg irrespective of their hypertensive treatment as suggested in the clinical practice guidelines for the management of hypertension).<sup>##UREF##3##14##</sup> BMI was calculated as weight in kilograms divided by height in metres squared. Generalised obesity was defined using the BMI cutoff values for Asians mentioned in the clinical practice guidelines for the management of obesity in Malaysia. The patients were considered to be of normal weight with a BMI of <23 kg/m<sup>2</sup>, overweight with a BMI of ≥23 kg/m<sup>2</sup>, and obese with a BMI of ≥27.5 kg/m<sup>2</sup>.<sup>##UREF##4##15##</sup></p>", "<title>Statistical analysis</title>", "<p>Data were analysed using IBM SPSS statistics for windows, version 25. The Kolmogorov-Smirnov test was used to evaluate the normality of the data, confirming that the data were normally distributed. Continuous variables were reported as means and standard deviations and categorical variables as frequencies and percentages. Student’s t-test was utilised to compare continuous variables across the study groups. Continuous variables with more than two subcategories were compared using one-way between-group analysis of variance (ANOVA). Tukey's Honestly significant differences (HSD) post -hoc test was applied for OneWay ANOVA. Multiple imputations were used to handle variables with missing values above 10%. Missing values were found in the BMI of the participants, which were imputed via multiple imputation methods. Five imputations were used, and Rubin’s rules were implemented to combine the findings. Logistic regression analysis was conducted to predict the factors independently associated with dyslipidaemia among the subjects with borderline diabetes. Clinically relevant and statistically tested variables were included in the univariable regression analysis. Variables with a P-value of <0.25 were included in the multivariable analysis.</p>", "<p>Correlation and multicollinearity between the independent variables were checked. The level of significance was set at P<0.05 for all tests.</p>" ]	[ "<title>Results</title>", "<title>General characteristics of the participants</title>", "<p>A total of 250 subjects with borderline diabetes were included in this study. Of them, 52.4% (n=131) were men, and 47.1% (n=119) were women. The mean age was 47.09±11.8 years, and the mean BMI was 27.0±5.54 kg/m<sup>2</sup>. The majority of the participants were middle-aged (<40 years), Malays (78%) and employed (80.4%). Dyslipidaemia was more common among men (n=122, 52.1%), Malays (n=184, 78.6%), and patients aged <40 years (n=90, 38.4%). Approximately 79.2% (n=198) of the participants were either overweight or obese, and 40.5% (n=95) of those who were overweight had dyslipidaemia. Comorbid hypertension was found among 47.6% (n=119) of the participants, among whom 48.7% (n=114) had abnormal lipid profiles (##TAB##0##Table 1##).</p>", "<p>The mean serum levels of the lipid parameters and their ratios with respect to age and sex were also calculated (##FIG##0##Figure 1##, ##TAB##0##Table S1## and ##TAB##1##Table S2##). Student’s t-test was used to compare the mean lipid levels according to sex. The mean serum levels of all lipid parameters (except TCs) were significantly higher among the male patients than among the female patients (P<0.05). The mean serum HDL-C levels were significantly lower among the male patients (1.29±0.35 mmol/L) than among the female patients (1.47±0.40 mmol/L) (P<0.01) (##FIG##0##Figure 1##). One-way ANOVA followed by Tukey’s post hoc test was conducted to explore the effect of age on the mean serum levels. The analysis revealed that the mean serum HDL-C levels significantly differed across the four age groups (P<0.05). Tukey’s post hoc test demonstrated significant differences in the serum HDL-C level between the patients aged <40 and ≥61 years but no significant differences in the serum levels of the other lipid parameters. When the sample was stratified according to sex, no significant age group-specific variations in the mean serum lipid levels were observed between the male and female patients (P>0.05) (##FIG##0##Figure 1## and ##TAB##1##Table S2##).</p>", "<title>Patterns of dyslipidaemia</title>", "<p>The patterns of dyslipidaemia among the male and female subjects with borderline diabetes are illustrated in ##TAB##1##Table 2##. The most prominent lipid abnormality was isolated dyslipidaemia, affecting 38.8% (n=97) of the participants with a high LDL-C level. The least common lipid abnormality was a low HDL-C level, affecting 1.6% (n=4) of the participants. Combined dyslipidaemia was the second most common pattern of dyslipidaemia found, with high LDL-C and TG levels comprising the majority (22.8%, n=57) of this pattern, followed by high LDL-C and low HDL-C levels (14%, n=35) and high TG and low HDL-C levels (2.8%, n=7). The prevalence of combined dyslipidaemia of high LDL-C and TG levels was significantly higher among the male participants (29.8%, n=39) than among the female participants (15.1%, n=18) (P=0.006). In contrast, combined dyslipidaemia of high LDL-C and low HDL-C levels was significantly more prevalent among the female participants (20.2%, n=24) than among the male participants (8.4%, n=11) (P=0.007). No significant difference was observed in the other patterns between the male and female participants (P>0.05).</p>", "<title>Factors associated with dyslipidaemia</title>", "<p>In the multivariable logistic regression analysis (##TAB##2##Table 3##), the male sex was found to be significantly (P=0.02) associated with hypertriglyceridaemia (adjusted odds ratio [AOR] = 1.86, 95% CI=1.09–3.1). The risk of having low HDL-C levels was significantly higher among the male participants than among the female participants (AOR=0.57, 95% CI=0.2–0.9) (P<0.05). The DBP was significantly associated with a low HDL-C level (A0R=2.09, 95% CI=1.0–4.1) (P=0.03). No significant association of the other variables with high TG, TC, and LDL-C levels was observed among the participants (P>0.05).</p>" ]	[ "<title>Discussion</title>", "<p>The majority of the participants had LDL-C levels higher than the recommended range in the Malaysian guidelines for dyslipidaemia management.<sup>##UREF##2##13##</sup> Herein, the most and least common lipid abnormalities were a high LDL-C level and a low HDL-C level, respectively. Similar findings were reported in previous studies conducted among Southeast Asian<sup>##REF##30641707##10##</sup> and Middle Eastern<sup>##REF##31920353##16##</sup> patients with hyperglycaemia, wherein the most frequent form of dyslipidaemia was a high LDL-C level with a frequency of 48.3% and 49%, respectively. High LDL-C levels (≥2.6 mmol/L) are known to pose a substantial risk for atherogenesis and the development of near-future CVD and coronary heart disease events.<sup>##REF##11955024##17##</sup> Similarly, low HDL-C levels (<1.0 mmol/L) are known to play a pivotal role in the atherogenic process. The coexistence of these two lipid abnormalities affected nearly 14% of the participants in this study, with the women (20.2%) being more affected than the men (8.4%). The prevalence of combined dyslipidaemia of high LDL-C and TG levels was significantly higher among the men than among the women (29.8% vs 15.1%). These findings are consistent with those of the study conducted among Nepalese patients with type 2 diabetes with a known prevalence of combined dyslipidaemia of high LDL-C and TG levels, which was significantly higher among men than women.<sup>##REF##28057050##18##</sup> In the present study, mixed and combined dyslipidaemia were observed in only fractions of the participants. Similarly, studies conducted among African<sup>##UREF##5##19##</sup> and Southeast Asian subjects with borderline diabetes<sup>##REF##27872574##20##</sup> showed that mixed dyslipidaemia was present in only 16% and 17.6%, respectively. Co-existing lipid abnormalities along with insulin resistance and hyperglycaemia for a longer duration may increase the risk of CVDs, suggesting the need to rectify such abnormalities at the initial stage.</p>", "<p>The mean serum levels of all lipid parameters (except HDL-C) were significantly higher among the male subjects with borderline diabetes than among their female counterparts in this study. This finding agrees with that in studies conducted among Chinese,<sup>##REF##30325950##7##</sup> Iranian<sup>##REF##25709992##21##</sup> and Indian<sup>##REF##27731552##22##</sup> subjects with borderline diabetes, wherein the mean serum levels of LDL-C and TG were higher, and the mean serum level of HDL-C was lower among male patients than among female patients. Differences in the sex hormones and body fat distribution between men and women could explain such discrepancies in the lipid profiles. The high prevalence among men may also be attributed to the lack of a cardio-protective effect of the female sex hormone, high visceral body fat distribution accompanied by reduced lipid metabolism and lipoprotein kinetics among men.<sup>##REF##17977473##23##</sup></p>", "<p>As age is a non-modifiable risk factor of CVDs,<sup>##REF##32688241##24##</sup> its effect on the serum lipid profile was also evaluated in this study across the four age groups using one-way ANOVA. A significant difference was observed only in the serum HDL-C level between the patients aged <40 and ≥61 years, irrespective of sex. This finding is comparable with other reports.<sup>##REF##28057050##18##,##REF##27872574##20##</sup> Further, the lipid levels among the participants were noted to increase with age, peaked at the age of 51–60 years and declined beyond the age of 60 years. Similarly, a previous review documented that the serum lipid levels (including TC and LDL-C) among older adults from 10 different countries<sup>##REF##23730531##25##</sup> were notably increased from the age of puberty to the age of 55 years, followed by a decline beyond the age of 60 years. The effect of such decline in the mean lipid levels with advancing age (beyond 60 years) could be attributed to a reduction in the cholesterol synthesis owing to the decline in the liver function with increasing age.<sup>##REF##23730531##25##</sup> Conversely, the HDL-C level increases with increasing age. Body weight and eating habits mainly including dietary fat intake may have a significant effect on HDL-C levels. Body weight has been reported to decline with age among Malaysian patients.<sup>##REF##28049454##26##</sup> Shift of dietary fat intake from saturated to non-saturated fatty lipids relative to increasing age could also explain the higher HDL-C levels among older adults than among young adults.<sup>##REF##28049454##26##</sup></p>", "<p>The risk factors associated with dyslipidaemia were also evaluated in this study using multivariable analysis. A high TG level was found to be significantly associated with the male sex. This finding is consistent with other reports that male patients had significantly high LDL-C and non-HDL-C levels.<sup>##REF##28057050##18##</sup> The high TG and LDL-C levels among men could be explained in part by the differences in the sex hormones and the central fat distribution between men and women.<sup>##REF##27159875##27##</sup> Oestrogen generally reduces the circulating TG and LDL-C levels but increases the HDL-C level, resulting in the inherited cardio-protective effect in women.<sup>##REF##7825640##28##</sup> Differences in the lipid metabolism and kinetics of lipoprotein also account for the sexual dimorphism in the plasma lipid levels between sexes.<sup>##REF##7825640##28##</sup> Women generally have a strong anti-inflammatory immune profile that acts as a compensatory mechanism to limit increases in the blood pressure, ultimately helping control dyslipidaemia.<sup>##REF##7825640##28##</sup> Women are also metabolically inclined to store fat in subcutaneous tissues rather than in the abdominal region.<sup>##REF##32688241##24##</sup> In contrast, men tend to store adipose fat preferentially in visceral tissues and the abdominal region. A high proportion of fat as visceral adipose tissue is known as a significant predictor of dyslipidaemia.<sup>##REF##17977473##23##</sup> This could explain why the men were more susceptible to dyslipidaemia than the women in our study. The women tended to show a good lipid profile apart from the low HDL-C level, which was significantly associated with the female sex. This might be linked to several other factors such as the onset of menopause, which mimics low HDL-C levels among women, or the intake of high-fat diet or low level of physical activity.<sup>##REF##31941004##29##</sup> However, the actual association remains uncertain, as these confounders were not evaluated in this study. A low HDL-C level was strongly associated with an increased DBP in this study. Similarly, a large population-based study reported that the HDL-C and total serum cholesterol levels were independently and positively associated with the DBP.<sup>##REF##2013148##30##</sup> This effect may be attributed to the insulin resistance already established in the prediabetic state, significantly contributing to increases in visceral adiposity, hypertension, glucose intolerance, and ultimately, dyslipidaemia.<sup>##REF##32688241##24##</sup></p>", "<title>Limitations</title>", "<p>The present study has some limitations. First, the study was conducted at a single healthcare centre in Malaysia, limiting the generalisability of the findings to the whole Malaysian population with borderline diabetes. Second, the study did not analyse the types and effects of lipid-lowering treatment among patients with dyslipidaemia. Data on antihypertensive drugs were not included, as this retrospective study depended mainly on data obtained from patient records. The findings might have been confounded by other factors, such as nutrition, physical activity, and concomitant morbidities. This aspect should be taken into account by future studies.</p>" ]	[ "<title>Conclusion</title>", "<p>A high LDL-C level was the most common pattern of dyslipidaemia, followed by high LDL-C and TG levels among the subjects with borderline diabetes. A high TG level was associated with the male sex, while a low HDL-C level was strongly associated with the female sex, suggesting a high risk of future CVDs among these populations. These findings highlight the extensive need for early screening of the lipid profiles of subjects with borderline diabetes. Effective interventions and targeted treatment approaches should also be implemented by healthcare professionals to prevent poor cardio-metabolic profiles and achieve optimum care.</p>" ]	[ "<title>Abstract</title>", "<title>Introduction:</title>", "<p>Diabetes is closely linked to cardiovascular diseases, with diabetic dyslipidaemia serving as an established marker of the acceleration of complications, contributing to an increased cardiovascular risk among patients. Timely detection and early characterization of lipid abnormalities can help clinicians in implementing effective preventive measures. This study aimed to determine the patterns and associated factors of dyslipidaemia among Malaysian subjects with borderline diabetes.</p>", "<title>Methods:</title>", "<p>A retrospective study was conducted among subjects with borderline diabetes aged ≥18 years who visited a primary healthcare centre at Universiti Sains Malaysia from January 2017 to December 2018. Sociodemographic, clinical and laboratory data were obtained from electronic medical records. Data were analysed using SPSS version 25.</p>", "<title>Results:</title>", "<p>A total of 250 participants with borderline diabetes were included in the analysis. Of them, 93.6% (n=234) had lipid abnormalities. Isolated dyslipidaemia characterised by a high low-density lipoprotein cholesterol (LDL-C) level (38.8%, n=97) was the most common pattern found, followed by combined dyslipidaemia of high LDL-C and triglyceride (TG) levels (22.8%, n=57). The male sex was found to be significantly associated with hypertriglyceridemia (adjusted odds ratio [AOR] = 1.86, 95% confidence interval [CI] =1.09–3.1)(P=0.02). Diastolic blood pressure ≥90mmHg was significantly associated with a low HDL-C level (A0R=2.09, 95% CI=1.0–4.1) (P=0.03).</p>", "<title>Conclusion:</title>", "<p>The majority of subjects with borderline diabetes have lipid abnormalities. Specifically, isolated dyslipidaemia characterised by a high LDL-C level is alarmingly prevalent. Further large-scale robust studies are needed to confirm the present findings.</p>", "<title>Keywords</title>" ]	[]	[ "<title>Acknowledgments</title>", "<p>We would like to thank Pusat Sejahtera USM staff members for their kind cooperation gave along the data collection process.</p>", "<title>Author Contributions</title>", "<p>SS conceptualised the study, collected and statistically analysed the data and drafted the manuscript. HZ conceptualised the study and reviewed technical aspects of the manuscript. SNH and SMSG critically reviewed the manuscript. NBAW and ABH provided administrative, technical, and logistic support during data collection. All authors read and approved the final version of the manuscript.</p>", "<title>Ethical approval</title>", "<p>The study was approved by the Research Advisory Committee (Jawatankuasa Etika Penyelidikan Manusia USM; reference no.: USM/JEPeM/18040197).</p>", "<title>Conflicts of interest</title>", "<p>All authors declare no conflicts of interest.</p>", "<title>Funding</title>", "<p>The study did not receive any grant from funding agencies in public commercial or not-for-profit sectors.</p>", "<title>Data sharing statement</title>", "<p>The datasets used and/or analysed during the current study are available from the corresponding author upon reasonable request.</p>" ]	[ "<fig position=\"float\" id=\"f1\"><label>Figure 1</label><caption><title>Sex- and age-specific mean lipid levels. Student’s t-test was used to compare the mean lipid levels according to sex. One-way ANOVA (Analysis of variance) was used to compare the mean serum lipid levels across the four age groups. Significant differences were observed in the serum HDL-C level across the age groups in one-way ANOVA (P<0.05) (##TAB##1##Table S2##).</title><p><italic toggle=\"yes\">Abbreviations;</italic> TC: Total cholesterol; TG: Triglycerides; HDL-C; High-density lipoprotein cholesterol; LDL-C: Low-density lipoprotein cholesterol</p></caption></fig>" ]	[ "<table-wrap position=\"float\" id=\"t1\"><label>Table 1</label><caption><title>General characteristics of the participants (N=250).</title></caption><table frame=\"box\" rules=\"groups\"><colgroup span=\"1\"><col align=\"left\" span=\"1\"/><col align=\"left\" span=\"1\"/><col align=\"left\" span=\"1\"/><col align=\"left\" span=\"1\"/><col align=\"left\" span=\"1\"/><col align=\"left\" span=\"1\"/></colgroup><thead valign=\"bottom\"><tr><th rowspan=\"2\" align=\"left\" valign=\"top\" colspan=\"1\">\n<p>Variables</p>\n</th><th rowspan=\"2\" align=\"left\" valign=\"top\" colspan=\"1\">\n<p>Categories</p>\n</th><th rowspan=\"2\" align=\"left\" valign=\"top\" colspan=\"1\">\n<p>Overall n(%)</p>\n</th><th colspan=\"3\" align=\"center\" valign=\"top\" rowspan=\"1\">\n<p>Dyslipidaemia n(%)</p>\n</th></tr><tr><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Yes (%)</p>\n</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>No (%)</p>\n</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>P-value</p>\n</th></tr></thead><tbody valign=\"top\"><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Sex</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Male</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>131 (52.4%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>122 (52.1%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>9 (56.2%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.75</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Female</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>119 (47.6%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>112 (47.9%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>7 (43.8%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td></tr><tr><td rowspan=\"2\" align=\"left\" valign=\"top\" colspan=\"1\">\n<p>Ethnicity</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Malay</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>195 (78.0%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>184 (78.6%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>11 (68.8%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.54</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Indian</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>37 (14.8%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>33(14.1%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>4 (25%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Chinese</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>18 (7.2%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>17 (7.3%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>1 (6.2%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td></tr><tr><td rowspan=\"2\" align=\"left\" valign=\"top\" colspan=\"1\">\n<p>Age (year)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p><40</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>92 (36.8%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>90 (38.5%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>2 (12.5%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.14</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>41-50</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>51 (20.4%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>47 (20.1%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>4 (25%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>51-60</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>81 (32.4%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>74(31.6%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>7 (43-8%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>≥61</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>26(10.4%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>23 (9-8%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>3(18.7%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td></tr><tr><td rowspan=\"2\" align=\"left\" valign=\"top\" colspan=\"1\">\n<p>Occupation</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Staff</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>201 (80.4%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>192 (82.0%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>9 (56.25%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.07</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Student</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>8 (3.2%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>7 (3.0%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>1 (6.25%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Unemployed/pensioner</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>41 (16.4%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>35(15.0%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>6 (37.5%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td></tr><tr><td rowspan=\"2\" align=\"left\" valign=\"top\" colspan=\"1\">\n<p>BMI (kg/m<sup>2</sup>)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Normal</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>52 (20.8%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>48 (20.5%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>4 (25.0%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.78</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Overweight</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>100 (40.0%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>93 (39.7%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>7 (43.8%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Obese</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>98 (39.2%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>93 (39.8%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>5(31.2%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td></tr><tr><td rowspan=\"2\" align=\"left\" valign=\"top\" colspan=\"1\">\n<p>Blood pressure status</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Normotensive</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>131 (52.4%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>120 (51.3%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>11 (68.75%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.17</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Hypertensive</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>119 (47.6%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>114 (48.7%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>5(31.25%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"t2\"><label>Table 2</label><caption><title>Patterns of dyslipidaemia among subjects with borderline diabetes (N=250).</title></caption><table frame=\"box\" rules=\"groups\"><colgroup span=\"1\"><col align=\"left\" span=\"1\"/><col align=\"left\" span=\"1\"/><col align=\"left\" span=\"1\"/><col align=\"left\" span=\"1\"/><col align=\"left\" span=\"1\"/><col align=\"left\" span=\"1\"/><col align=\"left\" span=\"1\"/><col align=\"left\" span=\"1\"/></colgroup><thead valign=\"bottom\"><tr><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</th><th colspan=\"3\" align=\"left\" valign=\"top\" rowspan=\"1\">\n<p>Isolated dyslipidaemia</p>\n</th><th colspan=\"3\" align=\"left\" valign=\"top\" rowspan=\"1\">\n<p>Combined dyslipidaemia</p>\n</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Mixed dyslipidaemia</p>\n</th></tr><tr><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>TG level of ≥1.7 mmol/L</p>\n</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>HDL-C level of ≤1.0/1.2 mmol/L</p>\n</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>LDL-C level of ≥2.6 mmol/L</p>\n</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>LDL-C level of ≥2.6 mmol/L + TG level of ≥1.7 mmol/L</p>\n</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>LDL-C level of ≥2.6 mmol/L + HDL-C level of ≤1.0/1.2 mmol/L</p>\n</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>TG level of ≥1.7 mmol/L + HDL-C level of ≤1.0/1.2 mmol/L</p>\n</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>LDL-C level of ≥2.6 mmol/L + TG level of ≥1.7 mmol/L + HDL-C level of ≤1.0/1.2 mmol/L</p>\n</th></tr></thead><tbody valign=\"top\"><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Men (n=131)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>2 (1.5%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>1 (0.76%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>51 (38.9%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>39 (29.8%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>11 (8.4%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>3 (2.3%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>15 (11.5%)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Women (n=119)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>5 (4.2%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>3 (2.5%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>46 (38.6%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>18 (15.1%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>24 (20.2%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>4 (3.4%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>12 (10.1%)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Total (N=250)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>7 (2.8%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>4 (1.6%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>97 (38.8%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>57 (22.8%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>35 (14.0%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>7 (2.8%)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>27 (10.8%)</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>P-value</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.18</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.27</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.72</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>\n<bold>0.006</bold>\n</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>\n<bold>0.007</bold>\n</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.89</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.7</p>\n</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"t3\"><label>Table 3</label><caption><title>Univariable and multivariable logistic regression analyses of the risk factors associated with various categories of dyslipidaemia among the subjects with borderline diabetes.</title></caption><table frame=\"box\" rules=\"groups\"><colgroup span=\"1\"><col align=\"left\" span=\"1\"/><col align=\"left\" span=\"1\"/><col align=\"left\" span=\"1\"/><col align=\"left\" span=\"1\"/><col align=\"left\" span=\"1\"/><col align=\"left\" span=\"1\"/><col align=\"left\" span=\"1\"/><col align=\"left\" span=\"1\"/><col align=\"left\" span=\"1\"/></colgroup><thead valign=\"bottom\"><tr><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</th><th colspan=\"4\" align=\"left\" valign=\"top\" rowspan=\"1\">\n<p>High TC level</p>\n</th><th colspan=\"4\" align=\"left\" valign=\"top\" rowspan=\"1\">\n<p>High TG level</p>\n</th></tr><tr><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</th><th colspan=\"2\" align=\"left\" valign=\"top\" rowspan=\"1\">\n<p>Univariable</p>\n</th><th colspan=\"2\" align=\"left\" valign=\"top\" rowspan=\"1\">\n<p>Multivariable</p>\n</th><th colspan=\"2\" align=\"left\" valign=\"top\" rowspan=\"1\">\n<p>Univariable</p>\n</th><th colspan=\"2\" align=\"left\" valign=\"top\" rowspan=\"1\">\n<p>Multivariable</p>\n</th></tr><tr><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>COR (95% CI)</p>\n</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>P-value</p>\n</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>AOR (95% CI)</p>\n</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>P-value</p>\n</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>COR (95% CI)</p>\n</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>P-value</p>\n</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>AOR (95% CI)</p>\n</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>P-value</p>\n</th></tr></thead><tbody valign=\"top\"><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Male sex</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>1.28 (0.7-2.2)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.37</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>--</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>--</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>1.80 (1.1-3.1)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.01</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>1.86 (1.09-3.1)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.02</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Age of >40 years</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>1.14 (0.6-2.0)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.65</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>--</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>--</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>1.15 (0.6-1.9)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.59</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>--</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>--</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>BMI of >23 kg/m<sup>2</sup></p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.58 (0.2-1.2)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.16</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.58 (0.2-1.2)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.16</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.88 (0.4–1.6)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.69</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>--</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>--</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Malay ethnicity</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>1.29 (0.6-2.5)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.43</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>--</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>--</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>1.49 (0.7-2.8)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.22</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>1.44 (0.7-2.8)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.28</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Unemployment</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>1.2 (0.6-2.2)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.55</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>--</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>--</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.68 (0.3-1.2)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.19</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.81 (0.4-1.4)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.49</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>SBP of >140 mmHg</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>1.59 (0.8-2.8)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.12</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>1.6 (0.8-2.9)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.11</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.98 (0.5-1.6)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.94</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>--</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>--</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>DBP of >90 mmHg</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>1.34 (0.6-2.6)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.38</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>--</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>--</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>1.36 (0.7-2.4)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.3</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>--</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>--</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>FPG level</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.98 (0.9-1.0)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.21</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.98 (0.9-1.0)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.19</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>1.0 (0.9-1.02)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.96</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>--</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>--</p>\n</td></tr><tr><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</th><th colspan=\"4\" align=\"left\" valign=\"top\" rowspan=\"1\">\n<p>Low HDL-C level</p>\n</th><th colspan=\"4\" align=\"left\" valign=\"top\" rowspan=\"1\">\n<p>High LDL-C level High TG level</p>\n</th></tr><tr><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</th><th colspan=\"2\" align=\"left\" valign=\"top\" rowspan=\"1\">\n<p>Univariable</p>\n</th><th colspan=\"2\" align=\"left\" valign=\"top\" rowspan=\"1\">\n<p>Multivariable</p>\n</th><th colspan=\"2\" align=\"left\" valign=\"top\" rowspan=\"1\">\n<p>Univariable</p>\n</th><th colspan=\"2\" align=\"left\" valign=\"top\" rowspan=\"1\">\n<p>Multivariable</p>\n</th></tr><tr><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p> </p>\n</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>COR (95% CI)</p>\n</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>P-value</p>\n</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>AOR (95% CI)</p>\n</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>P-value</p>\n</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>COR (95% CI)</p>\n</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>P-value</p>\n</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>AOR (95% CI)</p>\n</th><th align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>P-value</p>\n</th></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Male sex</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.61 (0.3-1.0)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.08</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.57 (0.2-0.9)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.02</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>1.1 (0.5-2.3)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.77</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>--</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>--</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Age of ≥40 years</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.57 (0.3-1.01)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.05</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.71 (0.3-1.3)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.27</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.56 (0.2-1.3)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.20</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.56 (0.2-1.3)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.20</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>BMI of ≥23 kg/m<sup>2</sup></p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>1.15 (0.5-2.2)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.68</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>--</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>--</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.73 (0.2-2.0)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.55</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>--</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>--</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Malay ethnicity</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>1.27 (0.6-2.5)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.49</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>--</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>--</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>1.33 (0.5-3.1)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.51</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>--</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>--</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>Unemployment</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.57 (0.3-1.07)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.08</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.58 (0.2-1.1)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.11</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.64 (0.2-1.4)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.27</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>--</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>--</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>SBP of ≥140 mmHg</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.68 (0.3-1.2)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.19</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.57 (0.3-1.0)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.08</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>1.13 (0.5-2.4)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.76</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>--</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>--</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>DBP of ≥90 mmHg</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>1.62 (0.8-2.99)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.11</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>2.09 (1.0–4.1)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.03</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>1.09 (0.4–2.6)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.84</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>--</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>--</p>\n</td></tr><tr><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>FPG level</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>1.0 (0.9-1.04)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.20</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>1.02 (0.9–1.0)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.09</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.9 (0.9–1.02)</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>0.57</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>--</p>\n</td><td align=\"left\" valign=\"top\" rowspan=\"1\" colspan=\"1\">\n<p>--</p>\n</td></tr></tbody></table></table-wrap>" ]	[]	[ "<boxed-text position=\"anchor\"><p>\n<bold>How does this paper make a difference in general practice?</bold>\n</p><list list-type=\"bullet\"><list-item><p>Diabetic dyslipidaemia is a major public health problem in developing countries and an independent predictor of cardiovascular diseases.</p></list-item><list-item><p>The findings provide useful insights into dyslipidaemia patterns commonly found among subjects with borderline diabetes.</p></list-item><list-item><p>High low-density lipoprotein cholesterol levels alone and in combination with high triglyceride levels are the most common patterns found.</p></list-item><list-item><p>High triglyceride levels and low high-density lipoprotein cholesterol levels are associated with the male and female sexes, respectively, suggesting a high risk of future cardiovascular diseases among them.</p></list-item><list-item><p>This study provides baseline findings that may help clinicians in deciding and implementing targeted treatment approaches to prevent poor metabolic profiles.</p></list-item></list></boxed-text>" ]	[]	[]	[]	[]	[ "<table-wrap-foot><fn id=\"t1n1\"><p>The chi-square test was used to calculate the frequencies and percentages of the general characteristics of the participants according to the dyslipidaemia status.</p><p>P<0.05 was considered statistically significant. BMI=body mass index</p></fn></table-wrap-foot>", "<table-wrap-foot><fn id=\"t2n1\"><p>Data are expressed as frequencies and percentages.</p><p>TG=triglyceride; HDL-C=high-density lipoprotein cholesterol; LDL-C=low-density lipoprotein cholesterol</p></fn></table-wrap-foot>", "<table-wrap-foot><fn id=\"t3n1\"><p>AOR=adjusted odds ratio; BMI=body mass index; CI=confidence interval; COR=crude odds ratio; DBP=diastolic blood pressure; FPG=fasting plasma glucose; SBP=systolic blood pressure; TC=total cholesterol; TG=triglyceride; HDL-C=high-density lipoprotein cholesterol; LDL-C=low-density lipoprotein cholesterol</p></fn></table-wrap-foot>" ]	[ "<graphic xlink:href=\"MFP-18-73-g1\" position=\"float\"/>" ]	[]	[{"label": ["8."], "surname": ["Rabeya", "Nabi", "Chowdhury", "Zaman", "Khan", "Hawlader"], "given-names": ["R", "MH", "AB", "S", "MNM", "MDH"], "article-title": ["Epidemiology of Dyslipidemia among Adult Population of Bangladesh."], "source": ["Rom JDiabetes, Nutr Metab Dis."], "year": ["2019"], "volume": ["26"], "issue": ["2"], "fpage": ["99"], "lpage": ["106"], "pub-id": ["10.2478/rjdnmd-2019-0011"]}, {"label": ["11."], "person-group": ["\n"], "surname": ["Arifin"], "given-names": ["WN"], "article-title": ["Introduction to sample size calculation."], "source": ["Educ Med J."], "year": ["2013"], "volume": ["5"], "issue": ["2"], "fpage": ["89"], "lpage": ["96"], "pub-id": ["10.5959/eimj.v5i2.130"]}, {"label": ["13."], "person-group": ["\n"], "collab": ["Ministry of Health Malaysia."], "source": ["Management of dyslipidaemia. MOH"], "publisher-name": ["National Heart association Malaysia, Academy of medicine Malaysia"], "comment": ["Published"], "year": ["2017", "2019"], "month": ["February"], "day": ["20"], "ext-link": ["http://www.acadmed.org.my/index.cfm?&menuid=67"]}, {"label": ["14."], "person-group": ["\n"], "collab": ["Ministry of health of Malaysia."], "source": ["Management of Hypertension."], "edition": ["Vol 5th Edition"], "year": ["2018", "2023"], "month": ["July"], "day": ["11"], "ext-link": ["http://www.acadmed.org.my/index.cfm?&menuid=67"]}, {"label": ["15."], "person-group": ["\n"], "collab": ["Minisry of Health Malaysia."], "source": ["Clinical practice guidelines on management of obesity."], "comment": ["Published"], "year": ["2003", "2019"], "month": ["June"], "day": ["6"], "ext-link": ["www.acadmed.org.my/view_file.cfm?fileid=183"]}, {"label": ["19."], "surname": ["Daya", "Bayat", "Raal"], "given-names": ["R", "Z", "FJ"], "article-title": ["Prevalence and pattern of dyslipidaemia in type 2 diabetes mellitus patients at a tertiary care hospital."], "source": ["J Endocrinol Metab Diabetes South Africa."], "year": ["2017"], "volume": ["22"], "issue": ["3"], "fpage": ["31"], "lpage": ["35"], "pub-id": ["10.1080/16089677.2017.1360064"]}]	{ "acronym": [], "definition": [] }	30	CC BY	no	2024-01-13 00:02:19	Malays Fam Physician. 2023 Dec 29; 18:73	oa_package/35/88/PMC10781612.tar.gz
PMC10781615	38213345	[ "<title>Introduction</title>", "<p>An osteochondroma (OC), also referred to as osteocartilaginous exostosis, is a benign neoplasm within the skeletal system [##REF##29132383##1##,##REF##33041593##2##]. Typically manifesting in endochondral bones, this condition is often devoid of symptoms and presents as a protruding growth on the bone surfaces [##REF##32089940##3##]. Approximately 85% of OCs occur as single lesions (pedunculated or sessile types), while the remaining 15% are multiple OCs, often as hereditary disorders associated with multiple exostoses (HME) [##REF##33622860##4##,##REF##31842965##5##].</p>", "<p>There are approximately 35.8% of benign bone tumors, with a reported 2% recurrence rate [##REF##29132383##1##,##REF##35968160##6##,##REF##33179614##7##]. The OC is a rarity in the head and neck region. It has been observed in diverse locations such as the skull base, maxillary sinus, zygomatic arch, and mandible [##REF##33041593##2##,##REF##25932269##8##, ####UREF##0##9##, ##UREF##1##10##, ##REF##25737946##11####25737946##11##]. The incidence in the craniofacial region is 0.6%. The mean patient age is 39.7 years and the peak age range is in the fourth decade. Female incidence is reported to be greater than males [##UREF##0##9##]. There are a total of 98 cases of mandibular condylar OCs reported between 1927 and 2010 [##REF##25737946##11##]. The OC has been reported to involve the mandibular condyle and coronoid process. Bilateral condylar involvement has also been noted [##REF##25932269##8##,##UREF##1##10##]. The OC involving the condyle has been linked to difficulties in mouth opening, dental malocclusion, and facial asymmetry [##REF##33041593##2##,##REF##32089940##3##,##REF##25737946##11##].</p>", "<p>In this report, we showcase a scenario involving a 27-year-old woman diagnosed with an OC located on the mandibular condyle. We present the clinical and histopathological observations, accompanied by an exploration of existing literature on the subject.</p>" ]	[]	[]	[ "<title>Discussion</title>", "<p>In 2002, the World Health Organization (WHO) defined the OC as a bony projection covered by cartilage, originating from the external surface of a bone, and possessing a marrow cavity that is connected to that of the underlying bone [##REF##33179614##7##]. This condition primarily emerges during the first to third decades of life, showing no significant gender preference, and typically involves the metaphysis of long bones [##REF##25932269##8##].</p>", "<p>Approximately 1% of OCs manifest in the head and neck region [##REF##35968160##6##]. Within the head and neck area, the most prevalent locations are the coronoid process and the mandibular condyle [##REF##33041593##2##]. Trauma and inflammation are considered predisposing factors for their development [##REF##33041593##2##,##UREF##0##9##]. In terms of the tumor's location, around 52% of OCs occur on the medial aspect of the condyle, 20% are positioned anteriorly, and only 1% are found laterally on the condyle [##UREF##1##10##].</p>", "<p>The OC of the mandibular condyle is typically accompanied by various signs and symptoms, encompassing facial asymmetry, malocclusion, cross-bite on the opposite side, lateral open-bite on the affected side, deviation during mouth opening, restricted movement, discomfort and clicking, and alterations in condylar morphology [##REF##35968160##6##,##UREF##1##10##,##REF##25737946##11##]. Our patient presented with mandibular deviation, altered bite, and restricted movement. Alterations in the condyle were observed during radiological investigations.</p>", "<p>A hypothesis concerning the development of an OC within the condyle suggests the presence of anomalous foci of epiphyseal cartilage on the surface of the bone. Accordingly, the growth of this tumor could be attributed to stress within the insertion area of the lateral pterygoid muscle, wherein a concentration of cells with cartilaginous potential accumulates. This hypothesis gains support from the observation that the tumor most often emerges on the medial aspect of the condyle [##REF##35968160##6##]. Hyperplasia of cartilaginous cells by tensional forces is the strongest hypothesis, because the condylar OC occurs commonly on the medial side (57%), followed by the anterior side (20%), and almost none occur in the lateral or superior aspects (<1%) [##REF##28361031##12##].</p>", "<p>When considering possible diagnoses, it is important to differentiate OCs from conditions such as condylar giant cell tumors, condylar hyperplasia, fibro-osseous lesions, vascular malformations, osteomas, chondromas, osteochondromatosis, and osteoblastomas [##REF##29132383##1##,##UREF##0##9##,##REF##25737946##11##]. Osteomas usually present as a pedunculated bony mass. Chondromas show irregular radiolucent mass with mature cartilage. Osteochondromatosis shows hot spots in areas other than the mandibular condyle on bone scans [##REF##28361031##12##]. Fibro-osseous lesions, giant cell tumors, condylar hyperplasia, vascular malformations, and osteoblastomas could be ruled out directly with histopathological findings. Our case presented with classical histopathological findings of OCs.</p>", "<p>Table ##TAB##0##1## outlines the key differences between OCs, unilateral condylar hyperplasia, and osteomas in terms of their nature, origin, clinical presentation, radiographic appearance, histopathology, and treatment considerations [##REF##33179614##7##,##UREF##1##10##] (Table ##TAB##0##1##).</p>", "<p>Table ##TAB##1##2## provides an overview of the diverse diagnostic methods available for radiographic diagnosis of the OC [##REF##33041593##2##,##UREF##0##9##]. </p>", "<p>Local recurrence for solitary OCs in instances involving long bones has been documented to be 2% [##REF##29132383##1##,##REF##35968160##6##,##REF##28361031##12##]. Kwon et al. reported that the recurrence rate of mandibular condylar OC was 1.3% (3 out of 236 cases) [##REF##28361031##12##].</p>", "<p>Sun et al. have reported a 3D evaluation of the OC [##REF##31473057##13##]. Saito et al. have reported a bilateral OC [##REF##11848198##14##]. Zhou et al. have specified two growth patterns including an OC with stalk or an OC with a sessile base [##REF##25932269##8##]. If it is a mild presentation, the condition can be evaluated through regular clinical monitoring and radiological assessments [##REF##25932269##8##]. Depending on factors such as symptoms, duration, and the size of the lesion, surgical approaches for OCs vary. These can include either solely removing the tumor or performing condylectomy in combination with tumor excision, followed by reconstructive surgery [##REF##35968160##6##,##REF##25932269##8##,##UREF##2##15##].</p>", "<p>The main treatment modality is condylectomy but it may result in lateral open bite on the contralateral side. Hence, simultaneous condylar reconstruction is required to maintain the ramus height and function of the TMJ. Reconstruction could be performed with grafts, distraction osteogenesis, and vertical ramus osteotomy [##REF##24856953##16##]. Costochondral graft is routinely used for reconstruction but requires surgery at the second site. If artificial grafts are placed as replacements, additional surgery is required for graft retrieval. Distraction osteogenesis could restore ramus height and maintain occlusion by condylar reconstruction. Its drawback is poor long-term stability showing condylar asymmetry. Vertical ramus osteotomy avoids surgery on the second site and has reduced risks. Its shortcoming is that the mandibular contour is damaged on the treated side [##REF##24856953##16##]. Mamatha et al. have reported that OCs can occur involving the mandibular angle, mandibular symphysis, and even in soft tissue without any connection to the mandible [##UREF##3##17##].</p>", "<p>Some authors suggest that the condyle can be preserved as much as possible as osteochondroma has a recurrence rate of just 2% in solitary osteochondroma cases of long bones. Preserving the condyle has the advantage of preserving the vertical ramus height and stable occlusion which will eliminate the need for reconstruction [##REF##28361031##12##]. We have summarized various therapeutic possibilities of OCs involving the mandibular condyle (Table ##TAB##2##3##).</p>", "<p>The majority of the literature suggests that the OC has a better prognosis and minimal recurrence rate but with constant clinical/radiological follow-up [##REF##33179614##7##,##REF##31473057##13##,##REF##11848198##14##,##UREF##2##15##,##UREF##3##17##].</p>", "<p>Mahajan et al. have recommended frequent measurement of cartilage cap thickness to rule out recurrence [##REF##35968160##6##]. Kwon et al. have also recommended further studies to identify the actual recurrence of the OC, as they have reported recurrence of the OC with conservative management [##REF##28361031##12##].</p>", "<p>Kishore et al. have recommended periodic measurement of mandibular length in younger patients to identify any discrepancies [##REF##24191218##18##]. Friedrich et al. have reported negative immunohistochemical assessment with Insulin-like growth factor in a case of OC [##REF##23060585##19##]. Gardner et al. have discussed various surgical options for OCs [##UREF##4##20##]. Chen et al. have classified OCs of the mandibular condyle based on the CT findings into Type-1 protruding expansion and Type-2 globular expansion [##REF##25119412##21##]. Type-1 could be treated with local excision and Type-2 requires condylectomy.</p>", "<p>There are EXT1 homozygous deletions in a solitary OC, whereas both EXT1 and EXT2 genes have been identified in multiple OCs [##REF##31842965##5##]. Somatic mutations have been identified in chromosomes 8 and 11 [##REF##24082753##22##].</p>", "<p>In certain situations, there exists potential for malignant transformation, with low-grade chondrosarcomas being the most prevalent form of a malignant tumor to develop [##REF##29132383##1##]. Malignant transformation is reported to be 1-2% in solitary OCs and 2-25% in osteochondromatosis [##REF##24191218##18##]. de Souza et al. have reported that rapid size increase, continuous growth, local pain, or erythema in a previously asymptomatic OC raises suspicion of a malignant change [##REF##26229862##23##]. Histopathological changes with architectural loss of cartilage, increased mitosis, cellular atypia, and necrosis indicate a malignant change.</p>", "<p>HME is a rare genetic disorder and it is also called hereditary multiple osteochondromas, hereditary deforming dyschondroplasia, diaphyseal aclasis, and multiple cartilaginous exostoses [##REF##31853203##24##]. Further studies on various biomarkers including heparan sulfate along with genetic analysis will help in understanding the pathogenesis of cartilage within this rare bone lesion.</p>" ]	[ "<title>Conclusions</title>", "<p>OCs constitute about 50% of benign bone tumors; however, their occurrence in the head and neck area is infrequent. Effective management entails thorough clinical, radiographic, and histopathological assessment. With a low recurrence rate of only 2%, the prognosis for this tumor is favorable following complete excision. We have presented such a rare presentation of an OC involving the left mandibular condyle of a young female along with its clinical, radiological, and histopathological findings.</p>" ]	[ "<p>Osteochondromas (OCs) are benign bone tumors characterized by their growth with a cartilage cap and typically occurring at the ends of long bones. Their occurrence in the head and neck region is infrequent, accounting for only around 1% of head and neck tumors. Notably, the mandibular coronoid process and the mandibular condyle are the primary sites where an OC is reported. Patients often exhibit facial asymmetry, limited mouth opening, and malocclusion. Possible treatment options depending on the condition include partial or total condylectomy, vertical ramus osteotomy, and supplementary orthognathic surgery. The recurrence rate of under 1%- 2% is reported after local resection. </p>", "<p>In this case report, we present a unique case of an OC in a 27-year-old woman. It involved the mandibular condyle, resulting in a left-sided mouth deviation while opening and closing her mouth. The purpose of this article is to detail the clinical and radiographic features, histopathological aspects, and treatment strategies and differentiate potential diagnoses, for such OCs.</p>" ]	[ "<title>Case presentation</title>", "<p>A 27-year-old woman presented herself at the outpatient department with a concern about her limited ability to open her mouth over the previous 12 months. Past medical, surgical, and dental history was non-contributory. Clinical examination showed a left-sided deviation of the mouth while opening and closing the mandible. Her mouth opening was around 18mm measured using a graduated scale. Extra-oral palpation in the temporomandibular joint region showed restricted movement (Figures ##FIG##0##1A##, ##FIG##0##1B##). The intra-oral view showed a crossbite on the left side (Figure ##FIG##0##1C##).</p>", "<p>The orthopantomogram revealed a single well-defined radiopaque bony overgrowth seen in the anterior aspect of the right condyle. A well-defined radiopaque structure is visible on the anteromedial surface of the right condyle, extending inferiorly to the neck of the condyle and superiorly to the intra-articular space measuring approximately 2 x 3 cm in size (Figure ##FIG##1##2##).</p>", "<p>Upon radiographic evaluation, a bony growth originating from the right condyle of the mandible, positioned in an anteromedial manner within the condyle itself, was noted. Computed tomographic 3D reconstruction revealed the presence of a large, lobulated dense osseous mass in continuity with the underlying bone, suggestive of an OC. Subsequently, a condylectomy procedure was carried out under general anesthesia (Figure ##FIG##2##3##). The excised specimen was fixed with 10% formaldehyde and submitted for histopathological examination. Upon gross macroscopic examination, bony hard tissue attached to soft tissue was seen. It was greyish-white in color, hard in consistency, and measured about 3.5 cm x 2 cm.</p>", "<p>Histopathological examination by routine hematoxylin and eosin (H and E) staining showed a fibrous perichondrium covering the cartilage cap and in continuity with the periosteum of the underlying bone. At lower magnification, clusters of chondrocytes with small nuclei were discernible (Figure ##FIG##3##4A##). These chondrocytes were arranged in parallel within lacunar spaces within the cartilaginous cap. Toward the base of the cartilaginous cap, areas of endochondral ossification were apparent, which merged with the trabecular bone (Figure ##FIG##3##4B##). Within the intertrabecular spaces, hematopoietic marrow was also noted. Moderate vascularity, areas of hemorrhage, muscle tissue, and adipocytes were identified. As it was diagnosed with classic histopathological features, special stains and IHC were not performed.</p>", "<p>Correlating the clinical, radiological, and histopathological findings, it was diagnosed as OC of the mandibular condyle. The patient remains disease-free on the eighth-month post-surgical follow-up.</p>" ]	[]	[ "<fig position=\"anchor\" fig-type=\"figure\" id=\"FIG1\"><label>Figure 1</label><caption><title>Clinical pictures</title><p>Clinical picture showing the extra-oral deviation to the left side (A, B) and intra-oral view of the open bite with crossbite on the left side (C)</p></caption></fig>", "<fig position=\"anchor\" fig-type=\"figure\" id=\"FIG2\"><label>Figure 2</label><caption><title>Orthopantomogram</title><p>Orthopantomogram showing the lesion involving the right mandibular condyle</p></caption></fig>", "<fig position=\"anchor\" fig-type=\"figure\" id=\"FIG3\"><label>Figure 3</label><caption><title>Descriptive images</title><p>(A) 3D reconstruction of the involved mandibular condyle and (B) the excised specimen</p></caption></fig>", "<fig position=\"anchor\" fig-type=\"figure\" id=\"FIG4\"><label>Figure 4</label><caption><title>Photomicrographs</title><p>Photomicrographs of hematoxylin and eosin stained sections in low power view showing fibrous perichondrium covering the cartilage cap (blue arrow) with periosteal continuity (black arrow) (A, H&E, 10x) and clusters of chondrocytes with small nuclei (white arrows with a blue outline) in the cartilaginous cap (B, H&E, 10x)</p></caption></fig>" ]	[ "<table-wrap position=\"float\" id=\"TAB1\"><label>Table 1</label><caption><title>Comparative table</title></caption><table frame=\"hsides\" rules=\"groups\"><tbody><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Characteristic</td><td rowspan=\"1\" colspan=\"1\">Osteochondroma (OC)</td><td rowspan=\"1\" colspan=\"1\">Unilateral Condylar Hyperplasia (UCH)</td><td rowspan=\"1\" colspan=\"1\">Osteomas</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Nature</td><td rowspan=\"1\" colspan=\"1\">Benign cartilage and bone tumor</td><td rowspan=\"1\" colspan=\"1\">Excessive growth of condyle</td><td rowspan=\"1\" colspan=\"1\">Benign bone tumor </td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Origin</td><td rowspan=\"1\" colspan=\"1\">Develops from the growth plate. The metaphyseal region of the long bones is the most common site of involvement.</td><td rowspan=\"1\" colspan=\"1\">Arises from hyperactivity of condylar growth, The etiology of condylar hyperplasia includes trauma, partial hemihypertrophy, osteochondromatosis, and neurotrophic disturbances. Genetic, acquired, functional factors, and age groups, also have a role in morphological changes in condyle. The occurrence of condylar hyperplasia in siblings suggested that it could be genetic in origin, either autosomal dominant or Y-linked, although with only a few cases and two generations of history, it will be difficult to determine it with any degree of certainty. </td><td rowspan=\"1\" colspan=\"1\">Arises from osteoblasts</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Growth Pattern</td><td rowspan=\"1\" colspan=\"1\">Projects away from the bone</td><td rowspan=\"1\" colspan=\"1\">Enlargement of condyle in the same direction</td><td rowspan=\"1\" colspan=\"1\">Nodule-like growth on bone surface</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Clinical Symptoms</td><td rowspan=\"1\" colspan=\"1\">Facial asymmetry, malocclusion</td><td rowspan=\"1\" colspan=\"1\">Facial asymmetry, malocclusion, pain</td><td rowspan=\"1\" colspan=\"1\">Asymptomatic or local symptoms</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Radiographic Features</td><td rowspan=\"1\" colspan=\"1\">Cartilage cap with bony stalk. Condyle shape is irregular and \"Cauliflower/mushroom-like\" appearance</td><td rowspan=\"1\" colspan=\"1\">An enlarged condyle with no stalk. The condyle shape is normal. Enlargement of condyle with distinct margins that could be identified with Scintigraphy</td><td rowspan=\"1\" colspan=\"1\">Well-defined uniformly opaque bony mass Expansion of condyle. Homogeneous density on CT </td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Histopathology</td><td rowspan=\"1\" colspan=\"1\">Cartilage cap and underlying bone</td><td rowspan=\"1\" colspan=\"1\">Hypertrophic condylar bone growth</td><td rowspan=\"1\" colspan=\"1\">Thick cortical lamellar bone</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Treatment</td><td rowspan=\"1\" colspan=\"1\">Surgical removal if symptomatic</td><td rowspan=\"1\" colspan=\"1\">Surgical reduction if symptomatic</td><td rowspan=\"1\" colspan=\"1\">Surgical removal if symptomatic</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"TAB2\"><label>Table 2</label><caption><title>Diagnostic modalities</title><p>PET-CT: Positron emission tomography-computed tomography</p></caption><table frame=\"hsides\" rules=\"groups\"><tbody><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Diagnostic Modality</td><td rowspan=\"1\" colspan=\"1\">Description</td></tr><tr><td rowspan=\"1\" colspan=\"1\">X-ray</td><td rowspan=\"1\" colspan=\"1\">Utilizes X-rays to visualize bone and cartilage structures.</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">CT Scan</td><td rowspan=\"1\" colspan=\"1\">Provides cross-sectional images for detailed assessment</td></tr><tr><td rowspan=\"1\" colspan=\"1\">MRI</td><td rowspan=\"1\" colspan=\"1\">Offers clear visualization of soft tissue and bone marrow involvement, also helpful in measuring the thickness of the cartilage cap.</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Ultrasound</td><td rowspan=\"1\" colspan=\"1\">Employs sound waves to examine superficial structures and evaluate the thickness of the cartilage cap. Ultrasound helps to evaluate the cartilage cap thickness. The cartilage cap appears as a hypoechoic region situated above a hyperechoic bone [##REF##33622860##4##].</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Nuclear Imaging (Bone Scans using Tc 99m - Methyl Diphosphonate)</td><td rowspan=\"1\" colspan=\"1\">Involves injecting a radioactive tracer to highlight areas.</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Bone Scintigraphy</td><td rowspan=\"1\" colspan=\"1\">Detects active bone growth and can assess multiple lesions in hereditary multiple exostoses. technetium-99m-labeled diphosphonates [ 99m Tc-MDP] scintigraphy, Thalium 201 scintigraphy is useful in identifying malignant change within benign lesions [##REF##33622860##4##]. </td></tr><tr><td rowspan=\"1\" colspan=\"1\">PET-CT</td><td rowspan=\"1\" colspan=\"1\">Useful for identifying malignant transformation of osteochondroma. Various fluorodeoxyglucose (FDG) spectrum uptake has been observed in primary and metastatic heterogeneous bone lesions. positron emission tomography (PET) and hybrid PET/computed tomography (PET/CT) systems have been focused on using 18 F-NaF for osseous imaging.</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Angiography</td><td rowspan=\"1\" colspan=\"1\">Visualizes blood vessels using intravenous contrast agents. Can be coordinated with computed tomographic angiographic images of the joint</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Arthroscopy</td><td rowspan=\"1\" colspan=\"1\">Directly examines joints and cartilage using a small fiberoptic endoscope.</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"TAB3\"><label>Table 3</label><caption><title>Treatment modalities</title></caption><table frame=\"hsides\" rules=\"groups\"><tbody><tr style=\"background-color:#ccc\"><td colspan=\"3\" rowspan=\"1\">Treatment options for the management of Osteochondroma Involving the Mandibular Condyle</td></tr><tr><td colspan=\"3\" rowspan=\"1\">\nMild presentation - Regular clinical monitoring and radiological assessment</td></tr><tr style=\"background-color:#ccc\"><td colspan=\"3\" rowspan=\"1\">\nPresentation with symptoms or functional difficulties - Surgical therapy is definitive and curative</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Types of surgical management</td><td rowspan=\"1\" colspan=\"1\">Reconstruction methods</td><td rowspan=\"1\" colspan=\"1\">Drawbacks</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Condylectomy with tumor excision and without reconstruction</td><td rowspan=\"1\" colspan=\"1\">No reconstruction</td><td rowspan=\"1\" colspan=\"1\">Functional defects</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Condylectomy with tumor excision and reconstruction</td><td rowspan=\"1\" colspan=\"1\">Reconstruction with autogenous grafts like Costochondral grafts, Sternoclavicular joints</td><td rowspan=\"1\" colspan=\"1\">Requires surgery at the second site</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Condylectomy with tumor excision and reconstruction</td><td rowspan=\"1\" colspan=\"1\">Reconstruction with alloplastic grafts like high molecular weight polyethylene joints, close-fitting custom-made prosthesis</td><td rowspan=\"1\" colspan=\"1\">Requires second surgery for graft retrieval</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Condylectomy with tumor excision and reconstruction</td><td rowspan=\"1\" colspan=\"1\">Reconstruction by distraction osteogenesis</td><td rowspan=\"1\" colspan=\"1\">Long-term stability is questionable</td></tr><tr style=\"background-color:#ccc\"><td rowspan=\"1\" colspan=\"1\">Condylectomy with tumor excision and reconstruction</td><td rowspan=\"1\" colspan=\"1\">Reconstruction by Vertical Ramus Osteotomy</td><td rowspan=\"1\" colspan=\"1\">Changes in mandibular contour</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Conservative management by recontouring the involved condyle</td><td rowspan=\"1\" colspan=\"1\">Not needed</td><td rowspan=\"1\" colspan=\"1\">Risk of recurrence at the involved site. </td></tr></tbody></table></table-wrap>" ]	[]	[]	[]	[]	[]	[]	[ "<fn-group content-type=\"other\"><title>Author Contributions</title><fn fn-type=\"other\"><p><bold>Concept and design:</bold> Karthikeyan Ramalingam, Varun Rastogi, Dilasha Dhungel, Sandhya Chaurasia, Nisha Maddheshiya</p><p><bold>Acquisition, analysis, or interpretation of data:</bold> Karthikeyan Ramalingam, Varun Rastogi, Dilasha Dhungel, Sandhya Chaurasia, Nisha Maddheshiya</p><p><bold>Drafting of the manuscript:</bold> Karthikeyan Ramalingam, Varun Rastogi, Sandhya Chaurasia</p><p><bold>Critical review of the manuscript for important intellectual content:</bold> Karthikeyan Ramalingam, Varun Rastogi, Dilasha Dhungel, Nisha Maddheshiya</p><p><bold>Supervision:</bold> Karthikeyan Ramalingam</p></fn></fn-group>", "<fn-group content-type=\"other\"><title>Human Ethics</title><fn fn-type=\"other\"><p>Consent was obtained or waived by all participants in this study</p></fn></fn-group>", "<fn-group content-type=\"competing-interests\"><fn fn-type=\"COI-statement\"><p>The authors have declared that no competing interests exist.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"cureus-0015-00000050355-i01\" position=\"float\"/>", "<graphic xlink:href=\"cureus-0015-00000050355-i02\" position=\"float\"/>", "<graphic xlink:href=\"cureus-0015-00000050355-i03\" position=\"float\"/>", "<graphic xlink:href=\"cureus-0015-00000050355-i04\" position=\"float\"/>" ]	[]	[{"label": ["9"], "article-title": ["Osteochondroma condyle: a journey of 20 years in a 52-year-old male patient causing severe facial asymmetry and occlusal derangement"], "source": ["J Oral Maxillofac Pathol"], "person-group": ["\n"], "surname": ["Mohapatra", "Banushree"], "given-names": ["M", "CS"], "fpage": ["162"], "volume": ["23"], "year": ["2019"]}, {"label": ["10"], "article-title": ["Osteochondroma of bilateral mandibular condyle with review of literature"], "source": ["J Clin Diagn Res"], "person-group": ["\n"], "surname": ["Kamble", "Rawat", "Kulkarni", "Pajnigara", "Dhok"], "given-names": ["V", "J", "A", "N", "A"], "fpage": ["0"], "lpage": ["2"], "volume": ["10"], "year": ["2016"]}, {"label": ["15"], "article-title": ["Computer-assisted local resection for exostosis osteochondroma of the mandibular condyle"], "source": ["J Craniofac Surg"], "person-group": ["\n"], "surname": ["Huang", "He", "Yang", "Chen", "Zhou", "Dong"], "given-names": ["D", "DM", "C", "MJ", "Q", "MJ"], "fpage": ["0"], "lpage": ["9"], "volume": ["24"], "year": ["2013"]}, {"label": ["17"], "article-title": ["Osteochondroma at the angle of mandible: a rare case"], "source": ["J Oral Maxillofac Pathol"], "person-group": ["\n"], "surname": ["Mamatha", "Shah", "Narayan", "Savita"], "given-names": ["NS", "A", "TV", "JK"], "fpage": ["110"], "volume": ["19"], "year": ["2015"]}, {"label": ["20"], "article-title": ["Osteochondroma of the mandibular condyle: an algorithm for treatment"], "source": ["Oral Surg Oral Med Oral Pathol Oral Radiol"], "person-group": ["\n"], "surname": ["Gardner", "Renapurkar"], "given-names": ["M", "S"], "fpage": ["0"], "volume": ["130"], "year": ["2020"], "uri": ["https://www.sciencedirect.com/science/article/abs/pii/S2212440320300109"]}]	{ "acronym": [], "definition": [] }	24	CC BY	no	2024-01-13 00:02:19	Cureus.; 15(12):e50355	oa_package/f4/a8/PMC10781615.tar.gz
PMC10781616	37985898	[]	[ "<title>Methods</title>", "<title>Ethics</title>", "<p id=\"Par30\">All experiments were performed in accordance with ethical applications approved by the Stockholm Ethical Board (Dnr:C50/12, N173/13 and 223/15). These applications are consistent with the Institutional Animal Care and Use Committee guidelines.</p>", "<title>Study system</title>", "<p id=\"Par31\">To evaluate the genetic architecture of sociability, we performed a series of experiments in guppies following artificial selection on coordinated motion. The laboratory population of guppies used originated from a downstream population of the Quare river in Trinidad, which is subject to high predation levels. The original collection was made in 1998<sup>##REF##24417444##66##</sup> and the laboratory population has since been kept in several large (>200 l) tanks of >200 individuals each to avoid inbreeding. The artificial selection procedure is outlined in detail in refs. <sup>##REF##33268362##22##,##UREF##7##23##</sup>. In brief, groups of female guppies were subjected to repeated open field tests and were subsequently sorted on the basis of their median polarization, measured by the degree of alignment exhibited by the individuals within the group when swimming together<sup>##REF##33268362##22##,##UREF##7##23##</sup>. For three generations, females from groups with higher polarization were mated with males from those cohorts to generate three lines of guppies that had been selected for high polarization. In parallel, random females were exposed to the same experimental conditions and were mated with unselected males to generate three control lines. Analysis of the third generation of polarization selection revealed that, on average, females exhibited a 15% higher level of polarization and a 10% higher level of group cohesiveness compared with control females<sup>##REF##33268362##22##</sup>.</p>", "<p id=\"Par32\">Throughout the selection experiment and the completion of experiments described below, all fish were removed from their parental tanks after birth, separated by sex at the first onset of sexual maturation and afterwards kept in single-sex groups of eight individuals in 7 l tanks containing 2 cm of gravel with continuously aerated water, a biological filter and plants for environmental enrichment. We allowed for visual contact between the tanks. The laboratory was maintained at 26 °C with a 12 h light:12 h dark schedule. Fish were fed a diet of flake food and freshly hatched brine shrimp daily.</p>", "<title>Heritability of sociability</title>", "<p id=\"Par33\">To investigate heritability and cross-sex genetic correlations of sociability in the guppy, we measured alignment and attraction with unfamiliar groups of conspecifics in parents and offspring from polarization-selected and control lines. Specifically, using offspring of the F<sub>3</sub> generation of selection, we bred 35 families for each of the three polarization-selected and for each of the three control lines. From our population of F<sub>3</sub> generation offspring (kept in single-sex groups before the breeding experiments), we used male and female guppies of the same age (~9 months old) and paired them in 3 l tanks to generate the parental generation. We collected offspring from the first two clutches of these pairs and transferred newborn offspring to 3 l tanks in groups of three siblings. We separated siblings by sex at the first onset of sexual maturation and afterwards kept them in single-sex groups of three individuals until behavioural testing. We phenotyped sociability for a total of 195 guppy families: mother, father and six offspring (three females and three males). Any family for which we did not collect at least three female and three male offspring was disregarded from further behavioural testing. Each of the six selection lines was represented by a minimum of 30 families in our heritability analyses.</p>", "<title>Behavioural assays</title>", "<p id=\"Par34\">To phenotype sociability in each member of our guppy families, we measured alignment and attraction of 1,495 guppies from our breeding experiment. For each fish, we performed an open field assay using white arenas with 55 cm diameter and 3 cm water depth in which our focal fish (guppies from the breeding experiment) interacted with a group of seven same-sex conspecifics. Non-focal guppies used in these assays were from a lab wild-type stock population and of similar age to our focal fish. Before the start of the test, focal fish and the seven-fish group were acclimated in the centre of the arena for 1 min in separate opaque white 15 cm PVC cylinders. After this acclimation period, we lifted the cylinders and filmed the arena for 10 min using a Point Grey Grasshopper 3 camera (FLIR Systems; resolution, 2,048 pixels by 2,048 pixels; frame rate, 25 Hz). Three weeks before assays, we tagged wild-type fish with small black elastomere implants (Northwest Marine Technology) to allow recognition of wild-type fish after completion of each assay. After completion, we gently euthanized focal fish from the parental generation with an overdose of benzocaine and kept them in ethanol for future genomic analyses. Focal fish from the offspring generation were transferred to group tanks for future experimental use. Groups of seven wild-type fish were transferred to holding tanks and used in a maximum of seven assays with focal fish.</p>", "<title>Data processing</title>", "<p id=\"Par35\">We tracked the movement of fish groups in the collected video recordings using idTracker<sup>##REF##24880877##67##</sup> and used fine-grained tracking data to calculate the following variables in Matlab (v.2020): (1) alignment, the median alignment of the focal fish to the group average direction across all frames in the assay. This was quantified by the total length of the sum of two-unit vectors, one representing the heading of the focal fish and the other representing the heading of the group centroid. Calculations of alignment were only obtained if six out of the eight members of the group presented tracks following the optimization of our tracking protocol in the setup in refs. <sup>##REF##33268362##22##,##UREF##7##23##,##UREF##16##68##</sup>; (2) attraction, the median nearest neighbour distance across all frames in the assay; and (3) activity; we obtained the median speed across all group members and across all frames by calculating the first derivatives of the <italic>x</italic> and <italic>y</italic> time series, followed by smoothing using a Savitzky–Golay filter with span of 12 frames (1/2 s) and degree 3. For all measurements, trials with less than 70% complete tracks (<italic>n</italic> = 8) were disregarded in further analyses. The proportion of frames used did not differ between polarization-selected and control fish for any comparison across different generations and sexes (Supplementary Fig. ##SUPPL##0##5##). We calculated these variables for the focal fish and the average for the seven-fish wild-type group. To recover focal fish id in the tracking data, we used idPlayer to visualize trials by projecting the raw tracking data onto experimental videos. We followed focal individuals for the first 2 min of the assay and used the stable identity assigned by idTracker in data collection. In trials with less than 85% complete tracks (<italic>n</italic> = 8), we followed focal individuals for the total duration of the recording to verify the consistency in identity assigned by idTracker. This approach has previously shown strong reliability in individuals that were observed using this protocol for 20 min recordings in the same experimental setup that quantified sexual behaviour of guppies in mixed-sex shoals<sup>##REF##31610058##69##</sup>.</p>", "<title>Statistical analyses</title>", "<p id=\"Par36\">Analyses were conducted using R statistical software (v.4.1.3)<sup>##UREF##17##70##</sup>, RStudio (v.2023.3.1.446)<sup>##UREF##18##71##</sup> and the tidyverse package<sup>##UREF##19##72##</sup>. We used LMMs with alignment and attraction as dependent variables to test for potential differences between polarization-selected and control lines in social interactions with unfamiliar individuals. Selection regime, sex, the interaction between these two factors and generation were included as fixed effects. The average activity of the wild-type group was coded as a covariate, with a random intercept for each replicated selection line, the breeding family and the number of tests previously performed with the wild-type group as random factors. All models were run using lme4 and lmerTest packages<sup>##UREF##20##73##,##UREF##21##74##</sup>. Model diagnostics showed that residual distributions were roughly normal with no evidence of heteroscedasticity.</p>", "<p id=\"Par37\">To estimate heritability, the degree of phenotypic variation due to genetic inheritance, and cross-sex genetic correlations of alignment and attraction, we used Bayesian animal models<sup>##UREF##22##75##</sup>. Animal models use a matrix of pedigree relationships set as a random effect to separate phenotypic variance for each response variable into additive genetic variance and the remaining variance. Given strong sex differences in social interactions in guppies, we performed three animal models for each trait: one including the data on the 1,495 phenotyped individuals and two including only the phenotyped females or males. Parameter values were estimated using the brms interface<sup>##UREF##23##76##,##UREF##24##77##</sup> to the probabilistic programming language Stan<sup>##UREF##25##78##</sup>. We used normal priors with a mean of 0 and s.d. of 3 for fixed effects, and Student-<italic>t</italic> priors with 5 degrees of freedom, a mean of 0 and s.d. of 5 for random effects. The full-pedigree model estimated cross-sex correlations with a Lewandowski–Kurowicka–Joe (LKJ) prior with <italic>η</italic> = 1, which is uniform over the range −1 to 1. Posterior distributions for full/same-sex pedigree models were obtained using Stan’s no-U-turn Hamiltonian Monte Carlo with 24/16 independent Markov chains of 2,500/4,000 iterations, discarding the first 1,500/2,000 iterations per chain as warm-up and resulting in 24,000/32,000 posterior samples overall. Convergence of the chains and sufficient sampling of posterior distributions were confirmed by a potential-scale-reduction metric (<italic>R</italic>) below 1.01 and an effective sample size of at least 1,000. For each model, posterior samples were summarized on the basis of the Bayesian point estimate (posterior median) and posterior uncertainty intervals by Highest Density Intervals. We calculated estimates of heritability by taking the ratio of the additive genetic variance to the total phenotypic variance in each independent model (see Supplementary Tables ##SUPPL##0##5## and ##SUPPL##0##6##).</p>", "<title>Genetic basis of sociability in guppies</title>", "<title>Pooled DNA sequencing</title>", "<p id=\"Par38\">We extracted DNA of muscle tissue from the caudal peduncle of polarization-selected females from the parental generation using Qiagen’s DNeasy Blood and Tissue kit following standard manufacturer protocol, with an additional on-column RNase A treatment. We quantified DNA concentration using fluorometry (Qubit, ThermoFisher). We next pooled samples from the 7 females that represented the top and bottom 20% polarization-selected guppy lines whose families presented higher and lower sociability in 6 final pools at equimolar amounts (Supplementary Fig. ##SUPPL##0##1##). We achieved a minimum of 3 μg genomic DNA per pool. We used a Nextera DNA Flex library preparation kit (Illumina) following manufacturer protocol. The final library containing 6 pooled samples was sequenced at SciLife Lab, Uppsala (Sweden) in one lane of an Illumina NovaSeq 6000 system. We obtained on average 31.8 million 150 bp read pairs per sample (26.9 million read pairs minimum per sample).</p>", "<title>Read quality control and trimming</title>", "<p id=\"Par39\">We assessed the quality of reads for each pool using FastQC v.0.11.4 (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.bioinformatics.babraham.ac.uk/projects/fastqc\">www.bioinformatics.babraham.ac.uk/projects/fastqc</ext-link>). After verifying initial read quality, reads were trimmed with Trimmomatic (v.0.35)<sup>##REF##24695404##79##</sup>. We filtered adaptor sequences and trimmed reads if the sliding window average Phred score over four bases was <15 or if the leading/trailing bases had a Phred score <4, removing reads post filtering if either read pair was <50 bases in length. Quality was verified after trimming with FastQC.</p>", "<title>Genome-wide allele frequency analysis</title>", "<p id=\"Par40\">Reads were mapped to the guppy reference genome assembly using default settings (Guppy_female_1.0 + MT, RefSeq accession: <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/assembly/GCA_000633615.2\">GCA_000633615.2</ext-link>)<sup>##REF##28033408##80##</sup> with bwa-mem (v0.7.17)<sup>##REF##27560171##81##</sup>. We used Samtools (v.1.6.0)<sup>##REF##19505943##82##</sup> to convert sam to bam files, sort bam files, remove duplicates and make mpileup files. First, to identify SNPs that significantly differed in their allele frequencies between guppies with high and low sociability, we merged sequences from high-sociability and low-sociability pools and used Popoolation2 (ref. <sup>##REF##22025480##27##</sup>) to create a synchronized file with allele frequencies for high and low sociability (mpileup2sync.pl –min-qual 20), compute allele frequency differences (mpileup2sync.pl –min-count 6 –min-coverage 25 –max-coverage 200), calculate Fst for every SNP (fst-sliding.pl) and perform a Fisher’s exact test (fisher-test.pl). Second, we similarly used Popoolation2 to detect consistent changes in allele frequencies of sociability pooled samples for our three replicated artificial selection lines. For this, we created one sync file per replicate (mpileup2sync.pl –min-qual 20) and performed a CMH test (cmh-test.pl –min-count 18 –min-coverage 25 –max-coverage 200). Using package qqman<sup>##UREF##26##83##</sup> in R (v.4.1.3)<sup>##UREF##17##70##</sup>, we made Manhattan plots for each chromosome by plotting the negative log<sub>10</sub>-transformed <italic>P</italic> values of the exact Fisher and CMH tests as a function of chromosome position.</p>", "<title>Significance tests and functional analyses</title>", "<p id=\"Par41\">We determined SNPs that were significantly different between high- and low-sociability merged pools in Fisher’s exact tests using the traditional genome-wide significance threshold (–log<sub>10</sub>(<italic>P</italic>) > 8)<sup>##REF##18348202##28##</sup>. We next used custom scripts to identify the overlap between the positions of these SNPs and genes present in the guppy reference annotated genome<sup>##REF##28033408##80##</sup> and to find homologous genes of this set in medaka (<italic>Oryzias latipes</italic>). We further used this set of unique genes (<italic>n</italic> = 160) to determine associated GO terms between our merged pools. For this, we performed enrichment tests in PANTHER<sup>##REF##23868073##84##</sup>, as implemented in the GO Ontology Consortium (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.geneontology.org/\">http://www.geneontology.org/</ext-link>). To test for enrichments of GO terms, we performed one-tail Fisher’s exact tests with a Bonferroni-corrected <italic>P</italic> value threshold of <italic>P</italic> < 0.05 using a full list of medaka genes orthologous to guppy genes as background. We used Revigo (<ext-link ext-link-type=\"uri\" xlink:href=\"http://revigo.irb.hr\">http://revigo.irb.hr</ext-link>)<sup>##REF##21789182##36##</sup> to find and visualize representative subsets of terms on the basis of semantic similarity measurements for our enriched GO terms related to biological processes, cellular components and molecular functions.</p>", "<p id=\"Par42\">For CMH test results, we determined SNPs that were significantly different between high- and low-sociability pools based on FDR-corrected <italic>P</italic> < 0.01. We used a custom script to identify the overlap between the positions of these SNPs and genes present in the guppy reference annotated genome<sup>##REF##28033408##80##</sup>.</p>", "<title>Neurogenomic response of schooling in guppies</title>", "<title>Behavioural assays and tissue collection</title>", "<p id=\"Par43\">Using offspring of the F<sub>3</sub> generation (6 months old), we placed an individual or groups of eight unfamiliar adult control and polarization-selected females in white 55 cm arenas. After 30 min, females were euthanized by transfer to ice water. After 30 s, with the aid of a Leica S4E microscope, we removed the top of the skull and after cutting transversally posterior of the optic tectum and anterior of the cerebellum, and horizontally through the optic chiasm, removed the brain from the skull and placed it into ice water. We severed the ‘telencephalon’ from the rest of the brain between the ventral telencephalon and thalamus at the ‘commissura anterioris’, including both the pallium and subpallium regions. Then we cut the laminated cup-like structures of the ‘optic tectum’. The remaining part of the brain was the ‘midbrain’. Dissections took under 2 min and tissue samples were immediately preserved in RNAlater (Ambion) at 4 °C for 24 h and then at −20 °C until RNA extraction.</p>", "<title>RNA extraction and sequencing</title>", "<p id=\"Par44\">For each treatment, we pooled tissue from 10 individuals into 2 non-overlapping pools of 5 for each replicate line. We used this strategy to reduce noise in transcript expression data during sample normalization procedures, potentially caused by outliers during behavioural experiments while maintaining each replicate as a comparable unit. Our experimental design represents a total of 120 individual females, constituting 6 pools per treatment per selection regime for a total of 24 pools per tissue. Each sample pool was homogenized and RNA was extracted using Qiagen’s RNeasy kits following standard manufacturer protocol. Libraries for each sample were prepared and sequenced by the Wellcome Trust Center for Human Genetics at the University of Oxford, United Kingdom. All samples were sequenced across nine lanes on an Illumina HiSeq 4000 system. We obtained on average 33.9 million 75 bp read pairs per sample (28.9 million read pairs minimum, 39.8 million maximum).</p>", "<title>Read quality control and trimming</title>", "<p id=\"Par45\">We assessed the quality of reads for each sample using FastQC v.0.11.4 (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.bioinformatics.babraham.ac.uk/projects/fastqc\">www.bioinformatics.babraham.ac.uk/projects/fastqc</ext-link>). After verifying initial read quality, reads were trimmed with Trimmomatic (v.0.35)<sup>##REF##24695404##79##</sup>. We filtered adaptor sequences and trimmed reads if the sliding window average Phred score over four bases was <15 or if the leading/trailing bases had a Phred score <3, removing reads post filtering if either read pair was <33 bases in length. Quality was verified after trimming with FastQC.</p>", "<title>Differential expression analysis</title>", "<p id=\"Par46\">We mapped RNA-seq reads against the latest release of the published guppy genome assembly<sup>##REF##28033408##80##</sup> using the HiSat 2.0.5–Stringtie v.1.3.2 suite<sup>##REF##27560171##81##</sup>. For each individual pool, reads were mapped to the genome and built into transcripts using default parameters. The resulting individual assemblies were then merged into a single, non-redundant assembly using the built-in StringTie-merge function. We filtered the resulting assembly for non-coding RNA using medaka and Amazon molly (<italic>Poecilia formosa</italic>) non-coding RNA sequences as reference in a nucleotide BLAST (Blastn) search. After eliminating all sequences matching non-coding RNAs, we kept only the longest isoform representative for each transcript for further analysis. Finally, we quantified expression by re-mapping reads to this filtered assembly using RSEM (v.1.2.20)<sup>##UREF##27##85##</sup>.</p>", "<p id=\"Par47\">Lowly expressed genes were removed by filtering transcripts with <2 reads per kilobase per million mapped reads, preserving only those transcripts that have expression above this threshold in at least half of the samples for each treatment within a line. After this final filter, a total of 26,140 optic tectum transcripts, 25,100 telencephalon transcripts and 26,514 midbrain transcripts were retained for further analysis. Using sample correlations in combination with multidimensional scaling plots based on all expressed transcripts, we determined that none of the 72 pools represented outliers, hence all samples were included in the analysis.</p>", "<p id=\"Par48\">We used DESeq2 (ref. <sup>##UREF##28##86##</sup>) to normalize filtered read counts using standard function to identify DE genes between the Alone and the Group treatment in control and polarization-selected lines separately and then examined the overlap in differentially expressed genes between them. A transcript was considered differentially expressed if it had an FDR-corrected <italic>P</italic> < 0.05. As behaviour could be modulated by small changes in expression, we did not filter differentially expressed genes on the basis of log fold-change in expression between the treatments.</p>", "<title>Differential co-expression analysis</title>", "<p id=\"Par49\">We used BFDCA<sup>##REF##27984044##30##</sup> to identify pairs of genes that have different correlation patterns in the two conditions<sup>##REF##32875689##32##,##REF##23505361##87##,##REF##23246976##88##</sup>. Here we compared the Alone and Group treatments within each line for each tissue separately, in the same manner as the previously described DE analysis. BFDCA is based on weighted gene co-expression network analysis and has been shown to be a reliable and accurate method<sup>##REF##27984044##30##</sup>. This untargeted approach to differential co-expression analysis uses a combined Bayes factor, a ratio of the marginal likelihoods of the data between the two alternative hypotheses, to evaluate which genes are differentially correlated in the two conditions. We controlled for false positives and accounted for multiple testing by integrating a random permutations approach<sup>##REF##32875689##32##</sup>. In short, we created 1,000 permutated datasets and considered a DC gene pair significant if the Bayes factor for the actual expression data was larger than the 1% tail of the permutated data Bayes factor distribution.</p>", "<title>Functional analyses</title>", "<p id=\"Par50\">To investigate the function of DE genes, we performed GO term enrichment tests. To accomplish this, we initially completed the annotation of the reference genome assembly. The transcripts without clear gene names from the reference genome, and the de novo transcripts identified by HiSat were annotated with blastX against the Swissprot non-redundant database. We then determined which GO terms were associated with differentially expressed genes and performed BP, CC and MF enrichment tests in PANTHER<sup>##REF##23868073##84##</sup>. To assess the level of concordance between genes of interest across experiments, we compared the proportions of BP, CC and MF GO terms that were significantly enriched in genomic analyses of sociability implemented in polarization-selected females and the proportions of BP, CC and MF GO terms enriched in differential expression analyses in brain tissue of polarization-selected females following exposure to Group and Alone experimental conditions. To assess their significance, we compared these values to mean proportions obtained from bootstrap analyses of 1,000 random sets of 158 (for comparison with telencephalon), 109 (midbrain) and 21 (optic tectum) genes from our medaka–guppy orthologous gene list. All analyses were based on one-tail Fisher’s exact tests with a Bonferroni-corrected <italic>P</italic> value threshold of <italic>P</italic> < 0.05 using medaka genes orthologous to guppy genes as the background. Bootstrap analyses with random sets of genes were automated using rbioapi package<sup>##REF##35561170##89##</sup> in R (v.4.1.3)<sup>##UREF##17##70##</sup>. We next summarized and visualized GO terms enrichment lists across experiments and tissues using REVIGO<sup>##REF##21789182##36##</sup> (settings: SimRel semantic similarity measure, 0.5 value). To investigate the function of differentially co-expressed genes, we used g:Profiler<sup>##REF##27098042##90##</sup> to identify the enriched BP GO terms and pathways that were altered across mating contexts associated with differentially co-expressed gene pairs. We determined overrepresented pathways among DC gene pairs in each tissue using the human (<italic>Homo sapiens</italic>) database in g:Profiler. We chose the human database for its completeness, acknowledging the distant phylogenetic relationship to guppies.</p>", "<title>Reporting summary</title>", "<p id=\"Par51\">Further information on research design is available in the ##SUPPL##1##Nature Portfolio Reporting Summary## linked to this article.</p>" ]	[ "<title>Results</title>", "<title>Heritability of sociability in guppies</title>", "<p id=\"Par7\">We first determined whether experimental evolution for higher schooling propensity affected social interactions with unfamiliar conspecifics. For this, we assessed sociability in 740 females and 746 males from multiple families of three replicate lines artificially selected for a 15% average higher polarization (polarization-selected lines hereafter) and three replicate control lines exposed to a group of non-kin unfamiliar conspecifics in an open field test. Specifically, we quantified their alignment and nearest neighbour distance (attraction), two measures of collective motion characteristics that are demonstrated to capture the most biologically relevant aspects of the sociability axis of personality in this species<sup>##REF##29581400##21##</sup>.</p>", "<p id=\"Par8\">Female guppies from polarization-selected lines presented higher alignment and higher attraction to an unfamiliar group compared with control lines (linear mixed model for alignment, LMM<sub>alignment</sub>: line: <italic>t</italic> = 2.27, d.f. = 9.68, <italic>P</italic> = 0.047; LMM<sub>attraction</sub>: line: <italic>t</italic> = −2.34, d.f. = 9.41, <italic>P</italic> = 0.043; Fig. ##FIG##0##1a## and Supplementary Table ##SUPPL##0##1##). No differences were observed in these traits between polarization-selected and control males (LMM<sub>alignment</sub>: line: <italic>t</italic> = −1.38, d.f. = 9.56, <italic>P</italic> = 0.20; LMM<sub>attraction</sub>: line: <italic>t</italic> = 0.88,; d.f. = 9.26, <italic>P</italic> = 0.40; Fig. ##FIG##0##1a## and Supplementary Table ##SUPPL##0##2##). Our analyses showed an effect of sex in alignment, with females exhibiting ~8% higher alignment than males (LMM<sub>alignment</sub>: sex: <italic>t</italic> = −3.02, d.f. = 690.08, <italic>P</italic> = 0.003), but no difference between sexes in attraction to a group of unfamiliar conspecifics (LMM<sub>attraction</sub>: sex: <italic>t</italic> = 0.51,d.f. = 447.05, <italic>P</italic> = 0.61; Fig. ##FIG##0##1a## and Supplementary Table ##SUPPL##0##1##). There were some differences in sociability between the parental and offspring generation tested in our experiment, with higher alignment to group average direction and lower distances to nearest neighbour observed in offspring (LMM<sub>alignment</sub>: generation: <italic>t</italic> = −10.13, d.f. = 1141.24, <italic>P</italic> < 0.001; LMM<sub>attraction</sub>: generation: <italic>t</italic> = 11.29, d.f. = 992.16, <italic>P</italic> < 0.001; Fig. ##FIG##0##1a## and Supplementary Table ##SUPPL##0##1##). Differences in body size between age classes in guppies may explain these results (see Supplementary Table ##SUPPL##0##3##), as the time restrictions involved in testing large numbers of fish required that we assessed individuals from the parental and offspring generations at different ages. However, these differences are unlikely to create large biases in our heritability estimates given that we tested all fish after sexual maturation and that polarization-selected and control fish were of similar age within parents tested (9 months old) and within offspring tested (5 months old). In addition, the difference in means between generations is accounted for in our statistical models (see Methods).</p>", "<p id=\"Par9\">To assess the heritability of sociability in this species, we fitted animal models with alignment and attraction phenotypes quantified from these 1,486 individuals comprising parents, three male and three female offspring for 195 families (99 polarization-selected and 96 control families). Given known differences between the sexes in social interaction patterns in guppies<sup>##UREF##8##24##–##REF##9784218##26##</sup>, we estimated heritability with animal models that only included relationships with same-sex individuals (same-sex pedigree) or that included relationships with individuals from both sexes (full pedigree).</p>", "<p id=\"Par10\">Using same-sex pedigree animal models, attraction heritability was similar in females (<italic>h</italic><sup>2</sup><sub>attraction</sub>, estimate (95% credible interval (CI))) = 0.18 (0.05, 0.34); Fig. ##FIG##0##1b##) and males (<italic>h</italic><sup>2</sup><sub>attraction</sub> = 0.19 (0.06, 0.34); Fig. ##FIG##0##1b##); however, alignment heritability was much higher in females (<italic>h</italic><sup>2</sup><sub>alignment</sub> = 0.34 (0.18, 0.49); Fig. ##FIG##0##1b##) than in males (<italic>h</italic><sup>2</sup><sub>alignment</sub> = 0.06 (0.00, 0.18); Fig. ##FIG##0##1b##). Full-pedigree models indicated lower heritability estimates than same-sex pedigree models (Supplementary Table ##SUPPL##0##5## and Fig. ##FIG##0##1b##), except for the heritability estimate of attraction in males (<italic>h</italic><sup>2</sup><sub>attraction</sub> = 0.26 (0.16, 0.37); Fig. ##FIG##0##1b##). Finally, animal models indicated a positive female–male genetic correlation in attraction (<italic>r</italic><sub>f–m,</sub>\n<sub>attraction</sub>: 0.68 (0.23, 0.98); Fig. ##FIG##0##1b##), although the magnitude of this correlation contained large CIs. For alignment, CIs for <italic>r</italic><sub>f–m</sub> are also wide and span zero (<italic>r</italic><sub>f–m,</sub>\n<sub>alignment</sub>: 0.44 (−0.17, 0.95)), and we can only conclude that the cross-sex genetic correlation is not strongly negative (Supplementary Table ##SUPPL##0##5## and Fig. ##FIG##0##1b##).</p>", "<title>Genetic basis of sociability in guppies</title>", "<p id=\"Par11\">Our quantitative genetic analyses of alignment and attraction suggest an important genetic influence on sociability phenotypes of guppies. As such, we sequenced DNA pools (Pool-seq) to identify genome-wide differences in allele frequencies between polarization-selected female guppies that presented high sociability and polarization-selected females that presented low sociability. Specifically, we focused on measurements obtained from females in analyses of alignment to an unfamiliar group. For this, we pooled the DNA from mothers whose families (normalized mother and daughters alignment score; see Methods) were in the top 25% and the bottom 25% quartiles from each of the three replicated polarization-selected lines (six total pooled samples with 7 individuals each; Supplementary Fig. ##SUPPL##0##1##).</p>", "<p id=\"Par12\">DNA reads were aligned to the guppy reference genome (Guppy_female_1.0 + MT, RefSeq accession: <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/assembly/GCA_000633615.2\">GCA_000633615.2</ext-link>) to compare genome-wide allele frequency differences between high- and low-sociability guppies. We ran two independent analyses with these aligned sequences. For our first analysis, we merged sequences from the three replicates with high-sociability pooled samples and sequences from the three replicates with low-sociability pooled samples. We filtered merged sequences to 3,004,974 single nucleotide polymorphisms (SNPs; see Methods) and performed a Fisher’s exact test in Popoolation2 (ref. <sup>##REF##22025480##27##</sup>) to identify SNPs that significantly differed in their allele frequencies between guppies with high and low sociability. Using this methodology, we identified 819 SNPs associated with our sociability phenotype (Fisher’s exact test, <italic>P</italic> < 10<sup>−8</sup>; Fig. ##FIG##1##2a##). SNPs over this standard genome-wide significance threshold<sup>##REF##18348202##28##</sup> were mostly found in single physically unlinked positions across the genome, consistent with a polygenic architecture of the trait.</p>", "<p id=\"Par13\">Out of these 819 significantly different SNPs, 421 were located within genes or gene promoter regions of the guppy genome and were used for further functional characterization in association with Gene Ontology (GO) annotations (273 unique genes). We clustered GO terms on the basis of semantic similarities and found significant overrepresentation of biological process terms related to learning and memory, synaptic functioning, response to stimulus, locomotion and growth (Fig. ##FIG##1##2b##). We likewise found significant overrepresentation of cadherin and calcium-dependent protein binding annotations (molecular components terms; Supplementary Fig. ##SUPPL##0##2##) and glutamatergic synapse annotations (cellular components terms; Supplementary Fig. ##SUPPL##0##3##).</p>", "<p id=\"Par14\">Second, we looked for consistent differences in allele frequencies between high- and low-sociability pooled samples in our three replicates by performing the Cochran–Mantel–Haenszel test (CMH test) in Popoolation2 (ref. <sup>##REF##22025480##27##</sup>). Convergent changes in allele frequency probably represent selected sites and are less likely the result of genetic drift in any one line. This stringent analysis identified 13 SNPs from 10 different chromosomes with consistent significant differences in allele frequencies across the three replicates (CMH test <italic>P</italic> < 0.01 with false discovery rate (FDR) correction). Five of these SNPs are located within known coding sequence of the guppy genome, of which three are within well-characterized genes in zebrafish and human homologues with important roles for cognitive function: ubiquitin-specific peptidase 11 (<italic>usp11</italic>), <italic>supt6</italic> histone chaperone and transcription elongation factor homologue (<italic>supt6h</italic>) and cadherin 13 (<italic>cdh13</italic>; Fig. ##FIG##1##2a## and Table ##TAB##0##1##). The other two are classified as novel genes, one of them being matched to an RNA-binding protein <italic>Nova-1</italic>-like gene, similarly associated with motor function and changes in synaptic function (Fig. ##FIG##1##2a## and Table ##TAB##0##1##).</p>", "<title>Neurogenomic response of schooling in guppies</title>", "<p id=\"Par15\">We used transcriptome sequencing to determine differences in gene expression in multiple brain regions of polarization-selected and control females in response to two different social contexts, swimming alone (the ‘Alone’ condition) or schooling in a group (groups of eight unfamiliar females; the ‘Group’ condition). We focused on three separate brain tissues that control distinct functions. The ‘optic tectum’ is involved in sensory processing of visual signals. The ‘telencephalon’ is implicated in decision making. The ‘midbrain’ is associated with motor function in response to auditory and visual stimuli<sup>##REF##25172472##29##,##REF##27984044##30##</sup>. Together, these three brain tissues contain the main components of the social brain network in fish<sup>##UREF##10##31##,##REF##32875689##32##</sup>.</p>", "<title>Differential expression analyses</title>", "<p id=\"Par16\">We identified genes differentially expressed between lines under each treatment condition and in each brain region separately to determine the neurogenomic response triggered by schooling in both lines. Gene expression analyses indicated very little overlap in differentially expressed (DE) genes between polarization-selected and control lines (Fig. ##FIG##2##3## and ##SUPPL##3##Supplementary Data##). Specifically, we found that only adipocyte enhancer-binding protein 2 gene (<italic>AEBP2;</italic> involved in adipocyte differentiation) in the midbrain and an unknown gene in the optic tectum were differentially expressed in both polarization-selected and control lines. Such little overlap suggests that females from different selection lines are activating different transcriptional cascades and biological pathways in response to social context. In polarization-selected lines we found an order of magnitude fewer DE genes in the optic tectum than in the other brain components (<italic>n</italic> = 21 for optic tectum, <italic>n</italic> = 158 for telencephalon, <italic>n</italic> = 109 for midbrain, each <italic>P</italic><sub>adj</sub> < 0.05). Moreover, in the telencephalon and midbrain, DE genes between Alone and Group treatment in the polarization-selected lines were enriched for GO annotations associated with cognition, memory, learning and social behaviour (##SUPPL##3##Supplementary Data##). We found enrichment for these annotation terms for DE genes expressed in the optic tectum but not in the midbrain or telencephalon of control lines.</p>", "<p id=\"Par17\">Hierarchical clustering analyses of DE genes showed that females from polarization-selected lines in the Group condition clustered uniquely from polarization-selected females in the Alone condition (Fig. ##FIG##2##3## and Supplementary Table ##SUPPL##0##6##). Similarly, females from polarization-selected lines in the Group condition clustered uniquely from females from control lines under the Group and Alone conditions in both the telencephalon and the midbrain, suggesting a unique response in the regions of the brain associated with behaviour to social exposure. This was not observed in samples from the optic tectum, suggesting that visual processing of social treatments did not differ between polarization-selected and control females. Hierarchical clustering analyses using all expressed genes clustered samples by selection line rather than by social context condition (Supplementary Fig. ##SUPPL##0##4##), suggesting that social context affects only a targeted subset of the overall transcriptome rather than the majority of genes.</p>", "<title>Differential co-expression analyses</title>", "<p id=\"Par18\">We used systems biology methods designed to compare the co-expression networks between conditions to identify genes that change in the way they are connected to other genes within the co-expression network across conditions, independent of whether they are differentially expressed<sup>##REF##25172472##29##–##UREF##10##31##</sup>. Specifically, we used Bayes approach for differential co-expression analysis (BFDCA)<sup>##REF##27984044##30##</sup> to identify differentially co-expressed (DC) gene pairs under the Group and Alone conditions (that is, pairs of genes that significantly change in correlation between the two social contexts for each line<sup>##REF##27984044##30##,##REF##32875689##32##</sup>). Similar to the findings in the DE analyses, we found little overlap in the genes forming DC gene pairs between comparisons implemented for control and polarization-selected lines (see Supplementary Tables ##SUPPL##0##7## and ##SUPPL##0##8##, and Fig. ##FIG##2##3##). Together, our results suggest that polarization-selected lines were activating different biological pathways compared with control lines to modulate coordinated movement.</p>", "<p id=\"Par19\">We additionally found a group of genes that are both DE and DC in the same tissue and line, suggesting that they might play an important role in mediating coordinated movement (Supplementary Tables ##SUPPL##0##8## and ##SUPPL##0##9##). Specifically, in the telencephalon, we identified 4 genes that are both DE and DC in polarization-selected lines: <italic>LRRC24</italic>, <italic>PTPRS, KHDR2</italic> and <italic>PP2BA</italic> (Supplementary Table ##SUPPL##0##9##). These genes are part of the calcineurin and the Wnt/oxytocin signalling pathways known to be involved in modulating social behaviour, learning and memory<sup>##UREF##11##33##–##REF##15522306##35##</sup>. Enrichment tests confirm the functional relevance of the DC gene pairs identified, revealing an overrepresentation of genes associated with the glutamatergic synapse, as well as with visual transduction among DC gene pairs in multiple comparisons (Supplementary Tables ##SUPPL##0##10## and ##SUPPL##0##11##).</p>", "<title>Functional characterization of genes of interest across experiments</title>", "<p id=\"Par20\">We combined the information from our genomic and transcriptomic analyses on polarization-selected and control lines to obtain an intersected delimitation of the gene functions that our analyses highlighted as important in the development and expression of social interactions with conspecifics. Specifically, we used functional analyses in the set of genes with differentiated SNPs between merged sequences of the three replicates with high and low sociability (273 unique genes) as reference and compared results to functional analyses of genes differentially expressed in three different brain tissues of females following exposure to multiple social conditions. We found a concordance of 79% in the combination of biological processes (BP), cellular components (CC) and molecular functions (MF) GO terms enriched following analyses of differentially expressed genes in the telencephalon (<italic>n</italic> = 158). This value represented a 1.7-fold increase in the concordance of terms in relation to mean values obtained from corresponding enrichment analyses of 1,000 random sets of 158 genes (see Methods; mean concordance (CI): 45% (43, 47)). We likewise found concordances of 64% and 4.5% for differentially expressed genes in the midbrain (<italic>n</italic> = 109) and in the optic tectum (<italic>n</italic> = 21), respectively. These represented 2.1-fold and 1.1-fold increases in relation to analyses with 1,000 random sets of 109 and 21 genes in midbrain and optic tectum, respectively (mean concordance midbrain: 30% (28, 31); mean concordance telencephalon: 3.8% (3.6, 4.2)). We summarized and visualized GO terms enrichment lists across experiments and tissues sampled using REVIGO<sup>##REF##21789182##36##</sup>. We found a strong overlap between enrichment of GO biological process terms associated with learning and memory, synaptic processes, neuron projection and cell growth, mostly constrained to the telencephalon and midbrain regions (Fig. ##FIG##3##4##). We found similar patterns in relation to cellular component GO terms, with strong overlap in neuronal components, in particular, with high enrichment of terms associated with glutamatergic synapse. Visualization of GO terms associated with molecular functions suggests a major role of genes with protein binding function across experiments, including a role for cadherin-binding related genes in the midbrain (Fig. ##FIG##3##4##).</p>" ]	[ "<title>Discussion</title>", "<p id=\"Par21\">We used behavioural phenotyping across guppy families, in conjunction with Pool-seq and RNA-seq to identify the genetic architecture of coordinated motion. Our broad range of analyses, spanning genomes, transcriptomes and phenotypes, provides an exceptional evaluation of the molecular mechanisms underlying sociability in this fish. Our work suggests that genes and gene networks involved in social decision-making through neuron migration and synaptic function are key in the evolution of schooling, highlighting a crucial role of glutamatergic synaptic function and calcium-dependent signalling processes.</p>", "<p id=\"Par22\">Our pedigree-based phenotyping analyses of 195 guppy families from polarization-selected and control lines indicate moderate levels of heritability (Alignment: 0.06–0.34; Attraction: 0.09–0.26), with pronounced sex differences in full-pedigree models (Alignment<sub>female–male</sub>: 0.10 ± 0.05; Attraction<sub>female–male</sub>: −0.17 ± 0.05) in estimates for key behavioural traits forming the sociability axis in this species. Our heritability estimates are similar to estimates for affiliative social behaviour traits in primates, ungulates and rodents<sup>##UREF##3##10##–##REF##29052939##13##,##REF##33583128##37##</sup>, and to overall estimates of heritability in personality traits across human and non-human animals<sup>##UREF##1##8##,##REF##25392476##38##</sup>. Given the importance of social behaviour in a range of survival and fitness components in natural systems<sup>##UREF##0##1##,##REF##15690039##39##,##REF##10102827##40##</sup>, our results suggest that complex genetic architectures can respond quickly to strong evolutionary pressures, even when only one sex is subject to selection<sup>##REF##33268362##22##</sup>, and that our lab population contained substantial amounts of standing genetic variation for these traits before selection.</p>", "<p id=\"Par23\">The complex genetic architecture makes it difficult to precisely characterize cross-sex genetic effects in our study. We nonetheless observed a positive cross-sex genetic correlation in attraction (0.68, CI: 0.25–0.98), suggesting similarities between males and females in the genetic architecture of this trait. This result is concordant with a study focused on the bold–shy continuum aspect of personality establishing that sex differences in risk-taking behaviours are weak and probably lack sex-specific genetic architecture in this species<sup>##REF##29795179##41##</sup>. Yet, sex differences in heritability estimates of alignment (♀<italic>h</italic><sup>2</sup><sub>alignment</sub> = 0.34 (0.18, 0.49); ♂<italic>h</italic><sup>2</sup><sub>alignment</sub> = 0.06 (0.00, 0.18)) suggest that it is important to account for sex-specific additive genetic variance when inferring the evolvability of personality traits. In addition, the low cross-sex heritability we observe in these latter traits is particularly interesting and suggests that selection for a complex trait in one sex need not result in a correlated response in the other sex. Overall, this indicates significant sex-specific genetic variation for sex-specific behaviours, and that sexually dimorphic behaviours need not require decoupling of male and female genetic architecture when sufficient sex-specific genetic variation is present.</p>", "<p id=\"Par24\">We next mapped the genomic and transcriptomic basis of phenotypic differences in polarization in female guppies. Our genome-wide association study was designed to compare individuals with high- and low-sociability phenotypes from within polarization-selected lines, rather than between polarization-selected and control lines. This may have resulted in compressed phenotypic spread but carries the important advantage of reducing the incidence of SNP frequencies that vary across alternative selection lines due to drift. As such, our design is conservative. In our most stringent Pool-seq analysis, we identified SNPs in four genes that consistently differed across all three replicate lines, these genes having been previously associated with cognition and functions relevant to social behaviour. The supt6 histone chaperone and transcription elongation factor homologue (<italic>supt6h</italic>) is important in the positive regulation of transcriptional elongation and a substrate of mTOR, a signalling pathway with a role in cognitive function<sup>##REF##25374355##42##,##REF##26669439##43##</sup>. The ubiquitin-specific peptidase 11 (<italic>usp11</italic>) homologue in humans has a critical function in the development of the neural cortex, and knockout studies in mice show that the locus protects females from cognitive impairment<sup>##UREF##12##44##,##REF##36198316##45##</sup>. Similarly, the novel RNA-binding protein <italic>Nova-1-</italic>like gene is associated with a neuron-specific nuclear RNA-binding protein in humans and regulates brain-specific splicing related to synaptic function<sup>##REF##9154818##46##</sup>.</p>", "<p id=\"Par25\">Finally, our Pool-seq analysis identified cadherin 13 (<italic>cdh13</italic>), the human homologue of which has a crucial role in GABAergic function<sup>##REF##26460479##47##</sup>, with involvement in neural growth and axonal guidance during early development<sup>##REF##20190755##48##,##REF##25468941##49##</sup>. Moreover, deficit of this gene has a major impact in neurodevelopmental disorders including attention-deficit/hyperactivity disorder and autism spectrum disorder<sup>##UREF##13##50##</sup>. Indeed, <italic>cdh13</italic> knockout mice display delayed acquisition of learning tasks and a decreased latency in sociability<sup>##REF##32113967##51##</sup>. Interestingly, repeated selection of genes involved in cadherin-signalling pathways<sup>##REF##34029313##52##</sup> has been shown in guppy populations experiencing different predation pressures. Together, natural selection imposed by differences in predation across these populations<sup>##REF##28564843##53##–##REF##28855361##55##</sup> and our combined findings in the genomic background of guppies suggest strong selective pressures for cadherin-signalling genes due to their modulation of affiliative behaviours.</p>", "<p id=\"Par26\">Our expression results revealed differences in regulation in genes associated with learning, behaviour and neural function, mainly in the telencephalon and midbrain, in comparisons of polarization-selected and control lines in different social contexts. Overall, this suggests that the regulation of highly demanding cognitive processes via synaptic function underlies variation in sociability. While the integration of visual signals is central in fish schools<sup>##UREF##14##56##</sup>, our results suggest that higher-order cognitive processes are the basis of variation in social affinity. Indeed, the differences in alignment and attraction observed when swimming with unfamiliar conspecifics are arguably highly cognitively demanding, as within a collective motion context, the tendency to copy the directional movements of other individuals implies a direct trade-off between personal goal-oriented behaviours and the benefits of social conformity<sup>##UREF##15##57##,##REF##26655156##58##</sup>. Together, our study of transcriptomic profiles of schooling fish suggests that the regulation of affiliative behaviours in this species is driven by an intricately linked social decision-making network in the brain<sup>##REF##24815200##59##</sup>, with strong links to functional groups governing social behaviours and personality across species. More broadly, our results offer insight into important questions about the evolution of behaviour and other traits with complex genetic architecture. First, our results of large-scale expression differences among selection lines are consistent with recent discussions of the role of gene regulatory networks in coordinating large numbers of genes associated with behaviours<sup>##REF##32661177##60##</sup>. It is highly likely that the genes with convergent expression changes in the selection lines are controlled via a modular regulatory architecture, as evidenced by our co-expression network analysis (Supplementary Tables ##SUPPL##0##7## and ##SUPPL##0##8##, and Fig. ##FIG##2##3##).</p>", "<p id=\"Par27\">We find a striking concordance in the functionality of genes independently identified in genomic and transcriptomic profiling of strongly differentiated experiments assessing social interactions of polarization-selected female guppies (Fig. ##FIG##3##4##). The overlap in significantly enriched GO terms, including learning, synaptic processes and neuron projection restricted to brain regions associated with decision-making and motor control, strongly reinforces the notion that genetic regulation of these cognitive processes is fundamental for sociability in guppies. In addition, the functional concordance we observe between the regulatory and protein differences among our selection lines is noteworthy in the context of the discussion of whether structural or regulatory variation is more important in adaptive phenotypes<sup>##REF##17492956##61##,##REF##18614008##62##</sup>. The overlap in functionality in our genomic and transcriptomic approaches suggests that both are important, with artificial selection for behaviour acting on coding and regulatory variation within the same pathway to achieve adaptive phenotypes.</p>", "<p id=\"Par28\">Our results indicate that the regulation of glutamatergic synaptic processes is a particularly promising network for future studies of affiliative behaviours. Interestingly, differential gene expression of glutamate receptor genes has been identified to regulate female mating preferences in guppies<sup>##REF##30297748##63##</sup> and is concordantly identified across species of vertebrates in the regulation of long-term affiliative mating behaviours<sup>##REF##30617061##64##</sup>. Guppies are livebearers, and this has hindered the use of functional genetic tools such as Clustered Regularly Interspaced Short Palindromic Repeats on this species. Although not feasible at this time, future functional validation via genetic manipulations of guppies for these pathways would prove extremely interesting.</p>", "<p id=\"Par29\">Our results are also concordant with other comparative transcriptomic studies of behavioural responses towards conspecific territorial intrusions, which identified calcium ion-binding regulation across phylogenetically distant species<sup>##REF##25453090##65##</sup>. Together, the consistency in our findings of specific genes and functional terms associated with calcium-dependent and cadherin-binding molecular functions across our experiments suggests that these are promising molecular targets for future research exploring the evolution and regulation of sociability and affiliative behaviours.</p>" ]	[]	[ "<p id=\"Par1\">The organization and coordination of fish schools provide a valuable model to investigate the genetic architecture of affiliative behaviours and dissect the mechanisms underlying social behaviours and personalities. Here we used replicate guppy selection lines that vary in schooling propensity and combine quantitative genetics with genomic and transcriptomic analyses to investigate the genetic basis of sociability phenotypes. We show that consistent with findings in collective motion patterns, experimental evolution of schooling propensity increased the sociability of female, but not male, guppies when swimming with unfamiliar conspecifics. This finding highlights a relevant link between coordinated motion and sociability for species forming fission–fusion societies in which both group size and the type of social interactions are dynamic across space and time. We further show that alignment and attraction, the two major traits forming the sociability personality axis in this species, showed heritability estimates at the upper end of the range previously described for social behaviours, with important variation across sexes. The results from both Pool-seq and RNA-seq data indicated that genes involved in neuron migration and synaptic function were instrumental in the evolution of sociability, highlighting a crucial role of glutamatergic synaptic function and calcium-dependent signalling processes in the evolution of schooling.</p>", "<p id=\"Par2\">The authors used multiple lines of evidence including behavioural assays, quantitative genetics and transcriptomics to explore schooling behaviour in guppies. Both genomic and transcriptomic analyses indicated that genes involved in neuron migration and synaptic function played key roles in the evolution of schooling behaviour.</p>", "<title>Subject terms</title>" ]	[ "<title>Main</title>", "<p id=\"Par3\">Living in groups, a widespread phenomenon across the animal kingdom, can lead to strikingly complex social behaviours, such as cooperative interactions, subdivision of labour or collective decision-making<sup>##UREF##0##1##</sup>. Sociability, the propensity to affiliate with other animals, can also vary across individuals. Sociability represents a fundamental aspect of personality which can influence social interactions and is often subject to strong selective processes<sup>##REF##17437562##2##,##REF##34894041##3##</sup>. Indeed, intraspecific differences in sociability are widespread (for example, refs. <sup>##REF##24047530##4##,##REF##29899075##5##</sup>) and individual genetic variation often underlies variability in personality and social behaviour phenotypes<sup>##REF##20346758##6##</sup>. However, heritability estimates of social behaviour traits are consistent with a complex, polygenic architecture<sup>##REF##18988841##7##</sup>. Human twin and family studies reveal that heritability estimates of personality traits generally ≈0.40 (reviewed in ref. <sup>##UREF##1##8##</sup>). In non-human animals, a meta-analysis estimated that mean heritability is 0.23 across social behaviours, including personality traits<sup>##UREF##2##9##</sup>, with heritability of affiliative associations ranging substantially from 0.11 to 0.51 (refs <sup>##UREF##3##10##–##REF##29052939##13##</sup>).</p>", "<p id=\"Par4\">Despite this complexity, multiple neural and genetic mechanisms underlying social behaviour have been identified<sup>##REF##36263954##14##</sup>. Many of the neural structures and neuromodulators (serotonin, dopamine, vasopressin and oxytocin) are highly conserved within the social decision-making network across vertebrates<sup>##REF##29044796##15##</sup>. Moreover, human personality traits associated with social decision-making have been linked to dopaminergic and serotonergic genes (reviewed in ref. <sup>##REF##21163292##16##</sup>), and the regulation of these neuromodulators has been connected to neurodevelopmental disorders that affect affiliative behaviours, such as autism spectrum disorder<sup>##REF##20346758##6##,##REF##24100617##17##,##UREF##5##18##</sup>. Studies in non-human organisms likewise point towards a major role of genes involved in the regulation of these neurochemical systems. For instance, mouse knockout mutants for genes involved in dopaminergic signalling exhibit altered sociability phenotypes<sup>##REF##30537522##19##</sup>, and changes in sociability in three-spined sticklebacks, <italic>Gasterosteus aculeatus</italic>, are predicted by natural variation in the expression of genes within the dopaminergic and stress pathways<sup>##UREF##6##20##</sup>. However, while specific groups of genes have been identified for a range of affiliative behaviours, we lack a deeper understanding of their role in inter-individual variation and evolutionary processes underlying sociability.</p>", "<p id=\"Par5\">In fish, group living often leads to spectacular forms of collective behaviour, with members of a school coordinating their movements to increase efficiency in foraging, travelling or predator avoidance<sup>##UREF##0##1##</sup>. The extent to which members of a school coordinate their movements is an integral part of the sociability axis of personality, that is, how individuals react to the presence or absence of conspecifics excluding aggressive behaviours<sup>##REF##29581400##21##</sup>. We previously showed that schooling behaviour has a repeatability of 0.43 at the individual level<sup>##REF##33268362##22##</sup> and this can increase substantially over just three generations of artificial selection in female guppies, <italic>Poecilia reticulata</italic>, generating a 15% increase in intrinsic schooling propensity compared with controls<sup>##REF##33268362##22##,##UREF##7##23##</sup>. Selection was based on a group phenotype, polarization, or the level of alignment between individuals moving together in a group.</p>", "<p id=\"Par6\">Understanding the genetic basis of this schooling phenotype requires linking individual phenotypic differences to genetic variation. In this study, we phenotyped alignment and attraction of 1,496 guppies across 195 families (father, mother, three female and three male offspring from our replicate experimental selection lines) to estimate the heritability of these two motion characteristics that previous factor analyses identified to be integral components for the sociability axis of personality in this species<sup>##REF##29581400##21##</sup>. Because many social interaction patterns in guppies have sex differences<sup>##UREF##8##24##–##REF##9784218##26##</sup>, and because our selection was performed solely on females, we are able to examine cross-sex genetic correlations in this ecologically relevant behavioural trait. Genomic and transcriptomic data from these lines reveal convergence in the genetic architecture of sociability, highlighting a series of genes with well-defined roles in neurodevelopmental processes. Our results provide a robust agreement across experiments about the genetic regulation of neural processes in decision making and motor control regions of the brain, and its importance for variation of personality within individuals of this species.</p>", "<title>Supplementary information</title>", "<p>\n\n\n\n\n</p>", "<title>Source data</title>", "<p>\n\n\n\n</p>" ]	[ "<title>Supplementary information</title>", "<p>The online version contains supplementary material available at 10.1038/s41559-023-02249-9.</p>", "<title>Acknowledgements</title>", "<p>We thank D. Sumpter, K. Pelckmans and J. Herbert-Read for important contributions to the conceptualization of the artificial selection procedure; J. Shu (jacelyndesigns.com) for composing guppy graphics for figures; A. Rennie, E. Trejo and A. Boussard for help with fish husbandry. This work was supported by the Knut and Alice Wallenberg Foundation (102 2013.0072 to N.K.), the Canada 150 Research Chair Program, the European Research Council (680951 to J.E.M), the Swedish Research Council (2016-03435 to N.K., 2017-04957), the Royal Swedish Academy of Sciences (BS2019-0046 to A.C-L.), Lars Hiertas Memorial Foundation (FO2019-0477 to A.C-L.), European Research Council (H2020 Marie Skłodowska-Curie Actions 654699 to N.I.B) and Universidad de los Andes (FAPA-4700000443 to N.I.B).</p>", "<title>Author contributions</title>", "<p>J.E.M., N.K. and A.C.-L. conceptualized and acquired funding for the project. N.K., A.K., A.S. and M.R. designed the selection procedure and behavioural experiments. A.C.-L. conducted research to obtain behavioural data. A.C.-L. and W.v.d.B. performed formal analyses and visualization of behavioural and heritability data. A.C.-L. and M.C.-C. conducted research to obtain genomic data. A.C.-L., M.C.-C. and I.D. performed formal analyses and visualization of genomic data. N.I.B., S.D.B. and A.K. conducted research to obtain transcriptomics data. N.I.B. and. A.C.-L. performed formal analyses and visualization of transcriptomics data. A.C.-L., J.E.M. and N.K. wrote the original draft. All authors contributed to the final version of the manuscript.</p>", "<title>Peer review</title>", "<title>Peer review information</title>", "<p id=\"Par52\"><italic>Nature Ecology & Evolution</italic> thanks the anonymous reviewers for their contribution to the peer review of this work. ##SUPPL##2##Peer reviewer reports## are available.</p>", "<title>Funding</title>", "<p>Open access funding provided by Uppsala University.</p>", "<title>Data availability</title>", "<p>Data needed to evaluate the conclusions in the paper are deposited in figshare under accession code 10.6084/m9.figshare.23805702. Genomic and transcriptomic data are deposited at NCBI under accession codes <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA994132\">PRJNA994132</ext-link> and <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA504011\">PRJNA504011</ext-link>. Video recordings related to this paper may be requested from the authors. <xref ref-type=\"sec\" rid=\"Sec31\">Source data</xref> are provided with this paper.</p>", "<title>Code availability</title>", "<p>Codes needed to evaluate the conclusions in the paper are deposited in figshare under accession code 10.6084/m9.figshare.23805702.</p>", "<title>Competing interests</title>", "<p id=\"Par53\">The authors declare no competing interests.</p>" ]	[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>Heritability of sociability in guppies.</title><p><bold>a</bold>, Female, but not male, guppies from polarization-selected lines (<italic>n</italic> = 763, orange) presented higher alignment to the group direction (left) and shorter distances to their nearest neighbour (higher alignment; right) than guppies from control lines (<italic>n</italic> = 724, grey) when swimming with unfamiliar same-sex conspecifics (see Supplementary Tables ##SUPPL##0##1## and ##SUPPL##0##2##). For all boxplots, horizontal lines indicate medians, boxes indicate the interquartile range and whiskers indicate all points within 1.5 times the interquartile range. Boxes in top left position of each facet indicate Tukey adjusted <italic>P</italic> values for multiple contrasts (<italic>P</italic> < 0.05 in bold) for statistical contrasts by sex in an LMM comparing alignment and attraction between selection line treatments (see Supplementary Tables ##SUPPL##0##1## and ##SUPPL##0##2##). <bold>b</bold>, Animal models using same-sex pedigrees and full pedigrees with alignment and attraction (nearest neighbour distance) phenotypes in 195 families of polarization-selected and control guppy lines indicated a moderate heritability in female guppies for both biologically relevant aspects of sociability measured, alignment (left) and attraction (right). In males, we found moderate heritability in attraction, but CIs in alignment estimates overlapped with 0, suggesting low heritability of this sociability aspect. Our full-pedigree animal models provided large CIs for male–female correlations in sociability, with estimates overlapping 0 in alignment, but a positive correlation between sexes in attraction (see Supplementary Tables ##SUPPL##0##4## and ##SUPPL##0##5##). Red diamonds indicate mean heritability values with 95% CIs.</p><p>##SUPPL##4##Source data##</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>Genetic basis of sociability in the guppy.</title><p><bold>a</bold>, Manhattan plot of −log<sub>10</sub>(<italic>P</italic>) values across linkage groups (LG) in the guppy genome resulting from a two-sided Fisher’s exact test comparing allele frequency differences between high- and low-sociability female guppies. We merged pooled DNA sequences of three independent replicates and found 819 SNPs to be significantly different (above genome-wide threshold highlighted in red), while a highly stringent analyses of consistent allele frequency differences across our three independent replicates (CMH test; see Methods) identified 13 SNPs (5 of them within genes) associated with sociability in the species (gene names and SNP location in the genome highlighted in orange). SNPs with −log<sub>10</sub>(<italic>P</italic>) < 2 are omitted. <bold>b</bold>, Clustering of statistically significant overrepresented GO annotations for biological processes associated with differences between high and low sociability in guppies. Point size and colour provide information on fold enrichment value from the statistical overrepresentation test performed in PANTHER<sup>##REF##23868073##84##</sup> (see Methods). Terms with fold enrichment lower than 8 are represented but not described in text. Axes have no intrinsic meaning and are based on multidimensional scaling which clustered terms based on semantic similarities<sup>##UREF##21##74##</sup>.</p><p>##SUPPL##5##Source data##</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><title>Neurogenomic response of schooling in guppies.</title><p><bold>a</bold>–<bold>c</bold>, Hierarchical clustering and relative expression levels for all differentially expressed genes between Alone and Group treatments in the optic tectum (<bold>a</bold>), the telencephalon (<bold>b</bold>) and the midbrain (<bold>c</bold>). Differentially expressed genes were identified separately in polarization and control line samples. Clustering, based on Euclidian distance, represents transcriptional similarity across all samples. Venn diagrams summarize the total number of DE genes and DC gene pairs in each tissue for polarization-selected and control lines.</p><p>##SUPPL##6##Source data##</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><title>Functional characterization of genes of interest across experiments.</title><p>Visualization of functional overlap based on GO annotations between genes of interest highlighted in strongly differentiated experimental setups evaluating social interactions of female guppies following experimental evolution for higher polarization: (1) genomic analyses of DNA comparing Pool-seq of high- and low-sociability female guppies (left column); (2) transcriptomic analyses evaluating differentially expressed genes in key brain regions of polarization-selected lines of female guppies exposed to two different social contexts: swimming alone or with a group of conspecifics (TEL, telencephalon; MBR, midbrain; OT, optic tectum; columns 2–4). Shades of green indicate fold enrichment from our statistical overrepresentation tests performed to gene lists obtained from each experiment (see ##SUPPL##3##Supplementary Dataset##).</p><p>##SUPPL##4##Source data##</p></caption></fig>" ]	[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Characterization of genes associated with sociability in guppies</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th>SNP location</th><th>Gene ID Ensemble</th><th>Gene name</th><th>Described cognitive function of homologues</th><th>References</th></tr></thead><tbody><tr><td>Chr 7: 30748139</td><td>00000004543</td><td><italic>usp11</italic></td><td><p>Control of cortical neurogenesis and neuronal migration</p><p>Mutations of the gene have been associated with neurological disorders.</p></td><td><p>ref. <sup>##UREF##12##44##</sup></p><p>ref. <sup>##REF##26539891##91##</sup></p></td></tr><tr><td>Chr 13: 31383940</td><td>00000018946</td><td>Novel gene (RNA-binding protein <italic>Nova-1</italic>-like)</td><td>Neuronal RNA-binding protein associated with motor function</td><td>ref. <sup>##REF##9154818##46##</sup></td></tr><tr><td>Chr 14: 4286109</td><td>00000009725</td><td><italic>supt6h</italic></td><td>Substrate of mTOR, a signalling pathway associated with brain function and neurodegenerative disorders</td><td><p>ref. <sup>##REF##25374355##42##</sup></p><p>ref. <sup>##REF##26669439##43##</sup></p></td></tr><tr><td>Chr 18: 4286109</td><td>00000014318</td><td>Novel gene</td><td>–</td><td>–</td></tr><tr><td>Chr 19: 3032099</td><td>00000019822</td><td><italic>cdh13</italic></td><td><p>Modulation of brain activity through GABAergic function</p><p>Organization of neuronal circuits</p></td><td><p>ref. <sup>##REF##26460479##47##</sup></p><p>ref. <sup>##REF##17133224##92##</sup></p></td></tr></tbody></table></table-wrap>" ]	[]	[]	[]	[]	[]	[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM3\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM4\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM5\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM6\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM7\"></supplementary-material>" ]	[ "<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"41559_2023_2249_Fig1_HTML\" id=\"d32e630\"/>", "<graphic xlink:href=\"41559_2023_2249_Fig2_HTML\" id=\"d32e782\"/>", "<graphic xlink:href=\"41559_2023_2249_Fig3_HTML\" id=\"d32e1027\"/>", "<graphic xlink:href=\"41559_2023_2249_Fig4_HTML\" id=\"d32e1143\"/>" ]	[ "<media xlink:href=\"41559_2023_2249_MOESM1_ESM.pdf\"><label>Supplementary Information</label><caption><p>Supplementary Figs. 1–5, Tables 1–10 and references.</p></caption></media>", "<media xlink:href=\"41559_2023_2249_MOESM2_ESM.pdf\"><caption><p>Reporting Summary</p></caption></media>", "<media xlink:href=\"41559_2023_2249_MOESM3_ESM.pdf\"><caption><p>Peer Review File</p></caption></media>", "<media xlink:href=\"41559_2023_2249_MOESM4_ESM.xlsx\"><label>Supplementary Data</label><caption><p>Source data for Supplementary Figs. 1–5.</p></caption></media>", "<media xlink:href=\"41559_2023_2249_MOESM5_ESM.xlsx\"><label>Source Data Figs. 1, 2, 4</label><caption><p>Sheet Fig. 1a. Data of alignment and nearest neighbour distance by sex/generation/selection line. Sheet Fig. 1b. Data of heritability and male–female correlations resulting from animal models. Sheet Fig. 2b. Results from REVIGO analyses on biological process GO terms from genomic data. Sheet Fig. 4. Fold changes in GO term categories found to be enriched across experiments.</p></caption></media>", "<media xlink:href=\"41559_2023_2249_MOESM6_ESM.zip\"><label>Source Data Fig. 2</label><caption><p>Fig. 2a. Extra source data file with results from GWAS and CMH tests of genomic data.</p></caption></media>", "<media xlink:href=\"41559_2023_2249_MOESM7_ESM.zip\"><label>Source Data Fig. 3</label><caption><p>Extra source data file with transcriptomic results of gene expression comparisons performed across multiple treatments and brain tissues + genes differentially expressed for each comparison.</p></caption></media>" ]	[{"label": ["1."], "mixed-citation": ["Krause, J. & Ruxton, G. D. 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PMC10781617	38200294	[]	[ "<title>Methods</title>", "<title>Ancient genomic analyses</title>", "<p id=\"Par30\">The 100 ancient Danish genomes analysed here contribute to the 317 shotgun-sequenced genomes in Allentoft et al.<sup>##UREF##0##3##</sup>. All details concerning sampling, DNA extraction, library preparation, sequencing, basic bioinformatics, authentication and dataset construction are found in ref. <sup>##UREF##0##3##</sup> together with all site descriptions and sample metadata. A condensed list of metainformation on the 100 Danish individuals is released here (Supplementary Data ##SUPPL##2##1##) together with a text summarizing the study sites and skeletons (Supplementary Note ##SUPPL##0##1##). In brief, laboratory work was carried out in dedicated ancient DNA cleanlab facilities (University of Copenhagen) using optimized ancient DNA methods<sup>##REF##26062507##1##,##REF##26081994##77##</sup>. Double-stranded blunt-end libraries were sequenced (80 bp and 100 bp single-end reads) on Illumina HiSeq 2500 and 4000 platforms. Initial shallow shotgun screening was used to identify samples with sufficient DNA preservation for deeper genomic sequencing. Of the 100 Danish samples that qualified for this, 65 were from tooth cementum, 29 were petrous bones, and 6 were obtained from other bones (Supplementary Data ##SUPPL##2##1##). Sequence reads were bioinformatically mapped to the human reference genome (build 37), filtered and merged to sample level followed by estimates of genomic overage, post-mortem DNA damage, contamination, and genetic sex ID (see<sup>##UREF##0##3##</sup>). For these 100 samples we observed C-to-T deamination fractions ranging from 12.2% to 66.7%, with an average of 34.9% across all samples (Supplementary Data ##SUPPL##2##1##), consistent with highly degraded ancient DNA. We genetically identified 67 males, 32 females and one undetermined in our dataset (Supplementary Data ##SUPPL##2##1##).</p>", "<p id=\"Par31\">We utilized a new computational method optimized for low-coverage data<sup>##REF##37339987##21##</sup>, to impute genotypes based on genotype likelihoods of ancient individuals with the samtools/bcftools pipeline, and using the 1000 Genomes phased data<sup>##REF##26432245##78##</sup> as a reference panel. To generate the main dataset in<sup>##UREF##0##3##</sup> this was jointly applied to 1,664 shotgun-sequenced ancient genomes, including our 100 ancient Danish genomes, and resulted in a dataset of 8.5 million common SNPs (>1% minor allele frequency and imputation info score > 0.5) for imputed diploid ancient genomes. After removing genomes with low coverage (<0.1X), low imputation quality (average genotype probability <0.98), contamination estimates >5%, or close relatives (first or second degree, lowest coverage relative removed), 67 of the 100 Danish genomes were retained as imputed in downstream analyses. The remaining 33 genomes were analysed as pseudo-haploid genotypes.</p>", "<p id=\"Par32\">For population genetic analyses, we combined ancient samples with two different modern reference panels:<list list-type=\"bullet\"><list-item><p id=\"Par33\">‘1000 G’ dataset: whole-genome sequencing data of 2,504 individuals from 26 world-wide populations from the 1000 Genomes project, with genotypes at 7,321,965 autosomal SNPs.</p></list-item><list-item><p id=\"Par34\">‘HO’ dataset: SNP array data of 2,180 modern individuals from 213 world-wide populations, with genotypes at 535,880 autosomal SNPs.</p></list-item></list></p>", "<p id=\"Par35\">Analyses were based on the 1000 G dataset unless otherwise noted. Individuals not passing imputation quality control cutoffs mentioned above were included in PCA and ADMIXTURE analyses as pseudo-haploid genotypes. Four Danish individuals showed possible signs of DNA contamination (Fig. ##FIG##2##3## and Supplementary Data ##SUPPL##2##1##) and were excluded from most analyses. To take full advantage of the extensive multiproxy data they were, however, included in Fig. ##FIG##2##3##. Individual metadata for all genetic analyses related to the ancient Danish individuals as well as selected subset of relevant West Eurasian individuals, are reported in Supplementary Data ##SUPPL##4##3##.</p>", "<p id=\"Par36\">For PCA combining ancient and modern Western Eurasians (Fig. ##FIG##0##1b##), we used the data and framework from<sup>##UREF##0##3##</sup> to capture West Eurasian genetic diversity based on n = 983 modern genomes and <italic>n</italic> = 1,105 ancient genomes (HO dataset). Data from a total of 179 ancient Danish genomes are shown in Fig. ##FIG##0##1b## of which 83 are previously published<sup>##REF##26062507##1##,##REF##31848342##47##,##REF##33444387##57##,##REF##32939067##76##</sup> (Supplementary Data ##SUPPL##4##3##)—the latter being primarily from the Bronze Age and Viking periods. To perform PCA projection for low-coverage individuals, we used smartpca with options ‘lsqproject: YES’ and ‘autoshrink: YES’.</p>", "<p id=\"Par37\">The ADMIXTURE results presented in this study represent subsets of individuals from the full ADMIXTURE runs in<sup>##UREF##0##3##</sup> where 1,593 ancient individuals were analysed (<italic>n</italic> = 1,492 imputed, <italic>n</italic> = 101 pseudo-haploid, <italic>n</italic> = 71 excluded as close relatives or with a contamination estimate >5%; HO dataset). Figure ##FIG##0##1c## represents 176 ancient Danish genomes after excluding three close relatives (Supplementary Data ##SUPPL##2##1## and ##SUPPL##5##4##).</p>", "<p id=\"Par38\">D-statistics were obtained using pseudo-haploid genotypes at transversion SNPs in the 1000 G dataset, grouping the non-Danish individuals into populations using their membership in the genetic clusters inferred from IBD sharing (Supplementary Data ##SUPPL##5##IV##). We computed D-statistics from genotypes in PLINK format using the qpdstat function implemented in the ADMIXTOOLS 2 R package<sup>##UREF##47##79##</sup>.</p>", "<p id=\"Par39\">Analysis of IBD sharing and mixture models were carried out as described<sup>##UREF##0##3##</sup>, using the same set of inferred genetic clusters (see Supplementary Data ##SUPPL##5##4##). In brief, we used IBDseq<sup>##REF##24207118##80##</sup> to detect IBD segments, a carried out genetic clustering of the individuals using hierarchical community detection on a network of pairwise IBD-sharing similarities. IBD-based PCA was carried out in R using the eigen function on a covariance matrix of pairwise IBD sharing between the respective ancient individuals. We estimated ancestry proportion in supervised modelling of target individuals as mixtures of different sets of putative source groups via non-negative least squares on relative IBD-sharing rate vectors.</p>", "<p id=\"Par40\">Admixture time inference for FBC-associated individuals was carried out using the linkage-disequilibrium-based method DATES<sup>##REF##35635751##44##</sup> (HO dataset). We estimated admixture time separately for each target individual from Denmark and Sweden, using hunter-gatherer individuals (<italic>n</italic> = 58) and early farmer individuals (<italic>n</italic> = 49) as the two source groups.</p>", "<p id=\"Par41\">For the PCAs presented in Fig. ##FIG##3##4## including modern Danish samples we projected 2,000 imputed samples<sup>##REF##36697501##81##</sup> of individuals born in 1981–2005 from the iPSYCH2012 case-cohort study<sup>##REF##28924187##62##</sup> onto the PCA space spanned by the 1,145 non-low coverage or related european and western Asian ancient imputed samples<sup>##UREF##0##3##</sup>. Otherwise, the analysis is identical to the one described above. The modern individuals were selected from a subset of the random population subcohort component of iPSYCH2012 having all four grandparents born in Denmark, and being of Danish or European ancestry as determined in a separate already existing PCA of main modern-day ancestry groups<sup>##REF##36697501##81##</sup>. This was done using Eigensoft 7.2.1 on the intersect of imputed SNPs from the ancient and modern samples, filtered by minor allele frequency 0.05, pruned using PLINK v1.90b6.21<sup>##REF##17701901##82##</sup> based on source samples (parameters: –indep-pairwise 1000 50 0.25) leaving 146,895 variants.</p>", "<p id=\"Par42\">The genetic predictions of eye and hair colour were done based on the HIrisPlex system<sup>##REF##22917817##83##</sup>. We used imputed effect allele dosages of 18 out of 24 main effect HIrisPlex variants, available for the ancient samples, to derive probabilities for brown, blue and grey/intermediate eye colour and blond, brown, black and red hair colour, following HIrisPlex formulas (see further details in Supplementary Note ##SUPPL##0##2##). We predicted relative ‘genetic height’ using allelic effect estimates from 310 common autosomal SNPs with robustly genome-wide significant allelic effects (<italic>P</italic> < 10<sup>−15</sup>) in a recent GWAS of height in the UK Biobank<sup>##REF##30305743##84##</sup>. Per-sample height polygenic score (PGS) was calculated for ancient individuals as well as 3,467 Danish ancestry male conscripts from the random population subcohort of the iPSYCH2012 case-cohort study<sup>##REF##28924187##62##</sup> by summing allelic effect multiplied with the effect allele imputed dosage<sup>##REF##36697501##81##</sup> across the 310 loci. For further details see Supplementary Note ##SUPPL##0##2##. Only a fraction of the 100 Danish skeletons were suitable for stature estimation by actual measurement, which is why these values are not reported here.</p>", "<title>Radiocarbon dates and Bayesian modelling of ancestry chronology</title>", "<p id=\"Par43\">For the 100 sample ages in this study we use midpoint estimates of the calibrated and reservoir corrected probability distribution of the radiocarbon age (Supplementary Data ##SUPPL##2##1##; further <sup>14</sup>C dates, associated isotopic measurements, calibrations and reservoir corrections are accessible in ref. <sup>##UREF##0##3##</sup>). Focusing on estimating the interval between the two major population turnovers, we established a precise chronology using 81 radiocarbon dates from 64 Danish sites relevant to this particular interval (Supplementary Note ##SUPPL##0##3##). A Bayesian approach applied to the radiocarbon dates unifies radiocarbon results, ancestry information, and the high precision curve into one calibration process, thereby gaining greater precision. All models and data calibrations were performed using OxCal v4.4<sup>##UREF##48##85##–##UREF##51##88##</sup> and the calibration dataset from Reimer et al.<sup>##UREF##52##89##</sup>. We used a trapezoidal phase prior<sup>##UREF##53##90##,##UREF##54##91##</sup> for the calculation of the transitional time interval to determine duration between the first appearance of Anatolian farmer-related ancestry to the first appearance of Steppe-related ancestry in Denmark. We corrected the reservoir effect on bones with significantly increased isotope values (δ<sup>13</sup>C, −18.00 and δ<sup>15</sup>N, +12.00) directly in the models using previously defined reservoir ages as input and calculated the diet reconstruction estimates for the individual in <sup>14</sup>C years based on the collagen isotope values (Supplementary Note ##SUPPL##0##3## and Supplementary Figs. ##SUPPL##0##3.1## and ##SUPPL##0##3.2##); for a similar method see refs. <sup>##UREF##55##92##,##UREF##56##93##</sup>. For combining radiocarbon dates related to the same individual we used the R_Combine() function.</p>", "<title>Stable isotope proxies for diet and mobility</title>", "<p id=\"Par44\">Bulk collagen isotope values of carbon (δ<sup>13</sup>C) and nitrogen (δ<sup>15</sup>N) represent protein sources consumed over several years before death, depending on the skeletal part and the age at death of the individual<sup>##REF##17405135##94##,##REF##18773469##95##</sup>. Generally, δ<sup>13</sup>C values inform on the proportion of marine versus terrestrial protein, whereas δ<sup>15</sup>N values reflect the trophic level from which the proteins were acquired<sup>##UREF##57##96##,##UREF##58##97##</sup>. See Supplementary Note ##SUPPL##0##4## for further discussion. Stable isotope values were measured in collagen from all 100 skeletons and the full assemblage of isotopic measurements is available in Supplementary Data ##SUPPL##3##2##, and further discussed in Supplementary Note ##SUPPL##0##4##. Most of the δ<sup>13</sup>C and δ<sup>15</sup>N measurements were conducted at the <sup>14</sup>C Centre, University of Belfast according to standard protocols<sup>##UREF##59##98##</sup>, based on a modified Longin method including ultra-filtration<sup>##UREF##59##98##,##REF##4926713##99##</sup>. Measured uncertainty was within the generally accepted range of ±0.2‰ (1 s.d.) and all samples were within the acceptable atomic C:N range of 2.9–3.6, showing low likelihood of diagenesis<sup>##REF##28311342##100##,##UREF##60##101##</sup>.</p>", "<p id=\"Par45\">Strontium isotope analyses can provide a proxy for individual mobility<sup>##UREF##61##102##–##UREF##63##104##</sup>. The <sup>87</sup>Sr/<sup>86</sup>Sr ratio in specific skeletal elements may reflect the local geological signature obtained through diet by the individual during early childhood and it will usually remain unchanged during life and after death<sup>##UREF##64##105##</sup>. Ongoing controversies exist over the exact use of geographically-defined baseline values<sup>##UREF##65##106##,##UREF##66##107##</sup>, which is why we restrict our observations and interpretations of Sr variation to patterns that are only relative to our own data. Measurements of <sup>87</sup>Sr/<sup>86</sup>Sr ratios in teeth and petrous bones were conducted at the Geochronology and Isotope Geochemistry Laboratory (Department of Geological Sciences, University of North Carolina- Chapel Hill) and data are found in Supplementary Data ##SUPPL##3##2##. For further details see Supplementary Note ##SUPPL##0##5##.</p>", "<title>Vegetation modelling</title>", "<p id=\"Par46\">Using a high-resolution pollen diagram from Lake Højby, Northwest Zealand<sup>##UREF##67##108##</sup>, we reconstruct the changes in vegetation cover during the period 5,000–2,400 cal. <sc>bc</sc> using the landscape-reconstruction algorithm (LRA<sup>##UREF##68##109##,##UREF##69##110##</sup>). Although the LRA has previously been applied at low temporal resolution regional scale (fer example, in refs. <sup>##UREF##70##111##,##UREF##71##112##</sup>.), and to Iron Age (and later) pollen diagrams<sup>##UREF##72##113##,##UREF##73##114##</sup>, to our knowledge, this is the first time that this quantitative method is applied at local scale to a pollen record spanning the Mesolithic and Neolithic periods in Denmark. In total 60 pollen samples between 6,900 and 4,400 cal. <sc>bp</sc> were included and the temporal resolution between samples is approximately 40 years. Regional vegetation was estimated with the model REVEALS<sup>##UREF##68##109##</sup> based on pollen data from six other lakes on Zealand (see Supplementary Fig. ##SUPPL##0##6.1##). From this, regional pollen rain is calculated and local scale vegetation around Højby Sø calculated using the LOVE model<sup>##UREF##69##110##</sup>. Average pollen productivity estimates for Europe<sup>##UREF##74##115##</sup> for 25 wind pollinated species were applied. The reconstructed cover for plant species were then combined into four land cover categories, crops (only cereals), grassland (all other herbs), secondary forest (<italic>Betula</italic> and <italic>Corylus</italic>) and primary forest (all other trees). The vegetation reconstruction from Højby Sø is used to illustrate the vegetation development at the Mesolithic/Neolithic transition in eastern Denmark. For more details see Supplementary Note ##SUPPL##0##6##.</p>", "<title>Reporting summary</title>", "<p id=\"Par47\">Further information on research design is available in the ##SUPPL##1##Nature Portfolio Reporting Summary## linked to this article.</p>" ]	[]	[]	[]	[ "<p id=\"Par1\">Major migration events in Holocene Eurasia have been characterized genetically at broad regional scales<sup>##REF##26062507##1##–##REF##36859578##4##</sup>. However, insights into the population dynamics in the contact zones are hampered by a lack of ancient genomic data sampled at high spatiotemporal resolution<sup>##REF##28619897##5##–##UREF##2##7##</sup>. Here, to address this, we analysed shotgun-sequenced genomes from 100 skeletons spanning 7,300 years of the Mesolithic period, Neolithic period and Early Bronze Age in Denmark and integrated these with proxies for diet (<sup>13</sup>C and <sup>15</sup>N content), mobility (<sup>87</sup>Sr/<sup>86</sup>Sr ratio) and vegetation cover (pollen). We observe that Danish Mesolithic individuals of the Maglemose, Kongemose and Ertebølle cultures form a distinct genetic cluster related to other Western European hunter-gatherers. Despite shifts in material culture they displayed genetic homogeneity from around 10,500 to 5,900 calibrated years before present, when Neolithic farmers with Anatolian-derived ancestry arrived. Although the Neolithic transition was delayed by more than a millennium relative to Central Europe, it was very abrupt and resulted in a population turnover with limited genetic contribution from local hunter-gatherers. The succeeding Neolithic population, associated with the Funnel Beaker culture, persisted for only about 1,000 years before immigrants with eastern Steppe-derived ancestry arrived. This second and equally rapid population replacement gave rise to the Single Grave culture with an ancestry profile more similar to present-day Danes. In our multiproxy dataset, these major demographic events are manifested as parallel shifts in genotype, phenotype, diet and land use.</p>", "<p id=\"Par2\">Integrated data, including 100 human genomes from the Mesolithic, Neolithic and Early Bronze Age periods show that two major population turnovers occurred over just 1,000 years in Neolithic Denmark, resulting in dramatic changes in the genes, diet and physical appearance of the local people, as well as the landscape in which they lived.</p>", "<title>Subject terms</title>" ]	[ "<title>Main</title>", "<p id=\"Par3\">The Mesolithic and Neolithic periods in southern Scandinavia are marked by a number of pivotal and well-described cultural transitions<sup>##UREF##3##8##</sup>. However, the genetic and demographic impacts of these events remain largely uncharacterized. The early postglacial human colonization of the Scandinavian peninsula (Sweden and Norway) is believed to comprise at least two distinct migration waves: a source related to western European hunter-gatherers (WHG) from the south, and an eastern European hunter-gatherer (EHG) source into the far north, before venturing south along the Atlantic coast of Norway<sup>##REF##29315301##9##,##REF##31123709##10##</sup>. However, insight into the fine-scale structure and mobility of Scandinavian Mesolithic populations is limited, including an almost complete absence of genetic data from southern Scandinavian populations associated with the consecutive Maglemose, Kongemose and Ertebølle cultures in Denmark.</p>", "<p id=\"Par4\">The Neolithic transition represents a watershed event in European prehistory, marked by the spread of domesticated crops and livestock from Southwest Asia, starting around 11,000 <sc>bp</sc>. Although migrations and population turnovers associated with this transition have been demonstrated at broad geographical and chronological scales<sup>##REF##26062507##1##–##REF##36859578##4##</sup>, coarse sampling and a one-sided focus on genetics have hindered insights on social interaction and detailed demographic processes in the contact zones between locals and newcomers<sup>##REF##28619897##5##–##UREF##2##7##</sup>. Southern Scandinavia occupies an enigmatic position in this discussion. The Neolithic transition was delayed here by a millennium compared to Central Europe, during which hunter-gatherer societies continued to flourish until around 5,900 calibrated years <sc>bp</sc> (cal. <sc>bp</sc>), only marginally affected by farmer populations to the south<sup>##UREF##4##11##</sup>. The substantial delay could suggest that the transition to farming in Denmark occurred by a different mechanism involving a stronger element of cultural diffusion<sup>##UREF##5##12##</sup> than the migration of people (demic diffusion) observed in the rest of Europe<sup>##REF##29144465##13##–##REF##30988490##15##</sup>.</p>", "<p id=\"Par5\">An extensive archaeological record has indicated that the Funnel Beaker culture (FBC) thrived for the first millennium of the Neolithic in Denmark, before an apparent decline<sup>##UREF##6##16##</sup> was followed by the appearance of the Single Grave culture (SGC). Owing to a lack of genetic data and a robust absolute chronology, the relation between the FBC and the SGC has been extensively debated<sup>##UREF##7##17##–##UREF##9##19##</sup>. Population dynamics associated with this second cultural transition in Neolithic Denmark are similarly unresolved, including its possible link to the ‘steppe migrations’ that transformed the gene pools elsewhere in Europe around the same time<sup>##REF##26062507##1##,##REF##25731166##2##</sup>.</p>", "<p id=\"Par6\">To investigate these defining events at high temporal and spatial resolution, we analyse a detailed and continuous dataset of 100 ancient Danish shotgun-sequenced genomes (0.01× to 7.1× autosomal coverage<sup>##UREF##0##3##</sup>), spanning about 7,300 years from the Early Mesolithic Maglemose, the Kongemose and Late Mesolithic Ertebølle epochs, the Early and Middle Neolithic FBC and the SGC, up until the Bronze Age (Fig. ##FIG##0##1## and Supplementary Data ##SUPPL##2##1##). The archaeological record in Denmark represents a very large assemblage of well-documented Mesolithic and Neolithic human skeletal remains, from a wide range of chronological, topographical and socio-cultural contexts. This is a result of an environment and climate that was amenable to both Mesolithic fisher-hunter-gatherer lifeways<sup>##REF##32332739##20##</sup> and the later Neolithic farming practices, combined with taphonomically favourable preservation conditions for skeletal remains, and a long, prolific history of archaeological research. We used a multiproxy approach, combining autosomal imputed genomes<sup>##UREF##0##3##,##REF##37339987##21##</sup> with Y chromosomal and mitochondrial haplogroups, <sup>14</sup>C-dating, genetic phenotype predictions, as well as <sup>87</sup>Sr/<sup>86</sup>Sr, δ<sup>13</sup>C and δ<sup>15</sup>N isotope data as proxies for mobility and diet. Moreover, to investigate a direct link between demographic and environmental processes, we align the genetic changes observed in the Danish population over time with changes in local vegetation, based on pollen analyses and quantitative vegetation cover reconstruction.</p>", "<title>The Mesolithic period</title>", "<p id=\"Par7\">It is not known whether shifts in southern Scandinavian Mesolithic material culture occurred in a population continuum or were facilitated by incoming migrants. The Early Mesolithic settlement in Denmark is associated with the Maglemose culture (around 11,000–8,400 cal. <sc>bp</sc>), characterized archaeologically by small flint projectiles in geometric shapes. Until the recent development of underwater archaeology, this culture was known mainly from inland locations along lakes and rivers<sup>##UREF##10##22##</sup>. During the succeeding Kongemose culture (around 8,400–7,400 cal. <sc>bp</sc>), trapeze-shaped flint points dominate the assemblages of arrowheads<sup>##UREF##11##23##</sup> along with high quality long blades. Most of the larger settlements cluster at good fishing locations along the coasts<sup>##UREF##12##24##</sup>, but there are also specialized hunting camps in the interior<sup>##UREF##13##25##</sup>. The Late Mesolithic Ertebølle culture (about 7,400–5,900 cal. <sc>bp</sc>), is characterized by flint points with transverse edges. Pottery was introduced from other hunter-gatherer groups to the east and perhaps the southwest<sup>##REF##36550220##26##</sup> and ‘exotic’ shaft-hole axes suggest exchange with farming societies south of the Baltic Sea<sup>##UREF##14##27##</sup>. The larger habitation sites, densely scattered along the coasts, probably represent multi-family, year-round occupation<sup>##UREF##12##24##,##UREF##15##28##</sup> and they have provided important insights into the physical anthropology and spiritual culture of the period.</p>", "<p id=\"Par8\">By analysing genomes from 38 Danish hunter-gatherers and inferring their ancestry, we examine whether cultural transitions observed in the Danish archaeological record are associated with any genetic changes in the population. Model-based clustering (ADMIXTURE), PCA and IBD-sharing analyses show that throughout the Maglemose (<italic>n</italic> = 4), Kongemose (<italic>n</italic> = 8) and Ertebølle (<italic>n</italic> = 27) epochs the region displayed a remarkable genetic homogeneity across a 4,500-year transect (Figs. ##FIG##0##1##–##FIG##2##3## and Extended Data Figs. ##FIG##4##1##–##FIG##6##3##), supporting interpretations of demographic continuity favoured by some archaeologists<sup>##UREF##11##23##–##UREF##13##25##</sup>. From the earliest known skeleton in Denmark, ‘Koelbjerg Man’ (NEO254, 10,648–10,282 cal. <sc>bp</sc><sup>##UREF##16##29##</sup>), to the most recent Mesolithic skeleton included here, ‘Rødhals Man’ (NEO645, 5,916–5,795 cal. <sc>bp</sc>), the individuals derive their ancestry almost exclusively from the same southern European source (Italy_15000BP_9000BP) that predominated in WHG ancestry in Mesolithic Western Europe<sup>##UREF##0##3##</sup>.</p>", "<p id=\"Par9\">In the IBD-based principal components analysis (PCA), the Danish Mesolithic individuals cluster closely together (Extended Data Fig. ##FIG##7##4a##), but beyond this tight local genetic connection they share most recent ancestry with the geographically and temporally proximate hunter-gatherer individuals from Western Europe (such as Cheddar Man, Loschbour and Bichon, commonly referred to as WHG; genetic cluster EuropeW_13500BP_8000BP; Fig. ##FIG##1##2##). A subtle shift of the earliest Danish individuals towards these western individuals probably reflects their closer temporal proximity captured through IBD sharing (Extended Data Fig. ##FIG##7##4a##). Although pressure-debitage of blades in the Maglemosian culture and pottery in the Ertebølle culture are both argued to have an eastern origin<sup>##REF##29315301##9##,##REF##31123709##10##,##UREF##17##30##,##UREF##18##31##</sup>, our data show no evidence for admixture with more eastern hunter-gatherers during those times. This points to cultural diffusion as the source of these introductions in Denmark. When tested with D-statistics, all Danish Mesolithic individuals form a clade with the earliest individual (NEO254), to the exclusion of Swedish Mesolithic hunter-gatherers (Sweden_10000BP_7500BP; Extended Data Fig. ##FIG##5##2a##) despite the close proximity to Sweden. However, a weak signal of gene flow with EHGs was shared across the whole Danish Mesolithic transect (Extended Data Fig. ##FIG##5##2b##), suggesting contact with communities further to the east prior to their expansion into Denmark before or during the earliest Mesolithic.</p>", "<p id=\"Par10\">Genetic phenotype predictions (Supplementary Note ##SUPPL##0##2##) indicate a high probability of blue eye pigmentation throughout the Mesolithic, consistent with previous findings<sup>##REF##26062507##1##,##REF##30988490##15##,##REF##26595274##32##</sup>, showing that this feature was present already in the early Mesolithic but was not fixed in the population. The Mesolithic hunter-gatherers from Denmark all display high probability of brown or black hair and height predictions generally suggest slightly lower and/or less variable stature than in the succeeding Neolithic period. We caution, however, that the relatively large genetic distance to modern individuals included in the genome-wide association studies (GWAS) panel produces scores that are less applicable to Mesolithic individuals than to more recent groups<sup>##UREF##19##33##</sup>.</p>", "<p id=\"Par11\">Stable isotope δ<sup>13</sup>C values in collagen can inform on the proportion of marine versus terrestrially-derived protein, whereas δ<sup>15</sup>N values reflect the trophic level of the protein sources<sup>##UREF##20##34##</sup>. The earliest skeleton (NEO254) shows depleted dietary isotopic values (Fig. ##FIG##2##3##) representing a lifestyle of inland hunter-gatherers of the Early Mesolithic. This result is mirrored in the second earliest known skeleton from Denmark (Tømmerupgårds Mose<sup>##UREF##20##34##</sup>). From later Maglemose (around 9,500 cal. BP) and throughout the Kongemose and Ertebølle epochs, we observe gradually increased δ<sup>13</sup>C and δ<sup>15</sup>N values (Extended Data Fig. ##FIG##8##5## and Supplementary Figs. ##SUPPL##0##4.1## and ##SUPPL##0##4.2##). This implies that marine foods progressed to constitute the major supply of proteins, as suggested previously based on data from more than 30 Mesolithic humans and dogs, from both coastal and inland sites in Denmark<sup>##UREF##20##34##,##UREF##21##35##</sup>. During this period global sea-level rise gradually transformed present-day Denmark into an archipelago, where all human groups had ample access to coastal resources within their annual territories<sup>##UREF##12##24##</sup>. The local Mesolithic population adapted their diet and culture over time to the changing landscape and our data show that this occurred in a continuous population, without any detectable influx of migrants over a 4,500-year period. Low variability in <sup>87</sup>Sr/<sup>86</sup>Sr isotope ratios throughout the Mesolithic (Fig. ##FIG##2##3## and Supplementary Note ##SUPPL##0##5##) could indicate limited long-range mobility and/or deriving dietary sources from more homogeneous environments (for example, marine) than in the succeeding Neolithic periods.</p>", "<p id=\"Par12\">Notably, some of the Danish Mesolithic individuals proved to be closely related<sup>##UREF##0##3##</sup>. Close kinship is demonstrated in the case of two individuals (NEO568/NEO569), father and son, interred next to each other in the <italic>locus classicus</italic> shell midden site of Ertebølle, and in the case of two individuals (NEO732/NEO733), mother and daughter, that were buried together at Dragsholm. The Ertebølle grave was the first discovered human skeleton in Denmark (excavated in the 1890s) that indisputably represented hunter-gatherers. After the excavation of this site, academic reasoning rooted in Biblical narration about early prehistory in Scandinavia lost momentum. The excavation data cannot reveal whether they were buried simultaneously; it can be ascertained only that the boy (infant, less than two years of age) was positioned less than one metre from his father (the ‘Ertebølle Man’). Excavations at Dragsholm in 1973 uncovered a well-preserved double burial containing a grave with two Mesolithic women as well as a male grave with grave goods suggesting an Early Neolithic date for the latter<sup>##UREF##22##36##</sup>. A close kin relationship was suggested for the two Dragsholm women on the basis of physical anthropological observations<sup>##UREF##23##37##</sup>. It was suggested that they were sisters, but this can now be corrected to a co-burial of a mother and daughter. Our data also show that the male in the adjacent burial (‘Dragsholm Man’, NEO962) was not related to the two women. These cases show that close biological kinship was socially relevant to Late Mesolithic groups in Northern Europe and affected the mortuary treatment of dead members of their society.</p>", "<title>Early Neolithic transition</title>", "<p id=\"Par13\">The emergence of the Neolithic FBC in Denmark has occupied a central position in archaeological research and debate throughout the past 175 years<sup>##UREF##3##8##,##UREF##24##38##,##UREF##25##39##</sup>. The defining element of the Neolithic, a food-producing economy based on domesticates of southwest Asian origin, was indisputably present in Denmark from around 5,900 cal. <sc>bp</sc><sup>##UREF##4##11##,##UREF##24##38##</sup>. The neolithization saw a boom of new shapes and types introduced in Danish material culture, including funnel-shaped beakers and polished flint axes. From about 5,800 cal. <sc>bp,</sc> monumental long barrows of wood and earth were added to the repertoire, and about 200 years later, burials built of soil, surrounded by raised stones and including stone-built chambers, were erected as dominant landmarks in the farmland<sup>##UREF##26##40##</sup>. After 5,300 cal. <sc>bp,</sc> larger and more complex stone-constructed passage graves in large earthen tumuli emerged<sup>##UREF##27##41##</sup>. Meanwhile, simple, non-monumental burials continued along with the megalithic tombs all through the FBC epoch<sup>##UREF##28##42##</sup>. Habitation deposits, dating to the earliest centuries of the Neolithic, on top of many Mesolithic Ertebølle coastal shell middens may be interpreted as a local continuation of marine gathering and fishing. By contrast, other settlements with regular long houses on easily farmed soils further inland are associated with remains of domestic plants and animals suggesting a very clear distinction from the previous Mesolithic Ertebølle period<sup>##UREF##25##39##,##UREF##29##43##</sup>.</p>", "<p id=\"Par14\">Regardless of these nuances, at around 5,900 cal. <sc>bp,</sc> our multiproxy dataset documents a marked and abrupt concomitant shift in genetic, phenotypic, dietary and vegetation parameters (Fig. ##FIG##2##3##). This is robust evidence for demic diffusion, settling a long-standing debate<sup>##UREF##3##8##,##UREF##24##38##</sup>. As observed elsewhere in Europe<sup>##REF##29144465##13##–##REF##30988490##15##</sup>, the introduction of farming in Denmark was unequivocally associated with the arrival of people with Anatolian farmer-related ancestry. This resulted in a population replacement with limited genetic contribution from the local hunter-gatherers. The earliest example of this typical Neolithic ancestry in our Danish dataset is observed in a bog skeleton of a female from Viksø Mose (NEO601) dated to 5,896–5,718 cal. <sc>bp</sc> (95%). In the PCA, all Danish Early Neolithic individuals cluster at the ‘late’ end of the European Neolithic farmer cline and consistently show some of the largest amounts of hunter-gatherer ancestry (10–35%) among all European Neolithic farmer genomes included (Figs. ##FIG##0##1## and ##FIG##2##3## and Extended Data Figs. ##FIG##4##1## and ##FIG##8##5a## and Supplementary Data ##SUPPL##5##4##). In IBD clustering analyses, the Danish individuals form part of a genetic cluster (Scandinavia_5600BP_4600BP) together with FBC-associated individuals from Sweden and Poland, and also show close affinity with Polish individuals from the Globular Amphora culture (GAC) (Extended Data Fig. ##FIG##7##4b##). This could suggest an eastern European proximate origin of the Early Neolithic farmers in Denmark. Using more proximate ancestry modelling, we find that Neolithic FBC-associated individuals across Denmark, Sweden and Poland derived their hunter-gatherer ancestry component predominantly from a source related to WHG (EuropeW_13500BP_8000BP). Ancestry related to Danish Mesolithic hunter-gatherers (Denmark_10500BP_6000BP) is found in smaller proportions (less than around 10%) and only in a subset of the FBC individuals from Denmark (Extended Data Fig. ##FIG##9##6##). Moreover, this tends to occur in more recent individuals (dated to around 5,400 cal. BP onwards) who are also showing the overall largest amount of total hunter-gatherer ancestry (for example, NEO945 and NEO886; Fig. ##FIG##2##3## and Extended Data Figs. ##FIG##6##3## and ##FIG##9##6a,b##). Using DATES<sup>##REF##35635751##44##</sup>, we found that admixture times for a large proportion of Danish Neolithic individuals predates 5,900 cal. <sc>bp</sc> when FBC emerged in Denmark, particularly for the earliest individuals (Extended Data Fig. ##FIG##10##7##). More recent admixture times (post dating the arrival of FBC in Denmark) were mainly observed in individuals dated to after about 5,400 cal. <sc>bp</sc>, and were associated with overall higher hunter-gatherer proportions. These observations were in marked contrast to FBC-associated individuals from Sweden, where admixture times and hunter-gatherer ancestry did not change over time, and no admixture with local Swedish hunter-gatherers was detected.</p>", "<p id=\"Par15\">Our results demonstrate a population turnover in Denmark at the onset of the neolithisation by incomers who displayed a mix of Anatolian Neolithic farmer ancestry and non-local hunter-gatherer ancestry. Ancestry related to the local Danish hunter-gatherers could be detected only late in the Danish Neolithic gene pool, suggesting gene flow with groups of late surviving hunter-gatherers, as also documented in other European regions (Iron Gates<sup>##REF##28552360##45##</sup>, Central Europe<sup>##REF##29144465##13##</sup> and Spain<sup>##REF##30880015##46##</sup>). We do not know how the Mesolithic Ertebølle population disappeared. Some may have been isolated in small ‘pockets’ of brief existence and/or adapted to a Neolithic lifestyle. The most recent individual in our Danish dataset with hunter-gatherer ancestry is the aforementioned Dragsholm Man (NEO962), dated to 5,947–5,664 cal. <sc>bp</sc> (95% confidence interval) and archaeologically assigned to the FBC based on his grave goods<sup>##UREF##23##37##</sup>. Our data confirm a typical Neolithic diet matching the cultural affinity but contrasting his hunter-gatherer ancestry. He clearly represents a local person of Mesolithic ancestry who lived in the short Mesolithic-Neolithic transition and adopted the culture and diet of the immigrant farmers. A similar case of late hunter-gatherer ancestry in Denmark was observed when analysing human DNA obtained from a piece of chewed birch pitch from the site of Syltholm on Lolland<sup>##REF##31848342##47##</sup>, dated to 5,858–5,661 cal. <sc>bp</sc> (95%). Thus, individuals with hunter-gatherer ancestry persisted for decades and perhaps centuries after the arrival of farming groups in Denmark, although they have left only a minor genomic imprint on the population of the subsequent centuries. Similar ‘relic’ hunter-gatherer ancestry is also found in the Evensås individual (NEO260) from west-coast Sweden, dated to 5913–5731 cal. <sc>bp</sc><sup>##UREF##0##3##</sup>.</p>", "<p id=\"Par16\">From the onset of the Neolithic in Denmark, diet shifted abruptly to a dominance of terrestrial sources as evidenced by δ<sup>13</sup>C values around −20‰ and δ<sup>15</sup>N values around 10‰ (Fig. ##FIG##2##3## and Extended Data Fig. ##FIG##8##5##). In line with archaeological evidence, these isotopic data show that domesticated crops and animals provided the main supply of proteins from this point onwards. Isotope values remained stable at these levels throughout the following periods, although with somewhat greater variation after about 4,500 cal. <sc>bp</sc> (Fig. ##FIG##2##3##). Five Neolithic and Early Bronze Age individuals have δ<sup>13</sup>C and δ<sup>15</sup>N values that indicate a substantial intake of high trophic marine food. This is especially pronounced for the individual NEO898 (Svinninge Vejle), one of two Danish Neolithic individuals displaying ancestry related to Swedish late hunter-gatherers (see below). A considerably higher variability in individual <sup>87</sup>Sr/<sup>86</sup>Sr values can be seen with the start of the Neolithic. This continues in the later periods (Supplementary Note ##SUPPL##0##5##) and is not easily explained by biases in sampling as most of our samples, regardless of ancestry and time period, are concentrated in the more easterly parts of Denmark where bone preservation conditions are generally good (Fig. ##FIG##0##1## and Supplementary Fig. ##SUPPL##0##5.3##). This pattern could suggest that the Neolithic farmers in Denmark occupied and/or consumed food from more diverse landscapes, or were more mobile than the preceding hunter-gatherers. The Neolithic transition also marks a considerable rise in frequency of major effect alleles associated with light hair pigmentation<sup>##REF##24463515##48##</sup>, whereas predictions throughout the first millennium of the Neolithic (FBC epoch) mostly indicate a lower stature than present day, echoing previous findings<sup>##REF##26595274##32##,##REF##31594846##49##</sup>.</p>", "<p id=\"Par17\">Pitted Ware culture (PWC) originated on the Scandinavian peninsula and the Baltic islands east of the Swedish mainland but emerged around 5,100–4,700 cal. <sc>bp</sc> in the northern and eastern part of Denmark, where it coexisted with the FBC<sup>##UREF##30##50##,##UREF##31##51##</sup>. It is characterized by coarse pottery that is often decorated with pits and subsistence based on a combination of marine species and agricultural products. No burials associated with the PWC have been discovered in Denmark. Of note, however, the genomes of two approximately 5,200-year-old male individuals (NEO33, NEO898) found in Danish wetland deposits proved to be of hunter-gatherer ancestry related to that of PWC individuals from Ajvide on the Baltic island of Gotland (Sweden)<sup>##REF##32497286##52##</sup> (Figs. ##FIG##1##2##, ##FIG##2##3## and Extended Data Fig. ##FIG##7##4a##). Of the two individuals, NEO033 (Vittrup, Northern Jutland) also displays an outlier Sr signature (Fig. ##FIG##2##3##), perhaps suggesting a non-local origin that matches his unusual ancestry. Overall, our results demonstrate direct contact across the sea between Denmark and the Scandinavian peninsula during this period, which is in line with archaeological findings<sup>##UREF##30##50##,##UREF##31##51##</sup>.</p>", "<title>Later Neolithic and Bronze Age</title>", "<p id=\"Par18\">Europe was transformed by large-scale migrations from the Pontic–Caspian Steppe around 5,000–4,800 cal. <sc>bp</sc>. This introduced steppe-related ancestry to most parts of the continent within a 1,000-year span and gave rise to the Corded Ware culture (CWC) complex<sup>##REF##26062507##1##,##REF##25731166##2##</sup>. In Denmark, this coincided with the transition from the FBC to the SGC, the regional manifestation of the CWC complex. The transition to single graves in round tumuli has been characterized archaeologically by two expansion phases: a primary and rapid occupation of central, western and northern Jutland (west Denmark) starting around 4,800 cal. <sc>bp</sc> and a later and slower expansion across the Eastern Danish Islands starting around 4,600 cal. <sc>bp</sc><sup>##UREF##32##53##,##UREF##33##54##</sup>. In the eastern parts of the country, SGC traits are less visible, whereas FBC traditions such as burial in megalithic grave chambers persisted<sup>##UREF##34##55##</sup>. This cultural shift represents another classical archaeological enigma, with explanations favouring immigration versus cultural acculturation competing for generations<sup>##UREF##9##19##,##UREF##35##56##</sup>.</p>", "<p id=\"Par19\">Insights from a few low-coverage genomes<sup>##REF##26062507##1##,##REF##33444387##57##</sup> have indeed shown a link to the Steppe expansions, but by mapping out ancestry components in the 100 ancient genomes we now uncover the full impact of this event and demonstrate a second near-complete population turnover in Denmark within just 1,000 years. This genetic shift was evident from PCA and ADMIXTURE analyses, in which Danish individuals dating to the SGC and Late Neolithic and Bronze Age (LNBA) cluster with other European LNBA individuals and show large proportions of ancestry components associated with Yamnaya groups from the Steppe (Figs. ##FIG##0##1## and ##FIG##2##3## and Extended Data Fig. ##FIG##4##1##). We estimate around 60–85% of ancestry related to Steppe groups (Steppe_5000BP_4300BP), with the remainder contributed from individuals with farmer-related ancestry associated with Eastern European GAC (Poland_5000BP_4700BP; 10–23%) and to a lesser extent from local Neolithic Scandinavian farmers (Scandinavia_5600BP_4600BP; 3–18%) (Extended Data Fig. ##FIG##9##6a,b##). Although the emergence of SGC introduced a major new ancestry component in the Danish gene pool, it was not accompanied by apparent shifts in dietary isotopic ratios, or Sr isotope ratios (Fig. ##FIG##2##3##). Our complex trait predictions, however, indicate an increase in height (Fig. ##FIG##2##3## and Supplementary Note ##SUPPL##0##2##), which is consistent with ancient Steppe individuals being predicted taller than average European Neolithic individuals before the steppe migrations<sup>##REF##26595274##32##,##REF##31594846##49##,##REF##26948573##58##</sup>.</p>", "<p id=\"Par20\">Because of poor preservation conditions in most of western Denmark, we do not have skeletons from the earliest phase of the SGC (around 4,800 cal. <sc>bp</sc>) so we cannot unequivocally demonstrate that these people carried steppe-related ancestry. SGC burial customs were implemented in different ways in the southern and the GAC-related northern parts of the peninsula, respectively<sup>##UREF##8##18##</sup> and considering recent genetic results in other regions<sup>##REF##34433570##59##</sup>, it is plausible that differing demographic processes unfolded within Denmark. However, we know that steppe ancestry was present 200 years later in SGC-associated skeletons from the Gjerrild grave<sup>##REF##33444387##57##</sup>. The age of the Gjerrild skeletons (from around 4,600 cal. <sc>bp</sc>) matches the earliest example of steppe-related ancestry in our current study, identified in a skeleton from a megalithic tomb at Næs (NEO792). We estimated around 85% of Steppe-related ancestry in this individual, the highest amount among all Danish LNBA individuals (Extended Data Fig. ##FIG##9##6a##). Notably, NEO792 is also contemporaneous with the two most recent individuals in our dataset showing Anatolian farmer-related ancestry without any steppe-related ancestry (NEO580, Klokkehøj and NEO943, Stenderup Hage) testifying to a short period of ancestry co-existence before the FBC disappeared—similar to the disappearance of the Mesolithic Ertebølle people of hunter-gatherer ancestry a thousand years earlier. Using Bayesian modelling we estimate the duration between the first appearance of Anatolian farmer-related ancestry to the first appearance of Steppe-related ancestry in Denmark to be between 876 and 1,100 years (95% prob. interval, Supplementary Note ##SUPPL##0##3##) implying that the former type of ancestry was dominant for less than 50 generations.</p>", "<p id=\"Par21\">The following Late Neolithic ‘Dagger’ epoch (around 4,300–3,700 cal. <sc>bp</sc>) in Denmark has been described as a time of integration of culturally and genetically distinct groups<sup>##UREF##33##54##</sup>. Bronze became dominant in the local production of weapons while elegantly surface-flaked daggers in flint were still the dominant male burial gift. Unlike the SGC epoch, this period is richly represented by human skeletal material. Although broad population genomic signatures suggest genetic stability in the LNBA (Figs. ##FIG##0##1## and ##FIG##2##3##), patterns of pairwise IBD-sharing and Y chromosome haplogroup distributions in a temporal transect of 38 LNBA Danish and southern Swedish individuals indicate at least three distinct ancestry phases during this approximately 1,000-year time span (Extended Data Figs. ##FIG##7##4c## and ##FIG##11##8##).</p>", "<p id=\"Par22\">LNBA phase I: an early stage between around 4,600 and 4,300 cal. <sc>bp</sc>, in which Scandinavians cluster with early CWC individuals from Eastern Europe, rich in Steppe-related ancestry and males with an R1a Y chromosomal haplotype (Extended Data Fig. ##FIG##11##8a,b##). Archaeologically, these individuals are associated with the later stages of the Danish SGC and the Swedish Battle Axe Culture.</p>", "<p id=\"Par23\">LNBA phase II: an intermediate stage largely coinciding with the Dagger epoch (around 4,300–3,700 cal. <sc>bp</sc>), in which Danish individuals cluster with central and western European LNBA individuals dominated by males with distinct sub-lineages of R1b-L51<sup>##UREF##0##3##</sup> (Extended Data Fig. ##FIG##11##8c,d##). Among them are individuals from Borreby (NEO735, 737) and Madesø (NEO752).</p>", "<p id=\"Par24\">LNBA phase III: a final stage from around 4,000 cal. <sc>bp</sc> onwards, in which a distinct cluster of Scandinavian individuals dominated by males with I1 Y-haplogroups appears (Extended Data Fig. ##FIG##11##8e##). Y chromosome haplogroup I1 is one of the dominant haplogroups in present-day Scandinavians, and we here document its earliest occurrence in an approximately 4,000-year-old individual from Falköping in southern Sweden (NEO220). The rapid increase in frequency of this haplogroup and associated genome-wide ancestry coincides with increase in human mobility seen in Swedish Sr isotope data, suggesting an influx of people from eastern or northeastern regions of Scandinavia, and the emergence of stone cist burials in Southern Sweden<sup>##UREF##36##60##</sup>, which were also introduced in eastern Denmark during that period<sup>##UREF##33##54##,##UREF##37##61##</sup>.</p>", "<p id=\"Par25\">Using genomes from LNBA phase III (Scandinavia_4000BP_3000BP) in supervised ancestry modelling, we find that they form the predominant ancestry source for later Iron and Viking Age Scandinavians (Extended Data Fig. ##FIG##9##6d##) and other ancient European groups with a documented Scandinavian or Germanic association (for example, Anglo-Saxons and Goths; Extended Data Fig. ##FIG##9##6e##). When projecting 2,000 modern Danish genomes<sup>##REF##28924187##62##</sup> on a PCA of ancient Eurasians, the modern individuals occupy an intermediate space on a cline between the LNBA and Viking Age individuals (Fig. ##FIG##3##4##). This result shows that the foundation for the present-day gene pool was already in place in LNBA groups 3,000 years ago, but the genetic structure of the Danish population was continually reshaped during succeeding millenia.</p>", "<title>Environmental impact</title>", "<p id=\"Par26\">The two documented major population turnovers were accompanied by substantial changes in land use, as apparent from the high-resolution pollen diagram from Lake Højby in Northwest Zealand (Fig. ##FIG##2##3##) reconstructed using the landscape-reconstruction algorithm (LRA; Supplementary Note ##SUPPL##0##6##). We uncovered a direct synchronic link between shifts in a populations’ ancestry profile and land use. During the Mesolithic, the landscape was dominated by primary forest trees (<italic>Tilia</italic>, <italic>Ulmus</italic>, <italic>Quercus</italic>, <italic>Fraxinus</italic>, <italic>Alnus</italic> and so on). At the onset of the Neolithic, the primary forest diminished, cleared by FBC farmers. A new type of forest with more secondary and early successional trees (<italic>Betula</italic> and then <italic>Corylus</italic>) appeared, whereas the proportion between forest and open land remained almost unaltered. From about 5,650 cal. <sc>bp</sc> deforestation intensified, resulting in an open grassland-dominated landscape. This open phase was short-lived, and the secondary forest expanded again from around 5,500 to 5,000 cal. <sc>bp</sc>, until another episode of forest clearance occurred during the last part of the FBC epoch. We conclude that the agricultural practice during the FBC was characterized by repeated clearing of the forest followed by regrowth. After about 4,600 cal <sc>bp,</sc> this strategy changed with the emergence of the SGC and the arrival of Steppe-related ancestry in Denmark. In Western Denmark (Jutland), the arrival of the SGC was characterized by permanent large-scale opening of the landscape to create pastureland<sup>##UREF##38##63##,##UREF##39##64##</sup> and we observe here a similar increase in grassland and cropland at Højby Sø in Eastern Denmark around 4,600 cal. <sc>bp</sc> (Fig. ##FIG##2##3##). Notably, this was accompanied by an increase in primary forest cover, especially <italic>Tilia</italic> and <italic>Ulmus</italic>, probably reflecting a development of a more permanent division of the landscape into open grazing areas and primary forests.</p>", "<title>Drivers of change</title>", "<p id=\"Par27\">We have demonstrated examples of both cultural and demic diffusion during the Mesolithic and Neolithic periods in Denmark. Shifts in the Mesolithic material culture appeared without any detectable levels of changes in ancestry, whereas the two cultural shifts in the Neolithic period were clearly driven by new people coming in. Accordingly, groupings of artefacts and monuments into archaeological cultures do not always represent genetically distinct populations and the underlying mechanisms responsible for prehistoric cultural shifts must be examined on a case-by-case basis.</p>", "<p id=\"Par28\">It remains a mystery why the Neolithic farming expansion came to a 1,000-year standstill before entering Southern Scandinavia. It may be that it was complicated by a high Mesolithic hunter-gatherer population density owing to a very productive marine and coastal environment<sup>##REF##32332739##20##,##UREF##40##65##</sup>. Further, the Danish Ertebølle population may have been acquainted with armed conflict<sup>##UREF##4##11##,##UREF##41##66##</sup> enabling territorial defence against intruders. Alternatively, it has been argued that changing climatic conditions around 6,000 cal. <sc>bp</sc> became a driver since it enhanced the potential for farming further north<sup>##REF##29127307##67##</sup>, but other studies have not confirmed this<sup>##UREF##42##68##</sup>. The second population turnover in the late Neolithic resulted in a short period of three competing cultural complexes in Denmark, namely the FBC, the PWC and the SGC. The latter introduced the steppe-related ancestry which has prevailed to this day. There is archaeological evidence that this was a violent time, both in Denmark<sup>##UREF##43##69##</sup> and elsewhere<sup>##UREF##44##70##,##REF##31061125##71##</sup>. Additionally, ancient DNA evidence has demonstrated that plague was widespread during this period<sup>##REF##26496604##72##,##REF##30528431##73##</sup>. In tandem with other indicators of population declines<sup>##UREF##45##74##</sup>, and widespread reforestation after 5,000 cal. <sc>bp</sc><sup>##UREF##46##75##</sup>, it suggests that the local populations of Central and Northern Europe may have been severely impacted prior to the arrival of newcomers with Steppe-related ancestry. This could explain the rapid population turnover and limited admixture with locals we observe.</p>", "<p id=\"Par29\">While the two major shifts in Danish Mesolithic and Neolithic material culture may have had different drivers and causes, the outcomes were ultimately the same: new people arrived and rapidly took over the territory. With this arrival, the local landscape was modified to fit the lifestyle and culture of the immigrants. This is the hallmark of the Anthropocene, observed here in high resolution in prehistoric Denmark.</p>", "<title>Online content</title>", "<p id=\"Par48\">Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41586-023-06862-3.</p>", "<title>Supplementary information</title>", "<p>\n\n\n\n\n\n\n</p>" ]	[ "<title>Extended data figures and tables</title>", "<p id=\"Par51\">\n\n</p>", "<p id=\"Par52\">\n\n</p>", "<p id=\"Par53\">\n\n</p>", "<p id=\"Par54\">\n\n</p>", "<p id=\"Par55\">\n\n</p>", "<p id=\"Par56\">\n\n</p>", "<p id=\"Par57\">\n\n</p>", "<p id=\"Par58\">\n\n</p>", "<title>Extended data</title>", "<p>is available for this paper at 10.1038/s41586-023-06862-3.</p>", "<title>Supplementary information</title>", "<p>The online version contains supplementary material available at 10.1038/s41586-023-06862-3.</p>", "<title>Acknowledgements</title>", "<p>The Lundbeck Foundation GeoGenetics Centre is supported by grants from the Lundbeck Foundation (R302-2018-2155, R155-2013-16338), the Novo Nordisk Foundation (NNF18SA0035006), the Wellcome Trust (WT214300), Carlsberg Foundation (CF18-0024), the Danish National Research Foundation (DNRF94, DNRF174), the University of Copenhagen (KU2016 programme) and Ferring Pharmaceuticals A/S, to E.W. This research has been conducted using the UK Biobank Resource and the iPSYCH Initiative, funded by the Lundbeck Foundation (R102-A9118 and R155-2014-1724). This work was further supported by the Swedish Foundation for Humanities and Social Sciences grant (Riksbankens Jubileumsfond M16-0455:1) to K.K. M.E.A. was supported by Marie Skłodowska-Curie Actions of the EU (grant no. 300554), The Villum Foundation (grant no. 10120) and Independent Research Fund Denmark (grant no. 7027-00147B). A.F. was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany´s Excellence Strategy–EXC 2150–390870439. R. Macleod was supported by an SSHRC doctoral studentship grant (G101449: ‘Individual Life Histories in Long-Term Cultural Change’). B.S.P. has received funding from the European Union’s Horizon 2020 research and innovation programme under the ERC-StG grant agreement No 94924. M.N. is funded by the Human Frontier Science Program Postdoctoral Fellowship LT000143/2019-L4. A.R.-M. was supported by the Lundbeck Foundation (grant R302-2018-2155) and the Novo Nordisk Foundation (grant NNF18SA0035006); E.K.I.-P. was supported by the Lundbeck Foundation (grant R302-2018-2155) and the Novo Nordisk Foundation (grant NNF18SA0035006). W.B. is supported by the Hanne and Torkel Weis-Fogh Fund (Department of Zoology, University of Cambridge). A.P. is funded by Wellcome grant WT214300. B.S.d.M. and O.D. are supported by the Swiss National Science Foundation (SFNS PP00P3_176977) and European Research Council (ERC 679330). G.R. is supported by a Novo Nordisk Foundation Fellowship (gNNF20OC0062491). N.N.J. is supported by Aarhus University Research Foundation. A.J.S. is supported by a Lundbeckfonden Fellowship (R335-2019-2318) and the National Institute on Aging (NIH award numbers U19AG023122, U24AG051129 and UH2AG064706). R. Maring was funded by the Aarhus University Research Foundation through a grant awarded to M. A. Mannino for the project titled Danish and European Diets in Time (AUFF-E-2015-FLS-8-2). S.R. was funded by the Novo Nordisk Foundation (NNF14CC0001). T.S.K. is funded by Carlsberg grant CF19-0712. R.D. is funded by the Wellcome Trust (WT214300). R.N. is funded by the National Institute of General Medical Sciences (NIH grant R01GM138634). T.W. is supported by the Lundbeck Foundation iPSYCH initiative (R248-2017-2003). We are indebted to P. Bennike for her contribution during the formative years of the project until shortly before her death in 2017. We acknowledge staff at the National Museum, the Anthropological Laboratory, and the regional museums in Denmark, responsible for collecting, recording and curating prehistoric skeletal remains studied herein. The process of identifying and sampling suitable archaeological remains for the current study was dependent on numerous specialists in Danish archaeology including researchers, museum employees and citizen scientists. We thank the following in this regard (alphabetically ordered): A. H. Andersen, S. Bergerbrant, K. Christensen, K. M. Gregersen, V. Grimes, E. Johansen, O. T. Kastholm, T. Lotz, E. Lundberg, M. Mannino, J. Olsen, K. Rosenlund and H. H. Sørensen. We also acknowledge those who have assisted in the sampling process or the gathering of provenance data on prehistoric human remains which were not analysed here owing to insufficient DNA preservation. These include H. Dahl (Tybrind Vig), I. B. Enghoff (Østenkær), A. B. Gurlev (Vedbæk Havn), L. Holten (Aldersro), O. Lass (Hesselbjerg/Ferle Enge and Nivå), L. Matthes (Knudsgrund/Knudshoved) and K. Randsborg (deceased) (Nivå). We are grateful for contributions from F. Racimo, and express our gratitude to the many researchers who have supported laboratory work, analytical procedures and evaluation of results that are presented here, including M. Mannino, P. Reimer and M. Thompson. E.W. thanks St. John’s College, Cambridge, for providing a stimulating environment of discussion and learning.</p>", "<title>Author contributions</title>", "<p>M.E.A., M.S. and A.F. contributed equally to this work. E.W. initiated the study. M.E.A., M.S., A.F., T.W., K.K. and E.W. led the study. M.E.A., M.S., A.F., M.M, R.N., T.W., K.K. and E.W. conceptualized the study. M.E.A., M.S., T.S.K., R.D., R.N., O.D., T.W., K.K. and E.W. supervised the research. M.E.A., R.D., R.N., T.W., K.K. and E.W. acquired funding for research. A.F., M.E.A., J.S., K.-G.S., M.L.S.J., T.Z.T.J., M.U.H., B.H.N, E.K., J.H., K.B.P., L.P., L.K., P. Lotz., P. Lysdahl, P.B., P.V.P., R. Maring, S.W., S.A.S, S.H.A, T.J. and N.L. were involved in sample collection. M.E.A., M.S., A.I., J.S., A.P., B.S.d.M., L.V., A.S., D.J.L., T.S.K., R.D., R.N., O.D., K.K. and E.W. were involved in developing and applying methodology. M.E.A., J.S. and L.V. led the DNA laboratory work research component. K.-G.S. led bioarchaeological data curation. M.E.A., M.S., A.R.-M., E.K.I.-P., W.B., A.I., A.P., B.S.d.M., B.S.P., R.A.H., T.V., H.M., A.V., A.B.N., P.R., G.R., A.D.R., A.J.S., A. Rosengren, R. Maring, S.R., T.S.K. and O.D. undertook formal analyses of data. M.E.A., M.S., A.F., K.-G.S., A.I., R. Macleod, A. Rosengren, B.S.P., M.F.M., A.B.N., M.U.H., N.N.J., L.P., N.L., T.W., K.K. and E.W. drafted the main text (M.E.A., M.S. and A.F. led this). M.E.A., M.S., A.F., K.-G.S., A.I., R. Macleod., A. Rosengren, B.S.P., M.L.S.J., M.N., J.S., T.D.P., M.F.M., A.B.N., M.U.H., L.S., P.O.N., P.R., A.R.-M, E.K.I.-P., W.B., A.P., B.S.d.M., F.D., R.A.H., T.V., H.M., A.V., L.V., A.S., A.J.S., A. Ruter, A.B.G., B.H.N., E.B.P., E.K., J.H., K.B.P., L.P., L.K., M.J., O.C.U., P.L., P.B., P.V.P., R. Maring, R.I., S.W., S.A.S., T.J., N.L., D.J.L., S.R., T.S.K., K.H.K., R.D., R.N., O.D., T.W. and K.K. drafted supplementary notes and materials. All authors read, commented on, and agreed upon the submitted manuscript.</p>", "<title>Peer review</title>", "<title>Peer review information</title>", "<p id=\"Par49\"><italic>Nature</italic> thanks Patricia Fall, Birgitte Skar and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.</p>", "<title>Data availability</title>", "<p>Sequencing data analysed in this study is released in the accompanying study ‘Population genomics of post-glacial western Eurasia’<sup>##UREF##0##3##</sup>. These are publicly available on the European Nucleotide Archive under accession <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ebi.ac.uk/ena/data/view/PRJEB64656\">PRJEB64656</ext-link>, together with sequence alignment map files, aligned using human build GRCh37. The full analysis dataset including both imputed and pseudo-haploid genotypes for all ancient individuals used in this study is available at 10.17894/ucph.d71a6a5a-8107-4fd9-9440-bdafdfe81455. Aggregated IBD-sharing data as well as hi-resolution versions of supplementary figures are available at Zenodo under accession 10.5281/zenodo.8196989. Maps were created in R using public domain Natural Earth map data.</p>", "<title>Competing interests</title>", "<p id=\"Par50\">The authors declare no competing interests.</p>" ]	[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>Overview of dataset.</title><p><bold>a</bold>, Geographic locations and age ranges relating to the 100 sequenced genomes from Denmark. Groupings are designated through a combination of chronology, culture, and ancestry (see Supplementary Notes 1 and 3). <bold>b</bold>, PCA for 179 ancient Danish individuals (Supplementary Data ##SUPPL##4##3##) ranging from the Mesolithic to the Viking Age, including previously published ones<sup>##REF##26062507##1##,##REF##31848342##47##,##REF##33444387##57##,##REF##32939067##76##</sup>, in the context of broader West Eurasian genetic diversity (<italic>n</italic> = 983 modern individuals, open grey circles; <italic>n</italic> = 1,105 ancient individuals, filled grey circles). Ancient individuals from Denmark are coloured according to the period as defined in <bold>a</bold> and <bold>c</bold>. <bold>c</bold>, Unsupervised model-based clustering (ADMIXTURE) for <italic>K</italic> = 8 ancestry components in Danish individuals, as well as contextual data from selected groups (left) that represent relevant ancestry components. See Extended Data Fig. ##FIG##4##1## for individual labels. Black crosses indicate low-coverage genomes represented by pseudo-haploid genotypes. BA, Bronze Age.</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>Identity-by-descent sharing patterns in ancient Danish individuals from circa 10,500–3000 cal. BP.</title><p>Heat map showing relative IBD-sharing rate of 72 imputed ancient individuals from Denmark (<italic>n</italic> = 67 individuals reported in this Article, <italic>n</italic> = 5 previously published individuals<sup>##REF##26062507##1##,##REF##31848342##47##,##REF##33444387##57##,##REF##32939067##76##</sup>) from the Mesolithic to the Bronze Age with selected genetic clusters. Individuals are grouped by their genetic cluster membership. See Supplementary Data ##SUPPL##4##3## for dataset and ancestry category definition.</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><title>Genetic, phenotypic, dietary and environmental shifts in Denmark through time.</title><p>Evidence of two population turnovers in chronologically sorted multiproxy data from 100 Danish Mesolithic, Neolithic and Early Bronze Age skeletons (Supplement Data ##SUPPL##2##1##). The figure shows concomitant changes in (from the top) admixture proportions in non-imputed genome-wide data, Y chromosomal and mitochondrial haplogroups, genetic phenotype predictions (based on imputed data) and <sup>87</sup>Sr/<sup>86</sup>Sr and δ<sup>13</sup>C and δ<sup>15</sup>N isotope data as proxies for mobility and diet, respectively. Predicted height values represent differences (in cm) from the average height of the present-day Danish population; probabilities for the hair colours (blond, brown, black and red) and eye colours (blue and brown) are shown, with grey denoting probability of intermediate eye colour (including grey, green and hazel). Lower panel shows the quantitative changes in vegetation cover, based on pollen analyses at Lake Højby in Zealand. Note that the vegetation panel covers a shorter time interval than the other panels. Black vertical lines mark the first presence of Anatolian Neolithic farmer ancestry and Steppe-related ancestry, respectively. Individuals with low genomic coverage, signs of possible contamination and/or low genotype prediction score (GP) are indicated (<xref rid=\"Sec7\" ref-type=\"sec\">Methods</xref>).</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><title>Genetic legacy of ancient Danish individuals.</title><p>PCA of 2,000 modern Danish genomes from the iPSYCH study<sup>##REF##28924187##62##</sup> in the context of ancient western Eurasian individuals. Coloured symbols indicate sample age for ancient Danish individuals, whereas grey symbols indicate 1,145 ancient imputed individuals from across Western Eurasia<sup>##UREF##0##3##</sup>. Modern Danish individuals are indicated by black filled circles and are shown on the right. Inset, a magnified view of the cluster with modern Danes. The colour scale in the inset represents the age range of the ancient samples within the magnified region only.</p></caption></fig>", "<fig id=\"Fig5\"><label>Extended Data Fig. 1</label><caption><title>Model-based clustering.</title><p>Unsupervised model-based clustering results (ADMIXTURE) for K = 2 to K = 15 assumed components for all published shotgun-sequenced ancient individuals from Denmark - including the herein presented 93 genomes (contamination <5% and close relative pairs excluded). Imputed genomes were used where available<sup>##UREF##0##3##</sup>. For low-coverage individuals (indicated with black cross) pseudo-haploid genotypes were used.</p></caption></fig>", "<fig id=\"Fig6\"><label>Extended Data Fig. 2</label><caption><title>Allele sharing of Danish Mesolithic individuals.</title><p><bold>a</bold>, D-statistic testing whether Danish Mesolithic individuals form a clade with the earliest Danish Mesolithic individual in the dataset (NEO254, Koelbjerg Man) to the exclusion of a genetic cluster of Mesolithic hunter-gatherer individuals from Sweden (Sweden_10000BP_7500BP). <bold>b</bold>, D-statistic testing whether Danish Mesolithic individuals form a clade with a genetic cluster of Western European HG individuals (EuropeW_13500BP_8000BP) to the exclusion of a genetic cluster of Eastern European HG individuals (RussiaNW_11000BP_8000BP). Error bars indicate three standard errors.</p></caption></fig>", "<fig id=\"Fig7\"><label>Extended Data Fig. 3</label><caption><title>IBD sharing among ancient individuals from Denmark.</title><p>Heatmap showing pairwise amount of total length of IBD shared between 72 ancient Danish individuals dated to older than 3,000 cal. BP. Colours in border and text indicate genetic cluster membership, and dendrograms show clustering hierarchy.</p></caption></fig>", "<fig id=\"Fig8\"><label>Extended Data Fig. 4</label><caption><title>Genetic affinities of ancient individuals from Denmark.</title><p>Panels show principal component analyses based on pairwise IBD-sharing of <bold>a</bold>, 30 imputed Danish Mesolithic individuals in context of 105 European HGs (right panel shows Danish individuals coloured by age); <bold>b</bold>, 22 imputed Danish early Neolithic individuals within the context of 170 Anatolian and European Neolithic farmers <bold>c</bold>, 21 imputed Danish LNBA individuals within the context of 127 European LNBA individuals. Symbol colour and shape indicate the genetic cluster of an individual (Supplementary Data ##SUPPL##4##III##). The extent of PCA positions of individuals from Denmark are indicated with a dotted line hull. Ancestry cluster categories defined in<sup>##UREF##0##3##</sup>.</p></caption></fig>", "<fig id=\"Fig9\"><label>Extended Data Fig. 5</label><caption><title>Dietary isotopic signatures.</title><p>δ<sup>13</sup>C and δ<sup>15</sup>N values in bone/dentine samples from 100 ancient Danish individuals, coloured according to their main genetic ancestry group. A fundamental dietary and genetic shift is observed at the transition from the Mesolithic to the Neolithic c. 5,900 cal. BP (dashed line). Four anomalous individuals are highlighted. Data from<sup>##UREF##0##3##</sup> and Supplementary Data ##SUPPL##3##II##.</p></caption></fig>", "<fig id=\"Fig10\"><label>Extended Data Fig. 6</label><caption><title>Ancestry modelling of ancient individuals.</title><p><bold>a</bold>, Heatmap of ancestry proportions for 72 ancient individuals from Denmark dated to older than 3,000 cal. BP estimated from supervised mixture models. Results for three different sets of ancestry source groups (deep, fEur, postNeol, Supplementary Data ##SUPPL##5##IV##) are distinguished in facet rows. Genetic cluster membership for Danish target individuals is indicated by column facets. <bold>b</bold>, Spatial distribution of estimated ancestry proportions of three different HG sources for Neolithic farmer individuals from Scandinavia and Poland. <bold>c</bold>, Spatial distribution of estimated ancestry proportions of two different farmer sources for LNBA individuals from Scandinavia and Poland. <bold>d</bold>, Ancestry proportions for Scandinavian Iron Age and Viking Age individuals (postBA reference set). <bold>e</bold>, Ancestry proportions for selected ancient European individuals with ancestry related to Scandinavian LNBA individuals (source Scandinavia_4000BP_3000BP, postBA reference set, Supplementary Data ##SUPPL##5##IV##).</p></caption></fig>", "<fig id=\"Fig11\"><label>Extended Data Fig. 7</label><caption><title>Contrasting hunter-gatherer admixture dynamics in Neolithic farmer individuals from Denmark and Sweden.</title><p><bold>a</bold>, Admixture time estimated using DATES<sup>##REF##35635751##44##</sup> as a function of age for Neolithic farmer individuals from Denmark (left) and Sweden (right). Pie charts indicate ancestry composition (light grey - farmer ancestry; dark grey - non-local hunter-gatherer ancestry; colour - local hunter-gatherer ancestry). <bold>b</bold>, Total amount of hunter-gatherer ancestry proportion as a function of admixture time for Neolithic farmer individuals from Denmark (left) and Sweden (right). Error bars indicate ± 1 standard error of admixture time estimate.</p></caption></fig>", "<fig id=\"Fig12\"><label>Extended Data Fig. 8</label><caption><title>Fine-scale structure in late Neolithic and Early Bronze Age (LNBA) Scandinavians c. 4,500-3,000 cal. BP.</title><p><bold>a</bold>–<bold>e</bold>, Geographic locations and PCA based on pairwise IBD sharing (middle) of 148 European LNBA individuals predating 3,000 cal. BP (Supplementary Data ##SUPPL##5##IV##). Geographic locations are shown for 65 individuals belonging to the five genetic clusters observed in 38 ancient Scandinavians (<bold>a</bold>,<bold>b</bold>, LNBA phase I; <bold>c</bold>,<bold>d</bold>, LNBA phase II; <bold>e</bold>, LNBA phase III; temporal sequence shown in timeline in centre of plot). Individual assignments and frequency distribution of major Y chromosome haplogroups are indicated in maps and timeline. Plot symbols with black circles indicate the 38 Scandinavian individuals in the PCA panels. Ancestry proportions for the 38 Scandinavian individuals estimated using proximal source groups from outside Scandinavia (postNeolScand source set) are shown on the right of the respective cluster results.</p></caption></fig>" ]	[]	[]	[]	[]	[]	[]	[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM3\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM4\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM5\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM6\"></supplementary-material>" ]	[ "<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>These authors contributed equally: Morten E. Allentoft, Martin Sikora, Anders Fischer</p></fn><fn><p>These authors jointly supervised this work: Thomas Werge, Kristian Kristiansen, Eske Willerslev</p></fn><fn><p>Deceased: Esben Kannegaard</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"41586_2023_6862_Fig1_HTML\" id=\"d32e1081\"/>", "<graphic xlink:href=\"41586_2023_6862_Fig2_HTML\" id=\"d32e1202\"/>", "<graphic xlink:href=\"41586_2023_6862_Fig3_HTML\" id=\"d32e1226\"/>", "<graphic xlink:href=\"41586_2023_6862_Fig4_HTML\" id=\"d32e1602\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6862_Fig5_ESM\" id=\"d32e2443\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6862_Fig6_ESM\" id=\"d32e2460\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6862_Fig7_ESM\" id=\"d32e2471\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6862_Fig8_ESM\" id=\"d32e2499\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6862_Fig9_ESM\" id=\"d32e2522\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6862_Fig10_ESM\" id=\"d32e2555\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6862_Fig11_ESM\" id=\"d32e2576\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6862_Fig12_ESM\" id=\"d32e2612\"/>" ]	[ "<media xlink:href=\"41586_2023_6862_MOESM1_ESM.docx\"><label>Supplementary Information</label><caption><p>Supplementary Notes 1–6: <bold>1</bold>, Overview of Danish Samples (including Figs S1.1 to S1.3); <bold>2</bold>, Polygenic prediction of height, eye colour and hair colour (including Table S2.1); <bold>3</bold>, Bayesian Chronological models of the transition (including Figs S3.1 to S3.6); <bold>4</bold>, Dietary variation in Mesolithic, Neolithic and Bronze Age Denmark (including Figs S4.1 to S4.2); <bold>5</bold>, Strontium Analysis of Danish Samples (including Figs S5.1 to S5.3, and Table S5.1); and <bold>6</bold>, Vegetation and landscape in Post-Glacia Denmark – illustrated using a high-resolution land cover reconstruction (LOVE) from Lake Højby, Northwest Zealand (including Figs S6.1 to S6.2).</p></caption></media>", "<media xlink:href=\"41586_2023_6862_MOESM2_ESM.pdf\"><caption><p>Reporting Summary</p></caption></media>", "<media xlink:href=\"41586_2023_6862_MOESM3_ESM.xlsx\"><label>Supplementary Data 1</label><caption><p>Basic overview of samples and genetic data.</p></caption></media>", "<media xlink:href=\"41586_2023_6862_MOESM4_ESM.xlsx\"><label>Supplementary Data 2</label><caption><p>Isotopic data from 100 Danish samples.</p></caption></media>", "<media xlink:href=\"41586_2023_6862_MOESM5_ESM.xlsx\"><label>Supplementary Data 3</label><caption><p>Isotopic data from 100 Danish samples: <bold>a</bold>, Metadata for ancient genomes from Denmark used in this study; <bold>b</bold>, Metadata for selected contextual ancient genomes from Western Eurasia.</p></caption></media>", "<media xlink:href=\"41586_2023_6862_MOESM6_ESM.xlsx\"><label>Supplementary Data 4</label><caption><p>Ancestry proportions for sample sets: <bold>a</bold>, Ancestry proportions for set “deep”; <bold>b</bold>, Ancestry proportions for set “fEur”; <bold>c</bold>, Ancestry proportions for set “postNeol”; <bold>d</bold>, Ancestry proportions for set “postBA”; <bold>e</bold>, Ancestry proportions for set “postNeolScand”.</p></caption></media>" ]	[{"label": ["3."], "mixed-citation": ["Allentoft, M. 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Palynol."], "year": ["2012"], "volume": ["187"], "fpage": ["38"], "lpage": ["49"], "pub-id": ["10.1016/j.revpalbo.2012.07.017"]}]	{ "acronym": [], "definition": [] }	115	CC BY	no	2024-01-13 00:02:19	Nature. 2024 Jan 10; 625(7994):329-337	oa_package/19/c5/PMC10781617.tar.gz
PMC10781618	38123683	[]	[ "<title>Methods</title>", "<title>Mice</title>", "<p id=\"Par15\">The following mouse lines were used: <italic>Dars2</italic><sup><italic>fl/fl</italic></sup> (ref. <sup>##REF##24606902##2##</sup>), <italic>Cox10</italic><sup><italic>fl/fl</italic></sup> (ref. <sup>##REF##16103131##4##</sup>), <italic>Vil1-cre</italic> (ref. <sup>##REF##12065599##7##</sup>) and <italic>Villin-creER</italic><sup><italic>T2</italic></sup> (ref. <sup>##REF##15282745##37##</sup>). <italic>Sdha</italic><sup><italic>tm2a</italic></sup> mice were obtained from the Knock Out Mouse Project repository (project ID: CSD48939) and bred to FLP deleter mice<sup>##REF##10835623##38##</sup> to delete the FRT-flanked region to generate <italic>Sdha</italic><sup><italic>fl/fl</italic></sup> mice. IEC-specific knockout mice were generated by intercrossing mice carrying the respective <italic>loxP</italic>-flanked alleles with <italic>Vil1-cre</italic> or <italic>Villin-creER</italic><sup><italic>T2</italic></sup> transgenic mice. Both female and male mice between 1 and 12 weeks of age were used in all in vivo experiments, whereas metabolic tracing studies were performed exclusively using male mice. All mice were maintained on the C57BL/6N background. Mice were housed at the specific-pathogen-free animal facilities of the CECAD Research Center of the University of Cologne under a 12-h dark–12-h light cycle in individually ventilated cages (Greenline GM500, Tecniplast) at 22 ± 2 °C and a relative humidity of 55 ± 5%. All mice had unlimited access to water and fed a standard chow diet (Harlan diet no. 2918 or Prolab Isopro RMH3000 5P76) ad libitum. For the experiments assessing the role of dietary fat, mice were fed a FFD (E15104-3474, ssniff-Spezialdiäten) containing only traces of fat (<0.5%). All animal procedures were conducted in accordance with European, national and institutional guidelines and protocols were approved by local government authorities (Landesamt für Natur, Umwelt und Verbraucherschutz Nordrhein-Westfalen) and Animal Welfare Officers of the University Medical Center Hamburg-Eppendorf and Behörde für Gesundheit und Verbraucherschutz Hamburg. Animals requiring medical attention were provided with appropriate care and were culled humanely when reaching pre-determined termination criteria to minimize suffering. No other exclusion criteria were applied. Villin-CreER<sup>T2</sup> recombinase activity was induced by five consecutive daily intraperitoneal administrations of 1 mg tamoxifen dissolved in corn oil and DMSO. Littermates not carrying the <italic>Vil1-cre</italic> or <italic>Villin-creER</italic><sup><italic>T2</italic></sup> transgenes were used as controls in all experiments.</p>", "<title>Tissue preparation</title>", "<p id=\"Par16\">The colon and SI were dissected and washed with PBS. Small pieces (about 0.5 cm) were isolated proximal (after the stomach) and distal (before the caecum) of the SI, snap-frozen on dry ice for RNA expression analysis and stored at –80 °C until further processing. The remaining SI tissue was cut longitudinally and washed in PBS to remove faeces. Intestinal tissue samples were rolled up from proximal to distal to form a Swiss roll and either fixed in 4% paraformaldehyde overnight at 4 °C or embedded in TissueTek for frozen sectioning.</p>", "<title>H&E staining of paraffin-fixed tissues</title>", "<p id=\"Par17\">Paraffin-embedded 3-μm-thick intestinal tissue sections were deparaffinized with xylene and rehydrated with decreasing amounts of ethanol solutions (100% ethanol, 96% ethanol and 75% ethanol). Sections were stained for 2 min in haematoxylin, differentiated in tap water for 15 min and incubated for 1 min in eosin. Stained sections were dehydrated using increasing amounts of ethanol solutions and fixed in xylene for 1 min. Slides were mounted with Entellan.</p>", "<title>COX and SDH and ORO staining of fresh-frozen tissues</title>", "<p id=\"Par18\">Fresh-frozen 7-μm-thick intestinal sections were sequentially stained for COX and SDH activity. Cryosections were dried and incubated for 45 min at 37 °C with COX solution. Then they were briefly washed with PBS and incubated for 40 min with SDH solution at 37 °C. Following dehydration through graded alcohol solutions, the sections were mounted with DPX and stored at room temperature. Fresh-frozen 10-μm-thick sections were fixed in 4% paraformaldehyde for 15 min at room temperature. After fixation, the sections were washed with ddH<sub>2</sub>O and stained with ORO in isopropanol/water (60:40) for 15 min. All sections were counterstained with haematoxylin for 5 min and mounted with Aquatex (EMD Millipore).</p>", "<title>Immunohistochemistry and immunofluorescence on intestinal sections</title>", "<p id=\"Par19\">Paraffin sections were rehydrated and heat-induced antigen retrieval was performed in 10 mM sodium citrate, 0.05% Tween-20 at pH 6.2 or with proteinase K treatment. Endogenous peroxidase was blocked in peroxidase blocking buffer for 15 min at room temperature. Sections were blocked in 1% BSA, 0.2% fish-skin gelatin, 0.2% Triton-X-100 and 0.05% Tween-20 in PBS for 1 h at room temperature. After blocking, the sections were incubated overnight at 4 °C with primary antibodies against adipophilin/PLIN2 (Progen, GP46, 1:500), Ki67 (Dako, M724901, clone 1O15, 1:1,000), OLFM4 (Cell Signaling, D6Y5A, clone D6X5A, 1:400), CC3 (Cell Signaling, 9661, 1:1,000), CC8 (Cell Signaling, 8592, 1:1,000), CD45 (BD Bioscience, 560510, clone 30-F11, 1:500) and F4/80 (AbD Serotec, MCA497, clone A3-1, 1:1,000). Sections were incubated with biotinylated anti-mouse IgG (H+L) (Vector Laboratories, BA-9200-1.5, 1:1,000), anti-rabbit IgG (H+L) (Vector Laboratories, BA-1000-1.5, 1:1,000) and anti-rat IgG (H+L) (Vector Laboratories, BA-9400-1.5, 1:1,000) secondary antibodies. Each staining was visualized using ABC Kit Vectastain Elite (Vector, PK6100) and DAB substrate (Dako and Vector Laboratories). Immunofluorescence was performed with primary antibodies against TGN38 (bio-techne, AF8059-SP, 1:200), E-cadherin (BD Biosciences, 610182, 1:1,000) and adipophilin/PLIN2 (Progen, GP46, 1:200). Nuclei were stained using DAPI (Vector Laboratories) and visualized with anti-sheep IgG NorthernLights NL557 (bio-techne, NL010, 1:300), anti-mouse Alexa 488 (Molecular Probes, A1101, 1:300) and anti-guinea pig Alexa 633 (Molecular Probes, A21105, 1:300) fluorescence-conjugated secondary antibodies. Periodic acid–Schiff (PAS) reaction was performed according to standard protocols. Endogenous alkaline phosphatase activity was visualized using a Fast Red Substrate kit according to the manufacturer’s instructions (ab64254, Abcam). For image acquisition, the intestinal sections were analysed using a light microscope equipped with a KY-F75U digital camera (JVC) (DM4000B, Leica Microsystems, Diskus 4.50 software), a TCS SP8 confocal laser scanning microscope (Inverse, DMi 8 CS, Leica Microsystems LAS X, Lightning software v.5.1.0) or a LSM Meta 710 confocal laser scanning microscope (Carl Zeiss Technology, ZEN 2009 software). Golgi quantification was performed using ImageJ software (v.2.0.0.-rc-46/1.50g) as previously described<sup>##REF##17543863##39##</sup>. The number and size of TGN38-positive fluorescent objects were quantified using the ‘analyse particles’ function after applying a fixed threshold on pictures derived from maximal 2D projections of the acquired confocal stacks. Each data point corresponds to the average values from at least three randomly selected intestinal areas of a single mouse. Representative pictures from 4–5 mice per genotype per time point were analysed. More than 100 IEC profiles per mouse with visible nuclei were quantified (<italic>n</italic> = 128–527).</p>", "<title>EM analysis</title>", "<p id=\"Par20\">A piece of 0.5 cm proximal SI tissue was fixed overnight in 2% glutaraldehyde (Merck) and 2% paraformaldehyde (Science Services) in 0.1 M cacodylate buffer (AppliChem). Tissue samples were treated with 1% OsO<sub>4</sub> (Science services) in 0.1 M cacodylate buffer for 2 h. After dehydration of the sample with ascending ethanol concentrations followed by propylene oxide, samples were embedded in Epon (Sigma-Aldrich). Ultrathin sections (70 nm thick) were cut, collected onto 100 mesh copper grids (Electron Microscopy Sciences) and stained with uranyl acetate (Plano) and lead citrate (Sigma Aldrich). Images were captured using a transmission electron microscope (Joel JEM2100 Plus) at an acceleration voltage of 80 kV, and pictures were acquired using a 4K-CCD camera, OneView (GATAN). Mitochondrial morphological integrity quantification was performed on randomly selected pictures of the proximal SI areas from four <italic>Dars2</italic><sup><italic>fl/fl</italic></sup> and four <italic>Dars2</italic><sup><italic>tam</italic></sup><sup><italic>IEC-KO</italic></sup> mice. Each mitochondrial profile was classified as normal, partly affected or severely damaged based on its electron density, the appearance of the cristae and the extent of matrix loss (Fig. ##FIG##1##2c##). The relative distribution of the analysed mitochondria per mouse into the three morphological groups is presented. A total of 663 mitochondrial profiles from 69 IECs versus 707 mitochondrial profiles from 80 IECs were quantified.</p>", "<title>Cell culture conditions and drug treatments</title>", "<p id=\"Par21\">IEC-6 cells (ACC 111) were purchased from the Leibniz Institute DSMZ–German Collection of Microorganisms and Cell Cultures and maintained in standard conditions at 37 °C and 5% CO<sub>2.</sub> The cell culture medium was composed of 45% Dulbecco’s modified Eagle medium (ThermoFisher, 41965-039), 45% RPMI 1640 (ThermoFisher, 11875093) and 0.1 U ml<sup>–1</sup> human insulin solution (Sigma, I9278) supplemented with 10% FCS (Bio&SELL). IEC-6 cells were routinely checked for mycoplasma contamination and tested negative. For induction of mitochondrial dysfunction, 70–80% confluent cells were treated for 48 h with 100 μM actinonin (A6671, Sigma-Aldrich) or 1 μM atpenin A5 (ab144194, Abcam). All compounds were solubilized in dimethyl sulfoxide (DMSO) (A3672, PanReac AppliChem). Control cells were treated with corresponding amounts of DMSO, which did not exceed 1% in culture medium. Treatments were renewed every 24 h. IEC-6 cells were incubated with 5 μg ml<sup>–1</sup> brefeldin A (B6542, Abcam) for 6 h. To induce LD formation, oleic acid (O1008, Sigma-Aldrich) was complexed to fatty acid-free BSA (A6003, Sigma-Aldrich) at a ratio of 6:1 and used at a concentration of 600 μM after titration for 24 h.</p>", "<title>Immunofluorescence of cultured cells</title>", "<p id=\"Par22\">Immunofluorescence staining was performed on IEC-6 cells cultured on coverslips and fixed in 4% paraformaldehyde for 15 min. Reactive aldehydes were quenched with 50 mM NH<sub>4</sub>Cl for 10 min and the cells were permeabilized with 0.1% Triton-X-100 in PBS for 5 min. After 20 min in blocking solution (0.2% fish-skin gelatin diluted in PBS), IEC-6 cells were incubated with primary antibodies against TGN38 (bio-techne, AF8059-SP, 1:200) and MTCO1/COX1 (Molecular Probes, 459600, 1D6E1A8, 1:100) for 30 min at room temperature, followed by incubation with anti-sheep IgG NorthernLights NL557 (bio-techne, NL010, 1:300) or anti-mouse Alexa 488 (Molecular Probes, A1101, 1:300) fluorescence-conjugated secondary antibodies for 30 min at room temperature. When LDs were stained, 5 μM of BODIPY 493/503 (D3922, Invitrogen) diluted in PBS was applied for 30 min. Finally, IEC-6 cells were mounted in Vectashield containing DAPI. For image acquisition, a TCS SP8 confocal laser scanning microscope (Inverse, DMi 8 CS, Leica Microsystems LAS X, Lightning software v.5.1.0) was used. Quantification of Golgi morphology was performed using ImageJ software (v.2.0.0.-rc-46/1.50g) on 2D projections from <italic>Z</italic>-stack images. A total of 4–6 randomly selected viewing fields per condition, capturing at least 30 cells per image, were used. Golgi morphology was classified into five distinct categories based on TGN38-positive fluorescent objects (Extended Data Fig. ##FIG##14##11a,b##) as follows: (1) normal (juxtanuclear Golgi ribbon composed of connected stacks); (2) ring (ring-like Golgi structures surrounding the entire nucleus); (3) condensed (bulb-shaped juxtanuclear Golgi structure); (4) fragmented (Golgi ribbon replaced by more and smaller tubules and vesicles positive for TGN38); and (5) dispersed (complete loss of Golgi ribbon and dispersal of the TGN38 signal). Quantification was performed by manually classifying the TGN38 pattern in each cell in one of the five Golgi phenotypes by the same observer, who was blinded to the experimental conditions. Three independent experiments were quantified.</p>", "<title>Measurement of serum parameters</title>", "<p id=\"Par23\">Glucose (GLU2), total cholesterol (CHOL2), triacylglycerol (TRIGL), high-density lipoprotein (HDLC4) and low-density lipoprotein (LDLC3) levels in the blood serum from mice aged 1–12 weeks old were measured using standard assays in a Cobas C111 Biochemical Analyzer (Roche Diagnostics).</p>", "<title>Isolation of mitochondria and analysis of mitochondrial respiratory complexes with blue native electrophoresis</title>", "<title>Mitochondria isolation</title>", "<p id=\"Par24\">The SI was chopped into small pieces and homogenized with a rotating Teflon potter (Potter S, Sartorius; 20 strokes, 1,000 r.p.m.) in a buffer containing 100 mM sucrose, 50 mM KCl, 1 mM EDTA, 20 mM TES and 0.2% fatty acid-free BSA, pH 7.6 followed by differential centrifugation at 850<italic>g</italic> and 8,500<italic>g</italic> for 10 min at 4 °C. Mitochondria were washed with BSA-free buffer, and protein concentrations were determined using Bradford reagent. Mitochondria were subjected to blue native polyacrylamide gel electrophoresis (BN-PAGE) followed by western blot analysis or determination of the in gel activity of respiratory complexes.</p>", "<title>BN-PAGE</title>", "<p id=\"Par25\">Mitochondrial protein concentrations were determined using Bradford reagent (Sigma). A total of 20 μg of mitochondria was lysed for 15 min on ice in dodecylmaltoside (5 g g<sup>–1</sup> of protein) for individual respiratory complexes, or digitonin (6.6 g g<sup>–1</sup> protein) for supercomplexes, and cleared from insoluble material for 20 min at 20,000<italic>g</italic>, 4 °C. Lysates were combined with Coomassie G-250 (0.25% final). Mitochondrial complexes were resolved by BN-PAGE using 4–16% NativePAGE Novex Bis-Tris mini gels (Invitrogen) in a Bis-Tris/Tricine buffering system with cathode buffer initially supplemented with 0.02% G-250 followed by the 0.002% G-250.</p>", "<title>Complex I in-gel activity</title>", "<p id=\"Par26\">Gels were incubated in a buffer containing 0.01 mg ml<sup>–1</sup> NADH and 2.5 mg ml<sup>–1</sup> nitrotetrazolium blue in 5 mM Tris-HCl pH 7.4.</p>", "<title>Western blot analysis</title>", "<p id=\"Par27\">Separated mitochondrial complexes were transferred onto a polyvinylidene fluoride membrane using a wet transfer methanol-free system. Membranes were immunodecorated with indicated antibodies followed by ECL-based signal detection. The following antibodies were used: anti-MTCO1 (Molecular Probes, 459600, clone 1D6E1A8, 1:5,000), anti-COX4L1 (Molecular Probes, A21348, clone 20E8C12, 1:1,000), anti-UQCRC1 (Molecular Probes, 459140, clone 16D10AD9AH5, 1:4,000), anti-NDUFS1 (Proteintech, 12444-1-AP, 1:1,000), anti-NDUFS2 (Abcam, ab96160, 1:1,000), anti-NDUFV2 (Proteintech, 15301-1-AP, 1:1,000), anti-UQCRFS1/RISP[5A5] (Abcam, ab14746, clone 5A5, 1:1,000), anti-ATP5A (Abcam, ab14748, 1:3,000), anti-SDHA (Molecular Probes, 459200, clone 2EGC12FB2AE2, 1:5,000) and anti-NDUFA9 (Molecular Probes, 459100, clone 20C11B11B11, 1:1,000).</p>", "<title>Isolation of IECs</title>", "<p id=\"Par28\">SI tissue was collected from mice, washed in DPBS (14190-094, Gibco) to remove faeces and cut longitudinally. IECs were isolated by sequential incubation of intestinal tissue in pre-heated 1 mM dithiothreitol and 1.5 mM EDTA solutions at 37 °C while shaking. Pellets of IECs were frozen at −80 °C for further processing.</p>", "<title>Protein lysate preparation</title>", "<p id=\"Par29\">IEC pellets were lysed in RIPA lysis buffer (10 mM Tris-Cl (pH 8), 140 mM NaCl, 1 mM EDTA, 0.5 mM EGTA, 1% Triton X-100, 0.1% sodium deoxycholate, 0.1% Triton X-100 and 0.1% SDS). Lysis buffer was supplemented with protease and phosphatase inhibitor tablets (Roche). The protein concentration was measured using Pierce 660 nm Protein Assay reagent (22660, Thermo Scientific) and a BSA standard pre-diluted set ranging from 0 to 2,000 μg ml<sup>–1</sup> (23208, Thermo Scientific). Cell lysates were separated on SDS–PAGE and transferred to polyvinylidene fluoride membranes (IPVH00010, Millipore). A protein size ladder (26620, Thermo Scientific) was used for size comparison. Membranes were blocked with 5% milk and 0.1% PBST and were probed overnight with primary antibodies against the following antibodies: DARS2 (Proteintech, 13807-1-AP, 1:1,200); total OXPHOS rodent WB antibody cocktail (Abcam, ab110413, 1:1,000); MTCO1 (Molecular Probes, 459600, clone 1D6E1A8, 1:5,000); COX4L1 (Molecular Probes, A21348, clone 20E8C12, 1:1,000); UQCRC1 (Molecular Probes, 459140, clone 16D10AD9AH5, 1:4,000); NDUFS1 (Proteintech, 12444-1-AP, 1:1,000); NDUFS2 (Abcam, ab96160, 1:1,000); NDUFV2 (Proteintech, 15301-1-AP, 1:1,000); UQCRFS1/RISP[5A5] (Abcam, ab14746, Clone 5A5, 1:1,000); ATP5A (Abcam, ab14748, 1:3,000); SDHA (Molecular Probes, 459200, clone 2EGC12FB2AE2, 1:5,000); NDUFA9 (Molecular Probes, 459100, clone 20C11B11B11, 1:1,000); α-tubulin (Sigma Aldrich, T6074, clone TUBA4A, 1:1,000); TOMM70 (Sigma, HPA014589, 1:500); β-actin (Santa Cruz, sc-1616, clone I-19, 1:1,000); adipophilin/PLIN2 (Progen, GP46, 1:500); FABP2 (Proteintech, 21252-1-AP, 1:500); FASN (Cell Signaling, 3189S, 1:1,000); vinculin (Cell Signaling, 13901, 1:1,000); and ApoB (Beckman Coulter, 467905, 1:500). Membranes were incubated for 1 h at room temperature with anti-rabbit IgG (GE Healthcare, NA934V, 1:5,000), anti-mouse IgG (GE Healthcare, NA931, 1:5,000), anti-goat IgG (Jackson Laboratories, 705-035-003, 1:5,000) or anti-guinea pig IgG (Progen, 90001, 1:5,000) secondary HRP-coupled antibodies and Amersham ECL Western Blotting Detection reagent (GE Healthcare) were used. The membranes were re-probed after incubation in Restore Western Blot stripping buffer (21059, ThermoFisher). The signal was measured with a Curix 60 Processor and a western blot imager (FUSION Solo X, Vilber).</p>", "<title>RNA isolation from tissues</title>", "<p id=\"Par30\">SI tissue samples were disrupted using a Precellys 24 tissue homogenizer (Bertin technologies). Isolation of RNA was performed using a NucleoSpin RNA isolation kit (Macherey Nagel ref. 740955.250) according to the manufacturer’s instructions.</p>", "<title>RT–qPCR</title>", "<p id=\"Par31\">cDNA was prepared using a Superscript III cDNA-synthesis kit (18080-044, Thermo Scientific). RT–qPCR was performed using TaqMan probes (Life Technologies) and SYBR Green (Thermo Scientific). The mRNA expression of each gene was normalized to the expression of the housekeeping genes <italic>Tbp</italic> or <italic>Hprt1</italic>. Relative expression of gene transcripts was analysed using the 2<sup>–ΔΔCt</sup> method. The RT–PCR data were collected using QuantStudio 12K Flex Software v.1.6 (Applied Biosystems). The following Taqman probes were used: <italic>Olfm4</italic> (Mm01320260_m1, Thermo Scientific), <italic>Lgr5</italic> (Mm00438890_m1, Thermo Scientific), <italic>Ascl2</italic> (Mm01268891_g1, Thermo Scientific), <italic>Tbp</italic> (Mm00446973_m1, Thermo Scientific), <italic>Prominin-1</italic> (Mm00477115_m1, Thermo Scientific) and <italic>Lrig-5</italic> (Mm00456116_m1, Thermo Scientific). Primer sequences for SYBR Green are described in Supplementary Table ##SUPPL##0##5##.</p>", "<title><italic>C.</italic><italic>elegans</italic> strains, maintenance and imaging</title>", "<p id=\"Par32\">Strains were cultured on OP50 <italic>Escherichia coli</italic>-seeded NGM plates, according to standard protocols<sup>##UREF##4##40##</sup>. Strains used in this study are Bristol N2, RT1315 <italic>unc-119(ed3)</italic>; <italic>pwIs503</italic>[p<italic>vha-6</italic>::<italic>mans</italic>::<italic>gfp</italic>;<italic>cbr</italic>-<italic>unc-119</italic>], VS25 hjIs14 [vha-6p::GFP::C34B2.10(SP12) + unc-119(+)] and RT130 pwIs23 [vit-2::GFP]. RNAi knockdown was performed as previously described<sup>##REF##11178279##41##</sup>. All the experiments were performed with hermaphrodite worms at days 1 and 4 of adulthood that were randomly selected and were not allocated into groups. <italic>dars-2</italic>, <italic>sar-1</italic>, <italic>sec-13 and fum-1</italic> clones were obtained from the Ahringer RNAi library<sup>##REF##11178279##41##</sup> and confirmed by sequencing. As a control, empty L4440 vector was used. For confocal imaging, animals were immobilized on 2% agarose pads in 5 mM levamisole buffer and imaging was performed using a spinning disc confocal microscope (Inverse, Nikon TiE, UltraView VoX, Perkin Elmer, Volocity software). For fluorescence imaging, worms were immobilized on 2% agarose pads in 50 mM sodium azide buffer and imaged using the optical Zeiss Axio Imager Z1 microscope (ZEN 2009 software). Images were analysed using the open-source software Fiji (ImageJ, v.1.53c).</p>", "<title>RNA isolation and RT–qPCR in <italic>C.</italic><italic>elegans</italic></title>", "<p id=\"Par33\">Worms were collected from a 9 cm plate and total RNA was isolated using Trizol (Invitrogen). DNAse treatment was performed using DNA-free, DNAse and removal (Ambion, Life technologies) according to the manufacturer’s protocol. RNA was quantified by spectrophotometry and 0.8 μg of total RNA was reverse transcribed using a High-Capacity cDNA Reverse Transcription kit (Applied Biosystems). For each condition, six independent samples were prepared. qPCR was performed using a Step One Plus Real-Time PCR system (Applied Biosystems) with the following PCR conditions: 3 min at 95 °C, followed by 40 cycles of 5 s at 95 °C and 15 s at 60 °C. Amplified products were detected using SYBR Green (Brilliant III Ultra-Fast SYBR Green qPCR Master Mix, Agilent Technologies). Relative quantification was performed against Y45F10D.4.</p>", "<p id=\"Par34\">The following primers were used: <italic>dars-2</italic> FW1 (5′-GTTTGCTGGGGAAATTCAGA-3′); <italic>dars-2</italic> RV1 (5′-AGTGGAGCCGTAAATGGATG-3′); Y45F10D.4 FW (5′-GTCGCTTCAAATCAGTTCAGC-3′); and Y45F10D.4 RV (5′-GTTCTTGTCAAGTGATCCGACA-3′). Data were analysed using ΔΔCt analysis.</p>", "<title>Lipidomics</title>", "<p id=\"Par35\">For lipid analyses, mouse tissue samples were homogenized in deionized water (10 μl per 1 mg wet weight) using a Precellys 24 homogenizer (Peqlab) at 6,500 r.p.m. for 30 s. The protein content of the homogenate was routinely determined using bicinchoninic acid.</p>", "<title>Liquid chromatography coupled to electrospray ionization tandem mass spectrometry</title>", "<p id=\"Par36\">Sphingolipid (ceramides and sphingomyelins) and cholesterol levels in mouse SI tissue were determined by liquid chromatography coupled to electrospray ionization tandem mass spectrometry (LC–ESI-MS/MS). For sphingolipid analyses, 50 μl of tissue homogenate was used. Lipid extraction and LC–ESI-MS/MS analysis were performed as previously described<sup>##REF##22927247##42##,##REF##28412693##43##</sup>. For the determination of cholesterol levels, 25 μl of tissue homogenate was extracted and processed as previously described<sup>##REF##25688136##44##</sup>.</p>", "<title>Nano-ESI-MS/MS</title>", "<p id=\"Par37\">Levels of cholesteryl esters (CEs), diacylglycerols (DAGs), TAGs and glycerophospholipids in mouse SI tissue were determined by nano-ESI-MS/MS). Next, 10 μl (for DAGs) or 5 μl (for TAGs and CEs) of tissue homogenate was diluted to 500 μl with Milli-Q water and mixed with 1.875 ml of chloroform, methanol and 37% hydrochloric acid 5:10:0.15 (v/v/v). Next, 20 μl of 4 µM d5-TG internal standard mixture I (for TAGs), 15 μl of 256 μM CE 19:0 (for CEs) or 20 μl of 4 μM d5-DG internal standard mixtures I and II (for DAGs) (Avanti Polar Lipids) were added. Lipid extraction and nano-ESI-MS/MS analyses of DAGs and TAGs were performed as previously described<sup>##UREF##5##45##</sup>. The detection of CE species was conducted in positive-ion mode by scanning for precursors of <italic>m/z</italic> 369 Da at a collision energy of 15 eV and with a declustering potential of 100 V, an entrance potential of 10 V and a cell exit potential of 14 V. Levels of glycerophospholipids (that is, phosphatidylcholines, phosphatidylethanolamines, phosphatidylinositols, phosphatidylserines and phosphatidylglycerols) were determined by performing extraction and nano-ESI-MS/MS measurement of 10 μl of tissue homogenate as previously described<sup>##REF##26644517##46##</sup>.</p>", "<title>Metabolomics</title>", "<title>Metabolite extraction</title>", "<p id=\"Par38\">Metabolite extraction solution (50% methanol, 30% acetonitrile, 20% water and 5 μM valine-d8 as internal standard) was added to 10–20 mg frozen SI tissue samples at an extraction ratio of 25 μl mg<sup>–1</sup> on dry ice. Samples were then homogenized using a Precellys 24 tissue homogenizer (Bertin Technologies). The resulting sample suspension was vortexed, mixed at 4 °C in a Thermomixer for 15 min at 1,500 r.p.m. and then centrifuged at 16,000<italic>g</italic> for 20 min at 4 °C. The supernatant was collected for LC–MS analysis.</p>", "<title>Metabolite measurement by LC–MS</title>", "<p id=\"Par39\">LC–MS chromatographic separation of metabolites was achieved using a Millipore Sequant ZIC-pHILIC analytical column (5 μm, 2.1 × 150 mm) equipped with a 2.1 × 20 mm guard column (both 5 mm particle size) with a binary solvent system. Solvent A was 20 mM ammonium carbonate and 0.05% ammonium hydroxide. Solvent B was acetonitrile. The column oven and autosampler tray were held at 40 °C and 4 °C, respectively. The chromatographic gradient was run at a flow rate of 0.200 ml min<sup>–1</sup> as follows: 0–2 min: 80% solvent B; 2–17 min: linear gradient from 80% solvent B to 20% solvent B; 17–17.1 min: linear gradient from 20% solvent B to 80% solvent B; 17.1–22.5 min: hold at 80% solvent B. Samples were randomized and analysed with LC–MS in a blinded manner with an injection volume of 5 μl. Pooled samples were generated from an equal mixture of all individual samples and analysed interspersed at regular intervals within the sample sequence as a quality control. Metabolites were measured using a Thermo Scientific Q Exactive Hybrid Quadrupole-Orbitrap mass spectrometer (HRMS) coupled to a Dionex Ultimate 3000 UHPLC. The mass spectrometer was operated in full-scan, polarity-switching mode, with the spray voltage set to +4.5 kV/–3.5 kV, the heated capillary held at 320 °C and the auxiliary gas heater held at 280 °C. The sheath gas flow was set to 25 units, the auxiliary gas flow was set to 15 units and the sweep gas flow was set to 0 unit. HRMS data acquisition was performed in a range of <italic>m/z</italic> = 70–900, with the resolution set at 70,000, the automatic gain control (AGC) target at 1 × 10<sup>6</sup> and the maximum injection time at 120 ms. Metabolite identities were confirmed using two parameters: (1) precursor ion <italic>m/z</italic> was matched within 5 ppm of theoretical mass predicted by the chemical formula; (2) the retention time of metabolites was within 5% of the retention time of a purified standard run with the same chromatographic method.</p>", "<title>Data analysis</title>", "<p id=\"Par40\">Chromatogram review and peak area integration were performed using the Thermo Fisher software Tracefinder (v.5.0). The peak area for each detected metabolite was subjected to the ‘Filtering 80% Rule’, half minimum missing value imputation and normalized against the total ion count of that sample to correct any variations introduced from sample handling through instrument analysis. Samples were excluded after performing testing for outliers based on geometric distances of each point in the PCA score plot as part of the muma package (v.1.4)<sup>##UREF##6##47##</sup>. Afterwards, differential metabolomics analysis was performed. In detail, the R package ‘gtools’ (v.3.8.2) (<ext-link ext-link-type=\"uri\" xlink:href=\"https://cran.r-project.org/web/packages/gtools/index.html\">cran.r-project.org/web/packages/gtools/index.html</ext-link>) was used to calculate the log<sub>2</sub>(fold change) using the functions ‘foldchange’ and ‘foldchange2logratio’ (parameter base = 2).The corresponding <italic>P</italic> value was calculated using the R base package ‘stats’ (v.4.0.5) (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.r-project.org\">www.r-project.org</ext-link>) with the function ‘t.test’ (SIMPLIFY = F). The <italic>P</italic> value was adjusted using the stats base function ‘p.adjust’ (method = “bonferroni”). Volcano plots were generated using the EnhancedVolcano package<sup>##UREF##7##48##</sup> (v.1.8.0).</p>", "<title>QuantSeq 3′ mRNA sequencing</title>", "<p id=\"Par41\">RNA quality was evaluated based on the RNA integrity number (RIN) and OD260/280 and OD260/230 ratios. RIN values were determined using TapeStation4200 and RNA Screen Tapes (Agilent Technologies). Gene expression was determined using a QuantSeq 3′ mRNA-Seq Library Prep kit FWD for Illumina (Lexogen). QuantSeq libraries were sequenced on an Illumina NovaSeq 6000 sequencer using Illumina RTA v.3.4.4 base-calling software. Sample exclusion criteria were OD260/280 < 1.8, OD260/230 < 1.5 and RIN < 7. Illumina adapters were clipped off the raw reads using Cutadapt with standard parameters and a minimum read length of 35 after trimming (shorter reads were discarded). QuantSeq-specific features were deliberately not removed to avoid loss of reads. Trimmed reads were mapped to a concatenation of the mouse genome (Mus_musculus.GRCm38.dna.chromosome.*.fa.gz, downloaded from <ext-link ext-link-type=\"uri\" xlink:href=\"ftp://ftp.ensembl.org/pub/release-100/fasta/mus_musculus/dna/\">ftp.ensembl.org/pub/release-100/fasta/mus_musculus/dna/</ext-link>) and the ERCC92 Spike In sequences (downloaded from <ext-link ext-link-type=\"uri\" xlink:href=\"https://assets.thermofisher.com/TFS-Assets/LSG/manuals/ERCC92.zip\">assets.thermofisher.com/TFS-Assets/LSG/manuals/ERCC92.zip</ext-link>) using subread-align version v.2.0.1 with parameters -t 0 -d 50 -D 600 --multiMapping -B 5. Genomic matches were counted using featureCounts with parameters -F “GTF” -t “exon” -g “gene_id” --minOverlap 20 -M --primary -O --fraction -J -Q 30 -T 4. The genome annotation used was Mus_musculus.GRCm38.100.gtf (downloaded from <ext-link ext-link-type=\"uri\" xlink:href=\"ftp://ftp.ensembl.org/pub/release-100/gtf/mus_musculus/\">ftp.ensembl.org/pub/release-100/gtf/mus_musculus/</ext-link>), augmented by entries for the ERCC92 Spike Ins.</p>", "<p id=\"Par42\">All analyses were done in R-4.0.0, using the functionality of Bioconductor v.3.11. For differential gene expression analysis, the package DESeq2 was used (<ext-link ext-link-type=\"uri\" xlink:href=\"https://bioconductor.org/packages/release/bioc/html/DESeq2.html\">bioconductor.org/packages/release/bioc/html/DESeq2.html</ext-link>).</p>", "<p id=\"Par43\">Pairwise comparisons were performed between genotypes TG and WT (differential_expression_DESeq2_tg_VS_wt.xlsx). Genes were excluded from a DESeq2 run if they had a zero count in more than half of the samples in either of the conditions compared. Note that DESeq2 sets the <italic>P</italic> value and the adjusted <italic>P</italic> value to NA for genes with too few counts or with extreme outlier counts. Such genes were removed after analysis from the DESeq2 output.</p>", "<p id=\"Par44\">The output tables were augmented by gene symbols and descriptions, which were derived from the org.Mm.eg.db annotation package using the function AnnotationDbi::mapIDs (<ext-link ext-link-type=\"uri\" xlink:href=\"https://bioconductor.org/packages/release/bioc/html/AnnotationDbi.html\">bioconductor.org/packages/release/bioc/html/AnnotationDbi.html</ext-link>). In addition, the raw read counts per gene and sample, as returned by featureCounts, were appended to the rows of each output table. The statistical test producing the <italic>P</italic> values is the Wald test, and <italic>P</italic>‐adjusted values were calculated using the false discovery rate and Benjamini–Hochberg approach. It was computed using the function nbinomWaldTest of the Bioconductor R package DESeq2, based on a negative binomial general linear model of the gene counts from a previously described method<sup>##REF##25516281##49##</sup>.</p>", "<title>Proteomics</title>", "<title>In-solution digestion for MS</title>", "<p id=\"Par45\">Samples for MS analysis were prepared by in-solution digestion. Protein (20 μg) was precipitated for at least 1 h in four volumes (v/v) of ice-cold acetone and protein pellets were extracted by centrifugation at 13,000<italic>g</italic> for 10 min and dissolved in urea buffer (6 M urea, 2 M thiourea in 10 mM HEPES, pH 8.0). Urea-containing samples were reduced by applying tris(2-carboxyethyl)phosphine at a final concentration of 10 mM, alkylated with chloroacetamide at a final concentration of 40 mM and incubated for 1 h at room temperature. Samples were then digested with 1 μl LysC for 2 h at room temperature, diluted with 50 mM ammonium bicarbonate to a urea concentration of 2 M, incubated with 1 μl 0.5 mg ml<sup>–1</sup> trypsin overnight at room temperature, acidified to 1% formic acid and purified using Stop and Go extraction tips (StageTips)<sup>##REF##17703201##50##</sup>.</p>", "<title>MS-based proteome analysis</title>", "<p id=\"Par46\">Proteome samples were analysed using LC–MS/MS on an Orbitrap Eclipse Tribrid mass spectrometer (Thermo Fisher) with a FAIMS Pro device using a combination of two compensation voltages of –50 V and –70 V. Chromatographic peptide separation was achieved on 50 cm reverse-phase nanoHPLC-columns (ID 75 μm, PoroShell C18 120, 2.4 μm) coupled to an EASY-nLC 1200 HPLC system and a binary buffer system A (0.1% formic acid) and B (80% acetonitrile/0.1% formic acid). Samples derived from in-solution digestion were measured over a 120 min gradient, raising the content of buffer acetonitrile from 3.2 to 22% over 102 min, from 22 to 45% over 8 min and from 45 to 76% over 2 min. The column was washed with 76% acetonitrile for 8 min. Full MS spectra (300–1,750 <italic>m/z</italic>) were recorded at a resolution of 60,000, maximum injection time of 20 ms and automatic gain control target of 6 × 10<sup>5</sup>. The 20 most abundant ion peptides in each full MS scan were selected for higher-energy collisional dissociation fragmentation at nominal collisional energy of 30. MS2 spectra were recorded at a resolution of 15,000, a maximum injection time of 22 ms and an automatic gain control target of 1 × 10<sup>5</sup>. This MS acquisition program was alternatively run for both FAIMS compensation voltages to cover different peptide fractions.</p>", "<title>MS data processing and analysis</title>", "<p id=\"Par47\">The generated MS raw data were analysed using MaxQuant analysis software and the implemented Andromeda software (v.1.6.14)<sup>##REF##19029910##51##,##REF##21254760##52##</sup>. Peptides and proteins were identified using the canonical mouse UniProt database (downloaded August 2019) with common contaminants. All parameters in MaxQuant were set to the default values. Trypsin was selected as the digestion enzyme, and a maximum of two missed cleavages was allowed. Methionine oxidation and amino-terminal acetylation were set as variable modifications, and carbamidomethylation of cysteines was chosen as a fixed modification. The label-free quantification algorithm was used to quantify the measured peptides and the ‘match between runs’ option was enabled to quantify peptides with a missing MS2 spectrum. Subsequent statistical analysis was performed using Perseus (1.5.8.5) software. Potential contaminants and reverse peptides were excluded, and values were log<sub>2</sub> transformed. Raw files were assigned to two groups (TG and WT) and protein groups were filtered for four valid values in at least one group before missing values were replaced from normal distribution (width of 0.3; down shift of 1.3). Welch’s Student <italic>t</italic>-test with S0 = 0.1 and a permutation-based false discovery rate of 0.01 with 500 randomizations was performed to obtain differentially regulated proteins between the two groups. Identified proteins were annotated with the gene ontology terms biological process, molecular function, and cellular compartment, and the Reactome Pathway database. Finally, graphical visualization was achieved using Instant Clue software<sup>##REF##30140043##53##</sup> (v.0.5.3).</p>", "<title>GSEA and data visualization</title>", "<p id=\"Par48\">Gene set enrichment methods were applied using GSEA and over-representation analysis (ORA). In detail, GSEA was performed by using gene sets published on the MsigDB (Reactome, KEGG, Biocarta and Hallmarks)<sup>##REF##16199517##54##</sup> and from a published study<sup>##REF##23624402##55##</sup> (ATF4) using the packages fgsea<sup>##UREF##8##56##</sup> (v.1.16.0) and GSEABase<sup>##UREF##9##57##</sup> (v.1.52.1). Volcano plots were generated using the EnhancedVolcano package<sup>##UREF##7##48##</sup> (v.1.8.0). The ORA was performed using the ‘enrich_GO’ function (parameters: keyType = “ENTREZID”, OrgDb = org.Mm.eg.db, ont = “ALL”, pAdjustMethod = “BH”, qvalueCutoff = 0.1) of the clusterProfiler package<sup>##REF##22455463##58##</sup>(v.3.16.1). The output data were plotted using the ‘emapplot’ function of the enrichplot package (v. 1.8.1) (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.bioconductor.org/packages/release/bioc/html/enrichplot.html\">www.bioconductor.org/packages/release/bioc/html/enrichplot.html</ext-link>) (parameters: pie_scale = 1, showCategory = 40, layout = “nicely”).</p>", "<title>Metabolic tracer studies</title>", "<title>Postprandial glucose and fat tolerance tests</title>", "<p id=\"Par49\">Mice were fasted for 2 h before receiving an oral gavage of 300 μl of a glucose–lipid emulsion containing triolein (3.6 g kg<sup>–1</sup> body weight), lecithin (0.36 g kg<sup>–1</sup> body weight) and glucose (2 g kg<sup>–1</sup> body weight), traced with [<sup>3</sup>H]triolein (1.4 MBq kg<sup>–1</sup> body weight) and [<sup>14</sup>C]DOG (1.7 MBq kg<sup>–1</sup> body weight). After 2 h, mice were anaesthetized and transcardially perfused with PBS containing 10 U ml<sup>–1</sup> heparin. Organs were collected, weighed and dissolved in 10× (v/w) Solvable (Perkin Elmer), and radioactivity (in d.p.m.) was measured by scintillation counting using a Perkin Elmer Tricarb scintillation counter. Uptake of radioactive tracers was calculated per total organ weight.</p>", "<title>CM production</title>", "<p id=\"Par50\">Mice were injected with tyloxapol (500 mg in 0.9% NaCl per kg body weight) to block vascular lipolysis. Mice received an oral gavage of a lipid emulsion with triolein (3.6 g kg<sup>–1</sup> body weight) and lecithin (0.36 g kg<sup>–1</sup> body weight) that were traced with [<sup>14</sup>C]cholesterol (1.4 MBq kg<sup>–1</sup> body weight) and [<sup>3</sup>H]triolein (1.7 MBq kg<sup>–1</sup> body weight). Blood was collected from the tail vein at 0, 30, 60 and 120 min after gavage. Plasma triglycerides were determined by standard colorimetric assays (Roche) and radioactivity was measured by scintillation counting.</p>", "<title>Plasma parameters</title>", "<p id=\"Par51\">Plasma was generated by centrifugation of EDTA-spiked blood for 10 min at 10,000 r.p.m. at 4 °C in a bench top centrifuge. Free glycerol was determined photometrically using Free Glycerol reagent (F6428, Sigma). For lipoprotein profiling, 150 μl pooled plasma was diluted with an equal amount of FPLC buffer (total 300 μl), which was separated by fast-performance liquid chromatography (FPLC) on a Superose 6 10/300 GL column (GE Healthcare) with a flow rate of 0.5 ml min<sup>–1</sup>. Forty fractions (0.5 ml each) were collected, and cholesterol and triglyceride concentrations were measured in each one.</p>", "<p id=\"Par52\">To isolate the TRL fractions, 200 μl of plasma was mixed with 200 μl density solution 1 (0.9% NaCl, 10 mM EDTA, 10 mM Tris-Cl pH 8.6 and 0.49 g ml<sup>–1</sup> KBr; density 1.3 g l<sup>–1</sup>). The density solution 2 (0.9% NaCl, 10 mM EDTA, 10 mM Tris-Cl pH 8.6; density 1.006 g l<sup>–1</sup>) was placed into a Beckman TL100 centrifuge tube (Beckman, 343778) and then the plasma carefully under layered. Ultracentrifugation was performed in a Beckman Optima MAX-XP ultracentrifuge for 2 h at 4 °C and 40,000 r.p.m. in a Beckman TL100 rotor. After centrifugation, 200 μl of the top containing TRL particles were collected using a syringe.</p>", "<title>Statistical analysis</title>", "<p id=\"Par53\">Data shown in column graphs represent the mean ± s.e.m., as indicated in the figure captions. The D’Agostino–Pearson omnibus normality test was applied to test normal (Gaussian) distribution. When data fulfilled the criteria for normality, unpaired two-sided Student’s <italic>t</italic>-tests with no assumption of equal variance were performed; otherwise, the nonparametric Mann–Whitney <italic>U</italic>-test was chosen. Multiple pairwise comparisons of groups over time by repeated measures were evaluated by two-way ANOVA with Bonferroni’s correction for multiple comparison (the corrected <italic>P</italic> values are given for comparison between genotypes at each time point). Survival curves were compared using Gehan–Breslow–Wilcoxon test. The chi-squared test was used for the comparison of the mitochondria integrity distribution between two groups and the assessment of the Golgi pattern distribution after various inhibitor treatments in IEC-6 cells. The number of mice analysed in each experiment is described in the respective figure captions. Statistical analyses were performed with GraphPad Prism 6 (v.6.01) and 9 (v.9.4.1).</p>", "<title>Reporting summary</title>", "<p id=\"Par54\">Further information on research design is available in the ##SUPPL##1##Nature Portfolio Reporting Summary## linked to this article.</p>" ]	[]	[ "<title>Discussion</title>", "<p id=\"Par14\">Taken together, our results revealed an essential and evolutionarily conserved role of mitochondria in dietary lipid processing by enterocytes. The LD accumulation phenotype caused by mitochondrial dysfunction in IECs is reminiscent of the pathology of human patients with mutations in the gene encoding SAR1B, who suffer from CM retention disease that manifests with chronic diarrhoea, intestinal distension and growth retardation in infancy<sup>##REF##32015520##1##,##REF##30640893##32##,##REF##17945526##33##</sup>. SAR1B deficiency prevents the trafficking of pre-CMs from the ER to the Golgi, which causes impaired transport of dietary lipids to the circulation and their accumulation within large LDs in enterocytes<sup>##REF##32015520##1##,##REF##30640893##32##,##REF##17945526##33##</sup>. Mice with IEC-specific ablation of MTTP, which is essential for CM production, showed impaired transport and accumulation of dietary lipids in enterocytes<sup>##REF##24019513##19##</sup>. Furthermore, brefeldin A treatment in rats suppresses CM production, which results in impaired transport of dietary fat and lipid accumulation in large LDs in enterocytes<sup>##REF##8141303##35##</sup>. These results provide evidence that Golgi disorganization inhibits dietary lipid processing. The precise mechanism by which mitochondrial defects affect secretory pathway organization and function remains to be fully elucidated. However, our findings suggest that mitochondrial dysfunction impairs CM formation and/or trafficking from the ER to the plasma membrane, which results in compromised transport of dietary lipids to peripheral tissues and their accumulation and storage within large cytoplasmic LDs in enterocytes. These findings could be relevant for the understanding of the mechanisms that cause intestinal complications associated with the severe inability to gain weight and failure to thrive in a subset of patients with mitochondrial disease<sup>##REF##11579428##6##,##REF##19659453##36##</sup>.</p>" ]	[]	[ "<p id=\"Par1\">Digested dietary fats are taken up by enterocytes where they are assembled into pre-chylomicrons in the endoplasmic reticulum followed by transport to the Golgi for maturation and subsequent secretion to the circulation<sup>##REF##32015520##1##</sup>. The role of mitochondria in dietary lipid processing is unclear. Here we show that mitochondrial dysfunction in enterocytes inhibits chylomicron production and the transport of dietary lipids to peripheral organs. Mice with specific ablation of the mitochondrial aspartyl-tRNA synthetase DARS2 (ref. <sup>##REF##24606902##2##</sup>), the respiratory chain subunit SDHA<sup>##REF##7550341##3##</sup> or the assembly factor COX10 (ref. <sup>##REF##16103131##4##</sup>) in intestinal epithelial cells showed accumulation of large lipid droplets (LDs) in enterocytes of the proximal small intestine and failed to thrive. Feeding a fat-free diet suppressed the build-up of LDs in DARS2-deficient enterocytes, which shows that the accumulating lipids derive mostly from digested fat. Furthermore, metabolic tracing studies revealed an impaired transport of dietary lipids to peripheral organs in mice lacking DARS2 in intestinal epithelial cells. DARS2 deficiency caused a distinct lack of mature chylomicrons concomitant with a progressive dispersal of the Golgi apparatus in proximal enterocytes. This finding suggests that mitochondrial dysfunction results in impaired trafficking of chylomicrons from the endoplasmic reticulum to the Golgi, which in turn leads to storage of dietary lipids in large cytoplasmic LDs. Taken together, these results reveal a role for mitochondria in dietary lipid transport in enterocytes, which might be relevant for understanding the intestinal defects observed in patients with mitochondrial disorders<sup>##REF##28286566##5##</sup>.</p>", "<p id=\"Par2\">Mitochondria have a pivotal role in the transport of dietary lipids in enterocytes, a finding that might have relevance to understanding the aberrant gastrointestinal function in patients with mitochondrial disorders.</p>", "<title>Subject terms</title>" ]	[ "<title>Main</title>", "<p id=\"Par3\">Mitochondrial dysfunction leads to deficiency in oxidative phosphorylation (OXPHOS) and metabolic defects that can affect almost any cell type and cause devastating diseases. Although mitochondrial diseases are usually described as encephalomyopathies, they often involve multiple organs, including the gastrointestinal (GI) tract<sup>##REF##28286566##5##,##REF##11579428##6##</sup>. The GI manifestations of mitochondrial diseases are frequently overlooked as they are considered either not life-threatening or nonspecific (for example, anorexia, abdominal pain, chronic constipation, diarrhoea or persistent vomiting). Defects in neuroendocrine and smooth muscle cells have been implicated in causing the GI manifestations, whereas the possible role of mitochondria in enterocytes remains largely unexplored<sup>##REF##28286566##5##,##REF##11579428##6##</sup>. Here we investigate the role of mitochondria in enterocytes, in particular in the processing and transport of dietary lipids.</p>", "<title>DARS2 deficiency causes lipid accumulation in IECs</title>", "<p id=\"Par4\">To study the role of mitochondria in intestinal epithelial cells (IECs), we generated mice lacking DARS2 specifically in IECs by crossing <italic>Dars2</italic><sup><italic>fl/fl</italic></sup> mice<sup>##REF##24606902##2##</sup> with <italic>Vil1</italic>-<italic>cre</italic> mice<sup>##REF##12065599##7##</sup> (<italic>Dars2</italic><sup><italic>fl/fl</italic></sup><italic>Vil1-cre</italic><sup><italic>tg/wt</italic></sup>, hereafter referred to as <italic>Dars2</italic><sup><italic>IEC-KO</italic></sup>). DARS2 deficiency inhibits the production of mitochondrial DNA (mtDNA)-encoded respiratory chain subunits and causes severe mitochondrial dysfunction<sup>##REF##24606902##2##</sup>. <italic>Dars2</italic><sup><italic>IEC-KO</italic></sup> mice were born at the expected Mendelian ratio but showed severely reduced body weight, failed to thrive and could not survive beyond the age of 4 weeks (Fig. ##FIG##0##1a,b##). Immunoblot analyses of total protein and mitochondrial protein extracts from primary IECs from 7-day-old <italic>Dars2</italic><sup><italic>IEC-KO</italic></sup> pups revealed efficient ablation of DARS2 and strongly reduced levels of mtDNA-encoded respiratory chain subunits (CI, CIII, CIV and CV) (Fig. ##FIG##0##1c## and Extended Data Fig. ##FIG##4##1a##). Consequently, reduced formation of OXPHOS supercomplexes was detected in mitochondria from the small intestine (SI) of <italic>Dars2</italic><sup><italic>IEC-KO</italic></sup> mice (Extended Data Fig. ##FIG##4##1b##). Enzyme histochemical staining showed strong cytochrome <italic>c</italic> oxidase (COX) deficiency, and electron microscopy (EM) analyses revealed swollen mitochondria with less densely packed and fragmented cristae in SI enterocytes of <italic>Dars2</italic><sup><italic>IEC-KO</italic></sup> mice (Fig. ##FIG##0##1d,e##).</p>", "<p id=\"Par5\">The SI of 7-day-old <italic>Dars2</italic><sup><italic>IEC-KO</italic></sup> pups were considerably shorter than those of <italic>Dars2</italic><sup><italic>fl/fl</italic></sup> littermates, and showed perturbed tissue architecture with blunted villi and lower numbers of Goblet cells and absorptive enterocytes (Fig. ##FIG##0##1f## and Extended Data Fig. ##FIG##4##1c,d##). Immunostaining for Ki67 revealed strongly decreased epithelial cell proliferation in intestinal crypts of <italic>Dars2</italic><sup><italic>IEC-KO</italic></sup> mice (Fig. ##FIG##0##1f##), and reduced expression of <italic>Olfm4</italic> and <italic>Lgr5</italic> indicated a depleted stem cell compartment (Extended Data Fig. ##FIG##4##1d##). Immunostaining for cleaved caspase-3 and caspase-8 did not reveal increased numbers of dying cells in the intestines of <italic>Dars2</italic><sup><italic>IEC-KO</italic></sup> mice. Similarly, immunostaining for CD45 and F4/80 did not reveal increased numbers of infiltrating immune cells (Extended Data Fig. ##FIG##4##1e##). A prominent microscopic feature of SI sections from <italic>Dars2</italic><sup><italic>IEC-KO</italic></sup> mice was the presence of large cytoplasmic vacuoles in enterocytes (Fig. ##FIG##0##1f##). These vacuoles did not stain with periodic acid–Schiff, which detects glycoproteins and mucins, but stained positive with oil red O (ORO), which detects neutral lipids (Fig. ##FIG##0##1f## and Extended Data Fig. ##FIG##4##1d##), which suggested that the vacuoles correspond to large LDs. Indeed, immunostaining for perilipin 2 (PLIN2), a protein that coats LDs<sup>##REF##32015520##1##,##REF##19698802##8##</sup>, confirmed that IECs in <italic>Dars2</italic><sup><italic>IEC-KO</italic></sup> mice contain large cytoplasmic LDs, which was in contrast to the few tiny LDs found in IECs from <italic>Dars2</italic><sup><italic>fl/fl</italic></sup> mice (Fig. ##FIG##0##1f##). Accordingly, mass spectrometry (MS)-mediated lipidomics analysis revealed strongly increased levels of lipids, particularly of triacylglycerol (TAG) species in the intestine of <italic>Dars2</italic><sup><italic>IEC-KO</italic></sup> pups (Fig. ##FIG##0##1g## and Extended Data Fig. ##FIG##5##2a–f##). By contrast, the livers of <italic>Dars2</italic><sup><italic>IEC-KO</italic></sup> pups displayed a strong reduction in TAG levels compared with their <italic>Dars2</italic><sup><italic>fl/fl</italic></sup> littermates (Fig. ##FIG##0##1h##). Moreover, reduced amounts of glucose and high-density lipoprotein (HDL) but normal levels of total cholesterol, low-density lipoprotein (LDL) and TAG were detected in the serum of <italic>Dars2</italic><sup><italic>IEC-KO</italic></sup> mice (Fig. ##FIG##0##1i##). Additionally, IECs from <italic>Dars2</italic><sup><italic>IEC-KO</italic></sup> mice showed reduced expression of several enzymes important for lipid biosynthesis (Extended Data Fig. ##FIG##5##2g##), which indicated that increased lipid synthesis is not the cause of fat accumulation in LDs. Collectively, these results suggest that DARS2 deficiency in enterocytes causes impaired transport of dietary lipids, which results in their accumulation within large LDs.</p>", "<title>LD accumulation in enterocytes lacking SDHA or COX10</title>", "<p id=\"Par6\">We then asked whether the intestinal pathology caused by DARS2 deficiency could be reproduced through the ablation of specific OXPHOS subunits. We therefore generated mice lacking succinate dehydrogenase A (SDHA), an enzyme involved in the tricarboxylic acid cycle (TCA) and OXPHOS complex II, or lacking protohaem IX farnesyltransferase (COX10), an assembly factor of complex IV<sup>##REF##7550341##3##,##REF##16103131##4##,##REF##29662174##9##</sup>, specifically in IECs by crossing mice carrying respective <italic>loxP</italic>-flanked alleles with <italic>Vil1-cre</italic> mice. Both <italic>Sdha</italic><sup><italic>IEC-KO</italic></sup> and <italic>Cox10</italic><sup><italic>IEC-KO</italic></sup> mice were born at Mendelian ratios but developed a postnatal phenotype similar to that of <italic>Dars2</italic><sup><italic>IEC-KO</italic></sup> animals; that is, reduced body weight, failure to thrive and severe hypoglycaemia (Extended Data Fig. ##FIG##6##3a–c,f–h##). No considerable differences in total cholesterol, HDL, LDL and TAGs were detected in the serum of either <italic>Sdha</italic><sup><italic>IEC-KO</italic></sup> mice or <italic>Cox10</italic><sup><italic>IEC-KO</italic></sup> mice compared with their control littermates (Extended Data Fig. ##FIG##6##3c,h##). Immunoblot analyses confirmed efficient ablation of complex II and complex IV in <italic>Sdha</italic><sup><italic>IEC-KO</italic></sup> and <italic>Cox10</italic><sup><italic>IEC-KO</italic></sup> IECs, respectively, without affecting other OXPHOS subunits (Extended Data Fig. ##FIG##6##3d,e,i,j##). Histological analyses revealed impaired IEC proliferation and lipid accumulation within large LDs in enterocytes from both <italic>Sdha</italic><sup><italic>IEC-KO</italic></sup> mice and <italic>Cox10</italic><sup><italic>IEC-KO</italic></sup> mice (Extended Data Fig. ##FIG##6##3d,i##), as observed in <italic>Dars2</italic><sup><italic>IEC-KO</italic></sup> mice. Therefore, loss of specific subunits of respiratory chain complexes II or IV phenocopied the intestinal pathology induced by DARS2 deficiency in IECs. This result shows that mitochondrial dysfunction causes impaired transport and accumulation of lipids in enterocytes.</p>", "<title>DARS2 loss in adult IECs causes LD accumulation</title>", "<p id=\"Par7\">The increased lipid accumulation in enterocytes of <italic>Dars2</italic><sup><italic>IEC-KO</italic></sup> pups could be related to the high fat content of milk or to developmental defects caused by DARS2 ablation during embryogenesis<sup>##REF##12065599##7##</sup>. We therefore assessed the consequences of tamoxifen-inducible DARS2 deletion in IECs of adult <italic>Dars2</italic><sup><italic>fl/fl</italic></sup><italic>Villin-creER</italic><sup><italic>T2</italic></sup> mice (hereafter referred to as <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup>) fed a normal chow diet (NCD). Tamoxifen administration on five consecutive days caused rapid weight loss that necessitated the euthanasia of <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice 7–8 days after the last injection (Fig. ##FIG##1##2a## and Extended Data Fig. ##FIG##7##4a##). Immunoblot and proteomics analyses of proximal IECs from <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice euthanized 7 days after tamoxifen injection confirmed efficient DARS2 ablation and severe depletion of OXPHOS subunits (Fig. ##FIG##1##2b## and Extended Data Fig. ##FIG##7##4b,c##). Gene set enrichment analysis (GSEA) of the proteomics data confirmed the depletion of OXPHOS subunits in IECs of <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice compared with <italic>Dars2</italic><sup><italic>fl/fl</italic></sup> littermates (Extended Data Fig. ##FIG##7##4d## and Supplementary Table ##SUPPL##2##1##). The proteomics analysis revealed that the ATF4-regulated pathway was among the most enriched signatures in DARS2-deficient enterocytes (Extended Data Fig. ##FIG##7##4d,e##). This result indicated that the mitochondrial integrated stress response was activated, a result previously reported in other models of mitochondrial dysfunction<sup>##UREF##0##10##–##REF##28566324##12##</sup>. RNA sequencing (RNA-seq) confirmed that the ATF4 signature was strongly upregulated in the proximal SI of <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice 7 days after the last tamoxifen injection, which was also observed at 3 days after tamoxifen albeit to a lesser extent (Extended Data Fig. ##FIG##7##4f,g## and Supplementary Table ##SUPPL##3##2##).</p>", "<p id=\"Par8\">GSEA of both the transcriptomics and proteomics data revealed downregulation of lipid metabolism pathways in DARS2-deficient enterocytes 7 days after tamoxifen induction (Extended Data Fig. ##FIG##7##4d,f##). These changes were not observed in RNA-seq data from <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice 3 days after tamoxifen administration (Supplementary Table ##SUPPL##3##2##), which suggested that they are not a primary consequence of DARS2 ablation but instead reflect a secondary response of the cells to the substantial metabolic alterations caused by mitochondrial dysfunction. Our data also showed reduced levels of proteins important for lipid biosynthesis at day 7 after tamoxifen injection, including fatty acid synthase (FASN) and fatty acid binding protein 2 (FABP2) (Extended Data Fig. ##FIG##8##5a,b##). Over-representation analysis using the gene ontology terms (Supplementary Table ##SUPPL##2##1##) of the significantly changed proteins revealed that many lipid metabolism pathways were downregulated in DARS2-deficient enterocytes, and ‘lipid droplet formation’ was one of the most upregulated terms (Extended Data Fig. ##FIG##8##5c,d##). PLIN2 was the most highly induced protein in DARS2-deficient IECs in the proteomics dataset, which was verified by immunoblotting (Extended Data Fig. ##FIG##8##5a,b##). To further investigate the consequences of DARS2 ablation, we performed metabolomics analysis, which showed a broad metabolic deregulation in DARS2-deficient proximal IECs at 8 days after tamoxifen induction. The suppression of mitochondrial metabolism in <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice was corroborated by a reduction in aspartate<sup>##REF##26232224##13##,##REF##26232225##14##</sup> and an accumulation of succinate, which are hallmarks of OXPHOS dysfunction<sup>##REF##25383517##15##</sup> (Extended Data Fig. ##FIG##8##5e,f## and Supplementary Table ##SUPPL##4##3##). Moreover, several glycolytic intermediates were accumulated in DARS2-deficient IECs, which suggested that the cells switched to glycolysis (Extended Data Fig. ##FIG##8##5e,f##). Consistent with a compensatory activation of glycolysis, the ratio of ATP to ADP, an indicator of the energy charge of the cell, was not reduced in DARS2-deficient enterocytes (Extended Data Fig. ##FIG##8##5f##). We also observed significant changes in purine and pyrimidine metabolism (Extended Data Fig. ##FIG##8##5e##), which was in line with the reported activation of mitochondrial integrated stress response in patients with mitochondrial disorders and in models of mitochondrial dysfunction<sup>##REF##28566324##12##,##REF##33855712##16##–##REF##26924217##18##</sup>. Furthermore, IECs from <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice showed a marked accumulation of acylcarnitines, a result indicative of impaired fatty acid oxidation (Extended Data Fig. ##FIG##8##5e##).</p>", "<p id=\"Par9\">Following necropsy of <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice, we observed a dilated, fluid-filled GI tract, with the proximal SI appearing white, which indicated massive lipid accumulation (Extended Data Fig. ##FIG##9##6a##). EM analyses showed that most mitochondria in <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> enterocytes appeared swollen, with less densely packed and fragmented cristae (Fig. ##FIG##1##2c##). Immunohistological evaluation revealed prominent respiratory chain deficiency, diminished numbers of proliferating cells, Goblet cells and absorptive enterocytes, and considerably reduced expression of stem cell markers in both the proximal and distal SI of <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice 7–8 days after tamoxifen induction (Fig. ##FIG##1##2d## and Extended Data Fig. ##FIG##9##6b,c,e##). Notably, immunostaining for cleaved caspase-3 and CD45 did not reveal increased numbers of dying cells or infiltrating immune cells, respectively, in the intestine of <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice (Extended Data Fig. ##FIG##9##6b,d##). This result shows that DARS2 deficiency does not induce enterocyte death or inflammation. As confirmation, an inflammatory gene expression signature was not observed in the RNA-seq data (Extended Data Fig. ##FIG##7##4f## and Supplementary Table ##SUPPL##3##2##). Similar to <italic>Dars2</italic><sup><italic>IEC-KO</italic></sup> mice, enterocytes in the proximal SI of <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice were filled with large LDs stained with ORO and PLIN2 (Fig. ##FIG##1##2e##). Lipidomics analyses also revealed increased TAG amounts in enterocytes from <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice (Fig. ##FIG##1##2f##). Serum glucose, TAGs and total cholesterol levels were not notably changed in <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice compared with control mice (Fig. ##FIG##1##2g##). In contrast to the proximal SI, enterocytes in the distal SI of <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice did not contain large LDs (Fig. ##FIG##1##2e##), which indicated that lipid accumulation occurred exclusively in proximal enterocytes, cells that are primarily responsible for the absorption, processing and transport of dietary fats<sup>##REF##32015520##1##</sup>. To obtain insight into the kinetics of lipid accumulation, we examined intestinal tissue from <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice 3 and 5 days after the last tamoxifen injection (Extended Data Fig. ##FIG##10##7##). Efficient DARS2 ablation, decreased expression of OXPHOS subunits and strong suppression of IEC proliferation were detected in enterocytes from <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice 3 and 5 days after tamoxifen administration (Extended Data Fig. ##FIG##10##7a–h##). Although we did not detect signs of lipid accumulation at day 3 after tamoxifen injection, proximal enterocytes from <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice 5 days after tamoxifen induction contained small LDs, which indicated that lipid accumulation occurs already at this stage (Extended Data Fig. ##FIG##10##7d,h##). Collectively, mitochondrial dysfunction caused by inducible DARS2 ablation in IECs causes substantial metabolic reprogramming and prominent accumulation of lipids in proximal enterocytes.</p>", "<title>Dietary lipids accumulate in <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> IECs</title>", "<p id=\"Par10\">The accumulation of large LDs in proximal but not distal enterocytes of <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice, together with the overall downregulation of lipid biosynthesis pathways, indicated that the stored lipids probably originate from dietary fat. Dietary lipids emulsified by bile acids are digested by pancreatic lipase within the intestinal lumen to produce fatty acids, monoacylglycerols, cholesterol and lysophospholipids. These lipids are then taken up by enterocytes in the proximal SI where they are re-esterified into TAGs, cholesteryl esters (CEs) and phospholipids<sup>##REF##32015520##1##</sup>. The majority of these lipids are then packaged into chylomicrons (CMs) that are released at the basolateral side and transported by the lymphatic system into the circulation and eventually to peripheral tissues<sup>##REF##32015520##1##</sup>. Enterocytes also temporarily store excess dietary TAGs in cytosolic LDs, which are then mobilized for release in the form of CMs to ensure a stable supply of lipids between meals<sup>##REF##32015520##1##</sup>. To assess the contribution of dietary fat, we examined whether feeding with a fat-free diet (FFD, containing <0.5% of fat), as opposed to a NCD (containing 3.4% fat), could prevent lipid accumulation in enterocytes of <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice (Extended Data Fig. ##FIG##11##8a##). Tamoxifen administration induced efficient DARS2 ablation, OXPHOS deficiency and weight loss in <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice fed the FFD. These mice also showed impaired IEC proliferation and reduced stem cell gene expression in the SI (Fig. ##FIG##2##3a## and Extended Data Fig. ##FIG##11##8##), consistent with our findings in mice fed the NCD. However, <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice fed the FFD exhibited only a few small LDs in enterocytes of the proximal SI 7 days after the last tamoxifen injection. This was in contrast to the large and highly abundant LDs observed in mice fed the NCD (Fig. ##FIG##2##3a##). Therefore, feeding a FFD could strongly reduce LD formation in DARS2-deficient enterocytes, thereby demonstrating that most accumulating lipids are derived from the diet. Notably, feeding a FFD delayed but could not ultimately prevent the substantial loss of body weight that necessitated the euthanasia of <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice. This result shows that lipid accumulation is not the primary cause of weight loss and death in these animals. This finding is in line with a previous study<sup>##REF##24019513##19##</sup> reporting that IEC-specific deficiency of microsomal triglyceride transfer protein (MTTP) induced lipid accumulation in enterocytes but did not cause death of the mice. Therefore, in addition to lipid accumulation, mitochondrial dysfunction causes defects in enterocytes such as the complete suppression of IEC proliferation, which probably led to the death of the animals.</p>", "<title>Impaired dietary lipid transport in <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice</title>", "<p id=\"Par11\">To directly assess whether DARS2 deficiency impairs the transport of dietary lipids by enterocytes, we performed metabolic tracing experiments. Specifically, we orally administered [<sup>3</sup>H]triolein and [<sup>14</sup>C]cholesterol to fasted <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice and <italic>Dars2</italic><sup><italic>fl/fl</italic></sup> littermates 7 days after tamoxifen injection and followed the appearance of the tracers in the plasma in the presence of the lipoprotein lipase inhibitor tyloxapol, which blocks intravascular lipoprotein processing (Extended Data Fig. ##FIG##12##9a–c##). Levels of [<sup>3</sup>H]triolein, [<sup>14</sup>C]cholesterol and TAGs were substantially decreased in the plasma of <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice compared with <italic>Dars2</italic><sup><italic>fl/fl</italic></sup> mice (Fig. ##FIG##2##3b##), which demonstrated that DARS2 deficiency inhibits the transport of dietary lipids by enterocytes. To further investigate how DARS2 deficiency in enterocytes affects the delivery of dietary lipids and glucose to peripheral organs, we orally administered [<sup>3</sup>H]triolein and [<sup>14</sup>C]deoxyglucose ([<sup>14</sup>C]DOG) in fasted <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice and <italic>Dars2</italic><sup><italic>fl/fl</italic></sup> mice 7 days after tamoxifen induction and measured the accumulation of the tracers in different tissues (Extended Data Fig. ##FIG##12##9d–f##). Compared with <italic>Dars2</italic><sup><italic>fl/fl</italic></sup> littermates, <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice showed strongly reduced transport of [<sup>3</sup>H]triolein to the plasma and most peripheral tissues, including the liver (Fig. ##FIG##2##3c##). By contrast, <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice showed normal [<sup>14</sup>C]DOG transport to the plasma (Fig. ##FIG##2##3d##), which indicated that loss of DARS2 in enterocytes predominantly affects the handling of dietary lipids. Notably, [<sup>14</sup>C]DOG uptake by the liver was unaffected in <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice but moderately decreased in other peripheral organs, including adipose tissue and heart (Fig. ##FIG##2##3d##). Moreover, profiling of plasma lipoproteins revealed reduced levels of TAG-rich lipoproteins (TRLs), including CMs and very low-density lipoproteins (VLDLs), in <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice compared with <italic>Dars2</italic><sup><italic>fl/fl</italic></sup> littermates, whereas HDL and LDL were not affected (Fig. ##FIG##2##3e##). The amount of plasma glycerol was increased whereas the weight of gonadal and inguinal white adipose tissue was reduced in <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice 7 days after tamoxifen administration (Fig. ##FIG##2##3e–g##). This result suggested that adipose tissues undergo increased lipolysis, probably as a compensatory response to the impaired supply of dietary lipids. Metabolic tracing studies performed 5 days after the last tamoxifen injection revealed mildly reduced levels of [<sup>14</sup>C]cholesterol and a trend towards reduced [<sup>3</sup>H]triolein in the plasma of <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice (Extended Data Fig. ##FIG##12##9g–n##). This finding indicated that already at this stage, the mice showed mild impairment of lipid transport. Uptake of [<sup>3</sup>H]triolein in the liver and most peripheral tissues, with the exception of white adipose tissue, was not reduced in <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice at 5 days after tamoxifen induction, a result consistent with its mildly impaired transport to the circulation (Extended Data Fig. ##FIG##12##9m##). Assessment of [<sup>14</sup>C]DOG levels 5 days after tamoxifen injection revealed increased accumulation of the tracer only in the intestine of <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice (Extended Data Fig. ##FIG##12##9n##), which could be related to the re-programming of cellular metabolism towards glycolysis in DARS2-deficient enterocytes. Taken together, the metabolic tracing experiments revealed a progressive impairment of dietary lipid transport to the circulation after enterocyte-specific ablation of DARS2.</p>", "<title>DARS2 loss impairs CM production and Golgi architecture</title>", "<p id=\"Par12\">Most dietary lipids absorbed by enterocytes are transported to the circulation in the form of CMs<sup>##REF##32015520##1##,##REF##24751933##20##</sup>. CM production requires MTTP-mediated packaging of lipids into pre-CMs with ApoB48 in the endoplasmic reticulum (ER), followed by their transfer within pre-CM transport vesicles to the Golgi for maturation and subsequent extracellular secretion<sup>##REF##32015520##1##</sup>. Primary enterocytes from <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice 5 and 7 days after tamoxifen injection expressed normal levels of ApoB48 (Fig. ##FIG##3##4a##). However, triglyceride-rich lipoproteins isolated from the plasma of <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice contained reduced levels of ApoB48 in relation to the liver-derived ApoB100 at 5 and 7 days after tamoxifen induction, a result consistent with decreased CM release from the intestine to the circulation (Fig. ##FIG##3##4b##). Ultrastructural examination of proximal SI sections showed that CMs were prominent within extended Golgi cisternae or were secreted across the basolateral surfaces of the intestinal epithelium of control <italic>Dars2</italic><sup><italic>fl/fl</italic></sup> mice (Fig. ##FIG##3##4c##). By contrast, an extensive disorganization of the secretory pathway with a substantial lack of Golgi cisternae containing CMs was observed in enterocytes of <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice (Fig. ##FIG##3##4c##). Instead, the cytoplasm of DARS2-deficient enterocytes was packed with very large LDs (Fig. ##FIG##3##4c##, arrowheads). Lipid particles were also often found within the ER lumen of enterocytes in <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice (Fig. ##FIG##3##4c##, arrows). To further examine the integrity of the Golgi and the secretory pathway in enterocytes, we immunostained proximal SI sections from <italic>Dars2</italic><sup><italic>fl/fl</italic></sup> and <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice for <italic>trans</italic>-Golgi network integral membrane protein 1 (TGN38), a transmembrane protein localized to the Golgi<sup>##REF##8436587##21##</sup>, and E-cadherin, an integral membrane protein that is transported to the plasma membrane through the secretory pathway. TGN38 staining revealed a typical compact juxtanuclear Golgi network in enterocytes from <italic>Dars2</italic><sup><italic>fl/fl</italic></sup> mice and a predominantly plasma membrane localization of E-cadherin (Extended Data Fig. ##FIG##13##10a##). By contrast, a substantial dispersal of TGN38 staining was observed in proximal SI enterocytes from <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice at 8 days after tamoxifen injection, which was accompanied by strongly reduced levels of E-cadherin at the plasma membrane (Extended Data Fig. ##FIG##13##10a##). Time course analyses revealed that the Golgi network was largely unaffected at day 3 and partially fragmented at day 5 after tamoxifen induction, which indicated the occurrence of progressive Golgi disorganization after DARS2 loss (Extended Data Fig. ##FIG##13##10a##). To address whether Golgi disorganization precedes LD formation, we immunostained intestinal tissue sections with antibodies against TGN38, E-cadherin and PLIN2. Substantial LD formation concomitant with strong Golgi dispersal was observed in DARS2-deficient proximal enterocytes 8 days after tamoxifen injection (Extended Data Fig. ##FIG##13##10b##). A clear fragmentation of the Golgi network was observed in most proximal enterocytes in <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice 5 days after tamoxifen treatment, whereas only a few LDs were detected in a small fraction of DARS2-deficient enterocytes at this stage (Fig. ##FIG##3##4d##). Therefore, Golgi dispersal occurs progressively after DARS2 ablation and precedes large LD formation. Notably, enterocytes in distal SI from <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice showed only a mild disorganization of Golgi network and absence of LDs 8 days after tamoxifen injection (Extended Data Fig. ##FIG##13##10b##). This result suggests that bulk transport and secretion of dietary lipids may accelerate the disorganization of the Golgi network in proximal SI enterocytes. Moreover, proximal enterocytes from FFD-fed <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice showed a partial fragmentation of Golgi network and absence of LDs at 7 days after tamoxifen induction (Extended Data Fig. ##FIG##13##10c##). Together, these results suggest that impaired production and/or ER-to-Golgi trafficking of CMs is probably an early event associated with Golgi disorganization. Our proteomics data confirmed that several proteins involved in CM production<sup>##REF##32015520##1##,##REF##25852563##22##,##REF##17449472##23##</sup> (CD36, APOA4, APOA1, MTTP and LSR) and COPII vesicle budding<sup>##REF##22265716##24##</sup> (SEC16, SEC23, SEC24 and SEC31) were downregulated in DARS2-deficient enterocytes (Extended Data Fig. ##FIG##8##5a##). We then assessed whether mitochondrial dysfunction could cause Golgi disorganization in IEC-6 cells, which are derived from rat SI epithelium and display typical characteristics of normal SI enterocytes<sup>##REF##88453##25##</sup>. Similar to our in vivo findings in <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> enterocytes, treatment with actinonin, a mitochondrial protein synthesis inhibitor<sup>##UREF##0##10##</sup> that mimics DARS2 deficiency, or atpenin A5, an inhibitor of SDH<sup>##UREF##3##26##</sup>, induced Golgi dispersal in IEC-6 cells (Extended Data Fig. ##FIG##14##11a,b##). As a positive control, we treated cells with brefeldin A, an inhibitor of the anterograde transport from the ER to Golgi and that causes Golgi membranes to be absorbed into the ER<sup>##REF##9382862##27##</sup>. Notably, addition of oleic acid in the medium exacerbated the Golgi disorganization induced by OXPHOS inhibitors, concomitant with lipid accumulation in large LDs (Extended Data Fig. ##FIG##14##11b##). Thus, inhibition of mitochondrial function induces Golgi disorganization in a cellular system and this effect is exacerbated in the presence of high levels of lipids in the medium.</p>", "<p id=\"Par13\">To assess whether DARS2 depletion affects the Golgi in the intestine of <italic>Caenorhabditis elegans</italic>, we took advantage of a transgenic strain that expresses the Golgi-specific a-mannosidase II fused to GFP under the control of the gut-specific <italic>vha-6</italic> promoter<sup>##REF##21620137##28##</sup>. <italic>Dars-2</italic> RNA-mediated interference considerably reduced the amount of Golgi puncta in the gut of <italic>C.</italic> <italic>elegans</italic> at adult days 1 and 4 without affecting ER morphology, as evaluated using a strain that expresses the ER-specific SPCS-1 fused to GFP<sup>##REF##22927462##29##</sup> (Extended Data Fig. ##FIG##14##11c,d##). Next, we investigated whether DARS-2 deficiency would affect the trafficking of lipids. To this end, we followed the transport of vitellogenin 2 (VIT-2) in <italic>C.</italic> <italic>elegans</italic><sup>##REF##17704769##30##</sup>. Vitellogenins are large lipo-glyco-phosphoproteins that recruit lipids and require COPII vesicle trafficking for their secretion into the circulation to be ultimately taken up by oocytes through receptor-mediated endocytosis<sup>##REF##3145737##31##</sup>. In <italic>C.</italic> <italic>elegans</italic>, VIT-2 (visualized using the VIT-2::GFP reporter) is predominantly found in oocytes, but in the absence of the small GTPase SAR-1, a homologue of SAR1B that is essential for budding of ER-derived COPII vesicles that mediate secretory cargo transport to the Golgi<sup>##REF##32015520##1##,##REF##30640893##32##,##REF##17945526##33##</sup>, it accumulates in the intestine<sup>##REF##17704769##30##</sup> (Extended Data Fig. ##FIG##14##11e##). Notably, <italic>dars-2</italic> depletion strongly prevented VIT-2 transport into oocytes (Extended Data Fig. ##FIG##14##11e##). By contrast, depletion of fumarate hydratase (FUM-1), a mitochondrial TCA cycle enzyme essential for numerous metabolic processes that also acts as a tumour suppressor<sup>##REF##31085323##34##</sup>, did not affect VIT-2 transport (Extended Data Fig. ##FIG##14##11e##). Further supporting our results and the role of mitochondria in the transport of lipoprotein complexes in <italic>C.</italic> <italic>elegans</italic>, a screen for factors essential for vitellogenin transport identified 40 mitochondrial proteins, out of which 28 directly regulate mitochondrial protein synthesis, including 8 mitochondrial tRNA amino-acyltransferases, enzymes that belong to the same family as DARS2 (ref. <sup>##REF##17704769##30##</sup>).</p>", "<title>Online content</title>", "<p id=\"Par55\">Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41586-023-06857-0.</p>", "<title>Supplementary information</title>", "<p>\n\n\n\n\n\n\n</p>", "<title>Source data</title>", "<p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n</p>" ]	[ "<title>Extended data figures and tables</title>", "<p id=\"Par58\">\n\n</p>", "<p id=\"Par59\">\n\n</p>", "<p id=\"Par60\">\n\n</p>", "<p id=\"Par61\">\n\n</p>", "<p id=\"Par62\">\n\n</p>", "<p id=\"Par63\">\n\n</p>", "<p id=\"Par64\">\n\n</p>", "<p id=\"Par65\">\n\n</p>", "<p id=\"Par66\">\n\n</p>", "<p id=\"Par67\">\n\n</p>", "<p id=\"Par68\">\n\n</p>", "<title>Extended data</title>", "<p>is available for this paper at 10.1038/s41586-023-06857-0.</p>", "<title>Supplementary information</title>", "<p>The online version contains supplementary material available at 10.1038/s41586-023-06857-0.</p>", "<title>Acknowledgements</title>", "<p>We are grateful to E. Gareus, J. Kuth, E. Stade, C. Uthoff-Hachenberg and J. von Rhein for their technical assistance; A. Schauss and staff at the CECAD Imaging Facility for microscopy support; A. Dilthey and U. Goebel from the CECAD Bioinformatics Facility for RNA-seq data analysis; S. Robine for <italic>Villin-creER</italic><sup><italic>T2</italic></sup> mice, D. Gumucio for <italic>Vil1-cre</italic> mice and C. Moraes for <italic>Cox10</italic><sup><italic>fl/fl</italic></sup> mice; and T. Langer and E. Rugarli for valuable discussions. Research reported in this publication was supported by funding from the European Research Council (grant agreement no. 787826 to M.P. and 819920 to C.F), the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), projects SFB1403 (project no. 414786233) and TRR259 (project no. 397484323) to M.P., projects SFB1218 (project no. 269925409), GRK2407 (project no. 360043781) to M.P. and A.T., project TR 1018/8-1 to A.T., as well as project SFB841 (Liver inflammation: Infection, immune regulation and consequences) and project SFB1328 (project number 335447717) to J.H. C.F. was also funded by the MRC Core award grant MRC_MC_UU_12022/6, the CRUK Programme Foundation award C51061/A27453 and by the Alexander von Humboldt Foundation in the framework of the Alexander von Humboldt Professorship endowed by the Federal Ministry of Education and Research.</p>", "<title>Author contributions</title>", "<p>C.M., A.T. and M.P. conceived the study and designed the experiments. C.M. performed and analysed most experiments. V.K., I.E., F.S., K.S., R.S., S.B., S.E., M. Heine and M.Y.J. performed and analysed experiments. M.Y., E.N. and C.S. performed and analysed the metabolomics experiments. T.B. performed the proteomics experiments. C.S., T.B. and M.K. analysed proteomics data. C.S. performed omics data visualization. M. Herholz. and L.B. performed and analysed the <italic>C.</italic> <italic>elegans</italic> experiments. V.K., C.F., J.H., A.T. and M.P. supervised the experiments. C.M., V.K., A.T. and M.P. interpreted data and wrote the paper.</p>", "<title>Peer review</title>", "<title>Peer review information</title>", "<p id=\"Par56\"><italic>Nature</italic> thanks Ömer Yilmaz and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. ##SUPPL##5##Peer reviewer reports## are available.</p>", "<title>Funding</title>", "<p>Open access funding provided by Universität zu Köln.</p>", "<title>Data availability</title>", "<p>The MS proteomics data have been deposited into the ProteomeXchange Consortium through the PRIDE<sup>##REF##30395289##59##</sup> partner repository with the dataset identifier <ext-link ext-link-type=\"uri\" xlink:href=\"http://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD026934\">PXD026934</ext-link>. Metabolomics data have been deposited into Metabolomics Workbench (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.metabolomicsworkbench.org\">www.metabolomicsworkbench.org</ext-link>) under the study ID <ext-link ext-link-type=\"uri\" xlink:href=\"https://doi.org/10.21228/M8X99S\">ST002184</ext-link> (datatrack_id: <ext-link ext-link-type=\"uri\" xlink:href=\"https://doi.org/10.21228/M8X99S\">3289</ext-link>). The RNA-seq data generated in this study have been deposited into NCBI’s Gene Expression Omnibus (GEO) and are accessible through GEO series accession number <ext-link ext-link-type=\"uri\" xlink:href=\"https://doi.org/10.21228/GSE207803\">GSE207803</ext-link>. Numerical source data underlying the graphical representations and statistical descriptions presented in Figs. ##FIG##0##1##–##FIG##3##4## and Extended Data Figs. ##FIG##4##1–####FIG##6##3##,##FIG##8##5##–##FIG##12##9## and ##FIG##14##11## are provided as source data files. Uncropped images of immunoblots presented in the figures are included in Supplementary Fig. ##SUPPL##0##1##. The number of mice analysed for histological purposes are presented in Supplementary Table ##SUPPL##0##4##. <xref ref-type=\"sec\" rid=\"Sec52\">Source data</xref> are provided with this paper.</p>", "<title>Code availability</title>", "<p>Detailed code for GSEA and metabolomics analysis, corresponding data tables and figures can be found at <ext-link ext-link-type=\"uri\" xlink:href=\"https://github.com/ChristinaSchmidt1/Mitochondria_in_Intestinal_Lipid-transport\">github.com/ChristinaSchmidt1/Mitochondria_in_Intestinal_Lipid-transport</ext-link>.</p>", "<title>Competing interests</title>", "<p id=\"Par57\">The authors declare no competing interests.</p>" ]	[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><title><italic>Dars2</italic><sup><italic>IEC-KO</italic></sup> mice develop severe intestinal pathology with massive lipid accumulation within large LDs in enterocytes.</title><p><bold>a</bold>,<bold>b</bold>, Kaplan–Meier survival curves (<bold>a</bold>) and body weight at the age of 7 days (<bold>b</bold>) of <italic>Dars2</italic><sup><italic>fl/fl</italic></sup> (<italic>n</italic> = 56 (<bold>a</bold>), <italic>n</italic> = 68 (<bold>b</bold>)) and <italic>Dars2</italic><sup><italic>IEC-KO</italic></sup> (<italic>n</italic> = 57 (<bold>a</bold>), <italic>n</italic> = 66 (<bold>b</bold>)) mice. <bold>c</bold>, Immunoblot of IEC protein extracts from 7-day-old <italic>Dars2</italic><sup><italic>fl/fl</italic></sup> (<italic>n</italic> = 3) and <italic>Dars2</italic><sup><italic>IEC-KO</italic></sup> (<italic>n</italic> = 3) pups with the indicated antibodies. β-actin was used as the loading control. <bold>d</bold>, Representative images of SI sections from <italic>Dars2</italic><sup><italic>fl/fl</italic></sup> and <italic>Dars2</italic><sup><italic>IEC-KO</italic></sup> mice stained with enzyme histochemical staining for COX and SDH. <bold>e</bold>, Representative transmission electron microscopy (TEM) micrographs of SI sections from 7-day-old <italic>Dars2</italic><sup><italic>fl/fl</italic></sup> and <italic>Dars2</italic><sup><italic>IEC-KO</italic></sup> mice (<italic>n</italic> = 3 per genotype). G, Golgi; M, mitochondria. <bold>f</bold>, Representative images of SI sections from <italic>Dars2</italic><sup><italic>fl/fl</italic></sup> and <italic>Dars2</italic><sup><italic>IEC-KO</italic></sup> mice stained with haematoxylin & eosin (H&E), ORO or immunostained for PLIN2 and Ki67. <bold>g</bold>,<bold>h</bold>, TAG species content in SI (<bold>g</bold>) and liver (<bold>h</bold>) of <italic>Dars2</italic><sup><italic>fl/fl</italic></sup> (<italic>n</italic> = 7) and <italic>Dars2</italic><sup><italic>IEC-KO</italic></sup> (<italic>n</italic> = 7 SI, <italic>n</italic> = 6 liver) mice. <bold>i</bold>, Concentration of glucose, total cholesterol, TAGs, HDL-cholesterol and LDL-cholesterol in sera from 7-day-old <italic>Dars2</italic><sup><italic>fl/fl</italic></sup> and <italic>Dars2</italic><sup><italic>IEC-KO</italic></sup> mice (<italic>n</italic> = 29 (glucose, total cholesterol) per genotype; <italic>n</italic> = 23 (HDL, LDL) per genotype; <italic>n</italic> = 28, <italic>n</italic> = 25 (TAG) for <italic>Dars2</italic><sup><italic>fl/fl</italic></sup> and <italic>Dars2</italic><sup><italic>IEC-KO</italic></sup>, respectively). In <bold>b</bold>,<bold>g</bold>–<bold>i</bold>, dots represent individual mice, bar graphs show the mean ± s.e.m. and <italic>P</italic> values were calculated using two-sided nonparametric Mann–Whitney <italic>U</italic>-test. In <bold>a</bold>, <italic>P</italic> values were calculated using two-sided Gehan–Breslow–Wilcoxon test. In <bold>d</bold>,<bold>f</bold>, histological images are representative of the number of mice analysed as indicated in Supplementary Table ##SUPPL##0##4##. In <bold>c</bold>, each lane represents one mouse. Scale bars, 1 μm (<bold>e</bold>) or 50 μm (<bold>d</bold>,<bold>f</bold>). For gel source data, see Supplementary Fig. ##SUPPL##0##1##.</p><p>##SUPPL##6##Source Data##</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>Inducible DARS2 ablation in IECs of adult mice causes lipid accumulation in proximal enterocytes.</title><p><bold>a</bold>, Relative body weight change of 8–12-week-old <italic>Dars2</italic><sup><italic>fl/fl</italic></sup> and <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice after tamoxifen administration (<italic>n</italic> = 21 per genotype). <bold>b</bold>, Immunoblot analysis with the indicated antibodies of protein extracts from SI IECs of <italic>Dars2</italic><sup><italic>fl/fl</italic></sup> and <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice 7 days after the last tamoxifen injection (<italic>n</italic> = 6 per genotype). β-actin was used as the loading control. <bold>c</bold>, Representative TEM micrographs of proximal SI sections and quantification of the mitochondria integrity distribution as a percentage of normal, partly affected and damaged mitochondria based on the electron density and cristae morphology in <italic>Dars2</italic><sup><italic>fl/fl</italic></sup> mice (<italic>n</italic> = 4 mice, <italic>n</italic> = 663 mitochondria in <italic>n</italic> = 69 IECs) and <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice (<italic>n</italic> = 4 mice, <italic>n</italic> = 707 mitochondria in <italic>n</italic> = 80 IECs) 7 days after tamoxifen. <bold>d</bold>, Representative images of sections from the proximal SI of <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> and <italic>Dars2</italic><sup><italic>fl/fl</italic></sup> mice stained with H&E, COX and SDH or immunostained with Ki67. <bold>e</bold>, Representative images of proximal and distal SI sections of <italic>Dars2</italic><sup><italic>fl/fl</italic></sup> and <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice stained with ORO or immunostained with PLIN2. <bold>f</bold>, TAG content in proximal SI of <italic>Dars2</italic><sup><italic>fl/fl</italic></sup> and <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice (<italic>n</italic> = 8 per genotype). <bold>g</bold>, Concentration of glucose, total cholesterol and TAGs in sera from <italic>Dars2</italic><sup><italic>fl/fl</italic></sup> mice (<italic>n</italic> = 9 (glucose, total cholesterol), <italic>n</italic> = 8 (TAG)) and <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice (<italic>n</italic> = 11 (glucose, total cholesterol), <italic>n</italic> = 7 (TAG)) 7 days after the last tamoxifen injection. In <bold>c</bold>, <bold>f</bold> and <bold>g</bold>, dots represent individual mice, bar graphs show the mean ± s.e.m. and <italic>P</italic> values were calculated using two-way analysis of variance (ANOVA) with Bonferroni’s correction for multiple comparison (<bold>a</bold>), two-sided chi-square test (<bold>c</bold>) or two-sided nonparametric Mann–Whitney <italic>U</italic>-test (<bold>f</bold>,<bold>g</bold>). In <bold>d</bold>,<bold>e</bold>, histological images are representative of the number of mice analysed as indicated in Supplementary Table ##SUPPL##0##4##. In <bold>b</bold>, each lane represents one mouse. Scale bars, 1 μm (<bold>c</bold>) or 50 μm (<bold>d</bold>,<bold>e</bold>). For gel source data, see Supplementary Fig. ##SUPPL##0##1##.</p><p>##SUPPL##7##Source Data##</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><title>DARS2 deficiency causes impaired transport of dietary lipids by enterocytes.</title><p><bold>a</bold>, Representative images from the proximal SI of 8–12-week-old <italic>Dars2</italic><sup><italic>fl/fl</italic></sup> and <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice fed with a FFD or NCD diet 7 days after the last tamoxifen injection, stained with H&E, ORO and COX and SDH or immunostained with antibodies against Ki67 and PLIN2. Scale bar, 50 μm. <bold>b</bold>, [<sup>3</sup>H]Triolein, [<sup>14</sup>C]cholesterol and TAG content in portal plasma of <italic>Dars2</italic><sup><italic>fl/fl</italic></sup> mice (<italic>n</italic> = 8) and <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice (<italic>n</italic> = 8) subjected to oral fat tolerance tests after intravenous injection of tyloxapol. <bold>c</bold>,<bold>d</bold>, Counts of [<sup>3</sup>H]triolein (<bold>c</bold>) and [<sup>14</sup>C]DOG (<bold>d</bold>) in different organs and plasma from <italic>Dars2</italic><sup><italic>fl/fl</italic></sup> mice (<italic>n</italic> = 7) and <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice (<italic>n</italic> = 8) determined 120 min after oral gavage. iBAT, interscapular brown adipose tissue; gWAT, gonadal white adipose tissue; iWAT, inguinal white adipose tissue; Prox. proximal. Liver 1 and liver 2 correspond to two different parts of the liver. <bold>e</bold>, Fast-protein liquid chromatography profiles of TAG and cholesterol in pooled portal plasma from fasted <italic>Dars2</italic><sup><italic>fl/fl</italic></sup> mice (<italic>n</italic> = 7) and <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice (<italic>n</italic> = 8) 120 min after gavage. IDL, intermediate-density lipoprotein. <bold>f</bold>, Free glycerol levels in plasma from <italic>Dars2</italic><sup><italic>fl/fl</italic></sup> mice (<italic>n</italic> = 7) and <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice (<italic>n</italic> = 8). <bold>g</bold>, Relative organ weight of <italic>Dars2</italic><sup><italic>fl/fl</italic></sup> mice (<italic>n</italic> = 7) and <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice (<italic>n</italic> = 8) 7 days after the last tamoxifen injection subjected to oral glucose fat tolerance test. In <bold>c</bold>,<bold>d</bold>,<bold>f</bold>,<bold>g</bold>, dots represent individual mice, bar graphs show the mean ± s.e.m. and <italic>P</italic> values were calculated using unpaired two-sided Student’s <italic>t</italic>-test with no assumption of equal variance (<bold>b</bold>–<bold>d</bold>,<bold>f</bold>,<bold>g</bold>). In <bold>a</bold>, histological images are representative of the number of mice analysed as indicated in Supplementary Table ##SUPPL##0##4##.</p><p>##SUPPL##8##Source Data##</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><title>DARS2 deficiency impairs CM production and induces progressive Golgi disorganization that precedes LD accumulation in enterocytes.</title><p><bold>a</bold>,<bold>b</bold>, Immunoblots depicting expression levels of ApoB48 in SI IECs (<bold>a</bold>) and ApoB48 and ApoB100 on TRLs isolated from plasma by ultracentrifugation (<bold>b</bold>) from <italic>Dars2</italic><sup><italic>fl/fl</italic></sup> and <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice 5 and 7 days after the last tamoxifen injection (<bold>a</bold>, <italic>n</italic> = 4 mice per genotype, per indicated time point; <bold>b</bold>, <italic>n</italic> = 5 mice per genotype at 7 days after tamoxifen, <italic>n</italic> = 4 mice per genotype at 5 days after tamoxifen). γ-tubulin was used as the loading control (<bold>a</bold>). AT, after tamoxifen. <bold>c</bold>, Representative TEM micrographs from proximal SI sections of <italic>Dars2</italic><sup><italic>fl/fl</italic></sup> mice (<italic>n</italic> = 4) and <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice (<italic>n</italic> = 4) 7 days after the last tamoxifen injection. Note the lack of CM-containing Golgi complexes and the appearance of aberrant numbers of LDs and damaged mitochondria in DARS2-deficient enterocytes. Asterisks indicate CMs secreted in the basolateral intercellular space. Arrows point at lipid particles within the ER lumen in rough and smooth ER neighbouring areas. Arrowheads point at LD lateral fusion. N, nucleus. <bold>d</bold>, Top, representative fluorescence microscopy images from the proximal SI of 8–12-week-old <italic>Dars2</italic><sup><italic>fl/fl</italic></sup> mice (<italic>n</italic> = 6) and <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice (<italic>n</italic> = 6) immunostained with antibodies against TGN38, E-cadherin and PLIN2. Nuclei stained with DAPI. Arrowheads point at LDs. Bottom, quantification of the TGN38-positive puncta size and the number of puncta per nucleus from confocal images of proximal SI sections of <italic>Dars2</italic><sup><italic>fl/fl</italic></sup> mice (<italic>n</italic> = 4) and <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> mice (<italic>n</italic> = 5) 5 days after the last tamoxifen injection. In <bold>d</bold>, dots represent individual mice, bar graphs show the mean ± s.e.m. and <italic>P</italic> values were calculated using unpaired two-sided Student’s <italic>t</italic>-test with no assumption of equal variance. In <bold>a</bold>,<bold>b</bold>, each lane represents one mouse from two independent experiments. Scale bars, 1 μm (<bold>c</bold>) or 50 μm (<bold>d</bold>). For gel source data, see Supplementary Fig. ##SUPPL##0##1##.</p><p>##SUPPL##9##Source Data##</p></caption></fig>", "<fig id=\"Fig5\"><label>Extended Data Fig. 1</label><caption><title>Depletion of respiratory complex subunits and intestinal pathology in DARS2<sup>IEC-KO</sup> mice.</title><p>BN-PAGE analysis of (<bold>a</bold>) individual respiratory complexes, or (<bold>b</bold>) supercomplexes in mitochondria isolated from the SI of 7-day-old <italic>Dars2</italic><sup>fl/fl</sup> (fl/fl) and DARS2<sup>IEC-KO</sup> (IEC-KO) mice. Respiratory complexes were visualized by immunoblotting with indicated antibodies (<bold>a, b</bold>). The activity of supercomplex-associated Complex I was determined with <italic>in-gel</italic> assay (<bold>b</bold>). Coomassie blue stains and Complex II levels (anti-SDHA) were used as the loading controls (<bold>a, b</bold>). <bold>c</bold>, Representative pictures and quantification of the length of the SI and colon in 7-day-old DARS2<sup>IEC-KO</sup> (<italic>n</italic> = 13) and <italic>Dars2</italic><sup>fl/fl</sup> littermates (<italic>n</italic> = 13). <bold>d</bold>, Representative images of SI sections from <italic>Dars2</italic><sup>fl/fl</sup> and DARS2<sup>IEC-KO</sup> mice stained with PAS and ALP or immunostained against OLFM4 and graph depicting relative mRNA expression of the indicated genes measured by RT-qPCR in the SI from 7-day-old <italic>Dars2</italic><sup>fl/fl</sup> (<italic>n</italic> = 6) and DARS2<sup>IEC-KO</sup> (<italic>n</italic> = 6) mice normalized to <italic>Tbp</italic>. <bold>e</bold>, Representative images of SI sections from <italic>Dars2</italic><sup>fl/fl</sup> and DARS2<sup>IEC-KO</sup> mice immunostained against CC3, CC8, CD45 and F4/80. Scale bars, 50 μm (<bold>d</bold>, <bold>e</bold>). PAS, Periodic acid- Schiff; ALP, Alkaline Phosphatase; <italic>OLFM4</italic>, Olfactomedin 4, CC3, Cleaved Caspase 3, CC8, Cleaved Caspase 8. In <bold>c</bold> and <bold>d</bold>, dots represent individual mice, bar graphs show mean ± s.e.m. and <italic>P</italic> values were calculated by two-sided nonparametric Mann-Whitney <italic>U</italic> -test. In <bold>a</bold> and <bold>b</bold>, each individual lane represents mitochondria isolated from one mouse (<italic>n</italic> = 3 per genotype). For gel source data, see Supplementary Fig. ##SUPPL##0##1##. In <bold>d</bold> and <bold>e</bold>, histological images shown are representative of the number of mice analysed as indicated in Supplementary Table ##SUPPL##0##4##.</p><p>\n##SUPPL##10##Source Data##\n</p></caption></fig>", "<fig id=\"Fig6\"><label>Extended Data Fig. 2</label><caption><title>Impaired lipid homeostasis in the intestines of DARS2<sup>IEC-KO</sup> mice.</title><p><bold>a-f</bold>, Graphs depicting quantification of DAG (<bold>a</bold>), GPLs (<bold>b</bold>), ceramides (<bold>c</bold>), sphingomyelins (<bold>d</bold>), CEs (<bold>e</bold>), and total cholesterol levels (<bold>f</bold>) in SI tissues from 7-day-old <italic>Dars2</italic><sup>fl/fl</sup> and DARS2<sup>IEC-KO</sup> mice (<italic>n</italic> = 7, DAG, GLPs, ceramides, sphingomyelins, total cholesterol and <italic>n</italic> = 4, CE per genotype). <bold>g</bold>, Graph depicting relative mRNA expression of lipid-regulating genes associated with the indicated processes and measured by RT-qPCR in the total distal SI of 7-day-old <italic>Dars2</italic><sup>fl/fl</sup> (<italic>n</italic> = 15) and DARS2<sup>IEC-KO</sup> (<italic>n</italic> = 15) mice normalized to <italic>Hprt1</italic>. In all graphs, dots represent individual mice, bar graphs show mean ± s.e.m. and <italic>P</italic> values were calculated by two-sided nonparametric Mann-Whitney <italic>U</italic> -test. DAG, Diacylglycerol, GLPs, Glycerophospholipids, PC, phosphatidylcholine, PE, phosphatidylethanolamine, PI, phosphatidylinositol, PS, phosphatidylserine, PG, phosphatidylglycerol, CE, cholesterol esters.</p><p>\n##SUPPL##11##Source Data##\n</p></caption></fig>", "<fig id=\"Fig7\"><label>Extended Data Fig. 3</label><caption><title>IEC-specific ablation of SDHA or COX10 causes lipid accumulation in large LDs in enterocytes.</title><p><bold>a-c</bold>, Graphs depicting Kaplan-Meier survival curve <bold>(a)</bold>, body weight <bold>(b)</bold> serum levels of glucose, total cholesterol, TAGs, HDL- and LDL-cholesterol <bold>(c)</bold> of <italic>Sdha</italic><sup>fl/fl</sup> (<italic>n</italic> = 29 <bold>(a)</bold>, <italic>n</italic> = 30 <bold>(b)</bold>, <italic>n</italic> = 19 (glucose, HDL- and LDL- cholesterol), <italic>n</italic> = 24 (total cholesterol, TAG) <bold>(c)</bold>) and SDHA<sup>IEC-KO</sup> (<italic>n</italic> = 32 <bold>(a)</bold>, <italic>n</italic> = 31 <bold>(b)</bold>, <italic>n</italic> = 17 (glucose, HDL- and LDL-cholesterol), <italic>n</italic> = 22 (total cholesterol, TAG) <bold>(c)</bold>) mice at the age of 7 days. <bold>d</bold>, Representative images of SI sections from <italic>Sdha</italic><sup>fl/fl</sup> and SDHA<sup>IEC-KO</sup> mice stained with H&E, COX-SDH and ORO or immunostained for PLIN2 and Ki67. Scale bars, 50 μm. <bold>e</bold>, Immunoblot analysis of IEC protein extracts from 7-day-old <italic>Sdha</italic><sup>fl/fl</sup> (<italic>n</italic> = 4) and SDHA<sup>IEC-KO</sup> (<italic>n</italic> = 3) mice with the indicated antibodies. <bold>f, g, h</bold> Graphs depicting Kaplan-Meier survival curve <bold>(f)</bold>, body weight <bold>(g)</bold> serum levels of glucose, total cholesterol, TAGs, HDL- and LDL-cholesterol <bold>(h)</bold> of 7-day-old <italic>Cox10</italic><sup>fl/fl</sup> (<italic>n</italic> = 35 <bold>(f)</bold>, <italic>n</italic> = 18 <bold>(g)</bold>, <italic>n</italic> = 11 (glucose, HDL- and LDL-cholesterol), <italic>n</italic> = 18 (total cholesterol, TAG) <bold>(h)</bold>) and COX10<sup>IEC-KO</sup> (<italic>n</italic> = 37 <bold>(f)</bold>, <italic>n</italic> = 21 <bold>(g)</bold>, <italic>n</italic> = 7 (glucose, HDL- and LDL-cholesterol), <italic>n</italic> = 22 (total cholesterol, TAG) <bold>(h)</bold>) mice. <bold>i</bold>, Representative images of SI sections from <italic>Cox10</italic><sup>fl/fl</sup> and COX10<sup>IEC-KO</sup> mice stained with H&E, COX-SDH and ORO or immunostained for PLIN2 and Ki67. Scale bars, 50 μm. <bold>j</bold>, Immunoblot analysis of IEC protein extracts from 7-day-old <italic>Cox10</italic><sup>fl/fl</sup> (<italic>n</italic> = 5) and COX10<sup>IEC-KO</sup> (<italic>n</italic> = 4) mice with the indicated antibodies. In <bold>b</bold>, <bold>c</bold>, <bold>g</bold> and <bold>h</bold>, dots represent individual mice, bar graphs show mean ± s.e.m. and <italic>P</italic> values were calculated by two-sided nonparametric Mann-Whitney<italic> U</italic> -test. In <bold>a</bold> and <bold>f</bold>, <italic>P</italic> values were calculated by two-sided Gehan-Breslow-Wilcoxon test. In <bold>d</bold> and <bold>i</bold>, histological images shown are representative of the number of mice analysed as indicated in Supplementary Table ##SUPPL##0##4##. In <bold>i</bold> and <bold>j</bold>, each lane represents one mouse and α-tubulin was used as loading control. For gel source data, see Supplementary Fig. ##SUPPL##0##1##.</p><p>\n##SUPPL##12##Source Data##\n</p></caption></fig>", "<fig id=\"Fig8\"><label>Extended Data Fig. 4</label><caption><title>Proteomics and transcriptomics analyses of intestinal tissue and enterocytes from DARS2<sup>tamIEC-KO</sup> mice.</title><p><bold>a</bold>, Schematic depicting the experimental design for inducible DARS2 deletion created with BioRender.com. Mice received daily intraperitoneal injections of tamoxifen (1 mg) for 5 consecutive days and were sacrificed 8 days upon the last injection as indicated. <bold>b</bold>, Volcano plot illustrating the protein expression profile of the mitochondria respiratory chain complex proteins detected in proximal IECs isolated from DARS2<sup>tamIEC-KO</sup> (<italic>n</italic> = 11) compared to <italic>Dars2</italic><sup>fl/fl</sup> (<italic>n</italic> = 9) 7 days upon the last tamoxifen injection. <bold>c</bold>, Immunoblot analysis of protein extracts from proximal SI IECs from <italic>Dars2</italic><sup>fl/fl</sup> and DARS2<sup>tamIEC-KO</sup> mice 7 days after the last tamoxifen injection with the indicated antibodies. α- tubulin was used as loading control. <bold>d</bold>, Volcano plot the profile of the different gene sets (colour coded) after performing GSEA analysis on the proteomics landscape of the DARS2<sup>tamIEC-KO</sup> (<italic>n</italic> = 11) mice compared to <italic>Dars2</italic><sup>fl/fl</sup> (<italic>n</italic> = 9) 7 days upon the last tamoxifen injection. Adjusted p-value (p.adj) and normalized enrichment score (NES) are the result of the GSEA analysis. <bold>e</bold>, Volcano plot illustrating the protein expression profile of genes that are part of the ATF4 signature based on Han et al<sup>##REF##25688136##44##</sup> comparing proximal small intestinal IECs from DARS2<sup>tamIEC-KO</sup> (<italic>n</italic> = 11) to <italic>Dars2</italic><sup>fl/fl</sup> mice (<italic>n</italic> = 9). <bold>f</bold>, Volcano plot illustrating the profile of the different gene sets (colour coded) after performing GSEA analysis on the transcriptomic profile of DARS2<sup>tamIEC-KO</sup> (<italic>n</italic> = 6) mice compared to <italic>Dars2</italic><sup>fl/fl</sup> (<italic>n</italic> = 6) 7 days upon the last tamoxifen injection. NES and p.adj are the result of the GSEA analysis. <bold>g</bold>, Volcano plot illustrating the mRNA expression profile of genes that are part of the ATF4 signature based on Han et. al<sup>##REF##25688136##44##</sup> comparing the proximal small intestine from DARS2<sup>tamIEC-KO</sup> mice to <italic>Dars2</italic><sup>fl/fl</sup> 7 days (red, <italic>n</italic> = 6) and 3 days (blue, <italic>n</italic> = 7) upon the last tamoxifen injection, respectively. In <bold>c</bold>, each lane represents one mouse (<italic>n</italic> = 6 per genotype). For gel source data, see Supplementary Fig. ##SUPPL##0##1##. In <bold>b</bold> and <bold>e</bold>, unpaired two-sided Welch’s Student <italic>t</italic>-test with S0 = 0.1 and a permutation-based FDR of 0.01 with 500 randomizations was performed to obtain differentially regulated proteins between the two groups. In <bold>d</bold> and <bold>f</bold>, normalized enrichment score (NES) and the statistics (p.adj) were calculated based on an algorithm described in Subramanian et al<sup>##REF##16199517##54##</sup>. In <bold>g</bold>, the statistical test producing the <italic>P</italic> values is Wald test and P‐adjusted values are calculated using the FDR/Benjamini-Hochberg approach. It is computed by function nbinomWaldTest of the Bioconductor DESeq2 package, based on a negative binomial general linear model of the gene counts<sup>##REF##25516281##49##</sup>.</p></caption></fig>", "<fig id=\"Fig9\"><label>Extended Data Fig. 5</label><caption><title>Proteomics and metabolomics analyses reveal downregulation of lipid biosynthesis and chylomicron production, increased lipid droplet formation and suppression of mitochondrial metabolism in DARS2-deficient enterocytes.</title><p><bold>a</bold>, Volcano plot presenting the proteome landscape comparing DARS2<sup>tamIEC-KO</sup>(<italic>n</italic> = 11) to <italic>Dars2</italic><sup>fl/fl</sup> (<italic>n</italic> = 9) mice 7 days after the last tamoxifen injection. <bold>b</bold>, Immunoblot analysis with the indicated antibodies of protein extracts from proximal SI IECs isolated from <italic>Dars2</italic><sup>fl/fl</sup> and DARS2<sup>tamIEC-KO</sup> mice 7 days after the last tamoxifen injection. α-tubulin and vinculin were used as loading controls. <bold>c-d</bold>, Emapplots of the Over Representation Analysis (ORA) performed on the significantly upregulated (Log2FC > 0.5 and p.adj < 0.05) proteins (<bold>c</bold>) and on the significantly downregulated (Log2FC < −0.5 and p.adj<0.05) proteins (<bold>d</bold>) when comparing DARS2<sup>tamIEC-KO</sup> (<italic>n</italic> = 11) to <italic>Dars2</italic><sup>fl/fl</sup> (<italic>n</italic> = 9) mice 7 days upon tamoxifen injection. <bold>e</bold>, Volcano plot illustrating the differential intracellular metabolite levels comparing SI IECs of DARS2<sup>tamIEC-KO</sup> (<italic>n</italic> = 15) to <italic>Dars2</italic><sup>fl/fl</sup> (<italic>n</italic> = 9) mice 7 days upon the last tamoxifen injection. <bold>f</bold>, Each data point in the bar graph represents the mean ± s.e.m of three technical replicates of one animal and is expressed as AUs relative to the average value of all control mouse samples for each metabolite detected in IECs from DARS2<sup>tamIEC-KO</sup> (<italic>n</italic> = 15) to <italic>Dars2</italic><sup>fl/fl</sup> (<italic>n</italic> = 8) mice 7 days upon the last tamoxifen injection. <italic>P</italic> values were calculated by unpaired two-sided Welch’s Student <italic>t</italic>-test with S0 = 0.1 and a permutation-based FDR of 0.01 with 500 randomizations (<bold>a</bold>), one-sided Fisher’s exact test with Benjamini-Hochberg multiple-testing correction (<bold>c</bold>, <bold>d</bold>), one-sided Student <italic>t</italic>-test with Benjamini-Hochberg multiple-testing correction (<bold>e</bold>) and unpaired two-sided Student’s <italic>t</italic>-test with no assumption of equal variance or two-sided nonparametric Mann-Whitney <italic>U</italic> -test (<bold>f</bold>).</p><p>\n##SUPPL##13##Source Data##\n</p></caption></fig>", "<fig id=\"Fig10\"><label>Extended Data Fig. 6</label><caption><title>Impaired IEC proliferation, stemness and differentiation upon tamoxifen-inducible DARS2 ablation in IECs of adult mice.</title><p><bold>a</bold>, Representative pictures from necropsy examination of <italic>Dars2</italic><sup>fl/fl</sup> (<italic>n</italic> = 4) and DARS2<sup>tamIEC-KO</sup> (<italic>n</italic> = 4) mice sacrificed 8 days after the last tamoxifen injection. Blue arrows indicate the proximal SI appearing white in DARS2<sup>tamIEC-KO</sup> mice. <bold>b, c, d</bold> Representative microscopic pictures of proximal SI sections stained with PAS and ALP and immunostained with CC3 and CD45 (<bold>b</bold>) distal SI sections stained with H&E, COX/SDH, PAS, ALP or immunostained with Ki67 (<bold>c</bold>) and distal SI sections immunostained with CC3 and CD45 from <italic>Dars2</italic><sup>fl/fl</sup> and DARS2<sup>tamIEC-KO</sup> (<bold>d</bold>). Scale bar, 50 μm (<bold>b</bold>, <bold>c</bold>, <bold>d</bold>). <bold>e</bold>, Graph depicting mRNA expression levels of stem cell genes in distal SI of <italic>Dars2</italic><sup>fl/fl</sup> (<italic>n</italic> = 6) and DARS2<sup>tamIEC-KO</sup> (<italic>n</italic> = 6) mice measured with RT-qPCR and normalized to <italic>Tbp</italic>. In <bold>e</bold>, dots represent individual mice, bar graphs show mean ± s.e.m. and <italic>P</italic> values were calculated by two-sided non-parametric Mann-Whitney U test. In <bold>b</bold>, <bold>c</bold> and <bold>d</bold>, histological images shown are representative of the number of mice analysed as indicated in Supplementary Table ##SUPPL##0##4##.</p><p>\n##SUPPL##14##Source Data##\n</p></caption></fig>", "<fig id=\"Fig11\"><label>Extended Data Fig. 7</label><caption><title>Analysis of respiratory subunit expression, IEC proliferation and lipid accumulation in DARS2<sup>tamIEC-KO</sup> mice 3 and 5 days after tamoxifen injection.</title><p><bold>a</bold>, Schematic depiction of the experimental design created with BioRender.com. <bold>b</bold>, Immunoblot analysis with the indicated antibodies of proximal SI IEC protein extracts from <italic>Dars2</italic><sup>fl/fl</sup> (<italic>n</italic> = 6) and DARS2<sup>tamIEC-KO</sup> (<italic>n</italic> = 6) mice 3 days after the last tamoxifen injection. <bold>c</bold>, Graph depicting relative body weight of <italic>Dars2</italic><sup>fl/fl</sup> (<italic>n</italic> = 16) and DARS2<sup>tamIEC-KO</sup> (<italic>n</italic> = 15) mice after tamoxifen injection. <bold>d</bold>, Representative microscopic images of proximal SI sections from <italic>Dars2</italic><sup>fl/fl</sup> and DARS2<sup>tamIEC-KO</sup> mice sacrificed 3 days upon the last tamoxifen injection stained with H&E, COX/SDH and ORO or immunostained with Ki67 and PLIN2. Scale bars, 50 μm. <bold>e</bold>, Schematic depiction of the experimental design created with BioRender.com. <bold>f</bold>, Immunoblot analysis with the indicated antibodies of proximal SI IEC protein extracts from <italic>Dars2</italic><sup>fl/fl</sup> (<italic>n</italic> = 5) and DARS2<sup>tamIEC-KO</sup> (<italic>n</italic> = 6) mice 5 days after the last tamoxifen injection. <bold>g</bold>, Graph depicting relative body weight of <italic>Dars2</italic><sup>fl/fl</sup> (<italic>n</italic> = 8) and DARS2<sup>tamIEC-KO</sup> (<italic>n</italic> = 11) mice after tamoxifen injection. <bold>h</bold>, Representative microscopic images of proximal SI sections from <italic>Dars2</italic><sup>fl/fl</sup> and DARS2<sup>tamIEC-KO</sup> mice sacrificed 5 days upon the last tamoxifen injection stained with H&E, COX/SDH and ORO or immunostained with Ki67 and PLIN2. Scale bars, 50 μm. In <bold>c</bold> and <bold>g</bold>, data are represented as mean ± s.e.m and <italic>P</italic> values were calculated by two-way ANOVA with Bonferroni’s correction for multiple comparison. In <bold>b</bold> and <bold>f</bold>, each individual lane represents one mouse and α-tubulin was used as loading control. For gel source data, see Supplementary Fig. ##SUPPL##0##1##. In <bold>d</bold> and <bold>h</bold>, histological images shown are representative of the number of mice analysed as indicated in Supplementary Table ##SUPPL##0##4##.</p><p>\n##SUPPL##15##Source Data##\n</p></caption></fig>", "<fig id=\"Fig12\"><label>Extended Data Fig. 8</label><caption><title>Tamoxifen-inducible ablation of DARS2 in adult mice fed with a fat-free diet (FFD).</title><p><bold>a</bold>, Schematic depiction of the experimental design for inducing DARS2 deletion in mice fed with a FFD created with BioRender.com. 8-12-week-old mice were fed with an equal mixture of normal chow diet (NCD) and FFD for 4 days, which was followed by 14 days FFD feeding and sacrifice 7 days after the last tamoxifen injection. <bold>b</bold>, Graph depicting relative body weight change in FFD-fed <italic>Dars2</italic><sup>fl/fl</sup> (<italic>n</italic> = 18) and DARS2<sup>tamIEC-KO</sup> (<italic>n</italic> = 20) mice after tamoxifen injection. <bold>c</bold>, Immunoblot analysis with the indicated antibodies of small intestinal IEC protein extracts from FFD-fed <italic>Dars2</italic><sup>fl/fl</sup> (<italic>n</italic> = 6) and DARS2<sup>tamIEC-KO</sup> (<italic>n</italic> = 8) mice. α-tubulin was used as loading control. <bold>d</bold>, Representative images of sections from the distal SI of FFD-fed <italic>Dars2</italic><sup>fl/fl</sup> and DARS2<sup>tamIEC-KO</sup> mice 7 days after the last tamoxifen injection stained with H&E, ORO and COX/SDH or immunostained for PLIN2 and Ki67. Scale bar, 50 μm. <bold>e</bold>, Graph depicting relative mRNA levels of stem cell genes analysed by RT-qPCR and normalized to <italic>Tbp</italic> in the SI of <italic>Dars2</italic><sup>fl/fl</sup> (<italic>n</italic> = 8) and DARS2<sup>tamIEC-KO</sup> (<italic>n</italic> = 8) mice 7 days after tamoxifen injection. <bold>f</bold>, Graphs depicting the concentration of glucose, total cholesterol and TAGs in sera from <italic>Dars2</italic><sup>fl/fl</sup> (<italic>n</italic> = 6) and DARS2<sup>tamIEC-KO</sup> (<italic>n</italic> = 6) mice fed with FFD 7 days upon the last tamoxifen injection. In <bold>e</bold> and <bold>f</bold>, dots represent individual mice. In <bold>b</bold>, <bold>e</bold> and <bold>f</bold> bar graphs data are represented as mean ± s.e.m. and <italic>P</italic> values were calculated by two-way ANOVA with Bonferroni’s correction for multiple comparison (<bold>b</bold>) and two-sided nonparametric Mann-Whitney<italic> U</italic> -test (<bold>e</bold>, <bold>f</bold>). In <bold>c</bold>, each individual lane represents one mouse. For gel source data, see Supplementary Fig. ##SUPPL##0##1##. In <bold>d</bold>, histological images shown are representative of the number of mice analysed as indicated in Supplementary Table ##SUPPL##0##4##.</p><p>\n##SUPPL##16##Source Data##\n</p></caption></fig>", "<fig id=\"Fig13\"><label>Extended Data Fig. 9</label><caption><title>Oral glucose fat tolerance and metabolic tracing in DARS2<sup>tamIEC-KO</sup> mice 5 and 7 days upon the last tamoxifen injection.</title><p><bold>a-n</bold>, Metabolic tracing studies performed in DARS2<sup>tamIEC-KO</sup> mice 7 (<bold>a</bold>-<bold>f</bold>) and 5 days (<bold>a</bold>, <bold>d</bold>, <bold>g</bold>-<bold>n</bold>) upon the last tamoxifen injection. <bold>a</bold>, Schematic depiction of the experimental design of the oral fat tolerance test (OFTT) created with Biorender.com. On day 5 or 7 after the last tamoxifen injection, mice received an intravenous injection of the lipoprotein lipase inhibitor tyloxapol and were fasted for 2 h, followed by oral gavage with a lipid solution containing <sup>3</sup>H-triolein and <sup>14</sup>C-cholesterol. Afterwards, blood was collected from the tail vein for the indicated time points and the plasma appearance of the tracers was measured. <bold>b, c, g</bold>, <bold>h</bold>, Relative body weight change over the indicated time period (<bold>b</bold>, <bold>g</bold>) and body weight recorded on the day of sacrifice (<bold>c</bold>, <bold>h</bold>) of <italic>Dars2</italic><sup>fl/fl</sup> (<italic>n</italic> = 8) and DARS2<sup>tamIEC-KO</sup> (<italic>n</italic> = 8) mice subjected to OFTT 7 days (<bold>b</bold>, <bold>c</bold>) or 5 days (<bold>g</bold>, <bold>h</bold>) upon tamoxifen. <bold>e, f, j, k</bold>, Relative body weight change over the indicated time period (<bold>e</bold>, <bold>j</bold>) and body weight recorded on the day of sacrifice (<bold>f</bold>, <bold>k</bold>) of <italic>Dars2</italic><sup>fl/fl</sup> (<italic>n</italic> = 7) and DARS2<sup>tamIEC-KO</sup> (<italic>n</italic> = 8) mice subjected to OGFTT 7 days (<bold>e</bold>, <bold>f</bold>) or <italic>Dars2</italic><sup>fl/fl</sup> (<italic>n</italic> = 9) and DARS2<sup>tamIEC-KO</sup> (<italic>n</italic> = 10) mice subjected to OGFTT 5 days (<bold>j</bold>, <bold>k</bold>) upon tamoxifen. <bold>d</bold>, Schematic depiction of the experimental design of the oral glucose fat tolerance test (OGFTT) created with BioRender.com. On day 5 or 7 after the last tamoxifen injection, mice were fasted for 2 h followed by oral gavage with <sup>3</sup>H-triolein and <sup>14</sup>C-DOG. Tissues were harvested 2 h after the oral gavage. <bold>i</bold>, Graphs depicting <sup>3</sup>H-triolein, <sup>14</sup>C-cholesterol and TAG content in portal plasma of <italic>Dars2</italic><sup>fl/fl</sup> (<italic>n</italic> = 8) and DARS2<sup>tamIEC-KO</sup> (<italic>n</italic> = 8) mice subjected to OFTT after intravenous tyloxapol injection. <bold>l</bold>, Graph depicting relative organ weight of <italic>Dars2</italic><sup>fl/fl</sup> (<italic>n</italic> = 9) and DARS2<sup>tamIEC-KO</sup> (<italic>n</italic> = 10) mice subjected to OGFTT. <bold>m, n</bold>, Graphs depicting counts of <sup>3</sup>H-triolein (<bold>m</bold>) or <sup>14</sup>C-DOG (<bold>n</bold>) in different organs and plasma from <italic>Dars2</italic><sup>fl/fl</sup> (<italic>n</italic> = 9) and DARS2<sup>tamIEC-KO</sup> (<italic>n</italic> = 10) mice determined 120 min after oral gavage 5 days upon the last tamoxifen injection. In <bold>c</bold>, <bold>f</bold>, <bold>h</bold>, <bold>k</bold>, <bold>l</bold>, <bold>m</bold> and <bold>n</bold>, dots represent individual mice. In bar graphs data are represented as mean ± s.e.m. and <italic>P</italic> values were calculated by two-way ANOVA with Bonferroni’s correction for multiple comparison (<bold>b</bold>, <bold>e</bold>, <bold>g</bold>, <bold>j</bold>), two-sided nonparametric Mann-Whitney <italic>U</italic> -test (<bold>c</bold>, <bold>f, h, k</bold>), and unpaired two-sided Student’s <italic>t</italic>-test with no assumption of equal variance <bold>(i</bold>, <bold>l</bold>, <bold>m</bold>, <bold>n)</bold>.</p><p>\n##SUPPL##17##Source Data##\n</p></caption></fig>", "<fig id=\"Fig14\"><label>Extended Data Fig. 10</label><caption><title>DARS2 depletion causes gradual Golgi disorganisation in proximal enterocytes that precedes LD formation and requires the presence of fat in the diet.</title><p><bold>a</bold>, Representative fluorescence microscopy images from the proximal SI of 8-12-week-old <italic>Dars2</italic><sup>fl/fl</sup> (<italic>n</italic> = 6) and DARS2<sup>tamIEC-KO</sup> (<italic>n</italic> = 6) mice sacrificed 3, 5 and 8 days upon the last tamoxifen injection and immunostained with antibodies against TGN38 (red) and E-cadherin (green). Insets shows only TGN38 staining in white. <bold>b</bold>, Representative fluorescence microscopy images from the proximal and distal SI of 8-12-week-old <italic>Dars2</italic><sup>fl/fl</sup> (<italic>n</italic> = 6) and DARS2<sup>tamIEC-KO</sup> (<italic>n</italic> = 6) mice fed with NCD sacrificed 3 and 8 days upon the last tamoxifen injection and immunostained with antibodies against TGN38 (red), E-cadherin (green) and PLIN2 (yellow). <bold>c</bold>, Representative fluorescence microscopy images from the proximal SI of 8-12-week-old <italic>Dars2</italic><sup>fl/fl</sup> (<italic>n</italic> = 6) and DARS2<sup>tamIEC-KO</sup> (<italic>n</italic> = 6) mice under NCD and FFD sacrificed 7 days upon the last tamoxifen injection and immunostained with antibodies against TGN38 (red), E-cadherin (green) and PLIN2 (yellow). Nuclei stained with DAPI (blue). Scale bars, 50 μm. Confocal images shown are representative of the number of mice analysed as indicated in Supplementary Table ##SUPPL##0##4##.</p></caption></fig>", "<fig id=\"Fig15\"><label>Extended Data Fig. 11</label><caption><title>Mitochondrial dysfunction causes impairment of Golgi organisation and lipid processing in IEC-6 cells and in <italic>C. elegans</italic>.</title><p><bold>a</bold>, Representative fluorescence microscopy images depicting IEC-6 cells treated for 48 h with actinonin (100 μM), Atpenin <italic>A5 (AA5, 1</italic>μ<italic>M)</italic> or 1% dimethyl sulfoxide; DMSO (control). Short treatment (6 h) with Brefeldin A (BFA, 5 μg/ml) was used as a positive control for Golgi dispersal. Scale bars, 50 μm <bold>b</bold>, Representative fluorescence microscopy images of IEC-6 cells grown under the same conditions as described in <bold>a</bold> were incubated with oleic acid (OA, 600 μM) for the last 24 h prior imaging. In this case, BFA was applied in the last 6 h of OA treatment to avoid cytotoxicity. Anti-TGN38 (red) antibody was used to visualize Golgi, Anti-COX1(green) to stain mitochondrial networks <bold>(a)</bold>, BODIPY (green) to stain lipid droplets <bold>(b)</bold>, and DAPI (blue) for nuclei (top). TGN38 staining is additionally depicted in white (bottom). Scale bars, 50 μm. Quantification of the observed Golgi morphology of the IEC-6 cells based on five distinct categories, as illustrated at the right (<italic>n</italic> = 100–300 inspected IEC-6 cells from three independent biological experiments). <bold>c</bold>, Representative confocal images and graphs depicting quantification of GFP signal in <italic>C. elegans</italic> expressing α-mannosidase II fused to GFP under the control of the gut-specific vha-6 promoter grown either on a control empty vector (EV) or RNAi against <italic>dars-2</italic> at the first (D1) and fourth day of adulthood (D4) (EV, <italic>n</italic> = 19 (<bold>D1</bold>), <italic>n</italic> = 13 (<bold>D4</bold>), <italic>dars-2</italic>, <italic>n</italic> = 20 (<bold>D1</bold>), <italic>n</italic> = 19 (<bold>D4</bold>)). Scale bars, 1 μm. <bold>d</bold>, Representative confocal images of GFP signal in <italic>C. elegans</italic> expressing SPCS-1 fused to GFP under the control of the gut-specific vha-6 promoter grown either on a control empty vector (EV) or RNAi against <italic>dars-2</italic> at the first (D1) and fourth day of adulthood (D4) EV, <italic>n</italic> = 19 (<bold>D1</bold>), <italic>n</italic> = 19 (<bold>D4</bold>), <italic>dars-2</italic>, <italic>n</italic> = 20 (<bold>D1</bold>), <italic>n</italic> = 19 (<bold>D4</bold>)). Scale bars, 1 μm. <bold>e</bold>, Immunofluorescence micrographs of <italic>C. elegans</italic> carrying <italic>vit-2::GFP</italic> reporter on a control empty vector (EV) (<italic>n</italic> = 10) and <italic>RNAi</italic> against <italic>sar-1</italic>, <italic>sec-13</italic>, <italic>fum-1</italic> and <italic>dars-2</italic> at D1 (<italic>n</italic> = 10). Insets show magnification of a selected area of the worm. Scale bars, 100 μm. In bar graphs data are represented as mean ± s.e.m. and <italic>P</italic> values were calculated by two-sided Chi-squared test (<bold>a</bold>, <bold>b</bold>) and two-sided Student’s <italic>t</italic>-test with assumption of equal variance (<bold>c</bold>).</p><p>\n##SUPPL##18##Source Data##\n</p></caption></fig>" ]	[]	[]	[]	[]	[]	[]	[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM3\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM4\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM5\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM6\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM7\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM8\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM9\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM10\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM11\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM12\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM13\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM14\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM15\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM16\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM17\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM18\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM19\"></supplementary-material>" ]	[ "<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"41586_2023_6857_Fig1_HTML\" id=\"d32e742\"/>", "<graphic xlink:href=\"41586_2023_6857_Fig2_HTML\" id=\"d32e1286\"/>", "<graphic xlink:href=\"41586_2023_6857_Fig3_HTML\" id=\"d32e1754\"/>", "<graphic xlink:href=\"41586_2023_6857_Fig4_HTML\" id=\"d32e2258\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6857_Fig5_ESM\" id=\"d32e3281\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6857_Fig6_ESM\" id=\"d32e3360\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6857_Fig7_ESM\" id=\"d32e3601\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6857_Fig8_ESM\" id=\"d32e3756\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6857_Fig9_ESM\" id=\"d32e3883\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6857_Fig10_ESM\" id=\"d32e3983\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6857_Fig11_ESM\" id=\"d32e4120\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6857_Fig12_ESM\" id=\"d32e4260\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6857_Fig13_ESM\" id=\"d32e4543\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6857_Fig14_ESM\" id=\"d32e4605\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6857_Fig15_ESM\" id=\"d32e4774\"/>" ]	[ "<media xlink:href=\"41586_2023_6857_MOESM1_ESM.pdf\"><label>Supplementary Information</label><caption><p>This file contains Supplementary Fig. 1 and Supplementary Tables 4 and 5. Supplementary Fig. 1: Uncropped gels from immunoblots presented in the manuscript. Supplementary Table 4: List of the genotypes and the numbers of mice for which histological sections were analysed for each experimental group. Supplementary Table 5: SYBR Green primer sequences used for RT–qPCR.</p></caption></media>", "<media xlink:href=\"41586_2023_6857_MOESM2_ESM.pdf\"><caption><p>Reporting Summary</p></caption></media>", "<media xlink:href=\"41586_2023_6857_MOESM3_ESM.xlsx\"><label>Supplementary Table 1</label><caption><p>Proteomics analysis of protein lysates from IECs isolated from mice 7 days after the last tamoxifen injection.</p></caption></media>", "<media xlink:href=\"41586_2023_6857_MOESM4_ESM.xlsx\"><label>Supplementary Table 2</label><caption><p>RNA-seq analysis of total proximal SI tissues isolated from <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> and <italic>Dars2</italic><sup><italic>fl/fl</italic></sup> mice 3 and 7 days after the last tamoxifen injection.</p></caption></media>", "<media xlink:href=\"41586_2023_6857_MOESM5_ESM.xlsx\"><label>Supplementary Table 3</label><caption><p>Metabolomics analysis of IECs isolated from <italic>Dars2</italic><sup><italic>tamIEC-KO</italic></sup> and <italic>Dars2</italic><sup><italic>fl/fl</italic></sup> mice 7 days after the last tamoxifen injection.</p></caption></media>", "<media xlink:href=\"41586_2023_6857_MOESM6_ESM.pdf\"><caption><p>Peer Review File</p></caption></media>", "<media xlink:href=\"41586_2023_6857_MOESM7_ESM.xlsx\"><caption><p>Source Data Fig. 1</p></caption></media>", "<media xlink:href=\"41586_2023_6857_MOESM8_ESM.xlsx\"><caption><p>Source Data Fig. 2</p></caption></media>", "<media xlink:href=\"41586_2023_6857_MOESM9_ESM.xlsx\"><caption><p>Source Data Fig. 3</p></caption></media>", "<media xlink:href=\"41586_2023_6857_MOESM10_ESM.xlsx\"><caption><p>Source Data Fig. 4</p></caption></media>", "<media xlink:href=\"41586_2023_6857_MOESM11_ESM.xlsx\"><caption><p>Source Data Extended Data Fig. 1</p></caption></media>", "<media xlink:href=\"41586_2023_6857_MOESM12_ESM.xlsx\"><caption><p>Source Data Extended Data Fig. 2</p></caption></media>", "<media xlink:href=\"41586_2023_6857_MOESM13_ESM.xlsx\"><caption><p>Source Data Extended Data Fig. 3</p></caption></media>", "<media xlink:href=\"41586_2023_6857_MOESM14_ESM.xlsx\"><caption><p>Source Data Extended Data Fig. 5</p></caption></media>", "<media xlink:href=\"41586_2023_6857_MOESM15_ESM.xlsx\"><caption><p>Source Data Extended Data Fig. 6</p></caption></media>", "<media xlink:href=\"41586_2023_6857_MOESM16_ESM.xlsx\"><caption><p>Source Data Extended Data Fig. 7</p></caption></media>", "<media xlink:href=\"41586_2023_6857_MOESM17_ESM.xlsx\"><caption><p>Source Data Extended Data Fig. 8</p></caption></media>", "<media xlink:href=\"41586_2023_6857_MOESM18_ESM.xlsx\"><caption><p>Source Data Extended Data Fig. 9</p></caption></media>", "<media xlink:href=\"41586_2023_6857_MOESM19_ESM.xlsx\"><caption><p>Source Data Extended Data Fig. 11</p></caption></media>" ]	[{"label": ["10."], "mixed-citation": ["Kaspar, S. et al. 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R package version 1.54.0. 10.18129/B9.bioc.GSEABase (2021)."]}]	{ "acronym": [], "definition": [] }	59	CC BY	no	2024-01-13 00:02:19	Nature. 2024 Dec 20; 625(7994):385-392	oa_package/04/d2/PMC10781618.tar.gz
PMC10781619	38057661	[]	[ "<title>Methods</title>", "<title>Neuropathological analyses</title>", "<p id=\"Par28\">Human tissue samples were from the Dementia Laboratory Brain Library at Indiana University School of Medicine (individuals 1 and 4) and from the Queen Square Brain Bank for Neurological Disorders at University College London Queen Square Institute of Neurology (individuals 2 and 3). Their use in this study was approved by the ethical review processes at each institution. Informed consent was obtained from patients’ next of kin.</p>", "<p id=\"Par29\">These individuals were selected based on a neuropathological diagnosis of FTLD–FET. All individuals had abundant neuronal cytoplasmic inclusions, as well as sparse neuronal intranuclear inclusions and dystrophic neurites, in the prefrontal and temporal cortices. The inclusions were immunoreactive against ubiquitin, p62, FUS, TAF15 and transportin 1. Immunohistochemistry for α-internexin did not show any neuronal intermediate filament inclusions. Haematoxylin and eosin staining did not show any basophilic inclusions. These results are consistent with the FTLD subtype atypical FTLD with ubiquitin-positive inclusions (aFTLD-U)<sup>##UREF##0##2##</sup>.</p>", "<p id=\"Par30\">Individuals 1 and 4 also exhibited upper and lower motor neuron pathology, both showing loss of upper and lower motor neurons. Luxol fast blue staining of myelin also showed extensive lateral and anterior corticospinal tract loss in the thoracic and lumbar spinal cord for individual 1. Corticospinal tract loss could not be assessed for individual 4 owing to a lack of available spinal cord. Individuals 1 and 4 also had abundant FUS-, TAF15- and transportin 1-positive cytoplasmic inclusions in upper and lower motor neurons in the motor cortex, brainstem and spinal cord.</p>", "<p id=\"Par31\">All individuals received a clinical diagnosis of behavioural variant FTD. Individual 4 also received a diagnosis of probable ALS based on electromyography results, lower extremity hyper-reflexia and bulbar symptoms. Additional clinicopathological details are given in Extended Data Table ##TAB##0##1##.</p>", "<title>Genetic analyses</title>", "<p id=\"Par32\">Whole-exome sequencing target enrichment was performed using the SureSelectTX human all-exon library (v.6, 58 megabase pairs, Agilent) and high-throughput sequencing was carried out using a HiSeq 4000 (sx75 base-pair, paired-end configuration, Illumina). Data corresponding to the coding regions of genes previously reported as being associated with FTLD were screened for potential pathogenic variants; these genes included <italic>CHMP2B</italic>, <italic>EWS</italic>, <italic>FUS</italic>, <italic>GRN</italic>, <italic>INA</italic>, <italic>MAPT</italic>, <italic>MATR3</italic>, <italic>OPTN</italic>, <italic>SQSTM1</italic>, <italic>TAF15</italic>, <italic>TARDBP</italic>, <italic>TBK1</italic>, <italic>TIA1</italic>, <italic>TMEM106B</italic>, <italic>UBQLN1</italic> and <italic>VCP</italic>. Analysis of <italic>C9orf72</italic> for hexanucleotide repeat expansion was performed using repeat-primed PCR as previously described<sup>##REF##21944779##54##</sup>. No mutations associated with FTLD were found, and the individuals had wild-type <italic>FUS</italic>, <italic>TAF15</italic> and <italic>EWS</italic></p>", "<title>Extraction of sarkosyl-insoluble proteins</title>", "<p id=\"Par33\">Sarkosyl-insoluble proteins were extracted from flash-frozen tissue (prefrontal, temporal and motor cortices, and medulla) as previously described<sup>##REF##34880495##5##</sup>. Grey matter was dissected and homogenized using a Polytron (Kinematica) in 40 volumes (v/w) of extraction buffer (10 mM Tris-HCl pH 7.4, 0.8 M NaCl, 10% sucrose, 1 mM dithiothreitol and 1 mM EGTA) containing protease and phosphatase inhibitor cocktail (Pierce). A 25% solution of sarkosyl in water was added to homogenates to achieve a final concentration of 2% sarkosyl. Homogenates were then incubated for 1 h or overnight at 37 °C with orbital shaking at 200 rpm, followed by centrifugation at 27,000<italic>g</italic> for 10 min. Supernatants were retained and centrifuged in 1 ml aliquots at 166,000<italic>g</italic> for 20 min. Each pellet was soaked in 20 µl of extraction buffer containing 1% sarkosyl at 37 °C for at least 20 min and then resuspended by pipetting. Six pellets were combined, topped up to 0.5 ml with the same buffer and sonicated for 5 min at 50% amplitude (Qsonica Q700). Samples were then diluted to 1 ml with the same buffer and centrifuged at 17,000<italic>g</italic> for 5 min. Supernatants were retained and centrifuged at 166,000<italic>g</italic> for 20 min. Pellets were soaked and resuspended as before. Samples were topped up to 1 ml with extraction buffer containing 1% sarkosyl and incubated overnight at 37 °C with orbital shaking at 200 rpm. Samples were centrifuged at 166,000<italic>g</italic> for 20 min and pellets resuspended in 25 μl g<sup>−1</sup> tissue of 20 mM Tris-HCl pH 7.4 and 150 mM NaCl by soaking at 37 °C, pipetting and sonication for 5 min at 50% amplitude. 1–2 g of tissue was used for each cryo-EM sample. All centrifugation steps were carried out at 25 °C.</p>", "<title>Immunolabelling</title>", "<p id=\"Par34\">For histology, brain hemispheres were fixed with 10% buffered formalin and embedded in paraffin. Deparaffinized sections (8 μm thickness) were treated with 88% formic acid for 5 min and incubated in 10 mM sodium citrate buffer at 105 °C for 10 min. After washing, sections were treated with non-fat dry milk in Tris-buffered saline and then incubated overnight with primary antibodies against either FUS (Proteintech, no. 11570-1-AP at a dilution of 1:1,000), TAF15 (Bethyl, no. IHC-00094 at a dilution of 1:500), EWS (Santa Cruz, no. sc-28327 at a dilution of 1:250) or transportin 1 (abcam, no. ab10303 at a dilution of 1:200) in Tris-buffered saline. Following incubation with biotinylated secondary antibodies overnight, labelling was detected using the ABC staining kit (Vector) with 3,3′-diaminobenzidine. Sections were counterstained with haematoxylin.</p>", "<p id=\"Par35\">For immunoblotting, samples were resolved using 4–12% BIS-Tris gels (Novex) at 200 V for 40 min and transferred to nitrocellulose membranes. Membranes were blocked in PBS containing 1% bovine serum albumin and 0.2% Tween for 30 min at room temperature and incubated with primary antibodies against either FUS (Proteintech, no. 11570-1-AP at a dilution of 1:5,000), TAF15 (Bethyl, no. A300-308A at a dilution of 1:5,000), EWS (Santa Cruz, no. sc-28327 at a dilution of 1:250) or transportin 1 (abcam, no. ab10303 at a dilution of 1:500) for 1 h at room temperature. Membranes were then washed three times with PBS containing 0.2% Tween and incubated with either Goat Anti-Mouse IgG StarBright Blue 700 (Bio-Rad) or Anti-Rabbit IgG DyLight 800 4× PEG Conjugate (Cell Signaling Technology) secondary antibodies for 1 h at room temperature. Membranes were then washed three times with PBS containing 0.2% Tween and imaged using a ChemiDoc MP (Bio-Rad).</p>", "<title>Mass spectrometry</title>", "<p id=\"Par36\">Sarkosyl-insoluble proteins were extracted from 0.2 g of grey matter. The final pellet was dried by vacuum centrifugation (Savant) then soaked in 20 μl of hexafluoroisopropanol, incubated at 37 °C for 1 h, resuspended and topped up to 100 µl of solvent. Samples were sonicated three times for 3 min each at 50% amplitude in a water bath (QSonica Q700). Any non-disassembled filaments were removed by centrifugation at 166,000<italic>g</italic> for 30 min. The supernatant was dried by vacuum centrifugation.</p>", "<p id=\"Par37\">Dried protein samples were resuspended in 8 M urea and 50 mM ammonium bicarbonate, reduced with 5 mM dithiothreitol and alkylated with 10 mM iodoacetamide. Samples were diluted to 1 M urea with 50 mM ammonium bicarbonate and incubated with chymotrypsin (Promega) overnight at 25 °C. Digestion was stopped by the addition of formic acid to a final concentration of 0.5%, followed by centrifugion at 16,000<italic>g</italic> for 5 min. Supernatants were desalted using home-made C18 stage tips (3M Empore) packed with Oligo R3 resin (Thermo Fisher Scientific) resin. Bound peptides were eluted with 5–60% acetonitrile in 0.5% formic acid and partially dried in a Speed Vac (Savant).</p>", "<p id=\"Par38\">Peptide mixtures were analysed by liquid chromatography–tandem mass spectrometry using an Ultimate 3000 RSLCnano system (Thermo Fisher Scientific) coupled to an Orbitrap Q Exactive HFX mass spectrometer (Thermo Fisher Scientific). Peptides were trapped using a 100 μm × 2 cm, PepMap100 C18 nanotrap column (Thermo Fisher Scientific) and separated on a 75 μm × 50 cm, EASY-Spray HPLC Column using a binary gradient consisting of buffer A (2% acetonitrile, 0.1% formic acid) and buffer B (80% acetonitrile, 0.1% formic acid) at a flow rate of 300 nl min<sup>−1</sup> for 190 min. For data independent acquisition, MS1 spectra were acquired at a resolution of 60,000, mass range 385–1,015 <italic>m/z</italic> and maximum injection time 60 ms. MS2 analysis was carried out at a resolution of 15,000, and 25 MS2 scans with 24 <italic>m/z</italic> isolation window.</p>", "<p id=\"Par39\">Liquid chromatography–tandem mass spectrometry data were processed using DIA-NN software (v.1.8.1) in library-free mode<sup>##REF##31768060##55##</sup>. The sequence database was automatically generated from the UP000005640_9606 human proteome fasta file (March 2023). Two chymotrypsin missed cleavages were allowed in the search parameters. Carbamidomethyl cysteine was set as static modification and methionine oxidation as variable modification. Precursor mass range was set as 370–1,100 <italic>m/z</italic> and default settings were used for other parameters. The files report.pg_matrix.tsv (protein) and report.pr_matrix.tsv (peptide) were used for analyses.</p>", "<title>Negative-stain electron microscopy</title>", "<p id=\"Par40\">Sarkosyl-insoluble proteins were extracted from 0.1 g of grey matter, the supernatant of the first ultracentrifugation being retained as the sarkosyl-soluble fraction. The final pellet of sarkosyl-insoluble protein was resuspended in 50 µl of 20 mM Tris-HCl pH 7.4 and 150 mM NaCl. Both sarkosyl-soluble and -insoluble fractions were sonicated for 5 min at 50% amplitude. Samples were diluted up to tenfold in 20 mM Tris-HCl pH 7.4 and 150 mM NaCl. Glow-discharged 400-mesh carbon-coated copper grids (Electron Microscopy Sciences) were incubated face-down on 4 µl of sample for 1 min. Grids were washed three times using Millipore-filtered water and once using 2% uranyl acetate, then stained for 30 s in 2% uranyl acetate before blotting with filter paper. Dried grids were imaged with a 120 keV Tecnai Spirit microscope (Thermo Fisher Scientific) with an Orius CCD detector (Gatan).</p>", "<title>Cryo-EM</title>", "<p id=\"Par41\">Extracted sarkosyl-insoluble proteins were incubated with 0.4 mg ml<sup>−1</sup> pronase (Sigma) for 1 h at room temperature and centrifuged at 3,000<italic>g</italic> for 15 s to remove large debris. Supernatants were retained and applied to glow-discharged 1.2/1.3 μm holey carbon-coated 200-mesh gold grids (Quantifoil) and plunge-frozen in liquid ethane using a Vitrobot Mark IV (Thermo Fisher Scientific). Images were acquired using a 300 keV Titan Krios microscope (Thermo Fisher Scientific) with either a Falcon 4 detector (Thermo Fisher Scientific) or a K3 detector (Gatan) and GIF-quantum energy filter (Gatan) operated at a slit width of 20 eV. Aberration-free image shift within the EPU software (Thermo Fisher Scientific) was used during image acquisition. Further details are given in Extended Data Table ##TAB##1##2##.</p>", "<title>Helical reconstruction</title>", "<p id=\"Par42\">Movie frames were gain-corrected, aligned, dose-weighted and summed using the motion correction programme in RELION-4.0 (ref. <sup>##REF##30713699##56##</sup>). Motion-corrected micrographs were used to estimate contrast transfer function (CTF) using CTFFIND-4.1 (ref. <sup>##REF##26278980##57##</sup>). All subsequent image processing was performed using helical reconstruction methods in RELION-4.0 (refs. <sup>##REF##28193500##58##,##REF##34783343##59##</sup>). Amyloid filaments were picked manually, and reference-free 2D classification was performed to remove suboptimal segments. Initial 3D reference models were generated de novo by producing sinograms from 2D class averages as previously described<sup>##REF##32038040##43##</sup>. Masked 3D autorefinements with optimization of helical twist were performed, followed by iterative Bayesian polishing and CTF refinement<sup>##REF##30713699##56##,##REF##32148853##60##</sup>. Where beneficial, 3D classification was used to further remove suboptimal segments; 3D autorefinement, Bayesian polishing and CTF refinement were then repeated. Final reconstructions were sharpened using the standard post-processing procedures in RELION-4.0, and overall resolutions were estimated from Fourier shell correlations of 0.143 between the two independently refined half-maps using phase-randomization to correct for convolution effects of a generous, soft-edged solvent mask<sup>##REF##23872039##61##</sup>. Local-resolution estimates were obtained using the same phase-randomization procedure but with a soft spherical mask that was moved over the entire map. Helical symmetry was imposed using the RELION Helix Toolbox. Further details are given in Extended Data Table ##TAB##1##2##.</p>", "<title>Atomic model building and refinement</title>", "<p id=\"Par43\">The atomic models were built de novo and refined in real space in COOT<sup>##UREF##7##62##</sup> using the best-resolved map. Rebuilding using molecular dynamics was carried out in ISOLDE<sup>##REF##29872003##63##</sup>. The model was refined in Fourier space using REFMAC5 (ref. <sup>##REF##25615868##64##</sup>), with appropriate symmetry constraints defined using Servalcat<sup>##REF##34605431##65##</sup>. To confirm the absence of overfitting the model was shaken, refined in Fourier space against the first half-map using REFMAC5 and compared with the second half-map. Geometry was validated using MolProbity<sup>##REF##29067766##66##</sup>. Molecular graphics and analyses were performed in ChimeraX<sup>##REF##32881101##67##</sup>. Model statistics are given in Extended Data Table ##TAB##1##2##.</p>", "<title>Reporting summary</title>", "<p id=\"Par44\">Further information on research design is available in the ##SUPPL##1##Nature Portfolio Reporting Summary## linked to this article.</p>" ]	[]	[ "<title>Discussion</title>", "<p id=\"Par18\">While amyloid assembly of TDP-43 or tau is the hallmark of the majority of cases of FTLD, the assembled protein that characterizes the remaining approximately 10% of cases, termed FTLD–FET, was previously unknown. Using cryo-EM, we found abundant TAF15 amyloid filaments with a shared filament fold in the brain of four individuals with FTLD–FET. In one individual we uniquely detected TAF15 filaments in the absence of Aβ and TMEM106B filaments. TAF15-immunoreactive inclusions and TAF15 filaments have not previously been observed in other neurodegenerative conditions, or in neurologically normal individuals<sup>##REF##21856723##23##–##REF##27500866##27##,##REF##35344985##38##</sup>. Together these results suggest that the formation of TAF15 amyloid filaments characterizes FTLD–FET, thereby adding TAF15 to the small group of proteins that form amyloid filaments associated with neurodegenerative disease alongside proteins such as tau, TDP-43 and α-synuclein<sup>##REF##37758888##45##</sup>.</p>", "<p id=\"Par19\">The presence of TAF15 amyloid filaments is consistent with the immunoreactivity of neuronal cytoplasmic inclusions against TAF15 (refs. <sup>##REF##21856723##23##,##REF##22497712##26##,##REF##27500866##27##</sup>) (Fig. ##FIG##0##1a##), as well as with the propensity of TAF15 to form amyloid filaments in vitro<sup>##REF##22065782##29##,##REF##24267890##31##,##REF##35643629##32##</sup>. We did not find amyloid filaments of FUS, despite the inclusions also exhibiting immunoreactivity against this protein<sup>##REF##19674978##12##,##REF##21752791##15##</sup> (Fig. ##FIG##0##1a##) and its propensity to also form filaments in vitro<sup>##REF##28942918##18##–##REF##35036880##20##</sup>. This was supported by the ability of mass spectrometry to discriminate between individuals with FTLD–FET and a neurologically normal individual by the presence of peptides from the TAF15 filament core, but not by FUS peptides (Extended Data Fig. ##FIG##6##3##).</p>", "<p id=\"Par20\">Three non-mutually exclusive scenarios may account for the immunoreactivity of the inclusions against FUS in the absence of FUS filaments. First, filaments of FUS may be present in significantly smaller numbers than those of TAF15, thereby escaping our cryo-EM analysis. However, we note that cryo-EM structures of filaments accounting for only a few percent of the total population have been determined using similar approaches<sup>##REF##34588692##3##,##REF##30894745##46##</sup>.</p>", "<p id=\"Par21\">Second, FUS may form filaments that were not captured by our extraction method, possibly because of differences in stability or solubility, although we did not find filaments in the sarkosyl-soluble brain fraction (Extended Data Fig. ##FIG##4##1a##). This scenario would imply that putative FUS filaments behave differently to filaments of tau, α-synuclein, TDP-43 and Aβ, which characterize most cases of neurodegenerative disease and which were all previously shown to be extracted from human brain using the method used in this study<sup>##REF##34588692##3##,##REF##34880495##5##,##REF##32461689##36##–##REF##35344985##38##</sup>. We also note that all of these neurodegenerative diseases are characterized by intracellular amyloid filament inclusions of only one protein.</p>", "<p id=\"Par22\">Third, non-filamentous FUS may be present in inclusions, as amyloid filament inclusions are known to sequester non-filamentous proteins<sup>##REF##30559480##47##</sup>. The overlapping protein and RNA interactions of FET proteins suggest that TAF15 amyloid filaments may sequester non-filamentous FUS<sup>##REF##25494299##28##</sup>, which may also account for reports of occasional inclusion immunoreactivity against EWS<sup>##REF##21856723##23##,##REF##22497712##26##,##REF##27500866##27##</sup>. Similarly, the immunoreactivity of the inclusions against transportin 1 may be due to its interaction with the NLS of TAF15 (refs. <sup>##REF##21847626##24##,##REF##22842875##25##</sup>) (Fig. ##FIG##0##1a##), which lies outside of the ordered core of the filaments (Fig. ##FIG##2##3a##). Future development of experimental models that reproduce the TAF15 amyloid filament structure identified in this work will enable testing of this hypothesis.</p>", "<p id=\"Par23\">We also found abundant TAF15 amyloid filaments with the same filament fold in the motor cortex and brainstem of two of the individuals (Fig. ##FIG##3##4b##), one of whom had received a clinical diagnosis of probable ALS before developing FTD. These individuals showed upper and lower motor neuron pathology and had FUS-, TAF15- and transportin 1-immunoreactive inclusions in upper and lower motor neurons (Fig. ##FIG##3##4a## and Extended Data Fig. ##FIG##11##8##). The other two individuals lacked motor neuron pathology and had scarce or absent motor neuron inclusions. These results suggest that the formation of TAF15 amyloid filaments can be associated with motor neuron pathology and may underlie a disease spectrum of FTLD and motor neuron disease. This may be analogous to FTLD and ALS with TDP-43 inclusions, which share a distinct TDP-43 filament fold<sup>##REF##34880495##5##</sup>. In support of this hypothesis, cases of sporadic ALS with FET protein- and transportin 1-immunoreactive inclusions in the absence of FTD have been reported<sup>##UREF##4##48##,##REF##30375034##49##</sup>. Future studies should examine the identities and structures of amyloid filaments from these cases to further test this hypothesis.</p>", "<p id=\"Par24\">Rare mutations in the genes that encode TDP-43 and tau give rise to amyloid filament assembly and inherited FTLD<sup>##REF##19655382##6##–##REF##9636220##10##</sup>. Rare mutations in the gene that encodes the filament-forming protein in FTLD–FET would, therefore, be expected give rise to inherited forms of this disorder. Mutations in <italic>FUS</italic> have not been linked to FTLD<sup>##REF##20124201##21##,##REF##21424531##22##</sup> whereas genetic studies of <italic>TAF15</italic> in FTLD have not been reported. Mutations in <italic>TAF15</italic> have been described in ALS, but their pathogenicity has not been confirmed<sup>##REF##22065782##29##,##UREF##5##50##,##UREF##6##51##</sup>. One of these mutations, A31T, is within the region that forms the TAF15 filament core and might stabilize the filament fold by the introduction of additional hydrogen bonds with the adjacent Q48 (Fig. ##FIG##2##3c##). Our finding that TAF15 forms filaments in individuals with FTLD–FET, including those with upper and lower motor neuron pathology, should motivate genetic analysis of patient cohorts to elucidate the potential contribution of rare <italic>TAF15</italic> mutations to FTLD and motor neuron disease.</p>", "<p id=\"Par25\">Rare mutations in <italic>FUS</italic> can cause ALS<sup>##REF##19251627##16##,##REF##19251628##17##</sup>. In these cases, because motor neuron inclusions are immunoreactive against FUS but not TAF15, EWS or transportin 1 (refs. <sup>##REF##21856723##23##,##REF##22842875##25##</sup>), it is unlikely that TAF15 forms amyloid filaments but possible that these inclusions may contain FUS amyloid filaments. This supports the hypothesis that the disease mechanisms of ALS caused by <italic>FUS</italic> mutations are distinct from those of FTLD–FET and sporadic ALS with FET protein-immunoreactive inclusions, as previously suggested<sup>##REF##21856723##23##,##UREF##4##48##,##REF##30375034##49##</sup>. Additional evidence for different disease mechanisms comes from the observation that FUS is hypomethylated in FTLD–FET but not in cases of ALS caused by <italic>FUS</italic> mutations<sup>##REF##22968170##52##</sup>. Future studies should focus on investigating the presence, identities and structures of amyloid filaments in cases of this rare form of familial ALS.</p>", "<p id=\"Par26\">We found that a single TAF15 filament fold characterized individuals with FTLD–FET in this study (Fig. ##FIG##1##2b##). For TDP-43, tau and α-synuclein, distinct filament folds characterize different neurodegenerative disorders<sup>##REF##37758888##45##</sup>. Two additional rare neurodegenerative disorders—neuronal intermediate filament inclusion body disease and basophilic inclusion body disease—also present with inclusions that are immunoreactive against FET proteins and transportin 1 (refs. <sup>##REF##21856723##23##,##REF##22497712##26##,##REF##27500866##27##</sup>). Potentially, distinct filament folds of TAF15 may underlie these disorders.</p>" ]	[ "<title>Conclusion</title>", "<p id=\"Par27\">The presence of abundant TAF15 amyloid filaments with a characteristic fold in FTLD establishes TAF15 as a member of the small group of proteins known to form neurodegenerative disease-associated amyloid filaments. This focuses attention on the role of TAF15 proteinopathy in neurodegenerative disease. In accordance with consensus recommendations for FTLD nomenclature<sup>##REF##19015862##53##</sup>, we strongly advocate that the frequently used term FTLD–FUS should be abandoned in favour of the previously suggested FTLD–FET<sup>##REF##21856723##23##</sup>, and that it may even be appropriate to consider the use of the term FTLD–TAF. The structures of TAF15 filaments will guide the development of model systems to enable studies of disease mechanisms, and will provide a basis for the design of diagnostic and therapeutic tools targeting TAF15 proteinopathy in neurodegenerative disease.</p>" ]	[ "<p id=\"Par1\">Frontotemporal lobar degeneration (FTLD) causes frontotemporal dementia (FTD), the most common form of dementia after Alzheimer’s disease, and is often also associated with motor disorders<sup>##REF##37563165##1##</sup>. The pathological hallmarks of FTLD are neuronal inclusions of specific, abnormally assembled proteins<sup>##UREF##0##2##</sup>. In the majority of cases the inclusions contain amyloid filament assemblies of TAR DNA-binding protein 43 (TDP-43) or tau, with distinct filament structures characterizing different FTLD subtypes<sup>##REF##34588692##3##,##UREF##1##4##</sup>. The presence of amyloid filaments and their identities and structures in the remaining approximately 10% of FTLD cases are unknown but are widely believed to be composed of the protein fused in sarcoma (FUS, also known as translocated in liposarcoma). As such, these cases are commonly referred to as FTLD–FUS. Here we used cryogenic electron microscopy (cryo-EM) to determine the structures of amyloid filaments extracted from the prefrontal and temporal cortices of four individuals with FTLD–FUS. Surprisingly, we found abundant amyloid filaments of the FUS homologue TATA-binding protein-associated factor 15 (TAF15, also known as TATA-binding protein-associated factor 2N) rather than of FUS itself. The filament fold is formed from residues 7–99 in the low-complexity domain (LCD) of TAF15 and was identical between individuals. Furthermore, we found TAF15 filaments with the same fold in the motor cortex and brainstem of two of the individuals, both showing upper and lower motor neuron pathology. The formation of TAF15 amyloid filaments with a characteristic fold in FTLD establishes TAF15 proteinopathy in neurodegenerative disease. The structure of TAF15 amyloid filaments provides a basis for the development of model systems of neurodegenerative disease, as well as for the design of diagnostic and therapeutic tools targeting TAF15 proteinopathy.</p>", "<p id=\"Par2\">Cryogenic electron microscopy structures of amyloid filaments extracted from patient brains reveal that the protein TAF15 forms filaments that characterize certain cases of frontotemporal lobar degeneration.</p>", "<title>Subject terms</title>" ]	[ "<title>Main</title>", "<p id=\"Par3\">Neuronal inclusions containing abnormally assembled TDP-43 or tau characterize approximately 50% and 40% of FTLD cases, respectively<sup>##UREF##0##2##</sup>. The assemblies have amyloid structure<sup>##REF##34588692##3##–##REF##34880495##5##</sup>. Amyloids are filamentous protein assemblies stabilized by intermolecular β-sheets along the filament axis. Although the proteins are wild type in most cases of disease, rare mutations in the genes encoding TDP-43 and tau that give rise to amyloid assembly and FTLD demonstrate a causal link<sup>##REF##19655382##6##–##REF##9636220##10##</sup>. Furthermore, distinct amyloid filament folds of TDP-43 and tau define different subtypes of FTLD<sup>##REF##34588692##3##–##REF##34880495##5##</sup>, which are associated with various behavioural and language variants of FTD, as well as with motor disorders<sup>##UREF##3##11##</sup>.</p>", "<p id=\"Par4\">By contrast, the presence, identities and structures of amyloid filaments within the neuronal inclusions of the remaining approximately 10% of FTLD cases are unknown. The inclusions were initially found to be immunoreactive for FUS, resulting in these cases being commonly referred to as FTLD–FUS<sup>##REF##19674978##12##–##REF##21752791##15##</sup>. The search for FUS was motivated by the discovery that rare mutations in <italic>FUS</italic> can cause the motor disorder amyotrophic lateral sclerosis (ALS) in the absence of FTLD<sup>##REF##19251627##16##,##REF##19251628##17##</sup>. Furthermore, recombinant fragments of the FUS LCD can assemble into amyloid filaments in vitro<sup>##REF##28942918##18##–##REF##35036880##20##</sup>. However, to date, mutations in <italic>FUS</italic> associated with FTLD have not been found<sup>##REF##20124201##21##,##REF##21424531##22##</sup> and amyloid filaments of FUS have not been identified in patient brains.</p>", "<p id=\"Par5\">It was subsequently shown that the inclusions of FTLD–FUS are also immunoreactive against TAF15 and transportin 1 (also known as importin β-2 and karyopherin β-2)<sup>##REF##21856723##23##–##REF##27500866##27##</sup>. For some of the cases in these studies, a subset of inclusions was also immunoreactive against Ewing’s sarcoma (EWS). FUS, EWS and TAF15 are homologous RNA-binding proteins, collectively known as the FET proteins<sup>##REF##25494299##28##</sup>. Owing to FET protein immunoreactivity, FTLD–FUS has also been referred to as FTLD–FET<sup>##REF##21856723##23##</sup>, a more comprehensive term that we, therefore, use from here on. Evidence suggests that, like FUS, the LCDs of TAF15 and EWS can also assemble into filaments in vitro<sup>##REF##22065782##29##–##REF##35643629##32##</sup>.</p>", "<p id=\"Par6\">In healthy cells, FET proteins are mainly localized in the nucleus and undergo nucleocytoplasmic shuttling<sup>##REF##25494299##28##</sup>. FET proteins have roles in transcription and in the splicing, processing and transport of RNA. Their N-terminal LCDs are enriched in glycine, tyrosine, glutamine and serine residues. They also contain a mid-region RNA recognition motif flanked by arginine–glycine–glycine (RGG) motif-rich segments, a zinc finger domain and a C-terminal nuclear localization signal (NLS). Transportin 1 binds to the NLS of FET proteins to mediate their nuclear import<sup>##REF##16901787##33##</sup>.</p>", "<p id=\"Par7\">To understand neurodegeneration at a molecular level and to provide a basis for diagnostic and therapeutic strategies, a structural understanding of pathological protein assembly is essential<sup>##REF##35969734##34##,##REF##36114178##35##</sup>. Here we investigated the presence, identities and structures of amyloid filaments in the brains of individuals with FTLD–FET.</p>", "<title>Amyloid filaments in FTLD–FET</title>", "<p id=\"Par8\">We analysed tissue from the prefrontal and temporal cortices of four individuals with FTLD–FET (Extended Data Table ##TAB##0##1##). Immunohistochemistry using antibodies against FUS, TAF15 and transportin 1 confirmed the presence of abundant neuronal cytoplasmic inclusions, and occasional neuronal intranuclear inclusions and glial cytoplasmic inclusions (Fig. ##FIG##0##1a##), as previously reported<sup>##REF##21856723##23##–##REF##27500866##27##</sup>. We did not detect inclusions using an antibody against EWS (Fig. ##FIG##0##1a##), consistent with previous reports of scarce or absent EWS inclusion immunoreactivity in FTLD–FET<sup>##REF##21856723##23##,##REF##22497712##26##,##REF##27500866##27##</sup>.</p>", "<p id=\"Par9\">We extracted insoluble material from tissues using differential centrifugation in the presence of the detergent <italic>N</italic>-lauroyl-sarcosine (sarkosyl). This method enriches for stable protein assemblies, including amyloid filaments from human brain<sup>##REF##34588692##3##,##REF##34880495##5##,##REF##32461689##36##–##REF##35344985##38##</sup>. Negative-stain electron microscopy of samples from individual 1 showed amyloid filaments in the insoluble fraction whereas none were observed in the soluble fraction (Extended Data Fig. ##FIG##4##1a##). Immunoblotting showed the presence of all three FET proteins and transportin 1 in the insoluble fraction (Fig. ##FIG##0##1b##). Previous studies have also observed these proteins in detergent-insoluble fractions of human brain by immunoblotting, including from neurologically normal individuals<sup>##REF##19674978##12##,##REF##21752791##15##,##REF##21856723##23##–##REF##22842875##25##</sup>. These results suggest that FET proteins and transportin 1 form detergent-stable assemblies in human brain but are not sufficient to determine whether they form amyloid filaments.</p>", "<p id=\"Par10\">We used cryo-EM to image amyloid filaments in the insoluble extracts from each individual. The majority of filaments were approximately 8 nm in width (Fig. ##FIG##1##2a## and Extended Data Fig. ##FIG##4##1b##). A minority of filaments from individuals 2–4 could be identified as those of transmembrane protein 106B (TMEM106B) based on their distinct width of either 12 nm (single protofilament) or 26 nm (double protofilament), smooth surface, striated appearance and blunt filament ends<sup>##REF##35344985##38##–##REF##35344984##40##</sup> (Extended Data Fig. ##FIG##4##1b##). We determined high-resolution cryo-EM structures of these filaments from individual 4, which confirmed their identity and showed that they had the type I TMEM106B fold<sup>##REF##35344985##38##</sup> (Extended Data Fig. ##FIG##5##2## and Extended Data Table ##TAB##1##2##). TMEM106B filaments were not observed in individual 1. In agreement with this finding, using mass spectrometry, peptides from the region forming the TMEM106B filament core were detected in insoluble extracts from individual 4 and an aged neurologically normal individual, but not from individual 1 (Extended Data Fig. ##FIG##6##3##). Individual 1 died at 30 years of age whereas the other individuals were over 50 years old at death, consistent with the previously reported age-dependent accumulation of TMEM106B filaments in human brain<sup>##REF##35344985##38##,##REF##36527486##41##</sup>. TMEM106B filaments were previously shown to accumulate in neurologically normal brains, as well as in the presence of neurodegenerative disease-characteristic amyloid filaments, with no clear relationship between disease status and TMEM106B filament fold<sup>##REF##35344985##38##,##REF##36527486##41##,##REF##35477998##42##</sup>.</p>", "<p id=\"Par11\">The cryo-EM images yielded 112,000–358,000 segments of non-TMEM106B filaments per individual (Extended Data Table ##TAB##1##2##). Reference-free two-dimensional (2D) classification showed the presence of a single predominant filament population for all individuals, with a helical cross-over spacing of approximately 405 Å (Extended Data Fig. ##FIG##7##4a##). The 2D classes of this predominant filament population did not correspond to any known amyloid filament structure. The 2D classes for individual 4 also showed the presence of amyloid-β 42 (Aβ42) filaments (Extended Data Fig. ##FIG##5##2##), consistent with sparse amyloid-β plaques in the prefrontal cortex of this individual (Extended Data Table ##TAB##0##1##). In agreement, peptides corresponding to Aβ42 were identified by mass spectrometry in the insoluble extracts from individual 4, but not from either individual 1 or a neurologically normal individual (Extended Data Fig. ##FIG##6##3##). We determined the cryo-EM structure of the Aβ42 filaments from individual 4 (Extended Data Fig. ##FIG##5##2## and Extended Data Table ##TAB##1##2##), which showed that they had the type II Aβ42 filament fold as previously found in individuals exhibiting amyloid-β plaque copathology, including in FTLD with TDP-43 and tau pathology<sup>##REF##35025654##37##</sup>.</p>", "<title>Structure of TAF15 filaments in FTLD–FET</title>", "<p id=\"Par12\">For the predominant, unassigned filament population we generated de novo initial three-dimensional (3D) maps from well-resolved 2D classes using the sinogram approach detailed in ref. <sup>##REF##32038040##43##</sup> (Extended Data Fig. ##FIG##7##4b,c##). Helical processing of each individual dataset yielded four superimposable maps at resolutions of between 2.0 and 2.7 Å (Fig. ##FIG##1##2b## and Extended Data Table ##TAB##1##2##). The protein backbone and amino acid side-chains were unambiguously resolved in our cryo-EM reconstructions (Extended Data Fig. ##FIG##8##5##), thereby identifying the filament-forming protein by its sequence. Contrary to our initial expectation, the filaments are formed from TAF15 and not FUS. Our mass spectrometry analysis of insoluble extracts from individuals 1 and 4, and from the neurologically normal individual, corroborated this finding. Whereas peptides mapping to FUS and TAF15 were detected for all individuals, only those mapping to the region forming the core of TAF15 filaments could distinguish between the individuals with FTLD–FET and the neurologically normal individual (Extended Data Fig. ##FIG##6##3##). These results suggest that the formation of TAF15 amyloid filaments characterizes FTLD–FET, in analogy to TDP-43 and tau amyloid filaments that characterize other types of FTLD<sup>##REF##34588692##3##,##REF##34880495##5##</sup>.</p>", "<p id=\"Par13\">The TAF15 filaments comprise a single protofilament with a left-handed helical twist, with the ordered filament fold formed by residues 7–99 of TAF15, which are part of its LCD (Fig. ##FIG##2##3a,b##). Perpendicular to the helical axis, the shape of the filament fold somewhat resembles a scooter, with the proximal N and C termini representing the two handlebars (Fig. ##FIG##2##3c##). The filament fold contains 13 β-strands of between two and eight residues in length, which encompass 57% of all residues. These β-strands, together with their counterparts in adjacent TAF15 molecules, form parallel, in-register β-sheets characteristic of amyloid filaments (Extended Data Fig. ##FIG##9##6a,b##). Eight of the β-sheets are stacked with interdigitated side-chains, forming zipper packing typical of amyloid filaments<sup>##REF##19781557##44##</sup> (Fig. ##FIG##2##3c##). Viewed along the helical axis, the termini of each TAF15 molecule are on different planes and interact with the molecules above and below (Extended Data Fig. ##FIG##9##6b##).</p>", "<p id=\"Par14\">The fold is enriched in glycine, tyrosine, glutamine and serine residues, each contributing 16–24% of all residues. Glycine residues mainly facilitate turns between β-strands (Extended Data Fig. ##FIG##10##7a##). Among the tyrosine residues, all non-solvent-exposed side-chains are hydrogen bonded in the model (Extended Data Fig. ##FIG##10##7b##). In addition, the off-centred, parallel orientation of their aromatic rings, with a distance of 3.2–3.5 Å between aromatic planes, allows for staggered stacking interactions (Extended Data Fig. ##FIG##10##7c##). The abundant glutamine residues, together with the six asparagine residues, engage in extended hydrogen-bonding networks (Extended Data Fig. ##FIG##10##7d,e##). The majority of their side-chain amide groups form hydrogen-bonded ladders with their counterparts in neighbouring TAF15 molecules, as often observed in amyloid filaments. The side-chain amides also form intra- and intermolecular hydrogen bonds with each other, main-chain carbonyl groups and ordered solvent. Further hydrogen bonding is provided by the abundance of serine residues, in addition to three threonine residues (Extended Data Fig. ##FIG##10##7f##). The side-chains of the four acidic residues in the fold face the solvent and one of these, E71, forms a salt bridge with the only basic residue, K74 (Extended Data Fig. ##FIG##10##7g##). Only four residues (P23, A31, P82 and M92) possess side-chains lacking polar groups. Consistent with a protein fold stabilized by intricate hydrogen-bonding networks, the high-resolution map shows that the TAF15 filament fold is well hydrated. We modelled 23 ordered water molecules per TAF15 molecule, each contributing between two and four hydrogen bonds with either polar amino acid side-chains, the backbone or other ordered solvent molecules (Fig. ##FIG##2##3c## and Extended Data Fig. ##FIG##10##7h##).</p>", "<p id=\"Par15\">Additional densities on the filament surface that could not be modelled confidently are present in the cryo-EM maps (Extended Data Fig. ##FIG##9##6c,d##). The smaller densities may be attributed to ordered solvent whereas the larger ones might indicate the binding of other molecules. The majority of these larger densities appear connected along the filament axis, suggesting that the molecules do not follow the same helical symmetry as TAF15. The additional densities do not appear to connect to the density for TAF15 and are thus unlikely to represent covalent post-translational modifications. A planar density adjacent to a flat surface formed by residues 60–64 is reminiscent of those seen in TDP-43 filaments from human brain<sup>##UREF##1##4##,##REF##34880495##5##</sup>. There is also an external density adjacent to the side-chain of Y83. Two densities are located in grooves along the filament axis formed by residues 85–89 and 94–98, with the orientation of the amino acid side-chains and main-chain carbonyl groups of these residues allowing for hydrogen bonding.</p>", "<title>TAF15 filaments in motor regions</title>", "<p id=\"Par16\">FTLD–FET is often associated with FET protein- and transportin 1-immunoreactive inclusions in upper and lower motor neurons<sup>##REF##19674978##12##,##REF##21752791##15##,##REF##21856723##23##,##REF##27500866##27##</sup>. Moreover, individuals 1 and 4 exhibited upper and lower motor neuron pathology and individual 4 had received a clinical diagnosis of probable ALS before being diagnosed with FTD (Extended Data Fig. ##FIG##11##8a,b##, Extended Data Table ##TAB##0##1## and <xref rid=\"Sec7\" ref-type=\"sec\">Methods</xref>). We therefore performed immunohistochemistry on the spinal cord, motor cortex and brainstem of the four individuals using antibodies against FET proteins and transportin 1. FUS-, TAF15- and transportin 1-immunoreactive inclusions were readily observed in upper and lower motor neurons for individuals 1 and 4, but were sparse or absent for individuals 2 and 3 (Fig. ##FIG##3##4a## and Extended Data Fig. ##FIG##11##8c–e##). We did not detect EWS-immunoreactive motor neuron inclusions.</p>", "<p id=\"Par17\">To investigate the presence, identities and structures of amyloid filaments in motor neuron inclusions of individuals with motor neuron pathology we determined the cryo-EM structures of filaments extracted from the motor cortex of individual 1 and from the medulla of individual 4. For both individuals we found abundant TAF15 filaments with the same fold as detected previously in the prefrontal and temporal cortices (Fig. ##FIG##3##4b## and Extended Data Table ##TAB##1##2##). We did not find filaments of FUS. For individual 4 we also found TMEM106B and Aβ42 filaments with the same folds as in the prefrontal cortex, in addition to TAF15 filaments (Extended Data Fig. ##FIG##12##9## and Extended Data Table ##TAB##1##2##). No TMEM106B or Aβ42 filaments were found for individual 1, mirroring our results from the prefrontal cortex. These results suggest that TAF15 form amyloid filaments in upper and lower motor neuron inclusions in FTLD–FET associated with upper and lower motor neuron pathology.</p>", "<title>Online content</title>", "<p id=\"Par45\">Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41586-023-06801-2.</p>", "<title>Supplementary information</title>", "<p>\n\n\n\n</p>" ]	[ "<title>Extended data figures and tables</title>", "<p id=\"Par48\">\n\n</p>", "<p id=\"Par49\">\n\n</p>", "<p id=\"Par50\">\n\n</p>", "<p id=\"Par51\">\n\n</p>", "<p id=\"Par52\">\n\n</p>", "<p id=\"Par53\">\n\n</p>", "<p id=\"Par54\">\n\n</p>", "<p id=\"Par55\">\n\n</p>", "<p id=\"Par56\">\n\n</p>", "<p id=\"Par57\">\n\n</p>", "<p id=\"Par58\">\n\n</p>", "<title>Extended data</title>", "<p>is available for this paper at 10.1038/s41586-023-06801-2.</p>", "<title>Supplementary information</title>", "<p>The online version contains supplementary material available at 10.1038/s41586-023-06801-2.</p>", "<title>Acknowledgements</title>", "<p>We thank the individuals and their families for donating brain tissue; the Queen Square Brain Bank for Neurological Disorders at University College London Queen Square Institute of Neurology, which receives support from the Reta Lila Weston Institute for Neurological Studies, for supplying tissue from individuals 2 and 3; J. Grafman for supplying tissue from individual 4; M. Jacobsen for help with neuropathological examinations; R. Richardson, K. Cox and N. Maynard for help with histology and immunohistochemistry; J. Grimmett, T. Darling and I. Clayson for help with high-performance computing; K. Yamashita and G. Murshudov for help with model refinements; and T. Behr, A. Bertolotti, R. Chen, S. Davies, M. Goedert, D. Hilvert and S. Scheres for discussions. This work was supported by the electron microscopy and scientific computing facilities at the MRC Laboratory of Molecular Biology and by the Center for Medical Genomics at the Indiana University School of Medicine. This work was supported by the Medical Research Council as part of United Kingdom Research and Innovation (also known as UK Research and Innovation) (no. MC_UP_1201/25 to B.R.-F.); the US National Institutes of Health (nos. U01-NS110437, RF1-AG071177 and R01-AG080001 to R.V. and B.G.); the Alzheimer’s Society (nos. AS-PG-18-004 and AS-PG-21-004 to T.L.); the Association for Frontotemporal Degeneration (no. 2019-0009 to Y.B. and T.L.); a Swiss National Science Foundation Postdoctoral Fellowship (no. P500PB_206890 to S.T.); and a Leverhulme Early Career Fellowship (no. ECF-2022-610 to D.A.). For the purposes of open access, the MRC Laboratory of Molecular Biology has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising.</p>", "<title>Author contributions</title>", "<p>K.L.N., L.G.A., T.L. and B.G. identified individuals. K.L.N., T.L. and B.G. performed neuropathology. Y.B., T.L. and B.G. performed immunohistochemistry. H.J.G. and R.V. performed genetic analyses. S.T. and D.A. prepared brain extracts. S.T. performed negative-stain electron microscopy. S.T. and S.Y.P.-C. performed mass spectrometry. S.T. performed immunoblot analysis. S.T. and D.A. collected cryo-EM data. S.T., A.G.M. and B.R.-F. analysed cryo-EM data. B.R.-F. supervised the study. All authors contributed to writing the manuscript.</p>", "<title>Peer review</title>", "<title>Peer review information</title>", "<p id=\"Par46\"><italic>Nature</italic> thanks Aaron Gitler, Henning Stahlberg and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. ##SUPPL##2##Peer reviewer reports## are available.</p>", "<title>Data availability</title>", "<p>Whole-exome data have been deposited in the National Institute on Ageing Alzheimer’s Disease Data Storage Site (NIAGADS) under accession code NG00107. Mass spectrometry data have been deposited to the Proteomics Identifications (PRIDE) database under accession code <ext-link ext-link-type=\"uri\" xlink:href=\"http://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD044821\">PXD044821</ext-link>. Cryo-EM datasets have been deposited to the Electron Microscopy Public Image Archive (EMPIAR) under accession code nos. EMPIAR-11735 (individual 1, prefrontal cortex), EMPIAR-11736 (individual 1, motor cortex), EMPIAR-11737 (individual 2, prefrontal and temporal cortex), EMPIAR-11738 (individual 3, prefrontal and temporal cortex), EMPIAR-11739 (individual 4, prefrontal cortex) and EMPIAR-11740 (individual 4, brainstem). Cryo-EM maps have been deposited to the Electron Microscopy Data Bank under accession codes <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ebi.ac.uk/pdbe/entry/emdb/EMD-16999\">EMD-16999</ext-link> and <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ebi.ac.uk/pdbe/entry/emdb/EMD-18236\">EMD-18236</ext-link> (TAF15 filaments from prefrontal cortex and motor cortex, respectively, of individual 1); <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ebi.ac.uk/pdbe/entry/emdb/EMD-17022\">EMD-17022</ext-link> (TAF15 filaments from prefrontal and temporal cortex of individual 2); <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ebi.ac.uk/pdbe/entry/emdb/EMD-17021\">EMD-17021</ext-link> (TAF15 filaments from prefrontal and temporal cortex of individual 3); <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ebi.ac.uk/pdbe/entry/emdb/EMD-17020\">EMD-17020</ext-link> and <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ebi.ac.uk/pdbe/entry/emdb/EMD-18227\">EMD-18227</ext-link> (TAF15 filaments from prefrontal cortex and brainstem, respectively, of individual 4); <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ebi.ac.uk/pdbe/entry/emdb/EMD-17109\">EMD-17109</ext-link> and <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ebi.ac.uk/pdbe/entry/emdb/EMD-18226\">EMD-18226</ext-link> (Aβ42 filaments from prefrontal cortex and brainstem, respectively, of individual 4); <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ebi.ac.uk/pdbe/entry/emdb/EMD-18240\">EMD-18240</ext-link> and <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ebi.ac.uk/pdbe/entry/emdb/EMD-18243\">EMD-18243</ext-link> (singlet TMEM106B filaments from prefrontal cortex and brainstem, respectively, of individual 4); and <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ebi.ac.uk/pdbe/entry/emdb/EMD-18242\">EMD-18242</ext-link> and <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ebi.ac.uk/pdbe/entry/emdb/EMD-18241\">EMD-18241</ext-link> (doublet TMEM106B filaments from prefrontal cortex and brainstem, respectively, of individual 4). The atomic model for TAF15 amyloid filaments has been deposited to the Protein Data Bank (PDB) under accession code 8ONS. Atomic models of singlet and doublet TMEM106B type I filaments are available at the PDB under accession codes 7QVC and 7QVF, respectively. The atomic model of Aβ42 type II filaments is available at the PDB under accession code 7Q4M.</p>", "<title>Competing interests</title>", "<p id=\"Par47\">The authors declare no competing interests.</p>" ]	[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>FET proteins and transportin 1 in FTLD–FET.</title><p><bold>a</bold>, FUS, EWS, TAF15 and transportin 1 immunoreactivity (brown) in the prefrontal cortex of individials 1–4 with FTLD–FET. Sections were counterstained with haematoxylin (blue). Scale bar, 50 μm. Neuronal cytoplasmic inclusions were immunoreactive for FUS, TAF15 and transportin 1 (examples indicated by magenta arrows for individual 1). Antibodies against EWS showed diffuse labelling of nuclei only (example indicated by cyan arrow for individual 1). <bold>b</bold>, Immunoblots of the total homogenate, sarkosyl-soluble fraction and sarkosyl-insoluble fraction of frontotemporal cortex grey matter from individuals 1–4 with FTLD–FET with antibodies against FUS, EWS, TAF15 and transportin 1. Asterisks indicate bands corresponding to full-length proteins. Bands of lower molecular weight probably correspond to protease cleavage products. For uncropped images of immunoblots see Supplementary Fig. ##SUPPL##0##1##. <bold>a</bold>,<bold>b</bold>, Results are representative of <italic>n</italic> ≥ 3 technical replicates per individual.</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>Cryo-EM characterization of amyloid filaments from individuals with FTLD–FET.</title><p><bold>a</bold>, Representative cryo-EM micrograph of the sarkosyl-insoluble fraction of frontotemporal cortex grey matter from individual 1 with FTLD–FET. Abundant amyloid filaments are indicated by arrows. Scale bar, 50 nm. Results are representative of <italic>n</italic> ≥ 3 technical replicates per individual. Additional micrographs for all four individuals are shown in Extended Data Fig. ##FIG##4##1b##. <bold>b</bold>, Cryo-EM reconstructions of amyloid filaments from individuals 1–4 with FTLD–FET showing a readily traceable protein backbone and well-resolved amino acid side-chain densities. All four reconstructions have an identical filament fold. Resolution estimates are indicated. Scale bars, 2 nm.</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><title>Cryo-EM structure of TAF15 amyloid filaments from FTLD–FET.</title><p><bold>a</bold>, Domain organization of TAF15. The region comprising the ordered core of TAF15 amyloid filaments is indicated. RRM, RNA recognition motif; ZnF, zinc finger domain. <bold>b</bold>, Sequence alignment of secondary structure elements of the TAF15 amyloid filament fold. Arrows indicate β-strands. <bold>c</bold>, Cryo-EM reconstruction and atomic model of the TAF15 amyloid filament structure, shown for a single TAF15 molecule perpendicular to the helical axis. The carbon atoms of residues forming β-strands are shown in yellow and ordered solvent as red spheres.</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><title>Motor neuron inclusions and TAF15 filaments.</title><p><bold>a</bold>, FUS, EWS, TAF15 and transportin 1 immunoreactivity (brown) in the spinal cord of individuals 1 and 4 with FTLD–FET. Sections were counterstained with haematoxylin (blue). Motor neuron inclusions were immunoreactive for FUS, TAF15 and transportin 1. Antibodies against EWS showed only diffuse labelling of nuclei. Results are representative of <italic>n</italic> ≥ 3 technical replicates per individual. Additional immunohistochemistry of spinal cord, motor cortex and brainstem for all four individuals is shown in Extended Data Fig. ##FIG##11##8##. Scale bar, 50 μm. <bold>b</bold>, Cryo-EM reconstructions of amyloid filaments from the motor cortex of individual 1 (left) and medulla of individual 4 (right), showing TAF15 filaments with a fold identical to those from prefrontal cortices. Resolution estimates are indicated. Scale bar, 2 nm.</p></caption></fig>", "<fig id=\"Fig5\"><label>Extended Data Fig. 1</label><caption><title>Electron micrographs of sarkosyl-soluble and -insoluble brain extracts from individuals with FTLD-FET.</title><p><bold>a</bold>, Representative negative stain electron micrographs of the sarkosyl-soluble and -insoluble fractions of prefrontal cortex grey matter from FTLD-FET individual 1. Amyloid filaments were only observed in the insoluble fraction (yellow arrows). Scale bar, 100 nm. <bold>b</bold>, Cryo-EM micrographs of the sarkosyl-insoluble fraction of frontotemporal cortex grey matter from FTLD-FET individuals 1–4. Yellow arrows indicate the predominant filament population. Cyan arrows indicate TMEM106B filaments. Magenta arrows indicate collagen fibres. Scale bars, 50 nm. For <bold>a</bold> and <bold>b</bold>, the results are representative of n ≥ 3 technical replicates per individual.</p></caption></fig>", "<fig id=\"Fig6\"><label>Extended Data Fig. 2</label><caption><title>Cryo-EM structure of TMEM106B and Aβ42 filaments from the prefrontal cortex of FTLD-FET individual 4.</title><p><bold>a-c</bold>, Cryo-EM reference-free 2D class averages of the filament segments used to reconstruct TMEM106B singlet (<bold>a</bold>), TMEM106B doublet (<bold>b</bold>), and Aβ42 (<bold>c</bold>) filaments from FTLD-FET individual 4. Scale bars, 10 nm. <bold>d-f</bold>, Cryo-EM reconstructions of TMEM106B singlet (<bold>d</bold>), TMEM106B doublet (<bold>e</bold>), and Aβ42 (<bold>f</bold>) filaments, viewed as central slices perpendicular to the helical axis. Scale bar, 2 nm. Resolution estimates are indicated. <bold>g</bold>, Fit of the published atomic model of singlet TMEM106B type 1 filaments (PDB- ID: 7QVC) into the density map (grey mesh), shown for a single peptide perpendicular to the helical axis. <bold>h</bold>, Fit of the published atomic model of doublet TMEM106B type 1 filaments (PDB-ID: 7QVF) into the density map (grey mesh), shown for two C2 symmetry-related peptides perpendicular to the helical axis. The two chains were fit individually, as their relative orientations were rotated compared to the published model (teal). <bold>i</bold>, Fit of the published atomic model of Aβ42 type II filaments (PDB-ID: 7Q4M) into the reconstruction (grey mesh), shown for two C2 symmetry-related peptides perpendicular to the helical axis. The overall structure remains similar, with only subtle shifts in the backbone towards the N-termini and His13 side chain flips (cyan arrows) observed in our reconstruction.</p></caption></fig>", "<fig id=\"Fig7\"><label>Extended Data Fig. 3</label><caption><title>Mass spectrometry analysis of TMEM106B, APP, TAF15 and FUS in insoluble extracts from individuals with FTLD-FET.</title><p>Peptides identified by mass spectrometry are mapped along the protein sequences of TMEM106B, the amyloid-β precursor protein (APP), TAF15 and FUS. The amyloid filament core regions of TMEM106B and TAF15, as well as the Aβ42 peptide, are highlighted in yellow. Peptides from TMEM106B were not detected for individual 1, in agreement with the cryo-EM data. Peptides from Aβ42 were only detected for individual 4, in agreement with cryo-EM data and histopathology (Extended Data Table ##TAB##0##1##). Peptides mapping to the TAF15 filament core were only identified for individuals with FTLD-FET. No differences could be identified in FUS peptides for individuals with or without disease. Peptides from EWS and transportin-1 were not detected.</p></caption></fig>", "<fig id=\"Fig8\"><label>Extended Data Fig. 4</label><caption><title>Cryo-EM 2D class averages and 3D initial model of amyloid filaments from FTLD- FET.</title><p><bold>a</bold>, The 50 most populated cryo-EM reference-free 2D class averages of amyloid filaments from FTLD- FET individual 1 are shown. Numbers indicate the percentage of filament segments in each class average with respect to the total number of classified segments. The displayed classes comprise ~80% of total filament segments. The circular mask has a diameter of 400 Å and corresponds to approximately one helical cross-over of the filaments. <bold>b,c</bold>, An initial 3D model of amyloid filaments from FTLD-FET individual 1, generated de novo from reference-free 2D class averages, viewed as a 2D projection along the helical axis (<bold>b</bold>) and as a central slice perpendicular to the helical axis (<bold>c</bold>). Scale bars, 2 nm.</p></caption></fig>", "<fig id=\"Fig9\"><label>Extended Data Fig. 5</label><caption><title>A high-resolution map of TAF15 amyloid filaments from FTLD-FET.</title><p><bold>a</bold>, Fourier shell correlation (FSC) curves for two independently refined half maps from individual 1 (black line); for the refined atomic model against the cryo-EM density map (blue); and for the atomic model shaken and refined against the first (green) or second (red) independent half map. FSC thresholds of 0.143 and 0.5, as well as a vertical line at the estimated map resolution of 1.97 Å are plotted. <bold>b</bold>, The map viewed along the helical axis, showing well-resolved individual TAF15 molecules. <bold>c</bold>, The map with rainbow-coloured local resolution estimates viewed perpendicular to the helical axis. <bold>d</bold>, The map, shown at contour levels of 0.015 (orange) and 0.0345 (blue), viewed for a single TAF15 molecule perpendicular to the helical axis, shows a well-resolved backbone and clear side-chain densities, including aromatic rings of tyrosine residues.</p></caption></fig>", "<fig id=\"Fig10\"><label>Extended Data Fig. 6</label><caption><title>The TAF15 amyloid filament fold of FTLD-FET.</title><p><bold>a,b</bold>, Atomic model of TAF15 amyloid filaments from FTLD-FET shown for five differently-coloured TAF15 molecules perpendicular to (<bold>b</bold>) and along (<bold>c</bold>) the helical axis, showing that individual TAF15 molecules are not planar and that the N- and C-termini of neighbouring molecules interact with each other. <bold>c,d</bold>, Unmodelled densities (yellow), calculated by subtracting modelled density from the cryo-EM map, shown perpendicular to the helical axis (<bold>d</bold>) and rotated by 40° (<bold>e</bold>).</p></caption></fig>", "<fig id=\"Fig11\"><label>Extended Data Fig. 7</label><caption><title>Structural features of the TAF15 amyloid filament fold of FTLD-FET.</title><p><bold>a–h</bold>, Views of the atomic model of TAF15 amyloid filaments from FTLD-FET, shown for three TAF15 molecules, highlighting glycine residues (yellow) (<bold>a</bold>); tyrosine residues (yellow), their hydrogen bonding network (dashed lines) and staggered stacking interactions of their aromatic side chain groups (<bold>b,c</bold>); glutamine (yellow) and asparagine (orange) residues and their hydrogen bonding network (dashed lines) (<bold>d,e</bold>); serine (yellow) and threonine (orange) residues and their hydrogen bonding network (dashed lines) (<bold>f</bold>); charged residues (yellow), with a salt bridge between K74 and E71 (<bold>g</bold>); and ordered solvent molecules (red) and their hydrogen bonding network (dashed lines) (<bold>h</bold>).</p></caption></fig>", "<fig id=\"Fig12\"><label>Extended Data Fig. 8</label><caption><title>Motor neuron pathology.</title><p><bold>a, b</bold>, Luxol fast blue staining of myelin in the thoracic (<bold>a</bold>) and lumbar (<bold>b</bold>) spinal cord of FTLD-FET individual 1 showing extensive corticospinal tract loss (cyan arrows). Scale bar, 1 mm. <bold>c–e</bold>, FUS, EWS, TAF15 and transportin 1 immunoreactivity (brown) in the spinal cord of individual 3 (<bold>c</bold>), the brainstem of individuals 1–4 (<bold>d</bold>); and the motor cortex of individuals 1–4 (<bold>e</bold>). Sections were counterstained with hematoxylin (blue). Scale bars, 50 μm. Abundant motor neuron inclusions were observed for individuals 1 and 4, whereas inclusions were scarce or absent in individuals 2 and 3. Motor neuron inclusions were immunoreactive for FUS, TAF15 and transportin 1. Antibodies against EWS only showed diffuse labelling of nuclei. For <bold>a–e</bold>, the results are representative of n ≥ 3 technical replicates per individual.</p></caption></fig>", "<fig id=\"Fig13\"><label>Extended Data Fig. 9</label><caption><title>Cryo-EM structure of TMEM106B and Aβ42 filaments from the brainstem of FTLD- FET individual 4.</title><p><bold>a-c</bold>, Cryo-EM reference-free 2D class averages of the filament segments used to reconstruct TMEM106B singlet (<bold>a</bold>), TMEM106B doublet (<bold>b</bold>), and Aβ42 (<bold>c</bold>) filaments from the brainstem of FTLD-FET individual 4. Scale bars, 10 nm. <bold>d-f</bold>, Cryo-EM reconstructions of TMEM106B singlet (<bold>d</bold>), TMEM106B doublet (<bold>e</bold>), and Aβ42 (<bold>f</bold>) filaments, viewed as a central slice perpendicular to the helical axis. Scale bar, 2 nm. Resolution estimates are indicated. <bold>g</bold>, Fit of the published atomic model of singlet TMEM106B type 1 filaments (PDB-ID: 7QVC) into the density map (grey mesh), shown for a single peptide perpendicular to the helical axis. <bold>h</bold>, Fit of the published atomic model of doublet TMEM106B type 1 filaments (PDB-ID: 7QVF) into the density map (grey mesh), shown for two C2 symmetry-related peptides perpendicular to the helical axis. The two chains were fit individually, as their relative orientations were rotated compared to the published model (teal). <bold>i</bold>, Fit of the published atomic model of Aβ42 type II filaments (PDB-ID: 7Q4M) into the reconstruction (grey mesh), shown for two C2 symmetry-related peptides perpendicular to the helical axis.</p></caption></fig>" ]	[ "<table-wrap id=\"Tab1\"><label>Extended Data Table 1</label><caption><p>Clinicopathological details</p></caption></table-wrap>", "<table-wrap id=\"Tab2\"><label>Extended Data Table 2</label><caption><p>CryoEM data collection, refinement and validation statistics</p></caption></table-wrap>" ]	[]	[]	[]	[]	[]	[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM3\"></supplementary-material>" ]	[ "<table-wrap-foot><p>M, male; F, female; y, years; NA, not applicable; bvFTD, behavioural variant frontotemporal dementia; ALS, amyotrophic lateral sclerosis; FTLD-FUS, frontotemporal lobar degeneration with FUS-immunoreactive inclusions; FTLD-FET, frontotemporal lobar degeneration with FET protein-immunoreactive inclusions; aFTLD-U, atypical frontotemporal lobar degeneration with ubiquitin-positive inclusions; MNP, upper and lower motor neuron pathology; PFC, prefrontal cortex; TC, temporal cortex; MC, motor cortex; SC, spinal cord; BS, brainstem; AD-NC, Alzheimer’s disease neuropathologic change; A, Thal phase; B, Braak stage; C, CERAD score.</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"41586_2023_6801_Fig1_HTML\" id=\"d32e455\"/>", "<graphic xlink:href=\"41586_2023_6801_Fig2_HTML\" id=\"d32e561\"/>", "<graphic xlink:href=\"41586_2023_6801_Fig3_HTML\" id=\"d32e659\"/>", "<graphic xlink:href=\"41586_2023_6801_Fig4_HTML\" id=\"d32e751\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6801_Fig5_ESM\" id=\"d32e1406\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6801_Fig6_ESM\" id=\"d32e1451\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6801_Fig7_ESM\" id=\"d32e1466\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6801_Fig8_ESM\" id=\"d32e1489\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6801_Fig9_ESM\" id=\"d32e1512\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6801_Fig10_ESM\" id=\"d32e1541\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6801_Fig11_ESM\" id=\"d32e1574\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6801_Fig12_ESM\" id=\"d32e1610\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6801_Fig13_ESM\" id=\"d32e1655\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6801_Tab1_ESM\" id=\"d32e1665\"><caption><p>Clinicopathological details</p></caption></graphic>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6801_Tab2_ESM\" id=\"d32e1681\"><caption><p>CryoEM data collection, refinement and validation statistics</p></caption></graphic>" ]	[ "<media xlink:href=\"41586_2023_6801_MOESM1_ESM.pdf\"><label>Supplementary Fig. 1</label><caption><p>Uncropped images of immunoblots shown in this study.</p></caption></media>", "<media xlink:href=\"41586_2023_6801_MOESM2_ESM.pdf\"><caption><p>Reporting Summary</p></caption></media>", "<media xlink:href=\"41586_2023_6801_MOESM3_ESM.pdf\"><caption><p>Peer Review File</p></caption></media>" ]	[{"label": ["2."], "surname": ["Neumann", "Mackenzie"], "given-names": ["M", "IRA"], "article-title": ["Review: neuropathology of non-tau frontotemporal lobar degeneration"], "source": ["Neuropath. Appl. Neurobiol."], "year": ["2019"], "volume": ["45"], "fpage": ["19"], "lpage": ["40"], "pub-id": ["10.1111/nan.12526"]}, {"label": ["4."], "mixed-citation": ["Arseni, D. et al. TDP-43 forms amyloid filaments with a distinct fold in type A FTLD-TDP. "], "italic": ["Nature"], "bold": ["620"]}, {"label": ["7."], "surname": ["Kovacs"], "given-names": ["GG"], "article-title": ["TARDBP variation associated with frontotemporal dementia, supranuclear gaze palsy, and chorea"], "source": ["Mov. Disord."], "year": ["2009"], "volume": ["24"], "fpage": ["1842"], "lpage": ["1847"], "pub-id": ["10.1002/mds.22697"]}, {"label": ["11."], "surname": ["Lashley", "Rohrer", "Mead", "Revesz"], "given-names": ["T", "JD", "S", "T"], "article-title": ["An update on clinical, genetic and pathological aspects of frontotemporal lobar degenerations"], "source": ["Neuropath. Appl. Neurobiol."], "year": ["2015"], "volume": ["41"], "fpage": ["858"], "lpage": ["881"], "pub-id": ["10.1111/nan.12250"]}, {"label": ["48."], "surname": ["Takeuchi"], "given-names": ["R"], "article-title": ["Transportin 1 accumulates in FUS inclusions in adult-onset ALS without FUS mutation"], "source": ["Neuropath. Appl. Neurobiol."], "year": ["2013"], "volume": ["39"], "fpage": ["580"], "lpage": ["584"], "pub-id": ["10.1111/nan.12022"]}, {"label": ["50."], "surname": ["Ticozzi"], "given-names": ["N"], "article-title": ["Mutational analysis reveals the FUS homolog TAF15 as a candidate gene for familial amyotrophic lateral sclerosis"], "source": ["Am. J. Med. Genet. B Neuropsychiatr. Genet."], "year": ["2011"], "volume": ["156"], "fpage": ["285"], "lpage": ["290"], "pub-id": ["10.1002/ajmg.b.31158"]}, {"label": ["51."], "mixed-citation": ["Van Daele, S. H. et al. Genetic variability in sporadic amyotrophic lateral sclerosis. "], "italic": ["Brain"], "bold": ["146"]}, {"label": ["62."], "surname": ["Casa\u00f1al", "Lohkamp", "Emsley"], "given-names": ["A", "B", "P"], "article-title": ["Current developments in Coot for macromolecular model building of electron cryo-microscopy and crystallographic data"], "source": ["Protein Sci."], "year": ["2020"], "volume": ["29"], "fpage": ["1055"], "lpage": ["1064"], "pub-id": ["10.1002/pro.3791"]}]	{ "acronym": [], "definition": [] }	67	CC BY	no	2024-01-13 00:02:19	Nature. 2024 Dec 6; 625(7994):345-351	oa_package/bc/45/PMC10781619.tar.gz
PMC10781620	38110579	[]	[]	[]	[]	[]	[ "<title>Subject terms</title>" ]	[ "<p id=\"Par1\">Correction to: <italic>Nature</italic> 10.1038/s41586-023-06837-4 Published online 13 December 2023</p>", "<p id=\"Par2\">This article was originally published under a standard Springer Nature licence (© The Author(s), under exclusive licence to Springer Nature Limited). It is now available as an open-access paper under a Creative Commons Attribution 4.0 International licence, © The Author(s). The error has been corrected in the HTML and PDF versions of the article.</p>" ]	[]	[]	[]	[]	[]	[]	[]	[]	[]	[]	[]	[]	[]	{ "acronym": [], "definition": [] }	0	CC BY	no	2024-01-13 00:02:19	Nature. 2024 Dec 18; 625(7994):E11	oa_package/71/53/PMC10781620.tar.gz
PMC10781621	37816907	[]	[ "<title>Methods</title>", "<p id=\"Par2\">Transplant-eligible myeloma patients aged ≥65 with ≤12 months of prior therapy were enrolled. Prior transplant was not allowed. Patients with progressive disease on initial therapy were also excluded. The study was approved by the Institutional Review Board of the University of Iowa and performed in accordance with the principles of the Declaration of Helsinki.</p>", "<p id=\"Par3\">Patients received one 4-day course of dexamethasone 40 mg/day, doxorubicin 10 mg/m<sup>2</sup>/day and a continuous infusion of cisplatin 10 mg/m<sup>2</sup>/day, cyclophosphamide 400 mg/m<sup>2</sup>/day, and etoposide 40 mg/m<sup>2</sup>/day (D-PACE). Pegfilgrastim was administered on d + 6 and d + 13 after the start of D-PACE and patients underwent apheresis to collect a target of >15 × 10<sup>6</sup> CD34+ cells/kg. Patients then proceeded to transplant within 3 months of D-PACE. The conditioning regimen consisted of dexamethasone 20 mg PO days −4 to −1 and + 2 to + 5, bortezomib 1 mg/m<sup>2</sup> IV days −4, −1, + 2, and + 5, thalidomide 100 mg PO days −4 to + 5, and melphalan 100 mg/m<sup>2</sup> days −4 and −1 (70 mg/m<sup>2</sup> days −4 and −1 if age >70), with autologous CD34+ cells on day 0. Maintenance therapy started as early as 4 weeks after transplant, and was delivered per institutional standard of care, allowing for substitutions in the case of toxicity. The institutional standard of care was considered 12 cycles of bortezomib, thalidomide and dexamethasone followed by 12 cycles of bortezomib, cyclophosphamide and dexamethasone, for a total fixed duration of 24 months. The most common substitution was lenalidomide for thalidomide in case of severe neuropathy.</p>", "<p id=\"Par4\">The primary endpoint was progression free survival (PFS), measured from the start of protocol treatment until progression per International Myeloma Working Group consensus criteria or death. Overall response rate (ORR) and overall survival (OS) were secondary endpoints. Patients alive and disease-free at the off-study date were censored. All statistical testing of secondary endpoints was two-sided and assessed for significance at the 5% level.</p>" ]	[ "<title>Results</title>", "<p id=\"Par5\">There were 41 eligible patients enrolled between May 2013 and June 2017. The median age at enrollment was 68 years, and the oldest patient enrolled was 75 years old. Prior to enrollment, 26 patients had received prior therapy resulting in a stringent complete response (sCR) in 8%, complete response (CR) in 19%, very good partial response (VGPR) in 12%, partial response (PR) in 38%, and stable disease (SD) in 23% (Table ##TAB##0##1##).</p>", "<p id=\"Par6\">Two patients dropped out prior to transplant due to intolerance of D-PACE (<italic>n</italic> = 1) or failure of adequate CD34+ cell collection (<italic>n</italic> = 1). A median of 19.1 × 10<sup>6</sup> CD34+ cells/kg (range: 11.3–73.8 × 10<sup>6</sup> CD34+ cells/kg) were collected and a median of 9.8 × 10<sup>6</sup> CD34+ cells/kg (range: 4.5–15.0 × 10<sup>6</sup> CD34+ cells/kg) were infused with the transplant. Patients’ responses following transplant and prior to maintenance were sCR in 46%, CR in 8%, VGPR in 28%, PR in 15% and not evaluable (NE) in 3%.</p>", "<p id=\"Par7\">Thirty-seven patients started maintenance therapy after a median of 34 days (range: 17–90 days). The median follow-up on study was 43.7 months (range: 2.9–80.5 months). The best response to therapy was sCr in 85%, VGPR in 7%, PR in 2%, and NE in 5%, with ORR of 95%. The estimated median PFS was 76.4 months (95% confidence interval [CI]: 42.0-not reached), and median OS was not reached. The PFS and OS at 48 months were 64% (95% CI: 45–77%) and 83% (95% CI: 66–92%), respectively (Fig. ##FIG##0##1a, b##). More than half of patients (54%) received all 24 cycles of maintenance therapy, 30% received 13–23 total cycles and 16% received 1–12 total cycles). For patients who completed the full 2-year course of therapy, the progression free survival and overall survival at 48 months were 81% (95% CI: 57–93%) and 100%, respectively.</p>", "<p id=\"Par8\">Grade 3 or 4 hematologic toxicities were common. Median times to neutrophil and platelet engraftment were 11 days (range: 3–13) and 11 days (range: 3–30), respectively. Common grade 3 or 4 non-hematologic adverse events during the transplant and maintenance phases were infections (31% and 27%), diarrhea (21% and 11%), and electrolyte abnormalities such as hypophosphatemia (56% and 41%). Substitutions for thalidomide or bortezomib were made for neuropathy during maintenance therapy in 9 patients (24%). No patients had grade 3 or 4 peripheral neuropathy.</p>", "<p id=\"Par9\">There was one transplant-related death within 100 days of transplant due to infectious complications.</p>" ]	[ "<title>Discussion</title>", "<p id=\"Par10\">The role of ASCT has increasingly been called into question, particularly among older patients [##REF##32501533##4##]. Our study is the first to prospectively evaluate the safety and efficacy of ASCT in older patients with multiple myeloma using a novel agent conditioning regimen followed by an intensive 2-year maintenance therapy. The favorable best overall responses of sCR in 85% of patients and estimated median PFS of 76.4 months, reflect the feasibility, efficacy and safety of this intensive approach. Caution is warranted in extrapolating the results of this single-arm, single-institution study.</p>", "<p id=\"Par11\">Prior reports of intensified conditioning regimens, combining alkylating agents, proteosome inhibitors, and/or immunomodulatory drugs (IMiDs), have reported rates of CR or better to be 22.1–38% [##REF##30910541##5##–##REF##31101891##8##], generally with acceptable safety parameters. In our study, the rate of CR or better prior to maintenance (46%) was comparable, and the rate of transplant-related mortality (2.6%) was similar to previously reported rates [##REF##28286198##9##]. The use of intensive combination maintenance therapy also proved feasible in this study and was an important contributor to the prolonged median estimated PFS. Greater than 50% of patients were able to complete the 24-month maintenance regimen, which exceeds the 18 months median duration of lenalidomide only maintenance reported in the Myeloma XI study [##REF##30559051##10##]. One important factor may have been the high stem cell dose used allowing subjects to start maintenance rapidly and preventing issues with cytopenias during maintenance. Other studies using intensive proteosome inhibitor/IMiD combination maintenance therapies have demonstrated their safety and have produced encouraging PFS. In the FORTE trial, patients randomized to the carfilzomib/lenalidomide maintenance arm had a 48-month PFS rate of 75%, while patients treated with carfilzomib/lenalidomide/dexamethasone maintenance in the ATLAS study had an estimated median PFS of 58 months [##REF##34774221##11##, ##REF##36642080##12##]. Our results add to this body of experience, which collectively demonstrates that even among this older population, intensified conditioning plus prolonged triplet therapy can be delivered safely and is associated with a high rate of deep responses and a prolonged PFS.</p>", "<p id=\"Par12\">The 24-month defined duration of maintenance treatment, along with the long median PFS, resulted in a significant treatment-free period. This may be valuable to patients and to health care systems. Financial toxicity from myeloma therapy is common even among insured patients in the United States, and use of fixed-duration maintenance regimens, such as demonstrated in this study, could play a large role in attenuating this growing issue [##REF##26686042##13##].</p>", "<p id=\"Par13\">Based on these results, HSCT should remain a viable option for patients 65 and older and probably the therapy of choice if transplant-eligible.</p>" ]	[]	[ "<title>Subject terms</title>" ]	[ "<title>To the Editor:</title>", "<p id=\"Par1\">Treatment with autologous stem cell transplant (ASCT) using melphalan 200 mg/m<sup>2</sup> conditioning chemotherapy is a well-established and efficacious therapy for patients with multiple myeloma, even among eligible patients 65 years and older [##REF##11806971##1##, ##REF##32965680##2##]. However, despite improved outcomes, the predominant cause of death for patients remains their myeloma, highlighting the need to improve treatment efficacy for all patients [##REF##31112311##3##]. This phase II study aimed to evaluate the tolerance, safety, and efficacy of an intensified conditioning with ASCT in patients ≥65 years of age with multiple myeloma.</p>" ]	[ "<title>Acknowledgements</title>", "<p>Supported by National Cancer Institute 1R01CA236814-01A1, 3R01-CA236814-03S1, and U54CA272691-01, U.S. Department of Defense (CA180190) as well as funding from the Myeloma Crowd Research Initiative Award and the Paula and Rodger Riney Foundation, and UAMS Winthrop P. Rockefeller Cancer Institute (WRCRI) Fund to FZ.</p>", "<title>Author contributions</title>", "<p>Conceptualization: GT. Validation: CS and SLM. Formal analysis: SLM and BJS. Investigation: CS, MMS, UF, YJ, FZ and GT. Writing—original draft: CS. Writing—review and editing: SLM, BJS, MMS, UF, FZ, YJ and GT.</p>", "<title>Competing interests</title>", "<p id=\"Par14\">The authors declare no competing interests.</p>" ]	[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>Efficacy Outcomes.</title><p><bold>a</bold> Progression free and (<bold>b</bold>) overall survival estimates and 95% confidence intervals.</p></caption></fig>" ]	[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Baseline characteristics at study enrollment.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th/><th><italic>N</italic> = 41</th></tr></thead><tbody><tr><td colspan=\"2\">Age</td></tr><tr><td> Median (range)</td><td>68 (65–75)</td></tr><tr><td colspan=\"2\">Sex</td></tr><tr><td> Male</td><td>22 (54%)</td></tr><tr><td> Female</td><td>19 (46%)</td></tr><tr><td colspan=\"2\">Isotype</td></tr><tr><td> IgG/IgA/LC/Others</td><td>26/6/8/1</td></tr><tr><td colspan=\"2\">Cytogenetic risk</td></tr><tr><td> High<sup>a</sup></td><td>17 (44%)</td></tr><tr><td> Standard</td><td>22 (56%)</td></tr><tr><td> Missing</td><td>2</td></tr><tr><td colspan=\"2\">Cytogenetic abnormalities</td></tr><tr><td> Amplification 1q</td><td>2 (5%)</td></tr><tr><td> +1q</td><td>10 (26%)</td></tr><tr><td> −17p</td><td>3 (8%)</td></tr><tr><td> T[4;14]</td><td>0 (0%)</td></tr><tr><td> T[11;14]</td><td>6 (15%)</td></tr><tr><td> T[14;16]</td><td>2 (5%)</td></tr><tr><td colspan=\"2\">ISS</td></tr><tr><td> Stage I</td><td>13 (35%)</td></tr><tr><td> Stage II</td><td>17 (46%)</td></tr><tr><td> Stage III</td><td>7 (19%)</td></tr><tr><td> Missing</td><td>4</td></tr><tr><td colspan=\"2\">Pre-protocol therapy</td></tr><tr><td> No</td><td>15 (37%)</td></tr><tr><td> Yes</td><td>26 (63%)</td></tr><tr><td> Proteosome inhibitor</td><td>22 (85%)</td></tr><tr><td> IMiD</td><td>13 (50%)</td></tr><tr><td> Alkylator therapy</td><td>10 (38%)</td></tr></tbody></table></table-wrap>" ]	[]	[]	[]	[]	[]	[]	[ "<table-wrap-foot><p><sup>a</sup>Defined as deletion of 17p, t(14;16), gain 1q (≥3 copies), or t(4;14) by fluorescence in situ hybridization (FISH) on CD-138 sorted plasma cells.</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"41409_2023_2119_Fig1_HTML\" id=\"d32e452\"/>" ]	[]	[]	{ "acronym": [], "definition": [] }	13	CC BY	no	2024-01-13 00:02:19	Bone Marrow Transplant. 2024 Oct 10; 59(1):128-130	oa_package/87/7c/PMC10781621.tar.gz
PMC10781622	37848556	[ "<title>Introduction</title>", "<p id=\"Par2\">Fanconi anemia (FA) is a rare, inherited bone marrow failure (BMF) syndrome, characterized by congenital abnormalities, pancytopenia and predisposition to malignancies, especially myelodysplastic syndrome (MDS), acute myeloid leukemia (AML) and squamous cell carcinoma (SCC) [##REF##19622403##1##]. While gene therapy trials for FA are ongoing, allogeneic hematopoietic cell transplantation (allo-HCT) is still the only standard of care curative option for hematologic malignancies of FA, but does not prevent the occurrence of solid tumors, mainly head and neck SCC. It has also been argued that graft versus host disease (GvHD) after allo-HCT might contribute to the development of solid tumors in these patients [##REF##15331448##2##].</p>", "<p id=\"Par3\">Outcomes after allo-HCT have improved significantly over the last 30 years by optimizing preparative regimens to decrease toxicities related to the high sensitivity of FA patients to DNA alkylating agents, specifically by the introduction of fludarabine [##REF##27470218##3##]. Furthermore, graft-versus-host-disease (GvHD) prophylaxis, human leucocyte antigen (HLA)-typing and supportive care have contributed to the improved outcomes [##REF##17038525##4##, ##REF##24144640##5##].</p>", "<p id=\"Par4\">Recent series describing outcomes after allo-HCT for FA have included relatively limited numbers of patients [##REF##28179273##6##, ##UREF##0##7##], or, when larger numbers of patients were included, have been registry studies, including data from centers with low volume of allo-HCT for FA [##REF##24144640##5##].</p>", "<p id=\"Par5\">In this study we describe our retrospective predictor analysis for outcomes of allo-HCT in FA patients performed between 2007 and 2020 across three large FA referral institutions using different transplant platforms.</p>" ]	[ "<title>Methods</title>", "<p id=\"Par6\">Data of patients with FA and BMF or FA related myeloid malignancies undergoing their first HCT at Memorial Sloan Kettering Cancer Center (New York, USA), the University Medical Center Utrecht/Princess Máxima Center for Pediatric Oncology (Utrecht, the Netherlands) and Leiden University Medical Center (Leiden, the Netherlands) between 2007 and 2020, with follow up through December 31, 2020, was collected prospectively. We conducted retrospective analysis of this data, with no restrictions in terms of age, gender, HLA matching, conditioning regimen, cell source (cord blood [CB], bone marrow [BM] and peripheral blood stem cells [PBSCs]) and/or graft source or manipulation. As part of clearance to proceed to transplant patients were required to have a performance score above 70 (Karnofsky or Lansky depending on age). Stem cell donors were defined as matched donors (HLA match 10/10, 8/8 [BM and PBSC] or 6/6 [CB]), including matched related donors (MRD) and matched unrelated donors (MUD), or mismatched donors (HLA match ≤9/10, or ≤5/6 for CB). HLA typing was performed by licensed laboratories according to state-of-the-art technologies. Supportive care was similar among the centers, including monitoring for viruses, and did not change over time. The study was approved by the Institutional Review Boards of all three institutions. Patients signed a general transplant consent as well as a data collection consent. For these specific analyses the need for an additional informed consent was waived.</p>", "<p id=\"Par7\">Indication for HCT was defined as moderate or severe BMF (with or without cytogenetic changes), MDS or AML. For patients with MDS/AML, the strategy from all centers was to proceed to HCT without prior treatment of MDS/AML. The severity of the cytopenia was defined per Camitta et al. 1976 classification [##REF##779871##8##]. MDS was defined as the presence of cytogenetic abnormalities and dysplastic changes in greater than 10% of cells morphologically [##REF##32267023##9##], based on reports from the institutional licensed pathologists. This definition was used to differentiate MDS from BMF with cytogenetic changes. For the definition of MDS/AML relapse, we also used reports from the institutional licensed pathologists.</p>", "<title>Outcomes</title>", "<p id=\"Par8\">Main outcomes of interest were overall survival (OS) and event free survival (EFS). OS time was defined as time from allo-HCT to time of death from any cause or time of last follow-up for survivors. EFS time was defined as time from allo-HCT to time of event or time of last follow-up for patient who did not have an event. Events were defined as relapse (MDS/AML), graft failure (GF) and treatment-related mortality (TRM). GF was defined by either no engraftment at day 30 (for PBSC and BM) or 42 (for CB) post-transplant or loss of the graft after initial engraftment (secondary graft failure) [##REF##34304802##10##]. We used the definition of engraftment of the first of three consecutive days with an absolute neutrophil count greater than 0.5 × 10<sup>9</sup>/L.</p>", "<p id=\"Par9\">Other outcomes of interest were TRM (death not due to relapse), acute graft versus host disease (aGvHD) at day 100 as defined by CIBMTR criteria, extensive chronic GvHD (cGvHD) defined by NIH criteria, engraftment, and post-transplant malignancies.</p>", "<title>Statistical analysis</title>", "<p id=\"Par10\">Continuous variables are displayed as median and range, discrete variables as counts and proportions. Cox proportional hazard models were used to study possible impact of variables on the outcomes of interest. Variables considered were age, gender, indication (BMF with or without cytogenetic abnormalities versus MDS/AML), HLA matching, conditioning regimen, graft source, graft manipulation, FANC complementation group and year of transplant before versus after the median (2014). The results are presented as hazard ratios (HRs), 95% confidence intervals (95% CIs), and log-likelihood test <italic>P</italic> values. Factors were assessed in univariable models first and subsequently entered into multivariable (MV) models if <italic>P</italic> ≤ 0.05. The Kaplan-Meier method was used to visualize and analyze the main outcomes of interest OS and EFS. For analysis of cumulative incidences of TRM, aGvHD and cGvHD Fine and Gray models for competing risk were used.</p>" ]	[ "<title>Results</title>", "<title>Patient and transplant characteristics</title>", "<p id=\"Par11\">A total of 89 consecutive FA patients were included. Patient and allo-HCT characteristics are summarized in Table ##TAB##0##1## and Supplementary Tables ##SUPPL##0##S1## and ##SUPPL##0##S2##. Subsets of patients have been reported previously; 29 patients by Smetsers et al. [##REF##27470218##3##], and 10 patients by Mehta et al. [##REF##28179273##6##]. Median age at transplant for the entire cohort was 9.2 (range 1.7–44) years. Sixteen of 89 patients were adults (≥19 years), who were transplanted at a median age of 30.2 (range 22.8–44) years. For the 73 (82%) patients <19 years old, median age at transplant was 8 (range 1.7–18.9) years. In total, 15 patients had MDS, and 4 patients had AML at time of transplant, the rest of the patients had BMF with or without cytogenetic changes. Of the patients <19 years old, 12 (16.4%) had MDS/AML while in the group ≥19 years old this number was 7 (43.8%).</p>", "<p id=\"Par12\">The most commonly used conditioning regimen was cyclophosphamide/fludarabine (Cy/Flu). The addition of busulfan (Bu) to the conditioning regimen was dependent on local institutional practice; at Memorial Sloan Kettering Cancer Center the Bu target was 18–22 mghr/L, while in the European centers it was 30 mghr/L. A minority of patients received total body irradiation (TBI; 12.4%), in a range of 3–4.5 Gy. Almost all patients received serotherapy (95.5%), and more than half of the patients had a matched donor. Bone marrow was the stem cell source in about half of the transplants, followed by peripheral blood and cord blood respectively. Ex vivo T-cell depletion (TCD) was used in 37 (41.6%) of the transplants, of which 20 (54.0%) were HLA-mismatched. Of the T-replete allo-HCTs, 40 (77%) were from bone marrow and 12 (23% [10 mismatched; 19.2%]) from cord blood.</p>", "<title>Outcomes</title>", "<p id=\"Par13\">For the full cohort, 5-year OS and EFS were 83.2% (75.3–91.9%) and 74% (65–84.2%), respectively (Fig. ##FIG##0##1a, b##). Sixteen of 89 patients died; causes of death were infection (fungal [<italic>n</italic> = 2] and viral [<italic>n</italic> = 5]), GvHD (<italic>n</italic> = 3), multiorgan failure (<italic>n</italic> = 2), secondary malignancy (SCC of the tongue and urothelial carcinoma; <italic>n</italic> = 2), graft failure (<italic>n</italic> = 1) and progression of leukemia (<italic>n</italic> = 1). 5-year OS by donor type was 91.9% (83.5–100%) for MUD, 87% (70.8–100%) for MRD, 74.7% (57.7–96.6%) for mismatched unrelated donor (MMUD) and 55.6% (31–99.7%) for mismatched related donor (MMRD). Patients with BMF +/− cytogenetic changes had a significantly better 5-year OS compared to patients with MDS/AML (90% [83.2–97.3%] versus 58.6% [38.2–90.1%], <italic>p</italic> = 0.015).</p>", "<p id=\"Par14\">In univariable Cox proportional hazard analysis for OS, EFS and TRM; age, indication for transplant, graft manipulation, conditioning regimen, HLA match and year of transplant were found to be significant predictors. Table ##TAB##1##2## summarizes the multivariable analysis of the full cohort for these outcomes. For OS age ≥19 (HR 13.4, 95% CI 2.3–77, <italic>P</italic> = 0.004), HLA mismatch (HR 4.7, 95% CI 1.3–16.6, <italic>P</italic> = 0.02) and transplant in 2014 or later (HR 0.12, 95% CI 0.02–0.57, <italic>P</italic> = 0.008) were found to be multivariable predictors. Likewise, for EFS the same multivariable predictors were found; age ≥19 (HR 8.8, 95% CI 2.6–30.2, <italic>P</italic> < 0.001, Fig. ##FIG##1##2a##), HLA mismatch (HR 6.2, 95% CI 2.3–16.7, <italic>P</italic> < 0.001) and HCT in 2014 of later (HR 0.31, 95% CI 0.11–0.84, <italic>P</italic> = 0.02). Finally, these covariates were also found to be multivariable predictors of TRM; age ≥19 (HR 29.9, 95% CI 2.8–319, <italic>P</italic> = 0.005, Fig. ##FIG##1##2b##), HLA mismatch (HR 10, 95% CI 1.8–54.2, <italic>P</italic> = 0.008) and transplant in 2014 or later (HR 0.06, 95% CI 0.01–0.52, <italic>P</italic> = 0.011).</p>", "<p id=\"Par15\">In total, 84 patients (94.4%) had sustainable neutrophil recovery. Median time to neutrophil recovery was 15 (range: 7–35) days. Five patients (5.6%) had primary graft failure (graft source: CB [<italic>n</italic> = 2], T-cell depleted PBSC [<italic>n</italic> = 3]; conditioning regimen: Cy/Flu [<italic>n</italic> = 2], Bu/Cy/Flu [<italic>n</italic> = 2], TBI/Cy/Flu [<italic>n</italic> = 1]); 4 patients (4.5%) had secondary graft failure (graft source: BM [<italic>n</italic> = 1], T-cell depleted PBSC [<italic>n</italic> = 3]; conditioning regimen: Cy/Flu [<italic>n</italic> = 2], Bu/Cy/Flu [<italic>n</italic> = 2]).</p>", "<p id=\"Par16\">The cumulative incidence of day 100 grade II-IV aGvHD, grade III-IV aGvHD, and 5-year extensive cGvHD were 5.6%, 2.2% and 4.6%, respectively. Relapse in the MDS/AML subgroup was only seen in 4 patients (16%). GF was seen in 9 patients (TCD 6/37 [16%]; T-replete 3/52 [5.7%]). Six patients developed malignancy after allo-HCT at a median time of 6.3 (range 0.9–13) years after transplant; 4 had received T-replete, 2 ex vivo TCD transplants. The malignancies were SCC of the oral mucosa (<italic>n</italic> = 3), basal cell carcinoma (<italic>n</italic> = 1), urothelial (<italic>n</italic> = 1) and hepatocellular carcinoma (<italic>n</italic> = 1). Preceding these diagnoses, 2 patients had grade I, 1 had grade II, and 1 had grade III aGvHD and limited cGvHD.</p>", "<p id=\"Par17\">In the pediatric patients (age <19 years, <italic>n</italic> = 73), 5-year OS and EFS were 90.4% (83–98.5%) and 83.8% (75.2–93.3%), respectively (Fig. ##FIG##0##1c, d##). Graft manipulation (TCD) and HLA-match were found to be univariable predictors for the main outcomes of interest: OS and EFS. In multivariable analysis including these variables (Table ##TAB##2##3##), TCD was found to be the only borderline multivariable predictor for inferior OS suggesting an 8-fold increased risk of an event (HR 8.4, 95% CI 0.9–76.6, <italic>P</italic> = 0.059) with 5-year OS of 73.0% (54.7–97.4%) in TCD vs 100% for T-replete HCT (Fig. ##FIG##2##3a##). HLA mismatch was a predictor of worse EFS (HR 9.8, 95%-CI 1.94–50, <italic>P</italic> = 0.0058), mainly driven by GF. For TRM, conditioning regimen was found to be the only predictor suggesting a 10-fold higher risk of TRM using a TBI based conditioning regimen (HR 10.9, 95% CI 1.2–99.2, <italic>P</italic> = 0.034). Age above or below the median within the pediatric cohort was not a predictor for OS, EFS or TRM.</p>" ]	[ "<title>Discussion</title>", "<p id=\"Par18\">With our contemporary data from three large FA referral centers, we show that survival after allo-HCT for FA (including pediatric and adult patients) is excellent, with an OS over 80%. For patients <19 years survival rates are even > 90% and up to 100% for those receiving a T-replete transplant. For the full cohort the main predictors for inferior outcome found for OS, EFS and TRM were age ≥19 years, HLA mismatch and HCT performed before 2014. In the <19-year group ex vivo TCD was found to be a borderline predictor (<italic>P</italic> = 0.059, HR 8) for OS as well. GvHD rates were very low across all transplant platforms (<10%).</p>", "<p id=\"Par19\">Due to the retrospective nature of this study, one of the limitations is that reliable data on androgen use and number of transfusions received prior to HCT, factors previously described as predictive of outcomes after HCT, is missing [##REF##10627445##11##, ##UREF##1##12##]. Although the definition of MDS/AML diagnosis and relapse was based on reports from institutional licensed pathologists, this was not a centralized review, which could be a limitation of this study. Another limitation is that due to center preference in general to either perform T-replete versus ex vivo TCD transplantations, it is not possible to correct for center effect and we cannot fully exclude possible variations in clinical management across centers may have contributed to outcomes. Nevertheless, we do believe our results are novel and of interest to the field, given the inclusion of a large number of patients treated in centers with particular expertise in FA and considering that a comparison between T-replete and ex vivo TCD transplants has not been reported before.</p>", "<p id=\"Par20\">Our outcomes are consistent with or better than previously described results. In a large registry study by the European Group for Blood and Marrow Transplantation, OS was 65% at 5 years [##REF##24144640##5##]; in contrast, a more recent study from Iran, including patients ≤18 years of age, showed 5-year OS of 82% [##UREF##2##13##]. These patients received in vivo TCD with rabbit anti-thymocyte globulin (ATG) and a busulfan based conditioning, without radiation. In our cohort of patients <19 years, only 26% of patients received busulfan, which may explain the higher toxicity seen in the Iranian series. This was also discussed previously by Smetsers et al., who demonstrated a 5-year OS of 87.8% in pediatric and young adult patients who received a fludarabine based conditioning regimen [##REF##27470218##3##]. The prospective multi-center study by Mehta et al., including pediatric and adult patients, demonstrated a 3-year OS of 80% for recipients of alternative-donor ex vivo TCD transplant with TBI-free conditioning [##REF##28179273##6##]. Other recent reports on outcomes after allo-HCT for Fanconi anemia also indicated similar outcomes as we see in our cohort, such as the Spanish multicenter study including 34 mainly pediatric patients (OS 73%) [##UREF##0##7##] and the report from Hadassah Medical Center, including 41 mostly pediatric patients (OS 82.9%) [##UREF##3##14##]</p>", "<p id=\"Par21\">For our full cohort, a predictor for inferior survival was age ≥19 years and although the percentage of patients with MDS/AML was higher in the group ≥19 years, inferior survival was driven by TRM and not relapse. Although in our cohort only 12% of patients received TBI, in the adult group this was 50%, which may have contributed to the higher rate of TRM as described previously [##REF##24144640##5##], but other factors, such as pre-treatment associated toxicities may have played a role as well. For patients <19 years, age was not found to be a predictor for any of the outcomes, which was also shown by Rostami et al. [##UREF##2##13##]. In contrast, Latour et al. and Mehta et al. both described age below and above 10 years to be an independent predictor of OS [##REF##24144640##5##, ##REF##28179273##6##]. Mehta showed superior OS in patients <10 years compared to patients ≥10 years, including adult patients (OS 92.3% versus 63.2%; <italic>P</italic> = 0.02). The more favorable OS may be explained by the generally better immune reconstitution seen in young children compared to adults in the ex vivo TCD setting, as positive viral serostatus (CMV, Adeno, EBV) is more common with increasing age. HLA mismatch and transplant performed before 2014 were also found to be multivariable predictors for inferior survival in the full cohort, but not in the cohort of patients <19 years of age, suggesting that these covariates mainly predicted outcomes in older FA patients. In addition to high resolution HLA typing, optimization of anti-microbial prophylaxis, could have contributed to better outcomes in more recent transplants.</p>", "<p id=\"Par22\">In the <19 group, ex vivo TCD was found to be a borderline predictor for inferior survival (8-fold higher risk of an event), compared to T-replete transplants (100% OS at 5 years; <italic>n</italic> = 45 versus 73% in 28 TCD patients), which is in line with Smetsers et al. [##REF##27470218##3##]. Although the <italic>P</italic> value did not reach significance at the 0.05 threshold (<italic>p</italic> = 0.059), the 95% confidence interval is wide and not clearly centered around 1, suggesting a lack of power, rather than a lack of effect. We acknowledge that this study was not designed to define which platform is superior, but our findings are relevant, especially in the context of recent results presented on behalf of the EBMT Severe Aplastic Anaemia Working Party and Paediatric Disease Working Party at the 64th American Society of Hematology Meeting and Exposition in 2022 [##UREF##4##15##]. Their outcomes for >800 children who underwent transplantation for FA, including only 85 (11%) ex vivo TCD transplantations, were excellent (5-year OS, EFS and GRFS to be 83%, 78% and 70%, respectively). Although ex vivo TCD is considered standard of care in some select US centers, T-replete transplantation seems to perform at least as well, and has the advantage of potential faster immune recovery reducing the risk of post transplantation complications. In addition, T-replete transplantation is not limited to centers with access to TCD and can be performed in more centers worldwide.</p>", "<p id=\"Par23\">The rates of GvHD in both T-replete and TCD recipients are low. Ex vivo TCD is being used in some centers to minimize the risk of GvHD, however in our analyses, there was no difference in GvHD incidence between TCD and T-replete HCT, whereas TCD was associated with inferior survival.</p>", "<p id=\"Par24\">Although follow-up time and numbers may be limited, there was no association found between a history of severe aGvHD (grade III-IV) and extensive cGvHD, and the development of malignancies later in life. This contrasts with Rosenberg et al., reporting aGvHD and cGvHD being significant risk factors for SCC in a cohort of 117 pediatric FA patients who received allo-HCT [##REF##15331448##2##]. Bonfim et al. described 12 patients with FA who developed SCC after transplant, 8 of which had preceding cGvHD [##UREF##5##16##]. Although the numbers were too small to statistically determine the significance of cGvHD as a risk factor for SCC, the cohort did demonstrate that patients with preceding cGvHD developed SCC earlier. In this cohort described by Bonfim et al., the 2-year cumulative incidence of cGvHD (35%) was much higher than what we describe in our cohort. In a large cohort of pediatric and adult patients who underwent allo-HCT for any indication, Rizzo et al. reported that 0.02% of patients developed SCC after HCT; cGvHD was found to be associated with a 5-fold increased risk of SCC (0.1%) [##REF##18971419##17##]. The proportion of patients with FA included was not determined.</p>", "<p id=\"Par25\">In comparison to the study by Mehta et al. [##REF##28179273##6##], describing patients who received ex vivo TCD, we show similar outcomes in a cohort that includes >50% T-replete transplants. Our data are also consistent with pediatric registry data from EBMT from 2021, demonstrating better outcomes after allo-HCT with haplo-identical transplants with only in vivo TCD (OS at 24 months 80%; <italic>n</italic> = 59), compared to haplo-identical transplant with both in vivo and ex vivo TCD (OS at 24 months 60%; <italic>n</italic> = 33) [##REF##33606297##18##]. The cumulative incidence of extensive cGvHD in that analysis for haplo-identical transplants with only in vivo versus in vivo and ex vivo TCD was 3% and 4%, respectively.</p>", "<p id=\"Par26\">In summary, we show excellent outcomes in this relatively large tri-institutional cohort of pediatric and young adult FA patients which includes the use of conventional grafts and ex vivo TCD. We show high survival and low toxicity (including low incidence of GvHD), particularly for those <19 years of age. For patients without a suitable matched sibling donor, matched unrelated donor and cord blood are good alternative cell sources. In the absence of a fully matched (un)related donor, ex vivo graft manipulation may be considered in centers with access and experience, but for other centers, alternative strategies, such as post-transplant cyclophosphamide, as explored by Bonfim et al. [##REF##35240077##19##], to decrease the risk of GvHD should be considered.</p>" ]	[]	[ "<p id=\"Par1\">Allogeneic hematopoietic cell transplantation (HCT) remains the only cure for the hematologic manifestations of Fanconi anemia (FA). We performed retrospective predictor analyses for HCT outcomes in FA for pediatric and young adult patients transplanted between 2007 and 2020 across three large referral institutions. Eighty-nine patients, 70 with bone marrow failure +/− cytogenetic abnormalities, 19 with MDS/AML, were included. Five-year overall survival (OS) was 83.2% and event-free survival (EFS) was 74%. Age ≥19, HLA mismatch and year of HCT were multivariable predictors (MVPs) for OS, EFS and treatment-related mortality (TRM). In the pediatric group, TCD was a borderline MVP (<italic>P</italic> = 0.059) with 5-year OS of 73.0% in TCD vs. 100% for T-replete HCT. The cumulative incidence of day 100 grade II-IV aGvHD and 5-year cGvHD were 5.6% and 4.6%, respectively. Relapse in the MDS/AML subgroup occurred in 4 patients (16%). Graft failure was seen in 9 patients (TCD 6/37 [16%]; T-replete 3/52 [5.7%]). Six patients developed malignancy after HCT. Survival chances after HCT for FA are excellent and associated with high engrafted survival and low toxicity. Age ≥19, HLA mismatch, year of transplant and ‘TCD in the <19 years group’ (although borderline) were found to be negative predictors for survival.</p>", "<title>Subject terms</title>" ]	[ "<title>Supplementary information</title>", "<p>\n\n</p>" ]	[ "<title>Supplementary information</title>", "<p>The online version contains supplementary material available at 10.1038/s41409-023-02121-1.</p>", "<title>Acknowledgements</title>", "<p>MC, AGT, EK and JJB acknowledge support from NIH/NCI Cancer Center Support Grant P30 CA008748. We thank Joseph Olechnowicz for editorial assistance. We thank Audrey Mauguen, PhD for guidance in the biostatistical analyses.</p>", "<title>Author contributions</title>", "<p>MC, AGT, JJB and SES contributed to the conception and design, analysis, and interpretation of the work. MC, AGT, JJB, SES contributed to the acquisition of the data for the work. MC and AGT wrote the initial draft and all authors contributed to revising it critically for important intellectual content and provided final approval of the submitted version.</p>", "<title>Data availability</title>", "<p>Data supporting the findings of the study are provided in the manuscript and supplementary data. Deidentified individual participant data reported in the manuscript will be shared under the terms of a Data Use Agreement and may only be used for approved proposals. Requests may be made to: crdatashare@mskcc.</p>", "<title>Competing interests</title>", "<p id=\"Par27\">The authors declare no competing interests.</p>" ]	[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>Outcomes for full cohort.</title><p>Overall survival (<bold>a</bold>) and event-free survival (<bold>b</bold>) in our full cohort and overall survival (<bold>c</bold>) and event-free survival (<bold>d</bold>) in patients younger than 19 years of age at time of transplant.</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>Outcomes by age.</title><p>Event-free survival (<bold>a</bold>) and treatment related mortality (death by other cause than relapse; <bold>b</bold>) by age <19 years compared to age ≥19 years.</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><title>Outcomes in patients <19 years old by T-cell depletion.</title><p>Overall survival (<bold>a</bold>) and event-free survival (<bold>b</bold>) in patients younger than 19 years, by T-replete transplants versus ex vivo T-cell depleted transplants.</p></caption></fig>" ]	[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Patient and HCT characteristics.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th>Total <italic>n</italic> = 89</th><th><italic>n</italic> (%)</th><th/><th><italic>n</italic> (%)</th></tr></thead><tbody><tr><td>Center</td><td/><td>Conditioning regimen</td><td/></tr><tr><td> Leiden University Medical Center</td><td>12 (13.5)</td><td> Cy/Flu</td><td>51 (57.3)</td></tr><tr><td> Memorial Sloan Kettering Cancer Center</td><td>35 (39.3)</td><td> Bu<sup>d</sup>/Cy/Flu</td><td>26 (29.2)</td></tr><tr><td> University Medical Center Utrecht/Princess Maxima Center for Pediatric Oncology</td><td>42 (47.2)</td><td> TBI/Cy/Flu</td><td>11 (12.4)</td></tr><tr><td>Age at HCT</td><td/><td> Cy/Thiotepa</td><td>1 (1.1)</td></tr><tr><td> Median (years)</td><td>9.2</td><td>Serotherapy</td><td/></tr><tr><td> Range (years)</td><td>1.7-44</td><td> ATG</td><td>84 (94.4)</td></tr><tr><td>Gender</td><td/><td> Alemtuzumab</td><td>1 (1.1)</td></tr><tr><td> Female</td><td>33 (37.1)</td><td> None</td><td>4 (4.5)</td></tr><tr><td> Male</td><td>56 (62.9)</td><td>HLA matching</td><td/></tr><tr><td>Complementation group</td><td/><td> Matched</td><td>59 (66.3)</td></tr><tr><td> <italic>FANCA</italic></td><td>52 (58.4)</td><td> Mismatched</td><td>30 (33.7)</td></tr><tr><td> <italic>FANCC</italic></td><td>22 (24.7)</td><td>Donor</td><td/></tr><tr><td> <italic>FANCE</italic></td><td>2 (2.2)</td><td> MUD</td><td>37 (41.6)</td></tr><tr><td> <italic>FANCG</italic></td><td>3 (3.4)</td><td> MRD</td><td>22 (24.7)</td></tr><tr><td> Other<sup>a</sup></td><td>4 (4.5)</td><td> MMUD</td><td>20 (22.5)</td></tr><tr><td> Unknown</td><td>6 (6.7)</td><td> MMRD</td><td>10 (11.2)</td></tr><tr><td>Disease status at time of HSCT</td><td/><td>Stem cell source</td><td/></tr><tr><td> Bone marrow failure +/− cytogenetic changes<sup>b</sup></td><td>70 (78.7)</td><td> Bone marrow</td><td>45 (50.6)</td></tr><tr><td> MDS/AML<sup>c</sup></td><td>19 (21.3)</td><td> Peripheral blood</td><td>32 (36)</td></tr><tr><td>Follow-up</td><td/><td> Cord blood</td><td>12 (13.5)</td></tr><tr><td> Median (years)</td><td>3.6</td><td>Graft manipulation</td><td/></tr><tr><td> Range (years)</td><td>0.9–14.3</td><td> Unmanipulated/conventional<sup>e</sup></td><td>52 (58.4)</td></tr><tr><td/><td/><td> Ex-vivo T cell depletion<sup>f</sup></td><td>37 (41.6)</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Multivariable Cox PH analysis all patients (<italic>n</italic> = 89).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th/><th colspan=\"2\">Overall survival</th><th colspan=\"2\">Event-free survival</th><th colspan=\"2\">Treatment-related mortality</th></tr><tr><th/><th>HR (95% CI)</th><th><italic>P</italic></th><th>HR (95% CI)</th><th><italic>P</italic></th><th>HR (95% CI)</th><th><italic>P</italic></th></tr></thead><tbody><tr><td colspan=\"7\">Age at transplant</td></tr><tr><td> ≤8 years</td><td>1</td><td/><td>1</td><td/><td>1</td><td/></tr><tr><td> 8–18.9 years</td><td>3.1 (0.54–17.6)</td><td>0.20</td><td>2.1 (0.6–7.3)</td><td>0.25</td><td>7.5 (0.67–83.4)</td><td>0.10</td></tr><tr><td> ≥19 years</td><td>13.4 (2.3–77)</td><td>0.004</td><td>8.8 (2.6–30.2)</td><td><0.001</td><td>29.9 (2.8–319)</td><td>0.005</td></tr><tr><td colspan=\"7\">Transplant indication</td></tr><tr><td> BMF</td><td>1</td><td/><td>1</td><td/><td>1</td><td/></tr><tr><td> MDS/AML</td><td>0.51 (0.12–2.1)</td><td>0.35</td><td>0.36 (0.12–1.1)</td><td>0.08</td><td>0.21 (0.04–1.2)</td><td>0.072</td></tr><tr><td colspan=\"7\">Ex vivo T-cell depletion</td></tr><tr><td> No</td><td>1</td><td/><td>1</td><td/><td>1</td><td/></tr><tr><td> Yes</td><td>1.4 (0.26–7.4)</td><td>0.69</td><td>2.5 (0.57–10.8)</td><td>0.22</td><td>1.6 (0.21–11.8)</td><td>0.66</td></tr><tr><td colspan=\"7\">Conditioning regimen<sup>a</sup></td></tr><tr><td> Including busulfan</td><td>1</td><td/><td>1</td><td/><td>1</td><td/></tr><tr><td> Without busulfan</td><td>0.33 (0.05–2.2)</td><td>0.25</td><td>0.68 (0.15–3.1)</td><td>0.62</td><td>0.17 (0.01–2.03)</td><td>0.16</td></tr><tr><td> Including TBI</td><td>0.87 (0.24–3.2)</td><td>0.84</td><td>0.91 (0.28–3.0)</td><td>0.88</td><td>0.68 (0.14–3.3)</td><td>0.64</td></tr><tr><td colspan=\"7\">HLA match</td></tr><tr><td> Matched</td><td>1</td><td/><td>1</td><td/><td>1</td><td/></tr><tr><td> Mismatched</td><td>4.7 (1.3–16.6)</td><td>0.02</td><td>6.2 (2.3–16.7)</td><td><0.001</td><td>10 (1.8–54.2)</td><td>0.008</td></tr><tr><td colspan=\"7\">HCT year</td></tr><tr><td> <2014</td><td>1</td><td/><td>1</td><td/><td>1</td><td/></tr><tr><td> ≥2014</td><td>0.12 (0.02–0.57)</td><td>0.008</td><td>0.31 (0.11–0.84)</td><td>0.02</td><td>0.06 (0.01–0.52)</td><td>0.011</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab3\"><label>Table 3</label><caption><p>Multivariable Cox PH analysis patients <19 years old (<italic>n</italic> = 73).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th/><th colspan=\"2\">Overall survival</th><th colspan=\"2\">Event-free survival</th></tr><tr><th/><th>HR (95% CI)</th><th><italic>P</italic></th><th>HR (95% CI)</th><th><italic>P</italic></th></tr></thead><tbody><tr><td colspan=\"5\">Ex vivo T-cell depletion</td></tr><tr><td> No</td><td>1</td><td/><td>1</td><td/></tr><tr><td> Yes</td><td>8.39 (0.92–76.6)</td><td>0.059</td><td>2.67 (0.44–16.4)</td><td>0.29</td></tr><tr><td colspan=\"5\">HLA match</td></tr><tr><td> Matched</td><td>1</td><td/><td>1</td><td/></tr><tr><td> Mismatched</td><td>3.15 (0.57–17.4)</td><td>0.19</td><td>9.84 (1.94–50)</td><td>0.0058<sup></sup></td></tr></tbody></table></table-wrap>" ]	[]	[]	[]	[]	[]	[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>" ]	[ "<table-wrap-foot><p><sup>a</sup>In the group ‘Other’ we included one of each of the following: <italic>FANCB</italic>, <italic>FANCD2</italic>, <italic>FANCL</italic>, and <italic>FANCM</italic>.</p><p><sup>b</sup>Cytogenetic changes observed in patients with bone marrow failure were: 3q26/EVI1 rearrangement, +X, 7-, 7q-, and 20q-.</p><p><sup>c</sup>Cytogenetic changes observed in patients with MDS/AML were: 3q26/EVI1 rearrangement, 1q+, 1q24+, 1q25+, 3q27+, 6p25-, 7q-, 9-, 9+, 11q-, 12p-, 12p13-, 15q+.</p><p><sup>d</sup>BU target was 18–22 mgh/L at MSKCC and 30 mgh/L at LUMC and UMCU/PMC.</p><p><sup>e</sup>GvHD prophylaxis used was dependent on graft source/manipulation. For bone marrow we used cyclosporine (CsA) and methotrexate (<italic>n</italic> = 31), CsA and mycophenolate mofetil (MMF; <italic>n</italic> = 3), CsA/MMF/prednisone (<italic>n</italic> = 1), CsA/MMF/tacrolimus/prednisone (<italic>n</italic> = 1), CsA/methotrexate/tacolimus (<italic>n</italic> = 1), MMF/prednisone (<italic>n</italic> = 1), MMF alone (<italic>n</italic> = 1), or CsA alone (<italic>n</italic> = 1). For cord blood we used CsA/prednisone (<italic>n</italic> = 9) or CsA alone (<italic>n</italic> = 3).</p><p><sup>f</sup>T-cell depletion devices used were Isolex (<italic>n</italic> = 8) and CliniMACS (<italic>n</italic> = 29).</p></table-wrap-foot>", "<table-wrap-foot><p><sup>a</sup>Although busulfan target AUCs were different between MSKCC and the centers in the Netherlands, only 3 patients in the Netherlands received busulfan as part of their conditioning regimen, so no further sub-analyses on busulfan exposure could be performed.</p><p><italic>P</italic> < 0.05.</p></table-wrap-foot>", "<table-wrap-foot><p><italic>P</italic> < 0.05.</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>These authors contributed equally: Maria Cancio, Alexandre G. Troullioud Lucas.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"41409_2023_2121_Fig1_HTML\" id=\"d32e736\"/>", "<graphic xlink:href=\"41409_2023_2121_Fig2_HTML\" id=\"d32e1060\"/>", "<graphic xlink:href=\"41409_2023_2121_Fig3_HTML\" id=\"d32e1234\"/>" ]	[ "<media xlink:href=\"41409_2023_2121_MOESM1_ESM.docx\"><caption><p>Supplementary Material</p></caption></media>" ]	[{"label": ["7."], "surname": ["Murillo-Sanju\u00e1n", "Gonz\u00e1lez-Vicent", "Argil\u00e9s Aparicio", "Badell Serra", "Rodr\u00edguez Villa", "Uria"], "given-names": ["L", "M", "B", "I", "A"], "article-title": ["Survival and toxicity outcomes of hematopoietic stem cell transplantation for pediatric patients with Fanconi anemia: a unified multicentric national study from the Spanish Working Group for Bone Marrow Transplantation in Children"], "source": ["Bone Marrow Transpl"], "year": ["2021"], "volume": ["56"], "fpage": ["1213"], "lpage": ["6"], "pub-id": ["10.1038/s41409-020-01172-y"]}, {"label": ["12."], "surname": ["Pasquini", "Carreras", "Pasquini", "Camitta", "Fasth", "Hale"], "given-names": ["R", "J", "MC", "BM", "AL", "GA"], "article-title": ["HLA-matched sibling hematopoietic stem cell transplantation for fanconi anemia: comparison of irradiation and nonirradiation containing conditioning regimens"], "source": ["Biol Blood Marrow Transpl"], "year": ["2008"], "volume": ["14"], "fpage": ["1141"], "lpage": ["7"], "pub-id": ["10.1016/j.bbmt.2008.06.020"]}, {"label": ["13."], "mixed-citation": ["Rostami T, Mousavi SA, Kiumarsi A, Kasaeian A, Rad S, Yaghmaie M, et al. Radiation-free reduced-intensity hematopoietic stem cell transplantation with in vivo T-cell depletion from matched related and unrelated donors for Fanconi anemia: prognostic factor analysis. Exp Hematol. 2022;109:27\u201334."]}, {"label": ["14."], "surname": ["Fink", "Even-Or", "Avni", "Grisariu", "Zaidman", "Schejter"], "given-names": ["O", "E", "B", "S", "I", "YD"], "article-title": ["Two decades of stem cell transplantation in patients with Fanconi anemia: analysis of factors affecting transplant outcomes"], "source": ["Clin Transpl"], "year": ["2023"], "volume": ["37"], "fpage": ["e14835"], "pub-id": ["10.1111/ctr.14835"]}, {"label": ["15."], "surname": ["Lum", "Samarasinghe", "Eikema", "Piepenbroek", "Dalissier", "Ayas"], "given-names": ["SH", "S", "D-J", "B", "A", "M"], "article-title": ["Outcome of haematopoietic cell transplantation in 813 children with fanconi anaemia: a study on behalf of the EBMT Severe Aplastic Anaemia Working Party and Paediatric Disease Working Party"], "source": ["Blood."], "year": ["2022"], "volume": ["140"], "fpage": ["657"], "lpage": ["9"], "pub-id": ["10.1182/blood-2022-163140"]}, {"label": ["16."], "surname": ["Bonfim", "Ribeiro", "Nichele", "Bitencourt", "Loth", "Koliski"], "given-names": ["C", "L", "S", "M", "G", "A"], "article-title": ["Long-term survival, organ function, and malignancy after hematopoietic stem cell transplantation for fanconi anemia"], "source": ["Biol Blood Marrow Transpl"], "year": ["2016"], "volume": ["22"], "fpage": ["1257"], "lpage": ["63"], "pub-id": ["10.1016/j.bbmt.2016.03.007"]}]	{ "acronym": [], "definition": [] }	19	CC BY	no	2024-01-13 00:02:19	Bone Marrow Transplant. 2024 Oct 17; 59(1):34-40	oa_package/dc/51/PMC10781622.tar.gz
PMC10781623	38200299	[]	[ "<title>Methods</title>", "<p id=\"Par27\">We use two approaches to evaluate the effects of anthropogenic climate change on spring snowpack. First, we follow an attribution that uses the correlation between observed historical snowpack trends from several SWE data products and those from climate model simulations. Second, we take a data–model fusion approach in which we generate a large observation-based ensemble of historical snowpack and estimate what March SWE would have been in the absence of anthropogenically forced changes to cold-season temperature and precipitation. The former indicates forced changes to hemispheric snowpack and the latter indicates forced snow changes at hydrologically relevant scales.</p>", "<title>Data</title>", "<p id=\"Par28\">Our ensemble of SWE observations consists of five long-term gridded datasets from the European Center for Medium-Range Weather Forecasting’s (ECMWF) ERA5-Land reanalysis<sup>##UREF##34##45##</sup>; the Japan Meteorological Agency’s JRA-55 reanalysis<sup>##UREF##35##46##</sup>; NASA’s MERRA-2 reanalysis<sup>##UREF##36##47##</sup>; the European Space Agency’s Snow-CCI, Version 2.0<sup>##UREF##37##48##</sup>; and TerraClimate<sup>##REF##29313841##49##</sup>. Products with a submonthly temporal resolution are averaged across all available March values. We focus on March because it is climatologically the month of maximum snow mass in the Northern Hemisphere<sup>##UREF##15##20##</sup> and there is an extensive collection of in situ measurements taken during March against which we can benchmark our results. Because the satellite remote-sensing-based Snow-CCI product is masked over mountainous terrain, we follow the approach of ref. <sup>##UREF##15##20##</sup> and fill SWE values in mountainous cells with the mean value from the other four data sources. For non-mountainous grid cells, we use the unaltered Snow-CCI data. In addition, we use in situ SWE data from the Snowpack Telemetry Network (SNOTEL) network in the western USA<sup>##UREF##38##50##</sup>; the Canadian historical Snow Water Equivalent dataset (CanSWE)<sup>##UREF##39##51##</sup>; and the Northern Hemisphere Snow Water Equivalent (NH-SWE) dataset, a hemispheric dataset that converts far more abundant snow depth observations to SWE using a well validated model<sup>##UREF##40##52##</sup>. Only in situ observations with records for at least 35 years between 1981 and 2020 are retained, resulting in a set of 550 from SNOTEL, 341 from CanSWE and 2,119 from NH-SWE.</p>", "<p id=\"Par29\">Gridded precipitation data come from the ECMWF’s ERA5 reanalysis<sup>##UREF##41##53##</sup>; the Global Precipitation Climatology Centre (GPCC)<sup>##UREF##42##54##</sup>; MERRA-2<sup>##UREF##36##47##</sup>; Multi-Source Weighted-Ensemble Precipitation (MSWEP), Version 2<sup>##UREF##43##55##</sup>; and TerraClimate<sup>##REF##29313841##49##</sup>. Gridded temperature data come from Berkeley Earth (BEST)<sup>##UREF##44##56##</sup>; NOAA’s Climate Prediction Center (CPC) Global Unified Temperature<sup>##UREF##45##57##</sup>; ERA5<sup>##UREF##41##53##</sup>; and MERRA-2<sup>##UREF##36##47##</sup>. Daily gridded runoff data come from the ECMWF’s Global Flood Awareness System (GloFAS)<sup>##UREF##46##58##</sup>. Details of all datasets used in the analysis are given in Extended Data Table ##TAB##0##1##.</p>", "<p id=\"Par30\">For the climate-model-based attribution and observation-based reconstructions, we regrid all data to 2° × 2° and 0.5° × 0.5° horizontal resolution, respectively, using conservative regridding. For all data except runoff, grid cells where March SWE is zero in more than half of all product years are masked out, as is Greenland.</p>", "<p id=\"Par31\">We also use climate model output from 12 models that archived monthly SWE (‘snw’) data from the pre-industrial control (PIC), historical (HIST), historical-nat (HIST-NAT) and SSP2-4.5 CMIP6 experiments, as well as monthly air temperature (‘tas’) and precipitation (‘pr’) data from the HIST, HIST-NAT and SSP2-4.5 experiments<sup>##UREF##21##27##,##UREF##22##28##</sup>. All model output are regridded and masked as with the gridded observational data. Consistent with the Detection and Attribution Model Intercomparison Project (DAMIP) protocol, the HIST simulations, which end in 2014, are extended to 2020 using the SSP2-4.5 scenario<sup>##UREF##47##59##</sup>. For simplicity, ‘historical’ (HIST) will always refer to these extended time series. Model details are given in Extended Data Table ##TAB##1##2##.</p>", "<p id=\"Par32\">To provide estimates of hydrologic quantities at decision-meaningful scales, we aggregate from the gridded to the river-basin scale using basin extents from the Global Runoff Data Center’s Major River Basins of the World database<sup>##UREF##33##44##</sup>. All empirically estimated grid-cell values of SWE, precipitation and runoff (in mm, or equivalently kg m<sup>−2</sup>) are multiplied by the grid cell area (in m<sup>2</sup>) before summing all grid cells within a basin to calculate basin-scale mass (in kg). Basin- and hemisphere-average temperatures are given by the area-weighted mean temperature of all snow-covered grid cells.</p>", "<p id=\"Par33\">All estimates of basin population are calculated using the 2020 values from the 15 arcmin Gridded Population of the World, Version 4 (GPWv4) dataset from NASA’s Socioeconomic Data and Applications Center<sup>##UREF##48##60##</sup>.</p>", "<title>Attributing SWE trends to anthropogenic forcing</title>", "<p id=\"Par34\">Our hemispheric attribution approach tests whether the similarity between observed and climate-model-simulated forced SWE trends exceeds what could be possible from natural climate variability alone<sup>##UREF##20##26##–##UREF##23##29##</sup>. To evaluate the null hypothesis that the pattern of SWE trends in the HIST simulations could be the result of natural variability alone, we calculate the spatial pattern of trends in March SWE from 1981 to 2020 in each model’s HIST simulation and for every unique 40-year period from those same models’ unforced PIC simulations (for example, for a 500-year PIC simulation, we generate 461 maps of 40-year trends). All trends are calculated using the Theil–Sen estimator, a non-parametric technique for estimating a linear trend that is more robust to data that is skewed or contains outliers than trends calculated using ordinary least squares regression. Then, we calculate the Spearman (rank) correlation coefficient between the spatial maps of HIST and PIC trends to quantify the pattern similarity. The resulting empirical distribution of 78,601 correlations (background histogram on Fig. ##FIG##1##2##) represents the likelihood that the pattern in the forced historical simulations could have arisen from natural variability alone.</p>", "<p id=\"Par35\">We quantify the similarity between the observed pattern of SWE trends and the model-estimated response to forcing by taking the Spearman spatial correlation between the map of trends from each observational product and the multimodel mean map from the HIST simulations (red symbols in Fig. ##FIG##1##2e##). For this analysis, the in situ observations are aggregated to the same 2° × 2° grid as the gridded observations and climate models by taking the mean trend of all stations within each grid cell (Fig. ##FIG##1##2a##). If the correlations between the observations and HIST simulations are greater than almost all of the correlations between the HIST and PIC simulations, we can reject the null hypothesis that the observed historical pattern could have arisen from natural variability alone and claim that a response to historical forcing is present in the observed pattern. Furthermore, if we cannot reject the null hypothesis using the correlations between the observations and HIST-NAT simulations with only solar and volcanic forcing, then it is unlikely that the observed pattern is the result of natural radiative forcing. Combined, these two lines of evidence strongly indicate that anthropogenic forcing is causing the observed patterns of SWE trends.</p>", "<title>Observation-based snow reconstructions</title>", "<p id=\"Par36\">As another means of attributing historical SWE change, and to better understand its patterns and drivers at scales more commensurate with the impacts of snow loss, we generate a large observation-based ensemble of historical March SWE with and without the effects of anthropogenic forcing. We do so by using the common random forest machine-learning algorithm, which fits randomized regression trees on bootstrapped samples of the data and averages their predictions together. The decision tree framework is particularly well suited to pick up nonlinear interactions, such as that between temperature and precipitation in the context of snow, as well as correlated predictors. The random forest algorithm has been applied to reconstruct a wide variety of biogeophysical variables that are shaped by temperature, precipitation and their interaction, including historical runoff<sup>##UREF##49##61##</sup>, crop yields<sup>##UREF##50##62##</sup> and climate-induced species range shifts<sup>##UREF##51##63##</sup>. In each instance, the random forest model was found to significantly outperform both other machine-learning algorithms and more traditional approaches such as linear regression. In addition, for this particular application of reconstructing historical snowpack, the model imposes no prior assumptions about temperature thresholds for rain–snow partitioning or snowmelt, which can vary substantially in space and are themselves a contributor to uncertainty in modelled estimates of SWE<sup>##REF##29559636##42##,##UREF##52##64##</sup>. We model March SWE as a function of average monthly temperature and cumulative monthly precipitation from the previous November to March:where SWE<sub><italic>y</italic>,<italic>i</italic></sub> is average March SWE in water year (October–September) <italic>y</italic> at grid cell <italic>i</italic>, <italic>f</italic> is the random forest model, <italic>T</italic><sub><italic>y</italic>,<italic>m</italic>,<italic>i</italic></sub> is the average temperature in month <italic>m</italic> of water year <italic>y</italic> and grid cell <italic>i</italic>, and <italic>P</italic><sub><italic>y</italic>,<italic>m</italic>,<italic>i</italic></sub> is the total precipitation in month <italic>m</italic> of water year <italic>y</italic> and grid cell <italic>i</italic>. We fit the model using the full spatiotemporal panel of 0.5° × 0.5° gridded data (that is, all grid-cell years from 1981 to 2020), then aggregate the predicted gridded values to the river-basin scale. We find that training a single model on the full panel of data offers two main advantages over training multiple models on more local data (for example, a model for each river basin). First is that the out-of-sample prediction skill of the full panel model is significantly higher in many highly populated mid-latitude basins of the western USA, western Europe and High Mountain Asia; local models are more skilful in fewer than 20% of basins, concentrated in sparsely populated high-latitude basins where the skill of the full panel model is already high (Extended Data Fig. ##FIG##6##3##). Second, training a single model on data from the entire hemisphere provides greater statistical stability of projections made with large perturbations to the input variables, such as adding an end-of-century climate change signal (Extended Data Fig. ##FIG##11##8##), which could exceed the support of local historical observations as records fall at an increasing rate<sup>##UREF##53##65##,##REF##22025683##66##</sup>.</p>", "<p id=\"Par37\">To adequately sample and quantify the observational uncertainty in snowpack, temperature and precipitation and create a sufficiently wide ensemble of possible SWE values, we repeat this procedure for all combinations of 6 SWE (5 gridded + in situ), 4 temperature and 5 precipitation datasets (Extended Data Table ##TAB##0##1##), providing 120 (6 × 4 × 5) estimates of basin-scale March SWE from 1981 to 2020. Our ensemble approach is motivated by two main considerations. First, it is difficult to determine what represents ‘true’ snowpack at hydrologically relevant scales. All methods of estimating spatially distributed snowpack (for example, remote sensing or reanalysis) have their intrinsic limitations that result in high levels of disagreement on snow mass, its variability and long-term trends<sup>##UREF##2##5##,##UREF##3##6##</sup>, as we show in Fig. ##FIG##0##1##. In situ measurements may represent truth at the locations at which they are collected, but are difficult to generalize, especially in complex terrain. As a result, using these point observations to adjudicate which gridded products (whose values represent averages over tens to tens of thousands of kilometres) lie closest to ‘truth’ is challenging. Given the inability to know the true state of snowpack or rigorously rule out any of its various gridded estimates, we choose to consider these observational products as equally valid estimates of truth in which we can attempt to identify shared responses. Second, the ensemble approach allows us to capture the structural uncertainty in how SWE responds to changes in temperature and precipitation, which are themselves subject to data uncertainties (Supplementary Fig. ##SUPPL##0##2##). Using all dataset combinations, we can sample and characterize uncertainty in SWE, temperature and precipitation and their covariance with one another. Such an approach has been used to estimate forced changes in components of the Earth system in which both the dependent and independent variables of interest are themselves uncertain<sup>##REF##37200445##32##,##UREF##54##67##</sup>.</p>", "<p id=\"Par38\">We compare the model-predicted time series generated through this process with the observational SWE product on which the model is trained, using the common <italic>R</italic><sup>2</sup> and RMSE metrics (Extended Data Fig. ##FIG##7##4##). In addition, as the emphasis of the analysis is on long-term trends in SWE, we compare the reconstructed trends with the observed trends over the study period and find that our models faithfully reproduce the spatial pattern and magnitude of the trends quite well, with correlations for all data products falling between 0.9 and 0.97 (Extended Data Fig. ##FIG##6##3##). Furthermore, the RMSE of the construction model predictions is comparable across the 10 coldest, 10 warmest and 20 ‘average’ years in the 1981–2020 period, indicating that the reconstructions are stable even in extreme years (Supplementary Fig. ##SUPPL##0##4##).</p>", "<p id=\"Par39\">As an additional test of model skill, we use the model trained on only the gridded observational products to predict fully out-of-sample March SWE at 2,961 in situ sites from the SNOTEL, CanSWE and NH-SWE datasets. Our reconstructions are able to capture the interannual variability in in situ SWE quite well, with a median <italic>R</italic><sup>2</sup> across stations of 0.59 and an RMSE of around 22% (Extended Data Fig. ##FIG##8##5##). The reconstruction model predictions are similarly able to capture skillfully the long-term SWE trends at the in situ sites, with a pattern correlation of 0.72 (Extended Data Fig. ##FIG##8##5##). Finally and crucially, we confirm that there are no systematic trends in time of the bias of our reconstructions against the in situ observations (Supplementary Fig. ##SUPPL##0##5##), indicating that the reconstruction models are capturing the real-world rate of change of snowpack with high fidelity.</p>", "<title>Counterfactual snowpack reconstructions</title>", "<p id=\"Par40\">To identify where and how anthropogenic climate change has altered spring snowpack at impact-relevant scales, we combine our observation-based reconstructions, which are highly skilful at capturing historical SWE trends at impact-relevant scales, with climate model simulations that allow us to estimate forced changes to temperature and precipitation. Such a data–model fusion approach has been used to attribute anthropogenically forced changes to a wide variety of systems, both physical (for example, soil moisture<sup>##REF##32299953##8##,##UREF##24##31##</sup>, wildfire<sup>##REF##27791053##30##</sup> and lake water storage<sup>##REF##37200445##32##</sup>) and socioeconomic (for example, crop indemnities<sup>##UREF##25##33##</sup> and climate damages<sup>##UREF##26##34##</sup>).</p>", "<p id=\"Par41\">We calculate the temperature response to anthropogenic forcing as the difference between the 30-year rolling mean average temperature for each month in the HIST and HIST-NAT runs. For precipitation, we calculate the forced response as the percentage difference between 30-year rolling mean monthly precipitation in HIST versus HIST-NAT. By differencing experiments from the same model, we hope to limit the influence of model biases in climatological temperature and precipitation, as each model is benchmarked to its own climatology. Systematic biases in the model-simulated trends (for example, too rapid warming or wetting), however, could potentially lead to over- or under-estimating the forced response. To address this possibility, we evaluate model biases in the 1981–2020 trends in winter temperature and precipitation against observed trends by taking the difference between the CMIP6 HIST ensemble mean and the mean of the observational products for each quantity (Extended Data Fig. ##FIG##9##6##). To test whether the observed and modelled trends are consistent, we ask whether the observed trend falls within a plausible range of forcing plus internal variability, given as the 2.5–97.5th percentile range of the CMIP6 HIST trends. Only 1% (3%) of grid cells fall outside this range for temperature (precipitation), indicating that the climate models capture realistic historical climate trends.</p>", "<p id=\"Par42\">Having estimated anthropogenically forced changes in gridded temperature and precipitation, we create counterfactual time series of temperature and precipitation by downscaling the output to the 0.5° × 0.5° resolution of the observational ensemble using conservative regridding and removing the forced response from each model realization from each gridded temperature and precipitation dataset. Temperature is adjusted by subtracting the forced change from the observations and precipitation is adjusted by the forced percentage change. Then, we use the reconstruction models trained on historical data (equation (##FORMU##0##1##)) to predict March SWE using the counterfactual temperature and precipitation data, giving an estimate of what SWE would have been absent human-caused climate change. In addition, we isolate the effects of forced changes to temperature and precipitation individually by removing the forced response of only one or the other quantity from the observations, while leaving the other at its observed historical values. These gridded counterfactual reconstructions are then similarly aggregated to the basin scale and linear trends in SWE for these counterfactual scenarios are calculated using the Theil–Sen estimator. The effect of forced changes to temperature and precipitation individually (Fig. ##FIG##2##3c,d##) and in combination (Fig. ##FIG##2##3e##) is calculated as the difference between each historical trend and the counterfactual trends based on the same SWE–temperature–precipitation dataset combination. For each of the 120 reconstruction ensemble members, we have 101 estimates of the anthropogenic effect (one from each climate model realization; Extended Data Table ##TAB##1##2##), for a total of 12,120 estimates for each basin. Using only the first realization from each climate model, rather than all available runs, produces nearly identical results (Supplementary Fig. ##SUPPL##0##6##).</p>", "<p id=\"Par43\">To further test the validity of this approach of using forced changes in temperature and precipitation to estimate counterfactual SWE, we repeat this protocol using exclusively climate model output in a ‘perfect model’ framework. For each model, we fit the empirical model described in equation (##FORMU##0##1##) using SWE, temperature and precipitation data from the CMIP6 HIST simulations over the 1981–2020 period, rather than observations. Then, we use the random forest trained on these HIST data to predict counterfactual SWE using temperature and precipitation from the HIST-NAT simulations. Finally, we compare the forced (HIST minus HIST-NAT) trends calculated from the reconstruction approach to the ‘true’ forced trends calculated by using the direct SWE output from the HIST and HIST-NAT climate model experiments (Extended Data Fig. ##FIG##12##9## and Supplementary Fig. ##SUPPL##0##7##). The strong similarity in the patterns of the ‘true’ and reconstructed forced responses indicates that using observations with forced changes in temperature and precipitation removed produces reasonable estimates of a forced SWE change.</p>", "<title>Uncertainty quantification</title>", "<p id=\"Par44\">The methods detailed above yield 12,120 estimates of the effect of climate change on March snowpack trends in each of 169 major river basins. Contributing to the spread of these estimates are four main sources of uncertainty: (1) uncertainty in the SWE data products on which the reconstructions are based; (2) uncertainty in the temperature and precipitation data products and their relationship with SWE; (3) differences in the forced response of temperature and precipitation due to structural differences between climate models; and (4) uncertainty due to internal climate variability in temperature and precipitation.</p>", "<p id=\"Par45\">To quantify the magnitude of uncertainty introduced by each source, we calculate the standard deviation of forced SWE trends across a single dimension, holding all others at their mean. For instance, the uncertainty due to differences in model structure is given by the standard deviation of forced SWE trends across the 12 climate models (considering only the first realization from each), taking the mean across all SWE–temperature–precipitation dataset combinations.</p>", "<p id=\"Par46\">To isolate the uncertainty from internal variability in temperature and precipitation, we use 50 pairs of HIST and HIST-NAT simulations from the MIROC6 model<sup>##UREF##55##68##</sup>, which differ in only their initial conditions. We take the standard deviation of forced SWE trends for all 50 realizations, taking the mean across all SWE, temperature and precipitation data product combinations.</p>", "<p id=\"Par47\">Consistent with previous work in uncertainty partitioning<sup>##UREF##14##19##,##UREF##31##41##,##UREF##56##69##</sup>, we consider total uncertainty <italic>U</italic> in the forced SWE trend in basin <italic>b</italic> to be the sum of all four sources:where <italic>S</italic> is the uncertainty from SWE observations, TP is the uncertainty from temperature and precipitation observations, <italic>M</italic> is the uncertainty from model structure, and <italic>I</italic> is the uncertainty from internal variability. To assess which sources are the largest contributor to uncertainty in each basin, we consider the fractional uncertainty of each (for example, <italic>S</italic><sub><italic>b</italic></sub>/<italic>U</italic><sub><italic>b</italic></sub> gives the proportion of uncertainty in basin <italic>b</italic> attribution to SWE observational uncertainty). This fractional uncertainty is reported in Supplementary Fig. ##SUPPL##0##12##. For each source, we hatch out basins where the magnitude of uncertainty is insufficient to change the sign of the ensemble mean estimate of the forced SWE trend (that is, the signal-to-noise ratio is >1).</p>", "<title>Temperature sensitivity of snowpack</title>", "<p id=\"Par48\">To better understand the drivers of the heterogeneous spatial response of SWE and its potential future changes with further warming, we evaluate the temperature sensitivity of March SWE across a gradient of climatological winter temperatures in in situ observations, gridded observations, our basin-scale reconstructions and climate models. The marginal effect of an additional degree of warming, ∂SWE/∂<italic>T</italic> or <italic>β</italic><sub>1</sub>, is calculated as the regression coefficient of March SWE on cold-season (November–March) temperature:where SWE<sub><italic>y</italic>,<italic>i</italic></sub> is March SWE in unit <italic>i</italic> (in situ station, grid cell or river basin) in water year <italic>y</italic> and <italic>T</italic><sub><italic>y</italic>,<italic>i</italic></sub> is average cold-season temperature in that same unit. We run this regression at each in situ location, for all 20 combinations of gridded SWE and temperature products, for all 12 climate models (using the HIST simulations), and for all 120 basin-scale reconstructions. We then calculate the average and standard deviation of all of the coefficients for a given type of data (in situ, gridded observations, climate models and basin-scale reconstructions) in a rolling 5° temperature window to produce the curves in Fig. ##FIG##3##4a##. As such, the uncertainty estimate includes both parametric and data uncertainty.</p>", "<title>Snowpack-driven runoff changes</title>", "<p id=\"Par49\">To evaluate the differential water security implications of the human-caused snowpack declines, we quantify the spring (April–July) runoff change due to forced March SWE changes. We once again use the random forest algorithm, modelling April–July run-off as a function of March SWE and monthly temperature and precipitation from the previous November to July:where <italic>Q</italic><sub><italic>y</italic>,<italic>b</italic></sub> is April–July total runoff in water year (October–September) <italic>y</italic> in basin <italic>b</italic>, SWE<sub><italic>y</italic>,<italic>b</italic></sub> is average March SWE in water year <italic>y</italic> in basin <italic>b</italic>—unlike the SWE reconstructions, which were fit at the grid-cell level and aggregated to the basin scale, the runoff model is fit using basin-scale data—<italic>T</italic><sub><italic>y</italic>,<italic>m</italic>,<italic>b</italic></sub> is the area-weighted basin-average temperature in month <italic>m</italic> of water year <italic>y</italic>, and <italic>P</italic><sub><italic>y</italic>,<italic>m</italic>,<italic>b</italic></sub> is the total basin-scale precipitation in month <italic>m</italic> of water year <italic>y</italic>. We fit this model using all 120 SWE–temperature–precipitation dataset combinations and the GloFAS runoff data (Extended Data Table ##TAB##0##1##). We evaluate model skill using the same methods as those used to validate our SWE reconstructions (Extended Data Fig. ##FIG##13##10##).</p>", "<p id=\"Par50\">Analogous to the basin-scale March SWE attribution described above, the spring runoff change due to forced changes to snowpack is given by the difference between runoff estimated with historical SWE and runoff estimated with the effects of forced temperature and precipitation changes on SWE removed.</p>", "<title>Future snowpack and runoff changes</title>", "<p id=\"Par51\">To better understand the differential water-availability implications of future warming-driven SWE changes, we combine our statistical models and projections of future temperature and precipitation change to produce estimates of end-of-century (2070–2099) snowpack under the SSP2-4.5 forcing scenario. Specifically, we use a ‘delta’ method in which we adjust the observed climatology for each month by the difference between the end-of-century and historical (1981–2020) climate from the climate models. We additively adjust temperature and adjust precipitation by the percentage change between historical and future climate. We then make predictions of future climatological snowpack using the adjusted data and the model described in equation (##FORMU##0##1##) trained on historical data.</p>", "<p id=\"Par52\">Future runoff changes due to changes in SWE are calculated using equation (##FORMU##3##4##), but substituting estimates of future SWE climatology for the historical, while keeping temperature and precipitation at their observed historical climatological values.</p>", "<title>Snow dominance</title>", "<p id=\"Par53\">To identify a priori the river basins considered to be snow dominant in Fig. ##FIG##0##1##, we use the ratio <italic>R</italic> of water year (October–September) cumulative snowfall to runoff<sup>##REF##16292301##1##</sup>, calculated from ERA5-Land<sup>##UREF##34##45##</sup>. Basins where the average <italic>R</italic> is greater than 0.5 are considered to be snowmelt dominant.</p>" ]	[]	[]	[]	[ "<p id=\"Par1\">Documenting the rate, magnitude and causes of snow loss is essential to benchmark the pace of climate change and to manage the differential water security risks of snowpack declines<sup>##REF##16292301##1##–##UREF##1##4##</sup>. So far, however, observational uncertainties in snow mass<sup>##UREF##2##5##,##UREF##3##6##</sup> have made the detection and attribution of human-forced snow losses elusive, undermining societal preparedness. Here we show that human-caused warming has caused declines in Northern Hemisphere-scale March snowpack over the 1981–2020 period. Using an ensemble of snowpack reconstructions, we identify robust snow trends in 82 out of 169 major Northern Hemisphere river basins, 31 of which we can confidently attribute to human influence. Most crucially, we show a generalizable and highly nonlinear temperature sensitivity of snowpack, in which snow becomes marginally more sensitive to one degree Celsius of warming as climatological winter temperatures exceed minus eight degrees Celsius. Such nonlinearity explains the lack of widespread snow loss so far and augurs much sharper declines and water security risks in the most populous basins. Together, our results emphasize that human-forced snow losses and their water consequences are attributable—even absent their clear detection in individual snow products—and will accelerate and homogenize with near-term warming, posing risks to water resources in the absence of substantial climate mitigation.</p>", "<p id=\"Par2\">Snowpack reconstructions for major river basins in the Northern Hemisphere reveal that the snowpack has declined in almost half of the basins, with roughly one-third of the declines attributable to human-induced warming.</p>", "<title>Subject terms</title>" ]	[ "<title>Main</title>", "<p id=\"Par3\">Seasonal snow is regarded as a sentinel system for climate change. Warm winter temperatures can favour rain over snow and enhance snowmelt, reducing snow water storage and posing hydrologic risks to people and ecosystems<sup>##REF##16292301##1##–##UREF##1##4##</sup>. Yet, puzzlingly, snow is not behaving as a sentinel (Fig. ##FIG##0##1##): although observations show consistent warming trends at the hemispheric, continental and river-basin scales (Fig. ##FIG##0##1##), there is no consistent pattern of snowpack loss across observational data products (Fig. ##FIG##0##1b–e##). As such, although the latest Intergovernmental Panel on Climate Change (IPCC) assessment concluded with high confidence that Northern Hemisphere springtime snow water equivalent (SWE; a typical measure of snow mass) has “generally declined” since 1981<sup>##UREF##4##7##</sup>, it remains unclear where, when and by how much anthropogenic climate change has actually altered snowpack so far, especially at decision-relevant scales. Absent a robust attribution of human-forced snowpack changes, it is difficult to identify the regions most vulnerable to snow loss and, by extension, to develop appropriate strategies to manage present and future water security risks from snow changes.</p>", "<p id=\"Par4\">At least three factors account for the inconsistent response of snowpacks to observed warming. Chief among them are the aforementioned observational uncertainties in estimates of SWE<sup>##UREF##2##5##,##UREF##3##6##</sup>. For example, in only one-third of the Northern Hemisphere’s major river basins—and fewer than half of the dozen most populated—is there agreement across products on the direction of long-term snow change (Fig. ##FIG##0##1c##). Second, snowpack is highly variable across a range of timescales, reflecting low-frequency modes of climate variability, such as the Pacific Decadal Oscillation<sup>##REF##32299953##8##–##UREF##6##10##</sup> or Atlantic Multidecadal Variability<sup>##UREF##7##11##</sup>. Disentangling the snowpack response to forcing thus also requires a robust estimate of regional snow responses to internal variability, such as those that come from initial condition large ensembles of climate simulations<sup>##UREF##8##12##</sup>. Attribution studies that rely on a small number of climate models and/or few model realizations (for example, refs. <sup>##REF##18239088##13##–##UREF##11##16##</sup>) may conflate internal variability and model structural uncertainties<sup>##UREF##12##17##–##UREF##14##19##</sup>, the latter of which are quite large for snowpack<sup>##UREF##15##20##–##UREF##17##22##</sup>, making attribution difficult. Lastly, the relationship between forcing and snowpack is not unidirectional: warming, for example, can enhance cold-season precipitation<sup>##UREF##18##23##</sup> and snowfall extremes<sup>##REF##25164753##24##</sup>, potentially offsetting warming-driven losses, particularly in cold, high-latitude or high-elevation regions<sup>##UREF##12##17##,##UREF##19##25##</sup>. Regional attribution studies (for example, refs. <sup>##REF##18239088##13##,##UREF##9##14##</sup>) have normalized SWE by cumulative cold-season precipitation in a rightful effort to reduce noise from precipitation variability and allow for a clearer identification of a temperature signal, but this strategy fails to capture the full effect of climate change on snow. Any attribution of human-caused snowpack declines must address these complications to be trustworthy and informative.</p>", "<p id=\"Par5\">We address these uncertainties by combining an observations-based ensemble of snowpack, temperature, precipitation and runoff data products with empirical and climate models to attribute snowpack changes to anthropogenic warming at the hemispheric and river-basin scales. We use these insights to assess how changes to temperature and precipitation have affected snow water storage and to generalize how snowpack and the runoff it generates will respond to additional warming. Together, our results provide a thorough documentation of the historical and future effects of climate change on snow water storage.</p>", "<title>A forced signal in snowpack observations</title>", "<p id=\"Par6\">Despite the substantial uncertainty in spatially distributed estimates of snowpack (Fig. ##FIG##0##1## and Extended Data Fig. ##FIG##4##1##), gridded snow products nevertheless share a distinct spatial pattern of historical trends that agrees well with in situ observations (Fig. ##FIG##1##2a,b##). Over the past 40 years, March SWE has sharply declined in the southwestern USA and much of western, central and northern Europe by 10% to 20% per decade. Strong snow decreases extend eastwards across the Eurasian continent into parts of central Asia, per the gridded products (Fig. ##FIG##1##2b## and Extended Data Fig. ##FIG##4##1##), although a lack of in situ reference points there makes it difficult to validate these trends. In contrast, the cold continental interiors of central North America and northern Eurasia have seen increasing spring snowpacks, with in situ observations indicating a deepening of over 20% per decade in the Northern Great Plains and parts of Siberia, whereas gridded products indicate more modest increases of 5% to 10% per decade. Snow-dominated regions that lack in situ observations, such High Mountain Asia and the Tibetan Plateau, show weak trends in the gridded observational ensemble mean (Fig. ##FIG##1##2b##), which belie directionally inconsistent trends in individual data products (Fig. ##FIG##0##1b,e## and Extended Data Fig. ##FIG##4##1##).</p>", "<p id=\"Par7\">Coupled climate model simulations forced with historical human and natural forcing capture some features of the observed historical spatial pattern of snow change, particularly the large snow loss over most of Europe and modest gains over Northern Eurasia (Fig. ##FIG##1##2c## and Extended Data Fig. ##FIG##5##2##). The historical climate model experiments capture parts of the spatial structure of snow change over North America, including declines in the southwest and northeast, but show modest declines in the continental interior where observations report deepening snowpacks (Extended Data Fig. ##FIG##5##2##). Meanwhile, simulations that exclude anthropogenic emissions fail to capture the observed pattern of snow change (Fig. ##FIG##1##2d##).</p>", "<p id=\"Par8\">To be able to claim that human interference in the climate system is responsible for the observed hemispheric pattern of snowpack trends, we calculate the chances that the observed pattern of snow change could have arisen from natural climate variability alone. We follow a widely used attribution approach<sup>##UREF##20##26##–##UREF##23##29##</sup> and generate a distribution of pattern correlations between 40-year SWE trends from forced (historical or HIST) and unforced (pre-industrial control or PIC) climate model simulations (<xref rid=\"Sec6\" ref-type=\"sec\">Methods</xref>). This exercise provides a null distribution (the grey background histogram in Fig. ##FIG##1##2e##) indicating how much a spatial pattern of SWE trends arising from model-simulated natural variability alone could resemble a pattern consistent with those that include anthropogenic forcing. We then correlate the spatial pattern of SWE trends in each observational dataset with those from the ensemble mean of two different climate experiments: the HIST simulations (red symbols in Fig. ##FIG##1##2e##), representing historical anthropogenic forcing and the historical-nat, or HIST-NAT, simulations (blue symbols in Fig. ##FIG##1##2e##), representing a historical climate without human-caused greenhouse gas emissions. Finally, we compare the observed correlations to the null distribution to calculate the probability that the degree of similarity between the observations and HIST and HIST-NAT simulations could have arisen from natural variability.</p>", "<p id=\"Par9\">We find that, in the language of the IPCC, it is virtually certain (>99% probability) that human emissions have contributed to the observed pattern of March snowpack trends in in situ observations and in the average of the gridded ensemble, as well as in the TerraClimate reanalysis and the Japanese 55-year Reanalysis (JRA-55). We note that the strength of this claim is subject to the choice of dataset, as the ERA5-Land reanalysis (97%) and the satellite remote sensing-based Snow-CCI product (97%) show a slightly lower, but still an ‘extremely likely’ probability, and there is no detectable influence when examining the MERRA-2 reanalysis (78%). Thus, despite the substantial observational uncertainty in long-term snow trends among data products, there seems to be a shared structure in the spatial pattern of observed change that is consistent with that from anthropogenic forcing. Crucially, this similarity is absent when these products are compared with simulations that include only solar and volcanic forcing on the climate system (HIST-NAT; blue symbols in Fig. ##FIG##1##2e##), as not a single pattern is distinguishable from natural variability. As such, we can considerably strengthen the recent IPCC claim about snow trends and say with a high degree of confidence that human emissions have contributed to the observed pattern of spring snowpack trends across the Northern Hemisphere over the past 40 years.</p>", "<title>River-basin-scale snowpack changes</title>", "<p id=\"Par10\">The coupled climate model experiments such as those presented in Fig. ##FIG##1##2## are a powerful tool for detecting and attributing human influence on the broad features of the hemispheric pattern of SWE trends. Yet the ability of these models to capture the magnitude and detailed spatial structure of observed trends is limited (see the range of the <italic>x</italic> axis in Fig. ##FIG##1##2e##), undermining the ability to assess forced snow change and its consequences at impact-relevant scales. To that end, we pursue a data–model fusion approach using a random forest machine-learning algorithm that has been applied in a wide variety of attribution contexts<sup>##REF##32299953##8##,##REF##27791053##30##–##UREF##26##34##</sup>, where we combine empirical models of SWE with climate model simulations to allow us to flexibly estimate how anthropogenic emissions have affected the temperature and precipitation that drive SWE at finer scales (<xref rid=\"Sec6\" ref-type=\"sec\">Methods</xref>). We combine a number of gridded snowpack, temperature and precipitation datasets (Extended Data Table ##TAB##0##1##) in an effort to produce an ensemble of empirical reconstructions of historical March SWE at the basin scale (<xref rid=\"Sec6\" ref-type=\"sec\">Methods</xref>) that skillfully reproduce observed trends and variability in those datasets, with the spatial pattern correlations of reconstructed and observed trends ranging from 0.9 to 0.97 (Extended Data Fig. ##FIG##6##3##) and a median root-mean-square error (RMSE) across all products and basins of under 8% (Extended Data Fig. ##FIG##7##4##). Furthermore, the snowpack reconstruction models are able to skillfully hindcast long-term trends and variability in out-of-sample in situ snow data, with a trend pattern correlation across roughly 3,000 sites of 0.72 and a median RMSE of 22% (Extended Data Fig. ##FIG##8##5##).</p>", "<p id=\"Par11\">Our strategy to empirically reconstruct basin-scale SWE many times using a large number of dataset combinations has three goals. First, we want to be able to effectively sample the observational uncertainty in snow and climate that has undermined snow attributions so far (Fig. ##FIG##0##1##). Second, we need to reconstruct snowpack as a function of temperature and precipitation to isolate how forced and unforced changes in those quantities have shaped observed snowpack changes at impact-relevant scales. Our ensemble of empirical snowpack reconstructions give us the experimental control to assess the drivers of snow changes. Lastly, we want to be able to assess whether signals of forced snowpack changes emerge above the noise of observational, internal variability and climate model uncertainties, and to quantify those sources of uncertainties to improve snowpack constraints<sup>##UREF##13##18##</sup> (Extended Data Fig. ##FIG##10##7##). By using all factorial combinations of observations and climate models, we can fully characterize and quantify these sources of uncertainty and achieve a better estimate of the true forced signal than could be achieved with any single dataset<sup>##UREF##2##5##,##REF##37200445##32##</sup>.</p>", "<p id=\"Par12\">Our ensemble of observations-based reconstructions of March SWE (Fig. ##FIG##2##3## and Extended Data Fig. ##FIG##7##4##) shows that spring snowpack has declined over the past four decades in many mid-latitude basins, with modest increases in cold, high-latitude basins (Fig. ##FIG##2##3a##). The largest decreases of around 10% per decade are seen in the river basins of the southwestern USA and Europe, in agreement with the long-term trends from in situ SWE measurements there<sup>##UREF##27##35##,##UREF##28##36##</sup>. Despite the substantial uncertainty in March SWE trends in the gridded observational products themselves (Fig. ##FIG##0##1##), our empirical reconstructions show a consistent direction of trends in about half of all major river basins (82 out of 169). At the same time, however, there are large concentrations of basins with insignificant March SWE trends in High Mountain Asia, northern North America and Siberia (outside of the Far East, where increases similarly agree with in situ observations<sup>##UREF##29##37##</sup>) driven largely by disagreement on the direction of trends across the ensemble of SWE reconstructions.</p>", "<p id=\"Par13\">The value of our basin-scale SWE reconstructions is that they allow us to isolate the influence of anthropogenically forced trends in temperature and precipitation on snowpack trends at hydrologically relevant scales while fully sampling observational, empirical and climate model uncertainties. We difference the Coupled Model Intercomparison Project Phase 6 (CMIP6) HIST and HIST-NAT experiments to estimate the forced response of temperature and precipitation. We then remove that from the observed temperature and precipitation time series and re-estimate our snowpack reconstructions, giving us an ensemble of counterfactual no-anthropogenic-climate-change snowpack (<xref rid=\"Sec6\" ref-type=\"sec\">Methods</xref>). Although fewer than a quarter of all basins (37 out of 169) show significant counterfactual trends (Supplementary Fig. ##SUPPL##0##1##), some basins, such as the Rio Grande (6.3% per decade), still show consistent declines over the past 40 years, even without human interference with the climate. Such declines are consistent with regional teleconnections to low-frequency oceanic variability, such as the Pacific Decadal Oscillation<sup>##UREF##6##10##</sup>, which can drive decadal-scale hydroclimate trends in these regions independent of those from anthropogenic warming.</p>", "<p id=\"Par14\">We note that the CMIP6 models tend to over-estimate the historical warming trend compared with observations in some regions, particularly over central North America and eastern Europe (Extended Data Fig. ##FIG##9##6## and Supplementary Fig. ##SUPPL##0##2##). At the same time, however, fewer than 1% of apparent biases over the hemisphere fall outside the range of model internal variability, suggesting that models are skillfully capturing Northern Hemisphere winter land-temperature trends<sup>##UREF##30##38##</sup>. The models also underestimate the multidecadal drying in the southwestern USA, which has seen historical precipitation declines driven by both internal ocean–atmosphere variability and anthropogenic forcing<sup>##REF##32299953##8##</sup>, and underestimate observed wetting over the Tibetan Plateau (Extended Data Fig. ##FIG##9##6## and Supplementary Fig. ##SUPPL##0##2##). Once again, however, fewer than 3% of precipitation biases lie outside that possible from modelled internal variability, suggesting these biases do not undermine our attribution.</p>", "<p id=\"Par15\">Our approach sifts through the observational and model noise to reveal that human-forced changes to temperature and precipitation have altered spring snowpack trends in 31 major river basins across the Northern Hemisphere (Fig. ##FIG##2##3e##). The spatial pattern of forced SWE trends is similar to the historical trends (compare Fig. ##FIG##2##3a## and ##FIG##2##3e##), with anthropogenic climate change having reduced spring snowpacks in the mid-latitudes (basins south of 60° N) by 4.1 ± 3.4% per decade (mean ± s.d.) and enhanced them in the cold, high-latitude basins that drain into the Arctic Ocean by 2.5 ± 1.8% per decade (Fig. ##FIG##2##3e##). Interestingly, we are able to detect a forced SWE decline in major basins such as the Columbia (4.8% per decade) where historical observations indicate modest increases since 1981 or the Saint Lawrence (6.9% per decade), where observed trends have been small and statistically insignificant. These examples suggest that internal variability in the climate system has been masking large forced snowpack reductions in some regions<sup>##UREF##12##17##</sup>. Likewise, there are basins like the Rio Grande, which have suffered large historical snowpack declines of over 10% per decade, but for which there is little agreement that forced temperature and precipitation changes have caused those declines, reinforcing the notion that low-frequency variability can overwhelm forced signals in snow and hydroclimate, even on multidecadal timescales<sup>##UREF##12##17##,##REF##32849911##39##</sup>. Indeed, internal variability is the dominant source of uncertainty in the magnitude of forced response—over climate model structural differences and observational uncertainty in SWE, temperature and precipitation—in roughly one in eight basins (Extended Data Fig. ##FIG##10##7##).</p>", "<p id=\"Par16\">Our isolation of the effects of forced changes in temperature (Fig. ##FIG##2##3c##) and precipitation (Fig. ##FIG##2##3d##) show that anthropogenic temperature changes have generally reduced March SWE across the hemisphere, except in the coldest basins, although uncertainty in the underlying SWE observations and in the regional temperature response of the climate models limits agreement over much of northern North America and Asia (Fig. ##FIG##1##2c## and Extended Data Fig. ##FIG##10##7##). Anthropogenically forced precipitation increases have offset some warming-driven losses (Fig. ##FIG##2##3d##) consistent with observed human-caused increases in winter precipitation in many of the Northern Hemisphere’s cold regions<sup>##UREF##18##23##</sup>. Outside of cold continental interiors<sup>##REF##24805239##40##</sup>, however, forced snowpack increases from precipitation are generally insignificant, reflecting both the greater model uncertainty in precipitation and the larger contribution of internal variability to hydroclimate uncertainty<sup>##UREF##14##19##,##UREF##31##41##</sup>.</p>", "<title>Nonlinear sensitivity of snow to warming</title>", "<p id=\"Par17\">Disentangling forced from unforced snow changes (as presented in Fig. ##FIG##2##3##) is essential to inform decisions to manage present and future snow loss. Our analysis makes clear that there is indeed a fingerprint of anthropogenically forced SWE trends across the Northern Hemisphere and that for some regions, natural variability has been sufficient to mask or reverse snow trends. But such an analysis is not just valuable for what it says about snow changes so far. It is valuable because it helps reveal the highly nonlinear sensitivity of snowpack to warming (Fig. ##FIG##3##4##), and in doing so, resolve the conundrum of why it is that—despite warming—there has not been a commensurate decline in snow water storage across the Northern Hemisphere (for example, Fig. ##FIG##0##1##). It also makes clear why we should expect snow losses to rapidly accelerate, with widespread water security consequences (Fig. ##FIG##3##4b##).</p>", "<p id=\"Par18\">Examining the shape of the relationship between average winter temperatures and the marginal sensitivity of snow change to temperature change clarifies why snow detection has been elusive so far and why even modest levels of warming suggest much sharper snow declines to come (Fig. ##FIG##3##4a##). The responsiveness of snow to 1 °C of warming depends on climatological winter temperatures. Below historical temperatures of about −8 °C (determined from change-point analysis), spring snowpack is little affected by warming; however, each additional 1 °C of warming beyond that point results in accelerating losses.</p>", "<p id=\"Par19\">There are several notable features in these curves. First, is their scale and data invariance: the location of the inflection point in temperature sensitivity is consistent when it is estimated from point measurements, gridded data products, climate models or our basin-scale reconstructions. This consistency suggests that despite substantial measurement and modelling uncertainties, simple thermodynamics can explain much of snow’s historical and future response to warming. As the climatological temperature of a location warms towards the freezing point, the likelihood of subseasonal temperatures exceeding thresholds where precipitation is partitioned towards rain over snow or accumulated snowpack will melt increases exponentially. We note, however, that these thresholds themselves are not constant in space, owing to factors such as topography and distance from oceanic moisture sources<sup>##REF##29559636##42##</sup>, which may account for some of the uncertainty in snow sensitivities at any one climatological temperature (shading in Fig. ##FIG##3##4a##). Second, the marginal sensitivity of snow to temperature change provides some intuition for the spatial pattern of SWE trends shared by the observations and climate models in Fig. ##FIG##1##2##: in general, the largest snowpack declines are seen in the climatologically warmest places, which sit just beyond the inflection point in the curve presented in Fig. ##FIG##3##4a##. There, small increases in temperature have led to large declines in snowpack. In contrast, cold regions see little change or in some cases, increased SWE. Such locations sit on the flat, insensitive part on the curve defining the relationship between climatological temperatures and snow sensitivity (Fig. ##FIG##3##4a##).</p>", "<p id=\"Par20\">Lastly, the fact that snow is relatively insensitive to warming below climatological winter temperatures of about −8 °C helps explain the lack of clear snow trends at the hemispheric scale despite substantial warming so far: over 80% of the March snow mass in the Northern Hemisphere is found in places to the left of this inflection point (upper inset distribution, Fig. ##FIG##3##4a##). In those regions, warming has little effect. Notably, much of the 20% of hemispheric snow mass remaining resides just to the right of the −8 °C inflection point, hovering near a snow-loss cliff, where marginal increases in temperature imply larger and larger snow losses to come. What is clear is that in these regions, snow declines so far have been relatively small compared with natural variability. Indeed, the likelihood of observing a statistically significant trend in SWE begins increasing around this inflection point in climatological temperature (Supplementary Fig. ##SUPPL##0##3##). Such a relationship suggests that further warming and thus additional time spent beyond this −8 °C threshold will homogenize snow trends towards more consistent declines, portending widespread and accelerating snow losses for many basins over the coming decades.</p>", "<p id=\"Par21\">Crucially, the highly nonlinear relationship between snow sensitivity and climatological temperature implies rapidly emerging water security risks to people. Although 80% of the Northern Hemisphere’s snow mass is found in cold places that have historically been insensitive to warming, 80% of the hemisphere’s inhabitants reside in the snow-dependent regions beyond this inflection point (lower inset distribution, Fig. ##FIG##3##4a##). As such, further warming is likely to have rapidly emerging impacts on snow water resources in the mid-latitude basins where people reside and place competing demands on fresh water.</p>", "<p id=\"Par22\">To assess this, we consider the population exposure to both projected snow loss and attendant spring snowmelt driven runoff change (Fig. ##FIG##3##4b##). Under Shared Socioeconomic Pathway (SSP) 2–4.5, a ‘middle-of-the-road’ emissions scenario, the most highly populated basins are expected to see strong declines in spring runoff as a result of nonlinear snow loss, even in the face of relatively modest warming projected in those regions (Fig. ##FIG##3##4b## and Extended Data Fig. ##FIG##11##8##). The western USA, for example, is poised to see particularly sharp spring runoff declines in the upper Mississippi (84 million people, 30.2% spring runoff decline), Colorado (14 million, 42.2%), Columbia (8.8 million, 32.7%) and San Joaquin (6.8 million, 40.9%) river basins (Extended Data Fig. ##FIG##11##8##). The most populous basins in Europe, such as the Danube (92 million, 41.0%), Volga (60 million, 39.5%), Rhine (51 million, 33.0%) and Po (18 million, 40.5%) could face water-availability challenges of a similar magnitude. Future changes to SWE-driven spring runoff in Asia, the continent with the greatest number of people living in snow-influenced basins, show substantially less agreement (hatching in Extended Data Fig. ##FIG##11##8##). Snowpack in cold and sparsely populated basins, meanwhile, is likely to be resilient to high levels of winter warming exceeding 5 °C, such as that arising from Arctic amplification<sup>##UREF##32##43##</sup>, and the coldest may see increased snowpacks and enhanced spring runoff into the Arctic Ocean of over 10% on average (Fig. ##FIG##3##4b## and Extended Data Fig. ##FIG##11##8##).</p>", "<title>Managing and leveraging snow uncertainty</title>", "<p id=\"Par23\">Our analysis uses snowpack observations, climate models and an observations-based ensemble of snowpack reconstructions to attribute changes in spring snow water storage at the hemispheric and river-basin scales. Our results explain why snowpack has been a poor sentinel system to assess the pace and magnitude of global warming so far, but why despite that, we should expect unprecedented snowpack declines with only modest additional warming. There is a highly nonlinear temperature sensitivity of snowpack, foreshadowing marked reductions in spring snowpack and associated snow-driven runoff in highly populated basins where snowmelt has an important role in water supply. Our analysis reveals that many of the world’s most populous basins are hovering on the precipice of rapid snow declines and that such losses may only be detected across all observational data products once the water security impacts of snow loss have already manifested. Thoughtful adaptive planning and risk mitigation—particularly around capital-intensive and contentious infrastructure to manage winter flood risks coupled with reduced warm-season streamflow—requires advance warning. The highly nonlinear marginal sensitivity to snow we identify clarifies why such warning in the observations so far has been elusive, and also why waiting until the impacts manifest could be too late to effectively manage their risks. Such warning, we show, will probably only come from the observations once warming is sufficient to push regions into this highly nonlinear snow-loss regime.</p>", "<p id=\"Par24\">We emphasize that we can report these findings to provide meaningful warning because of—rather than despite—uncertainty. Snow datasets may not agree with one another on the magnitude of snowpack or its variability and long-term trends through time (Fig. ##FIG##0##1a## and Extended Data Fig. ##FIG##4##1##). Yet in situ measurements and all gridded data products, apart from one, show a spatial structure consistent with anthropogenic forcing of the climate system. The consistency across diverse datasets allows for a much higher degree of confidence in the identification of forced snowpack trends than could be achieved using a single snow dataset alone. Furthermore, the lack of precise knowledge about the true state of snowpack over time, cold-season temperature and precipitation, and their response to anthropogenic emissions allows us to leverage multiple sources of uncertainty to produce over 12,000 estimates of the effects of anthropogenic climate change on spring snowpack in each of the major river basins of the Northern Hemisphere and identify a statistically stable estimate of the forced signal.</p>", "<p id=\"Par25\">In addition, there is value in identifying and quantifying these sources of uncertainty in forced snowpack changes (Extended Data Fig. ##FIG##10##7##), as it can guide future scientific and operational decision-making<sup>##UREF##13##18##</sup>. For instance, uncertainty in the forced response of temperature and precipitation arising from structural differences between climate models is the dominant source of uncertainty in the magnitude of forced March SWE trends in over half (95 out of 169) of all basins (Extended Data Fig. ##FIG##10##7##), suggesting that improving the skill of climate models in capturing regional climate would go a long way towards constraining historical and future snow change. Uncertainty in SWE data products themselves is also a limiting factor in many basins where in situ observations are sparse or non-existent (Extended Data Fig. ##FIG##10##7##), suggesting that constraining observational estimates of SWE would be valuable. Finally, identifying the contribution of irreducible uncertainty in SWE trends from internal variability in the climate system (Extended Data Fig. ##FIG##10##7##) is also essential, as it indicates the range of physically consistent snowpack trajectories for which water resource managers and stakeholders must be prepared<sup>##UREF##12##17##,##UREF##13##18##</sup>.</p>", "<p id=\"Par26\">Together, our findings portend serious water-availability challenges in basins where snowmelt runoff constitutes a major component of the water supply portfolio. Improving our understanding of where and how climate change has and will affect snow water resources is vital to informing the difficult water resource management decisions that a less snowy future will require.</p>", "<title>Online content</title>", "<p id=\"Par54\">Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41586-023-06794-y.</p>", "<title>Supplementary information</title>", "<p>\n\n\n</p>", "<title>Source data</title>", "<p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n</p>" ]	[ "<title>Extended data figures and tables</title>", "<p id=\"Par57\">\n\n</p>", "<p id=\"Par58\">\n\n</p>", "<p id=\"Par59\">\n\n</p>", "<p id=\"Par60\">\n\n</p>", "<p id=\"Par61\">\n\n</p>", "<p id=\"Par62\">\n\n</p>", "<p id=\"Par63\">\n\n</p>", "<p id=\"Par64\">\n\n</p>", "<p id=\"Par65\">\n\n</p>", "<p id=\"Par66\">\n\n</p>", "<p id=\"Par67\">\n\n</p>", "<p id=\"Par68\">\n\n</p>", "<title>Extended data</title>", "<p>is available for this paper at 10.1038/s41586-023-06794-y.</p>", "<title>Supplementary information</title>", "<p>The online version contains supplementary material available at 10.1038/s41586-023-06794-y.</p>", "<title>Acknowledgements</title>", "<p>We thank Dartmouth’s Research Computing and the Discovery Cluster for computing resources; the World Climate Research Programme, which, through its Working Group on Coupled Modeling, coordinated and promoted CMIP6; and all climate modelling groups for producing and making available their model output. We acknowledge funding for this research from NOAA MAPP NA20OAR4310425 (J.S.M.) and DOE DESC0022302 (J.S.M. and A.R.G.).</p>", "<title>Author contributions</title>", "<p>Both authors designed the analysis. A.R.G. performed the analysis. Both authors interpreted the results and wrote the paper. J.S.M. funded the research.</p>", "<title>Peer review</title>", "<title>Peer review information</title>", "<p id=\"Par55\"><italic>Nature</italic> thanks Jouni Pulliainen, Ryan Webb and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. ##SUPPL##1##Peer reviewer reports## are available.</p>", "<title>Data availability</title>", "<p>All data that support this study are publicly available at the following locations: CMIP6 model outputs, <ext-link ext-link-type=\"uri\" xlink:href=\"https://esgf-node.llnl.gov/\">https://esgf-node.llnl.gov/</ext-link>; SNOTEL, <ext-link ext-link-type=\"uri\" xlink:href=\"https://wcc.sc.egov.usda.gov/nwcc/tabget\">https://wcc.sc.egov.usda.gov/nwcc/tabget</ext-link>; CanSWE, <ext-link ext-link-type=\"uri\" xlink:href=\"https://zenodo.org/records/5889352\">https://zenodo.org/records/5889352</ext-link>; NH-SWE, <ext-link ext-link-type=\"uri\" xlink:href=\"https://zenodo.org/records/7565252\">https://zenodo.org/records/7565252</ext-link>; Snow-CCI, <ext-link ext-link-type=\"uri\" xlink:href=\"https://climate.esa.int/en/projects/snow/Snow_data/\">https://climate.esa.int/en/projects/snow/Snow_data/</ext-link>; ERA5, ERA5-Land and GloFAS, <ext-link ext-link-type=\"uri\" xlink:href=\"https://cds.climate.copernicus.eu/\">https://cds.climate.copernicus.eu/</ext-link>. JRA-55, <ext-link ext-link-type=\"uri\" xlink:href=\"https://rda.ucar.edu/datasets/ds628.0/\">https://rda.ucar.edu/datasets/ds628.0/</ext-link>; MERRA-2, <ext-link ext-link-type=\"uri\" xlink:href=\"https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/\">https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/</ext-link>; TerraClimate, <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.climatologylab.org/terraclimate.html\">https://www.climatologylab.org/terraclimate.html</ext-link>; GPCC, <ext-link ext-link-type=\"uri\" xlink:href=\"https://psl.noaa.gov/data/gridded/data.gpcc.html\">https://psl.noaa.gov/data/gridded/data.gpcc.html</ext-link>; MSWEPv280, <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.gloh2o.org/mswep/\">http://www.gloh2o.org/mswep/</ext-link>; Berkeley Earth, <ext-link ext-link-type=\"uri\" xlink:href=\"https://berkeleyearth.org/data/\">https://berkeleyearth.org/data/</ext-link>; Climate Prediction Center (CPC), <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.cpc.ncep.noaa.gov/\">https://www.cpc.ncep.noaa.gov/</ext-link>; Gridded Population of the World (GPW), <ext-link ext-link-type=\"uri\" xlink:href=\"https://sedac.ciesin.columbia.edu/data/collection/gpw-v4\">https://sedac.ciesin.columbia.edu/data/collection/gpw-v4</ext-link>; Global Runoff Data Center Major River Basins, <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.bafg.de/GRDC/\">https://www.bafg.de/GRDC/</ext-link>. <xref ref-type=\"sec\" rid=\"Sec19\">Source data</xref> are provided with this paper.</p>", "<title>Code availability</title>", "<p>All code that supports this study is available at 10.5281/zenodo.10035276.</p>", "<title>Competing interests</title>", "<p id=\"Par56\">The authors declare no competing interests.</p>" ]	[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>Observed long-term warming trends are robust throughout the Northern Hemisphere, but snowpack trends are not.</title><p><bold>a</bold>,<bold>b</bold>, Agreement across observational products (Supplementary Table ##SUPPL##0##1##) on the sign of trends in November–March average temperature (winter <italic>T</italic>, <bold>a</bold>) and March SWE (<bold>b</bold>) from 1981 to 2020. Numbers in bottom left show the percentage of basins with each category of agreement indicated on the colour bar. Insets: the hemispheric trends for each individual product. <bold>c</bold>–<bold>e</bold>, The trends for the four most populous river basins in North America (<bold>c</bold>), Europe (<bold>d</bold>) and Asia (<bold>e</bold>) that are generally considered snow dominated, as well as each continent (<xref rid=\"Sec6\" ref-type=\"sec\">Methods</xref>). The locations of the basins are indicated on the map in <bold>a</bold>, corresponding to the number in parentheses. Temperature (red triangles) is referenced to the top <italic>x</italic> axis and SWE (blue squares) is referenced to the bottom <italic>x</italic> axis. The 2020 basin population is indicated in the top-right corner. Maps were generated using cartopy v0.18.0. River basin boundaries come from the Global Runoff Data Centre’s Major River Basins of the World database<sup>##UREF##33##44##</sup>.</p><p>##SUPPL##2##Source Data##</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>Climate model experiments reveal that human-caused warming has influenced Northern Hemisphere snowpack trends.</title><p><bold>a</bold>–<bold>d</bold>, Trend in March SWE from 1981 to 2020 in in situ observations (<bold>a</bold>), the ensemble mean of five long-term gridded SWE products (<bold>b</bold>), and the multimodel mean of CMIP6 historical simulations with (<bold>c</bold>) and without (<bold>d</bold>) anthropogenic emissions. <bold>e</bold>, Spatial pattern correlation (<italic>ρ</italic>) of 1981–2020 March SWE trends between the CMIP6 multimodel mean HIST (red symbols) and HIST-NAT (blue symbols) simulations and each observational (OBS) SWE product (see legend). The grey histogram indicates the empirical probability density function of spatial correlations between trends from the historical simulations and all possible 40-year trends from unforced pre-industrial control (PIC) simulations (<italic>N</italic> = 78,601). The red (orange) vertical dashed line indicates the 99th (95th) percentile of this empirical distribution. Maps were generated using cartopy v.0.18.0.</p><p>##SUPPL##3##Source Data##</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><title>Empirical snowpack reconstructions reveal the countervailing effects of human-forced temperature and precipitation trends on basin-scale snow changes.</title><p><bold>a</bold>, Average observed 1981–2020 March SWE trends from 5 long-term SWE data products in 169 major Northern Hemisphere river basins. <bold>b</bold>, As in <bold>a</bold> but for our observation-based reconstructions. <bold>c</bold>, Effect of anthropogenically forced temperature changes on March SWE trends, given by the ensemble mean difference between the statistically reconstructed historical trend and the reconstructed trend with forced changes to temperature removed. <bold>d</bold>, As in <bold>c</bold> but for forced precipitation changes. <bold>e</bold>, As in <bold>c</bold> and <bold>d</bold> but for forced changes to both temperature and precipitation. The hatching indicates basins where fewer than 80% of observations or reconstructed estimates agree on the sign of the trend or forced effect. Maps were generated using cartopy v.0.18.0. River basin boundaries come from the Global Runoff Data Centre’s Major River Basins of the World database<sup>##UREF##33##44##</sup>.</p><p>##SUPPL##4##Source Data##</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><title>The nonlinear sensitivity of snowpack to warming augers accelerating water security risks for highly populous snow-dependent basins.</title><p><bold>a</bold>, Temperature sensitivity of March SWE across a range of climatological winter temperatures in in situ observations (green), gridded data products (blue), climate models (red) and our basin-scale statistical reconstructions (orange). The solid line (shading) indicates the average sensitivity (±1 s.d.) in a rolling 5 °C temperature window across all in situ locations, grid cells or river basins. The red vertical line indicates the change point at which the temperature sensitivity of snowpack becomes nonlinear (based on a change-point analysis using the basin-scale reconstructions). The bottom histograms show the distribution of climatological Northern Hemisphere March SWE and human population in 2° temperature bins, with the values indicating how much of each distribution falls on each side of the change point. Temperatures on the <italic>x</italic> axis are the average November–March temperature over the 1981–2020 period from each in situ location or grid cell. Only climatologically snow-covered grid cells are used to calculate the basin-average temperature. <bold>b</bold>, Percentage change in basin-scale March SWE-driven April–June runoff in 2070–2099 under SSP2-4.5 relative to 1981–2020 (<xref rid=\"Sec6\" ref-type=\"sec\">Methods</xref>) versus basin population. The dots are coloured by the percentage change in March SWE in 2070–2099 relative to 1981–2020 and sized by the CMIP6 ensemble mean projected end-of-century temperature change.</p><p>##SUPPL##5##Source Data##</p></caption></fig>", "<fig id=\"Fig5\"><label>Extended Data Fig. 1</label><caption><title>Heterogenous long-term trends in observed March SWE make claims about snow responses to warming a challenge.</title><p><bold>a</bold>-<bold>e</bold>, Trend in March SWE from 1981 to 2020 from individual gridded SWE data products. <bold>f</bold>, Average trend across all 5 products. Grid cells where fewer than 4 products agree on the sign of the trend are hatched. Maps were generated using cartopy v0.18.0.</p><p>\n##SUPPL##6##Source Data##\n</p></caption></fig>", "<fig id=\"Fig6\"><label>Extended Data Fig. 2</label><caption><title>Historical trends in March SWE from CMIP6 models exhibit uncertainty outside of the Western United States, Europe, and Northern Eurasia.</title><p><bold>a</bold>-<bold>k</bold>, Trend in March SWE from 1981 to 2020 from historical climate model simulations. Details of models can be found in Extended Data Table ##TAB##1##2##. <bold>l</bold>, Ensemble mean trend. Grid cells where fewer than 80% of models agree on the sign of the trend are hatched. Maps were generated using cartopy v0.18.0.</p><p>\n##SUPPL##7##Source Data##\n</p></caption></fig>", "<fig id=\"Fig7\"><label>Extended Data Fig. 3</label><caption><title>Ensemble reconstructions based on the Random Forest model skillfully reproduce the pattern and magnitude of long-term SWE trends in each snow product.</title><p>Observed (<bold>a</bold>-<bold>f</bold>) and reconstructed (<bold>g</bold>-<bold>l</bold>) 1981–2020 March SWE trends for 5 gridded SWE data products and their mean. <bold>m</bold>-<bold>r</bold>, Scatterplot of reconstructed versus observed trends, where each dot represents a river basin. Dashed line denotes perfect reconstruction. Pearson’s correlation is shown in bottom right corner. Maps were generated using cartopy v0.18.0. River basin boundaries come from the Global Runoff Data Centre’s Major River Basins of the World database<sup>##UREF##33##44##</sup>.</p><p>\n##SUPPL##8##Source Data##\n</p></caption></fig>", "<fig id=\"Fig8\"><label>Extended Data Fig. 4</label><caption><title>The Random Forest model exhibits high snowpack reconstruction skill based on temperature and precipitation data.</title><p>Basin-scale R<sup>2</sup> (<bold>a</bold>-<bold>e</bold>) and root-mean-square error (RMSE; <bold>f</bold>-<bold>j</bold>) for 5 gridded SWE data products over the period 1981–2020. Each metric shows the skill of the mean of all reconstructions for a single SWE product versus the observed values from that product. Insets show the distribution of skill across basins, with the red line and value indicating the median. Maps were generated using cartopy v0.18.0. River basin boundaries come from the Global Runoff Data Centre’s Major River Basins of the World database<sup>##UREF##33##44##</sup>.</p><p>\n##SUPPL##9##Source Data##\n</p></caption></fig>", "<fig id=\"Fig9\"><label>Extended Data Fig. 5</label><caption><title>The ensemble reconstruction based on the Random Forest model skillfully predicts the variability and trends in out-of-sample <italic>in situ</italic> snowpack data.</title><p>R<sup>2</sup> (<bold>a</bold>) and RMSE (<bold>b</bold>) of Random Forest model predictions of <italic>in situ</italic> March SWE at 2,961 locations over the period 1981–2020. Insets show the distribution of skill across sites, with the red line and value indicating the median. Observed (<bold>c</bold>) and reconstructed (<bold>d</bold>) 1981–2020 March SWE trends. <bold>c</bold>, Scatterplot of reconstructed versus observed trends, where each dot represents an <italic>in situ</italic> location. Points are colored by their density. Dashed line denotes perfect agreement between reconstructed and observed trends. Pearson’s correlation is shown in bottom right corner. Maps were generated using cartopy v0.18.0.</p><p>\n##SUPPL##10##Source Data##\n</p></caption></fig>", "<fig id=\"Fig10\"><label>Extended Data Fig. 6</label><caption><title>CMIP6 model bias in winter temperature and precipitation trends largely within range of natural variability.</title><p>Observed trends in November-March average temperature (<bold>a</bold>) and total precipitation (<bold>d</bold>) from 1981 to 2020. <bold>b</bold>, <bold>e</bold>. Ensemble mean of historical CMIP6 simulations. <bold>c</bold>, <bold>f</bold>. Average bias in trends across all observation-model combinations. Hatching indicates regions where the observed trend falls outside the 2.5–97.5th percentile range of the CMIP6 trends. Maps were generated using cartopy v0.18.0.</p><p>\n##SUPPL##11##Source Data##\n</p></caption></fig>", "<fig id=\"Fig11\"><label>Extended Data Fig. 7</label><caption><title>Uncertainty in the attribution of human-caused snowpack trends resides with climate model structure and modeled internal variability, not observations.</title><p><bold>a</bold>, Dominant source of uncertainty in reconstruction-based estimates of forced March SWE trends from 1981 to 2020. <bold>b</bold>-<bold>e</bold>, Percentage of total uncertainty in forced SWE trends attributable to (<bold>b</bold>) observational uncertainty in gridded SWE products, (<bold>c</bold>) observational uncertainty in temperature and precipitation data products, (<bold>d</bold>) uncertainty in the forced response of temperature and precipitation across different climate models, and (<bold>e</bold>) uncertainty in the forced response of temperature and precipitation arising from internal variability (Methods). Hatching indicates basins where the uncertainty attributable to a given source is insufficient to change the sign of the ensemble mean estimate of the forced SWE trend. Maps were generated using cartopy v0.18.0. River basin boundaries come from the Global Runoff Data Centre’s Major River Basins of the World database<sup>##UREF##33##44##</sup>.</p><p>\n##SUPPL##12##Source Data##\n</p></caption></fig>", "<fig id=\"Fig12\"><label>Extended Data Fig. 8</label><caption><title>Historical associations among climate, snowpack, and snow-driven runoff portend accelerating changes to snow hydrology.</title><p>Column 1: Historical change in November-March average temperature (<bold>a</bold>), total precipitation (<bold>c</bold>), March average SWE (<bold>e</bold>), and snowpack-driven April-July runoff (<bold>g</bold>) over the period 1981–2020. Values represent averages across all data products and hatching indicated basins where fewer than 80% of products agree on the sign of the change. Column 2: 2070–2099 changes under the SSP2-4.5 forcing scenario relative 1981–2020. Temperature (<bold>b</bold>) and precipitation (<bold>d</bold>) are calculated as the difference within each model realization between the end-of-century and climatological periods and future SWE (<bold>f</bold>) and runoff (<bold>h</bold>) changes are calculated according to Equations ##FORMU##0##1## and ##FORMU##1##2##, respectively. Maps were generated using cartopy v0.18.0. River basin boundaries come from the Global Runoff Data Centre’s Major River Basins of the World database<sup>##UREF##33##44##</sup>.</p><p>\n##SUPPL##13##Source Data##\n</p></caption></fig>", "<fig id=\"Fig13\"><label>Extended Data Fig. 9</label><caption><title>The Random Forest snowpack reconstruction methodology exhibits high skill based on a perfect model framework.</title><p>CMIP6 ensemble mean forced (HIST minus HIST-NAT) trends in March SWE from 1981–2020 based on (<bold>a</bold>) climate model SWE output and (<bold>b</bold>) SWE estimated using climate model temperature and precipitation and Random Forest model. <bold>c</bold>, Scatterplot of reconstructed versus original trends, where each dot represents a grid cell. Points are colored by their density. Dashed line denotes perfect agreement between reconstructed and original trends. Spatial correlation is shown in the bottom right corner.</p><p>\n##SUPPL##14##Source Data##\n</p></caption></fig>", "<fig id=\"Fig14\"><label>Extended Data Fig. 10</label><caption><title>The Random Forest model is extended to predict runoff from snowmelt skillfully.</title><p>R<sup>2</sup> (<bold>a</bold>) and RMSE (<bold>b</bold>) of Random Forest model predictions of April-July basin-scale runoff from 1981 to 2020. Insets show the distribution of skill across sites, with the red line and value indicating the median. Observed (<bold>c</bold>) and reconstructed ensemble mean (<bold>d</bold>) 1981–2020 April-July runoff trends. <bold>e</bold>, Scatterplot of reconstructed versus observed trends, where each dot represents a basin. Dashed line denotes perfect agreement between reconstructed and observed trends. Spatial correlation is shown in center left. Maps were generated using cartopy v0.18.0. River basin boundaries come from the Global Runoff Data Centre’s Major River Basins of the World database<sup>##UREF##33##44##</sup>.</p><p>\n##SUPPL##15##Source Data##\n</p></caption></fig>" ]	[ "<table-wrap id=\"Tab1\"><label>Extended Data Table 1</label><caption><p>Summary of observational data products used in the analysis</p></caption></table-wrap>", "<table-wrap id=\"Tab2\"><label>Extended Data Table 2</label><caption><p>Summary of CMIP6 models used in the analysis</p></caption></table-wrap>" ]	[ "<disp-formula id=\"Equ1\"><label>1</label><alternatives><tex-math id=\"M1\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{\\rm{SWE}}}_{y,i}=f({T}_{y,11,i},{P}_{y,11,i},{T}_{y,12,i},{P}_{y,12,i},{T}_{y,1,i},{P}_{y,1,i},{T}_{y,2,i},{P}_{y,2,i},{T}_{y,3,i},{P}_{y,3,i})$$\\end{document}</tex-math><mml:math id=\"M2\" display=\"block\"><mml:mrow><mml:msub><mml:mrow><mml:mi 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"<disp-formula id=\"Equ2\"><label>2</label><alternatives><tex-math id=\"M3\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${U}_{b}={S}_{b}+{{\\rm{T}}{\\rm{P}}}_{b}+{M}_{b}+{I}_{b}$$\\end{document}</tex-math><mml:math id=\"M4\" display=\"block\"><mml:msub><mml:mrow><mml:mi>U</mml:mi></mml:mrow><mml:mrow><mml:mi>b</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mi>b</mml:mi></mml:mrow></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mrow><mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">T</mml:mi><mml:mi 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]	[]	[]	[]	[]	[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM3\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM4\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM5\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM6\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM7\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM8\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM9\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM10\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM11\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM12\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM13\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM14\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM15\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM16\"></supplementary-material>" ]	[ "<table-wrap-foot><p>Monthly snow water equivalent “snw” is used from the “piControl”, “historical” (1850–2015), “historical-nat” (1850–2020) and “ssp245” (2015–2100) experiments. Monthly air temperature (“tas”) and precipitation (“pr”) is used from the “historical”, “historical-nat”, and “ssp245” experiments. *No monthly snow water equivalent from the pre-industrial control run archived.</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"41586_2023_6794_Fig1_HTML\" id=\"d32e307\"/>", "<graphic xlink:href=\"41586_2023_6794_Fig2_HTML\" id=\"d32e449\"/>", "<graphic xlink:href=\"41586_2023_6794_Fig3_HTML\" id=\"d32e619\"/>", "<graphic xlink:href=\"41586_2023_6794_Fig4_HTML\" id=\"d32e755\"/>", "<graphic xlink:href=\"41586_2023_6794_Article_Equ1.gif\" position=\"anchor\"/>", "<graphic xlink:href=\"41586_2023_6794_Article_Equ2.gif\" position=\"anchor\"/>", "<graphic xlink:href=\"41586_2023_6794_Article_Equ3.gif\" position=\"anchor\"/>", "<graphic xlink:href=\"41586_2023_6794_Article_Equ4.gif\" position=\"anchor\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6794_Fig5_ESM\" id=\"d32e1888\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6794_Fig6_ESM\" id=\"d32e1916\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6794_Fig7_ESM\" id=\"d32e1956\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6794_Fig8_ESM\" id=\"d32e1992\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6794_Fig9_ESM\" id=\"d32e2037\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6794_Fig10_ESM\" id=\"d32e2073\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6794_Fig11_ESM\" id=\"d32e2115\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6794_Fig12_ESM\" id=\"d32e2167\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6794_Fig13_ESM\" id=\"d32e2193\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6794_Fig14_ESM\" id=\"d32e2232\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6794_Tab1_ESM\" id=\"d32e2243\"><caption><p>Summary of observational data products used in the analysis</p></caption></graphic>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6794_Tab2_ESM\" id=\"d32e2256\"><caption><p>Summary of CMIP6 models used in the analysis</p></caption></graphic>" ]	[ "<media xlink:href=\"41586_2023_6794_MOESM1_ESM.pdf\"><caption><p>Supplementary Information</p></caption></media>", "<media xlink:href=\"41586_2023_6794_MOESM2_ESM.pdf\"><caption><p>Peer Review File</p></caption></media>", "<media xlink:href=\"41586_2023_6794_MOESM3_ESM.xlsx\"><caption><p>Source Data Fig. 1</p></caption></media>", "<media xlink:href=\"41586_2023_6794_MOESM4_ESM.xlsx\"><caption><p>Source Data Fig. 2</p></caption></media>", "<media xlink:href=\"41586_2023_6794_MOESM5_ESM.xlsx\"><caption><p>Source Data Fig. 3</p></caption></media>", "<media xlink:href=\"41586_2023_6794_MOESM6_ESM.xlsx\"><caption><p>Source Data Fig. 4</p></caption></media>", "<media xlink:href=\"41586_2023_6794_MOESM7_ESM.csv\"><caption><p>Source Data Extended Data Fig. 1</p></caption></media>", "<media xlink:href=\"41586_2023_6794_MOESM8_ESM.csv\"><caption><p>Source Data Extended Data Fig. 2</p></caption></media>", "<media xlink:href=\"41586_2023_6794_MOESM9_ESM.csv\"><caption><p>Source Data Extended Data Fig. 3</p></caption></media>", "<media xlink:href=\"41586_2023_6794_MOESM10_ESM.csv\"><caption><p>Source Data Extended Data Fig. 4</p></caption></media>", "<media xlink:href=\"41586_2023_6794_MOESM11_ESM.xlsx\"><caption><p>Source Data Extended Data Fig. 5</p></caption></media>", "<media xlink:href=\"41586_2023_6794_MOESM12_ESM.xlsx\"><caption><p>Source Data Extended Data Fig. 6</p></caption></media>", "<media xlink:href=\"41586_2023_6794_MOESM13_ESM.csv\"><caption><p>Source Data Extended Data Fig. 7</p></caption></media>", "<media xlink:href=\"41586_2023_6794_MOESM14_ESM.csv\"><caption><p>Source Data Extended Data Fig. 8</p></caption></media>", "<media xlink:href=\"41586_2023_6794_MOESM15_ESM.csv\"><caption><p>Source Data Extended Data Fig. 9</p></caption></media>", "<media xlink:href=\"41586_2023_6794_MOESM16_ESM.xlsx\"><caption><p>Source Data Extended Data Fig. 10</p></caption></media>" ]	[{"label": ["3."], "surname": ["Mankin", "Viviroli", "Singh", "Hoekstra", "Diffenbaugh"], "given-names": ["JS", "D", "D", "AY", "NS"], "article-title": ["The potential for snow to supply human water demand in the present and future"], "source": ["Environ. 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PMC10781624	38200293	[]	[]	[]	[ "<title>Discussion</title>", "<p id=\"Par29\">The fundamental changes in diet resulting from the transitions from hunting and gathering to farming and subsequently to pastoralism, precipitated far-reaching consequences for the physical and mental health of present-day Eurasian populations. These dramatic cultural changes created a heterogeneous mix of selection pressures, probably related to changes in diet and increased population densities, including selection for resistance to new infectious challenges. Owing to the highly pleiotropic nature of each sweep region, it is difficult to ascribe causal factors to any of our selection signals and we did not exhaustively test all non-trait-associated variants. However, our results show that selection during the Holocene has had a substantial impact on present-day genetic disease risk, as well as the distribution of genetic factors affecting metabolic and anthropometric traits. Our analyses have also shown that the ability to detect signatures of natural selection in present-day human genomes is drastically limited by conflicting selection pressures in different ancestral populations masking the signals. Developing methods to trace selection while accounting for differential admixture allowed us to effectively double the number of genome-wide significant selection peaks and helped clarify the trajectories of several variants related to diet and lifestyle. Furthermore, we have shown that numerous complex traits thought to have been under local selection are better explained by differing genetic contributions of ancient individuals to present-day variation. Overall, our results emphasize how the interplay between ancient selection and the main admixture events occurring in the Mesolithic, Neolithic and Bronze Age have profoundly shaped the patterns of genetic variation observed in present-day humans across Eurasia.</p>", "<title>Reporting summary</title>", "<p id=\"Par30\">Further information on research design is available in the ##SUPPL##1##Nature Portfolio Reporting Summary## linked to this article.</p>" ]	[]	[ "<p id=\"Par1\">The Holocene (beginning around 12,000 years ago) encompassed some of the most significant changes in human evolution, with far-reaching consequences for the dietary, physical and mental health of present-day populations. Using a dataset of more than 1,600 imputed ancient genomes<sup>##UREF##0##1##</sup>, we modelled the selection landscape during the transition from hunting and gathering, to farming and pastoralism across West Eurasia. We identify key selection signals related to metabolism, including that selection at the FADS cluster began earlier than previously reported and that selection near the <italic>LCT</italic> locus predates the emergence of the lactase persistence allele by thousands of years. We also find strong selection in the HLA region, possibly due to increased exposure to pathogens during the Bronze Age. Using ancient individuals to infer local ancestry tracts in over 400,000 samples from the UK Biobank, we identify widespread differences in the distribution of Mesolithic, Neolithic and Bronze Age ancestries across Eurasia. By calculating ancestry-specific polygenic risk scores, we show that height differences between Northern and Southern Europe are associated with differential Steppe ancestry, rather than selection, and that risk alleles for mood-related phenotypes are enriched for Neolithic farmer ancestry, whereas risk alleles for diabetes and Alzheimer’s disease are enriched for Western hunter-gatherer ancestry. Our results indicate that ancient selection and migration were large contributors to the distribution of phenotypic diversity in present-day Europeans.</p>", "<p id=\"Par2\">Analyses of imputed ancient genomes and of samples from the UK Biobank indicate that ancient selection and migration were large contributors to the distribution of phenotypic diversity in present-day Europeans.</p>", "<title>Subject terms</title>" ]	[ "<title>Main</title>", "<p id=\"Par3\">One of the central goals of human evolutionary genetics is to understand how natural selection has shaped the genomes of present-day people in response to changes in culture and environment. The transition from hunter-gatherers to farmers, and subsequently pastoralists, during the Holocene in Eurasia, involved some of the most dramatic changes in diet, health and social organization experienced during recent human evolution. These changes represent big shifts in environmental exposure, impacting the evolutionary forces acting on the human gene pool and imposing a series of heterogeneous selection pressures. As human lifestyles changed, close contact with domestic animals and higher population densities are likely to have increased exposure to infectious diseases, introducing new challenges to our immune system<sup>##REF##27071109##2##,##UREF##1##3##</sup>.</p>", "<p id=\"Par4\">Our understanding of the genetic architecture of complex traits in humans has been substantially advanced by genome-wide association studies (GWAS), which have identified many genetic variants associated with phenotypes of interest<sup>##REF##28686856##4##,##REF##30305743##5##</sup>. However, the extent to which these variants have been under directional selection during recent human evolution remains unclear. Although signatures of selection can be identified from patterns of genetic diversity in extant populations<sup>##REF##24274750##6##</sup>, this can be challenging in humans as they have been exposed to highly diverse and dynamic local environments through time and space. In the complex mosaic of genetic affinities that constitute a present-day human genome, any putative signatures of selection may misrepresent the timing and magnitude of the selective process. For example, episodes of admixture between ancestral populations can result in present-day haplotypes that contain no evidence of selective processes occurring further back in time. Ancient DNA (aDNA) provides the potential to resolve these issues, by directly observing changes in trait-associated allele frequencies over time.</p>", "<p id=\"Par5\">Whilst numerous previous studies have used aDNA to infer patterns of selection in Eurasia during the Holocene (for example, refs. <sup>##REF##26595274##7##–##REF##24616518##9##</sup>), many key questions remain unanswered. To what extent are present-day genetic differences due to natural selection or to differential patterns of admixture? What are the genetic legacies of Mesolithic, Neolithic and Bronze Age populations in present-day complex traits? How has the complex admixture history of Holocene Eurasia affected our ability to detect natural selection in genetic data? To investigate these questions, we tested for traces of divergent selection in health- and lifestyle-related genetic variants using three broad approaches. First, we looked for evidence of selection by identifying strong differentiation in allele frequencies between ancient populations. Second, we reconstructed the allele frequency trajectories and selection coefficients of tens of thousands of trait-associated variants, using a new chromosome painting technique to model ancestry-specific allele frequency trajectories through time. This allowed us to identify many trait-associated variants with new evidence for directional selection and to answer long-standing questions about the timing of selection for key health-, dietary- and pigmentation-associated loci. Finally, we used ancient genomes to infer local ancestry tracts in more than 400,000 present-day samples from the UK Biobank (UKB)<sup>##REF##30305743##5##</sup> and calculated ancestry-specific polygenic risk scores for 35 complex traits. This allowed us to characterize the genetic legacy of Mesolithic, Neolithic and Bronze Age populations in present-day phenotypes.</p>", "<title>Samples and data</title>", "<p id=\"Par6\">Our analyses are undertaken on a large collection of shotgun-sequenced ancient genomes presented in ref. <sup>##UREF##0##1##</sup>. This dataset comprises 1,664 imputed diploid ancient genomes and more than 8.5 million single nucleotide polymorphisms (SNPs), with an estimated imputation error rate of 1.9% and a phasing switch error rate of 2.0% for 1X genomes. Full details of the validation and benchmarking of the imputation and phasing of this dataset are provided in ref. <sup>##REF##37339987##10##</sup>. These samples represent a considerable transect of Eurasia, ranging longitudinally from the Atlantic coast to Lake Baikal and latitudinally from Scandinavia to the Middle East (Fig. ##FIG##0##1##). The included genomes constitute a thorough temporal sequence from 11,000 to 1,000 cal <sc>bp</sc>. This dataset allowed us to characterize in fine detail the changes in selective pressures exerted by major transitions in human culture and environment.</p>", "<title>Genetic legacy of ancient Eurasians</title>", "<p id=\"Par7\">We began our analysis by inferring local ancestry tracts in present-day populations by chromosome ‘painting’<sup>##REF##22291602##11##</sup> the UKB with Mesolithic, Neolithic and Bronze Age individuals as tract sources. We used a pipeline adapted from GLOBETROTTER<sup>##REF##24531965##12##</sup> and estimated admixture proportions by means of non-negative least squares (Supplementary Note ##SUPPL##0##2##). In total, we painted 433,395 present-day samples, including 24,511 from individuals born outside the United Kingdom, from 126 countries (Supplementary Note ##SUPPL##0##1##). Our results show that none of the Mesolithic, Neolithic or Bronze Age ancestries are homogeneously distributed among present-day Eurasian populations (Fig. ##FIG##1##2##). Western hunter-gatherer (WHG)-related ancestries are highest in present-day individuals from the Baltic States, Belarus, Poland and Russia; Eastern hunter-gatherer (EHG)-related ancestries are highest in Mongolia, Finland, Estonia and Central Asia; and Caucasus hunter-gatherer (CHG)-related ancestries are highest in countries east of the Caucasus, in Pakistan, India, Afghanistan and Iran, in accordance with previous results<sup>##REF##31168093##13##</sup>. The CHG-related ancestries probably reflect affinities to both CHG and Iranian Neolithic individuals, explaining the relatively high levels in South Asia<sup>##REF##31495572##14##</sup>. Consistent with expectations<sup>##REF##27274049##15##</sup>, Neolithic Anatolian-related farmer ancestries are concentrated around the Mediterranean basin, with high levels in southern Europe, the Near East and North Africa, including the Horn of Africa, but are less frequent in Northern Europe. This is in direct contrast to the Steppe-related ancestries, which are found in high levels in northern Europe, peaking in Ireland, Iceland, Norway and Sweden and decreasing further south. There is also evidence for their spread into southern Asia. Overall, these results refine global patterns of spatial distributions of ancient ancestries amongst present-day individuals. We caution, however, that absolute admixture proportions should be interpreted with caution in regions where our ancient source populations are less directly related to present-day individuals, such as in Africa and East Asia. Although these values are dependent on the reference samples used, as well as the treatment of pre- or post-admixture drift, the relative geographical variation and associations should remain consistent.</p>", "<p id=\"Par8\">The availability of many present-day samples (<italic>n</italic> = 408,884) from self-identified ‘white British’ individuals who share similar positions on a principal component analysis<sup>##REF##30305743##5##</sup> allowed us to further examine the distribution of ancient ancestries at high resolution in present-day Britain (Supplementary Note ##SUPPL##0##2##). Although regional ancestry distributions differ by only a few percentage points, we find clear evidence of geographical heterogeneity across the United Kingdom. This can be visualized by averaging ancestry proportions per county on the basis of place of birth (Fig. ##FIG##1##2##). The proportion of Neolithic farmer ancestries is highest in southern and eastern England today and lower in Scotland, Wales and Cornwall. Steppe-related ancestries are inversely distributed, peaking in the Outer Hebrides and Ireland, a pattern only previously described for Scotland<sup>##REF##27773431##16##</sup>. This regional pattern was already evident in the Pre-Roman Iron Age and persists to the present day even though immigrating Anglo-Saxons had relatively less affinities to Neolithic farmers than the Iron Age individuals of southwest Britain. Although this Neolithic farmer/Steppe-related dichotomy mirrors the modern ‘Anglo-Saxon’/‘Celtic’ ethnic divide, its origins are older, resulting from continuous migration from a continental population relatively enriched in Neolithic farmer ancestries, starting as early as the Late Bronze Age<sup>##REF##34937049##17##,##REF##29466337##18##</sup>. By measuring haplotypes from these ancestries in present-day individuals, we show that these patterns differentiate Wales and Cornwall as well as Scotland from England. We also find higher levels of WHG-related ancestries in central and northern England. These results demonstrate clear ancestry differences within an ‘ethnic group’ (white British), highlighting the need to account for subtle population structure when using resources such as the UKB<sup>##REF##33200985##19##</sup>.</p>", "<title>Ancestry-stratified selective sweeps</title>", "<p id=\"Par9\">Having identified that significant differences in ancestries persist in seemingly homogeneous present-day populations, we sought to disentangle these effects by developing a chromosome painting technique that allows us to label haplotypes on the basis of their genetic affinities to ancient individuals. To achieve this, we built a quantitative admixture graph model (Fig. ##FIG##2##3## and Supplementary Note ##SUPPL##0##3##) that represents the four main ancestry flows contributing to present-day European genomes over the last 50,000 years<sup>##REF##26567969##20##</sup>. We used this model to simulate genomes at time periods and in sample sizes equivalent to our empirical dataset and inferred tree sequences using Relate<sup>##REF##31477933##21##,##REF##34129037##22##</sup>. We trained a neural network classifier to estimate the path backwards in time through the population structure taken by each simulated individual, at each position in the genome. Our trained classifier was then used to infer the ancestral paths taken at each site, using 1,015 imputed ancient genomes from West Eurasia that passed quality filters. Using simulations, we show that our chromosome painting method has an average accuracy of 94.6% for the four ancestral paths leading to present-day Europeans and is robust to model misspecification.</p>", "<p id=\"Par10\">We then adapted CLUES<sup>##REF##31518343##23##</sup> to model aDNA time-series data (Supplementary Notes ##SUPPL##0##4## and ##SUPPL##0##5##) and used it to infer allele frequency trajectories and selection coefficients for 33,341 quality-controlled trait-associated variants from the GWAS Catalog<sup>##REF##30445434##24##</sup>. An equal number of putatively neutral, frequency-paired variants were used as a control set (Supplementary Note ##SUPPL##0##4##). To control for possible confounders, we built a causal model to distinguish direct effects of age on allele frequency from indirect effects mediated by read depth, read length and/or error rates (Supplementary Note ##SUPPL##0##6##) and developed a mapping bias test used to evaluate systematic differences between data from ancient and present-day populations (Supplementary Note ##SUPPL##0##4##). Because admixture between groups with differing allele frequencies can confound interpretation of allele frequency changes through time, we used the local ancestry paths from our chromosome painting model to stratify haplotypes in our selection tests. By conditioning on these path labels, we are able to infer selection trajectories while controlling for changes in admixture proportions through time.</p>", "<p id=\"Par11\">Our analysis identified no genome-wide significant (<italic>P</italic> < 5 × 10<sup>−8</sup>) selective sweeps when using genomes from present-day individuals alone (1000 Genomes Project populations GBR, FIN and TSI<sup>##REF##26432245##25##</sup>), although trait-associated variants were enriched for evidence of selection compared to the control group (<italic>P</italic> < 7.29 × 10<sup>−35</sup>, Wilcoxon signed-rank test). By contrast, when using imputed aDNA genotype probabilities, we identified 11 genome-wide significant selective sweeps in the GWAS group (<italic>n</italic> = 476 SNPs with <italic>P</italic> < 5 × 10<sup>−8</sup>) and no sweeps in the control group, despite some SNPs exhibiting evidence of selection (<italic>n</italic> = 51). These results are consistent with selection preferentially acting on trait-associated variants. We then conditioned our selection analysis on each of our four local ancestry pathways—that is, local ancestry tracts passing through WHG, EHG, CHG or Anatolian farmers (ANA)—and identified 21 genome-wide significant selection peaks (Fig. ##FIG##3##4## and Extended Data Figs. ##FIG##5##1##–##FIG##14##10##). This suggests that admixture between ancestral populations has masked evidence of selection at many trait-associated loci in Eurasian populations<sup>##REF##36316412##26##</sup>.</p>", "<title>Selection on diet-associated loci</title>", "<p id=\"Par12\">We find strong changes in selection associated with lactose digestion after the introduction of farming but before the expansion of the Steppe pastoralists into Europe around 5,000 years ago<sup>##REF##26062507##27##,##REF##25731166##28##</sup>, the timing of which is a long-standing controversy<sup>##REF##18179885##29##–##REF##32511234##32##</sup>. The strongest overall signal of selection in the pan-ancestry analysis is observed at the <italic>MCM6/LCT</italic> locus (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/snp/?term=rs4988235\">rs4988235</ext-link>: A; <italic>P</italic> = 1.68 × 10<sup>−59</sup>; <italic>s</italic> = 0.0194), where the derived allele results in lactase persistence<sup>##REF##11788828##33##</sup>. The trajectory inferred from the pan-ancestry analysis indicates that the lactase persistence allele began increasing in frequency about 6,000 years ago and has continued to increase up to the present (Fig. ##FIG##3##4##). In the ancestry-stratified analyses, this signal is driven primarily by sweeps in two of the ancestral backgrounds, associated with EHG and CHG. We also observed that many selected SNPs within this locus exhibited earlier evidence of selection than at <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/snp/?term=rs4988235\">rs4988235</ext-link>, suggesting that selection at the <italic>MCM6/LCT</italic> locus is more complex than previously thought. To investigate this further, we expanded our selection scan to include all SNPs within the ~2.6 megabase (Mb)-wide sweep locus (<italic>n</italic> = 5,608) and checked for the earliest evidence of selection. We observed that most genome-wide significant SNPs at this locus began rising in frequency earlier than <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/snp/?term=rs4988235\">rs4988235</ext-link>, indicating that strong positive selection at this locus predates the emergence of the lactase persistence allele by thousands of years. Among the alleles showing much earlier frequency rises was <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/snp/?term=rs1438307\">rs1438307</ext-link>: T (<italic>P</italic> = 9.77 × 10<sup>−24</sup>; <italic>s</italic> = 0.0146), which began rising in frequency about 12,000 years ago (Fig. ##FIG##3##4##). This allele has been shown to regulate energy expenditure and contribute to metabolic disease and it has been suggested to be an ancient adaptation to famine<sup>##REF##33058756##34##</sup>. The high linkage disequilibrium between <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/snp/?term=rs1438307\">rs1438307</ext-link> and <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/snp/?term=rs4988235\">rs4988235</ext-link> in present-day individuals (<italic>R</italic><sup>2</sup> = 0.89 in GBR) may explain the recently observed correlation between frequency rises in the lactase persistence allele and archaeological proxies for famine and increased pathogen exposure<sup>##REF##35896751##35##</sup>. To control for potential bias introduced by imputation, we replicated these results using genotype likelihoods, called directly from the aDNA sequencing reads, and with publicly available 1240k capture array data from the Allen Ancient DNA Resource v.52.2 (ref. <sup>##UREF##2##36##</sup>) (Supplementary Note ##SUPPL##0##4##).</p>", "<p id=\"Par13\">We also found strong selection in the FADS gene cluster—<italic>FADS1</italic> (rs174546: C; <italic>P</italic> = 4.41 × 10<sup>−19</sup>; <italic>s</italic> = 0.0126) and <italic>FADS2</italic> (rs174581: G; <italic>P</italic> = 2.21 × 10<sup>−19</sup>; <italic>s</italic> = 0.0138)—which are associated with fatty acid metabolism and known to respond to changes in diet from a more/less vegetarian to a more/less carnivorous diet<sup>##REF##24097068##37##–##REF##30272210##41##</sup>. In contrast to previous results<sup>##REF##28333262##39##–##REF##30272210##41##</sup>, we find that much of the selection associated with a more vegetarian diet occurred in Neolithic populations before they arrived in Europe, then continued during the Neolithic (Fig. ##FIG##3##4##). The strong signal of selection in this region in the pan-ancestry analysis is driven primarily by a sweep occurring across the EHG, WHG and ANA haplotypic backgrounds (Fig. ##FIG##3##4##). Interestingly, we do not find statistically significant evidence of selection at this locus in the CHG background but most of the allele frequency rise in the EHG background occurs after their admixture with CHG (around 8,000 years ago<sup>##REF##29960127##42##</sup>), within whom the selected alleles were already close to present-day frequencies. This suggests that the selected alleles may already have existed at substantial frequencies in early farmer populations in the Middle East and among Caucasus hunter-gatherers (associated with the ANA and CHG backgrounds, respectively) and were subject to continued selection as eastern groups moved northwards and westwards during the late Neolithic and Bronze Age periods.</p>", "<p id=\"Par14\">When specifically comparing selection signatures differentiating ancient hunter-gatherer and farmer populations<sup>##REF##27601374##43##</sup>, we also observe many regions associated with lipid and sugar metabolism and various metabolic disorders (Supplementary Note ##SUPPL##0##7##). These include, for example, a region in chromosome 22 containing <italic>PATZ1</italic>, which regulates the expression of <italic>FADS1</italic> and <italic>MORC2</italic>, which plays an important role in cellular lipid metabolism<sup>##REF##24286864##44##</sup>. Another region in chromosome 3 overlaps with <italic>GPR15</italic>, which is both related to immune tolerance and to intestinal homoeostasis<sup>##REF##23661644##45##,##UREF##3##46##</sup>. Finally, in chromosome 18, we recover a selection candidate region spanning <italic>SMAD7</italic>, which is associated with inflammatory bowel diseases such as Crohn’s disease<sup>##REF##25785968##47##</sup>. Taken together these results indicate that the transition to agriculture imposed a substantial amount of selection for humans to adapt to a new diet and lifestyle and that the prevalence of some diseases observed today may be a consequence of these selective processes.</p>", "<title>Selection on immunity-associated loci</title>", "<p id=\"Par15\">We also observe evidence of strong selection in several loci associated with immunity and autoimmune disease (Supplementary Note ##SUPPL##0##4##). Some of these putative selection events occurred earlier than previously claimed and are probably associated with the transition to agriculture, which may help explain the high prevalence of autoimmune diseases today. Most notably, we detect an 8 Mb-wide selection sweep signal in chromosome 6 (chr6: 25.4–33.5 Mb), spanning the full length of the human leukocyte antigen (HLA) region. The selection trajectories of the variants within this locus support several independent sweeps, occurring at different times and with differing intensities. The strongest signal of selection at this locus in the pan-ancestry analysis is at an intergenic variant, located between <italic>HLA-A</italic> and <italic>HLA-W</italic> (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/snp/?term=rs7747253\">rs7747253</ext-link>: A; <italic>P</italic> = 7.56 × 10<sup>−32</sup>; <italic>s</italic> = −0.0178), associated with protection against chickenpox (odds ratio (OR) 0.888; ref. <sup>##REF##30305743##5##</sup>), increased risk of intestinal infections (OR 1.08; ref. <sup>##REF##36653562##48##</sup>) and decreased heel bone mineral density (OR 0.98; ref. <sup>##REF##30598549##49##</sup>). This allele rapidly decreased in frequency, beginning about 8,000 years ago (Extended Data Fig. ##FIG##7##3##), reducing the risk of intestinal infections, at the cost of increasing the risk of chickenpox. By contrast, the signal of selection at <italic>C2</italic> (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/snp/?term=rs9267677\">rs9267677</ext-link>: C; <italic>P</italic> = 6.60 × 10<sup>−26</sup>; <italic>s</italic> = 0.0441), also found within this sweep, shows a gradual increase in frequency beginning around 4,000 years ago, before rising more rapidly about 1,000 years ago. In this case, the favoured allele is associated with protection against some sexually transmitted diseases (STDs) (OR 0.786; ref. <sup>##REF##36653562##48##</sup>), primarily those caused by human papillomavirus, and with increased psoriasis risk (OR 2.2; ref. <sup>##REF##30305743##5##</sup>). This locus provides a good example of the possibility that the high prevalence of autoimmune diseases in present-day populations may, in part, be due to genetic trade-offs; by which selection increased protection against pathogens with the pleiotropic effect of increased susceptibility to autoimmune diseases<sup>##REF##25458997##50##</sup>.</p>", "<p id=\"Par16\">These results also highlight the complex temporal dynamics of selection at the HLA locus, which not only plays a role in the regulation of the immune system but is also associated with many non-immune-related phenotypes. The high pleiotropy in this region makes it difficult to determine which selection pressures may have driven these increases in frequencies at different periods of time. However, profound shifts in lifestyle in Eurasian populations during the Holocene have been suggested to be drivers for strong selection on loci involved in immune response. These include a change in diet and closer contact with domestic animals, combined with higher mobility and increasing population density. We further explore the complex pattern of ancestry-specific selection at the HLA locus in our companion paper<sup>##UREF##4##51##</sup>.</p>", "<p id=\"Par17\">We also identify selection signals at the <italic>SLC22A4</italic> (rs35260072: C; <italic>P</italic> = 8.49 × 10<sup>−20</sup>; <italic>s</italic> = 0.0172) locus, associated with increased itch intensity from mosquito bites (OR 1.049; ref. <sup>##REF##28199695##52##</sup>), protection against childhood and adult asthma (OR 0.902 and 0.909; ref. <sup>##REF##36653562##48##</sup>) and asthma-related infections (OR 0.913; ref. <sup>##REF##36653562##48##</sup>) and we find that the derived variant has been steadily rising in frequency since around 9,000 years ago (Extended Data Fig. ##FIG##13##9##). However, in the same <italic>SLC22A4</italic> candidate region as rs35260072, we find that the frequency of the previously reported allele <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/snp/?term=rs1050152\">rs1050152</ext-link>: T (which also protects against asthma (OR 0.90; ref. <sup>##REF##36653562##48##</sup>) and related infections) plateaued about 1,500 years ago, contrary to previous reports suggesting a recent rise in frequency<sup>##REF##26595274##7##</sup>. Similarly, we detect selection at the <italic>HECTD4</italic> (rs11066188: A; P = 9.51 × 10<sup>−31</sup>\n<italic>s</italic> = 0.0198) and <italic>ATXN2</italic> (rs653178: C; <italic>P</italic> = 3.73 × 10<sup>−29</sup>; <italic>s</italic> = 0.0189) loci, both of which have been rising in frequency for about 9,000 years (Extended Data Fig. ##FIG##8##4##), also contrary to previous reports of a more recent rise in frequency<sup>##REF##26595274##7##</sup>. These SNPs are associated with protection against urethritis and urethral syndrome (OR 0.769 and 0.775; ref. <sup>##REF##36653562##48##</sup>), which are often caused by STDs or accumulated urethral damage from having more than five births. Both SNPs are also linked to increased risk of intestinal infectious diseases (OR 1.03 and 1.04), several non-specific parasitic diseases (OR 1.44 and 1.59; ref. <sup>##REF##36653562##48##</sup>), schistosomiasis (OR 1.13 and 1.32; ref. <sup>##REF##36653562##48##</sup>), helminthiases (OR 1.29 and 1.28; ref. <sup>##REF##36653562##48##</sup>), spirochaetes (OR 1.14 and 1.12; ref. <sup>##REF##36653562##48##</sup>), pneumonia (OR 1.03 and 1.03; ref. <sup>##REF##36653562##48##</sup>) and viral hepatitis (OR 1.15 and 1.15; ref. <sup>##REF##30305743##5##</sup>). These SNPs also increase the risk of coeliac disease and rheumatoid arthritis<sup>##REF##26546613##53##</sup>. Thus, several highly pleiotropic disease-associated loci, which were previously thought to be the result of recent adaptation, may have been subject to selection for a much longer period of time.</p>", "<title>Selection on the 17q21.31 locus</title>", "<p id=\"Par18\">We further detect signs of strong selection in a 2 Mb sweep on chromosome 17 (chr. 17: 44.0-46.0 Mb), spanning a locus on 17q21.3, implicated in neurodegenerative and developmental disorders. The locus includes an inversion and other structural polymorphisms with indications of a recent positive selection sweep in some human populations<sup>##REF##15654335##54##,##REF##22751100##55##</sup>. Specifically, partial duplications of the <italic>KANSL1</italic> gene probably occurred independently on the inverted (H2) and non-inverted (H1) haplotypes (Extended Data Fig. ##FIG##15##11a##) and both are found in high frequencies (15–25%) among current European and Middle Eastern populations, but are much rarer in Sub-Saharan African and East Asian populations. We used both SNP genotypes and WGS read depth information to determine inversion (H1/H2) and <italic>KANSL1</italic> duplication (d) status in the ancient individuals studied here (Supplementary Note ##SUPPL##0##8##).</p>", "<p id=\"Par19\">The H2 haplotype is observed in two of three previously published genomes<sup>##REF##27498567##56##</sup> of Anatolian aceramic-associated Neolithic individuals (Bon001 and Bon004) from around 10,000 <sc>bp</sc> but data were insufficient to identify <italic>KANSL1</italic> duplications. The oldest evidence for <italic>KANSL1</italic> duplications is observed in an early Neolithic individual (AH1 from 9,900 <sc>bp</sc>; ref. <sup>##REF##27417496##57##</sup>) from present-day Iran, followed by two Mesolithic individuals (NEO281 from 9,724 <sc>bp</sc> and KK1 (ref. <sup>##REF##28162894##58##</sup>) from 9,720 <sc>bp</sc>), from present-day Georgia, all of whom are heterozygous for the inversion and carry the inverted duplication. The <italic>KANSL1</italic> duplications are also detected in two Neolithic individuals, from present-day Russia (NEO560 from 7,919 <sc>bp</sc> (H1d) and NEO212 from 7,390 <sc>bp</sc> (H2d)). With both H1d and H2d having spread to large parts of Europe with Anatolian Neolithic farmer ancestries, their frequency seems unchanged in most of Europe as Steppe-related ancestries become dominant in large parts of the subcontinent (Extended Data Fig. ##FIG##15##11c##). The fact that both H1d and H2d are found in apparently high frequencies in both early Anatolian farmers and the earliest Steppe-related ancestry groups suggests that any selective sweep acting on the H1d and H2d variants would probably have occurred in populations ancestral to both.</p>", "<p id=\"Par20\">We note that the strongest signal of selection observed in the pan-ancestry analysis at this locus is at <italic>MAPT</italic> (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/snp/?term=rs4792897\">rs4792897</ext-link>: G; <italic>P</italic> = 1.33 × 10<sup>−18</sup>; <italic>s</italic> = 0.0299 (Extended Data Fig. ##FIG##12##8## and Supplementary Note ##SUPPL##0##4##), which codes for the tau protein<sup>##REF##1420178##59##</sup> and is associated with protection against mumps (OR 0.776; ref. <sup>##REF##36653562##48##</sup>) and increased risk of snoring (OR 1.04; ref. <sup>##REF##30804565##60##</sup>). More generally, polymorphisms in <italic>MAPT</italic> have been associated with increased risk of several neurodegenerative disorders, including Alzheimer’s disease and Parkinson’s disease<sup>##REF##25687773##61##</sup>. However, we caution that this region is also enriched for evidence of reference bias in our dataset—especially around the <italic>KANSL1</italic> gene—due to complex structural polymorphisms (Supplementary Note ##SUPPL##0##10##).</p>", "<title>Selection on pigmentation loci</title>", "<p id=\"Par21\">Our results identify strong selection for lighter skin pigmentation in groups moving northwards and westwards, consistent with the idea that selection is caused by reduced ultraviolet exposure and resulting vitamin D deficiency. We find that the most strongly selected alleles reached near-fixation several thousand years ago, suggesting that this process was not associated with recent sexual selection as previously proposed<sup>##REF##12573076##62##</sup>. In the pan-ancestry analysis, we detect strong selection at the <italic>SLC45A2</italic> locus (rs35395: C; <italic>P</italic> = 1.60 × 10<sup>−44</sup>; <italic>s</italic> = 0.0215)<sup>##REF##33443182##8##,##REF##31315583##63##</sup>, with the selected allele (responsible for lighter skin) increasing in frequency from around 13,000 years ago, until plateauing around 2,000 years ago (Fig. ##FIG##3##4##). The predominant hypothesis is that high melanin levels in the skin are important in equatorial regions owing to its protection against ultraviolet radiation, whereas lighter skin has been selected for at higher latitudes (where ultraviolet radiation is less intense) because some ultraviolet penetration is required for cutaneous synthesis of vitamin D<sup>##REF##10896812##64##,##REF##22254036##65##</sup>. Our findings confirm pigmentation alleles as key targets of selection during the Holocene<sup>##REF##26595274##7##,##REF##16494531##66##</sup>, particularly on a small proportion of loci with large effect sizes<sup>##REF##33443182##8##</sup>.</p>", "<p id=\"Par22\">Our results also provide detailed information about the duration and geographic spread of these processes (Fig. ##FIG##3##4##), suggesting that an allele associated with lighter skin was selected for repeatedly, probably as a consequence of similar environmental pressures occurring at different times in different regions. In the ancestry-stratified analysis, all marginal ancestries show broad agreement at the <italic>SLC45A2</italic> locus (Fig. ##FIG##3##4##) but differ in the timing of their frequency shifts. The ANA-associated ancestry background shows the earliest evidence for selection at rs35395, followed by EHG and WHG around 10,000 years ago and CHG about 2,000 years later. In all ancestry backgrounds, except ANA, the selected haplotypes plateau at high frequency by about 2,000 years ago, whilst the ANA haplotype background reaches near-fixation 1,000 years earlier. We also detect strong selection at the <italic>SLC24A5</italic> locus (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/snp/?term=rs1426654\">rs1426654</ext-link>: A; <italic>P</italic> = 2.28 × 10<sup>−16</sup>; <italic>s</italic> = 0.0185), which is also associated with skin pigmentation<sup>##REF##31315583##63##,##REF##29195075##67##</sup>. At this locus, the selected allele increased in frequency even earlier than <italic>SLC45A2</italic> and reached near-fixation around 3,500 years ago. Selection on this locus thus seems to have occurred early on in groups that were moving northwards and westwards and only later in the WHG background after these groups encountered and admixed with the incoming populations.</p>", "<title>Selection among major axes of variation</title>", "<p id=\"Par23\">Beyond patterns of genetic change at the Mesolithic–Neolithic transition, much genetic variability observed today reflects high genetic differentiation in the hunter-gatherer groups that eventually contributed to present-day European genetic diversity<sup>##REF##27601374##43##</sup>. Indeed, many loci associated with cardiovascular disease, metabolism and lifestyle diseases trace their genetic variability before the Neolithic transition to ancient differential selection in ancestry groups occupying different parts of the Eurasian continent (Supplementary Note ##SUPPL##0##7##). These may represent selection episodes that preceded the admixture events described above and led to differentiation between ancient hunter-gatherer groups in the late Pleistocene and early Holocene. One of these overlaps with the <italic>SLC24A3</italic> gene, which is a salt-sensitivity gene significantly expressed in obese individuals<sup>##REF##31886193##68##</sup>. Another spans <italic>ROPN1</italic> and <italic>KALRN</italic>, two genes involved in vascular disorders<sup>##UREF##5##69##</sup>. A further region contains <italic>SLC35F3</italic>, which codes for a thiamine transport<sup>##REF##24509276##70##</sup> and has been associated with hypertension in a Han Chinese cohort<sup>##UREF##6##71##</sup>. Finally, there is a candidate region containing several genes (<italic>CH25H</italic> and <italic>FAS</italic>) associated with obesity and lipid metabolism<sup>##REF##32229247##72##,##REF##32519954##73##</sup> and another peak with several genes (<italic>ASXL2, RAB10, HADHA</italic> and <italic>GPR113</italic>) involved in glucose homoeostasis and fatty acid metabolism<sup>##REF##26051940##74##–##UREF##7##77##</sup>. These loci predominantly reflect ancient patterns of extreme differentiation between Eastern and Western Eurasian genomes and may be candidates for selection after the separation of the Pleistocene populations that occupied different environments across the continent (roughly 45,000 years ago<sup>##REF##31168093##13##</sup>).</p>", "<title>Pathogenic structural variants</title>", "<p id=\"Par24\">Rare, recurrent copy-number variants (CNVs) are known to cause neurodevelopmental disorders and are associated with a range of psychiatric and physical traits with variable expressivity and incomplete penetrance<sup>##REF##21854229##78##,##REF##22396478##79##</sup>. To understand the prevalence of pathogenic structural variants over time we examined 50 genomic regions susceptible to recurrent CNVs, known to be the most prevalent drivers of human developmental pathologies<sup>##REF##22970919##80##</sup>. The analysis included 1,442 ancient shotgun genomes passing quality control for CNV analysis (Supplementary Note ##SUPPL##0##10##) and 1,093 present-day human genomes for comparison<sup>##REF##27654912##81##,##REF##32193295##82##</sup>. We identified CNVs in ancient individuals at ten loci using a read depth-based approach and digital comparative genomic hybridization<sup>##REF##21030649##83##</sup>. Although most of the observed CNVs (including duplications at 15q11.2 and <italic>CHRNA7</italic> and CNVs spanning parts of the TAR locus and 22q11.2 distal) have not been unambiguously associated with disease in large studies, the identified CNVs include deletions and duplications that have been associated with developmental delay, dysmorphic features and neuropsychiatric abnormalities such as autism (most notably at 1q21.1, 3q29, 16p12.1 and the DiGeorge/VCFS locus but also deletions at 15q11.2 and duplications at 16p13.11). Overall, the carrier frequency in the ancient individuals is similar to that reported in the UKB genomes (1.25% versus 1.6% at 15q11.2 and <italic>CHRNA7</italic> combined and 0.8% versus 1.1% across the remaining loci combined)<sup>##REF##30343275##84##</sup>. These results indicate that large, recurrent CNVs, which can lead to several pathologies, were present at similar frequencies in the ancient and present-day populations included in this study.</p>", "<title>Phenotypic legacy of ancient Eurasians</title>", "<p id=\"Par25\">In addition to identifying evidence of selection for trait-associated variants, we also estimated the contribution from different genetic ancestries (associated with EHG, CHG, WHG, Steppe pastoralists and Neolithic farmers) to variation in complex traits in present-day individuals. We calculated ancestry-specific polygenic risk score—hereafter ancestral risk scores (ARS)—on the basis of chromosome painting of over 400,000 UKB samples using ChromoPainter<sup>##REF##22291602##11##</sup> (Fig. ##FIG##4##5## and Supplementary Note ##SUPPL##0##9##). This allowed us to identify which ancient ancestry components are over-represented in present-day UK populations at loci significantly associated with a given trait and is analogous to the genetic risk that a present-day individual would possess if they were composed entirely of one of the ancestry groupings defined in this study. This analysis avoids issues related to the portability of polygenic risk scores between populations<sup>##REF##28366442##85##</sup>, as our ARSs are calculated from the same individuals used to estimate the effect sizes. Working with many imputed ancient genomes provides high statistical power to use ancient populations as ancestral sources. We focused on 35 phenotypes whose polygenic scores were significantly overdispersed among the ancient populations (Supplementary Note ##SUPPL##0##9##), as well as well as three large effect alleles at the <italic>APOE</italic> gene (ApoE2, ApoE3 and ApoE4) known to significantly mediate risk of developing Alzheimer’s disease<sup>##REF##8346443##86##</sup>. We emphasize that this approach makes no direct reference to ancient phenotypes but instead describes how these genetic ancestry components contributed to the present-day phenotypic landscape.</p>", "<p id=\"Par26\">We find that for many anthropometric traits—such as trunk predicted mass, forced expiratory volume in 1-second (FEV1) and basal metabolic rate—the ARS for Steppe ancestry was the highest, followed by EHG and CHG/WHG, whilst Neolithic farmer ancestry consistently scored the lowest for these measurements. Consistent with previous studies, hair and skin pigmentation also showed significant differences, with scores for skin colour for WHG, EHG and CHG higher (that is, darker) than for Neolithic farmer and Steppe-associated ancestries<sup>##REF##33443182##8##,##REF##24616518##9##,##REF##26062507##27##,##REF##25731166##28##</sup>; and scores for traits related to malignant neoplasms of the skin were elevated in Neolithic farmer-associated ancestries. Both Neolithic farmer- and Steppe-associated ancestries have higher scores for blonde and light brown hair, whereas the hunter-gatherer-associated ancestries have higher scores for dark brown hair and CHG-associated ancestries had the highest score for black hair.</p>", "<p id=\"Par27\">In terms of genetic contributions to risk for diseases, the WHG ancestral component had strikingly high scores for traits related to cholesterol, blood pressure and diabetes. The Neolithic farmer component scored the highest for anxiety, guilty feelings and irritability; CHG and WHG ancestry components consistently scored the lowest for these three traits. We found the ApoE4 allele (rs429358: C and rs7412: C, which increases risk of Alzheimer’s disease) preferentially painted with a WHG/EHG haplotypic background, suggesting it was probably brought into Western Eurasia by early hunter-gatherers (Supplementary Note ##SUPPL##0##9##). This result is in line with the present-day European distribution of this allele, which is highest in northeastern Europe, where the proportion of these ancestries is larger than in other regions of the continent<sup>##REF##30844401##87##</sup>. By contrast, we found the ApoE2 allele (rs429358: T and rs7412: T, which decreases the risk for Alzheimer’s disease) on a haplotypic background with affinities to Steppe pastoralists. Our pan-ancestry analysis identified positive selection favouring ApoE2 (<italic>P</italic> = 6.99 × 10<sup>−3</sup>; <italic>s</italic> = 0.0130), beginning about 7,000 years ago and plateauing around 2,500 years ago (Supplementary Note ##SUPPL##0##4##). However, we did not identify evidence of selection for either ApoE3 (rs429358: T and rs7412: C) or ApoE4, contrary to a recent study with a smaller sample size and unphased genotypes<sup>##REF##36951219##88##</sup>. The selective forces probably favouring ApoE2 in Steppe pastoralists may be associated with protective immune responses against infectious challenges, such as protection against malaria or an unknown viral infection (Supplementary Note ##SUPPL##0##9##).</p>", "<p id=\"Par28\">In light of the ancestry gradients within the United Kingdom and across Eurasia (Fig. ##FIG##1##2##), these results support the suggestion that migration-mediated geographic variation in phenotypes and disease risk is commonplace, and points to a way forward for explaining geographically structured disease prevalence through differential admixture processes between present-day populations. These results also help to clarify the famous discussion of selection in Europe relating to height<sup>##REF##26595274##7##,##UREF##8##89##</sup>. Our finding that the Steppe- and EHG-associated ancestral components have elevated genetic values for height in the UKB demonstrates that height differences between Northern and Southern Europe may be a consequence of differential ancestry, rather than selection, as claimed in many previous studies<sup>##REF##27738015##90##</sup>. However, our results do not preclude the possibility that height has been selected for in specific populations<sup>##REF##32533944##91##,##REF##35534559##92##</sup>.</p>", "<title>Online content</title>", "<p id=\"Par31\">Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41586-023-06705-1.</p>", "<title>Supplementary information</title>", "<p>\n\n\n\n\n</p>" ]	[ "<title>Extended data figures and tables</title>", "<p id=\"Par34\">\n\n</p>", "<p id=\"Par35\">\n\n</p>", "<p id=\"Par36\">\n\n</p>", "<p id=\"Par37\">\n\n</p>", "<p id=\"Par38\">\n\n</p>", "<p id=\"Par39\">\n\n</p>", "<p id=\"Par40\">\n\n</p>", "<p id=\"Par41\">\n\n</p>", "<p id=\"Par42\">\n\n</p>", "<p id=\"Par43\">\n\n</p>", "<p id=\"Par44\">\n\n</p>", "<title>Extended data</title>", "<p>is available for this paper at 10.1038/s41586-023-06705-1.</p>", "<title>Supplementary information</title>", "<p>The online version contains supplementary material available at 10.1038/s41586-023-06705-1.</p>", "<title>Acknowledgements</title>", "<p>We thank all the former and current staff at the Lundbeck Foundation GeoGenetics Centre and the GeoGenetics Sequencing Core and colleagues across the many institutions detailed below. We are particularly grateful to L. Olsen as project manager for the Lundbeck Foundation GeoGenetics Centre project. We thank UKB for access to the UKB genomic resource. We want to acknowledge the participants and investigators of the FinnGen study. We are thankful to Illumina for collaboration. E.W. thanks St. John’s College, Cambridge, for providing a stimulating environment of discussion and learning. The Lundbeck Foundation GeoGenetics Centre is supported by the Lundbeck Foundation (R302-2018-2155 and R155-2013-16338), the Novo Nordisk Foundation (NNF18SA0035006), the Wellcome Trust (214300), Carlsberg Foundation (CF18-0024), the Danish National Research Foundation (DNRF94 and DNRF174), the University of Copenhagen (KU2016 programme), Ferring Pharmaceuticals A/S and a COREX ERC Synergy grant (ID 951385). This research has been conducted using the UKB Resource and the iPSYCH Initiative, funded by the Lundbeck Foundation (R102-A9118 and R155-2014-1724). This work was further supported by the Swedish Foundation for Humanities and Social Sciences grant (Riksbankens Jubileumsfond M16-0455:1) to K.K.. E.K.I.-P. and A.R.-M. were supported by the Lundbeck Foundation (R302-2018-2155) and the Novo Nordisk Foundation (NNF18SA0035006). A.P., R.D. and E.W. were supported by the Wellcome Trust (214300). R.M. was supported by a SSHRC doctoral studentship (G101449). R.A.H. and T.K. were supported by the Carlsberg Foundation (CF19-0712). L.S. was supported by a Sir Henry Wellcome fellowship (220457/Z/20/Z). L.F. was supported by the OAK Foundation (OCAY-15-520). P.H.S. was supported by the Institute of General Medical Sciences (R35GM142916) and a Vallee Scholars Award. R.N. was supported by the National Institutes of Health (R01GM138634). F.R. was supported by a Villum Young Investigator Grant (project no. 00025300), a Novo Nordisk Fonden Data Science Ascending Investigator Award (NNF22OC0076816) and by the European Research Council (ERC) under the European Union’s Horizon Europe programme (grant agreements No. 101077592 and 951385).</p>", "<title>Author contributions</title>", "<p>E.K.I.-P., A.R.-M., W.B., A.I., A.P. and A.F. contributed equally to this work. P.H.S., D.J.L., R.D., T.K., T.W., M.E.A., M.S., R.N., F.R. and E.W. led the study. A.F., T.W., M.E.A., M.S. and E.W. conceptualized the study. P.H.S., D.J.L., R.D., T.K., T.W., M.E.A., M.S., R.N., F.R. and E.W. supervised the research. M.E.A., K.K., R.D., T.W., R.N. and E.W. acquired funding for research. E.K.I.-P., A.R.-M., A.I., A.P., W.B., A.H.V., L.S., A. J. Stern, K.K., D.J.L., R.D., T.K. M.E.A., M.S., R.N. and F.R. were involved in developing and applying methodology. E.K.I.-P., A.R.-M., A.I., A.P., W.B., A.S.H., R.A.H., T.V., H.M., A.H.V., L.S., A. Ramsøe, A. J. Schork, A. Rosengren, L.Z., P.H.S., T.K., M.E.A., M.S. and F.R. undertook formal analyses of data. E.K.I.-P., A.R.-M., A.I., A.P., A.F., W.B., K.-G.S., A.S.H., R.A.H., T.V., A. J. Stern, A. Ramsøe, A. Rosengren, L.Z., A.K.N.I., L.F., P.H.S., D.J.L., T.K., M.S., F.R. and E.W. drafted the main text. E.K.I.-P., A.R.-M., A.I., A.P., A.F., W.B., K.-G.S., A.S.H., R.A.H., T.V., A. J. Stern, G.S., A. Ramsøe, A. Rosengren, L.Z., A.K.N.I., L.F., P.H.S., D.J.L., M.S. and E.W. drafted the ##SUPPL##0##Supplementary Information## and ##SUPPL##3##Tables##. E.K.I.-P., A.R.-M., A.I., A.P., A.F., W.B., K.-G.S., A.S.H., R.M., F.D., R.A.H., T.V., H.M., A. Ramsøe, A. J. Schork, L.Z., K.K., A.K.N.I., L.F., P.H.S., D.J.L., R.D., T.K., T.W., M.E.A., M.S., R.N., F.R. and E.W. were involved in reviewing drafts and editing. All co-authors read, commented on and agreed on the submitted manuscript.</p>", "<title>Peer review</title>", "<title>Peer review information</title>", "<p id=\"Par32\"><italic>Nature</italic> thanks the anonymous reviewers for their contribution to the peer review of this work. ##SUPPL##2##Peer reviewer reports## are available.</p>", "<title>Data availability</title>", "<p>All ancient genomic data used in this study are already published and listed in Supplementary Table ##SUPPL##3##1##. Data were aligned to the human reference GRCh37. Modern human genomes were obtained from the 1000 Genomes Project<sup>##REF##26432245##25##</sup>, the Simons Genome Diversity Project<sup>##REF##27654912##81##</sup> and the Human Genome Diversity Project<sup>##REF##32193295##82##</sup>. GWAS data were obtained from the GWAS Catalog<sup>##REF##30445434##24##</sup>, the FinnGen Study<sup>##REF##36653562##48##</sup> and the UKB<sup>##REF##30305743##5##</sup>.</p>", "<title>Code availability</title>", "<p>The scripts used to run the chromosome painting (Supplementary Note ##SUPPL##0##2##) and calculate ARS in the UKB (Supplementary Note 9) are available at <ext-link ext-link-type=\"uri\" xlink:href=\"https://github.com/will-camb/mesoneo_selection_paper\">https://github.com/will-camb/mesoneo_selection_paper</ext-link> (10.5281/zenodo.8301166). The software to perform the ancestral path chromosome painting described in Supplementary Note ##SUPPL##0##3## is available on GitHub at <ext-link ext-link-type=\"uri\" xlink:href=\"https://github.com/AliPearson/AncestralPaths\">https://github.com/AliPearson/AncestralPaths</ext-link> (10.5281/zenodo.8319452) and the demographic model is available in the stdpopsim library (<ext-link ext-link-type=\"uri\" xlink:href=\"https://popsim-consortium.github.io/stdpopsim-docs/stable/catalog.html#sec_catalog_homsap_models_ancienteurope_4a21\">https://popsim-consortium.github.io/stdpopsim-docs/stable/catalog.html#sec_catalog_homsap_models_ancienteurope_4a21</ext-link>). The analysis pipeline and ‘conda’ environment necessary to replicate the analysis of allele frequency trajectories of trait-associated variants in Supplementary Note ##SUPPL##0##4## are available at <ext-link ext-link-type=\"uri\" xlink:href=\"https://github.com/ekirving/mesoneo_paper\">https://github.com/ekirving/mesoneo_paper</ext-link> (10.5281/zenodo.8289755). The modified version of CLUES used in this study is available from <ext-link ext-link-type=\"uri\" xlink:href=\"https://github.com/standard-aaron/clues\">https://github.com/standard-aaron/clues</ext-link> (10.5281/zenodo.8228252). The pipeline to replicate the analyses for Supplementary Note ##SUPPL##0##7## can be found at <ext-link ext-link-type=\"uri\" xlink:href=\"https://github.com/albarema/neo\">https://github.com/albarema/neo</ext-link> (10.5281/zenodo.8301253). All other analyses relied on available software which has been fully referenced in the manuscript and detailed in the relevant ##SUPPL##0##Supplementary notes##.</p>", "<title>Competing interests</title>", "<p id=\"Par33\">The authors declare no competing interests.</p>" ]	[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>Geographic and temporal distribution of the 1,015 ancient genomes from West Eurasia.</title><p><bold>a</bold>, Map of West Eurasia showing sampling locations and ages of the ancient samples. <bold>b</bold>, Raincloud plot of the sample ages, grouped by sampling region: Western Europe (<italic>n</italic> = 156), Central/Eastern Europe (<italic>n</italic> = 268), Southern Europe (<italic>n</italic> = 136), Northern Europe (<italic>n</italic> = 432) and Central/Western Asia (<italic>n</italic> = 23). Boxplot shows the median and first and third quartiles of the sample ages and whiskers extend to the largest value no further than 1.5× the interquartile range.</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>The genetic legacy of ancient Eurasian ancestries in present-day populations.</title><p><bold>a</bold>–<bold>e</bold>, Maps showing the average ancestry of: WHG (<bold>a</bold>); EHG (<bold>b</bold>); CHG (<bold>c</bold>); Neolithic farmer (<bold>d</bold>); and Steppe pastoralist (<bold>e</bold>) ancestry components per country (left) and per county or unitary authority within Great Britain and per country for the Republic of Ireland and Northern Ireland (right). Estimation was performed using ChromoPainter and NNLS, on samples of a ‘typical ancestral background’ for each non-UK country (<italic>n</italic> = 24,511) and Northern Ireland. For Great Britain, an average of self-identified ‘white British’ samples was used to represent each UK county and unitary authority, based on place of birth (<italic>n</italic> = 408,884). Countries with less than 4 and counties with less than 15 samples are shown in grey. Map uses ArcGIS layers World Countries Generalized and World Terrain.</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><title>A schematic of the model of population structure in Europe.</title><p>Quantitative admixture model used to simulate genomes to train the local ancestry neural network classifier. The model begins with the Out-of-Africa population, before splitting into basal Northern Europeans (NE) and West Asians (WA), who further split into EHG, WHG, CHG and ANA. These then admix to form Steppe pastoralist (Yam) and Neolithic farmer (Neo) populations. Moving down the figure is forwards in time and the population split times and admixture times are given in generations ago. Each branch is labelled with the effective population size of the population. Coloured lines represent the populations declared in the simulation that extend through time.</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><title>Genome-wide selection scan for trait-associated variants.</title><p><bold>a</bold>, Manhattan plot of <italic>P</italic> values from selection scan with CLUES, based on a time-series of imputed aDNA genotype probabilities. Twenty-one genome-wide significant selection peaks highlighted in grey and labelled with the gene closest to the most significant SNP within each locus. Within each sweep, SNPs are positioned on the <italic>y</italic> axis and coloured by their most significant marginal ancestry. Outside of the sweeps, SNPs show <italic>P</italic> values from the pan-ancestry analysis and are coloured grey. Red dotted lines indicate genome-wide significance (<italic>P</italic> < 5 × 10<sup>−8</sup>). <bold>b</bold>, Detailed plots for three genome-wide significant sweep loci: (1) <italic>MCM6</italic>, lactase persistence; (2) <italic>SLC45A2</italic>, skin pigmentation; and (3) <italic>FADS2</italic>, lipid metabolism. Rows show results for the pan-ancestry analysis (ALL) plus the four marginal ancestries: WHG, EHG, CHG and ANA. The first column of each locus shows zoomed Manhattan plots of the <italic>P</italic> values for each ancestry and column two shows allele frequency trajectories for the top SNPs across all ancestries (grey shading for the marginal ancestries indicates approximate temporal extent of the pre-admixture population).</p></caption></fig>", "<fig id=\"Fig5\"><label>Fig. 5</label><caption><title>ARSs for 35 complex traits.</title><p>Showing the genetic risk that a present-day individual would possess if they were composed entirely of one ancestry. On the basis of chromosome painting of the UKB, for 35 complex traits found to be significantly overdispersed in ancient populations. Confidence intervals (95%) are estimated by bootstrapping present-day samples (<italic>n</italic> = 408,884) and centred on the mean estimate.</p></caption></fig>", "<fig id=\"Fig6\"><label>Extended Data Fig. 1</label><caption><title>Selection at the <italic>MCM6</italic> locus.</title><p>CLUES selection results for the most significant sweep locus, showing the pan-ancestry analysis (ALL) plus the four marginal ancestries: Western hunter-gatherers (WHG), Eastern hunter-gatherers (EHG), Caucasus hunter-gatherers (CHG) and Anatolian farmers (ANA). Row one shows zoomed Manhattan plots of the p values for each ancestry and row two shows allele trajectories for the top SNPs across all ancestries (grey shading for the marginal ancestries indicates approximate temporal extent of the pre-admixture population).</p></caption></fig>", "<fig id=\"Fig7\"><label>Extended Data Fig. 2</label><caption><title>Selection at the <italic>SLC45A2</italic> locus.</title><p>CLUES selection results for the second most significant sweep locus, showing the pan-ancestry analysis (ALL) plus the four marginal ancestries: Western hunter-gatherers (WHG), Eastern hunter-gatherers (EHG), Caucasus hunter-gatherers (CHG) and Anatolian farmers (ANA). Row one shows zoomed Manhattan plots of the p values for each ancestry and row two shows allele trajectories for the top SNPs across all ancestries (grey shading for the marginal ancestries indicates approximate temporal extent of the pre-admixture population).</p></caption></fig>", "<fig id=\"Fig8\"><label>Extended Data Fig. 3</label><caption><title>Selection at the HLA locus.</title><p>CLUES selection results for the third most significant sweep locus, showing the pan-ancestry analysis (ALL) plus the four marginal ancestries: Western hunter-gatherers (WHG), Eastern hunter-gatherers (EHG), Caucasus hunter-gatherers (CHG) and Anatolian farmers (ANA). Row one shows zoomed Manhattan plots of the p values for each ancestry and row two shows allele trajectories for the top SNPs across all ancestries (grey shading for the marginal ancestries indicates approximate temporal extent of the pre-admixture population).</p></caption></fig>", "<fig id=\"Fig9\"><label>Extended Data Fig. 4</label><caption><title>Selection at the <italic>ACAD10</italic> locus.</title><p>CLUES selection results for the fourth most significant sweep locus, showing the pan-ancestry analysis (ALL) plus the four marginal ancestries: Western hunter-gatherers (WHG), Eastern hunter-gatherers (EHG), Caucasus hunter-gatherers (CHG) and Anatolian farmers (ANA). Row one shows zoomed Manhattan plots of the p values for each ancestry and row two shows allele trajectories for the top SNPs across all ancestries (grey shading for the marginal ancestries indicates approximate temporal extent of the pre-admixture population).</p></caption></fig>", "<fig id=\"Fig10\"><label>Extended Data Fig. 5</label><caption><title>Selection at the <italic>CCDC12</italic> locus.</title><p>CLUES selection results for the fifth most significant sweep locus, showing the pan-ancestry analysis (ALL) plus the four marginal ancestries: Western hunter-gatherers (WHG), Eastern hunter-gatherers (EHG), Caucasus hunter-gatherers (CHG) and Anatolian farmers (ANA). Row one shows zoomed Manhattan plots of the p values for each ancestry and row two shows allele trajectories for the top SNPs across all ancestries (grey shading for the marginal ancestries indicates approximate temporal extent of the pre-admixture population).</p></caption></fig>", "<fig id=\"Fig11\"><label>Extended Data Fig. 6</label><caption><title>Selection at the <italic>RNA5SP158</italic> locus.</title><p>CLUES selection results for the sixth most significant sweep locus, showing the pan-ancestry analysis (ALL) plus the four marginal ancestries: Western hunter-gatherers (WHG), Eastern hunter-gatherers (EHG), Caucasus hunter-gatherers (CHG) and Anatolian farmers (ANA). Row one shows zoomed Manhattan plots of the p values for each ancestry and row two shows allele trajectories for the top SNPs across all ancestries (grey shading for the marginal ancestries indicates approximate temporal extent of the pre-admixture population).</p></caption></fig>", "<fig id=\"Fig12\"><label>Extended Data Fig. 7</label><caption><title>Selection at the <italic>GATA4</italic> locus.</title><p>CLUES selection results for the seventh most significant sweep locus, showing the pan-ancestry analysis (ALL) plus the four marginal ancestries: Western hunter-gatherers (WHG), Eastern hunter-gatherers (EHG), Caucasus hunter-gatherers (CHG) and Anatolian farmers (ANA). Row one shows zoomed Manhattan plots of the p values for each ancestry and row two shows allele trajectories for the top SNPs across all ancestries (grey shading for the marginal ancestries indicates approximate temporal extent of the pre-admixture population).</p></caption></fig>", "<fig id=\"Fig13\"><label>Extended Data Fig. 8</label><caption><title>Selection at the <italic>ARL17B</italic> locus.</title><p>CLUES selection results for the eighth most significant sweep locus, showing the pan-ancestry analysis (ALL) plus the four marginal ancestries: Western hunter-gatherers (WHG), Eastern hunter-gatherers (EHG), Caucasus hunter-gatherers (CHG) and Anatolian farmers (ANA). Row one shows zoomed Manhattan plots of the p values for each ancestry and row two shows allele trajectories for the top SNPs across all ancestries (grey shading for the marginal ancestries indicates approximate temporal extent of the pre-admixture population).</p></caption></fig>", "<fig id=\"Fig14\"><label>Extended Data Fig. 9</label><caption><title>Selection at the <italic>IRF1</italic> locus.</title><p>CLUES selection results for the ninth most significant sweep locus, showing the pan-ancestry analysis (ALL) plus the four marginal ancestries: Western hunter-gatherers (WHG), Eastern hunter-gatherers (EHG), Caucasus hunter-gatherers (CHG) and Anatolian farmers (ANA). Row one shows zoomed Manhattan plots of the p values for each ancestry and row two shows allele trajectories for the top SNPs across all ancestries (grey shading for the marginal ancestries indicates approximate temporal extent of the pre-admixture population).</p></caption></fig>", "<fig id=\"Fig15\"><label>Extended Data Fig. 10</label><caption><title>Selection at the <italic>KRT18P51</italic> locus.</title><p>CLUES selection results for the tenth most significant sweep locus, showing the pan-ancestry analysis (ALL) plus the four marginal ancestries: Western hunter-gatherers (WHG), Eastern hunter-gatherers (EHG), Caucasus hunter-gatherers (CHG) and Anatolian farmers (ANA). Row one shows zoomed Manhattan plots of the p values for each ancestry and row two shows allele trajectories for the top SNPs across all ancestries (grey shading for the marginal ancestries indicates approximate temporal extent of the pre-admixture population).</p></caption></fig>", "<fig id=\"Fig16\"><label>Extended Data Fig. 11</label><caption><title>The 17q21.31 inversion locus.</title><p>A) Haplotypes of the 17q21.31 locus: the ancestral (non-inverted) H1 17q21.31 and the inverted H2 haplotype. Duplications of the <italic>KANSL1</italic> gene have occurred independently on both lineages yielding H1D and H2D haplotypes. B) Frequency of the 17q21.31 inversion and duplication haplotypes across present-day global populations (Human Genome Diversity Project<sup>##REF##32193295##82##</sup>). C) Change in the frequency of the 17q21.31 inversion haplotype through time.</p></caption></fig>" ]	[]	[]	[]	[]	[]	[]	[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM3\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM4\"></supplementary-material>" ]	[ "<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>These authors contributed equally: Evan K. Irving-Pease, Alba Refoyo-Martínez, William Barrie, Andrés Ingason, Alice Pearson, Anders Fischer</p></fn><fn><p>These authors jointly supervised this work: Daniel J. Lawson, Richard Durbin, Thorfinn Korneliussen, Thomas Werge, Morten E. Allentoft, Martin Sikora, Rasmus Nielsen, Fernando Racimo, Eske Willerslev</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"41586_2023_6705_Fig1_HTML\" id=\"d32e701\"/>", "<graphic xlink:href=\"41586_2023_6705_Fig2_HTML\" id=\"d32e771\"/>", "<graphic xlink:href=\"41586_2023_6705_Fig3_HTML\" id=\"d32e831\"/>", "<graphic xlink:href=\"41586_2023_6705_Fig4_HTML\" id=\"d32e940\"/>", "<graphic xlink:href=\"41586_2023_6705_Fig5_HTML\" id=\"d32e1667\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6705_Fig6_ESM\" id=\"d32e1919\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6705_Fig7_ESM\" id=\"d32e1934\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6705_Fig8_ESM\" id=\"d32e1945\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6705_Fig9_ESM\" id=\"d32e1960\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6705_Fig10_ESM\" id=\"d32e1975\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6705_Fig11_ESM\" id=\"d32e1990\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6705_Fig12_ESM\" id=\"d32e2005\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6705_Fig13_ESM\" id=\"d32e2020\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6705_Fig14_ESM\" id=\"d32e2035\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6705_Fig15_ESM\" id=\"d32e2050\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6705_Fig16_ESM\" id=\"d32e2070\"/>" ]	[ "<media xlink:href=\"41586_2023_6705_MOESM1_ESM.pdf\"><caption><p>Supplementary Information</p></caption></media>", "<media xlink:href=\"41586_2023_6705_MOESM2_ESM.pdf\"><caption><p>Reporting Summary</p></caption></media>", "<media xlink:href=\"41586_2023_6705_MOESM3_ESM.pdf\"><caption><p>Peer Review File</p></caption></media>", "<media xlink:href=\"41586_2023_6705_MOESM4_ESM.xlsx\"><caption><p>Supplementary Tables</p></caption></media>" ]	[{"label": ["1."], "mixed-citation": ["Allentoft, M. E. et al. Population genomics of post-glacial western Eurasia. "], "italic": ["Nature"]}, {"label": ["3."], "mixed-citation": ["Marciniak, S., Bergey, C., Silva, A. M. & Ha\u0142uszko, A. An integrative skeletal and paleogenomic analysis of prehistoric stature variation suggests relatively reduced health for early European farmers. "], "italic": ["Proc. Natl Acad. Sci. USA"], "bold": ["119"]}, {"label": ["36."], "mixed-citation": ["Mallick, S. et al. The Allen Ancient DNA Resource (AADR): a curated compendium of ancient human genomes. Preprint at "], "italic": ["bioRxiv"]}, {"label": ["46."], "mixed-citation": ["Nguyen, L. P. et al. Role and species-specific expression of colon T cell homing receptor GPR15 in colitis. "], "italic": ["Nat. Immunol."], "bold": ["16"]}, {"label": ["51."], "mixed-citation": ["Barrie, W. et al. Elevated genetic risk for multiple sclerosis emerged in steppe pastoralist populations. "], "italic": ["Nature"]}, {"label": ["69."], "mixed-citation": ["Wang, L. et al. Peakwide mapping on chromosome 3q13 identifies the kalirin gene as a novel candidate gene for coronary artery disease. "], "italic": ["Am. J. Hum. Genet."], "bold": ["80"]}, {"label": ["71."], "mixed-citation": ["Zang, X.-L. et al. Association of a SNP in SLC35F3 gene with the risk of hypertension in a Chinese Han population. "], "italic": ["Front. Genet."], "bold": ["7"]}, {"label": ["77."], "mixed-citation": ["Ong, H. S. & Yim, H. C. H. in "], "italic": ["Regulation of Inflammatory Signaling in Health and Disease"]}, {"label": ["89."], "surname": ["Rosenstock"], "given-names": ["E"], "article-title": ["Human stature in the Near East and Europe ca. 10,000\u20131000\u2009"], "sc": ["bc"], "source": ["Archaeol. Anthropol. Sci."], "year": ["2019"], "volume": ["11"], "fpage": ["5657"], "lpage": ["5690"], "pub-id": ["10.1007/s12520-019-00850-3"]}]	{ "acronym": [], "definition": [] }	92	CC BY	no	2024-01-13 00:02:19	Nature. 2024 Jan 10; 625(7994):312-320	oa_package/2c/b0/PMC10781624.tar.gz
PMC10781625	38200297	[]	[ "<title>Methods</title>", "<title>Ambient pressure XPS</title>", "<p id=\"Par20\">All XPS data are collected with total external reflection X-rays and normalized to core levels of the substrate. The emitted photoelectrons and gases pass into the spectrometer through orifices in the front cone to be detected in a hemispherical analyser. The overall resolution in the measurement was 0.2 eV, all spectra were normalized with respect to the Fe 2<italic>p</italic><sub>3/2</sub> or Ru 3<italic>d</italic><sub>5/2</sub> core levels unless stated otherwise and spectra are presented in counts per second (c.p.s.).</p>", "<p id=\"Par21\">POLARIS is an ambient pressure X-ray photoelectron spectrometer that operates with several key differences from typical ambient pressure XPS systems. The sample is approached to 30 µm from a set of roughly 20-µm-diameter apertures that lead to the analyser; the X-rays used are in the tender range 4.6 keV for all data collected. Most importantly, though, is that the gas is delivered through the front cone directly to the sample, making a virtual pressure cell in which only the sample and aperture to the analyser are pressurized. To achieve surface sensitivity, grazing incidence X-rays are used within the total external reflection range 0.3° for iron and 0.45° for ruthenium. This geometry allows for surface sensitivity despite high kinetic energy electron detection; the probe depths are 15.5 Å and 12.6 Å for Fe and Ru, respectively<sup>##REF##33955763##30##</sup>. The electron spectrometer is a HiPP-2 hemispherical analyser manufactured by Scienta Omicron; see ref. <sup>##UREF##10##13##</sup> for more details. The single-crystal samples (Surface Preparation Laboratory, 99.99% purity) are mounted in a steel sample holder and heated from the back side with a resistive heater. The temperature of the sample is measured with a type C thermocouple pressed between the sample and the heater. The separation between the sample and the apertures is held constant by PID feedback based on the pressure over the sample<sup>##REF##32597682##31##</sup>. A Si(311) double-crystal monochromator was used, yielding a photon-energy bandwidth of approximately 130 meV, a 0.8 mm curved entry slit and 100 pass energy was used in the electron analyser.</p>", "<p id=\"Par22\">Extended Data Fig. ##FIG##4##1a## shows an example of an N1s spectrum of 1:3 N<sub>2</sub>:H<sub>2</sub> gases at 200 mbar and the sample at 423 K, indicating adsorbed N atoms on the surface. The measurements were conducted at a photon flux at which no detectable X-ray-beam-induced changes could be seen. Individual spectra were gathered for 30 to 300 min with no decreeable spectral changes when hydrogen was present in the gas phase. Extended Data Fig. ##FIG##6##3## shows an example time interval of 2 h over Fe(210) at 423 K and 500 mbar in 1:3 N<sub>2</sub>:H<sub>2</sub> gas mixture. Extended Data Fig. ##FIG##6##3a,b## shows the data over this time for mass fragments 15, 16, 17 and 18, with and without processing. Extended Data Fig. ##FIG##6##3c## shows the XPS spectra evolution with time and Extended Data Fig. ##FIG##6##3d## shows the time-averaged results. From these, it is clear that the only change observed with time is the decrease in water signal owing to the slow improvement of vacuum conditions under constant hydrogen conditions.</p>", "<title>XPS data processing</title>", "<p id=\"Par23\">All presented spectra are scaled by the number of sweeps and dwell time per data point. Further scaling is done based on the relative cross-section of the materials, as mentioned in the main text. To fit the spectra, CasaXPS was used with linear or Shirley backgrounds as needed. Peaks were fitted with modified Voigt function (LA) line shapes, which allows for asymmetry. Asymmetry was tuned for each component.</p>", "<title>Sample preparation</title>", "<p id=\"Par24\">Sample cleaning was performed by ion sputtering with 3 keV Ar<sup>+</sup> for iron and 1 keV for ruthenium. The samples were annealed to 900 K for Fe and 1,100 K for Ru. Chemical cleaning was performed as needed by exposing the sample to either hydrogen or oxygen at elevated temperatures to remove oxygen or carbon, respectively. Small contaminations of sulfur and silicon were present, but the atomic composition was maintained at or below 1%.</p>", "<title>Coverage</title>", "<p id=\"Par25\">To model the coverage of the surfaces, the Fe<sub>4</sub>N and RuN nitrides were used as the physical representation of the surface species. Although not a perfect model, reference data of commercial nitrides verify that the surface constituents are similar in atomic bonding. We used the method previously established<sup>##REF##33955763##30##</sup> and typical XPS coverage formulation<sup>##UREF##24##32##</sup> to calculate the coverages. Elemental cross-section data was taken from ref. <sup>##UREF##25##33##</sup>.</p>", "<p id=\"Par26\">To calculate the probe depth, the X-ray and electron mean free path need to be combined; this is done by calculating the X-ray field in the material<sup>##UREF##26##34##</sup> at a given angle and using the TTP2M electron mean free path<sup>##UREF##27##35##</sup> to determine the electron-signal intensity as a function of depth within the sample. Then the integral is evaluated over all depths to determine the effective probe depth. Once the probe depth is determined, the coverage is then calculated on the basis of the ratio of substrate to surface species intensity-weighted by the cross-sections and atomic densities<sup>##UREF##24##32##</sup>.</p>", "<title>Mass spectrometry</title>", "<p id=\"Par27\">To determine the amount of NH<sub>3</sub> formed by the catalyst, a differentially pumped mass spectrometer (Hiden HAL/3F RC 301 PIC system) was attached to the first differential pumping stage of the XPS analyser. By leaking a small amount of gas from the pumping stage to the mass spectrometer, the composition of the gas over the sample was determined. To ascertain the gas composition, mass fragments of all relevant peaks were monitored. Impurities in the N<sub>2</sub> and H<sub>2</sub> gases were predominantly H<sub>2</sub>O and CO<sub>2</sub>. These contributions to the NH<sub>3</sub> fragments were subtracted on the basis of the measured ratio of pure gas to contaminate. Owing to the marked overlap of water and NH<sub>3</sub> ionization patterns, <italic>m</italic>/<italic>z</italic> = 15 and 16 were used as the markers for NH<sub>3</sub>. Further smoothing is done with a third-order Savitzky–Golay filter over a window of 1 s. The result of this analysis is shown in Fig. ##FIG##0##1##. As the mass spectrometer is highly sensitive, there is signal before any experiment from the chamber at all masses, including masses 15 and 16, most likely because of hydrocarbons. With the high-flow conditions required to establish the pressure for the XPS measurements, the amount of ammonia in the gas stream into the mass spectrometer is small and the signal becomes noisy. Therefore, to make a more accurate measurement of the NH<sub>3</sub> production, time integration was done between the background level in pure N<sub>2</sub> and that of pure H<sub>2</sub>. The background was subtracted from the time integration during ammonia production. Extended Data Fig. ##FIG##7##4a## shows an example mass spectrometer time trace in which there is negligible NH<sub>3</sub> production, showing how the background change with gas flow is within the noise of the measurement and therefore requires time integration. Owing to the specific design of the high-flow virtual cell, unwanted gas molecules originating from reactions of the sample holder or heater cannot reach the single-crystal surface area that is examined by the opening into the electron spectrometer. Thereby, all measurement conditions are constant. The increase in ammonia production at higher temperatures is as expected according to refs. <sup>##UREF##7##9##,##UREF##15##21##,##UREF##16##22##</sup>, providing further confidence that ammonia is produced.</p>", "<p id=\"Par28\">The relative chemical activity (RCA) was calculated using the following equation. Time-averaged NH<sub>3</sub>% was calculated from the amount of signal from ammonia as described above per total signal from the mass spectrometer. Volume (<italic>V</italic>) of gas is the total volume of gas used during the measurement, pressure (<italic>P</italic>) over the sample, temperature (<italic>T</italic>) of the sample, the gas constant (<italic>R</italic>), time is the duration of the time when ammonia could have been produced, <italic>A</italic><sub>n</sub> is Avogadro’s number and sites is the number of active sites under the high-pressure area. Finally, the highest activity on any surface is a normalization to the maximum of any surface. The normalization is to account for systematic errors, such as the fact that most of the volume of gas used does not pass over the sample or the fact that not all sites in the high-pressure region under the front cone would be examined by the mass spectrometer.</p>", "<p id=\"Par29\">With an instrument exposed to many gases over the years, there are signals at all masses, including masses 15 and 16, before any ammonia-synthesis experiment is performed, owing to desorption from the chamber walls. This desorption in the first differential pumping stage most likely comes from hydrocarbons. In the mass spectrometer, it is possible for crosstalk between channels or other instrumental errors to affect the signal. This is particularly true when the signal is very near the noise level, as in the work presented herein. Extended Data Fig. ##FIG##6##3## shows the masses 15, 16, 17 and 18. Mass 17, corresponding to ammonia, is strongly affected by water production from H<sub>2</sub> interaction with the chamber walls and the mass spectrometer itself, making quantitative analysis impossible. To decrease the possibility of the ammonia signal originating from instrumental errors, both masses 16 and 15 are included in the signal of ammonia. As discussed above, mass 17 is not included because of the large water signal. Extended Data Fig. ##FIG##6##3a,b## shows the effect of both processing and long acquisition times. The sample is Fe(210), 423 K, 500 mbar, 1:3 N<sub>2</sub>:H<sub>2</sub> ratio. Here we can see that the atomic mass units of both 15 and 16 are constantly above the background signal from hydrocarbons or water. Meanwhile, mass 17 is not, owing to the strong overlap of OH and NH<sub>3</sub> masses. Note that the background signal removed at this point in the processing does not account for all of the background signals. As described above, to determine the relative chemical activity, the signal of ammonia (masses 15 and 16) above the background signal in either pure N<sub>2</sub> or pure H<sub>2</sub> is taken. That ammonia signal is then compared with the total signal in the mass spectrometer over the same time period. By this method, the plotted data do not remove all of the background signals, yet when the data are processed for relative chemical activity, the entirety of the background is removed.</p>", "<p id=\"Par30\">The error of the relative chemical activity is estimated on the basis of the signal-to-noise ratio of the background of the ammonia signal. Part of the calculation is to subtract the background, seen in Fig. ##FIG##0##1b##, between times 15 and 23 min; the fluctuations in the background can have a notable effect on the calculation of ammonia content. To ascertain the estimated error, the 95% confidence interval of the noise average and standard deviation over the collected time were introduced as an error source in the equation for relative chemical activity. Because the background signal and noise are similar for all experiments, the estimated error introduced is also similar. The relative chemical activity is meant to be a semiquantitative description of the abundance of ammonia, only as a comparative description of these similar systems, and to demonstrate that the trends follow previous more absolute activity measurements. Extended Data Fig. ##FIG##7##4a## shows an example of when there is no ammonia production at the lowest temperature with the least active catalyst, Fe(110) at 523 K (300 mbar, 1:1 ratio). Here we can see that extremely small ammonia production occurs and this is most likely the background level. Extended Data Fig. ##FIG##7##4b##, by contrast, shows the same surface and experiment at the higher temperature of 673 K and clearly shows that ammonia production increases with increasing temperature.</p>", "<title>Beam effects</title>", "<p id=\"Par31\">To determine the effect of the X-ray beam intensity on the observed species, two types of beam damage test were performed, attenuation and dark spectra, both carried out at 423 K, 500 mbar and a 3:1 mixture of H<sub>2</sub> and N<sub>2</sub>. The dark spectra were performed by aligning the sample and gathering highly attenuated spectra, then closing off the light, cleaning the sample in hydrogen, then opening the shutter and obtaining a new spectrum. The result showed no change owing to the amount of time the sample was exposed to X-rays. The second test was done by gradually increasing the intensity of the beam to determine whether any beam damage would accumulate; here no change in the spectra once normalized to the attenuation factor was observed.</p>", "<p id=\"Par32\">Nitrogen spectra gathered outside reaction conditions (that is, without hydrogen), such as those shown in Fig. ##FIG##1##2##, show not only chemical changes inherent to the reaction but also accumulation of beam-induced effects. The build-up of beam-induced nitride formation is slower than chemical activity but is not possible to fully avoid. For this reason, no attempt is made to quantify the formation rate of the various nitrides. The main finding of the paper is the distinct lack of nitrogen on the surface during the reaction and the slow nitride formation compared with fast reduction caused by hydrogen. The beam-induced nitride formation only serves to increase nitrogen formation. Therefore, the beam effects do not alter any conclusions of the activity of N<sub>2</sub> compared with H<sub>2</sub> but rather strengthen the finding.</p>", "<p id=\"Par33\">To determine whether the beam has any effect on the mass spectrometer findings, two experiments of gas switching (in which the sample was exposed to pure N<sub>2</sub> then 1:1 N<sub>2</sub>:H<sub>2</sub> then pure H<sub>2</sub>) were performed with X-ray light and without. Extended Data Fig. ##FIG##7##4b,c## shows the mass fragments of all relevant species for these experiments. For these data, the sample was Fe(110) at 673 K. Extended Data Fig. ##FIG##7##4b## shows the measurement with beam, whereas the measurement in Extended Data Fig. ##FIG##7##4c## was collected without beam. Although there are some minor differences in the ammonia signal between the two datasets, none of the changes are what would be expected from beam effects. It is expected that beam effects in the mass spectrometer would strictly cause an increase or decrease in ammonia signal. The change in the relative chemical activity between the test done with and without beam is approximately 2% and well within the error of the experiment. From this, it is clear that the X-ray beam does not have any effect on the mass spectrometer findings.</p>", "<p id=\"Par34\">Extended Data Fig. ##FIG##8##5a## shows the effect of photon flux on the nitrogen content. Note that the line of best fit shown in grey has a forced intercept to zero. Extended Data Fig. ##FIG##8##5b## shows the effect of flux on nitrogen speciation, showing no change to the components of the N1s spectra. These spectra were gathered at 500 mbar in a 1:3 gas ratio at 423 K over Ru, the conditions expected to be the most sensitive to beam damage. Equivalent tests were performed for all catalysts.</p>", "<title>Trace contaminations</title>", "<p id=\"Par35\">Although highly pure gas (5N for nitrogen and hydrogen) was used with in-line chemical purifiers (N<sub>2</sub>, model no. MC45-804; H<sub>2</sub> gas, model no. MC45-904, SAES Group), trace impurities are still present; on the basis of the mass spectrometer data, approximately 6 ppm of water and 3 ppm of CO<sub>2</sub>. Owing to the high flows used, these small contaminants can react and build on the surface instantly. Furthermore, molecules will readily react with iron to form iron oxides; the same is not true for ruthenium. As a result, the iron surface at low temperatures will form a partial oxide but, as the temperature increases, the reduction by hydrogen outpaces the oxidation of the contaminants, yielding a metallic surface at relevant conditions.</p>", "<title>Nitrogen species binding energies</title>", "<p id=\"Par36\">Extended Data Table ##TAB##0##1## presents previously published and computed binding energies for various amine and nitrogen species over iron and ruthenium. Note that, for comparison with studies at low photon energy, the recoil effect, in which the energy of the electron causes the nucleus to recoil, thereby decreasing the kinetic energy of the emitted electrons, needs to be considered<sup>##REF##18851493##36##</sup>.</p>" ]	[]	[]	[]	[ "<p id=\"Par1\">The large-scale conversion of N<sub>2</sub> and H<sub>2</sub> into NH<sub>3</sub> (refs. <sup>##UREF##0##1##,##UREF##1##2##</sup>) over Fe and Ru catalysts<sup>##UREF##2##3##</sup> for fertilizer production occurs through the Haber–Bosch process, which has been considered the most important scientific invention of the twentieth century<sup>##UREF##3##4##</sup>. The active component of the catalyst enabling the conversion was variously considered to be the oxide<sup>##UREF##4##5##</sup>, nitride<sup>##UREF##1##2##</sup>, metallic phase or surface nitride<sup>##UREF##5##6##</sup>, and the rate-limiting step has been associated with N<sub>2</sub> dissociation<sup>##REF##10032161##7##–##UREF##7##9##</sup>, reaction of the adsorbed nitrogen<sup>##UREF##8##10##</sup> and also NH<sub>3</sub> desorption<sup>##UREF##9##11##</sup>. This range of views reflects that the Haber–Bosch process operates at high temperatures and pressures, whereas surface-sensitive techniques that might differentiate between different mechanistic proposals require vacuum conditions. Mechanistic studies have accordingly long been limited to theoretical calculations<sup>##REF##15681379##12##</sup>. Here we use X-ray photoelectron spectroscopy—capable of revealing the chemical state of catalytic surfaces and recently adapted to operando investigations<sup>##UREF##10##13##</sup> of methanol<sup>##REF##35511988##14##</sup> and Fischer–Tropsch synthesis<sup>##REF##35815066##15##</sup>—to determine the surface composition of Fe and Ru catalysts during NH<sub>3</sub> production at pressures up to 1 bar and temperatures as high as 723 K. We find that, although flat and stepped Fe surfaces and Ru single-crystal surfaces all remain metallic, the latter are almost adsorbate free, whereas Fe catalysts retain a small amount of adsorbed N and develop at lower temperatures high amine (NH<sub><italic>x</italic></sub>) coverages on the stepped surfaces. These observations indicate that the rate-limiting step on Ru is always N<sub>2</sub> dissociation. On Fe catalysts, by contrast and as predicted by theory<sup>##REF##25013071##16##</sup>, hydrogenation of adsorbed N atoms is less efficient to the extent that the rate-limiting step switches following temperature lowering from N<sub>2</sub> dissociation to the hydrogenation of surface species.</p>", "<p id=\"Par2\">Using X-ray photoelectron spectroscopy, the surface composition of iron and ruthenium catalysts during ammonia synthesis at pressures up to 1 bar and temperatures as high as 723 K can be revealed.</p>", "<title>Subject terms</title>" ]	[ "<title>Main</title>", "<p id=\"Par3\">Figure ##FIG##0##1a## shows how surface-sensitive operando X-ray photoelectron spectroscopy (XPS) is measured together with reaction-product detection during the Haber–Bosch process in the POLARIS instrument<sup>##UREF##10##13##</sup>. XPS is a powerful technique for investigating the chemical state of catalytic surfaces through core-level shifts that traditionally required vacuum conditions, but operando studies can be conducted using a differential pumping scheme<sup>##UREF##11##17##</sup>. The Fe and Ru single-crystal surfaces are mounted in front of the electron spectrometer with a gap of 30 µm and gases are fed through the front cone of the electron lens, creating a localized virtual catalytic reactor of elevated pressure with a rapid gas flow<sup>##UREF##10##13##</sup>. The typical operational pressure for ammonia synthesis is 50–200 bar (ref. <sup>##UREF##12##18##</sup>), at which the gas-phase equilibrium is strongly shifted towards the product, giving a high final conversion to ammonia. However, during the initial phase of the Haber–Bosch process, when not much ammonia has yet been produced, the reaction also proceeds with a high rate at our operational pressures of up to 1 bar (refs. <sup>##UREF##13##19##,##UREF##14##20##</sup>).</p>", "<p id=\"Par4\">The incoming X-rays were set to an energy of 4,600 eV and the incidence at an angle below total reflection, allowing for high surface sensitivity despite high kinetic energy electron detection. The emitted photoelectrons will pass into the spectrometer through orifices in the front cone and be detected in a hemispherical analyser. The inset in Fig. ##FIG##0##1a## shows an example of an N1s spectrum of 1:3 N<sub>2</sub>:H<sub>2</sub> gases at 1 bar at 673 K, indicating NH<sub>3</sub> (blue), NH<sub>2</sub> (purple), NH (red), surface N (green) and nitride surface (yellow) components. The measurements were conducted at a photon flux at which no detectable X-ray-beam-induced changes could be seen during the Haber–Bosch process (see <xref rid=\"Sec2\" ref-type=\"sec\">Methods</xref> for further details).</p>", "<p id=\"Par5\">To track the production of NH<sub>3</sub>, masses 15 and 16 were monitored in the mass spectrometer (see <xref rid=\"Sec2\" ref-type=\"sec\">Methods</xref>), as shown in Fig. ##FIG##0##1b##. The relative chemical reactivities shown in Fig. ##FIG##0##1c## were determined by measuring the mass spectrometer ammonia signal with respect to the signal of all constituents to compute the number of ammonia molecules formed per second per surface site, which is then further normalized to the highest activity shown by any surface at any temperature (see <xref rid=\"Sec2\" ref-type=\"sec\">Methods</xref> for further details). The reaction rate increases with increasing temperature and is higher for the stepped Fe(210) than the flat Fe(110) surface, in agreement with previous high-pressure-reactor studies<sup>##UREF##7##9##</sup>. The highest rate is seen for the surface, as expected based on polycrystalline studies showing that Ru has higher activity than Fe (ref. <sup>##UREF##15##21##</sup>). The maximum rate for Ru is not at the highest temperature of 723 K, as for the Fe surfaces, but at 623 K, also in accordance with catalytic-reactor studies<sup>##UREF##16##22##</sup>.</p>", "<p id=\"Par6\">On exposure to pure N<sub>2</sub> gas at 150 mbar, the two Fe surfaces have a delayed but eventually rapid increase in the N1s intensity, showing bulk nitride formation (Fig. ##FIG##1##2a,b##). On the basis of the binding-energy position of the N1s peaks in the spectra, this corresponds to the formation of γ′-nitride and ε-nitride plus some small amount of chemisorbed N atoms on the bare Fe surface (see Extended Data Table ##TAB##0##1##). The nitride formation is more rapid on the Fe(210) surface, specifically the γ′-nitride, whereas on the Fe(110) surface, there is an equal amount of the two nitrides and slower growth. The thicknesses of the nitride layers are greater than ten monolayers; exact quantification depends on the reaction time, as the surface continues to evolve even after hours of observation (see <xref rid=\"Sec2\" ref-type=\"sec\">Methods</xref> for details on monolayer calculations). We attribute the faster growth on the Fe(210) facet to the higher probability of N<sub>2</sub> dissociation on the stepped surface<sup>##UREF##17##23##</sup>. At temperatures below 523 K, no nitride formation is observed.</p>", "<p id=\"Par7\">The reacts completely differently. Almost instantaneously after N<sub>2</sub> exposure, the N1s intensity saturates and remains constant, corresponding to a coverage of 5% of a monolayer, and there is no bulk nitride formation at 623 K (Fig. ##FIG##1##2c##). The coverage is comparable with previous work, which predicts 17% of a monolayer at 500 K and a pressure of 100 mbar (ref. <sup>##UREF##17##23##</sup>). The small amount of N<sub>2</sub> on the Ru surface indicates a much weaker N–metal interaction than on Fe, as expected from theoretical predictions<sup>##REF##25013071##16##</sup>. The two components are at 397.4 eV and 397.9 eV, and we tentatively assign these to N adsorbed on terraces and steps, respectively (Extended Data Fig. ##FIG##4##1##). It is interesting that a weak, broad feature is seen at approximately 399–400 eV, with a binding energy consistent with adsorbed N<sub>2</sub> (ref. <sup>##UREF##18##24##</sup>); see Extended Data Fig. ##FIG##4##1##.</p>", "<p id=\"Par8\">When the pure N<sub>2</sub> gas is replaced by 1:1 N<sub>2</sub>:H<sub>2</sub> at 300 mbar, a marked change on the two Fe surfaces occurs within the first spectral sweep (90 s), shown at the bottom of Fig. ##FIG##1##2a,b##. The nitrides instantaneously disappear and only a small amount of adsorbed N atoms with a coverage of 2% of a monolayer on Fe(110) and 5% on Fe(210) remains. At the same time as the gas mixture is introduced, NH<sub>3</sub> is detected by the mass spectrometer. The rapid removal of the nitrides shows the strong reduction ability of the H<sub>2</sub>. The slow growth of nitrides (10–15 min) compared with the fast reduction (<100 ms) shows the difference in rates of N<sub>2</sub> and H<sub>2</sub> dissociation. The adsorbed N atom coverage is also substantially lowered on the surface following the introduction of the 1:1 N<sub>2</sub>:H<sub>2</sub> mixture at 300 mbar and decreases from 5% to <0.05% of a monolayer as NH<sub>3</sub> is produced.</p>", "<p id=\"Par9\">Next, we address the question of oxides potentially not being reduced on Fe under operando conditions owing to trace contaminations of water or CO<sub>2</sub> in the gas phase<sup>##UREF##4##5##</sup>. Iron is known to oxidize in trace amounts of water or CO<sub>2</sub> at room temperature, yet iron oxide is not readily reduced below 500 K and, as a result, even under pure hydrogen, iron will oxidize with high flows (see <xref rid=\"Sec2\" ref-type=\"sec\">Methods</xref> for a detailed description). Figure ##FIG##2##3## shows data collected at 500 mbar, 1:3 N<sub>2</sub>:H<sub>2</sub> and various temperatures. The Fe 2p<sub>2/3</sub> peaks in Fig. ##FIG##2##3a## from metallic iron at 706.5 eV and 707.4 eV are split owing to exchange interactions with the ferromagnetic valence electrons, and there is a broad Fe oxide peak at 710.8–709.8 eV, indicated by the grey rectangle. The Fe(110) sample is fully reduced as the temperature reaches 523 K at 500 mbar and the Fe(210) surface requires a higher temperature of 573 K, as seen in Fig. ##FIG##2##3b##. Fe(210) needs a higher temperature because of the stronger binding of oxygen on a stepped surface. Ru is metallic at all conditions. All surfaces are in a metallic state during the Haber–Bosch process, as expected because of the high concentration of adsorbed hydrogen (Fig. ##FIG##2##3c##). Note that these measurements were gathered simultaneously with the data in Fig. ##FIG##3##4##.</p>", "<p id=\"Par10\">The adsorbed nitrogen species can be measured operando as NH<sub>3</sub> is produced. First, focusing on the two Fe single-crystal surfaces (Fig. ##FIG##3##4a,b##), we observe only adsorbed N atoms on the surface at a binding energy of 397.4 eV, consistent with previous surface-science vacuum experiments once the recoil effect of the emitted atoms is considered (see Extended Data Table ##TAB##0##1##). Adsorbed molecular N<sub>2</sub> could not be detected and would have been observed at 399.0, 401.2 or 405.9 eV (Extended Data Table ##TAB##0##1##), depending on the adsorption site and bonding type. The coverage of adsorbed N is 1.3% at 200 mbar and 0.6% at 500 mbar on the Fe(110) surface and increases on the Fe(210) surface to 5.0% and 1.5%, respectively. The higher coverage on the stepped surface is related to availability and stronger bonding of undercoordinated sites<sup>##REF##25013071##16##</sup>. What is most surprising is that the coverage is not increasing at higher pressures; on the contrary, the coverage decreases slightly with increased pressure. Inspecting the N1s spectra in Fig. ##FIG##3##4d##, measured at 1 bar and 673 K, the peak is barely distinguishable from the noise, implying an even lower coverage. It would be tempting to expect an increase in N coverage with increasing pressure because the impinging rate of N<sub>2</sub> molecules increases, but obviously also does the rate of H adsorption. Although we cannot determine the H coverage with XPS, our data suggest that the hydrogenation ability of the surface increases with the total pressure; this would explain a more efficient further reaction of the adsorbed N atoms. Extrapolating to much higher pressures, we predict that the Fe surface is an almost pristine metal under realistic conditions. The fact that no amines (NH or NH<sub>2</sub>) or NH<sub>3</sub> are observed at the reaction temperature of 673 K indicates that the rate-limiting step after N<sub>2</sub> dissociation is the hydrogenation of adsorbed N, and the rates of the other hydrogenation steps of NH and NH<sub>2</sub> as well as NH<sub>3</sub> desorption are much faster. At high temperatures, the Ru surface (Fig. ##FIG##3##4g##) has adsorbed N at 397.4 eV and the adsorbate coverage is almost negligible, with <0.1% of a monolayer of both NH and NH<sub>2</sub> species, independent of pressure within the noise limit. Here the surface is almost entirely clean of any species at conditions of high reaction rate.</p>", "<p id=\"Par11\">At 523 K, for which the reaction proceeds very slowly, the population of the adsorbates changes. There is a slight increase of the adsorbed N on Fe(110) at 500 mbar to 2.3% of a monolayer (Fig. ##FIG##3##4d##). The Fe(210) surface shows large differences compared with the higher-temperature spectra (Fig. ##FIG##3##4e##). Further peaks at 398.0 eV, 398.9 eV and 400.2 eV formed, corresponding to NH, NH<sub>2</sub> and NH<sub>3</sub>, as determined by previous XPS vacuum studies<sup>##UREF##7##9##,##UREF##19##25##,##UREF##20##26##</sup> and calculated relative peak positions (Extended Data Table ##TAB##0##1##). Note that the peak at 399 eV is not related to adsorbed N<sub>2</sub> because ex situ XPS studies observed the peak when the Fe catalyst was cooled down to room temperature in the reaction mixture and moved to a vacuum, in which all molecular N<sub>2</sub> would desorb. We observe a relatively high coverage of NH<sub>2</sub> (24.8%), adsorbed N (4.3%), NH (6.7%) and NH<sub>3</sub> (5.2%) at 200 mbar. There is a slight pressure dependence, for which—in particular—the NH<sub>2</sub> decreases to 9.3%. Clearly, there exist conditions in which the adsorbed N and NH<sub><italic>x</italic></sub> species are strongly adsorbed on step sites owing to a substantially lower hydrogenation rate. Decreasing the temperature further to 423 K, adsorbed NH<sub><italic>x</italic></sub> and NH<sub>3</sub> become visible on the Fe(110) surface. These trends are seen across 423 to 623 K (Extended Data Fig. ##FIG##5##2##).</p>", "<p id=\"Par12\">On Ru at 523 K at 500 mbar (Fig. ##FIG##3##4c##), we still see very low coverages, although the coverage of adsorbed N at steps has increased to 0.5%, as well as adsorbed NH<sub>2</sub> to 0.1% and adsorbed NH<sub>3</sub> to 0.1% at around 400 eV. The NH signal increases with pressure, but the nitrogen coverage quantification of these results is nearly within the margin of error. If there is an increase in coverage with pressure for Ru, it may indicate that the H<sub>2</sub>–metal interaction for Ru is weaker than for Fe, possibly leading to higher coverages at operational pressures. The adsorbed N species is much more reactive on Ru than Fe, supporting previous theoretical predictions<sup>##REF##25013071##16##</sup>.</p>", "<p id=\"Par13\">We can discriminate the various proposed hypotheses and put forward ideas consistent with the data on the chemical state of the catalysts and reaction mechanism in terms of rate-limiting steps. We have shown that nitride formation is far slower than nitride reduction and that the surface states are all metallic with low coverages of atomic nitrogen. There is no evidence for interstitial nitrogen, oxides or high coverage of any species of nitrogen, especially over the most active catalysts. It is interesting to compare the hydrogenation reactions of CO and N<sub>2</sub>, which are isoelectronic molecules. In the case of the Fischer–Tropsch reaction on Fe(110), a thick carbide is formed<sup>##REF##35815066##15##</sup>, whereas in the Haber–Bosch process, on the same surface, only a pristine metallic phase is generated. Clearly, the difference in the bond breaking of the CO molecule with respect to N<sub>2</sub> and the strength of the adsorbed C and N play an essential role.</p>", "<p id=\"Par14\">The different reaction steps in NH<sub>3</sub> synthesis have been proposed as the following<sup>##UREF##8##10##</sup>:in which * means surface species and <italic>θ</italic>* indicate empty sites available for bonding.</p>", "<p id=\"Par15\">The simplest case is the surface, for which we can directly explain that steps 3–6 are extremely rapid with no build-up of intermediates, pointing to 1 and 2 as the rate-limiting steps. We observe that the population of adsorbed N<sub>2</sub> is extremely low at high temperatures. The adsorbed molecular state is indeed observed at the low reaction temperature of 523 K, at which its dissociation limits the reaction. We conclude that the rate-limiting step of NH<sub>3</sub> production is the dissociation of the adsorbed N<sub>2</sub>, fully in line with theoretical estimations<sup>##REF##15681379##12##</sup>. Even at low temperatures, the surface is mostly adsorbate free, with little adsorbed NH<sub><italic>x</italic></sub> seen, because of the strong bonding to step sites in comparison with terrace atoms<sup>##UREF##17##23##</sup>. Although we have not observed definitive pressure dependence in the population of adsorbed N, it is plausible that the step sites will become more populated but are expected to remain well below a monolayer.</p>", "<p id=\"Par16\">On Fe it is well established that the rate-limited steps is the molecular dissociation<sup>##REF##10032161##7##–##UREF##7##9##</sup>, supported by the correlation between the NH<sub>3</sub> production rate and the N<sub>2</sub> dissociative sticking coefficient for the different single-crystal surface facets<sup>##UREF##7##9##,##UREF##21##27##</sup>. However, the results here show that, at all temperatures, a factor of around 100 times higher population of adsorbates is observed in comparison with the stepped Ru surface and we can no longer postulate that the reaction proceeds with a high rate after the molecular dissociative steps. Furthermore, there are no signs of molecularly adsorbed N<sub>2</sub> even at the lowest temperatures, indicative of a much higher rate of step 1b. Above 573 K, we observe adsorbed N that is more populated on the stepped crystal, indicating that the hydrogenation step 3 also partly controls the rate<sup>##REF##15681379##12##</sup>.</p>", "<p id=\"Par17\">The coverage of N species on the Fe surfaces decreases with increasing total pressure at a constant N<sub>2</sub>:H<sub>2</sub> ratio, implying that the N<sub>2</sub> dissociation step is slower than the hydrogenation step<sup>##UREF##8##10##</sup>. Most likely, the coverage of adsorbed H increases with pressure, resulting in faster hydrogenation. Because the coverage of H<sub>2</sub> at the reaction temperatures is expected to be low, we can assume that there is no inhibition of N<sub>2</sub> dissociation caused by the adsorbed hydrogen<sup>##UREF##21##27##</sup>.</p>", "<p id=\"Par18\">The population of intermediates shows that, as the reaction temperature lowers, the rate-limiting step switches to become hydrogenation of N, NH and NH<sub>2</sub> as well as NH<sub>3</sub> desorption (steps 3–6), demonstrating differences in the bonding at different high and low coordinated Fe sites. This agrees with earlier observations of the activation energy for hydrogenation being much higher than for N<sub>2</sub> dissociation<sup>##UREF##8##10##</sup> and the difference in the barriers of these two steps thus becoming prominent at low temperatures: although the N<sub>2</sub> dissociation rate at high temperatures is low owing to a low sticking coefficient that limits N<sub>2</sub> adsorption<sup>##UREF##8##10##</sup>, we see a large population of amines NH<sub><italic>x</italic></sub> and NH<sub>3</sub> on Fe at low temperatures. This trend, not seen with Ru, points to the hydrogenation steps affecting the overall rate on Fe. At higher pressures at which more N<sub>2</sub> is converted and the NH<sub>3</sub> content is higher, the back reaction may become important. Indeed, for Ru, it has been theoretically predicted that the coverage of nitrogen species may become substantially higher<sup>##UREF##22##28##</sup>.</p>", "<p id=\"Par19\">In closing, we note that, although concerns over the environmental impact of ammonia synthesis have spurned interest in low-pressure alternatives and these might indeed be feasible<sup>##UREF##23##29##</sup>, the Haber–Bosch process looks set to remain the primary method of ammonia production for many years to come. A better understanding of the mechanism at play might help to further improve the efficiency and, thereby, lower the environmental impact of this important industrial process. We anticipate that our approach to operando studies will contribute to this endeavour, by making it possible to explore the surface chemistry associated with ammonia formation in the presence of promotors and by making it possible, once measurements at higher pressures and with a higher NH<sub>3</sub> content are feasible, to explore the impact of the ammonia decomposition back reaction.</p>", "<title>Online content</title>", "<p id=\"Par37\">Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41586-023-06844-5.</p>", "<title>Supplementary information</title>", "<p>\n\n</p>" ]	[ "<title>Extended data figures and tables</title>", "<p id=\"Par40\">\n\n</p>", "<p id=\"Par41\">\n\n</p>", "<p id=\"Par42\">\n\n</p>", "<p id=\"Par43\">\n\n</p>", "<p id=\"Par44\">\n\n</p>", "<p id=\"Par45\">\n\n</p>", "<title>Extended data</title>", "<p>is available for this paper at 10.1038/s41586-023-06844-5.</p>", "<title>Supplementary information</title>", "<p>The online version contains supplementary material available at 10.1038/s41586-023-06844-5.</p>", "<title>Acknowledgements</title>", "<p>This research was financed by the Swedish Research Council (Vetenskapsrådet, VR) under award numbers VR 2013-8823 and 2017-00559, as well as the Knut and Alice Wallenberg Foundation under award number KAW 2016.0042. We acknowledge Deutsches Elektronen-Synchrotron DESY (Hamburg, Germany), a member of the Helmholtz Association (HGF), for the provision of experimental facilities. Parts of this research were carried out at PETRA III using beamline P22. Beamtime was allocated for proposals I-20200291 EC, I-20200292 EC and II-20211048 EC. J.K.M. is grateful for financial support from VILLUM FONDEN (research grant 41388).</p>", "<title>Author contributions</title>", "<p>A.N. and C.M.G. proposed the study. C.S. developed the beamline. C.M.G., A.N., D.D. and P.A. developed the experimental set-up. C.M.G., P.L., D.D., M.S., P.A., F.G.-M., S.K., B.D., J.K.M. and R.R. performed the experiments. C.M.G. analysed the data. G.L.S.R. performed the binding-energy calculations. A.N., C.M.G. and B.D. wrote the manuscript.</p>", "<title>Peer review</title>", "<title>Peer review information</title>", "<p id=\"Par38\"><italic>Nature</italic> thanks Jorge Boscoboinik and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. ##SUPPL##0##Peer reviewer reports## are available.</p>", "<title>Funding</title>", "<p>Open access funding provided by Stockholm University.</p>", "<title>Data availability</title>", "<p>Experimental data were generated at the PETRA III facility at the DESY Research Centre of the Helmholtz Association. Raw datasets are available from the corresponding authors on reasonable request.</p>", "<title>Competing interests</title>", "<p id=\"Par39\">The authors declare no competing interests.</p>" ]	[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>Experimental set-up and relative turnover-frequency measurements.</title><p><bold>a</bold>, The sample faces a set of apertures that deliver the reaction gas while simultaneously gathering products and emitted electrons. The grazing incidence X-rays enter from the left, producing photoelectrons. The mix of gas and electrons is separated by an electrostatic lens and analysed in an electron analyser and a mass spectrometer. The inset shows XPS spectra of the chemical state of N at 200 mbar over the Fe(110) surface with a 1:3 N<sub>2</sub>:H<sub>2</sub> gas ratio. <bold>b</bold>, Mass spectrometer readout of masses 15 and 16 corresponding to NH<sub>3</sub> production as the gas ratio changes from 150 mbar pure N<sub>2</sub> (blue region showing flow) to 300 mbar 1:1 N<sub>2</sub>:H<sub>2</sub> (green region showing flow) over Ru at 673 K. Note that the flows of the gases are shown as the filled blocks plotted on the left axis. <bold>c</bold>, The enhanced mass spectrometer signals were time averaged during the interval of the 1:1 N<sub>2</sub>:H<sub>2</sub> mixture to estimate the relative chemical reactivity. a.u., arbitrary units.</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>Nitride formation and depletion.</title><p>The formation and depletion of nitride on the surface of each catalyst are shown as a function of time. At the top, the N<sub>2</sub> gas is introduced with a total pressure of 150 mbar and spectral collection begins. Then, after the nitride begins to stabilize, H<sub>2</sub> gas is introduced immediately in a 1:1 ratio with N<sub>2</sub> with a total pressure of 300 mbar, reducing the surface within the frame of the detector. Next to each time series are example spectra normalized to the background, with a grey arrow showing the frame it represents. <bold>a</bold>, The data for 673 K over Fe(110). <bold>b</bold>, The data for 673 K over Fe(210). <bold>c</bold>, The data for 623 K over . For Ru, the spectra shown are the summation of the entire time series. Note the difference in <italic>y</italic>-axis scale in the spectral figures.</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><title>Oxides and metal.</title><p>Owing to trace contaminations in the gases, the surfaces can form oxides. <bold>a</bold>, Two cases in which a thick oxide forms at low temperatures and 500 mbar in a 1:3 N<sub>2</sub>:H<sub>2</sub> gas mixture, but the oxide thins and disappears as the temperature increases. The grey rectangle shows the region in which iron oxide peaks are present. <bold>b</bold>, The ratio of oxide to metal as a function of pressure and temperature for the Fe catalysts. The Fe(110) is grey, whereas the Fe(210) is blue. The solid line shows the lower-pressure data at 200 mbar, whereas the dashed line is the higher-pressure data at 500 mbar; at no point was the Ru catalyst oxidized. <bold>c</bold>, Example spectra of the metal peaks during NH<sub>3</sub> formation at 623 K, showing a singular metallic peak for all catalysts. a.u., arbitrary units.</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><title>Effects on adsorbates of temperature and pressure.</title><p>The steady-state population of the N species on the surface is shown for each catalyst at 200 mbar and 500 mbar at 523 K and 673 K in a 1:3 N<sub>2</sub>:H<sub>2</sub> gas mixture. Each set of spectra is normalized and corrected for the cross-section of the corresponding metal substrate. <bold>a</bold>–<bold>c</bold>, The data over Fe(110), Fe(210) and at 523 K, respectively. <bold>d</bold>, The data over Fe(210) at 673 K and at 1 bar. <bold>e</bold>–<bold>g</bold>, The data over Fe(110), Fe(210) and at 673 K, respectively. Note the change in scale owing to the Ru data in <bold>c</bold> and <bold>g</bold>; nitrogen coverage of N species on the Ru surface is incredibly low.</p></caption></fig>", "<fig id=\"Fig5\"><label>Extended Data Fig. 1</label><caption><title>Comparison of N1s over ruthenium under pure N<sub>2</sub> and low-pressure condition.</title><p>The spectra of N1s on under 200 mbar 1:3 N<sub>2</sub>:H<sub>2</sub> gas mixture at 423 K and the thickest nitride film made on the same surface at 623 K. The black lines show the offset between the NH species in red in the top spectra to N on terraces and light green in the bottom spectra cumulating to 150 meV.</p></caption></fig>", "<fig id=\"Fig6\"><label>Extended Data Fig. 2</label><caption><title>N1s data at every temperature and pressure.</title><p><bold>a</bold>–<bold>c</bold>, 200 mbar 1:3 N<sub>2</sub>:H<sub>2</sub> nitrogen spectra over Fe(110), Fe(210) and , respectively. <bold>d</bold>–<bold>f</bold>, 500 mbar 1:3 N<sub>2</sub>:H<sub>2</sub> nitrogen spectra over Fe(110), Fe(210) and , respectively. Temperature increases from top to bottom, from 423 K to 723 K.</p></caption></fig>", "<fig id=\"Fig7\"><label>Extended Data Fig. 3</label><caption><title>Stability of long acquisition times.</title><p><bold>a</bold>, The set of mass spectrometer data before any processing gathered over a 2-h window. The trend in masses 16, 17 and 18 are because of the slow improvement of the vacuum conditions. <bold>b</bold>, The same data as in <bold>a</bold> with processing as described in <xref rid=\"Sec2\" ref-type=\"sec\">Methods</xref>. <bold>c</bold>, XPS spectra gathered simultaneously normalized to background signal. <bold>d</bold>, The sum of the spectra with the fitted peaks shown according to the colour scheme used throughout the paper.</p></caption></fig>", "<fig id=\"Fig8\"><label>Extended Data Fig. 4</label><caption><title>Mass spectrometer time trace with and without ammonia production.</title><p><bold>a</bold>, The equivalent experiment as for the data shown in Fig. ##FIG##0##1b## but at 523 K over Fe(110) with the complete set of mass fragments shown before any data processing, as well as the ammonia signal with all of the data processing. <bold>b</bold>, The equivalent experiment but at 673 K over Fe(110). Note here that the timescale is far longer than in Fig. ##FIG##0##1b## to ensure sufficient statistics, ensuring that the relative reactivity is representative of the lack of ammonia production. Note that, in <bold>a</bold>, there is a glitch in the mass spectrometer background as a response to the removal of N<sub>2</sub>, but it is clear that mass 15 is decreasing in pure H<sub>2</sub> after the switchover. <bold>c</bold>, The equivalent experiment as <bold>b</bold> but performed without any X-rays on the sample.</p></caption></fig>", "<fig id=\"Fig9\"><label>Extended Data Fig. 5</label><caption><title>Beam flux effect on surface species.</title><p><bold>a</bold>, The trend of N1s intensity over Ru at 500 mbar, 423 K, 1:3 N<sub>2</sub>:H<sub>2</sub> gas ratio as beam flux increased, with a line of best fit in grey (intercept set to zero) showing that the beam does not have an appreciable effect on total coverage. <bold>b</bold>, The same experiment showing that the N1s spectra gathered sequentially from lowest flux to full flux, showing no change in speciation with beam flux.</p></caption></fig>" ]	[ "<table-wrap id=\"Tab1\"><label>Extended Data Table 1</label><caption><p>Nitrogen 1s binding energies</p></caption></table-wrap>" ]	[ "<inline-formula id=\"IEq1\"><alternatives><tex-math id=\"M1\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\rm{Ru}}(10\\bar{1}3)$$\\end{document}</tex-math><mml:math id=\"M2\"><mml:mrow><mml:mi mathvariant=\"normal\">Ru</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mn>10</mml:mn><mml:mover accent=\"true\"><mml:mrow><mml:mn>1</mml:mn></mml:mrow><mml:mo>¯</mml:mo></mml:mover><mml:mn>3</mml:mn></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq2\"><alternatives><tex-math id=\"M3\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\rm{Ru}}(10\\bar{1}3)$$\\end{document}</tex-math><mml:math id=\"M4\"><mml:mrow><mml:mi mathvariant=\"normal\">Ru</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mn>10</mml:mn><mml:mover accent=\"true\"><mml:mrow><mml:mn>1</mml:mn></mml:mrow><mml:mo>¯</mml:mo></mml:mover><mml:mn>3</mml:mn></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq3\"><alternatives><tex-math id=\"M5\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\rm{Ru}}(10\\bar{1}3)$$\\end{document}</tex-math><mml:math id=\"M6\"><mml:mrow><mml:mi mathvariant=\"normal\">Ru</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mn>10</mml:mn><mml:mover accent=\"true\"><mml:mrow><mml:mn>1</mml:mn></mml:mrow><mml:mo>¯</mml:mo></mml:mover><mml:mn>3</mml:mn></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq4\"><alternatives><tex-math id=\"M7\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\rm{Ru}}(10\\bar{1}3)$$\\end{document}</tex-math><mml:math id=\"M8\"><mml:mrow><mml:mi mathvariant=\"normal\">Ru</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mn>10</mml:mn><mml:mover accent=\"true\"><mml:mrow><mml:mn>1</mml:mn></mml:mrow><mml:mo>¯</mml:mo></mml:mover><mml:mn>3</mml:mn></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq5\"><alternatives><tex-math id=\"M9\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\rm{Ru}}(10\\bar{1}3)$$\\end{document}</tex-math><mml:math id=\"M10\"><mml:mrow><mml:mi mathvariant=\"normal\">Ru</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mn>10</mml:mn><mml:mover accent=\"true\"><mml:mrow><mml:mn>1</mml:mn></mml:mrow><mml:mo>¯</mml:mo></mml:mover><mml:mn>3</mml:mn></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq6\"><alternatives><tex-math id=\"M11\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\rm{Ru}}(10\\bar{1}3)$$\\end{document}</tex-math><mml:math id=\"M12\"><mml:mrow><mml:mi mathvariant=\"normal\">Ru</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mn>10</mml:mn><mml:mover accent=\"true\"><mml:mrow><mml:mn>1</mml:mn></mml:mrow><mml:mo>¯</mml:mo></mml:mover><mml:mn>3</mml:mn></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:math></alternatives></inline-formula>", "<disp-formula id=\"Equ1\"><label>1a</label><alternatives><tex-math id=\"M13\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{\\rm{N}}}_{2}({\\rm{g}})+{\\theta }^{* }\\to {{\\rm{N}}}_{2}^{* }$$\\end{document}</tex-math><mml:math id=\"M14\" display=\"block\"><mml:mrow><mml:msub><mml:mrow><mml:mi mathvariant=\"normal\">N</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi mathvariant=\"normal\">g</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>+</mml:mo><mml:msup><mml:mrow><mml:mi>θ</mml:mi></mml:mrow><mml:mrow><mml:mo></mml:mo></mml:mrow></mml:msup><mml:mo>→</mml:mo><mml:msubsup><mml:mrow><mml:mi mathvariant=\"normal\">N</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow><mml:mrow><mml:mo></mml:mo></mml:mrow></mml:msubsup></mml:mrow></mml:math></alternatives></disp-formula>", "<disp-formula id=\"Equ2\"><label>1b</label><alternatives><tex-math id=\"M15\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{\\rm{N}}}_{2}^{* }+{\\theta }^{* }\\to 2{{\\rm{N}}}^{* }$$\\end{document}</tex-math><mml:math id=\"M16\" display=\"block\"><mml:mrow><mml:msubsup><mml:mrow><mml:mi mathvariant=\"normal\">N</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow><mml:mrow><mml:mo></mml:mo></mml:mrow></mml:msubsup><mml:mo>+</mml:mo><mml:msup><mml:mrow><mml:mi>θ</mml:mi></mml:mrow><mml:mrow><mml:mo></mml:mo></mml:mrow></mml:msup><mml:mo>→</mml:mo><mml:mn>2</mml:mn><mml:msup><mml:mrow><mml:mi mathvariant=\"normal\">N</mml:mi></mml:mrow><mml:mrow><mml:mo></mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math></alternatives></disp-formula>", "<disp-formula id=\"Equ3\"><label>2</label><alternatives><tex-math id=\"M17\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{\\rm{H}}}_{2}({\\rm{g}})+{\\theta }^{ }\\to 2{{\\rm{H}}}^{* }$$\\end{document}</tex-math><mml:math id=\"M18\" display=\"block\"><mml:mrow><mml:msub><mml:mrow><mml:mi mathvariant=\"normal\">H</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi mathvariant=\"normal\">g</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>+</mml:mo><mml:msup><mml:mrow><mml:mi>θ</mml:mi></mml:mrow><mml:mrow><mml:mo></mml:mo></mml:mrow></mml:msup><mml:mo>→</mml:mo><mml:mn>2</mml:mn><mml:msup><mml:mrow><mml:mi mathvariant=\"normal\">H</mml:mi></mml:mrow><mml:mrow><mml:mo></mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math></alternatives></disp-formula>", "<disp-formula id=\"Equ4\"><label>3</label><alternatives><tex-math id=\"M19\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{\\rm{N}}}^{* }+{{\\rm{H}}}^{* }\\to {{\\rm{NH}}}^{* }$$\\end{document}</tex-math><mml:math id=\"M20\" display=\"block\"><mml:mrow><mml:msup><mml:mrow><mml:mi mathvariant=\"normal\">N</mml:mi></mml:mrow><mml:mrow><mml:mo></mml:mo></mml:mrow></mml:msup><mml:mo>+</mml:mo><mml:msup><mml:mrow><mml:mi mathvariant=\"normal\">H</mml:mi></mml:mrow><mml:mrow><mml:mo></mml:mo></mml:mrow></mml:msup><mml:mo>→</mml:mo><mml:msup><mml:mrow><mml:mi mathvariant=\"normal\">NH</mml:mi></mml:mrow><mml:mrow><mml:mo></mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math></alternatives></disp-formula>", "<disp-formula id=\"Equ5\"><label>4</label><alternatives><tex-math id=\"M21\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{\\rm{NH}}}^{ }+{{\\rm{H}}}^{* }\\to {{\\rm{NH}}}_{2}^{* }$$\\end{document}</tex-math><mml:math id=\"M22\" display=\"block\"><mml:mrow><mml:msup><mml:mrow><mml:mi mathvariant=\"normal\">NH</mml:mi></mml:mrow><mml:mrow><mml:mo></mml:mo></mml:mrow></mml:msup><mml:mo>+</mml:mo><mml:msup><mml:mrow><mml:mi mathvariant=\"normal\">H</mml:mi></mml:mrow><mml:mrow><mml:mo></mml:mo></mml:mrow></mml:msup><mml:mo>→</mml:mo><mml:msubsup><mml:mrow><mml:mi mathvariant=\"normal\">NH</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow><mml:mrow><mml:mo></mml:mo></mml:mrow></mml:msubsup></mml:mrow></mml:math></alternatives></disp-formula>", "<disp-formula id=\"Equ6\"><label>5</label><alternatives><tex-math id=\"M23\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{\\rm{NH}}}_{2}^{ }+{{\\rm{H}}}^{* }\\to {{\\rm{NH}}}_{3}^{* }$$\\end{document}</tex-math><mml:math id=\"M24\" display=\"block\"><mml:mrow><mml:msubsup><mml:mrow><mml:mi mathvariant=\"normal\">NH</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow><mml:mrow><mml:mo></mml:mo></mml:mrow></mml:msubsup><mml:mo>+</mml:mo><mml:msup><mml:mrow><mml:mi mathvariant=\"normal\">H</mml:mi></mml:mrow><mml:mrow><mml:mo></mml:mo></mml:mrow></mml:msup><mml:mo>→</mml:mo><mml:msubsup><mml:mrow><mml:mi mathvariant=\"normal\">NH</mml:mi></mml:mrow><mml:mrow><mml:mn>3</mml:mn></mml:mrow><mml:mrow><mml:mo></mml:mo></mml:mrow></mml:msubsup></mml:mrow></mml:math></alternatives></disp-formula>", "<disp-formula id=\"Equ7\"><label>6</label><alternatives><tex-math id=\"M25\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{\\rm{NH}}}_{3}^{ }\\to {{\\rm{NH}}}_{3}({\\rm{g}})+{\\theta }^{* }$$\\end{document}</tex-math><mml:math id=\"M26\" display=\"block\"><mml:mrow><mml:msubsup><mml:mrow><mml:mi mathvariant=\"normal\">NH</mml:mi></mml:mrow><mml:mrow><mml:mn>3</mml:mn></mml:mrow><mml:mrow><mml:mo></mml:mo></mml:mrow></mml:msubsup><mml:mo>→</mml:mo><mml:msub><mml:mrow><mml:mi mathvariant=\"normal\">NH</mml:mi></mml:mrow><mml:mrow><mml:mn>3</mml:mn></mml:mrow></mml:msub><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi mathvariant=\"normal\">g</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>+</mml:mo><mml:msup><mml:mrow><mml:mi>θ</mml:mi></mml:mrow><mml:mrow><mml:mo></mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math></alternatives></disp-formula>", "<inline-formula id=\"IEq7\"><alternatives><tex-math id=\"M27\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\rm{Ru}}(10\\bar{1}3)$$\\end{document}</tex-math><mml:math id=\"M28\"><mml:mrow><mml:mi mathvariant=\"normal\">Ru</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mn>10</mml:mn><mml:mover accent=\"true\"><mml:mrow><mml:mn>1</mml:mn></mml:mrow><mml:mo>¯</mml:mo></mml:mover><mml:mn>3</mml:mn></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:math></alternatives></inline-formula>", "<disp-formula id=\"Equa\"><alternatives><tex-math id=\"M29\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\rm{RCA}}=\\frac{\\overline{{{\\rm{NH}}}_{3} \\% }\\times V\\times P}{{\\rm{sites}}\\times {\\rm{time}}\\times T\\times R\\times {A}_{{\\rm{n}}}}/{\\rm{highest}}\\,{\\rm{a}}{\\rm{c}}{\\rm{t}}{\\rm{i}}{\\rm{v}}{\\rm{i}}{\\rm{t}}{\\rm{y}}\\,{\\rm{o}}{\\rm{n}}\\,{\\rm{a}}{\\rm{n}}{\\rm{y}}\\,{\\rm{s}}{\\rm{u}}{\\rm{r}}{\\rm{f}}{\\rm{a}}{\\rm{c}}{\\rm{e}}$$\\end{document}</tex-math><mml:math id=\"M30\" display=\"block\"><mml:mrow><mml:mi mathvariant=\"normal\">RCA</mml:mi><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mover accent=\"true\"><mml:mrow><mml:msub><mml:mrow><mml:mi mathvariant=\"normal\">NH</mml:mi></mml:mrow><mml:mrow><mml:mn>3</mml:mn></mml:mrow></mml:msub><mml:mi>%</mml:mi></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover><mml:mo>×</mml:mo><mml:mi>V</mml:mi><mml:mo>×</mml:mo><mml:mi>P</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">sites</mml:mi><mml:mo>×</mml:mo><mml:mi mathvariant=\"normal\">time</mml:mi><mml:mo>×</mml:mo><mml:mi>T</mml:mi><mml:mo>×</mml:mo><mml:mi>R</mml:mi><mml:mo>×</mml:mo><mml:msub><mml:mrow><mml:mi>A</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">n</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfrac><mml:mo>/</mml:mo><mml:mi mathvariant=\"normal\">highest</mml:mi><mml:mspace width=\"0.25em\"/><mml:mi mathvariant=\"normal\">a</mml:mi><mml:mi mathvariant=\"normal\">c</mml:mi><mml:mi mathvariant=\"normal\">t</mml:mi><mml:mi mathvariant=\"normal\">i</mml:mi><mml:mi mathvariant=\"normal\">v</mml:mi><mml:mi mathvariant=\"normal\">i</mml:mi><mml:mi mathvariant=\"normal\">t</mml:mi><mml:mi mathvariant=\"normal\">y</mml:mi><mml:mspace width=\"0.25em\"/><mml:mi mathvariant=\"normal\">o</mml:mi><mml:mi mathvariant=\"normal\">n</mml:mi><mml:mspace width=\"0.25em\"/><mml:mi mathvariant=\"normal\">a</mml:mi><mml:mi mathvariant=\"normal\">n</mml:mi><mml:mi mathvariant=\"normal\">y</mml:mi><mml:mspace width=\"0.25em\"/><mml:mi mathvariant=\"normal\">s</mml:mi><mml:mi mathvariant=\"normal\">u</mml:mi><mml:mi mathvariant=\"normal\">r</mml:mi><mml:mi mathvariant=\"normal\">f</mml:mi><mml:mi mathvariant=\"normal\">a</mml:mi><mml:mi mathvariant=\"normal\">c</mml:mi><mml:mi mathvariant=\"normal\">e</mml:mi></mml:mrow></mml:math></alternatives></disp-formula>", "<inline-formula id=\"IEq8\"><alternatives><tex-math id=\"M31\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\rm{Ru}}(10\\bar{1}3)$$\\end{document}</tex-math><mml:math id=\"M32\"><mml:mrow><mml:mi mathvariant=\"normal\">Ru</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mn>10</mml:mn><mml:mover accent=\"true\"><mml:mrow><mml:mn>1</mml:mn></mml:mrow><mml:mo>¯</mml:mo></mml:mover><mml:mn>3</mml:mn></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq9\"><alternatives><tex-math id=\"M33\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\rm{Ru}}(10\\bar{1}3)$$\\end{document}</tex-math><mml:math id=\"M34\"><mml:mrow><mml:mi mathvariant=\"normal\">Ru</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mn>10</mml:mn><mml:mover accent=\"true\"><mml:mrow><mml:mn>1</mml:mn></mml:mrow><mml:mo>¯</mml:mo></mml:mover><mml:mn>3</mml:mn></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq10\"><alternatives><tex-math id=\"M35\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\rm{Ru}}(10\\bar{1}3)$$\\end{document}</tex-math><mml:math id=\"M36\"><mml:mrow><mml:mi mathvariant=\"normal\">Ru</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mn>10</mml:mn><mml:mover accent=\"true\"><mml:mrow><mml:mn>1</mml:mn></mml:mrow><mml:mo>¯</mml:mo></mml:mover><mml:mn>3</mml:mn></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:math></alternatives></inline-formula>" ]	[]	[]	[]	[]	[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>" ]	[ "<table-wrap-foot><p>*Calibration binding energy.</p><p><sup>†</sup>On the basis of the average position of both nitrogen atoms in N<sub>2</sub>. Refs. <sup>##UREF##6##8##,##UREF##11##17##,##UREF##17##23##–##UREF##19##25##,##UREF##21##27##,##UREF##28##37##–##UREF##33##42##</sup>.</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"41586_2023_6844_Fig1_HTML\" id=\"d32e413\"/>", "<inline-graphic xlink:href=\"41586_2023_6844_Article_IEq1.gif\"/>", "<inline-graphic xlink:href=\"41586_2023_6844_Article_IEq2.gif\"/>", "<graphic xlink:href=\"41586_2023_6844_Fig2_HTML\" id=\"d32e549\"/>", "<inline-graphic xlink:href=\"41586_2023_6844_Article_IEq3.gif\"/>", "<inline-graphic xlink:href=\"41586_2023_6844_Article_IEq4.gif\"/>", "<graphic xlink:href=\"41586_2023_6844_Fig3_HTML\" id=\"d32e709\"/>", "<inline-graphic xlink:href=\"41586_2023_6844_Article_IEq5.gif\"/>", "<inline-graphic xlink:href=\"41586_2023_6844_Article_IEq6.gif\"/>", "<graphic xlink:href=\"41586_2023_6844_Fig4_HTML\" id=\"d32e786\"/>", "<graphic xlink:href=\"41586_2023_6844_Article_Equ1.gif\" position=\"anchor\"/>", "<graphic xlink:href=\"41586_2023_6844_Article_Equ2.gif\" position=\"anchor\"/>", "<graphic xlink:href=\"41586_2023_6844_Article_Equ3.gif\" position=\"anchor\"/>", "<graphic xlink:href=\"41586_2023_6844_Article_Equ4.gif\" position=\"anchor\"/>", "<graphic xlink:href=\"41586_2023_6844_Article_Equ5.gif\" position=\"anchor\"/>", "<graphic xlink:href=\"41586_2023_6844_Article_Equ6.gif\" position=\"anchor\"/>", "<graphic xlink:href=\"41586_2023_6844_Article_Equ7.gif\" position=\"anchor\"/>", "<inline-graphic xlink:href=\"41586_2023_6844_Article_IEq7.gif\"/>", "<graphic xlink:href=\"41586_2023_6844_Article_Equa.gif\" position=\"anchor\"/>", "<inline-graphic xlink:href=\"41586_2023_6844_Article_IEq8.gif\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6844_Fig5_ESM\" id=\"d32e1743\"/>", "<inline-graphic xlink:href=\"41586_2023_6844_Article_IEq9.gif\"/>", "<inline-graphic xlink:href=\"41586_2023_6844_Article_IEq10.gif\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6844_Fig6_ESM\" id=\"d32e1817\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6844_Fig7_ESM\" id=\"d32e1846\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6844_Fig8_ESM\" id=\"d32e1883\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6844_Fig9_ESM\" id=\"d32e1904\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6844_Tab1_ESM\" id=\"d32e1914\"><caption><p>Nitrogen 1s binding energies</p></caption></graphic>" ]	[ "<media xlink:href=\"41586_2023_6844_MOESM1_ESM.pdf\"><caption><p>Peer Review File</p></caption></media>" ]	[{"label": ["1."], "surname": ["Haber", "le Rossignol"], "given-names": ["F", "R"], "article-title": ["\u00dcber die technische Darstellung von Ammoniak aus den Elementen"], "source": ["Z. 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PMC10781626	38093015	[]	[ "<title>Methods</title>", "<title>Parasites and human cell cultures</title>", "<p id=\"Par29\">Primary HFFs (ATCC CCL-171) were cultured in Dulbecco’s modified Eagle medium (Invitrogen) supplemented with 10% heat-inactivated fetal bovine serum (Invitrogen), 10 mM 4-(2-hydroxyethyl)-1-piperazine-ethanesulfonic acid (HEPES) buffer pH 7.2, 2 mM <sc>l</sc>-glutamine and 50 μg ml<sup>−1</sup> penicillin and streptomycin (Invitrogen). Cells were incubated at 37 °C and 5% CO<sub>2</sub>. <italic>Toxoplasma</italic> strains used in this study and listed in Supplementary Table ##SUPPL##3##1## were maintained in vitro by serial passage on monolayers of HFFs. Cultures were free of mycoplasma as determined by qualitative PCR.</p>", "<title>Reagents</title>", "<p id=\"Par30\">The following primary antibodies were used in the immunofluorescence, immunoblotting and/or ChIP assays: rabbit anti-TgHDAC3 (Research Resource Identifier (RRID): AB_2713903), rabbit anti-TgGAP45 (gift from Prof. Dominique Soldati), mouse anti-HA tag (Roche, RRID: AB_2314622), rabbit anti-HA Tag (Cell Signaling Technology, RRID: AB_1549585), rabbit anti-mCherry (Cell Signaling Technology, RRID: AB_2799246), rabbit anti-Flag (Cell Signaling Technology, RRID: AB_2798687), mouse anti-MYC clone 9B11 (RRID: AB_2148465), H3K9me3 (Diagenode, RRID: AB_2616044), rabbit anti-acetyl-histone H4, pan (Lys5,8,12) (Millipore, RRID: AB_310270), rat anti-IMC7 and centrin 1 (gift from Prof. M. J. Gubbels), mouse anti-IMC1 (gift from Prof. G. E. Ward), mouse anti-AtRx antibody clone 11G8 (ref. <sup>##REF##18586952##24##</sup>) and mouse anti-GRA11b<sup>##REF##28526607##14##</sup>. We have also raised homemade antibodies to linear peptides in rabbits corresponding to the following proteins: MORC_Peptide2 (C+SGAPIWTGERGSGA); AP2XI-2 (C+HAFKTRRTEAAT); TGME49_273980 or GRA80 (C+RPPWAPGAGPEN); TGME49_243940 or GRA81 (C+QKELAEVAQRALEN); TGME49_277230 or GRA82 (C+SDVNTEGDATVANPE); TGME49_209985 or ROP26 (CQETVQGNGETQL); SRS48 family (CKALIEVKGVPK); SRS59B or K (C+IHVPGTDSTSSGPGS); TGME49_314250 or BRP1 (C+QVKEGTKNNKGLSDK); TGME49_307640 or CK2 kinase (C+IRAQYHAYKGKYSHA); and TGME49_306455 (C+DGRTPVDRVFEE). They were manufactured by Eurogentec and used for immunofluorescence, immunoblotting and/or ChIP. Secondary immunofluorescent antibodies were coupled with Alexa Fluor 488 or Alexa Fluor 594 (Thermo Fisher Scientific). Secondary antibodies used in western blotting were conjugated to alkaline phosphatase (Promega).</p>", "<title>Auxin-induced degradation</title>", "<p id=\"Par31\">Degradation of AP2XII-1–mAID–HA, AP2XI-2–mAID–HA and AP2XII-1–mAID–HA plus AP2XI-2–mAID–MYC was achieved with IAA (Sigma-Aldrich number 45533). A stock of 500 mM IAA dissolved in 100% ethanol at a ratio of 1:1,000 was used to degrade mAID-tagged proteins to a final concentration of 500 μM. The mock treatment consisted of an equivalent volume of 100% ethanol at a final concentration of 0.0789% (w/v). To monitor the degradation of mAID-tagged proteins, parasites grown in HFF monolayers were treated with auxin or ethanol alone for various time intervals at 37 °C. After treatment, parasites were collected and analysed by immunofluorescence or western blotting.</p>", "<title>Immunofluorescence microscopy</title>", "<p id=\"Par32\"><italic>Toxoplasma</italic>-infected HFF cells grown on coverslips were fixed in 3% formaldehyde for 20 min at room temperature, permeabilized with 0.1% (v/v) Triton X-100 for 15 min, and blocked in phosphate-buffered saline (PBS) containing 3% (w/v) BSA. Cells were then incubated with primary antibodies for 1 h, followed by the addition of secondary antibodies conjugated to Alexa Fluor 488 or 594 (Molecular Probes). Nuclei were stained with Hoechst 33258 (2 μg ml<sup>−1</sup> in PBS) for 10 min at room temperature. After washing four times in PBS, coverslips were mounted on a glass slide with Mowiol mounting medium, and images were acquired with a fluorescence microscope ZEISS ApoTome.2 and processed with ZEN software (Carl Zeiss).</p>", "<p id=\"Par33\">For IFA of in vivo stages in the cat, small intestines of infected kittens from a previous study<sup>##REF##30728393##16##</sup> embedded in paraffin were sectioned to 3 μm and dried overnight at 37 °C. Deparaffinization was carried out first three times for 2 min in xylene, and then the sections were washed twice for 1 min in 100% ethanol and finally rehydrated sequentially for 1 min in 96% ethanol, and then 70% ethanol and water. For antigen retrieval, samples were boiled in a pressure cooker for 20 min in citrate buffer at pH 6.1 (Dako Target Retrieval Solution, S2369) and transferred to water. Cells were permeabilized in 0.3% Triton X-100 in PBS and blocked with fetal calf serum (FCS). Staining was carried out overnight at 4 °C using the following combinations: mouse anti-GRA11b<sup>##REF##28526607##14##</sup> with rabbit anti-GRA80, anti-IMC1 or anti-GAP45 (the last two being gifts from Prof. Dominique Soldati) or rabbit anti-GRA80 with rat immune serum recognizing merozoite proteins in 20% FCS and 0.3% Triton X-100 in PBS. The samples were then washed and incubated with 1 μg ml<sup>−1</sup> DAPI, 20% FCS and 0.3% Triton X-100 in PBS and appropriate combinations of anti-rabbit Alexa Fluor 488, 555 or 594 (Invitrogen, A11070, A32794 or A11072) with anti-mouse Alexa Fluor 488, 594 or 647 (Invitrogen, A11017, A11005 or A21235) or anti-rat Alexa Fluor 488 (Invitrogen, A11006) with anti-rabbit Alexa Fluor 594 (Invitrogen, A11072) for 1 h at room temperature. After three washes, samples were mounted with Vectashield and imaged either with a Leica DMI 6000 B epi-fluorescence microscope or a Leica SP8 confocal microscope. Confocal images were deconvoluted using SVI Huygens Professional. Maximum-intensity projections were carried out using FIJI 2.9.1.</p>", "<title>Transmission electron microscopy</title>", "<p id=\"Par34\">For ultrastructural observations, <italic>Toxoplasma</italic>-infected HFFs grown as monolayers on a 6-well dish were exposed to 500 µM IAA or ethanol solvent as described above before fixation 24 h or 40 h post-infection in 2.5% glutaraldehyde in 0.1 mM sodium cacodylate (pH 7.4) and processed as described previously<sup>##REF##12972565##44##</sup>. Ultrathin sections of infected cells were stained with osmium tetraoxide before examination with a Hitachi 7600 electron microscope under 80 kV equipped with a dual AMT CCD camera system.</p>", "<title>Western blot</title>", "<p id=\"Par35\">Immunoblot analysis of protein was carried out as described in ref. <sup>##REF##35921477##45##</sup>. Briefly, about 10<sup>7</sup> cells were lysed and sonicated in 50 μl lysis buffer (10 mM Tris-HCl, pH 6.8, 0.5% SDS (v/v), 10% glycerol (v/v), 1 mM EDTA and protease inhibitor cocktail). Proteins were separated using SDS–polyacrylamide gel electrophoresis, transferred by liquid transfer to a polyvinylidene fluoride membrane (Immobilon-P; EMD Millipore), and western blots were probed with the appropriate primary antibodies and alkaline phosphatase-conjugated or horseradish peroxidase-conjugated secondary goat antibodies. Signals were detected using NBT-BCIP (Amresco) or an enhanced chemiluminescence system (Thermo Scientific).</p>", "<title>Plasmid construction</title>", "<p id=\"Par36\">The plasmids and primers used in this work for the genes of interest (GOIs) are listed in Supplementary Table ##SUPPL##3##1##. To construct the vector pLIC-GOI-HA-Flag, pLIC-GOI-mAID-HA or pLIC-GOI-mAID-(MYC)2, the coding sequence of the GOI was amplified with the primers LIC-GOI-Fwd and LIC-GOI-Rev using genomic <italic>Toxoplasma</italic> DNA as a template. The resulting PCR product was cloned into the vector pLIC-HF-dhfr or pLIC-mCherry-dhfr using the ligation-independent cloning (LIC) method. Specific guide RNA for the GOI, based on the CRISPR–cas9 editing method, was cloned into the plasmid pTOXO_Cas9-CRISPR<sup>##REF##32094587##1##</sup>. Twenty-base oligonucleotides corresponding to specific GOIs were cloned using the Golden Gate strategy. Briefly, the primers GOI-gRNA-Fwd and GOI-gRNA-Rev containing the single guide RNA targeting the genomic sequence of the GOI were phosphorylated, annealed and ligated into the pTOXO_Cas9-CRISPR plasmid linearized with BsaI, resulting in pTOXO_Cas9-CRISPR::sgGOI.</p>", "<title><italic>Toxoplasma</italic> transfection</title>", "<p id=\"Par37\">Parasite strains were electroporated with vectors in Cytomix buffer (120 mM KCl, 0.15 mM CaCl<sub>2</sub>, 10 mM K<sub>2</sub>HPO<sub>4</sub> and KH<sub>2</sub>PO<sub>4</sub> pH 7.6, 25 mM HEPES pH 7.6, 2 mM EGTA, 5 mM MgCl<sub>2</sub>) using a BTX ECM 630 machine (Harvard Apparatus). Electroporation was carried out in a 2-mm cuvette at 1,100 V, 25 Ω and 25 µF. Antibiotics (concentration) used were chloramphenicol (20 µM), mycophenolic acid (25 µg ml<sup>−1</sup>) with xanthine (50 µg ml<sup>−1</sup>), pyrimethamine (3 µM) or 5-fluorodeoxyuracil (10 µM) as needed. Stable transgenic parasites were selected with the appropriate antibiotic, cloned in 96-well plates by limiting dilution, and verified by immunofluorescence assay or genomic analysis.</p>", "<title>Chromatographic purification of Flag-tagged proteins</title>", "<p id=\"Par38\"><italic>Toxoplasma</italic> extracts from RH∆ku80 or Pru∆ku80 cells stably expressing HA–Flag-tagged AP2XII-1 or AP2XI-2 proteins, respectively, were incubated with anti-Flag M2 affinity gel (Sigma-Aldrich) for 1 h at 4 °C. Beads were washed with 10 column volumes of BC500 buffer (20 mM Tris-HCl, pH 8.0, 500 mM KCl, 20% glycerol, 1 mM EDTA, 1 mM dithiothreitol, 0.5% NP-40 and protease inhibitors). Bound polypeptides were eluted stepwise with 250 μg ml<sup>−1</sup> Flag peptide (Sigma-Aldrich) diluted in BC100 buffer. For size-exclusion chromatography, protein eluates were loaded onto a Superose 6 HR 10/30 column equilibrated with BC500. The flow rate was set at 0.35 ml min<sup>−1</sup>, and 0.5-ml fractions were collected.</p>", "<title>MS-based quantitative analyses of parasite proteomes and AP2 interactomes</title>", "<title>Sample preparation</title>", "<p id=\"Par39\">For proteome-wide analyses, HFF cells were grown to confluence, infected with the RH (<italic>AP2XII-1</italic> and <italic>AP2XI-2</italic> KD) strain and treated with IAA for 24 h, 32 h and 48 h or mock-treated. Proteins were extracted using cell lysis buffer (Invitrogen). Three biological replicates were prepared and analysed for each condition. For characterization of HA–Flag-tagged AP2XII-1 or AP2XI-2 interactomes, eluted proteins were solubilized in Laemmli buffer. Three biological replicates were prepared for each bait protein and for the negative control. Proteins were stacked in the top of a 4–12% NuPAGE gel (Invitrogen) and stained with Coomassie blue R-250 (Bio-Rad) before in-gel digestion using modified trypsin (Promega, sequencing grade) as previously described<sup>##REF##32094587##1##</sup>.</p>", "<title>Nanoliquid chromatography coupled to MS/MS analyses</title>", "<p id=\"Par40\">The resulting peptides were analysed by online nanoliquid chromatography coupled to an MS/MS instrument (Ultimate 3000 RSLCnano and Q-Exactive HF, Thermo Fisher Scientific) using a 360-min gradient for proteome-wide analysis and a 200-min gradient for interactome characterization. For this, peptides were sampled on a 300 μm × 5 mm PepMap C18 precolumn and separated in a 200 cm µPAC column (PharmaFluidics) or a 75 μm × 250 mm C18 column (Aurora Generation 2, 1.7 µm, IonOpticks) for, respectively, proteome-wide and interactome analyses. MS and MS/MS data were acquired using Xcalibur software version 4.0 (Thermo Scientific).</p>", "<title>Protein identification and quantification</title>", "<p id=\"Par41\">Peptides and proteins were identified by Mascot (version 2.8.0, Matrix Science) through concomitant searches against the <italic>T. gondii</italic> database (ME49 taxonomy, version 58 downloaded from ToxoDB), the Uniprot database (<italic>Homo sapiens</italic> taxonomy) and a homemade database containing the sequences of classical contaminant proteins found in proteomic analyses (human keratins, trypsin and so on). Trypsin was chosen as the enzyme and two missed cleavages were allowed. Precursor and fragment mass error tolerances were set, respectively, at 10 and 20 ppm. Peptide modifications allowed during the search were: carbamidomethyl (C, fixed), acetyl (protein amino terminus, variable) and oxidation (M, variable). The Proline software (version 2.2.0) was used for the compilation, grouping and filtering of the results (conservation of rank 1 peptides, peptide length ≥ 6 amino acids, false discovery rate of peptide-spectrum-match identifications < 1% and minimum of one specific peptide per identified protein group). Proline was then used to carry out an MS1 label-free quantification of the identified protein groups based on razor and specific peptides.</p>", "<title>Statistical analyses</title>", "<p id=\"Par42\">Statistical analyses were carried out using ProStaR<sup>##UREF##4##46##</sup>. Proteins identified in the reverse and contaminant databases or matched to human sequences were discarded. For proteome-wide analyses, only proteins identified by MS/MS in a minimum of two replicates of one condition and quantified in the three replicates of one condition were conserved. After log<sub>2</sub> transformation, abundance values were normalized using the variance-stabilizing normalization method, before missing-value imputation (structured least squares algorithm (SLSA) for partially observed values in the condition and DetQuantile algorithm for totally absent values in the condition). For comparison of each IAA-treated condition to the mock-treated condition, statistical testing was conducted with limma, whereby differentially expressed proteins were selected using a log<sub>2</sub>[FC] cutoff of 1 and a <italic>P</italic>-value cutoff of 0.01, allowing one to reach a false discovery rate inferior to 5% according to the Benjamini–Hochberg estimator. Proteins found differentially abundant but identified by MS/MS in fewer than two replicates, and detected in fewer than three replicates, in the condition in which they were found to be more abundant were invalidated (<italic>P</italic> value = 1). Protein abundances measured in the four different conditions were also compared globally by ANOVA using Perseus; <italic>q</italic> values were obtained by Benjamini–Hochberg correction.</p>", "<p id=\"Par43\">For interactome analysis, only proteins identified by MS/MS in the three replicates of one condition and proteins quantified with a minimum of five peptides were conserved. After log<sub>2</sub> transformation, abundance values were normalized by condition-wise median centring, before missing-value imputation (SLSA algorithm for partially observed values in the condition and DetQuantile algorithm for totally absent values in the condition). Statistical testing was conducted with limma, whereby differentially expressed proteins were selected using a <italic>P</italic>-value cutoff of 0.01 and FC cutoffs of 5 and 3, respectively, for comparison of each AP2 interactome with negative control and AP2 interactomes together, allowing one to reach a false discovery rate inferior to 1% according to the Benjamini–Hochberg estimator. The relative abundances of AP2-associated proteins were determined using the iBAQ metrics; only proteins with an iBAQ ratio of at least 0.1 in relation to the bait protein were considered.</p>", "<title>MS-based proteomic analyses of SEC fractions</title>", "<p id=\"Par44\">Protein bands were excised from colloidal blue-stained gels (Thermo Fisher Scientific) before in-gel digestion using modified trypsin (Promega, sequencing grade) as previously described<sup>##REF##32094587##1##</sup>. Resulting peptides were analysed by online nanoliquid chromatography coupled to MS/MS (UltiMate 3000 RSLCnano and Orbitrap Exploris 480, Thermo Scientific). Peptides were sampled on a 300 µm × 5 mm PepMap C18 precolumn and separated on a 75 µm × 250 mm C18 column (Aurora Generation 2, 1.6 µm, IonOpticks) using a 25-min gradient. MS and MS/MS data were acquired using Xcalibur version 4.0 (Thermo Scientific). Peptides and proteins were identified using Mascot (version 2.8.0) through concomitant searches against the <italic>T. gondii</italic> database (ME49 taxonomy, version 58 downloaded from ToxoDB), the Uniprot database (<italic>T. ni</italic> taxonomy) and a homemade database containing the sequences of classical contaminant proteins found in proteomic analyses (human keratins, trypsin and so on). Trypsin/P was chosen as the enzyme and two missed cleavages were allowed. Precursor and fragment mass error tolerances were set, respectively, at 10 and 20 ppm. Peptide modifications allowed during the search were: carbamidomethyl (C, fixed), acetyl (protein N terminus, variable) and oxidation (M, variable). The Proline software (version 2.2.0) was used for the compilation, grouping and filtering of the results (conservation of rank 1 peptides, peptide length ≥ 6 amino acids, false discovery rate of peptide-spectrum-match identifications < 1% and minimum of one specific peptide per identified protein group). iBAQ values were calculated for each protein group in Proline using MS1 intensities of specific and razor peptides.</p>", "<title>ChIP coupled with Illumina sequencing</title>", "<title>ChIP</title>", "<p id=\"Par45\">HFF cells were grown to confluence and infected with KD strains as indicated in the figure legends. Collected intracellular parasites were crosslinked with formaldehyde (final concentration 1%) for 8 min at room temperature, and crosslinking was stopped by addition of glycine (final concentration 0.125 M) for 5 min at room temperature. The parasites were lysed in ice-cold lysis buffer A (50 mM HEPES KOH pH 7.5, 140 mM NaCl, 1 mM EDTA, 10% glycerol, 0.5% NP-40, 0.125% Triton X-100 and protease inhibitor cocktail) and after centrifugation, crosslinked chromatin was sheared in buffer B (1 mM EDTA pH 8.0, 0.5 mM EGTA pH 8.0, 10 mM Tris pH 8.0 and protease inhibitor cocktail) by sonication with a Diagenode Biorupter. Samples were sonicated for 16 cycles (30 s on and 30 s off) to achieve an average size of 200–500 base pairs. Sheared chromatin, 5% BSA, a protease inhibitor cocktail, 10% Triton X-100, 10% deoxycholate, magnetic beads coated with DiaMag protein A (Diagenode) and antibodies to epitope tags (HA or MYC) or the protein of interest (MORC or HDAC3) were used for immunoprecipitation. A rabbit IgG antiserum served as a control mock. After overnight incubation at 4 °C on a rotating wheel, chromatin–antibody complexes were washed and eluted from the beads using the iDeal ChIP–seq kit for transcription factors (Diagenode) according to the manufacturer’s protocol. Samples were de-crosslinked by heating for 4 h at 65 °C. DNA was purified using the IPure kit (Diagenode) and quantified using Qubit Assays (Thermo Fisher Scientific) according to the manufacturer’s protocol. For ChIP–seq, the purified DNA was used for library preparation and subsequently sequenced by Arraystar (USA).</p>", "<title>Library preparation, sequencing and data analysis (Arraystar)</title>", "<p id=\"Par46\">ChIP–seq libraries were prepared according to the Illumina protocol “Preparing Samples for ChIP Sequencing of DNA”. For library preparation, 10 ng of DNA from each sample was converted to blunt-end phosphorylated DNA fragments using T4 DNA polymerase, Klenow polymerase and T4 polymerase (NEB); an ‘A’ base was added to the 3′ end of the blunt-end phosphorylated DNA fragments using the polymerase activity of Klenow (Exo-Minus) polymerase (NEB); Illumina genomic adapters were ligated to the A-tailed DNA fragments; PCR amplification to enrich the ligated fragments was carried out using Phusion High Fidelity PCR Master Mix with HF Buffer (Finnzymes Oy). The enriched product of about 200–700 bp was excised from the gel and purified. For sequencing, the library was denatured with 0.1 M NaOH to generate single-stranded DNA molecules and loaded into flow cell channels at a concentration of 8 pM and amplified in situ using TruSeq Rapid SR cluster kit (number GD-402-4001, Illumina). Sequencing was carried out for 100 cycles on the Illumina HiSeq 4000 according to the manufacturer’s instructions. For data analysis, after the sequencing platform generated the sequencing images, the stages of image analysis and base calling were carried out using Off-Line Basecaller software (OLB V1.8). After passing the Solexa CHASTITY quality filter, the clean reads were aligned to the <italic>T. gondii</italic> reference genome (TGME49) using BOWTIE V2 and then converted and sorted using Bamtools. Aligned reads were used for peak calling of the ChIP-enriched peaks using MACS v2.2 with a cutoff <italic>P</italic> value of 10<sup>−4</sup>. For Integrated Genome Browser visualization and gene-centred analysis using Deeptools, MACS2-generated bedgraph files were processed with the command ‘sort -k1,1 -k2,2n 5_treat_pileup.bdg > 5_treat_pileup-sorted.bdg’, and then converted using the BedGraphToBigWig program (ENCODE project). The Deeptools analysis was generated using the command computeMatrix reference point, with the following parameters: –minThreshold 2, –binSize 10 and –averageTypeBins sum. Plotprofile or heat map was then used with <italic>k</italic>-means clustering when applicable. Inter-sample comparisons were obtained using the nf-core ChIP–seq workflow with standard parameters<sup>##REF##32055031##47##</sup>. From this pipeline, HOMER (annotatePeaks) was used to analyse peak distribution relative to gene features. All of these raw and processed files can be found at <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE222819\">GSE222819</ext-link>.</p>", "<title>RNA-seq and sequence alignment</title>", "<p id=\"Par47\">Total RNAs were extracted and purified using TRIzol (Invitrogen) and RNeasy Plus Mini Kit (Qiagen). RNA quantity and quality were measured using a NanoDrop 2000 (Thermo Scientific). For each condition, RNAs were prepared from three biological replicates. RNA integrity was assessed by standard non-denaturing 1.2% TBE agarose gel electrophoresis. RNA-seq was carried out following standard Illumina protocols, by Novogene (Cambridge, UK). Briefly, RNA quantity, integrity and purity were determined using the Agilent 5400 Fragment Analyzer System (Agilent Technologies). The RNA quality number ranged from 7.8 to 10 for all samples, which was considered sufficient. mRNAs were purified from total RNA using poly-T oligonucleotide-attached magnetic beads. After fragmentation, the first-strand cDNA was synthesized using random hexamer primers. Then the second-strand cDNA was synthesized using dUTP, instead of dTTP. The directional library was ready after end repair, A-tailing, adapter ligation, size selection, USER enzyme digestion, amplification and purification. The library was checked with Qubit and real-time PCR for quantification and a bioanalyser for size distribution detection. Quantified libraries were pooled and sequenced on Illumina platforms, according to effective library concentration and data amount. The samples were sequenced on the Illumina NovaSeq platform (2 × 150 bp, strand-specific sequencing) and generated about 40 million paired-end reads for each sample. The quality of the raw sequencing reads was assessed using FastQC (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.bioinformatics.babraham.ac.uk/projects/fastqc/\">https://www.bioinformatics.babraham.ac.uk/projects/fastqc/</ext-link>) and MultiQC. For quantification and normalization of the expression data, the FASTQ reads were aligned to the ToxoDB-49 build of the <italic>T. gondii</italic> ME49 genome using Subread version 2.0.1 with the following options: subread-align -d 50 -D 600–sortReadsByCoordinates. Read counts for each gene were calculated using featureCounts from the Subread package. Differential expression analysis was conducted using DESeq2 and default settings within the iDEP.96 web interface<sup>##REF##30567491##48##</sup>. Transcripts were quantified and normalized using TPMCalculator. The Illumina RNA-seq dataset generated during this study is available at the National Center for Biotechnology Information: BioProject number <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA921935\">PRJNA921935</ext-link>.</p>", "<title>Nanopore direct RNA-seq</title>", "<p id=\"Par48\">The mRNA library preparation followed the SQK-RNA002 kit (Oxford Nanopore)–recommended protocol; the only modification was the input mRNA quantity increased from 500 to 1,000 ng, and all other consumables and parameters were standard. Final yields were evaluated using the Qubit HS dsDNA kit (Thermo Fisher Scientific, Q32851) with minimum RNA preparations reaching at least 200 ng. For all conditions, sequencing was carried out on FLO-MIN106 flow cells using either a MinION MK1C or MinION sequencer. All datasets were subsequently base called (high-accuracy base calling) with a Guppy version higher than 5.0.1 with a <italic>Q</italic> score cutoff of >7. Long-read alignment was carried out by Minimap2 as previously described<sup>##REF##34263725##49##</sup>. Sam files were converted to bam and sorted using Samtools 1.4. Alignments were converted and sorted using Samtools 1.4.1. For the three described samples, <italic>Toxoplasma</italic> aligned reads range between 600,000 and 800,000. The Nanopore direct RNA-seq dataset is available at the National Center for Biotechnology Information: BioProject number <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA921935\">PRJNA921935</ext-link>.</p>", "<title>ATAC–seq</title>", "<p id=\"Par49\">Intracellular tachyzoites (non-treated or IAA treated for 24 h) were prepared using HFF cell monolayers in a T175 format, which was freshly scraped, gently homogenized by pipetting and centrifuged at 500<italic>g</italic>. Before initiating the transposition protocol, the pellet was gently washed with warm Dulbecco’s PBS (Life technologies) and resuspended in 500 μl of cold PBS + protease inhibitor (Diagenode kit). Nuclei preparation, permeabilization, Tn5 transposition and library preparation was carried out following precisely the Diagenode ATAC–seq kit protocol (C01080002). Nucleus permeabilization was carried out on an estimated 100,000 tachyzoites by diluting 10 μl of Dulbecco’s cell suspension (from one T175 resuspended in 500 μl) in 240 μl of Dulbecco’s PBS + protease inhibitor (1/25 dilution). From this dilution, 50 μl was then taken to carry out the transposition reaction. Of note, the permeabilization protocol used a 3-min 0.02% digitonin (Promega) exposure. Following the Tn5 reaction, libraries were amplified using the Diagenode 24 UDI kit 1 (ref 01011034) following standard protocol procedures. Libraries were multiplexed and sequenced on a single Novaseq6000 lane by Fasteris (Genesupport SA) using 2 × 50 cycles, generating on average 27 million reads. Demultiplexing of raw reads was performed by bcl2fastq V3, and trimming, quality control, alignment to the ME49 reference genome (using bwa2) and duplicate read merging (using Picard) were carried out by the nf-core ATAQ-SEQ pipeline<sup>##REF##32055031##47##</sup>. For Integrated Genome Browser visualization and gene-centred analysis using Deeptools, Picard merged bam files were converted to bigWig file format using a bin size of 5 by bamCoverage (Deeptools). The Deeptools analysis was then generated using ‘computeMatrix reference point’, with the following parameters: –minThreshold 2,–binSize 10 and –averageTypeBins sum. Quantitative analysis of untreated versus 24-h IAA conditions was carried out by nf-core through a broad peak calling and annotation (MACS2) followed by HOMER (annotatePeaks) to analyse peak distribution relative to gene features. Reads were counted on annotated peaks by featureCounts and counts were processed by DeSeq2 to generate global statistical analysis of peak intensities between conditions using biological duplicates. All of these raw and processed files can be found at <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE222832\">GSE222832</ext-link>.</p>", "<title>Gene synthesis for recombinant coexpression of <italic>T. gondii</italic> AP2XI-2 and AP2XII-I</title>", "<p id=\"Par50\">Gene synthesis for all insect cell codon-optimized constructs was provided by GenScript. (Strep)<sub>2</sub>–APXI-2 and AP2XII-1–Flag or AP2IX-6–Flag genes were cloned within the coexpression donor vector pFastBac dual, which accepts two constructs. The (Strep)<sub>2</sub>–AP2XI-2 expression cassette was derived from the TGME49_310900 gene with a fused dual Strep tag and tobacco etch virus (TEV) site in the N terminus. AP2XII-1–Flag or AP2IX-6–Flag was derived from the full-length TGME49_218960 and TGME49_290180 genes, respectively, with an additional non-cleavable Flag tag on the carboxy terminus. The (Strep)<sub>2</sub>–AP2XI-2 expression cassette was under the control of the polyhedrin promoter; the AP2XII-1–Flag or AP2IX-6–Flag was under the control of the P10 promoter.</p>", "<title>Generation of baculovirus</title>", "<p id=\"Par51\">Bacmid cloning steps and baculovirus generation were carried out using EMBacY baculovirus (gift from I. Berger), which contains a yellow fluorescent protein reporter gene in the virus backbone. The established standard cloning and transfection protocols set up within the EMBL Grenoble eukaryotic expression facility were used. Although baculovirus synthesis (V0) and amplification (to V1) were carried out with SF21 cells cultured in SF900 III medium (Life Technologies), large-scale expression cultures were carried out with Hi-5 cells cultured in the protein-free ESF 921 insect cell culture medium (Expression System) and infected with 0.8–1.0% (v/v) of generation 2 (V1) baculovirus suspensions and collected 72 h after infection.</p>", "<title>(Strep)<sub>2</sub>–AP2XI-2 and AP2XII-1–Flag or (Strep)<sub>2</sub>–AP2XI-2 and AP2XIX-6–Flag expression and purification</title>", "<p id=\"Par52\">For purification, three cell pellets of bout 500 ml of Hi-5 culture were each resuspended in 50 ml of lysis buffer (50 mM Tris (pH 8.0), 400 mM NaCl and 2 mM β-mercaptoethanol (BME)) in the presence of an anti-protease cocktail (Complete EDTA-free, Roche) and 1 μl of Benzonase (Merck Millipore, 70746). Lysis was carried out on ice by sonication for 3 min (30-s on/ 30-s off, 45° amplitude). After the lysis step, 10% of glycerol was added. Clarification was then carried out by centrifugation for 1 h at 16,000<italic>g</italic> and 4 °C and vacuum filtration using 45-μm nylon filter systems (SteriFlip, Merck Millipore). Before purification, tetrameric avidin (Biolock, IBA Lifescience) was added to the clarified lysate (1/1,000 v/v), which was then batch incubated for 20 min with 3 ml of Strep-Tactin XT (IBA Lifescience). Following the incubation, the resin was retained on a glass column and washed three times using 6 ml of lysis buffer (W1), 6 ml of lysis buffer with NaCl content at 1 M (W2) and 6 ml of lysis buffer (W3). The elution was then carried out using 1× BXT buffer (IBA Lifescience) containing 50 mM biotin, 100 mM Tris pH 8 and 150 mM NaCl. This initial 1× solution was further supplemented with 300 mM NaCl, 2 mM BME and 10% glycerol. Following Strep-Tactin XT elution, the sample was concentrated to 500 μl using a 100-kDa concentrator (Amicon Ultra 4, Merck Millipore) injected on an ÄKTA pure FPLC using a Superose 6 Increase column 10/300 GL (Cytiva) running in 50 mM Tris pH 8, 400 mM NaCl and 1 mM BME.</p>", "<title>Mouse infection and experimental survey</title>", "<p id=\"Par53\">Six-week-old NMRI, CD1 or BALB/c mice were obtained from Janvier Laboratories. Female mice were used for all studies. For intraperitoneal infection, tachyzoites were grown in vitro and extracted from host cells by passage through a 27-gauge needle, washed three times in PBS, and quantified with a haemocytometer. Parasites were diluted in Hank’s balanced salt solution (Life), and mice were inoculated by the intraperitoneal route with tachyzoites of each strain (in 200 μl volume) using a 28-gauge needle. Animal euthanasia was completed in an approved CO<sub>2</sub> chamber. For immunolabelling on histological sections of the brains, the brains were removed from mice, entirely embedded in a paraffin wax block and cut into 5-μm-thick layers using a microtome.</p>", "<title>Statistics and reproducibility</title>", "<p id=\"Par54\">Sample sizes were not predetermined and were chosen according to previous literature. Experiments were carried out in biological replicates and provided consistent statistically relevant results. No method of randomization was used. All experiments were carried out in independent biological replicates as stated for each experiment in the manuscript. All corresponding treatment and control samples from ChIP–seq and RNA-seq were processed at the same time to minimize technical variation. Investigators were not blinded during the experiments. Statistical significance was evaluated using <italic>P</italic> values from unpaired two-tailed Student <italic>t</italic>-tests. Data are presented as the mean ± s.d. Significance was set to a <italic>P</italic> value of <0.05. All of the micrographs shown are representatives from three independently conducted experiments, with similar results obtained.</p>", "<title>Ethics statement</title>", "<p id=\"Par55\">Mouse care and experimental procedures were carried out under pathogen-free conditions in accordance with established institutional guidance and approved protocols from the Institutional Animal Care and Use Committee of the University Grenoble Alpes (APAFIS number 4536-2016031 017075121 v5). Animal experiments were carried out under the direct supervision of a veterinary specialist, and according to Swiss law and guidelines on Animal Welfare and the specific regulations of the Canton of Zurich under permit numbers 130/2012 and 019/2016, as approved by the Veterinary Office and the Ethics Committee of the Canton of Zurich (Kantonales Veterinäramt Zürich).</p>", "<title>Reporting summary</title>", "<p id=\"Par56\">Further information on research design is available in the ##SUPPL##1##Nature Portfolio Reporting Summary## linked to this article.</p>" ]	[]	[ "<title>Discussion</title>", "<p id=\"Par27\">Simultaneous KD of AP2XII-1 and AP2XI-2 efficiently triggers the pre-sexual transcriptional program in <italic>Toxoplasma</italic>, outperforming other methods. This study advances our understanding of pre-gamete biology, illustrating endopolygeny with karyokinesis as the meronts’ preferred division mode, similar to the case for <italic>Cystoisospora suis</italic> but distinct from that for <italic>Sarcocystis neurona</italic><sup>##REF##18310354##29##,##REF##32582569##37##</sup>. All predefined morphotypes (A–E), including type E expected to evolve into gametes, were observed. However, fully matured microgametocytes and macrogametocytes were not found, possibly owing to complex genetic requirements<sup>##REF##36477538##38##,##REF##36634679##39##</sup> or specialized metabolic conditions<sup>##REF##31430281##40##</sup>.</p>", "<p id=\"Par28\">Converging pieces of evidence support the hypothesis that AP2XII-1 and AP2XI-2 are able to form homodimers but also heterodimers to silence merozoite genes in tachyzoites by binding to their promoters and recruiting MORC and HDAC3 (Extended Data Fig. ##FIG##17##12a,b##). MORC in turn forms dimers that topologically entrap DNA loops<sup>##REF##31442422##41##</sup>, leading to chromatin condensation that limits DNA accessibility to transcription factors and suppresses gene expression<sup>##UREF##3##42##</sup>. AP2XII-1 and AP2XI-2 also control the expression of secondary AP2 proteins specific to pre-gametes in the tachyzoite (Extended Data Fig. ##FIG##17##12c##). Operating as downstream activators or repressors, these transcription factors have the potential to significantly influence developmental trajectories post-merogony (for example, sex determination as shown for <italic>Plasmodium falciparum</italic><sup>##REF##36477538##38##,##REF##36634679##39##</sup>). Fine-tuning their activity in mature in vitro-cultured merozoites holds promise for functional gamete production and in vitro fertilization<sup>##REF##37313339##43##</sup>.</p>" ]	[]	[ "<p id=\"Par1\">Sexual reproduction of <italic>Toxoplasma gondii</italic>, confined to the felid gut, remains largely uncharted owing to ethical concerns regarding the use of cats as model organisms. Chromatin modifiers dictate the developmental fate of the parasite during its multistage life cycle, but their targeting to stage-specific cistromes is poorly described<sup>##REF##32094587##1##,##REF##19349466##2##</sup>. Here we found that the transcription factors AP2XII-1 and AP2XI-2 operate during the tachyzoite stage, a hallmark of acute toxoplasmosis, to silence genes necessary for merozoites, a developmental stage critical for subsequent sexual commitment and transmission to the next host, including humans. Their conditional and simultaneous depletion leads to a marked change in the transcriptional program, promoting a full transition from tachyzoites to merozoites. These in vitro-cultured pre-gametes have unique protein markers and undergo typical asexual endopolygenic division cycles. In tachyzoites, AP2XII-1 and AP2XI-2 bind DNA as heterodimers at merozoite promoters and recruit MORC and HDAC3 (ref. <sup>##REF##32094587##1##</sup>), thereby limiting chromatin accessibility and transcription. Consequently, the commitment to merogony stems from a profound epigenetic rewiring orchestrated by AP2XII-1 and AP2XI-2. Successful production of merozoites in vitro paves the way for future studies on <italic>Toxoplasma</italic> sexual development without the need for cat infections and holds promise for the development of therapies to prevent parasite transmission.</p>", "<p id=\"Par2\">A study describes the molecular basis of sexual development of <italic>Toxoplasma gondii</italic> entirely in vitro, demonstrating the role and interaction of AP2XII-1 and AP2XI-2 in the developmental program of this protozoan parasite.</p>", "<title>Subject terms</title>" ]	[ "<title>Main</title>", "<p id=\"Par3\"><italic>Toxoplasma</italic>, the cause of the global zoonotic infection toxoplasmosis, presents a multifaceted life cycle with distinctive stages (Fig. ##FIG##0##1a##). Much is understood about its fast-growing tachyzoites and semi-dormant bradyzoites, but its sexual reproduction, confined to felid guts, is less explored. Each stage has a distinctive transcriptional signature and switching between them is regulated by intricate transcriptional cascades in which covalent and noncovalent epigenetic mechanisms act as driving forces<sup>##REF##29932347##3##,##REF##34456144##4##</sup>. The chromatin modifiers MORC and HDAC3, key players in gene silencing<sup>##REF##32094587##1##,##REF##19349466##2##</sup>, act as critical checkpoints for sexual commitment, and when conditionally depleted or inhibited in tachyzoites, they trigger broad activation of chronic and sexual gene expression<sup>##REF##32094587##1##,##REF##19349466##2##</sup>. Previous studies overlooked their combined presence in nucleosomal telomeric repeats<sup>##REF##32094587##1##</sup>, suggesting a secondary role in maintaining genome stability through the formation of telomeric heterochromatin (Extended Data Fig. ##FIG##6##1a##). MORC detachment from chromosome ends disrupts subtelomeric gene silencing (Extended Data Fig. ##FIG##6##1b##), causing telomere dysfunction, mitotic bypass and aberrant polyploid zoite accumulation (Extended Data Fig. ##FIG##6##1c##), reminiscent of aneuploidy in human cancer<sup>##REF##20371347##5##</sup>. In MORC-depleted parasites, disorganized telomeres may in turn disrupt gene-level transcriptional regulation, leading to misguided sexual development. An alternative way to explore the modus operandi of MORC involves its partners, the apetala proteins<sup>##REF##32094587##1##</sup> (AP2; Extended Data Fig. ##FIG##7##2a##), which are considered important regulators of life-cycle transitions in all apicomplexan species<sup>##REF##29932347##3##,##REF##29975590##6##</sup>. This study underscores the role of two AP2 repressors in coordinating the expression of stage-specific genetic programs, and thus controlling <italic>Toxoplasma</italic> merogony.</p>", "<title>AP2-mediated merozoite gene silencing</title>", "<p id=\"Par4\">In the 1970s, <italic>Toxoplasma</italic>’s sexual cycle was partially studied in infected kittens through meticulous examination of the ultrastructure of the pre-gametes zoites and sexual dimorphic stages in the intestinal lining of <italic>Felis catus</italic><sup>##REF##4903651##7##–##UREF##1##11##</sup>. Merozoites—the initiators of the sexual cycle—have a unique transcriptional profile<sup>##REF##24885521##12##,##REF##25757795##13##</sup>, but their study has been difficult owing to the lack of specific markers. The only recognized marker so far, GRA11b, is used to track the development of this stage in the gut of <italic>Toxoplasma</italic>-infected cats<sup>##REF##28526607##14##</sup> (Fig. ##FIG##0##1b##). For a more in-depth exploration of merogony, we identified three potential merozoite-specific proteins with gene expression profiles mirroring those of GRA11b and produced matching antibodies. Notably, antibodies to TGME49_273980 showed robust reactivity with feline merozoites (Fig. ##FIG##0##1b##), but not with tachyzoites or bradyzoites, whether converted in vitro by overexpressing BFD1 (ref. <sup>##REF##31955846##15##</sup>) or present in tissue cysts in mouse brain (Extended Data Fig. ##FIG##7##2b,c##). This protein, together with GRA11b, is localized in the vacuolar space and on parasitophorous vacuole membranes, and has the typical characteristics of a dense granule protein; it is hereafter referred to as GRA80 (Fig. ##FIG##0##1b##). We were unable to produce antibodies compatible with immunofluorescence assay (IFA) for TGME49_243940, but the epitope-tagged protein shows typical features of a dense granule protein (GRA81) that accumulates in the vacuolar space after MORC depletion<sup>##REF##32094587##1##</sup> (Extended Data Fig. ##FIG##7##2d##). Another protein, GRA82 (TGME49_277230), co-occurs with GRA11b in in vivo schizonts specifically (Extended Data Figs. ##FIG##7##2e## and ##FIG##8##3d##).</p>", "<p id=\"Par5\">Using these new markers, we investigated which MORC- and HDAC3-associated AP2 is responsible for repressing merozoite-specific gene expression in tachyzoites. In a CRISPR loss-of-function screen, inactivation of only <italic>AP2XI-2</italic> or <italic>AP2XII-1</italic> among the 14 MORC-associated AP2 proteins resulted in a significant increase in the expression level of GRA11b, GRA80 and GRA81–HA to varying degrees (Fig. ##FIG##0##1c–e## and Extended Data Fig. ##FIG##7##2f##). This suggests a common or overlapping role of these two transcription factors. To examine the genetic relationship between AP2XI-2 and AP2XII-1, we knocked out both genes simultaneously and observed a synergistic increase in the expression level of all three merozoite markers that exceeded threefold the levels observed in the individual knockouts (Fig. ##FIG##0##1c–e##). By contrast, <italic>MORC</italic> knockout led to only a low level of expression of GRA81–HA (Fig. ##FIG##0##1e##). AP2XI-2 and AP2XII-1 are essential for tachyzoite proliferation (Extended Data Fig. ##FIG##7##2a##), hindering the study of merogony with knockouts. To investigate this further, we used the minimal auxin-inducible degron system (mAID; Supplementary Table ##SUPPL##3##1##) and transiently knocked down each AP2 factor individually or simultaneously (Extended Data Fig. ##FIG##7##2g##). Single knockdowns (KDs) of AP2XI-2 and AP2XII-1 had limited effects on merozoite marker expression, as less than 25% of vacuoles were co-labelled with GRA11b and GRA80 (Extended Data Fig. ##FIG##8##3b##). However, simultaneous KD resulted in efficient merozoite differentiation with more than 98% of vacuoles showing both markers after 48 h (Extended Data Fig. ##FIG##8##3a,b##). By contrast, MORC-depleted vacuoles did not exhibit a significant level of coexpression of GRA11b and GRA80 (Extended Data Fig. ##FIG##8##3b##). Additionally, KD of three genes encoding other MORC-associated AP2 proteins (AP2VII-3a, AP2VIII-4 and AP2VIII-7) failed to induce merozoite marker expression (Extended Data Fig. ##FIG##8##3c##).</p>", "<p id=\"Par6\">Notably, GRA82 showed delayed but significant coexpression with GRA11b in 98% of vacuoles depleted of both AP2XII-1 and AP2XI-2, 48 h after 3-indoleacetic acid (IAA) addition. This suggests a potential temporal regulation or association with a specific merozoite morphotype (Extended Data Fig. ##FIG##8##3d,e##). The in vitro pre-sexual parasite population that emerged following the acute depletion of AP2XI-2 and AP2XII-1 is homogeneous and does not express the typical markers of tachyzoites (for example, GRA2; Extended Data Fig. ##FIG##8##3f,g##) and bradyzoites (for example, BCLA (ref. <sup>##REF##32094587##1##</sup>), BAG1 and DBA; Extended Data Fig. ##FIG##8##3h–j##). In comparison, accumulation of Shield-protected BFD1 (ref. <sup>##REF##31955846##15##</sup>) induced bradyzoite differentiation in more than 95% of parasites (Extended Data Fig. ##FIG##8##3h–j##), without the expression of merozoite markers (Extended Data Figs. ##FIG##7##2b## and ##FIG##8##3b##). In contrast to the AP2 double-KD mutant, MORC-depleted parasites showed asynchronous development<sup>##REF##32094587##1##</sup> with vacuoles expressing either bradyzoite (BCLA<sup>+</sup>) or merozoite (GRA81<sup>+</sup>) markers in a mutually exclusive manner (Extended Data Fig. ##FIG##8##3k##).</p>", "<p id=\"Par7\">To gain a more complete understanding of in vitro merozoite differentiation beyond the limited perspective offered by GRA11b and GRA80 co-staining, we carried out RNA sequencing (RNA-seq) on all KD strains to investigate genome-wide transcriptional changes resulting from individual or simultaneous depletion of AP2XI-2 and AP2XII-1. Analysing the RNA-seq data using DESeq2, we identified 490 differentially expressed transcripts (fold change (FC) threshold of ≥8 and <italic>P</italic> value < 0.05), including 295 upregulated genes and 195 downregulated genes when both AP2XI-2 and AP2XII-1 were depleted (Fig. ##FIG##0##1f## and Supplementary Table ##SUPPL##4##2##).</p>", "<p id=\"Par8\">Hierarchical clustering analysis revealed that the co-depletion of AP2XI-2 and AP2XII-1 resulted in gene expression profiles reminiscent of those observed in vivo in enteroepithelial stages (EESs)<sup>##REF##24885521##12##,##REF##25757795##13##,##REF##30728393##16##</sup>. Their acute degradation triggered the induction of pre-gamete-specific genes at different developmental stages (clusters 1 and 2), while concurrently repressing a subset of tachyzoite-specific genes (clusters 3 and 4; Fig. ##FIG##0##1f##). These transcriptional changes mirror the gene expression profile observed in merozoites in the cat intestine<sup>##REF##24885521##12##,##REF##25757795##13##</sup>. Using principal component analysis, we observed consistent clustering of biological replicates within each treatment, indicating excellent reproducibility. In addition, the samples showed substantial clustering based on genetic background with significant separation between single-KD and double-KD samples (PC1 = 58%), suggesting synergistic regulation of gene expression by AP2XI-2 and AP2XII-1 in <italic>Toxoplasma</italic> (Extended Data Fig. ##FIG##9##4a##). DESeq2 analysis revealed that depletion of both AP2 factors was found to be necessary to upregulate 65% (194/295) of the identified genes (FC ≥ 8; <italic>P</italic> value < 0.05; Extended Data Fig. ##FIG##9##4b##). By contrast, single KDs of AP2XI-2 and AP2XII-1 resulted in expression of a lower proportion of genes, 9% and 13.5%, respectively (Extended Data Fig. ##FIG##9##4b##). This transcriptional trend also extends to tachyzoite-specific genes, whose repression is quantitatively more pronounced when AP2 proteins are simultaneously depleted (Extended Data Fig. ##FIG##9##4c##).</p>", "<p id=\"Par9\">Changes in mRNA levels in response to IAA treatment translated into changes in protein abundance. Principal component analysis showed consistent clustering of biological replicates within each condition, indicating high reproducibility (Extended Data Fig. ##FIG##9##4d##). Proteomic analysis revealed robust changes in 18% of the parasite proteins (<italic>n</italic> = 3,020 detected; log<sub>2</sub>[FC] ≥ 1; <italic>P</italic> value ≤ 0.01), with a highly polarized response to the merozoite stage. IAA-treated parasites exhibited increased expression levels of 276 proteins associated with pre-gamete stages, whereas 285 tachyzoite proteins were suppressed (Extended Data Fig. ##FIG##9##4e## and Supplementary Table ##SUPPL##5##3##). Overall, the RNA and protein expression patterns of in vitro merozoites mirrored those observed in their enteroepithelial counterparts<sup>##REF##24885521##12##,##REF##25757795##13##</sup>.</p>", "<title>AP2 depletion causes stage conversion</title>", "<p id=\"Par10\">The process of invasion of <italic>Toxoplasma</italic> tachyzoites has been thoroughly examined, revealing the cryptic functions of organelle-resident proteins<sup>##REF##35671531##17##</sup>. Micronemes secrete adhesin proteins (MICs) for attachment, facilitating motility and invasion. Rhoptries release neck (RON) and bulb (ROP) proteins, which interact with MICs to breach the host cell membrane and form the parasitophorous vacuole. Dense granules (GRA) release proteins involved in intravacuolar function, such as the tubulovesicular network, and act as effectors at the parasitophorous vacuole membrane and beyond, manipulating host signalling<sup>##REF##35587934##18##</sup>. Most MIC, ROP and GRA proteins secreted by tachyzoites and bradyzoites are absent in merozoites isolated from cats<sup>##REF##24885521##12##,##REF##25757795##13##,##REF##10072322##19##,##REF##15003495##20##</sup>. In vitro, we also observed substantial changes in the expression of many known MIC, ROP and GRA proteins after the addition of IAA, providing evidence for a developmental switch (Fig. ##FIG##1##2a–c## and Supplementary Table ##SUPPL##4##2##). Specifically, MIC complexes that play important roles in tachyzoites (for example, MIC2 and AMA1) were repressed, whereas merozoite- and bradyzoite-specific MICs (for example, MIC17a, MIC17b, MIC17c and AMA2) were markedly induced. Notably, the reassortment of organelle-resident proteins seems to be highly specific, as MICs restricted to sporozoites (SporoAMA1) were not induced in response to IAA (Fig. ##FIG##1##2a##).</p>", "<p id=\"Par11\">The combined depletion of AP2XI-2 and AP2XII-1 silenced 80% of the 143 rhoptry proteins described or predicted by hyperLOPIT to be tachyzoite specific (Fig. ##FIG##1##2b##). These include the components of the tachyzoite complex of RON2, RON4, RON5 and RON8 as well as ROP16, ROP18 and ROP5, which function as effectors to protect parasites from host cell-autonomous immune defences<sup>##REF##35587934##18##</sup>. The number of ROP proteins reported to be exclusively specific to pre-gametes is rather limited (Fig. ##FIG##1##2b##). Among them, BRP1 stands out as the first protein described to be expressed in both merozoites and bradyzoites<sup>##REF##16182390##21##</sup> (Fig. ##FIG##1##2d##). ROP26 is also expressed in both in vitro merozoites and BFD1-expressing bradyzoites (Fig. ##FIG##1##2b,d## and Extended Data Fig. ##FIG##9##4f##). These proteins, present in both stages, may facilitate the transition process from bradyzoites to merozoites.</p>", "<p id=\"Par12\">When we examined GRA mRNA and protein levels in response to IAA treatment, we observed extensive and comparable shutdown of the tachyzoite program and activation of the merozoite program (Fig. ##FIG##1##2c##). Notably, the levels of core proteins of the tubulovesicular network, including the GRA2 archetype, declined significantly in in vitro merozoites over time (Extended Data Fig. ##FIG##8##3f,g##). The MYR-dependent effectors GRA16, GRA24 or TgIST also showed a comparable decrease (Fig. ##FIG##1##2c##). By contrast, confirmed GRA markers of pre-gametes (for example, GRA11b (ref. <sup>##REF##28526607##14##</sup>), GRA80, GRA81 and GRA82) were induced in response to IAA (Fig. ##FIG##1##2c,d## and Extended Data Fig. ##FIG##8##3a,b,d,e##).</p>", "<p id=\"Par13\">During the transition from tachyzoite to merozoite, surface proteins on the zoite also undergo significant restructuring, including the SAG-related surface (SRS) protein family<sup>##REF##24885521##12##,##REF##25757795##13##</sup>. Compared to tachyzoites, merozoites express a broader range of SRS proteins (Extended Data Fig. ##FIG##9##4g##)—for example, SRS48 (Fig. ##FIG##1##2d##) and SRS59—which may contribute to gamete development and fertilization<sup>##REF##24885521##12##,##REF##25757795##13##</sup>. IAA treatment induces the expression of 90% of the known 88 SRS proteins, effectively mimicking the phenotypic features of in vivo merozoites, whereas suppressing tachyzoite-specific SRS proteins (Extended Data Fig. ##FIG##9##4g##). In addition, 29 of the 33 family A members, representing the major membrane-associated merozoite proteins, are expressed in vitro during this transition to pre-gametes (Extended Data Fig. ##FIG##9##4h##).</p>", "<p id=\"Par14\">In vitro-produced merozoites are deficient in essential proteins necessary for tachyzoite functions, including motility, attachment and invasion. As a result, these zoites exhibited reduced infectivity in human fibroblasts, as indicated by a notable decrease in the number of lytic plaques compared to that of untreated parasites (Extended Data Fig. ##FIG##10##5a##).</p>", "<title>AP2-depleted zoites undergo merogony</title>", "<p id=\"Par15\">In 1972, Dubey and Frenkel characterized pre-sexual stages at the cellular level and identified five morphological stages (A–E) that form sequentially during colonization of the cat intestinal epithelium before gamete formation<sup>##REF##5008846##22##</sup> (Fig. ##FIG##0##1a##). These morphotypes can be differentiated on the basis of their distinct subcellular structures and nuclear content<sup>##REF##29062899##23##</sup>. Using transmission electron microscopy and immunofluorescence, we tracked the development of these stages during in vitro merogony, including the dynamic behaviour of the inner membrane complex (IMC), a unique organelle essential for daughter cell formation during replication. During development in the cat intestine, the functions of various IMC proteins diverge from their assigned roles in tachyzoites<sup>##REF##29062899##23##</sup>. For example, at the later stages of schizogony, IMC1 and IMC3 staining revealed daughter IMC, whereas IMC7 staining was restricted to the periphery of the mother cell<sup>##REF##29062899##23##</sup>. In agreement with these findings, our results show that IMC1 but also GAP45 are valuable markers for tracking merozoite division during their development in the intestinal mucosa of cats (Fig. ##FIG##1##2e,f##).</p>", "<p id=\"Par16\">As a first step, 24 h post-IAA addition, the nucleus of the mother cell undergoes several fission events while maintaining the nuclear envelope, leading to individualized nuclei (in even numbers, ranging from 4 up to 10; Fig. ##FIG##2##3a##). Concomitantly, single organelles such as the apicoplast and the Golgi apparatus expand and multiply to match the number of nuclei. Transversal cross-sections of the apicoplast (limited by four membranes) reveal its elongation and constriction, suggestive of replication by scission (Fig. ##FIG##2##3b##), which was also visualized by IFA with the ATrx1 antibody<sup>##REF##18586952##24##</sup> (Extended Data Fig. ##FIG##10##5b##) and is in line with maternal inheritance of the apicoplast seen in meronts in the cat intestine<sup>##REF##15821140##25##</sup>. Multiple Golgi complexes are formed at different sites of the nucleus, sometimes in opposing orientations, suggestive of de novo formation (Fig. ##FIG##2##3c##). The manner in which other organelles, such as secretory organelles, multiply is not yet known. At this stage, the multinucleated mother cell contains several sets of organelles randomly distributed throughout the cytoplasm. Despite the increase in size of the mother cell, the subpellicular IMC is still prominently present beneath the plasma membrane. The parasites exhibiting a characteristic ovoid shape with four and eight nuclei are morphologically related to the cryptic and early meronts, namely B and C morphotypes<sup>##REF##5008846##22##</sup> (Fig. ##FIG##2##3d## and Extended Data Fig. ##FIG##10##5c##).</p>", "<p id=\"Par17\">As a second step, new flattened vesicles of the IMC emerge in the mother cytoplasm, and progressively elongate allowing the sub-compartmentalization of organelles destined for each daughter cell (Fig. ##FIG##2##3e##). This process of internal budding of more than two daughter cells, referred to here as endopolygeny<sup>##REF##15710440##26##–##REF##19249305##30##</sup>, differs from the tachyzoite division by endodyogeny in which the two daughter cells are generated symmetrically and in a synchronous manner in the mother cell (Fig. ##FIG##2##3f##). Alongside the expansion of daughter buds, the mother IMC and conoid undergo partial disassembly. Notably, rhoptries inside daughter cells are different in shape and electron density from mother rhoptries dispersed in the cytoplasm, suggesting de novo biogenesis of rhoptries (Fig. ##FIG##2##3g##), which can also be traced with ROP26 (Fig. ##FIG##2##3h##). This finding aligns with the observation that the bulbous end of the rhoptry in in vivo meronts remains spherical, in contrast to that in tachyzoites and bradyzoites<sup>##UREF##2##31##</sup>.</p>", "<p id=\"Par18\">In these multinucleated structures, daughter IMC could be identified with IMC1 staining, whereas GAP45 staining was restricted to the periphery of the mother cell (Fig. ##FIG##2##3i## and Extended Data Fig. ##FIG##10##5d##). Daughter cells become polarized with the formation of a conoid and apical distribution of micronemes, rhoptries, the apicoplast, and the Golgi apparatus (Fig. ##FIG##3##4a## and Extended Data Fig. ##FIG##10##5e##). At this stage, it is evident that the maternal conoid coexists with the newly formed conoids of the progeny, as shown by the labelling of apically methylated proteins (Extended Data Fig. ##FIG##10##5f##). After final assembly, the daughter cells emerge separately, wrapped by the plasma membrane of the mother cell (cortical or peripheral budding; Fig. ##FIG##3##4b,c##), forming fan-like structures as previously described in infected cat cells<sup>##REF##15710440##26##</sup>. Compared to tachyzoites, these newly formed parasites are thinner and do not form a rosette-like structure within the parasitophorous vacuole but instead are aligned, with their apex facing the parasitophorous vacuole membrane (Extended Data Fig. ##FIG##10##5g,h##); these features are reminiscent of those of type-D-like merozoites arising in cat intestinal cells from meront entities at the onset of infection<sup>##REF##25312612##28##–##REF##19249305##30##</sup>. Notably, the parasitophorous vacuole membrane of merozoites also forms physical interactions with host ER and mitochondria, potentially for nutrient acquisition<sup>##REF##32680452##32##</sup> (Extended Data Fig. ##FIG##10##5h##).</p>", "<title>In vitro merozoites mimic cat pre-gametes</title>", "<p id=\"Par19\">Extending the IAA treatment for another 16 h reveals the presence of very large meronts containing numerous daughter cells in formation (Fig. ##FIG##3##4d,e##). These schizonts are detected in the same parasitophorous vacuole together with fully formed in vitro merozoites (Fig. ##FIG##3##4e##), the latter being very similar to their counterpart in the cat intestine (Fig. ##FIG##1##2e,f##). Mature polyploid meronts can be visualized by IMC7 staining on their surface, whereas fully formed merozoites were completely negative for IMC7 (Fig. ##FIG##2##3d,h## and Extended Data Fig. ##FIG##10##5c##), a phenotype that has also been observed in pre-gametes developing in the cat gut<sup>##REF##29062899##23##</sup>. Notably, new pre-merozoite or merozoite-specific markers such as ROP26 and GRA80, respectively, distinguish the two morphotypic populations. ROP26 marks zoites undergoing schizogonic replication, whereas GRA11b and GRA80 are expressed exclusively in mature in vitro merozoites (Fig. ##FIG##2##3h## and Extended Data Fig. ##FIG##11##6a##). As merozoites undergo several cycles of endopolygeny, they acquire new distinct morphological features compared to first-generation merozoites (24 h post-IAA), probably type E (ref. <sup>##REF##15710440##26##</sup>). Some in vitro merozoites are sausage shaped, with a diameter of 1.5–1.8 μm, packed in the parasitophorous vacuole without any spatial organization (Extended Data Fig. ##FIG##11##6b,c##). These forms contain similar organelles found in tachyzoites, but they exhibit an extruded conoid (Extended Data Fig. ##FIG##11##6d##). Other parasitophorous vacuoles contain peripherally arranged parasites, leaving a large empty space (Extended Data Fig. ##FIG##11##6e,f##), reminiscent of schizont parasitophorous vacuoles formed in cat intestinal cells<sup>##REF##15710440##26##</sup>. Notably, these parasites at the parasitophorous vacuole edge adopt two configurations: a very large cell body (trapezoid) with a diameter of up to 5 μm or a very thin and elongated shape (tubular) with a diameter of 200–250 nm (Extended Data Fig. ##FIG##11##6g,h##). The latter do not contain nuclei but mitochondrion profiles and ribosomes are observed. Their origin and formation remain to be determined but their abundance in parasitophorous vacuoles probably suggests a physiological relevance in the <italic>Toxoplasma</italic> life cycle.</p>", "<p id=\"Par20\">Morphotypes A–E are difficult to study in vivo because they vary in size and shape and develop asynchronously in different regions of the digestive tract<sup>##REF##10072322##19##,##REF##15003495##20##,##REF##15710440##26##,##UREF##2##31##,##REF##9564564##33##</sup>. Here we were able to follow the initial steps of in vitro merogony using several stage-specific markers. Asynchronous nuclear division cycles were detected by DNA, histone or centrosome staining (Extended Data Fig. ##FIG##12##7a–d##). At 12-h post-IAA treatment, a significant proportion of the tachyzoite population transitioned into morphotype B (3–4 nuclei and ROP26<sup>+</sup>), reaching up to 75% (Fig. ##FIG##3##4f##). Within 24 h, morphotypes C and D (8–32 nuclei and ROP26<sup>+</sup>) and mononuclear merozoites (GRA11b<sup>+</sup>GRA80<sup>+</sup>) coexisted in culture (Fig. ##FIG##3##4f##). After 48 h, nearly 98% of the parasite population expressed merozoite markers (GRA11b<sup>+</sup> and GRA80<sup>+</sup>), whereas typical tachyzoite (GRA2) or bradyzoite (BAG1) markers were absent (Fig. ##FIG##3##4f##), aligning with our extensive transcriptome and proteome analyses.</p>", "<title>AP2 proteins bind to MORC and HDAC3</title>", "<p id=\"Par21\">AP2XI-2 and AP2XII-1 probably synergize to suppress gene expression in tachyzoites, but their modus operandi is still enigmatic. Both proteins were originally found in a MORC pulldown along with HDAC3 in tachyzoites<sup>##REF##32094587##1##</sup>. We confirmed their strong and specific association with MORC and HDAC3 by reverse immunoprecipitation combined with mass spectrometry (MS)-based quantitative proteomic and western blot analyses using knock-in parasite lines expressing a Flag-tagged version of AP2XI-2 or AP2XII-1 (Fig. ##FIG##4##5a,b##). Each AP2 protein shows significant enrichment in the eluate of its corresponding counterpart (FC > 90; <italic>P</italic> value < 1.5 × 10<sup>−10</sup>), indicating their association within the same functional complex along with MORC and HDAC3 (Fig. ##FIG##4##5a##). Notably, this interaction is specific and exclusive to these two AP2 proteins, as no such association was observed with other AP2 proteins, or with other chromatin modifiers (Supplementary Table ##SUPPL##6##4##). HDAC3 and MORC exhibited comparatively lower levels of enrichment (FC of 8 and 14, respectively, Fig. ##FIG##4##5a##), suggesting that AP2XI-2 or AP2XII-1 proteins independently form heterodimers in cellular contexts. To further test this hypothesis, we used baculoviruses to transiently coexpress epitope-tagged AP2XII-1–Flag and (Strep)<sub>2</sub>–AP2XI-2 in insect cells, with AP2IX-6–Flag serving as an internal control (Fig. ##FIG##4##5c##). AP2XII-1 was purified by Strep-Tactin affinity chromatography, and the partnership between AP2XII-1 and AP2XI-2 was confirmed through western blot analysis (Fig. ##FIG##4##5d##), whereas no co-enrichment was detected with AP2IX-6 (Fig. ##FIG##4##5e##). Consistent with AP2XI-2 and AP2XII-1 being part of a heterodimer, our findings show these two proteins coelute in the same gel filtration fractions, in a MORC- and HDAC3-independent manner, as confirmed by MS-based proteomics (Fig. ##FIG##4##5f,g## and Supplementary Table ##SUPPL##7##5##). Many transcription factors, including apicomplexan AP2, were reported to form homodimers and heterodimers with different partners that modulate DNA-binding specificity and affinity<sup>##REF##20109158##34##,##REF##19913037##35##</sup>. In this context, AP2XI-2 and AP2XII-1 probably bind cooperatively as a heterodimer to DNA to selectively and synergistically repress merozoite gene expression, and only their simultaneous depletion leads to achievement of the developmental program critical for merozoite formation.</p>", "<title>AP2XI-2 and AP2XII-1 limit chromatin access</title>", "<p id=\"Par22\">To further explore gene repression by AP2XI-2 and AP2XII-1, we used chromatin immunoprecipitation with sequencing (ChIP–seq; <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE222819\">GSE222819</ext-link>) to analyse their genome-wide distribution during conditional single or double KD. Simultaneously, we examined recruitment of their partners MORC and HDAC3 to chromatin under KD conditions. In the AP2XII-1-specific cistrome, we identified genes from cluster 2 that are exclusively expressed in pre-gametes and have a discrete and highly enriched peak centred on their transcription start site (TSS; Fig. ##FIG##5##6a,b##). Examining the co-occupancy in cluster 2, we observed a strong overlap between the binding sites of AP2XI-2 or AP2XII-1 cistromes in the untreated condition (Fig. ##FIG##5##6c,d## and Extended Data Fig. ##FIG##13##8a##) with approximately 30–50% of the peaks located at the promoter or TSS (Fig. ##FIG##5##6e##). Consistently, AP2XII-I and AP2XI-2 showed similar genome-wide occupancy when immunoprecipitated from single or double KD strains (Fig. ##FIG##5##6b## and Extended Data Fig. ##FIG##13##8##). HDAC3 and MORC are both enriched at AP2XII-I and AP2XI-2 peaks (Fig. ##FIG##5##6c,d## and Extended Data Fig. ##FIG##13##8c,d##). Addition of IAA triggers the acute release of AP2XI-2 and AP2XII-1 from chromatin and a concomitant reduction in HDAC3 and MORC occupancy at the TSS at cluster 2 genes, which is more pronounced in the context of double KD (Fig. ##FIG##5##6d## and Extended Data Fig. ##FIG##13##8a,e##).</p>", "<p id=\"Par23\">AP2XI-2 and AP2XII-1 are expected to alter chromatin compaction and accessibility, a function attributed to their partners MORC and HDAC3. To investigate this assumption, we carried out assay for transposase-accessible chromatin with high-throughput sequencing (ATAC–seq; <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE222832\">GSE222832</ext-link>), a robust and streamlined method for profiling chromatin accessibility<sup>##REF##24097267##36##</sup>. At the genome level, there is a slight decrease in average accessibility between untreated and treated conditions (Fig. ##FIG##5##6f##) with most of the peaks located at the TSS (Extended Data Fig. ##FIG##14##9a,b##). However, when we plotted ATAC–seq data for the subset of downregulated and upregulated genes (as defined in Fig. ##FIG##0##1f##), the changes in occupancy were more pronounced and consistent with expected increase or decrease in accessibility of induced or repressed clusters, respectively (Fig. ##FIG##5##6g,h##). At the gene level, dynamic release of AP2XI-2 and AP2XII-1 from DNA induced by IAA resulted in a substantial decrease in MORC and HDAC3 enrichment, which enhanced local chromatin hyperaccessibility and led to a concomitant increase in target gene mRNA abundance, a pattern largely shared by representative merozoite genes (Fig. ##FIG##5##6i## and Extended Data Fig. ##FIG##14##9c–e##).</p>", "<p id=\"Par24\">We next examined how AP2XII-1 degradation influences AP2XI-2 binding genome-wide. Initially, depletion of AP2XII-1 did not affect the nuclear localization or signal intensity of AP2XI-2 (Extended Data Fig. ##FIG##15##10a##). Notably, AP2XI-2 did not dissociate from chromatin post AP2XII-1 degradation (Extended Data Fig. ##FIG##15##10b##), indicating its ability to form independent homodimers and repress merozoite-specific genes (Extended Data Fig. ##FIG##15##10c,d##). At the transcriptional level, the persistence of homodimers on chromatin explains the sustained repression of merozoite gene expression when a single AP2 is depleted, with peak expression reached only following simultaneous KD (Fig. ##FIG##0##1f## and Extended Data Figs. ##FIG##9##4c## and ##FIG##15##10d##).</p>", "<title>Secondary regulators guide merogony</title>", "<p id=\"Par25\">Some genes escaped direct regulation by AP2XI-2 and AP2XII-1 because they expressed increased levels of RNA and had hyperaccessible chromatin signatures after the addition of IAA but lacked the characteristic recruitment of MORC and HDAC3 to their promoters in the untreated state (for example, <italic>PNP</italic>; Extended Data Fig. ##FIG##15##10e##). This indirect regulation is also typical of tachyzoite genes that are repressed when AP2XI-2 and AP2XII-1 are depleted. They show a strong decrease in ATAC–seq signals after addition of IAA, but no apparent binding of the repressive MORC complex to their TSS (Fig. ##FIG##5##6i## and Extended Data Figs. ##FIG##15##10f,g## and ##FIG##16##11a##).</p>", "<p id=\"Par26\">This suggests that AP2XI-2 and AP2XII-1 operate on gene expression through an indirect mechanism that is not reliant on their DNA-binding activities or their functional partners MORC and HDAC3. This transcriptional outcome may stem from secondary transcription factors that govern the establishment of specific predetermined transcriptional programs for distinct stages<sup>##REF##32094587##1##,##REF##34456144##4##</sup> (Extended Data Fig. ##FIG##17##12##). Supporting this hypothesis, our observations show that co-depletion of AP2XI-2 and AP2XII-1 leads to the activation of seven AP2 transcription factors and one C2H2 zinc finger transcription factor, all of which are subject to control by MORC and HDAC3 (Extended Data Fig. ##FIG##16##11b–d##).</p>", "<title>Online content</title>", "<p id=\"Par57\">Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41586-023-06821-y.</p>", "<title>Supplementary information</title>", "<p>\n\n\n\n\n\n\n\n\n</p>", "<title>Source data</title>", "<p>\n\n\n\n\n</p>" ]	[ "<title>Extended data figures and tables</title>", "<p id=\"Par60\">\n\n</p>", "<p id=\"Par61\">\n\n</p>", "<p id=\"Par62\">\n\n</p>", "<p id=\"Par63\">\n\n</p>", "<p id=\"Par64\">\n\n</p>", "<p id=\"Par65\">\n\n</p>", "<p id=\"Par66\">\n\n</p>", "<p id=\"Par67\">\n\n</p>", "<p id=\"Par68\">\n\n</p>", "<p id=\"Par69\">\n\n</p>", "<p id=\"Par70\">\n\n</p>", "<p id=\"Par71\">\n\n</p>", "<title>Extended data</title>", "<p>is available for this paper at 10.1038/s41586-023-06821-y.</p>", "<title>Supplementary information</title>", "<p>The online version contains supplementary material available at 10.1038/s41586-023-06821-y.</p>", "<title>Acknowledgements</title>", "<p>We are grateful to the developers of the <ext-link ext-link-type=\"uri\" xlink:href=\"https://ToxoDB.org\">ToxoDB.org</ext-link> Genome Resource. ToxoDB and EuPathDB are part of the National Institutes of Health National Institutes of Allergy and Infectious Diseases (NIH NIAID)-funded Bioinformatics Resource Center. We thank the technical staff of the Electron Microscopy Core Facility at the Johns Hopkins University School of Medicine Microscopy Facility; M. J. Gubbels for providing several antibodies, encompassing rat anti-IMC7 and centrin 1; and S. Lourido for providing <italic>ΔBFD1</italic>/DD-BFD1-Ty<sup>##REF##31955846##15##</sup>. This work was supported by MSD Avenir (Project LatentToxoDiag, DS-2022-0017), the Laboratoire d’Excellence (LabEx) ParaFrap (ANR-11-LABX-0024), the Agence Nationale pour la Recherche (Project ApiNewDrug, ANR-21-CE35-0010-01; Project ApiMORCing, ANR-21-CE15-0002-01; Project ToxoP53, ANR−19-CE15-0026) and Fondation pour la Recherche Médicale (FRM Equipe, EQU202103012571). I.C. was supported by an NIH grant (R01 AI060767). MS-based proteomic experiments were partially supported by Agence Nationale de la Recherche under projects ProFI (Proteomics French Infrastructure, ANR-10-INBS-08) and GRAL, a program from the Chemistry Biology Health (CBH) Graduate School of University Grenoble Alpes (ANR-17-EURE-0003).</p>", "<title>Author contributions</title>", "<p>M.-A.H. supervised the research and coordinated the collaboration. A.V.A., M.S., C.S., D.C.F., A.B., M.G.R., D.C., C.C., A.B.H. and M.-A.H. designed, carried out and interpreted the experimental work. Y.C. and C.B. carried out the MS analyses. I.C. carried out transmission electron microscopy and interpreted the associated results. C.R. carried out confocal microscopy of infected cat intestines. M.-A.H. wrote the paper with editorial support from I.C. and comments from all other authors.</p>", "<title>Peer review</title>", "<title>Peer review information</title>", "<p id=\"Par58\"><italic>Nature</italic> thanks Laura Knoll, T. Siegel and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. ##SUPPL##2##Peer reviewer reports## are available.</p>", "<title>Data availability</title>", "<p>Nanopore and Illumina RNA-seq data that support the findings of this study have been deposited under the BioProject number <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA921935\">PRJNA921935</ext-link>. The ChIP–seq and ATAC–seq data have been deposited to the Gene Expression Omnibus database under accession numbers <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE222819\">GSE222819</ext-link> and <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE222832\">GSE222832</ext-link>, respectively. The MS proteomics data have been uploaded to the ProteomeXchange Consortium through the PRIDE partner repository with the dataset identifiers <ext-link ext-link-type=\"uri\" xlink:href=\"http://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD039400\">PXD039400</ext-link> and <ext-link ext-link-type=\"uri\" xlink:href=\"http://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD042658\">PXD042658</ext-link> for, respectively, the proteome-wide and interactome analyses. Processed proteomics data are available in Supplementary Table ##SUPPL##5##3##. <xref ref-type=\"sec\" rid=\"Sec42\">Source data</xref> are provided with this paper.</p>", "<title>Competing interests</title>", "<p id=\"Par59\">The authors declare no competing interests.</p>" ]	[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>Dual depletion of AP2XI-2 and AP2XII-1 induces merozoite-specific gene expression.</title><p><bold>a</bold>, <italic>T. gondii</italic> has a multistage life cycle. The enteroepithelial cycle begins when a cat ingests tissue cysts, initiating asexual replication of merozoites (morphotypes A–E) leading to production of macro-gametes (MaG) and micro-gametes (MiG). These form oocysts that sporulate in an oxygen-rich environment. After ingestion, released sporozoites develop into tachyzoites causing acute infection. Later, owing to host immunity, they convert into bradyzoites forming tissue cysts. Created with <ext-link ext-link-type=\"uri\" xlink:href=\"https://biorender.com\">BioRender.com</ext-link>. <bold>b</bold>, IFA images of infected cat intestine using GRA80 and GRA11b antibodies and rat serum; nuclei were counterstained with 4′,6-diamidino-2-phenylindole (DAPI). Scale bars, 25 μm (left column of images), 2 μm (top right four images) and 5 μm (bottom right three images). <bold>c</bold>–<bold>e</bold>, Expression of the merozoite markers GRA11b (<italic>TGME49_237800</italic>; <bold>c</bold>), GRA80 (<italic>TGME49_273980</italic>; <bold>d</bold>) and GRA81 (<italic>TGME49_243940</italic>; <bold>e</bold>) was quantified in tachyzoites in which MORC, 1 of 14 MORC-associated AP2 proteins or AP2XII-1 and AP2XI-2 were genetically disrupted. Cas9–GFP measures genetic disruption efficacy (Extended Data Fig. ##FIG##7##2f##). Horizontal bars represent mean ± s.d. of vacuolar proteins intensity from <italic>n</italic> = 3 (<bold>c</bold>,<bold>d</bold>) and <italic>n</italic> = 4 (<bold>e</bold>) independent experiments (<italic>n</italic> = 50 GFP<sup>+</sup> vacuoles per dot). The <italic>P</italic> values were determined using one-way analysis of variance (ANOVA) and Tukey’s test. PV, parasitophorous vacuole. <bold>f</bold>, Differential expression analysis was carried out with DESeq2 on raw rRNA-subtracted data, with Benjamini–Hochberg correction (<italic>P</italic> value threshold of 0.05). In the IAA (24 h)-induced double-KD parasites, 295 genes exhibited upregulation (log<sub>2</sub>[FC] > 2) and 195 genes exhibited downregulation (log<sub>2</sub>[FC] < −1), compared with untreated (UT) parasites. The heat map uses mean-centred data and <italic>k</italic>-means clustering (Pearson correlation) using iDEP.96, and shows log<sub>2</sub>-transformed data. Hierarchical clustering grouped genes and samples to elucidate expression patterns across different in vivo stages—merozoites, enteroepithelial stages (EESs) 1–5, tachyzoites, sporozoites and cysts—as documented in previous studies<sup>##REF##24885521##12##,##REF##25757795##13##,##REF##30728393##16##</sup>. We also examined these patterns in the context of in vitro MORC KD and HDAC3 inhibition with FR235222 (FR; ref. <sup>##REF##32094587##1##</sup>). Transcript abundance is shown as log<sub>2</sub>[FC] based on the log-transformed mean transcripts per million kilobases (TPM) values from three biological replicates. Magenta indicates upregulation, and green indicates downregulation.</p><p>##SUPPL##8##Source Data##</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>Co-depletion of AP2XI-2 and AP2XII-1 causes rewiring of organellar proteomes specialized in invasion and host–parasite interaction.</title><p><bold>a</bold>–<bold>c</bold>, Heat map showing hierarchical clustering analysis of selected microneme (<bold>a</bold>), rhoptry (<bold>b</bold>) and dense granule (<bold>c</bold>) mRNA transcripts and their corresponding proteins, which were significantly upregulated (log<sub>2</sub>[FC] > 2; <italic>P</italic> value < 0.05) or downregulated (log<sub>2</sub>[FC] < −1; <italic>P</italic> value < 0.05) following the simultaneous depletion of AP2XII-1 and AP2XI-2. The abundance of these transcripts is presented across different in vivo stages—merozoites, EES1–EES5 stages, tachyzoites, sporozoites and cysts—as documented in previous studies<sup>##REF##24885521##12##,##REF##25757795##13##,##REF##30728393##16##</sup>. Pertinent examples of tachyzoite and merozoite stage genes are highlighted in blue and red, respectively. Analysis parameters are those of Fig. ##FIG##0##1f##. <bold>d</bold>, Time-course western blot analysis of protein expression levels after depletion of AP2XII-1–mAID–HA and AP2XI-2–mAID–MYC. Samples were collected at the indicated time points after addition of IAA and probed with antibodies to HA, MORC, HDAC3, rhoptry proteins (ROP26 and BRP1), dense granule proteins (GRA11b and GRA80), the SRS48 family, a CK2 kinase (encoded by <italic>TGME49_307640</italic>) and the protein encoded by <italic>TGME49_306455</italic>. The experiment was repeated three times and a representative blot is shown. <bold>e</bold>,<bold>f</bold>, Maximum-intensity projection of a confocal microscopy <italic>z</italic>-stack from a meront in infected small intestine of a kitten. Antibodies to GRA11b (green) mark the dense granules and those to IMC1 (red) or GAP45 (magenta) mark the IMC of individual merozoites. Nuclei were counterstained with DAPI. Scale bars, 5 μm (<bold>e</bold>) and 2 μm (<bold>f</bold>).</p><p>##SUPPL##9##Source Data##</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><title>Zoites depleted of AP2XI-2 and AP2XII-1 undergo endopolygeny with karyokinesis.</title><p><bold>a</bold>–<bold>c</bold>,<bold>e</bold>–<bold>g</bold>, Electron micrographs of human foreskin fibroblast (HFF) cells infected with the RH (<italic>AP2XII-1</italic> and <italic>AP2XI-2</italic> KD) strain of <italic>T. gondii</italic>, untreated (<bold>f</bold>) or treated for 24 h (<bold>a</bold>–<bold>c</bold>,<bold>e</bold>,<bold>g</bold>) with IAA. <bold>a</bold>, Emphasis on karyokinesis with fission. n1 to n3, nuclear profiles; a, apicoplast; m, mitochondrion; mIMC, mother IMC; HC, host cell. The arrowhead highlights an area of nuclear fission. <bold>b</bold>, Emphasis on apicoplast multiplication by growth and scission. <bold>c</bold>, Emphasis on Golgi multiplication from either side of the nucleus (n). Go1 and Go2, two Golgi apparatus. <bold>d</bold>, IFA of tachyzoites (untreated) and zoites depleted of AP2XII-1 and AP2XI-2 (12 h post-IAA). GAP45 staining marks the mother cell and its progeny. IMC7 staining specifically marks the diploid (left) and polyploid (right) mother cell. Cells were stained with Hoechst DNA-specific dye. Type B meronts are marked in yellow. <bold>e</bold>, Emphasis on appearance and role of the IMC segregating daughter buds in the mother cytoplasm. mc, mother conoid; rh, rhoptry. The arrowheads show areas devoid of the IMC and the asterisks highlight areas of nuclear fission. <bold>f</bold>, Emphasis on contrasting endodyogeny in tachyzoites (untreated condition). Two daughter buds formed apically and symmetrically (arrows). <bold>g</bold>, Emphasis on endopolygeny showing up to eight daughter buds and ultrastructure of rhoptries. mrh, mother rhoptry. <bold>h</bold>,<bold>i</bold>, Tachyzoites (untreated) and merozoites depleted of AP2XII-1 and AP2XI-2 (24 h post-IAA) were fixed and stained for ROP26 (<italic>TGME49_209985</italic>, in red) and IMC7 (green; <bold>h</bold>) or IMC1 (red) and GAP45 (green) along with Hoechst DNA-specific dye (<bold>i</bold>). Yellow arrows indicate IMC7<sup>−</sup> mature merozoites, and type C meronts are shown. Scale bars, 2 μm (<bold>a</bold>), 500 nm (<bold>b</bold>,<bold>c</bold>,<bold>e</bold>–<bold>g</bold>), 5 μm (<bold>d</bold>,<bold>i</bold>) and 10 μm (<bold>h</bold>). G1, cell growth phase before DNA synthesis; S/M, DNA synthesis (S phase) and mitosis (M phase).</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><title>In vitro-induced merogony typified by the emergence of multiple zoite stages.</title><p><bold>a</bold>,<bold>b</bold>,<bold>e</bold>, Electron micrographs of RH (<italic>AP2XII-1</italic> and <italic>AP2XI-2</italic> KD)-infected HFFs treated for 24 h (<bold>a,b</bold>) or 48 h (<bold>e</bold>) with IAA. <bold>a</bold>, Emphasis on protruding daughters sharing the mother plasma membrane (mPM). DG, dense granule; dc, daughter conoid. <bold>b</bold>, Emphasis on daughter cell emergence. mi, microneme; ER, endoplasmic reticulum. <bold>c</bold>, Representative image of neatly aligned elongated merozoites and forming fan-like structures as they hatch from the mother cell. Mature merozoites are co-stained with GAP45 (green), IMC1 (red) and Hoechst DNA-specific dye (blue). <bold>d</bold>, Image of a giant schizont delineated by IMC7 (green) showing polyploidy (<italic>n</italic> = 16). The nuclear structure is co-stained with Hoechst DNA-specific dye and hyperacetylated histone H4 (H4ac; red). <bold>e</bold>, Emphasis on a large parasitophorous vacuole containing a mega meront with many daughter buds residing with merozoites in the same parasitophorous vacuole. <bold>f</bold>, Time-course analysis of marker expression following AP2XII-1 and AP2XI-2 co-depletion. Data represent mean ± s.d. of vacuole staining for GRA2<sup>+</sup>GRA80<sup>−</sup>, ROP26<sup>+</sup>, GRA11b<sup>+</sup>GRA80<sup>+</sup> or BAG1<sup>+</sup> from three experiments (<italic>n</italic> = 50 vacuoles per dot). Statistical evaluation was conducted using one-way ANOVA, followed by Tukey’s multiple comparison test. Graph at bottom: longitudinal tracking of tachyzoites and developmental morphotypes within vacuoles post-IAA treatment, integrating stage-specific aforementioned markers and parasite nuclei count (Hoechst and histone staining). Scale bars, 500 nm (<bold>a</bold>,<bold>b</bold>), 5 μm (<bold>c</bold>), 10 μm (<bold>d</bold>) and 2 μm (<bold>e</bold>).</p></caption></fig>", "<fig id=\"Fig5\"><label>Fig. 5</label><caption><title>AP2XII-1 and AP2XI-2 heterodimerize and interact with HDAC3 and MORC to form a repressive core complex.</title><p><bold>a</bold>, Flag immunoprecipitation (IP) eluates in HFF cells infected with wild-type parasite (mock) or parasites stably expressing HA–Flag-tagged AP2XII-1 or AP2XI-2 protein were examined through label-free quantitative proteomics using MS (three replicates per condition) and are represented as a volcano plot. Each dot represents a protein. Black dashed lines show −log<sub>10</sub>[<italic>P</italic> value] < 7 and log<sub>2</sub>[FC] = 2.5 cutoffs; proteins above these thresholds are coloured in orange, with specific proteins in blue, purple and green as indicated. Raw data and a detailed statistical analysis are shown in Supplementary Table ##SUPPL##6##4##. <bold>b</bold>, Flag affinity eluates were analysed by western blot to detect MORC and HDAC3. This was repeated three times, and a representative blot is shown. <bold>c</bold>, Purification scheme for AP2XI-2–Strep and AP2XII-1–Flag or (Strep)<sub>2</sub>–AP2XI-2 and AP2IX-6–Flag coexpressed in <italic>Trichoplusia ni</italic> (Hi-5) insect cells. Created with <ext-link ext-link-type=\"uri\" xlink:href=\"https://biorender.com\">BioRender.com</ext-link>. <bold>d</bold>, Co-purification of AP2XII-1–Flag through the pulldown of (Strep)<sub>2</sub>–AP2XI-2 by Strep-Tactin purification. <bold>e</bold>, AP2IX-6–Flag is not co-purified by (Strep)<sub>2</sub>–AP2XI-2. In <bold>d</bold>,<bold>e</bold>, (Strep)<sub>2</sub>–AP2XI-2 was detected using an in-house anti-AP2XII-2 antibody (Ab) and AP2XII-1–Flag or AP2IX-6–Flag was detected using an anti-Flag antibody. Input, soluble fraction (SF), flow-through (FT), high-salt wash (W2) and final wash (W3) deposits were deposited using 8 μl whereas eluted fractions (E1 to E8), separated by the protein molecular weight marker (PW) were deposited using 15 μl. Experiments were carried out at least twice for <bold>d</bold>,<bold>e</bold>. <bold>f</bold>, Strep-Tactin XT-purified (Strep)<sub>2</sub>–AP2XI-2 and AP<sub>2</sub>XII-1–Flag proteins were fractionated on a Superose 6 Increase gel filtration column. Input (concentrated Strep-Tactin elution) and gel filtration fractions were separated by SDS–polyacrylamide gel electrophoresis and analysed by both western blots using anti-Flag and in-house anti-AP2XII-2 antibodies and colloidal blue staining. Fraction numbers are indicated at the top of the gel. A.U. at 280 nm, absorbance units at 280 nm. <bold>g</bold>, MS-based proteomic analyses of fraction (F) 10 and fraction 14. Number of identified peptides (Pept.) and intensity-based absolute quantification (iBAQ) values are indicated (Supplementary Table ##SUPPL##7##5##).</p><p>##SUPPL##10##Source Data##</p></caption></fig>", "<fig id=\"Fig6\"><label>Fig. 6</label><caption><title>Chromatin recruitment of HDAC3 and MORC by AP2XII-1 and AP2XI-2.</title><p><bold>a</bold>, Heat map and profile analysis of the ChIP peak intensity for AP2XII-1 in parasites, either untreated or following co-depletion of AP2XII-1 and AP2XI-2. <italic>k</italic>-means clustering applied to the untreated sample shows a centred enrichment of AP2XII-1 at the TSS in genes from cluster 2. <bold>b</bold>, Profile comparison of AP2XII-1 ChIP peaks in untreated single- or double-KD strains, centred at the TSS (±8 kb) within cluster 2 genomic loci. <bold>c</bold>, Superposed profile plots indicate AP2XII-1, AP2XI-2, HDAC3 and MORC co-enrichment around the TSS of cluster 2 genes in the untreated state. The profile plots in <bold>a</bold>–<bold>c</bold> were generated by Deeptools using summed occupancies over a bin size of 10. <bold>d</bold>, Integrated Genome Browser view illustrates AP2XII-1, AP2XI-2, MORC and HDAC3 enrichment and ATAC–seq chromatin accessibility before and after simultaneous <italic>AP2XII-1</italic> and <italic>AP2XI-2</italic> KD. The <italic>y</italic> axis shows the read density. <bold>e</bold>, HOMER analysis reveals global distribution of significant peaks within genomic features for AP2XI-2, AP2XII-1, HDAC3 and MORC ChIP experiments. TTS, transcription termination site. <bold>f</bold>, Profile and heat maps of summed occupancy using bin sizes of 10 show Tn5 transposase accessibility (ATAC–seq) for parasite genes centred at the TSS (±1 kb) in untreated and IAA-treated samples. High read intensity is in blue, with average signal profiles plotted above. <bold>g</bold>,<bold>h</bold>, The Tn5 transposase accessibility plot predicts nucleosomal occupancy. Mean occupancy profiles with a bin size of 10 of untreated and IAA-treated ATAC–seq signals across downregulated (<italic>n</italic> = 226) and upregulated genes (<italic>n</italic> = 281) show a nucleosome-depleted region (NDR) at the TSS and enriched, phased mono-nucleosome fragments at surrounding regions. <bold>i</bold>, Integrated Genome Browser screenshots of representative merozoite and tachyzoite genes highlight ChIP–seq signal occupancy for HA, MYC, MORC and HDAC3, in untreated and post-depletion conditions. RNA-seq data at different induction times and Tn5 transposase accessibility profiles for both conditions are also shown.</p></caption></fig>", "<fig id=\"Fig7\"><label>Extended Data Fig. 1</label><caption><title>MORC binds to telomere and its depletion leads to subtelomeric non-coding RNAs reactivation and cell cycle disruption.</title><p><bold>a</bold>, IGB views of MORC and HDAC3 ChIP-seq enrichment at chromosomal ends following MORC or AP2XII-1/AP2XI-2 depletion. Read density is on the y-axis, with telomeric repeats (TTTAGGG) marked. <bold>b</bold>, Nanopore direct RNA sequencing (DRS) read alignment of initially suppressed non-coding RNAs, observable post-MORC knockdown via IAA on the subtelomeric ends of chromosome III and XII. The y-axis shows read-depth. Positive strand reads are colored in magenta while negative strand reads are colored in blue. <bold>c</bold>, Expression levels of MORC over time are presented through IFA on cells infected with RH MORC–mAID–HA. Cells were fixed, permeabilized, and probed with HA antibodies (green) and Hoechst DNA-specific dye.</p></caption></fig>", "<fig id=\"Fig8\"><label>Extended Data Fig. 2</label><caption><title>Evaluating stage-specific markers both in vitro and in vivo across varied genetic backgrounds.</title><p><bold>a</bold>, Domain architectures of MORC AP2 partners, originally identified via immunoprecipitation and MS-proteomics, showcasing the AP2 (APETALA2) and ACDC (AP2-Coincident Domain primarily at the Carboxy-terminus) domains, as predicted by SMART and PFAM. <bold>b</bold>, Representative vacuoles of ΔBFD1/DD-BFD1-Ty parasites grown for 48 h with vehicle or 3 μM Shield-1, stained for Ty or DBA (red) and GRA80 (green). The graph on the right quantifies results (n = 50 vacuoles/dot), statistical analysis performed using one-tailed Student’s t-test. Data are presented as mean values ± s.d. <bold>c</bold>, DBA staining (red) highlighted the glycosylated cyst wall in brain sections of mice chronically infected with ME49 type II strain. The sections were counterstained against GRA80 (green) and Hoechst DNA-specific dye. <bold>d</bold>, IFA on HFFs infected with RH MORC KD lineage harboring mCherry-tagged GRA81 (<italic>TGME49_243940</italic>), a merozoite protein. UT and IAA-treated zoites were probed for GRA11b (green) and mCherry (red), and stained with Hoechst DNA-specific dye. <bold>e</bold>, Confocal microscopy of a meront in infected small intestine of a kitten co-stained with anti-GRA11b (cyan) and anti-GRA82 (red) antibodies, with DAPI employed for nuclear counterstaining. <bold>f</bold>, Representative images of intracellular parasites with disrupted AP2 genes due to transient CRISPR/Cas9 plasmid transfection. Cas9-GFP expression (green) indicates disruption, while merozoite marker GRA80 (red) is monitored in AP2 inactivated (GFP-positive) zoites. <bold>g</bold>, Expression levels of AP2XII-1 and AP2XI-2 in the single and double KD strains were monitored over time using Western blot. Post-IAA addition, samples were collected at the indicated time points and probed with HA, MYC, and HDAC3 antibodies. This was repeated three times, and a representative blot is displayed.</p><p>\n##SUPPL##11##Source Data##\n</p></caption></fig>", "<fig id=\"Fig9\"><label>Extended Data Fig. 3</label><caption><title>Quantification of stage-specific markers expression levels in the context of AP2XII-1/AP2XI-2 co-depletion, MORC depletion and BDF1 overexpression.</title><p><bold>a-b</bold>, MORC and single or double AP2XII-1/AP2XI-2 KD parasites, untreated or IAA-treated (24 and 48 h), were compared with ΔBFD1/DD-BFD1-Ty parasites treated with vehicle or 3 μM Shield-1 (48 h). Parasites were stained with GRA11b (red) and GRA80 (green) antibodies, and Hoechst DNA-specific dye. Representative images of double KD vacuoles are shown on the left (<bold>a</bold>) the right graph (<bold>b</bold>) displays quantified results (n = 50 vacuoles/dot), analyzed using one-way ANOVA and Tukey’s test. Data are presented as mean values ± s.d. <bold>c</bold>, Levels of merozoite markers after AP2VIIa-3, AP2VIII-4, and AP2VIII-7 depletion were compared with those after AP2XII-1/AP2XI-2 co-depletion (48 h). Displayed data represent mean ± s.d. of GRA11b(+)/GRA80(+) vacuole staining from three experiments (n = 50 vacuoles/dot). Statistics involved one-way ANOVA and Tukey’s multiple comparison test. <bold>d</bold>-<bold>e</bold>, MORC and single or double AP2XII-1/AP2XI-2 KD parasites, untreated or IAA-treated (24 and 48 h) were co-stained with GRA11b (red) and GRA82 (green) antibodies. Representative images of double KD vacuoles are shown on the left (<bold>d</bold>) the right graph (<bold>e</bold>) displays quantified results (n = 50 vacuoles/dot), analyzed using one-way ANOVA and Tukey’s test. Data are presented as mean values ± s.d. <bold>f-g</bold>, Representative vacuoles of AP2XII-1/AP2XI-2 KD parasites, untreated or IAA-treated (48 h) (<bold>f</bold>), stained for GRA2 (red) and GRA80 (green); the graph on the right (<bold>g</bold>) quantifies results (n = 50 vacuoles/dot, 3 replicates/conditions), using one-tailed Student’s t-test. <bold>h</bold>-<bold>j</bold>, Representative images of MORC, double AP2XII-1/AP2XI-2 KD, and ΔBFD1/DD-BFD1-Ty treated with respective inducers and stained with DBA and bradyzoite markers BAG1 (red) or BCLA (green) (<bold>h</bold>). Graphs show % of BAG1 (<bold>i</bold>) or BCLA (<bold>j</bold>) positive vacuoles across three experiments (n = 50 vacuoles/dot), statistical analysis by one-way ANOVA and Tukey’s test. Data are presented as mean values ± s.d. <bold>k</bold>, IAA-induced (24 h) vacuoles of MORC KD parasites containing GRA81 tagged with mCherry (in red), co-stained with a bradyzoite marker (BCLA, green), and Hoechst DNA-specific dye.</p></caption></fig>", "<fig id=\"Fig10\"><label>Extended Data Fig. 4</label><caption><title>Co-depletion of AP2XI-2 and AP2XII-1 induces the expression of merozoite proteins, including a large repertoire of parasite surface proteins.</title><p><bold>a</bold>, Principal Component Analysis (PCA) of mRNA sequencing data from biological triplicates of single KD or double KD parasites. Samples were collected from untreated conditions or after 24 or 48 h of IAA treatment. <bold>b</bold>, Venn diagram illustrating the overlapping genes that were upregulated in the three IAA-treated knockdown strains. Significant genes (FC > 8) were identified using DESeq2 with an independent-hypothesis-weighted approach and Benjamini–Hochberg false discovery rate (FDR) < 0.1. <bold>c</bold>, M-pileup representation of aligned Nanopore DRS reads at genes differentially expressed following IAA-induced knockdown of AP2XII-1 and AP2XI-2 individually or in combination. <bold>d</bold>, PCA illustrates the biological and technical variance between triplicate proteome samples extracted after 24-, 32-, and 48-hours post AP2XII-1/AP2XI-2 knockdown induction, juxtaposed with the untreated sample (UT). <bold>e</bold>, Histogram delineating the distribution of up- and down-regulated proteins (n = 276 and 285, respectively) post AP2XII-1 and AP2XI-2 knockdown, categorized by their life stage association. <bold>f</bold>, Representative vacuoles of <italic>ΔBFD1</italic>/DD-BFD1-Ty parasites grown for 48 h with vehicle or 3 μM Shield-1, stained for ROP26 (green), DBA (red) and Hoechst DNA-specific dye (blue). <bold>g-h</bold>, Heat map showing hierarchical clustering analysis of selected SRS (<bold>g</bold>) and Family A (<bold>h</bold>) mRNA transcripts and their corresponding proteomic enrichments, which were significantly upregulated (Log2 FC > 2; <italic>P</italic>-value < 0.01) or downregulated (Log2 FC < −1; <italic>P</italic>-value < 0.01) following the simultaneous depletion of AP2XII-1 and AP2XI-2. The abundance of these transcripts is presented across different in vivo stages - merozoites, EES1-EES5 stages, tachyzoites, sporozoites, and cysts, as documented in prior studies<sup>##REF##24885521##12##,##REF##25757795##13##,##REF##30728393##16##</sup>. Analysis parameters are those of Fig. ##FIG##0##1f##. <bold>g</bold>, Created with <ext-link ext-link-type=\"uri\" xlink:href=\"https://biorender.com\">BioRender.com</ext-link>.</p></caption></fig>", "<fig id=\"Fig11\"><label>Extended Data Fig. 5</label><caption><title>In vitro development of pre-gametes stages.</title><p><bold>a</bold>, Infectivity of the RH_AP2XII-1-mAID-HA/AP2XI-2-mAID-MYC strain was assessed using plaque assays, comparing untreated to IAA-treated for 7 days. Statistical significance was evaluated using Mann-Whitney. n.d. = not detected. <bold>b-d</bold>, IFA of tachyzoites (UT) and AP2XII-1/AP2XI-2-depleted zoites (at indicated time post-IAA) were fixed and stained with (<bold>b</bold>) antibodies of AtRX clone 11G8 (red) and IMC7 (green), (<bold>c</bold>) IMC7 or pan-acetylated histone H4 (red) and GAP45 or IMC7 (green), (<bold>d</bold>) IMC1 (red) and GAP45 (green). The cells were co-stained with DNA-specific Hoechst dye (white or blue). Morphotypes B, C and D meronts are highlighted in yellow. <bold>e</bold>, An electron micrograph of RH (AP2XII-1 KD/AP2XI-2 KD)-infected HFFs treated with IAA for 24 h shows an advanced stage of daughter individualization, characterized by polarized inner membrane complex (IMC) and apical conoid. <bold>f</bold>, Untreated tachyzoites (UT) and merozoites depleted of AP2XII-1/AP2XI-2 (24 h post-IAA) were marked with H3K9me3 (red), IMC7 (green), and Hoechst DNA-specific dye. Yellow and white arrows respectively highlight mother and daughter conoids. <bold>g</bold>, Fully formed merozoites (24 h post-IAA) were stained with GAP45 (green) and DNA-specific Hoechst dye. <bold>h</bold>, An electron micrograph of IAA-treated parasites shows fully formed merozoites aligned in the PV with apex directed towards the PV membrane. Go: Golgi apparatus, rh: rhoptry, mi: microneme, n: nucleus (plus posterior that in tachyzoite), PLVAC: plant-like vacuolar compartment, dc: daughter conoid, IMC: inner membrane complex, DG: dense granule, Ac: acidocalcisome, LD: lipid droplet, ER: endoplasmic reticulum, hER: host endoplasmic reticulum, m: mitochondrion, hm: host mitochondrion.</p></caption></fig>", "<fig id=\"Fig12\"><label>Extended Data Fig. 6</label><caption><title>Mature merozoites and their special substructural features.</title><p><bold>a</bold>, AP2XII-1/AP2XI-2-depleted meronts (24 h post-IAA) were fixed and stained with ROP26 (red), GRA11b (green) and Hoechst DNA-specific dye. Yellow arrows indicate fully developed merozoites. (<bold>b, d, e, g, h</bold>) Electron micrograph images of RH (AP2XII-1 KD/AP2XI-2 KD)-infected HFFs treated for 48 h with IAA. <bold>b</bold>, Emphasis on changes in body shape of merozoite (sausage). <bold>c</bold>, AP2XII-1/AP2XI-2-depleted type E meronts (48 h post-IAA) were fixed and stained with IMC7 (red), GAP45 (green) and Hoechst DNA-specific dye. <bold>d</bold>, Emphasis on conoid extrusion and same organelle content as in tachyzoite. n: nucleus, Go: Golgi apparatus, rh: rhoptry, m: mitochondrion, mi: microneme, DG: dense granule, LD: lipid droplet, mp: micropore, PLVAC: plant-like vacuolar compartment. Red circles showing extruded conoid. <bold>e</bold>, Emphasis on two other morphological transformations of merozoites, either very large (asterisks) or tubular (arrows) in PV with large lumen. <bold>f</bold>, AP2XII-1/AP2XI-2-depleted type E meronts (48 h post-IAA) were fixed and stained with IMC1 (red), GAP45 (green) and Hoechst DNA-specific dye. <bold>(g</bold> and <bold>h</bold>) Emphasis on thin parasitic forms (arrows) containing mitochondria and ribosomes. m: mitochondrion.</p></caption></fig>", "<fig id=\"Fig13\"><label>Extended Data Fig. 7</label><caption><title>Microscopic analysis of endopolygeny post-AP2XII-1/AP2XI-2 co-depletion.</title><p><bold>a</bold>, Untreated tachyzoites (UT) and zoites depleted of AP2XII-1/AP2XI-2 (24 h post-IAA) are stained with HA (red) and MYC (green) to identify AP2XII-I-mAID-HA and AP2XI-2-mAID-MYC respectively. DNA staining was accomplished using Hoechst dye. Polyploid meronts are indicated by a yellow arrow. <bold>b</bold>, Centrin distribution (red) within the dividing mother fibroblast cell. <bold>c</bold>, Untreated tachyzoites (UT) and AP2XII-1/AP2XI-2-depleted polyploid zoites, 24 h post-IAA treatment, stained with centrin (red), GAP45 (green), and Hoechst DNA-specific dye. <bold>d</bold>, Detailed observation of the diversity in pre-gamete stages 16 h after simultaneous depletion of AP2XII-1 and AP2XI-2. Both zoite and host cell nuclei are marked with pan-acetylated histone H4 (red) and Hoechst DNA-specific dye (blue), whereas polyploid meronts are identified by IMC7 staining. Red arrows designate completely developed merozoites, while morphotypes A and B are highlighted in orange and red, respectively.</p></caption></fig>", "<fig id=\"Fig14\"><label>Extended Data Fig. 8</label><caption><title>Contribution of AP2XII-1 and AP2XI-2 to MORC and HDAC3 recruitment on chromatin.</title><p><bold>a</bold>, Heatmap and profile analysis of APXII-1 (HA), AP2XI-2 (MYC), HDAC3, and MORC ChIP-seq peak intensity in double KD parasites, comparing untreated (UT) to co-depleted (24 h post-IAA) conditions. Density profiles centered at TSS (± 8 kb) and heat maps of peak density are shown, with color scales indicating signal intensity. <bold>b-d</bold>, Comparison of chip peak profiles in cluster 2 genomic loci (as defined in Fig. ##FIG##5##6a##), centered at TSS (± 8 kb), for (<bold>b</bold>) AP2XI-2 (HA or MYC antibody) in single AP2XI-2 KD or double knockdown in absence of auxin treatment; (<bold>c</bold>) AP2XII-1 (HA), HDAC3, and MORC in AP2XII-1 KD; (<bold>d</bold>) AP2XI-2 (HA), HDAC3, and MORC in AP2XI-2 KD. <bold>e</bold>, Heatmap and profile analysis of APXII-1 (HA), AP2XI-2 (MYC), HDAC3, and MORC ChIP-seq peak intensity in single KD parasites, comparing untreated (UT) to depleted (24 h post-IAA) conditions. Average signal profiles centered at TSS (± 8 kb) and heat maps of peak density are shown, with color scales indicating signal intensity. For all panels, Deeptools analysis used summed occupancies within a bin size of 10.</p></caption></fig>", "<fig id=\"Fig15\"><label>Extended Data Fig. 9</label><caption><title>Representative merozoite genes and their regulation by AP2XII-1 and AP2XI-2 and their repressive partners MORC and HDAC3.</title><p><bold>a</bold>, HOMER analysis reveals global distribution of significant Tn5 accessibility peaks across all genomic features in the AP2XII-1/AP2XI-1 double knockdown strain. Similar profiles were observed between untreated and treated conditions. <bold>b</bold>, Comparison profile of ChIP-seq summed occupancies over a bin size of 10 for AP2XI-2 (HA) and Tn5 accessibility density at all gene loci centered at TSS (± 3 kb) in the AP2XII-1/AP2XI-2 double knockdown strain without auxin treatment. <bold>(c-e)</bold>, IGB screenshots illustrate representative genomic regions containing merozoite genes, displaying ChIP-seq signal occupancy, ATAC-seq chromatin accessibility profiles, and nanopore DRS data, similar to Fig. ##FIG##5##6i##.</p></caption></fig>", "<fig id=\"Fig16\"><label>Extended Data Fig. 10</label><caption><title>AP2XI-2 can bind to chromatin even in the absence of AP2XII-1.</title><p><bold>a</bold>, AP2XII-1 (HA) and AP2XI-2 (MYC) expression levels and subcellular localization were assessed using IFA following AP2XII-1 depletion. Hoechst DNA-specific dye was used for counterstaining. <bold>b</bold>, Heatmap and profile analysis of APXII-1 (HA) and AP2XI-2 (MYC) ChIP-seq mean occupancy over a bin size of 10 in APXII-1 KD parasites with AP2XI-2-MYC knock-in (KI), comparing untreated (UT) and co-depleted (24 h post-IAA) conditions. Average signal profiles centered at TSS ( ± 8 kb) and heat maps of peak density display signal intensity. <bold>c</bold>, IGB screenshots depict the enrichment of AP2XII-1 and AP2XI-2 at the GRA80 and GRA81 loci in the context of AP2XII-1 single knockdown. <bold>d</bold>, M-pileup representation of aligned Nanopore DRS reads at genes up-regulated following IAA-induced knockdown of AP2XII-1 and AP2XI-2 individually or in combination. <bold>e</bold>, IGB screenshot highlighting the genomic region of the merozoite-specific purine nucleoside phosphorylase (PNP) gene, which is up-regulated upon IAA treatment independently of MORC and HDAC3. <bold>f</bold>, IGB screenshots displaying the genomic regions of the rhoptry genes (ROP16, ROP18) and the microneme gene (MIC1), demonstrating their repression in IAA-treated parasites. <bold>g</bold>, IGB screenshots showcasing the AMA1 gene family. (<bold>e-g</bold>) ChIP-seq signal occupancy, ATAC-seq chromatin accessibility profiles, and nanopore DRS are visualized in a manner consistent with Fig. ##FIG##5##6i##.</p></caption></fig>", "<fig id=\"Fig17\"><label>Extended Data Fig. 11</label><caption><title>AP2XII-1 and AP2XI-2 co-depletion induces a downstream network of secondary transcription factors to guide merogony.</title><p><bold>a</bold>, IGB screenshots displaying the genomic regions of the rhoptry genes (RON4, ROP39, ROP5) repressed in IAA-treated parasites in an AP2XII-1/AP2XI-2-independent manner. ChIP-seq signal occupancy, ATAC-seq chromatin accessibility profiles, and nanopore DRS are visualized in a manner consistent with Fig. ##FIG##5##6i##. <bold>b</bold>, Heat map showing hierarchical mRNA clustering analysis (Pearson correlation) of AP2 TFs regulated by simultaneous depletion of AP2XII-1 and AP2XI-2. Shown is the abundance of their transcripts at different developmental stages, namely merozoites, EES, tachyzoites, and cysts. The color scale indicates the log2-transformed fold changes. <bold>c-d</bold>, IGB screenshots of genomic regions with secondary transcription factors, including C2H2 Zinc Finger and AP2s, exhibiting activated expression in IAA-treated parasites. Displayed are ChIP-seq signal occupancy, ATAC-seq chromatin accessibility profiles, and nanopore DRS data, following the same representation as in Fig. ##FIG##5##6i##.</p></caption></fig>", "<fig id=\"Fig18\"><label>Extended Data Fig. 12</label><caption><title>Modeling of the <italic>modus operandi</italic> of AP2XII-1/AP2XI-2.</title><p><bold>a</bold>, Inspired by a 2.2 A crystal structure (pdb: 3IGM), the model shows that AP2XII-1 and AP2XI-2 can interact with DNA through either homodimeric or heterodimeric forms. These configurations influence DNA looping by stabilizing or forming domain-swapped dimers. <bold>b</bold>, AP2XI-2 and AP2XII-1 together fully silence merozoite gene expression (red light). A single KD only partially represses gene expression (orange light). When both are knocked down, genes are fully expressed (green light). Even with one AP2 missing, the remaining one can form a homodimer to partially repress transcription (Extended Data Fig. ##FIG##15##10c, d##). We hypothesize that each AP2 forms a fragile but stable homodimer, allowing mild gene expression due to easy disassociation from DNA. In contrast, heterodimers bind strongly to DNA, resulting in complete gene silencing. <bold>c</bold>, MORC/HDAC3 forms multiple partnerships with primary AP2, keeping chronic and sexual-stage genes in a persistently repressed chromatin state. The repressor complex drive the hierarchical expression of secondary AP2s, which may enforce the unidirectionality of the life cycle by influencing the gene expression of their respective stages. In tachyzoites, genes specific to this stage (<italic>SAG1</italic>) are expressed, while those related to merozoites (<italic>GRA11b</italic>) are repressed by AP2XII-1/AP2XI-2. Other primary AP2s help in recruiting MORC to other stage-specific genes, such as those for sporozoites and bradyzoites, to silence their expression. Additionally, MORC has a non-genic function where it binds to telomeric repeats, possibly facilitating silencing at the chromosome ends (Extended Data Fig. ##FIG##6##1##). Co-depletion of AP2XII-1/AP2XI-2 trigger merozoite gene activation, speeding up merogony. Secondary AP2s like AP2IX-1 then emerge, repressing tachyzoite-specific genes like SAG1. This second wave of TFs could also act as activators for pre-sexual genes not directly controlled by the primary AP2s (e.g., <italic>PNP</italic>; Extended Data Fig. ##FIG##15##10e##), offering an alternative route to merozoite development. The MORC/HDAC3-containing complexes maintain stage-specific and telomeric gene silencing, demonstrating their selective roles. <bold>a</bold>–<bold>c</bold>, Created with <ext-link ext-link-type=\"uri\" xlink:href=\"https://biorender.com\">BioRender.com</ext-link>.</p></caption></fig>" ]	[]	[]	[]	[]	[]	[]	[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM3\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM4\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM5\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM6\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM7\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM8\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM9\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM10\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM11\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM12\"></supplementary-material>" ]	[ "<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>These authors contributed equally: Ana Vera Antunes, Martina Shahinas, Christopher Swale</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"41586_2023_6821_Fig1_HTML\" id=\"d32e475\"/>", "<graphic xlink:href=\"41586_2023_6821_Fig2_HTML\" id=\"d32e816\"/>", "<graphic xlink:href=\"41586_2023_6821_Fig3_HTML\" id=\"d32e1069\"/>", "<graphic xlink:href=\"41586_2023_6821_Fig4_HTML\" id=\"d32e1222\"/>", "<graphic xlink:href=\"41586_2023_6821_Fig5_HTML\" id=\"d32e1449\"/>", "<graphic xlink:href=\"41586_2023_6821_Fig6_HTML\" id=\"d32e1550\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6821_Fig7_ESM\" id=\"d32e2153\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6821_Fig8_ESM\" id=\"d32e2194\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6821_Fig9_ESM\" id=\"d32e2258\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6821_Fig10_ESM\" id=\"d32e2327\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6821_Fig11_ESM\" id=\"d32e2366\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6821_Fig12_ESM\" id=\"d32e2405\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6821_Fig13_ESM\" id=\"d32e2428\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6821_Fig14_ESM\" id=\"d32e2461\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6821_Fig15_ESM\" id=\"d32e2484\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6821_Fig16_ESM\" id=\"d32e2523\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6821_Fig17_ESM\" id=\"d32e2550\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6821_Fig18_ESM\" id=\"d32e2608\"/>" ]	[ "<media xlink:href=\"41586_2023_6821_MOESM1_ESM.pdf\"><label>Supplementary Information</label><caption><p>Legends for Supplementary Tables 1–5.</p></caption></media>", "<media xlink:href=\"41586_2023_6821_MOESM2_ESM.pdf\"><caption><p>Reporting Summary</p></caption></media>", "<media xlink:href=\"41586_2023_6821_MOESM3_ESM.pdf\"><caption><p>Peer Review File</p></caption></media>", "<media xlink:href=\"41586_2023_6821_MOESM4_ESM.xlsx\"><caption><p>Supplementary Table 1</p></caption></media>", "<media xlink:href=\"41586_2023_6821_MOESM5_ESM.xlsx\"><caption><p>Supplementary Table 2</p></caption></media>", "<media xlink:href=\"41586_2023_6821_MOESM6_ESM.xlsx\"><caption><p>Supplementary Table 3</p></caption></media>", "<media xlink:href=\"41586_2023_6821_MOESM7_ESM.xlsx\"><caption><p>Supplementary Table 4</p></caption></media>", "<media xlink:href=\"41586_2023_6821_MOESM8_ESM.xlsx\"><caption><p>Supplementary Table 5</p></caption></media>", "<media xlink:href=\"41586_2023_6821_MOESM9_ESM.xlsx\"><caption><p>Source Data Fig. 1</p></caption></media>", "<media xlink:href=\"41586_2023_6821_MOESM10_ESM.xlsx\"><caption><p>Source Data Fig. 2</p></caption></media>", "<media xlink:href=\"41586_2023_6821_MOESM11_ESM.pdf\"><caption><p>Source Data Fig. 5</p></caption></media>", "<media xlink:href=\"41586_2023_6821_MOESM12_ESM.xlsx\"><caption><p>Source Data Extended Data Fig. 2</p></caption></media>" ]	[{"label": ["9."], "surname": ["Colley", "Zaman"], "given-names": ["FC", "V"], "article-title": ["Observations on the endogenous stages of "], "italic": ["Toxoplasma gondii"], "source": ["Southeast Asian J. Trop. Med. Public Health"], "year": ["1970"], "volume": ["1"], "fpage": ["465"], "lpage": ["480"]}, {"label": ["11."], "surname": ["Ferguson"], "given-names": ["DJ"], "article-title": ["Ultrastructural study of early stages of asexual multiplication and microgametogony of "], "italic": ["Toxoplasma gondii"], "source": ["Acta Pathol. Microbiol. Scand. B"], "year": ["1974"], "volume": ["82"], "fpage": ["167"], "lpage": ["181"]}, {"label": ["31."], "mixed-citation": ["Ferguson, D. J. and Dubremetz, J. F. in "], "italic": ["Toxoplasma gondii, the Model Apicomplexan - Perspectives and Methods"]}, {"label": ["42."], "mixed-citation": ["Zhong, Z. et al. MORC proteins regulate transcription factor binding by mediating chromatin compaction in active chromatin regions. "], "italic": ["Genome Biol."], "bold": ["24"]}, {"label": ["46."], "mixed-citation": ["Wieczorek, S. et al.\u00a0DAPAR & ProStaR: software to perform statistical analyses in quantitative discovery proteomics. "], "italic": ["Bioinformatics"], "bold": ["33"]}]	{ "acronym": [], "definition": [] }	49	CC BY	no	2024-01-13 00:02:19	Nature. 2024 Dec 13; 625(7994):366-376	oa_package/67/42/PMC10781626.tar.gz
PMC10781627	38200295	[]	[ "<title>Methods</title>", "<title>Generation and authentication of ancient DNA data</title>", "<p id=\"Par31\">Sampling of ancient human remains was undertaken in collaboration with co-authors responsible for the curation and contextual analyses of these, and with the approval of the relevant institutions responsible for the archaeological remains (detailed in the Reporting Summary). Laboratory work was undertaken in dedicated ancient DNA clean-lab facilities (Globe Institute, University of Copenhagen) following optimized ancient DNA protocols<sup>##REF##26062507##1##,##REF##26081994##77##</sup> (Supplementary Note ##SUPPL##0##1##). Double-stranded blunt-end libraries were constructed from the extracted DNA using NEBNext DNA Prep Master Mix Set E6070 (New England Biolabs) and sequenced (80 bp and 100 bp single read) on Illumina HiSeq 2500 and 4000 platforms. Initial shallow shotgun screening identified 317 of 962 ancient samples with sufficient DNA preservation for deeper sequencing. Of these, 211 were teeth, 91 were petrous bones and 15 were sampled from long bones, ribs and cranial bones (Supplementary Data ##SUPPL##3##2##). Reads were mapped to the human reference genome build 37 and also to the mitochondrial genome (rCRS) alone. Mapped reads were filtered for mapping quality 30 and sorted using Picard (v.1.127) (<ext-link ext-link-type=\"uri\" xlink:href=\"http://picard.sourceforge.net\">http://picard.sourceforge.net</ext-link>) and SAMtools<sup>##REF##19505943##78##</sup>. Data were merged to library level and duplicates were removed using Picard MarkDuplicates (v.1.127) and merged to sample level. Sample-level BAMs were re-aligned using GATK (v.3.3.0) and hereafter had the md-tag updated and extended BAQs calculated using samtools calmd (v.1.10)<sup>##REF##19505943##78##</sup>. Read depth and coverage were determined using pysam (<ext-link ext-link-type=\"uri\" xlink:href=\"https://github.com/pysam-developers/pysam\">https://github.com/pysam-developers/pysam</ext-link>) and BEDtools (v.2.23.0)<sup>##REF##20110278##79##</sup>. Post-mortem DNA damage patterns were determined using mapDamage2.0 (ref. <sup>##REF##23613487##80##</sup>). For the 317 samples we observed C-to-T deamination fractions ranging from 10.4% to 67.8%, with an average of 38.3% across all samples (Supplementary Data ##SUPPL##2##1##). These numbers indicate DNA-molecule degradation consistent with a millennia-scale depositional age. Three methods were used to estimate DNA contamination: two based on mitochondrial sequences<sup>##REF##26458810##81##,##REF##25341783##82##</sup> and one method investigating X-chromosomal data in males (ANGSD, Supplementary Note ##SUPPL##0##1##). All contamination estimates are reported in Supplementary Data ##SUPPL##4##5## (summary values in Supplementary Data ##SUPPL##2##1##). On the basis of this approach, we had a total of 15 samples flagged as ‘possibly contaminated’ in our downstream analyses (Supplementary Note ##SUPPL##0##1##).</p>", "<title>Imputation of ancient genomes</title>", "<p id=\"Par32\">We imputed the ancient genomes in this study using the imputation and phasing tool GLIMPSE v.1.0.0 (ref. <sup>##REF##33473199##35##</sup>) and 1000 Genomes phase 3 (ref. <sup>##REF##26432245##36##</sup>) as a reference panel. We first generated genotype likelihoods at the biallelic 1000 Genomes variant sites from the bam files with bcftools v.1.10 and the command bcftools mpileup with parameters -I -E -a ‘FORMAT/DP’ --ignore-RG, followed by bcftools call -Aim -C alleles. Using GLIMPSE_chunk, the genotype likelihood data were first split into chunks of sizes between 1 and 2 Mb with a buffer region of 200 kb at each side. We then imputed each chunk with GLIMPSE_phase with parameters --burn 10, --main 15 and --pbwt-depth 2. Finally, the imputed chunks were ligated with GLIMPSE_ligate. To validate the accuracy of the imputation, 42 high-coverage (5× to 39×) genomes, including a Neolithic trio, were downsampled for testing<sup>##REF##37339987##83##</sup> (Supplementary Note ##SUPPL##0##2##). We evaluated imputation accuracy on the basis of depth of coverage; MAF; and ancestry and time frame of ancient genomes, using high-coverage ancient genomes<sup>##REF##37339987##83##</sup>. Genomes with higher than 1× coverage provided a notably high imputation accuracy (closely matching that obtained for modern samples; Extended Data Fig. ##FIG##7##2##), except for African genomes, which had lower accuracy owing to the poor representation of this ancestry in the reference panel. Imputation accuracy was influenced by both MAF and coverage (Supplementary Fig. ##SUPPL##0##2.3##). We found that coverage as low as 0.1× and 0.4× was sufficient to obtain <italic>r</italic><sup>2</sup> imputation accuracies of 0.8 and 0.9 at common variants (MAF ≥ 10%), respectively. We conclude that ancient genomes can be imputed confidently from coverages above 0.4×, and that genome-wide aggregate analyses relying on common SNPs (for example, PCA and admixture modelling) can be performed with a low amount of bias for genome coverage from as low as 0.1× when using specific quality control on the imputed data (although at very low coverage a bias arises towards the major allele; see Supplementary Note ##SUPPL##0##2##). We also tested for possible effects of bias affecting inferred ancestry components<sup>##REF##37339987##83##</sup> propagating biases in individual-level pairwise analyses, using D-statistics, which indicated that imputed ancient genomes down to 0.1× coverage are not significantly affected (Supplementary Note ##SUPPL##0##2##).</p>", "<title>Demographic inference</title>", "<p id=\"Par33\">We determined the genetic sex of the study individuals using the ratio of reads aligning to either of the sex chromosomes (<italic>R</italic><sub>Y</sub> statistic)<sup>##UREF##25##84##</sup>. Y chromosomes of inferred male individuals were further analysed using phylogenetic placement<sup>##REF##30165689##85##</sup>. We built a reference phylogenetic tree of 1,244 male individuals from the 1000 Genomes project with RAxML-NG (ref. <sup>##REF##31070718##86##</sup>), using the general time-reversible model including among-site rate heterogeneity and ascertainment correction (model GTR+G+ASC_LEWIS). For each ancient sample, haploid genotypes given the positions and alleles in the reference panel were called using ‘bcftools call’ (options -C alleles –ploidy 1 -i). The resulting genotypes were converted to fasta format and placed onto the reference tree using EPA-ng (ref. <sup>##REF##30165689##85##</sup>). Phylogenetic placements were processed and visualized using gappa (ref. <sup>##REF##32016344##87##</sup>). To convert phylogenetic placements into haplogroup calls, we assigned each branch of the reference phylogeny to its representing haplogroup, using SNP annotations from ISOGG (v.15.73). For each ancient sample, haplogroups were then called using the most basal branch accumulating 99% of the placement weights, obtained using ‘accumulate’ in gappa. Phylogenetic analyses of reconstructed mitochondrial genomes were also undertaken using RAxML-ng (ref. <sup>##REF##30165689##85##</sup>; see Supplementary Note ##SUPPL##0##3a##).</p>", "<p id=\"Par34\">To infer genetic relatedness between the study individuals, we used the allele-frequency-free inference method introduced previously<sup>##REF##30462358##88##</sup>. For each pair of individuals, three relatedness estimators were calculated, R0, R1 and KING-robust (ref. <sup>##REF##20926424##89##</sup>) using the site-frequency-spectrum (SFS)-based approach. We used the realSFS method<sup>##REF##22911679##90##</sup> implemented in the ANGSD package<sup>##REF##25420514##91##</sup> to infer the 2D-SFS, selecting the SFS with the highest likelihood across ten replicates. We used a set of 1,191,529 autosomal transversion SNPs with MAF ≥ 0.05 from the 1000 Genomes Project<sup>##REF##26432245##36##</sup> for the analysis. Previously established cut-offs<sup>##REF##20926424##89##</sup> for the KING-robust estimator were applied to assign individual pairs to first-, second- or third-degree relationships. Parent–offspring relationships were distinguished from sibling relationships using R0 and R1 ratios, by requiring that R0 ≤ 0.02 and 0.4 ≤ R1 ≤ 0.6 to infer a parent–offspring relative pair. Individual pairs with fewer than 20,000 sites contributing to the estimators were excluded.</p>", "<p id=\"Par35\">We generated a dataset for population genetic analysis by combining the 317 newly sequenced individuals with 1,347 previously published ancient genomes with genomic coverage higher than 0.1× generated using shotgun sequencing (Supplementary Data ##SUPPL##5##7##). Imputed genotype data (Supplementary Note ##SUPPL##0##2##) for this set of 1,664 ancient genomes were merged with genotypes of 2,504 modern individuals from the 1,000 Genomes project<sup>##REF##26432245##36##</sup> used as a reference panel in the imputation. We retained only SNPs that passed the 1000 Genomes strict mask, resulting in a final dataset of 4,168 individuals genotyped at 7,321,965 autosomal SNPs (‘1000G’ dataset). As well as imputed genotypes, we also generated pseudo-haploid genotypes for each ancient individual by randomly sampling an allele from sequencing reads covering those SNPs. For population structure analyses in the context of global genetic diversity, we generated a second dataset by intersecting the ancient genotype data with SNP array data of 2,180 modern individuals from 213 worldwide populations<sup>##REF##25230663##3##,##REF##27459054##4##,##REF##22960212##92##,##REF##23072811##93##</sup> (‘HO’ dataset).</p>", "<p id=\"Par36\">To facilitate filtering for downstream analyses, we flagged individuals to potentially exclude according to the following criteria: (i) contamination estimate greater than 5% (‘contMT5pct’, ‘contNuc5pct’; Supplementary Note ##SUPPL##0##1##); (ii) autosomal coverage less than 0.1× (‘lowcov’); (iii) genome-wide average imputation genotype probability less than 0.98 (‘lowGpAvg’); (iv) individual is the lower-quality sample in a close relative pair (‘1d_rel’, ‘2d_rel’; Supplementary Note ##SUPPL##0##3c##). A total of 1,492 individuals (213 newly reported) passed all filters, which were used in most of the downstream analyses unless otherwise noted.</p>", "<p id=\"Par37\">We investigated overall population structure among the dataset individuals using PCA and model-based clustering (ADMIXTURE<sup>##REF##27216439##94##</sup>). We performed PCA using different subsets of individuals in the ‘HO’ dataset. For the PCA including only imputed diploid samples, we used GCTA (ref. <sup>##UREF##26##95##</sup>), excluding SNPs with MAF < 0.05 in the respective panel. For PCA projecting low coverage or flagged individuals, we used smartpca (refs. <sup>##REF##17194218##96##,##REF##16862161##97##</sup>) with options ‘lsqproject: YES’ and ‘autoshrink: YES’ on a fixed set of 400,186 SNPs with MAF ≥ 0.05 in non-African individuals passing all filters. We ran ADMIXTURE on a set of 1,593 ancient individuals from the ‘1000G’ dataset, excluding individuals flagged as close relatives or with a contamination estimate greater than 5%. For the 1,492 individuals passing all filters we used imputed genotypes; the remaining 101 lower-coverage samples were represented by pseudo-haploid genotypes. We restricted the analysis to transversion SNPs with imputation INFO score ≥ 0.8 and MAF ≥ 0.05. We further performed linkage-disequilibrium pruning and filtering for missingness using plink<sup>##REF##25722852##98##</sup> (options --indep-pairwise 500 50 0.4 –geno 0.8), for a final analysis set of 142,550 SNPs.</p>", "<p id=\"Par38\">We performed admixture graph fitting (qpGraph) to investigate deep Eurasian population structure using ADMIXTOOLS2 (ref. <sup>##UREF##27##99##</sup>). For these analyses, pairwise <italic>f</italic><sub>2</sub>-statistics were pre-computed from pseudo-haploid genotypes in the ‘1000G’ dataset using the ‘extract_f2’ function with ‘afProd=TRUE’. We grouped individuals into populations using their membership in the genetic clusters inferred from IBD sharing (Supplementary Note ##SUPPL##0##3f##), with the exception of the Upper Palaeolithic European individual Kostenki 14, who was treated as a separate population (new cluster label ‘Europe_37000BP_33000BP_Kostenki’). We carried out admixture graph fitting using a semi-automatic iterative approach (Supplementary Note ##SUPPL##0##3d##).</p>", "<p id=\"Par39\">We used IBDseq<sup>##REF##24207118##100##</sup> to detect genomic segments shared IBD between all individuals in the ‘1000G’ dataset, restricting to transversion SNPs with imputation INFO score ≥ 0.8 and MAF ≥ 0.01. We filtered the resulting IBD segments for LOD score ≥ 3 and a minimum length of 2 centimorgans (cM), and further removed regions of excess long IBD as described previously<sup>##REF##26299365##101##</sup>. First, we used the GenomicRanges<sup>##REF##23950696##102##</sup> package in R to calculate the total number of long IBD segments (greater than 10 cM) overlapping each position along the genome, and calculated their 3% trimmed mean and s.d. We then called regions of excess IBD if they were more than 10 trimmed s.d. from the trimmed mean, and removed any segments overlapping the excess IBD regions. For analyses of ROH we used a shorter length cut-off of 1 cM.</p>", "<p id=\"Par40\">We performed genetic clustering of the ancient individuals using hierarchical community detection on a network of pairwise IBD-sharing similarities<sup>##REF##31694865##103##</sup>. To facilitate the detection of clusters at a finer scale, we ran IBDseq (v.r1206) on a dataset restricting to ancient samples only, and applied more lenient filters of imputation INFO score > 0.5, and minimum IBD segment length of 1 cM. We constructed a weighted network of the individuals using the igraph<sup>##UREF##28##104##</sup> package in R, with the fraction of the genome shared IBD between pairs of individuals as weights. We then performed iterative community detection on this network using the Leiden algorithm<sup>##REF##30914743##105##</sup> implemented in the leidenAlg R package (v1.01; <ext-link ext-link-type=\"uri\" xlink:href=\"https://github.com/kharchenkolab/leidenAlg\">https://github.com/kharchenkolab/leidenAlg</ext-link>). We used a resolution parameter of <italic>r</italic> = 0.5 as the starting value for each level of community detection. If more than one community was detected, we split the network into the respective communities, and repeated the community detection step. If no communities were detected, we incremented the resolution parameter in steps of 0.5 until a maximum value of <italic>r</italic> = 3. The initial clustering was completed when no more communities were detected at the highest resolution parameter, across all subcommunities. To convert the resulting hierarchy into a final clustering, we simplified the initial clustering by collapsing nodes into single clusters on the basis of observed spatiotemporal annotations of the samples. We note that the obtained clusters should not be interpreted as ‘populations’ in the sense of a local community of individuals, but rather as sets of individuals with shared ancestry. Although this approach is an oversimplification of the complex spatiotemporally structured populations investigated here, the obtained clusters nevertheless captured real effects, grouping individuals within restricted spatiotemporal ranges and/or archaeological contexts and recapitulating known relationships between clusters.</p>", "<p id=\"Par41\">To circumvent some of the pitfalls of grouping individuals into discrete clusters, we used supervised ancestry modelling in which sets of ‘target’ individuals were modelled as mixtures of ‘source’ groups, selected to represent particular ancestry components. As an illustrative case, an individual of European HG ancestry with a minor contribution of Neolithic farmer admixture might be inferred to be a member of a HG genetic cluster, but will be modelled as a mixture of a HG and Neolithic farmer sources in the ancestry modelling. To estimate ancestry proportions from patterns of pairwise IBD sharing, we applied an approach akin to ‘chromosome painting’<sup>##REF##22291602##106##</sup>. We first inferred an IBD-based ‘painting profile’ for each target individual, by summing up the total amount of IBD shared with each ‘donor’ group (using population labels for modern donors or IBD-based genetic clusters for ancient donors), and normalizing them to the interval [0,1]. We used a leave-one-out approach<sup>##REF##27274049##38##</sup> to account for the fact that recipient individuals cannot be included as donors from their own group. We then used these painting profiles in supervised modelling of target individuals as mixtures from different sets of putative source groups<sup>##REF##27274049##38##,##REF##24531965##107##</sup>, using non-negative least squares implemented in the R package limSolve<sup>##UREF##29##108##</sup>. We estimated standard errors of ancestry proportions using a weighted block jacknife, leaving out each chromosome in turns. A comparison of results obtained using this approach to other commonly used methods (supervised ADMIXTURE, qpAdm) is shown in Supplementary Note ##SUPPL##0##3f##). We focused our analyses on three panels of putative source clusters reflecting different temporal depths: ‘deep’, using a set of deep ancestry source groups reflecting major ancestry poles; ‘postNeol’, using diverse Neolithic and earlier source groups; and ‘postBA’, using Late Neolithic and Bronze Age source groups (Extended Data Figs. ##FIG##10##5##–##FIG##12##7##). We also used additional source sets in follow-up analyses of more restricted spatiotemporal contexts (Supplementary Data ##SUPPL##5##7##–##SUPPL##6##13##).</p>", "<p id=\"Par42\">Finally, we aimed to infer the geographical and temporal spread of major ancestries (Supplementary Note ##SUPPL##0##3e##). We used a method<sup>##REF##32238559##46##</sup> applying spatiotemporal ordinary kriging on latent ancestry proportion estimates from ancient and present-day genomes. This way, we obtained spatiotemporal maps reflecting the dynamics of the spread of ancestry during the transition from the Mesolithic to the Neolithic, Bronze Age, Iron Age and more recent periods. We obtained ancestry proportions estimated using ADMIXTURE<sup>##REF##19648217##109##</sup> with <italic>K</italic> = 9 latent ancestry clusters (Supplementary Note ##SUPPL##0##3d##) on a sequence dataset including both whole-genome shotgun-sequenced genomes and genomic sequences obtained through SNP capture (Supplementary Note ##SUPPL##0##2##, intersection with ‘HO’ dataset). We performed spatiotemporal kriging<sup>##UREF##30##110##</sup> of these proportions over the last 12,900 years, in intervals of 300 years, with a 5,000-point spatial grid spanning western and central Eurasia. We used the R package gstat to fit a spatiotemporal variogram via a metric covariance model, and perform ordinary kriging<sup>##UREF##31##111##</sup>. We focused on the ancestry clusters for which we could fit variogram models that were not static over time.</p>", "<title><sup>14</sup>C chronology and reservoir effects</title>", "<p id=\"Par43\">Of the 317 individuals sequenced in this study, 272 were <sup>14</sup>C-dated in the project, 30 <sup>14</sup>C-dates were obtained from literature and 15 were dated by archaeological context (Supplementary Note ##SUPPL##0##4## and Supplementary Data ##SUPPL##3##2##). Some individuals were dated twice. Most of the dates (<italic>n</italic> = 242) were performed at the <sup>14</sup>CHRONO Centre laboratory at Queen’s University, Belfast, following published sample pretreatment and laboratory protocols<sup>##UREF##32##112##</sup>. Additional samples were analysed by the Oxford Radiocarbon Accelerator Unit (ORAU) laboratory (<italic>n</italic> = 24) and by the Keck-CCAMS Group (<italic>n</italic> = 6) (see previous reports<sup>##UREF##33##113##,##UREF##34##114##</sup> for laboratory procedures). Only datings with a C/N ratio of 2.9–3.6 were accepted; both δ<sup>13</sup>C and δ<sup>15</sup>N collagen measurements were also performed, and were used in estimates of marine and freshwater reservoir effects (MRE and FRE, respectively) (see Supplementary Note ##SUPPL##0##4## and Supplementary Data ##SUPPL##3##4##). Published values of MRE and FRE were used where available, but for some regions, such as sites in western Russia, a standard FRE value of 500 years was applied. A diet-weighted reservoir offset was then applied to the <sup>14</sup>C central value before calibration. Calibrations were made in Oxcal 4.4 using the Intcal20 calibration curve<sup>##UREF##35##115##</sup>. For display and calculation purposes a midpoint of the reservoir-corrected and calibrated 95% interval was calculated. Full details of the reservoir correction and calibration procedure are given in Supplementary Note ##SUPPL##0##4## and the calculations are in Supplementary Table##SUPPL##0## 4.1##.</p>", "<title>Reporting summary</title>", "<p id=\"Par44\">Further information on research design is available in the ##SUPPL##1##Nature Portfolio Reporting Summary## linked to this article.</p>" ]	[]	[]	[]	[ "<p id=\"Par1\">Western Eurasia witnessed several large-scale human migrations during the Holocene<sup>##REF##26062507##1##–##REF##29466330##5##</sup>. Here, to investigate the cross-continental effects of these migrations, we shotgun-sequenced 317 genomes—mainly from the Mesolithic and Neolithic periods—from across northern and western Eurasia. These were imputed alongside published data to obtain diploid genotypes from more than 1,600 ancient humans. Our analyses revealed a ‘great divide’ genomic boundary extending from the Black Sea to the Baltic. Mesolithic hunter-gatherers were highly genetically differentiated east and west of this zone, and the effect of the neolithization was equally disparate. Large-scale ancestry shifts occurred in the west as farming was introduced, including near-total replacement of hunter-gatherers in many areas, whereas no substantial ancestry shifts happened east of the zone during the same period. Similarly, relatedness decreased in the west from the Neolithic transition onwards, whereas, east of the Urals, relatedness remained high until around 4,000 <sc>bp</sc>, consistent with the persistence of localized groups of hunter-gatherers. The boundary dissolved when Yamnaya-related ancestry spread across western Eurasia around 5,000 <sc>bp</sc>, resulting in a second major turnover that reached most parts of Europe within a 1,000-year span. The genetic origin and fate of the Yamnaya have remained elusive, but we show that hunter-gatherers from the Middle Don region contributed ancestry to them. Yamnaya groups later admixed with individuals associated with the Globular Amphora culture before expanding into Europe. Similar turnovers occurred in western Siberia, where we report new genomic data from a ‘Neolithic steppe’ cline spanning the Siberian forest steppe to Lake Baikal. These prehistoric migrations had profound and lasting effects on the genetic diversity of Eurasian populations.</p>", "<p id=\"Par2\">An analysis involving the shotgun sequencing of more than 300 ancient genomes from Eurasia reveals a deep east–west genetic divide from the Black Sea to the Baltic, and provides insight into the distinct effects of the Neolithic transition on either side of this boundary.</p>", "<title>Subject terms</title>" ]	[ "<title>Main</title>", "<p id=\"Par3\">Genetic diversity in west Eurasian human populations was largely shaped by three major prehistoric migrations: anatomically modern hunter-gatherers (HGs) occupying the area from around 45,000 <sc>bp</sc> (refs. <sup>##REF##27459054##4##,##REF##26853362##6##</sup>); Neolithic farmers expanding from the Middle East from around 11,000 <sc>bp (</sc>ref. <sup>##REF##27459054##4##</sup>); and steppe pastoralists coming out of the Pontic Steppe around 5,000 <sc>bp</sc> (refs. <sup>##REF##26062507##1##,##REF##25731166##2##</sup>). Palaeogenomic analyses have uncovered the early post-glacial colonization routes<sup>##REF##36859578##7##</sup> that led to a basal ancestral dichotomy between HGs in central and western Europe and HG groups represented further east<sup>##REF##26595274##8##</sup>. Western HG (WHG) ancestry appears to be derived directly from ancestry sources related to Epigravettian, Azilian and Epipalaeolithic cultures (the Villabruna cluster)<sup>##REF##27135931##9##</sup>, whereas eastern HG (EHG) ancestry shows further admixture with an Upper Palaeolithic Siberian source (Ancient North Eurasian; ANE)<sup>##REF##24256729##10##</sup>. The WHG ancestry composition was regionally variable in the Mesolithic populations. There is evidence for continuous local admixture in Iberian HGs<sup>##REF##30880015##11##</sup>, which contrasts with the more homogenous WHG ancestry profile in Britain and northwestern continental Europe, suggesting ancestry formation before expansion<sup>##REF##30988490##12##</sup>. The timing of the ancestry admixture that formed EHG has been estimated at 13,000–15,000 <sc>bp</sc>, and the composition seems to follow a cline that is broadly correlated with geography, with Baltic and Ukrainian HGs showing more affinity to the Villabruna Upper Palaeolithic cluster ancestry, as compared with HGs in Russia, who exhibited more ANE ancestry<sup>##REF##29466330##5##,##REF##36859578##7##,##REF##29743352##13##,##REF##33523926##14##</sup>. Genomic analyses of Mesolithic skeletal material from the Scandinavian Peninsula has revealed varied mixes of WHG and EHG ancestry among the later Mesolithic populations<sup>##REF##25230663##3##,##REF##29315301##15##,##REF##31123709##16##</sup>.</p>", "<p id=\"Par4\">Beyond these broad-scale characterizations, our knowledge about Mesolithic population structure and demographic admixture processes is limited, and has substantial chronological and geographical information gaps. This is partly owing to a relative paucity of well-preserved Mesolithic human skeletons older than 8,000 years, and partly because most ancient DNA studies on the Mesolithic and Neolithic periods have been restricted to individuals from Europe. The archaeological record indicates a boundary from the eastern Baltic to the Black Sea, east of which HG societies persisted for much longer than in western Europe, despite the similar distance to the distribution centre for early agriculture in the Middle East<sup>##UREF##0##17##</sup>. Components of eastern and western HG ancestry appear highly variable in this boundary region<sup>##REF##29466330##5##,##REF##28162894##18##,##REF##29382937##19##</sup> but the wider spatiotemporal genetic implications of the east–west division are unclear. The spatiotemporal mapping of population dynamics east of Europe, including northern and central Asia during the same time period, is limited. In these regions, the term ‘Neolithic’ is characterized by cultural and economic changes including societal-network differences, changes in lithic technology and use of pottery. For instance, the Neolithic cultures of the central Asian steppe and the Russian taiga belt possessed pottery, but retained a HG economy alongside stone-blade technology, similar to the preceding Mesolithic cultures<sup>##UREF##1##20##</sup>. A fundamental lack of data from some key regions and periods has made it difficult to gain a deeper understanding of how the neolithization differed in its timing, mechanisms and effects across northern and western Eurasia.</p>", "<p id=\"Par5\">The transition from hunting and gathering to farming was based on domesticated plants and animals of Middle Eastern origin, and represents one of the most fundamental shifts in demography, health, lifestyle and culture in human prehistory. The neolithization process in large parts of Europe was accompanied by the arrival of immigrants of Anatolian descent<sup>##UREF##2##21##</sup>. For example, in Iberia, the Neolithic began with the abrupt spread of immigrant farmers of Anatolian–Aegean ancestry along the Mediterranean and Atlantic coasts, after which admixture with local HGs gradually took place<sup>##REF##30880015##11##</sup>. Similarly, in southeastern and central Europe, farming rapidly spread with Anatolian Neolithic farmers, who were to some extent subsequently admixed with local HGs<sup>##REF##29773666##22##–##UREF##4##27##</sup>. Conversely, in Britain, data suggest that there was a complete replacement of the HG population when agriculture was introduced by incoming continental farmers, without a subsequent resurgence of local HG ancestry<sup>##REF##30988490##12##,##REF##26712024##28##</sup>. In the east Baltic region, a markedly different neolithization trajectory occurred, with the introduction of domesticates only at the emergence of the Corded Ware complex (CWC) around 4,800 calibrated years before present (cal. <sc>bp)</sc> (refs. <sup>##REF##28162894##18##,##REF##29382937##19##</sup>). Similarly, in eastern Ukraine, HGs of Mesolithic ancestry co-existed for millennia with farming groups further west<sup>##REF##29466330##5##,##UREF##5##29##</sup>. These studies have all provided important regional contributions to the understanding of west Eurasian population history, but from a broader cross-continental perspective, our knowledge is still patchy.</p>", "<p id=\"Par6\">From approximately 5,000 <sc>bp</sc>, an ancestry component related to Early Bronze Age steppe pastoralists such as the Yamnaya culture rapidly spread across Europe through the expansion of the CWC and related cultures<sup>##REF##26062507##1##,##REF##25731166##2##</sup>. Although previous studies have identified these large-scale migrations into Europe and central Asia, central aspects concerning the demographic processes are not resolved. Yamnaya ancestry (that is, ‘steppe’ ancestry) has been characterized broadly as a mix between EHG ancestry and Caucasus hunter-gatherer (CHG), formed in a hypothetical admixture between a ‘northern’ steppe source and a ‘southern’ Caucasus source<sup>##REF##36007055##30##</sup>. However, the exact origins of these ancestry sources have not been identified. Furthermore, with a few exceptions<sup>##REF##33444387##31##–##UREF##6##33##</sup>, published Yamnaya Y-chromosomal haplogroups do not match those found in Europeans after 5,000 <sc>bp</sc>, and the origin of this patrilineal lineage is also unresolved. Finally, in Europe, ‘steppe’ ancestry has hitherto been identified only in admixed form, but the origin of this admixture event and the mechanism by which the ancestry subsequently spread with the CWC have remained elusive.</p>", "<p id=\"Par7\">To investigate these formative processes at a cross-continental scale, we sequenced the genomes of 317 radiocarbon-dated (by accelerator mass spectrometry) individuals of mainly Mesolithic and Neolithic origin, covering major parts of Eurasia. We combined these with published shotgun-sequenced data to impute a dataset of more than 1,600 diploid ancient genomes. Of the 317 sampled ancient skeletons (Fig. ##FIG##0##1##, Extended Data Fig. ##FIG##6##1## and Supplementary Data ##SUPPL##2##1##), 272 were radiocarbon-dated within the project, 30 dates were derived from published literature and 15 examples were dated by archaeological context. Dates were corrected for marine and freshwater reservoir effects (Supplementary Note ##SUPPL##0##4##) and ranged from the Upper Palaeolithic around 25,700 cal. <sc>bp</sc> to the mediaeval period (around 1,200 cal. <sc>bp</sc>). However, 97% of the individuals (<italic>n</italic> = 309) date to between 11,000 and 3,000 cal. <sc>bp</sc>, with a heavy focus on individuals associated with various Mesolithic and Neolithic cultures. Geographically, the 317 sampled skeletons cover a vast territory across Eurasia, from Lake Baikal to the Atlantic coast and from Scandinavia to the Middle East, deriving from contexts that include burial mounds, caves, bogs and the sea floor (Supplementary Notes ##SUPPL##0##6## and ##SUPPL##0##7##). Broadly, we can divide our research area into three large regions: (1) central, western and northern Europe; (2) eastern Europe, including western Russia, Belarus and Ukraine; and (3) the Urals and western Siberia (Supplementary Notes ##SUPPL##0##6## and ##SUPPL##0##7##). Samples cover many of the key Mesolithic and Neolithic cultures in western Eurasia, such as the Maglemose, Ertebølle, Funnel Beaker (TRB) and Corded Ware/Single Grave cultures in Scandinavia; the Cardial in the Mediterranean; the Körös and Linear Pottery (LBK) in southeastern and central Europe; and many archaeological cultures in Ukraine, western Russia and the trans-Ural region (for example, Veretye, Lyalovo, Volosovo and Kitoi). Our sampling was particularly dense in Denmark, from where an accompanying paper presents a detailed and continuous sequence of 100 genomes spanning the Early Mesolithic to the Bronze Age<sup>##UREF##7##34##</sup>. Dense sampling was also obtained from Ukraine, western Russia and the trans-Ural region, spanning the Early Mesolithic through to the Neolithic, up to around 5,000 <sc>bp</sc>.</p>", "<title>Broad-scale genetic structure</title>", "<p id=\"Par8\">Ancient DNA was extracted from either dental cementum or petrous bones, and the 317 genomes were shotgun-sequenced to a depth of coverage ranging between 0.01× and 7.1× (mean, 0.75×, median, 0.26×), with more than 1× coverage for 81 genomes (Supplementary Note ##SUPPL##0##1##). We used a computational method optimized for low-coverage data<sup>##REF##33473199##35##</sup> to impute genotypes using the 1000 Genomes phased data<sup>##REF##26432245##36##</sup> as a reference panel. This method was jointly applied to more than 1,300 previously published shotgun-sequenced genomes (Supplementary Data ##SUPPL##5##7##), resulting in a dataset of 8.5 million common single-nucleotide polymorphisms (SNPs) (with a minor allele frequency (MAF) greater than 1% and an imputation INFO score greater than 0.5) for 1,664 imputed diploid ancient genomes (Extended Data Fig. ##FIG##7##2##). For most downstream analyses, <italic>n</italic> = 71 individuals were excluded because they were found to be close relatives or because the estimated contamination was greater than 5%. This resulted in 1,593 genomes, of which 1,492 were analysed as imputed (213 sequenced in this study) and 101 were analysed as pseudo-haploids owing to low coverage (less than 0.1×) and/or low imputation quality (average genotype probability lower than 0.98).</p>", "<p id=\"Par9\">We conducted a broad-scale characterization of this dataset using principal component analysis (PCA) and model-based clustering (ADMIXTURE), which recapitulated previously described ancestry clines in ancient Eurasian populations at increased resolution (Fig. ##FIG##0##1##, Extended Data Fig. ##FIG##6##1## and Supplementary Note ##SUPPL##0##3d##). Our imputed whole genomes allowed us to perform PCA using ancient genomes as input, instead of projecting onto a space defined by modern variation. Notably, this resulted in much higher differentiation among the ancient individuals than observed previously (Extended Data Fig. ##FIG##6##1##). This is particularly notable in a PCA of west Eurasian individuals, in which the variance explained by the first two PCs increases more than 1.5-fold, and present-day populations are confined within a small central area of the PCA space (Fig. ##FIG##0##1d## and Extended Data Fig. ##FIG##6##1c,d##). These results are consistent with the genetic differentiation between ancient Europeans being higher than is observed in present-day populations, reflecting more genetic isolation and lower effective population sizes among ancient groups.</p>", "<p id=\"Par10\">To obtain a finer-scale characterization of genetic ancestries across space and time, we used an approach similar to the widely used ChromoPainter–FineSTRUCTURE workflow<sup>##REF##25788095##37##–##REF##27324836##39##</sup>. We first performed community detection on a network constructed from pairwise identity-by-descent (IBD)-sharing similarities between ancient individuals to group them into hierarchically related clusters of similar genetic ancestry (Extended Data Fig. ##FIG##8##3## and Supplementary Note ##SUPPL##0##3c##). At higher levels of the hierarchy, the resulting clusters represented previously described ancestry groups reflecting broad genetic structure, such as EHGs and WHGs (‘HG_EuropeE’ and ‘HG_EuropeW’; Extended Data Fig. ##FIG##8##3##). Clusters at the lowest level resolved fine-scale genetic structure, grouping individuals within restricted spatiotemporal ranges and/or archaeological contexts but also revealing previously unknown connections across broader geographical areas (Extended Data Fig. ##FIG##8##3## and Supplementary Note ##SUPPL##0##3f##). These resulting clusters were subsequently used in supervised ancestry modelling, in which sets of ‘target’ individuals were modelled as mixtures of ‘source’ groups (<xref rid=\"Sec8\" ref-type=\"sec\">Methods</xref>).</p>", "<title>Population structure of HGs after the LGM</title>", "<p id=\"Par11\">Our study comprises 113 shotgun-sequenced and imputed HG genomes, of which 79 were sequenced in this study. Among them, we report a 0.83× (0.83-fold coverage) genome of an Upper Palaeolithic skeleton from Kotias Klde Cave in Georgia, Caucasus (NEO283), directly dated to 26,052–25,323 cal. <sc>bp</sc> (95% confidence interval). In the PCA of all non-African individuals, this individual occupied a position distinct from those of other previously sequenced Upper Palaeolithic individuals—shifted towards west Eurasians along PC1 (Supplementary Note ##SUPPL##0##3d##). Using admixture graph modelling, we find that a well-fitting graph for this Caucasus Upper Palaeolithic lineage derives it as a mixture of predominantly west Eurasian Upper Palaeolithic HG ancestry (76%), with a contribution of about 24% from a ‘basal Eurasian’ ghost population, first observed in west Asian Neolithic individuals<sup>##REF##27459054##4##</sup> (Supplementary Note ##SUPPL##0##3d## and Supplementary Fig. ##SUPPL##0##3d.16##). To further explore the fine-scale structure of later European HGs, we then performed supervised ancestry modelling using sets of increasingly proximate source clusters (Extended Data Fig. ##FIG##9##4##). We replicate previous results of broad-scale genetic differentiation between HGs in eastern and western Europe after the Last Glacial Maximum (LGM)<sup>##REF##29466330##5##,##REF##36859578##7##</sup>. We show that the deep ancestry divisions in the Eurasian human gene pool that were established during early post-LGM dispersals<sup>##REF##36859578##7##</sup> persisted throughout the Mesolithic (Extended Data Fig. ##FIG##9##4##). Using distal sets of pre-LGM HGs as sources, we modelled western HGs as predominantly derived from a source related to the herein-reported Caucasus Upper Palaeolithic individual from Kotias Klde cave (Caucasus_25000BP), whereas eastern HGs showed varying amounts of ancestry related to a Siberian HG from Mal’ta (Malta_24000BP; Extended Data Fig. ##FIG##9##4a## and Supplementary Data ##SUPPL##6##12##). Using post-LGM sources, this divide is best represented by ancestry related to southern European (Italy_15000BP_9000BP) and Russian (RussiaNW_11000BP_8000BP) HGs, respectively, corresponding to the ‘WHG’ and ‘EHG’ labels commonly used in previous studies.</p>", "<p id=\"Par12\">Adding extra proximate sources allowed us to further refine the ancestry composition of northern European HGs. In Denmark, our 28 sequenced and imputed HG genomes derived almost exclusively from a southern European source (Italy_15000BP_9000), with notable homogeneity across a 5,000-year transect<sup>##UREF##7##34##</sup> (Extended Data Fig. ##FIG##9##4a## and Supplementary Data ##SUPPL##6##12##). By contrast, we observed marked geographical variation in the ancestry composition of HGs from other parts of Scandinavia. Mesolithic individuals from Scandinavia were broadly modelled as mixtures with varying proportions of eastern and western HGs using distal post-LGM sources (‘hgEur1’; Extended Data Fig. ##FIG##9##4a##), as previously reported<sup>##REF##29315301##15##</sup>. In Mesolithic individuals from southern Sweden, the eastern HG ancestry component was largely replaced by a southeastern European source (Romania_8800BP) in more proximate models, making up between 60% and 70% of the ancestry (Extended Data Fig. ##FIG##9##4a## and Supplementary Data ##SUPPL##6##12##). Ancestry related to Russian HGs increased in a cline towards the far north, peaking at around 75% in a late HG from Tromso (VK531; around 4,350 <sc>bp</sc>) (Extended Data Fig. ##FIG##9##4a,c## and Supplementary Data ##SUPPL##6##12##); this was also reflected in the fact that those individuals shared the highest IBD with northern Russian HGs (Extended Data Fig. ##FIG##9##4d##). During the late Mesolithic, we observed higher southern European HG ancestry in coastal individuals (NEO260 from Evensås and NEO679 from Skateholm) than in earlier individuals from further inland. Adding Danish HGs as a proximate source substantially improved the fit for those two individuals (‘hgEur3’; Extended Data Fig. ##FIG##9##4b##), with an estimated 58–76% of ancestry derived from Danish HGs (‘hgEur3’; Extended Data Fig. ##FIG##12##7a## and Supplementary Data ##SUPPL##6##12##), suggesting a population genetic link with Denmark, where this ancestry prevailed (Extended Data Fig. ##FIG##9##4c##). These results indicate that there were at least three distinct waves of northwards HG ancestry into Scandinavia: (1) a predominantly southern European source into Denmark and coastal southwestern Sweden; (2) a source related to southeastern European HGs into the Baltic and southeastern Sweden; and (3) a northwest Russian source into the far north, which then spread south along the Atlantic coast of Norway<sup>##REF##29315301##15##</sup> (Extended Data Fig. ##FIG##9##4c##). These movements are likely to represent post-glacial expansions from refugial areas shared with many plant and animal species<sup>##REF##17439649##40##</sup>.</p>", "<p id=\"Par13\">On the Iberian Peninsula, the earliest individuals, including an approximately 9,200-year-old HG (NEO694) from Santa Maira (eastern Spain), sequenced in this study, showed predominantly southern European HG ancestry, with a minor contribution from Upper Palaeolithic HG sources (Extended Data Fig. ##FIG##9##4a##). This observed Upper Palaeolithic HG ancestry source mix is likely to reflect the pre-LGM Magdalenian-related ancestry component that has previously been reported in Iberian HGs<sup>##REF##30880015##11##</sup>, for which a good source population proxy is lacking in our dataset. By contrast, later individuals from northern Iberia were more similar to HGs from southeastern Europe, deriving around 30–40% of their ancestry from a source related to HGs from the Balkans in more proximate models<sup>##REF##30880015##11##,##REF##30872528##41##</sup> (Extended Data Fig. ##FIG##9##4a## and Supplementary Data ##SUPPL##6##12##). The earliest evidence for this gene flow was observed in a Mesolithic individual from El Mazo, Spain (NEO646) who was dated, calibrated and reservoir-corrected to around 8,200 <sc>bp</sc> (8,365–8,182 cal. <sc>bp</sc>; 95%) but dated slightly earlier by context<sup>##REF##35444222##42##</sup> (8,550–8,330 <sc>bp</sc>). The directly dated age coincides with some of the oldest Mesolithic geometric microliths in northern Iberia, appearing around 8,200 <sc>bp</sc> at this site<sup>##REF##35444222##42##</sup>. An influx of southeastern European HG-related ancestry in Ukrainian individuals after the Mesolithic (Extended Data Fig. ##FIG##9##4a## and Supplementary Data ##SUPPL##6##12##) suggests a similar eastward expansion in southeastern Europe<sup>##REF##29466330##5##</sup>. Of note, two newly reported approximately 7,300-year-old genomes from the Middle Don River region in the Pontic-Caspian steppe (Golubaya Krinitsa, NEO113 & NEO212) were found to be predominantly derived from earlier Ukrainian HGs, but with around 18-24% of their ancestry contributed from a source related to HGs from the Caucasus (Caucasus_13000BP_10000BP) (Extended Data Fig. ##FIG##9##4a## and Supplementary Data ##SUPPL##6##12##). Further lower-coverage (non-imputed) genomes from the same site project in the same PCA space (Fig. ##FIG##0##1d##) shifted away from the European HG cline towards Iran and the Caucasus. Using the linkage-disequilibrium-based method DATES<sup>##REF##31488661##43##</sup>, we dated this admixture to around 8,300 <sc>bp</sc> (Supplementary Data ##SUPPL##7##14##). These results document genetic contact between populations from the Caucasus and the steppe region that is much earlier than previously known, providing evidence of admixture before the advent of later nomadic steppe cultures—in contrast with recent hypotheses—and further to the west than has been previously reported<sup>##REF##29466330##5##,##REF##30713341##44##</sup>.</p>", "<title>Major genetic transitions in Europe</title>", "<p id=\"Par14\">Previous ancient genomics studies have documented several episodes of large-scale population turnover in Europe within the past 10,000 years (see, for example, refs. <sup>##REF##26062507##1##,##REF##25731166##2##,##REF##29466330##5##,##REF##29144465##45##</sup>), but the 317 genomes reported here fill important knowledge gaps. Our analyses reveal profound differences in the spatiotemporal neolithization dynamics across Europe. Supervised admixture modelling (using the ‘deep’ ancestry set; Supplementary Data ##SUPPL##6##11##) and spatiotemporal kriging<sup>##REF##32238559##46##</sup> document a broad east–west distinction along a boundary zone running from the Black Sea to the Baltic. On the western side of this ‘great divide’, the Neolithic transition is accompanied by large-scale shifts in genetic ancestry from local HGs to farmers with Anatolian-related ancestry (Boncuklu_10000BP; Fig. ##FIG##1##2a## and Fig. ##FIG##2##3## and Extended Data Figs. ##FIG##10##5##–##FIG##12##7##). The arrival of Anatolian-related ancestry in different regions spans an extensive time period of more than 3,000 years, from its earliest evidence in the Balkans (Lepenski Vir) at around 8,700 <sc>bp</sc> (ref. <sup>##REF##29466330##5##</sup>) to around 5,900 <sc>bp</sc> in Denmark.</p>", "<p id=\"Par15\">Furthermore, we corroborate previous reports (for example, refs. <sup>##REF##25731166##2##,##REF##29466330##5##,##REF##29144465##45##,##REF##28749934##47##</sup>) of widespread, low-level admixture between early European farmers and local HGs, resulting in a resurgence of HG ancestry in many regions of Europe during subsequent centuries (Extended Data Fig. ##FIG##13##8b,c## and Supplementary Data ##SUPPL##6##8##). The resulting estimated proportions of HG ancestry rarely exceeded 10%, with notable exceptions observed in individuals from southeastern Europe (Iron Gates) and Sweden (Pitted Ware Culture), as well as in the herein-reported Early Neolithic genomes from Portugal (western Cardial), which are estimated to contain 27%–43% Iberian HG ancestry (Iberia_9000BP_7000BP). The latter result, together with an estimated admixture date of just 200 years earlier (‘Iberia farmer early’ in Supplementary Data ##SUPPL##7##14##), suggests extensive first-contact admixture, and is in agreement with archaeological inferences derived from modelling the spread of farming across west Mediterranean Europe<sup>##REF##28096413##48##</sup>. Neolithic individuals from Denmark showed some of the highest overall proportions of HG ancestry (up to around 25%), but this was mostly derived from non-local western European-related HGs (EuropeW_13500BP_8000BP), with only a small contribution from local Danish HG groups in some individuals (Extended Data Fig. ##FIG##13##8b## and Supplementary Note ##SUPPL##0##3f##).</p>", "<p id=\"Par16\">We find evidence for regional stratification in early Neolithic farmer ancestries in subsequent Neolithic groups. Specifically, southern European early farmers were found to have provided major genetic ancestry to Neolithic groups of later dates in western Europe, whereas central European early farmer ancestry was mainly observed in subsequent Neolithic groups in eastern Europe and Scandinavia (Extended Data Fig. ##FIG##13##8e##). These results are consistent with distinct migratory routes of expanding farmer populations, as previously suggested<sup>##REF##32632332##49##</sup>.</p>", "<p id=\"Par17\">On the eastern side of the great divide, no ancestry shifts can be observed during this period. In the east Baltic region<sup>##REF##28712569##50##</sup>, Ukraine and western Russia, local HG ancestry prevailed until around 5,000 <sc>bp</sc> without a noticeable input of Anatolian-related farmer ancestry (Figs. ##FIG##1##2## and ##FIG##2##3## and Extended Data Figs. ##FIG##10##5##–##FIG##12##7##). This eastern genetic continuity is in congruence with the archaeological record, which shows the persistence of pottery-using forager groups in this wide region, and a delayed introduction of cultivation and animal husbandry by several thousand years (Supplementary Note ##SUPPL##0##5##). Around 5,000 <sc>bp</sc>, major demographic events unfolded on the Eurasian Steppe, resulting in steppe-related ancestry spreading rapidly both eastwards and westwards<sup>##REF##26062507##1##,##REF##25731166##2##</sup>, marking the end of the great population genomic divide (Figs. ##FIG##2##3## and ##FIG##5##6##). We find that this second transition happened at a faster pace than during the neolithization, reaching most parts of Europe within an approximately 1,000-year time period after first appearing in the eastern Baltic region around 4,800 cal. <sc>bp</sc> (Fig. ##FIG##2##3##). In line with previous reports, we observe that by around 4,200 cal. <sc>bp</sc>, steppe-related ancestry was already dominant in individuals from Britain, France and the Iberian Peninsula<sup>##REF##30988490##12##,##REF##33434506##51##</sup>. Notably, because of the delayed neolithization in southern Scandinavia, these dynamics resulted in two episodes of large-scale genetic turnover in Denmark and southern Sweden within a period of roughly 1,000 years<sup>##UREF##7##34##</sup> (Fig. ##FIG##2##3##).</p>", "<p id=\"Par18\">Although the broader effects of the steppe migrations around 5,000 cal. <sc>bp</sc> are well known, the origin of this ancestry has remained a mystery. Here we show that the steppe ancestry composition (Steppe_5000BP_4300BP) can be modelled as a mixture of around 65% ancestry related to herein-reported HG genomes from the Middle Don River region (MiddleDon_7500BP) and around 35% ancestry related to HGs from Caucasus (Caucasus_13000BP_10000BP) (Extended Data Fig. ##FIG##11##6## and Supplementary Data ##SUPPL##6##9##). Thus, Middle Don HGs, who already carried ancestry related to Caucasus HGs (Extended Data Fig. ##FIG##9##4a##), serve as a hitherto-unknown proximal source for the majority ancestry contribution into Yamnaya-related genomes. The individuals in question derive from the burial ground Golubaya Krinitsa (Supplementary Note ##SUPPL##0##3##). Material culture and burial practices at this site are similar to the Mariupol-type graves, which are widely found in neighbouring regions of Ukraine; for instance, along the Dnepr River. They belong to the group of complex pottery-using HGs mentioned above, but the genetic composition at Golubaya Krinitsa is different from that in the remaining Ukrainian sites (Fig. ##FIG##1##2a## and Extended Data Fig. ##FIG##10##5##). A previous study<sup>##REF##36007055##30##</sup> suggested a model for the formation of Yamnaya ancestry that includes a ‘northern’ steppe source (EHG + CHG ancestry) and a ‘southern’ Caucasus Chalcolithic source (CHG ancestry), but did not identify the exact origin of these sources. The Middle Don genomes analysed here show the appropriate balance of EHG and CHG ancestry, suggesting that they are candidates for the missing northern proximate source for Yamnaya ancestry.</p>", "<p id=\"Par19\">The dynamics of the continent-wide transition from Neolithic farmer ancestry to steppe-related ancestry also differ markedly between geographical regions. The contribution of local Neolithic ancestry to the incoming groups was high in eastern, western and southern Europe, reaching more than 50% on the Iberian Peninsula<sup>##REF##30872528##41##</sup> (‘postNeol’ set; Extended Data Fig. ##FIG##11##6## and Supplementary Data ##SUPPL##6##10##). Scandinavia, however, shows a very different picture, with much lower contributions (less than 15%), including near-complete replacement of the local population in some regions (Extended Data Fig. ##FIG##14##9b##). Steppe-related ancestry accompanies and spreads with the formation of the CWC across Europe, and our results provide new evidence on the foundational admixture event. Individuals associated with the CWC carry a mix of steppe-related and Neolithic farmer-related ancestry; we show that the latter can be modelled as deriving exclusively from a genetic cluster associated with the Late Neolithic Globular Amphora culture (GAC) (Poland_5000BP_4700BP), and that this ancestry co-occurred with steppe-related ancestry across all sampled European regions (Fig. ##FIG##3##4a## and Extended Data Fig. ##FIG##11##6##). This suggests that the spread of steppe-related ancestry was predominantly mediated through groups already admixed with GAC-related farmer groups of the eastern European plains—an observation that has major implications for understanding the emergence of the CWC.</p>", "<p id=\"Par20\">A stylistic connection between GAC and CWC ceramics has long been suggested, including the use of amphora-shaped vessels and the development of cord decoration patterns<sup>##UREF##8##52##</sup>. Moreover, shortly before the emergence of the earliest CWC groups, eastern GAC and western Yamnaya groups exchanged cultural elements in the forest–steppe transition zone northwest of the Black Sea, where GAC ceramic amphorae and flint axes were included in Yamnaya burials, and the typical Yamnaya use of ochre was included in GAC burials<sup>##UREF##9##53##</sup>, indicating close interactions between these groups. Previous ancient genomic data from a few individuals suggested that this was limited to cultural influences and not population admixture<sup>##REF##29167359##54##</sup>. However, in the light of our new genetic evidence, it seems that this zone—and possibly other similar zones of contact between GAC and groups from the steppe (for example, the Yamnaya)—were key in the formation of the CWC, through which steppe-related ancestry and GAC-related ancestry co-dispersed far towards the west and the north<sup>##UREF##10##55##</sup>. This resulted in regionally diverse situations of interaction and admixture<sup>##REF##33523926##14##,##REF##34433570##32##</sup>, but a substantial part of the CWC dispersal happened through corridors of cultural and demic transmission that had been established by the GAC during the preceding period<sup>##UREF##6##33##,##UREF##11##56##</sup>. Differences in Y-chromosomal haplogroups between CWC and Yamnaya suggest that the currently published Yamnaya-associated genomes do not represent the most direct source for the steppe ancestry component in CWC<sup>##REF##34433570##32##,##UREF##6##33##</sup>. This notion was supported by proximate ancestry modelling using published genomes<sup>##REF##26062507##1##</sup> associated with Yamnaya or Afanasievo cultural contexts as separate sources, which revealed a subtle increase in affinity for an Afanasievo-related source over a Yamnaya-related source in early individuals with European steppe ancestry before 3,000 cal. <sc>bp</sc> (Fig. ##FIG##3##4b## and Extended Data Fig. ##FIG##14##9d##). The result confirms the subtle population genomic structure in the population associated with Yamnaya or Afanasievo, showing that more dense sampling across the steppe horizon will be required to find the direct source or sources of steppe ancestry in the early CWC.</p>", "<title>HG resilience east of the Urals</title>", "<p id=\"Par21\">In contrast to the considerable number of ancient HG genomes from western Eurasia that have been studied so far, genomic data from HGs east of the Urals have remained sparse. These regions are characterized by an early introduction of pottery from areas further east, and were inhabited by complex forager societies with permanent and sometimes fortified settlements<sup>##UREF##1##20##,##UREF##12##57##</sup>. Here, we substantially expand knowledge on ancient populations of this region by reporting genomic data from 38 individuals, 28 of whom date to pottery-associated HG contexts between 8,300 and 5,000 cal. <sc>bp</sc> (Supplementary Data ##SUPPL##3##2##). Most of these genomes form a previously only sparsely sampled<sup>##REF##29743352##13##,##REF##31488661##43##</sup> ‘Neolithic steppe’ cline that spans the Siberian forest steppe zones of the Irtysh, Ishim, Ob, and Yenisei River basins to the Lake Baikal region (Fig. ##FIG##0##1c## and Extended Data Figs. ##FIG##6##1a## and ##FIG##8##3e##). Supervised admixture modelling (using the ‘deep’ set of ancestry sources; Supplementary Data ##SUPPL##6##9##) revealed contributions from three major sources in these HGs from east of the Urals: early west Siberian HG ancestry (SteppeC_8300BP_7000BP) dominated in the western forest steppe; northeast Asian HG ancestry (Amur_7500BP) was highest at Lake Baikal; and Palaeo-Siberian ancestry (SiberiaNE_9800BP) was observed in a cline of decreasing proportions from northern Lake Baikal westwards across the forest steppe<sup>##REF##29743352##13##</sup> (Extended Data Figs. ##FIG##12##7## and ##FIG##15##10a##).</p>", "<p id=\"Par22\">We used these Neolithic HG clusters (‘postNeol’ ancestry source set; Extended Data Fig. ##FIG##12##7##) as putative source groups in more proximal admixture modelling to investigate the spatiotemporal dynamics of ancestry compositions across the steppe and the Lake Baikal region after the Neolithic period. We replicate previously reported evidence for a genetic shift towards higher forest steppe HG ancestry (source SteppeCE_7000BP_3600BP) in Late Neolithic and Early Bronze Age (LNBA) individuals at Lake Baikal (clusters Baikal_5600BP_5400BP and Baikal_4800BP_4200BP)<sup>##REF##29743352##13##,##REF##32437661##58##</sup>. However, ancestry related to this cluster is also already observed at around 7,000 <sc>bp</sc> in herein-reported Neolithic HG individuals both at Lake Baikal (NEO199 and NEO200) and along the Angara river to the north (NEO843) (Extended Data Fig. ##FIG##12##7##). Both male individuals at Lake Baikal belonged to the Y-chromosome haplogroup Q1b1, characteristic of the later LNBA groups in the same region (Supplementary Note ##SUPPL##0##3b## and Supplementary Fig. ##SUPPL##0##3b.5##). Together with an early estimated admixture time (upper bound of around 7,300 cal. <sc>bp</sc>) for the LNBA groups (Supplementary Data ##SUPPL##7##14##), these results suggest that gene flow between HGs of Lake Baikal and those of the south Siberian forest steppe regions already occurred during the eastern Early Neolithic, consistent with archaeological interpretations of contact. In this region, bifacially flaked tools first appeared near Baikal<sup>##UREF##13##59##</sup>, from where the technique spread far to the west. We find echoes of such bifacial flaking in archaeological complexes (Shiderty 3, Borly, Sharbakty 1, Ust-Narym and so on) in northern and eastern Kazakhstan, around 6,500–6,000 cal. <sc>bp </sc>(refs. <sup>##UREF##14##60##,##UREF##15##61##</sup>). Here, Mesolithic cultural networks with southwest Asia have also been recorded, as evidenced by pebble and flint lithics known from southwest Asia cultures<sup>##UREF##16##62##</sup>.</p>", "<p id=\"Par23\">Genomes reported here also shed light on the genetic origins of the Early Bronze Age Okunevo Culture in the Minusinsk Basin in Southern Siberia. In contrast to previous results, we find no evidence for Lake Baikal HG-related ancestry in the Okunevo<sup>##REF##29743352##13##,##REF##32437661##58##</sup> when using our newly reported Siberian forest steppe HG genomes jointly with Lake Baikal LNBA genomes as putative proximate sources. Instead, we find that they originate from the admixture of a forest steppe HG source (best modelled as a mixture of clusters Steppe_6700BP_4600BP and SteppeCE_7000BP_3600BP) and steppe-related ancestry (Steppe_5300BP_4000BP; Extended Data Fig. ##FIG##12##7##, set ‘postBA’ and Supplementary Data ##SUPPL##6##11##). We date the admixture with steppe-related ancestry to around 4,600 <sc>bp</sc> (Supplementary Data ##SUPPL##7##14##), and find it to be modelled exclusively from an Afanasievo-related source in proximate modelling separating the Yamnaya and Afanasievo steppe ancestries (Extended Data Figs. ##FIG##14##9d## and ##FIG##15##10c,e##). This is direct evidence for gene flow from peoples of the Afanasievo Culture, who were closely related to the Yamnaya and existed near Altai and Minusinsk Basin during the era of the steppe migrations<sup>##REF##26062507##1##,##REF##32437661##58##</sup>.</p>", "<p id=\"Par24\">From around 3,700 cal. <sc>bp</sc>, individuals across the steppe and Lake Baikal regions show markedly different ancestry profiles (Fig. ##FIG##4##5## and Extended Data Figs. ##FIG##12##7## and ##FIG##14##9b##). We document a sharp increase in non-local ancestries, with only limited ancestry contributions from local HGs. The early stages of this transition are characterized by an influx of steppe-related ancestry, which decays over time from its peak of around 70% in the earliest individuals. Similar to the dynamics in western Eurasia, steppe-related ancestry is here correlated with GAC-related farmer ancestry (Poland_5000BP_4700BP; Fig. ##FIG##4##5## and Extended Data Fig. ##FIG##15##10b##), recapitulating the previously documented gene flow from GAC groups into neighbouring groups of the steppe and the forest steppe, and the eastward expansion of admixed western steppe pastoralists from the Sintashta and Andronovo complexes during the Bronze Age<sup>##REF##31488661##43##,##REF##29743675##63##</sup>. However, GAC-related ancestry is notably absent in individuals of the Okunevo culture, and individuals with steppe ancestry after 3,700 <sc>bp</sc> show a slight excess in affinity to Yamnaya over Afanasievo in proximate modelling (Extended Data Fig. ##FIG##15##10d##), providing further support for two distinct eastward migrations of western steppe pastoralists during the early (Yamnaya-related) and later (Sintashta and Andronovo) Bronze Age. The later stages of the transition are characterized by increasing central Asian (Turkmenistan_7000 BP_5000BP) and northeast Asian-related (Amur_7500BP) ancestry components (Fig. ##FIG##4##5## and Extended Data Fig. ##FIG##15##10b##). Together, these results show that deeply structured HG ancestry dominated the eastern Eurasian steppe substantially longer than in western Eurasia, before successive waves of population expansions swept across the steppe within the last 4,000 years. These included a large-scale introduction of domesticated horse lineages concomitant with new equestrian equipment and spoke-wheeled chariotry<sup>##REF##29743675##63##,##REF##34671162##64##</sup>, as well as the adoption of millet as a robust subsistence crop<sup>##UREF##17##65##</sup>.</p>", "<title>Sociocultural insights</title>", "<p id=\"Par25\">We used patterns of pairwise IBD sharing between individuals to examine our data for temporal shifts in relatedness within genetic clusters. We found clear trends of a reduction of within-cluster relatedness over time, in both western and eastern Eurasia (Extended Data Fig. ##FIG##16##11a##). This pattern is consistent with a scenario of increasing effective population sizes during this period<sup>##REF##23103233##66##</sup>. Nevertheless, we observe notable differences in temporal relatedness patterns between western and eastern Eurasia, mirroring the wider difference in population dynamics discussed above. In the west, within-group relatedness changed substantially during the Neolithic transition (around 9,000–6,000 <sc>bp</sc>), in which clusters of individuals with Anatolian farmer-related ancestry show overall reduced IBD sharing compared with clusters of individuals with HG-associated ancestry (Extended Data Fig. ##FIG##16##11a##). In the east, genetic relatedness remained high until around 4,000 <sc>bp</sc>, consistent with a much longer persistence of smaller localized HG groups (Fig. ##FIG##5##6## and Extended Data Fig. ##FIG##16##11a##).</p>", "<p id=\"Par26\">Next, we examined the data for evidence of recent parental relatedness, by identifying individuals in which more than 50 centimorgans (cM) of their genomes was contained in long (more than 20 cM) runs of homozygosity (ROH) segments<sup>##REF##34521843##67##</sup>. We detected only 29 such individuals out of a total sample of 1,396 imputed ancient genomes from across Eurasia (Extended Data Fig. ##FIG##16##11b##). This suggests that close kin mating was not common in the regions and periods covered by our data. No obviously discernible spatiotemporal or cultural clustering were observed among the individuals with recent parental relatedness. Notably, an approximately 1,700-year-old Sarmatian individual from Temyaysovo (tem003)<sup>##REF##30417088##68##</sup> was found to be homozygous for almost the entirety of chromosome 2, but without evidence of ROH elsewhere in the genome, suggesting that this is the first documented case of uniparental disomy in an ancient individual (Extended Data Fig. ##FIG##16##11c##). Among several noteworthy familial relationships (see Supplementary Fig. ##SUPPL##0##3c.2##), we report a Mesolithic father–son burial at Ertebølle (NEO568 and NEO569), as well as a Mesolithic mother–daughter burial at Dragsholm (NEO732 and NEO733), Denmark<sup>##UREF##7##34##</sup>.</p>", "<title>Formation and dissolution of the divide</title>", "<p id=\"Par27\">We have provided evidence for the existence of a clear east–west genetic division extending from the Black Sea to the Baltic, mirroring archaeological observations, and persisting for several millennia. We show that this deep ancestry division in the Eurasian human gene pool that was established during early post-LGM dispersals<sup>##REF##36859578##7##</sup> was maintained throughout the Mesolithic and Neolithic ages (Fig. ##FIG##5##6##). Accordingly, we show that the genetic effect of the Neolithic transition was highly distinct east and west of this boundary. These observations raise a series of questions related to understanding the underlying drivers.</p>", "<p id=\"Par28\">In eastern Europe, the expansion of Neolithic farming was halted for around 3,000 years, and this delay could be linked to environmental factors, with regions east of the division having more continental climates and harsher winters, possibly less suited for Middle Eastern agricultural practices<sup>##UREF##18##69##</sup>. Here, highly developed HG societies persisted with stable, complex and sometimes fortified settlements, long-distance exchange and large cemeteries<sup>##UREF##19##70##,##UREF##20##71##</sup>. A diet including freshwater fish is clear both from our isotopic data (Supplementary Data ##SUPPL##3##2##) and from analyses of lipids in pottery<sup>##UREF##20##71##</sup>. In the northern forested regions of this boundary zone, HG societies persisted until the emergence of the CWC around 5,000 cal. <sc>bp</sc>, whereas in the southern and eastern steppe regions, hunting and gathering was eventually complemented with some animal husbandry (cattle and sheep), and possibly horse herding in central Asia<sup>##UREF##21##72##</sup>. Some of these groups, such as Khvalynsk at the Volga, saw the emergence of male sodalities involved in wide-ranging trade connections of copper objects from east central Europe and the Caucasus<sup>##UREF##5##29##</sup>. Settlements were confined mainly to the flat flood plains and river valleys, whereas the steppe belt remained largely unexploited.</p>", "<p id=\"Par29\">The eventual dissolution of this genetic, economic and social border was driven by events that unfolded in the steppe region. Here, two temporal phases of technological innovations can be observed archaeologically: the widespread dispersal of ox-drawn wheeled vehicles around 5,500 cal. <sc>bp</sc> and the later development of horse riding. Combined with possible changing environmental conditions<sup>##UREF##22##73##</sup>, this opened up the steppe as an economic zone, allowing Yamnaya groups to exploit the steppe as pastoral nomads around 5,000 cal. <sc>bp</sc> (ref. <sup>##REF##36867690##74##</sup>). Eneolithic settlements along river valleys were replaced by this new mobile economy<sup>##UREF##23##75##</sup>, which finally dissolved the great genomic boundary that had persisted in the preceding millennia (Fig. ##FIG##5##6##).</p>", "<p id=\"Par30\">By 4,000 cal. <sc>bp</sc>, the invention of chariot warfare and the adoption of millet as a food crop allowed the final eastward expansion into central Asia and beyond by the Andronovo and related groups, with global legacies for the expansion of Indo-European languages<sup>##UREF##24##76##</sup>. Our study has provided new genetic knowledge on these steppe migrations on two levels: we have identified a hitherto-unknown source of ancestry in HGs from the Middle Don region contributing ancestry to the steppe pastoralists, and we have documented how the later spread of steppe-related ancestry into Europe through the CWC was first mediated through peoples associated with the GAC. In a contact zone that included forested northern regions, the CWC was rapidly formed from a cultural and genetic amalgamation of steppe-groups related to the Yamnaya and the GAC groups in eastern Europe. In accordance with their mixed cultural and genetic background, the CWC practised a mixed economy, using various subsistence strategies in different environments. This flexibility would have contributed substantially to their success in settling and adapting to very different ecological and climatic settings over a very short period of time<sup>##UREF##6##33##</sup>.</p>", "<title>Online content</title>", "<p id=\"Par45\">Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41586-023-06865-0.</p>", "<title>Supplementary information</title>", "<p>\n\n\n\n\n\n\n\n\n</p>" ]	[ "<title>Extended data figures and tables</title>", "<p id=\"Par48\">\n\n</p>", "<p id=\"Par49\">\n\n</p>", "<p id=\"Par50\">\n\n</p>", "<p id=\"Par51\">\n\n</p>", "<p id=\"Par52\">\n\n</p>", "<p id=\"Par53\">\n\n</p>", "<p id=\"Par54\">\n\n</p>", "<p id=\"Par55\">\n\n</p>", "<p id=\"Par56\">\n\n</p>", "<p id=\"Par57\">\n\n</p>", "<p id=\"Par58\">\n\n</p>", "<title>Extended data</title>", "<p>is available for this paper at 10.1038/s41586-023-06865-0.</p>", "<title>Supplementary information</title>", "<p>The online version contains supplementary material available at 10.1038/s41586-023-06865-0.</p>", "<title>Acknowledgements</title>", "<p>We acknowledge P. Bennike, who was involved in initiating this project, for her substantial contributions to its conception and to prehistoric research more broadly; she passed away in 2017. We thank L. Olsen and P. Selmer Olsen for administrative and technical assistance, respectively; the UK Biobank for access to the UK Biobank genomic resource; Illumina for collaboration; and S. Ellingvåg for assistance with sample access. E.W. thanks St John’s College, Cambridge, for providing a stimulating environment of discussion and learning. The Lundbeck Foundation GeoGenetics Centre is supported by grants from the Lundbeck Foundation (R302-2018-2155 and R155-2013-16338), the Novo Nordisk Foundation (NNF18SA0035006), the Wellcome Trust (214300), the Carlsberg Foundation (CF18-0024), the Danish National Research Foundation (DNRF94, DNRF174), the University of Copenhagen (KU2016 programme) and Ferring Pharmaceuticals A/S to E.W. This research has been conducted using the UK Biobank Resource and the iPSYCH Initiative, funded by the Lundbeck Foundation (R102-A9118 and R155-2014-1724). This work was further supported by the Swedish Foundation for Humanities and Social Sciences grant (Riksbankens Jubileumsfond M16-0455:1) to K.K. M.E.A. was supported by Marie Skłodowska-Curie Actions of the EU (grant no. 300554), The Villum Foundation (grant no. 10120) and Independent Research Fund Denmark (grant no. 7027-00147B). W.B. is supported by the Hanne and Torkel Weis-Fogh Fund (Department of Zoology, University of Cambridge). A.P. is funded by the Wellcome grant WT214300; B.S.d.M and O.D. by the Swiss National Science Foundation (SFNS PP00P3_176977) and the European Research Council (ERC 679330); R. Macleod by an SSHRC doctoral studentship grant (G101449: ‘Individual Life Histories in Long-Term Cultural Change’); G.R. by a Novo Nordisk Foundation Fellowship (gNNF20OC0062491); N.N.J. by Aarhus University Research Foundation; B.S.P. by an ERC-Starter Grant 'NEOSEA' (grant no. 949424); H.S. by a Carlsberg Foundation Fellowship (CF19-0601); G.S. by Marie Skłodowska-Curie Individual Fellowship ‘PALAEO-ENEO’ (grant agreement number 751349); A. J. Schork by a Lundbeckfonden Fellowship (R335-2019-2318) and the National Institute on Aging (NIH award numbers U19AG023122, U24AG051129 and UH2AG064706); A.L. and I.S. by the Science Committee, Ministry of Education and Science of the Republic of Kazakhstan (AP08856317); B.G.-R. and M.G.-M. by the Spanish Ministry of Science and Innovation (project HAR2016-75605-R); C.M.-L. and O.R. by the Italian Ministry for the Universities (grants 2010-11 prot.2010EL8TXP_001, ‘Biological and cultural heritage of the central-southern Italian population through 30 thousand years’ and 2008 prot. 2008B4J2HS_001, ‘Origin and diffusion of farming in central-southern Italy: a molecular approach’); and D.C.-S. and I.G.-Z. by the Spanish Ministry of Science and Innovation (project HAR2017-86262-P). D.C.S.-G. acknowledges funding from the Generalitat Valenciana (CIDEGENT/2019/061) and the Spanish Government (EUR2020-112213). D.B. was supported by the NOMIS Foundation and Marie Skłodowska-Curie Global Fellowship 'CUSP' (grant no. 846856); E.R.U. by the Science Committee, Ministry of Education and Science of the Republic of Kazakhstan (АР09261083: ‘Transcultural Communications in the Late Bronze Age (Western Siberia–Kazakhstan–Central Asia)‘); E.C. by Villum Fonden (17649); J.E.A.T. by the Spanish Ministry of Economy and Competitiveness (HAR2013‐46861‐R) and Generalitat Valenciana (Aico/ 2018/125 and Aico 2020/97); and P.K. by the Russian Ministry of Science and Higher Education (Ural Federal University Program of Development within the Priority-2030 Program). P.K. also acknowledges the Museum of the Institute of Plant and Animal Ecology (UB RAS, Ekaterinburg). L.Y. acknowledges funding by the Science Committee of the Armenian Ministry of Education and Science (project 21AG-1F025); L.O. by the ERC Consolidator Grant ‘PEGASUS’ (agreement no. 681605); M. Sablin by the Russian Ministry of Science and Higher Education (075-15-2021-1069); N.C. by Historic Environment Scotland; S.V. and E.V.V. by the Russian Ministry of Science and Higher Education (075-15-2022-328); and V.M. by the Science Committee, Ministry of Education and Science of the Republic of Kazakhstan (AR08856925). V.A. is supported by a Lundbeckfonden Fellowship (R335-2019-2318); P.H.S. by the National Institute of General Medical Sciences (R35GM142916); S.R. by the Novo Nordisk Foundation (NNF14CC0001); T.S.K. is funded by Carlsberg grant CF19-0712; R.D. by the Wellcome Trust (WT214300); R.N. by the National Institute of General Medical Sciences (NIH grant R01GM138634); and F. Racimo by a Villum Fonden Young Investigator Grant (no. 00025300); by a Novo Nordisk Fonden Data Science Ascending Investigator Award (NNF22OC0076816) and by the European Research Council (ERC) under the European Union’s Horizon Europe programme (grant agreement No. 101077592). T.W. and V.A. are supported by the Lundbeck Foundation iPSYCH initiative (R248-2017-2003).</p>", "<title>Author contributions</title>", "<p>E.W. initiated the study. M.E.A., M.S., T.S.K., R.D., R.N., O.D., T.W., F. Racimo, K.K. and E.W. led the study. M.E.A., M.S., A.F., M.M., C.L.-F., R.N., E.C., T.W., K.K. and E.W. conceptualized the study. M.E.A., M.S., H.S., L.O., T.S.K., R.D., R.N., O.D., T.W., F. Racimo, K.K. and E.W. supervised the research. M.E.A., L.O., R.D., R.N., T.W., K.H.K., K.K. and E.W. acquired funding for research. M.E.A., A.F., J.S., K.-G.S., M.L.S.J., M.U.H., A.A.T., A.C., A.Z., A.M.S., A.J.H, A.G., A.L., B.H.N., B.G.-R, C.B., C.L., C.M.-L., D.V.P., D.C.-S., D.O.L., D.E., D.C.S.-G., D.B., E.B.P., E.K., E.V.V., E.R.U., E. Kannegaard, F. Radina, H.D., I.G.-Z., I.P., I.S., J.G., J.H., J.E.A.T., J.Z., J.V., K.B.P., K.T., L.N., L.L., L.N.M., L.Y., L.P., L. Sarti, L. Slimak, L.K., M.G.-M., M. Silvestrini, M.D., M.V., M.S.N., M.R., M.S., M.P., M.C., M. Sablin, N.C., N.S., O.P., O.R., O.V.L., P.A., P.K., P.C., P. Ríos, P. Lotz, P. Lysdahl, P.M., P.P., P.B., P.d.B.D., P.V.P., P.P.M., P.W., R.V.S., R. Maring, R. Menduiña, R.B., R.T., S.V., S.W., S.B., S.S., S.A.S., S.H.A., T.D.P., T.Z.T.J., Y.B.S., V.I.M., V.S., V.M., Y.M., I.M., O.G. and N.L. were involved in sample collection. M.E.A., M.S., A.R.-M., E.K.I.-P., W.B., A.I., J.S., A.P., B.S.d.M., M.I., M.M., L.V., A. Stern, C.G., F.E.Y, D.J.L., T.S.K., R.D., R.N., O.D., F. Racimo, K.H.K. and E.W. were involved in developing and applying methodology. M.E.A., J.S., C.G. and L.V. led the DNA laboratory work research component. K.-G.S., A.F. and M.E.A. led bioarchaeological data curation. M.E.A., M.S., A.R.-M., E.K.I.-P., W.B., A.I., A.P., B.S.d.M., B.S.P., A.H., R.A.H., T.V., H.M., A.M., A.V., A.B.N., P. Rasmussen, G.R., A. D. Ramsøe, A. Skorobogatov, A. J. Schork, A. Rosengren, C.J.M., I.A., L.Z., R. Maring, V.S., V.A., P.H.S., S.R., T.S.K., O.D. and F. Racimo undertook formal analyses of data. M.E.A., M.S., A.R.-M., E.K.I.-P., A.F., W.B., A.I., K.-G.S., R. Macleod, D.J.L., P.H.S., T.S.K., F. Racimo and E.W. drafted the main text (M.E.A. and M.S. led this). M.E.A., M.S., A.R.-M., E.K.I.-P., A.F., W.B., A.I., K.-G.S., A.P., B.S.d.M., B.S.P., A.H., R. Macleod, R.A.H., T.V., M.F.M., A.B.N., M.U.H., P. Rasmussen, A. J. Stern, N.N.J., H.S., G.S., A. Ramsøe, A. Skorobogatov, A. Rosengren, A.O., A.B., A.C., A.G., A.L., A.B.G., C.J.M., D.C.S.-G., E.B.P., E. Kostyleva, E.R.U., E. Kannegaard, I.G.-Z., I.P., I.S., J.G., J.H., J.E.A.T., K.H.K., L.Z, L.Y., L.P., L.K., M.B., M.G.-M., M.V., M.R., M.J., N.B., O.V.L., O.C.U., P.K., P. Lysdahl, P.B., P.W., R.V.S., R. Maring, R.B., R.I., S.V., S.W., S.B., S.H.A., T.Z.T.J., V.S., D.J.L., P.H.S., S.R., T.S.K., O.D. and F. Racimo drafted supplementary notes and materials. M.E.A., M.S., A.R.-M., E.K.I.-P., A.F., W.B., A.I., G.S., A.H., M.L.S.J., F.D., R. Macleod, L. Sørensen, P.O.N., R.A.H., T.V., H.M., A.M., N.N.J., H.S., A. Ramsøe, A. Skorobogatov, A. J. Schork, A. Ruter, A.O., B.H.N., B.G.-R., D.C.-S., D.C.S.-G., I.G.-Z., I.P., J.G., J.E.A.T., L.Z., L.O., L.K., M.G.-M., P.d.B.D., R.I., S.A.S., D.J.L., I.M., O.G., P.H.S., T.S.K., R.D., R.N., O.D., T.W., F. Racimo, K.K. and E.W. were involved in reviewing drafts and editing (M.E.A., M.S., A.F., K.-G.S., R. Macleod and E.W. led this, and subsequent finalization of the study). All co-authors read, commented on and agreed on the submitted manuscript.</p>", "<title>Peer review</title>", "<title>Peer review information</title>", "<p id=\"Par46\"><italic>Nature</italic> thanks Benjamin Peter and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.</p>", "<title>Data availability</title>", "<p>All adapter-trimmed sequence data (fastq) for the samples sequenced in this study are publicly available on the European Nucleotide Archive under accession <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ebi.ac.uk/ena/data/view/PRJEB64656\">PRJEB64656</ext-link>, together with sequence alignment map files, aligned using human build GRCh37. The full analysis dataset including both imputed and pseudo-haploid genotypes for all ancient individuals used in this study is available at 10.17894/ucph.d71a6a5a-8107-4fd9-9440-bdafdfe81455. Aggregated IBD-sharing data as well as high-resolution versions of supplementary figures are available at Zenodo (10.5281/zenodo.8196989). Previously published ancient genomic data used in this study are detailed in Supplementary Data ##SUPPL##5##7##, and are all already publicly available. Bioarchaeological data (including accelerator mass spectrometry results) are included in the online supplementary materials of this submission. Map figures were created using Natural Earth Data (in Figs. ##FIG##0##1##– ##FIG##2##3## and ##FIG##5##6## and Extended Data Figs. ##FIG##6##1##, ##FIG##8##3##, ##FIG##9##4## and ##FIG##13##8##–##FIG##16##11##.).</p>", "<title>Code availability</title>", "<p>All analyses relied on available software, which has been fully referenced in the manuscript and is detailed in the relevant supplementary notes. A collection of R functions for IBD-based mixture model inference is available at <ext-link ext-link-type=\"uri\" xlink:href=\"https://github.com/martinsikora/mixmodel_ibd\">https://github.com/martinsikora/mixmodel_ibd</ext-link>.</p>", "<title>Competing interests</title>", "<p id=\"Par47\">The authors declare no competing interests.</p>" ]	[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>Sample overview and broad-scale genetic structure.</title><p><bold>a</bold>,<bold>b</bold>, Geographical (<bold>a</bold>) and temporal (<bold>b</bold>) distribution of the 317 ancient genomes sequenced and reported in this study. Insert shows dense sampling in Denmark<sup>##UREF##7##34##</sup>. The age and the geographical region of ancient individuals are indicated by the colour and the shape of the symbols, respectively. Colour scale for age is capped at 15,000 years; older individuals are indicated with black. Random jitter was added to geographical coordinates to avoid overplotting. <bold>c</bold>,<bold>d</bold>, PCA of 3,316 modern and ancient individuals from Eurasia, Oceania and the Americas (<bold>c</bold>), and restricted to 2,126 individuals from western Eurasia (west of the Urals) (<bold>d</bold>). Principal components were defined using both modern and imputed ancient (<italic>n</italic> = 1,492) genomes passing all filters, with the remaining low-coverage ancient genomes projected. Ancient genomes sequenced in this study are indicated with black circles (imputed genomes passing all filters, <italic>n</italic> = 213) or grey diamonds (pseudo-haploid projected genomes; <italic>n</italic> = 104). Genomes of modern individuals are shown in grey, with population labels corresponding to their median coordinates. BA, Bronze Age.</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>Genetic ancestry transects of western Eurasia.</title><p><bold>a</bold>, Regional timelines of genetic ancestry compositions within the past 12,000 years in western Eurasia. Ancestry proportions in 1,012 imputed ancient genomes (representing populations west of the Urals) inferred using supervised ancestry modelling with the ‘deep’ HG ancestry source groups. Coloured bars within the timelines represent ancestry proportions for temporally consecutive individuals, with the width corresponding to their age difference. Individuals with identical age were offset along the time axis by adding random jitter. <bold>b</bold>, Map highlighting geographical areas (coloured areas) for samples included in the individual regional timelines, and excavation locations (black crosses). Only shotgun-sequenced genomes were used in our study, so the exact timing of ancestry shifts might differ slightly from previous studies if they are based on different types of data from different individuals.</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><title>Spatiotemporal kriging analysis of major ancestries.</title><p>The temporal transects show how WHG ancestry (Italy_15000BP_9000BP) was replaced by Neolithic farmer ancestry (Boncuklu_10000BP) during the Neolithic transition in Europe. Later, the steppe migrations around 5,000 cal. <sc>bp</sc> introduced both EHG (MiddleDon_7500BP) and CHG (Caucasus_13000BP_10000BP) ancestry into Europe, thereby reducing Neolithic farmer ancestry.</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><title>Fine-scale structure and temporal dynamics of steppe-related ancestry during the second transition in Europe.</title><p><bold>a</bold>, Correlation between the estimated proportions of steppe-related and GAC farmer-related ancestries (‘postNeol’ source set), across west Eurasian target individuals. <bold>b</bold>, Timeline of difference in estimated steppe-related ancestry proportions, using individuals from the genetic cluster ‘Steppe_5000BP_4300BP’ associated with either Yamnaya or Afanasievo cultural contexts as separate sources. Individuals from European post-Neolithic genetic clusters before 3,000 cal. <sc>bp</sc> are indicated with coloured symbols; other west Eurasian target individuals are indicated with grey symbols. Symbols with black outlines highlight early steppe-related individuals associated with either Corded Ware or related (for example, Battle Axe) cultural contexts.</p></caption></fig>", "<fig id=\"Fig5\"><label>Fig. 5</label><caption><title>Genetic transects east of the Urals.</title><p>Timelines of genetic ancestry compositions within the past 6,000 years east of the Urals. Shown are ancestry proportions in 148 imputed ancient genomes from this region, inferred using supervised ancestry modelling (‘postNeol’ source set). Panels separate ancestry proportions from local forest steppe HGs (HG) and sources representing ancestries originating further east or west.</p></caption></fig>", "<fig id=\"Fig6\"><label>Fig. 6</label><caption><title>Genetic relatedness across western Eurasia.</title><p>Maps showing networks of highest IBD sharing (top 10 highest sharing per individual) during different time periods for 579 imputed genomes predating 3,000 cal. <sc>bp</sc> and located in the geographical region shown. Shading and thickness of lines are scaled to represent the amount of IBD shared between two individuals. In the earliest periods, sharing networks exhibit strong links within relatively narrow geographical regions, representing predominantly close genetic ties between small HG communities, and rarely crossing the East–West divide extending from the Baltic to the Black Sea. From around 9,000 cal. <sc>bp</sc> onwards, a more extensive network with weaker individual ties appears in the south, linking Anatolia to the rest of Europe, as early Neolithic farmer communities spread across the continent. The period 7,000–5,000 cal. <sc>bp</sc> shows more connected subnetworks of western European and eastern/northern European Neolithic farmers, while locally connected networks of HG communities prevail on the eastern side of the divide. From c. 5,000 <sc>bp</sc> onwards the divide finally collapses, and continental-wide genetic relatedness unifies large parts of western Eurasia.</p></caption></fig>", "<fig id=\"Fig7\"><label>Extended Data Fig. 1</label><caption><title>Genetic structure of the 317 herein-reported ancient genomes.</title><p><bold>a</bold>–<bold>d</bold>, PCA of 3,316 modern and ancient individuals from Eurasia, Oceania and the Americas (<bold>a</bold>,<bold>b</bold>), as well as restricted to 2,126 individuals from western Eurasia (west of the Urals) (<bold>c</bold>,<bold>d</bold>). Shown are analyses with principal components inferred either using both modern and imputed ancient genomes passing all filters, and projecting low coverage ancient genomes (<bold>a</bold>,<bold>c</bold>); or only modern genomes and projecting all ancient genomes (<bold>b</bold>,<bold>d</bold>). Ancient genomes sequenced in this study are indicated either with black circles (imputed genomes) or grey diamonds (projected genomes). <bold>e</bold>, Model-based clustering results using ADMIXTURE for 284 newly reported genomes (excluding close relatives and individuals flagged for possible contamination). Results shown are based on ADMIXTURE runs from K = 2 to K = 15 on 1,593 ancient individuals, corresponding to the full set of 1,492 imputed genomes passing filters as well as 101 low coverage genomes represented by pseudo-haploid genotypes (flags “lowcov” or “lowGpAvg”, Supplementary Data ##SUPPL##5##7##; indicated with alpha transparency in plot).</p></caption></fig>", "<fig id=\"Fig8\"><label>Extended Data Fig. 2</label><caption><title>Imputation accuracy of ancient DNA.</title><p><bold>a</bold>, Imputation accuracy across 42 high-coverage ancient genomes when downsampled to lower depth of coverage values (see Supplementary Note ##SUPPL##0##2## and Supplementary Table ##SUPPL##0##2.1##). <bold>b</bold>, Imputation accuracy for 1× depth of coverage across 9 prehistoric European genomes; <bold>c</bold>, across 5 Viking age genomes; and <bold>d</bold>, across 7 ancient genomes from Early Medieval Hungary. In all panels, imputation accuracy is shown as the squared Pearson correlation between imputed and true genotype dosages as a function of MAF of the target variant sites.</p></caption></fig>", "<fig id=\"Fig9\"><label>Extended Data Fig. 3</label><caption><title>Genetic clustering of ancient individuals.</title><p>Characterization of genetic clusters for 1,401 imputed ancient individuals from Eurasia (that is, excluding 91 individuals from Africa and Americas), inferred from pairwise IBD sharing (indicated using coloured symbols throughout), <bold>a</bold>, Temporal distribution of clustered individuals, grouped by broad ancestry cluster. <bold>b</bold>,<bold>c</bold>, Geographical distribution of clustered individuals, shown for individuals predating 3,000 <sc>bp</sc> (<bold>b</bold>) and after 3,000 <sc>bp</sc> (<bold>c</bold>). <bold>d</bold>, Network graph of pairwise IBD sharing between 596 ancient Eurasians predating 3,000 <sc>bp</sc>, highlighting within- and between-cluster relationships. Each node represents an individual, and the width of edges connecting nodes indicates the fraction of the genome shared IBD between the respective pair of individuals. Network edges were restricted to the 10 highest sharing connections for each individual, and the layout was computed using the force-directed Fruchterman-Reingold algorithm. <bold>e</bold>, Neighbour-joining tree showing relationships between genetic clusters, inferred using total variation distance (TVD) of IBD painting palettes. <bold>f</bold>,<bold>g</bold>, PCA of 3,119 Eurasian (<bold>f</bold>) or 2,126 west Eurasian (<bold>g</bold>) ancient and modern individuals (“HO” dataset).</p></caption></fig>", "<fig id=\"Fig10\"><label>Extended Data Fig. 4</label><caption><title>Genetic structure of European HGs after the LGM.</title><p><bold>a</bold>, Supervised ancestry modelling using non-negative least squares on IBD sharing profiles. Panels show estimated ancestry proportions for target individuals from genetic clusters representing European HGs, using different sets of increasingly proximal source groups. Individuals used as sources in a particular set are indicated with black crosses and coloured bars with 100% ancestry proportion. Black lines indicate 1 standard error for the respective ancestry component. <bold>b</bold>, Residuals for model fit of target individuals from selected genetic clusters across different source sets. <bold>c</bold>, Moon charts showing spatial distribution of ancestry proportions in European HGs deriving from four European source groups (set “hgEur2”; source origins shown with coloured symbol). Estimated ancestry proportions are indicated by both size and amount of fill of moon symbols. Note that ‘Italy_15000BP_9000BP’ and ‘RussiaNW_11000BP_8000BP’ correspond to ‘WHG’ and ‘EHG’ labels used in previous studies. <bold>d</bold>, Maps showing networks of highest between-cluster IBD sharing (top 10 highest sharing per individual) for individuals from two genetic clusters representing Scandinavian HGs. See Supplementary Data ##SUPPL##2##1## and ##SUPPL##5##7## for details of individual sample IDs presented here.</p></caption></fig>", "<fig id=\"Fig11\"><label>Extended Data Fig. 5</label><caption><title>Ancestry modelling for HG and Neolithic farmer-associated genetic clusters.</title><p>Supervised ancestry modelling using non-negative least squares on IBD sharing profiles. Panels show estimated ancestry proportions of two global Eurasian clusters, corresponding to European HGs before 4,000 <sc>bp</sc> and individuals from Europe and western Asia from around 10,000 <sc>bp</sc> until historical times, including Anatolian-associated (Neolithic) farmers, Caucasus HGs and recent individuals with genetic affinity to the Levant. Columns show results of modelling target individuals using three panels of increasingly distal source groups: “postBA”: Bronze Age and Neolithic source groups; “postNeol”, Bronze Age and later targets using Late Neolithic/early Bronze Age and earlier source groups; “deep”, Mesolithic and later targets using deep ancestry source groups. Individuals used as sources in a particular set are indicated with black crosses and coloured bars with 100% ancestry proportion. Black lines indicate 1 standard error for the respective ancestry component.</p></caption></fig>", "<fig id=\"Fig12\"><label>Extended Data Fig. 6</label><caption><title>Ancestry modelling for post-Neolithic genetic clusters.</title><p>Supervised ancestry modelling using non-negative least squares on IBD sharing profiles. Panels show estimated ancestry proportions of a global Eurasian cluster corresponding to European individuals after 5,000 <sc>bp</sc>, as well as pastoralist groups from the Eurasian steppe. Columns show results of modelling target individuals using three panels of increasingly distal source groups: “postBA”: Bronze Age and Neolithic source groups; “postNeol”, Bronze Age and later targets using Late Neolithic/early Bronze Age and earlier source groups; “deep”, Mesolithic and later targets using deep ancestry source groups. Individuals used as sources in a particular set are indicated with black crosses and coloured bars with 100% ancestry proportion. Black lines indicate 1 standard error for the respective ancestry component.</p></caption></fig>", "<fig id=\"Fig13\"><label>Extended Data Fig. 7</label><caption><title>Ancestry modelling for genetic clusters east of the Urals.</title><p>Supervised ancestry modelling using non-negative least squares on IBDaring profiles. Panels show estimated ancestry proportions of a global Eurasian cluster corresponding to central, east and north Asian individuals with east Eurasian genetic affinities. Columns show results of modelling target individuals using three panels of increasingly distal source groups: “postBA”: Bronze Age and Neolithic source groups; “postNeol”, Bronze Age and later targets using Late Neolithic/early Bronze Age and earlier source groups; “deep”, Mesolithic and later targets using deep ancestry source groups. Individuals used as sources in a particular set are indicated with black crosses and coloured bars with 100% ancestry proportion. Black lines indicate 1 standard error for the respective ancestry component.</p></caption></fig>", "<fig id=\"Fig14\"><label>Extended Data Fig. 8</label><caption><title>Dynamics of the Neolithic transition in Europe.</title><p><bold>a</bold>, Supervised ancestry modelling using non-negative least squares on IBD sharing profiles. Panels show estimated ancestry proportions for target individuals from genetic clusters representing European Neolithic farmer individuals (“fEur” source set). Individuals used as sources in a particular set are indicated with black crosses and coloured bars with 100% ancestry proportion. Black lines indicate 1 standard error for the respective ancestry component. <bold>b</bold>, Composition of HG ancestry proportions from different source groups in individuals with Neolithic farmer ancestry, shown as bar plots. Grey bars represent contributions from a source with ancestry related to local HGs. <bold>c</bold>, Moon charts showing spatial distribution of estimated ancestry proportions related to local HGs across Europe. Estimated ancestry proportions are indicated by size and amount of fill of moon symbols. Coloured areas indicate the geographical extent of individuals included as local sources in the respective regions. <bold>d</bold>, Estimated time of admixture between local HG groups and Neolithic farmers. Black diamonds and error bars represent point estimate and standard errors of admixture time, coloured bars show temporal range of included target individuals. The time to admixture was adjusted backwards by the average age of individuals for each region. <bold>e</bold>, Moon charts showing spatial distribution of estimated ancestry proportions derived from genetic clusters of early Neolithic European farmers (locations indicated with coloured symbols). Estimated ancestry proportions are indicated by size and amount of fill of moon symbols. Red symbols indicate individuals where standard errors exceed the point estimates for the respective ancestry source.</p></caption></fig>", "<fig id=\"Fig15\"><label>Extended Data Fig. 9</label><caption><title>Dynamics of the steppe transition in Europe.</title><p><bold>a</bold>, Estimated time of admixture between local HG groups and Neolithic farmers. Black diamonds and error bars represent point estimate and standard errors of admixture time, coloured bars show temporal range of included target individuals. The time to admixture was adjusted backwards by the average age of individuals for each region. <bold>b</bold>, Moon charts showing spatial distribution of estimated ancestry proportions related to local Neolithic farmers across Europe. Estimated ancestry proportions are indicated by size and amount of fill of moon symbols. Coloured areas indicate the geographical extent of individuals included as local sources in the respective regions. <bold>c</bold>, Map showing networks of highest between-cluster IBD sharing (top 10 highest sharing per individual) for individuals from genetic cluster “Steppe_5000BP_4300BP” representing the major steppe ancestry source for Europeans. <bold>d</bold>, Distributions of difference in estimated steppe-related ancestry proportions, using individuals from the genetic cluster “Steppe_5000BP_4300BP”, associated with either Yamnaya or Afanasievo cultural contexts as separate sources.</p></caption></fig>", "<fig id=\"Fig16\"><label>Extended Data Fig. 10</label><caption><title>Genetic transformations across the Eurasian steppe.</title><p><bold>a</bold>, Moon charts showing spatial distribution of estimated ancestry proportions of Siberian HGs from the “deep” Siberian ancestry sources (names and locations indicated with coloured symbols). Estimated ancestry proportions are indicated by size and amount of fill of moon symbols. <bold>b</bold>, Timelines of ancestry proportions from “postNeol” sources in central and north Asian ancient individuals after 5,000 <sc>bp</sc>. Symbol shape and colour indicate the genetic cluster of each individual. Black lines indicate 1 standard error. <bold>c</bold>,<bold>d</bold>, Difference in estimated steppe-related ancestry proportions, using individuals from genetic cluster “Steppe_5000BP_4300BP” associated with either Yamnaya or Afanasievo cultural contexts as separate sources, as a function of time (<bold>c</bold>) or total estimated steppe-ancestry proportion (<bold>d</bold>). Individuals from genetic clusters of individuals associated with Okunevo (blue stars) or Sintashta/Andronovo (green diamonds) contexts are indicated with coloured symbols.</p></caption></fig>", "<fig id=\"Fig17\"><label>Extended Data Fig. 11</label><caption><title>Patterns of co-ancestry.</title><p><bold>a</bold>, Panels show within-cluster genetic relatedness over time, measured as the total length of genomic segments shared IBD between individuals. Results for both measures are shown separately for individuals from western versus eastern Eurasia. Small grey dots indicate estimates for individual pairs, with larger coloured symbols indicating median values within genetic clusters. Ranges of median values for major ancestry groups are indicated with labelled convex hulls. <bold>b</bold>, Distribution of ROH lengths for 29 individuals with evidence for recent parental relatedness (>50 cM total in ROHs > 20 cM). <bold>c</bold>, Karyogram showing genomic distribution of ROH in individual tem003, an ancient case of uniparental disomy for chromosome 2. Regions within ROH are indicated with blue colour.</p></caption></fig>" ]	[]	[]	[]	[]	[]	[]	[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM3\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM4\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM5\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM6\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM7\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM8\"></supplementary-material>" ]	[ "<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>These authors contributed equally: Morten E. Allentoft, Martin Sikora, Alba Refoyo-Martínez, Evan K. Irving-Pease, Anders Fischer, William Barrie, Andrés Ingason</p></fn><fn><p>Deceased: Esben Kannegaard, Peder Mortensen</p></fn><fn><p>These authors jointly supervised this work: Thorfinn Korneliussen, Richard Durbin, Rasmus Nielsen, Olivier Delaneau, Thomas Werge, Fernando Racimo, Kristian Kristiansen, Eske Willerslev</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"41586_2023_6865_Fig1_HTML\" id=\"d32e2392\"/>", "<graphic xlink:href=\"41586_2023_6865_Fig2_HTML\" id=\"d32e2852\"/>", "<graphic xlink:href=\"41586_2023_6865_Fig3_HTML\" id=\"d32e2864\"/>", "<graphic xlink:href=\"41586_2023_6865_Fig4_HTML\" id=\"d32e2881\"/>", "<graphic xlink:href=\"41586_2023_6865_Fig5_HTML\" id=\"d32e2911\"/>", "<graphic xlink:href=\"41586_2023_6865_Fig6_HTML\" id=\"d32e3169\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6865_Fig7_ESM\" id=\"d32e3875\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6865_Fig8_ESM\" id=\"d32e3904\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6865_Fig9_ESM\" id=\"d32e3960\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6865_Fig10_ESM\" id=\"d32e3989\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6865_Fig11_ESM\" id=\"d32e4007\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6865_Fig12_ESM\" id=\"d32e4022\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6865_Fig13_ESM\" id=\"d32e4033\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6865_Fig14_ESM\" id=\"d32e4059\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6865_Fig15_ESM\" id=\"d32e4082\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6865_Fig16_ESM\" id=\"d32e4115\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6865_Fig17_ESM\" id=\"d32e4136\"/>" ]	[ "<media xlink:href=\"41586_2023_6865_MOESM1_ESM.pdf\"><label>Supplementary Information</label><caption><p>Supplementary Notes 1–7: <bold>1</bold>, Data Generation and Authentication; <bold>2</bold>, Imputation of ancient DNA (including Figures S2.1 to S2.11, and Tables S2.1 and S2.2); <bold>3</bold>, Demographic Inference, comprising: 3a ‘Phylogenetic analysis of mtDNA sequences’ (including Figures S3a.1 to S3a.3), 3b ‘Y chromosome / sex determination’ (including Figures S3b.1 to S3b.8), 3c ‘Relatedness’ (including Figures S3c.1 and S3c.2, and Tables S3c.1 and S3c.2), 3d ‘Overall Population Structure’ (including Figures S3d.1 to S3d.16), 3e ‘Inferring the spatiotemporal spread of population movements in the past 13 millennia’ (including Figures S3e.1 to S3e.5, and animations S3.1 to s3e.11), 3f ‘HBD/ IBD sharing/ROH/clustering’ (including Figures S3f.1 to S3f.53); <bold>4</bold>, <sup>14</sup>C chronology and estimates of reservoir effects (including Table S4.1); <bold>5</bold>, From forager to farmer in western Eurasia: an archaeological overview (including Figures S5.1 to S5.3); <bold>6</bold>, Catalogue of Danish archaeological sites (including Figures S6.1 to S6.15); and <bold>7</bold>, Catalogue of non-Danish archaeological sites (including Figures S7.1 to S7.3, and Tables S7.1 to S7.3).</p></caption></media>", "<media xlink:href=\"41586_2023_6865_MOESM2_ESM.pdf\"><caption><p>Reporting Summary</p></caption></media>", "<media xlink:href=\"41586_2023_6865_MOESM3_ESM.xlsx\"><label>Supplementary Data 1</label><caption><p>Summary details of samples presented with novel genome data.</p></caption></media>", "<media xlink:href=\"41586_2023_6865_MOESM4_ESM.xlsx\"><label>Supplementary Data 2–4</label><caption><p>Supplementary Data 2 contains dates, isotopes and context. Supplementary Data 3 includes reservoir correction calculations, and Supplementary Data 4 contains isotopes and all individual samples.</p></caption></media>", "<media xlink:href=\"41586_2023_6865_MOESM5_ESM.xlsx\"><label>Supplementary Data 5 and 6</label><caption><p>Supplementary Data 5 contains DNA contamination estimates and Supplementary Data 6 contains relatedness estimates.</p></caption></media>", "<media xlink:href=\"41586_2023_6865_MOESM6_ESM.xlsx\"><label>Supplementary Data 7</label><caption><p>Full ancient genomes dataset.</p></caption></media>", "<media xlink:href=\"41586_2023_6865_MOESM7_ESM.xlsx\"><label>Supplementary Data 8–13</label><caption><p>Supplementary Data 8 contains mixture model sets. 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PMC10781628	38200302	[]	[ "<title>Methods</title>", "<title>Using CZ states in large-scale quantum-computing architectures</title>", "<p id=\"Par26\">Magic states are distilled to complete a universal set of fault-tolerant logic gates (see Extended Data Fig. ##FIG##4##1## for an overview of the details of a generic magic-state distillation protocol). In any such protocol, input magic states with some inherent error are encoded on quantum error-correcting codes. The encoded magic states are then used in distillation circuits to produce better magic states with higher fidelity. We can use magic states with near-perfect fidelity to perform fault-tolerant logic gates.</p>", "<p id=\"Par27\">We choose different magic-state distillation protocols depending on the magic states we prepare. In the next section, we review details on how we can use CZ states in large-scale fault-tolerant quantum computing. Specifically, we can convert CZ states into Toffoli states using Pauli measurements and Clifford operations<sup>##UREF##20##28##</sup>, such that we can adopt well-known magic-state distillation protocols for further rounds of distillation<sup>##UREF##4##11##</sup>. This conversion technique is probabilistic, as it depends on obtaining the correct outcome from a Pauli measurement. In addition to these results, we show how we can recover a CZ state from the output state, assuming we get the incorrect outcome of the Pauli measurement, thereby conserving the available resource states.</p>", "<p id=\"Par28\">We also provide some examples of how we can inject small codes into larger codes in the section ‘<xref rid=\"Sec8\" ref-type=\"sec\">Injecting small codes into larger codes</xref>’, as it will be required to take the encoded state we prepared in the main text and use it in subsequent rounds of magic-state distillation. Specifically, we show how to take distance-2 codes and encode their state on the surface code, the heavy-hex code and the colour code with a higher distance. Notably, the heavy-hex code is readily implemented on the heavy-hex lattice geometry on which we conducted the experiment. For each of these injection schemes, we argue that we can detect any single error that may occur. This is important to maintain the error suppression obtained when preparing the CZ state.</p>", "<p id=\"Par29\">In the case of the colour-code state-injection protocol, we inject the error-detecting code described in the main text directly into the larger code. In the case of the surface code and heavy-hex code, however, we inject a related code, that we call the [[4, 1, 2]] code. To complete these injection protocols, we need to take the CZ state prepared on the error-detecting code and copy its logical state onto two copies of the [[4, 1, 2]] code. We give a fault-tolerant procedure for this transformation in the section ‘<xref rid=\"Sec12\" ref-type=\"sec\">Encoding the CZ state on two [[4, 1, 2]] codes using the heavy-hex lattice geometry</xref>’.</p>", "<title>Magic-state distillation with the CZ state</title>", "<p id=\"Par30\">Although magic-state distillation for the CZ state has not been well-studied in the literature, it is known that two copies of the state can be probabilistically converted into a Toffoli state using Pauli measurements and Clifford operations<sup>##UREF##20##28##</sup>. Given there are known methods for distilling Toffoli states<sup>##UREF##4##11##</sup>, let us review how the Toffoli state is produced from the copies of the CZ state. In the following sections, we show how to inject the CZ state into larger quantum error-correcting codes that are capable of performing fault-tolerant Clifford operations<sup>##UREF##10##17##,##REF##35594359##18##,##UREF##33##45##</sup> to complete these circuits.</p>", "<p id=\"Par31\">The Toffoli state is defined as follows:where we sum over the bitwise values <italic>j</italic>, <italic>k</italic> = 0, 1.</p>", "<p id=\"Par32\">Given two copies of the CZ state, , if we project qubits 2 and 3 onto the <italic>Z</italic><sub>2</sub><italic>Z</italic><sub>3</sub> = −1 eigenspace, then we obtain the intermediate stateWe then obtain with the following unitary circuitwhere indices <italic>C</italic> and <italic>T</italic> of the controlled-not gate <italic>C</italic><italic>X</italic><sub><italic>C</italic>,<italic>T</italic></sub> denote the control and target qubit, respectively.</p>", "<p id=\"Par33\">We obtain the −1 outcome by measuring <italic>Z</italic><sub>2</sub><italic>Z</italic><sub>3</sub> of state with probability 4/9. Beyond the work in ref. <sup>##UREF##20##28##</sup>, we find that we can recover a single copy of the CZ state given the <italic>Z</italic><sub>2</sub><italic>Z</italic><sub>3</sub> = +1 outcome at this step, thereby saving magic resource states. In the event that we obtain this measurement outcome, we produce the stateApplying the unitary operation and obtaining the two-qubit parity measurement outcome <italic>Z</italic><sub>3</sub><italic>Z</italic><sub>4</sub> = −1, we obtain the state . We obtain this state with probability 3/5, assuming we obtained the <italic>Z</italic><sub>2</sub><italic>Z</italic><sub>3</sub> = +1 outcome previously.</p>", "<title>Injecting small codes into larger codes</title>", "<p id=\"Par34\">Magic-state distillation takes encoded magic states, and then processes these input states to probabilistically prepare a magic state with better fidelity. As such, it is necessary to encode magic states into quantum error-correcting codes. This process is commonly known as state injection.</p>", "<p id=\"Par35\">Ideally, the injection process will introduce a minimal amount of noise to the logical state that is encoded, as this will reduce the noise of the output magic state. To this end, we look for ways to inject the magic state prepared on the four-qubit error-detecting code in larger quantum-error-correcting codes in such a way that local errors can be detected.</p>", "<p id=\"Par36\">In what follows, we show how to inject the state encoded on the error-detecting code onto the surface code, the heavy-hex code and the colour code, thereby increasing the distance of the code that supports the magic state. Furthermore, we argue that we can detect any single error that may occur during the injection procedure. This enables us to maintain the error suppression we demonstrated experimentally in the main text.</p>", "<p id=\"Par37\">In the main text, we showed how to prepare the CZ state on a four-qubit error-detecting code shown in Extended Data Fig. ##FIG##5##2## (left). As we show later, states on this code can be injected directly onto the colour code. Two of the injection schemes, encoding onto the surface code, or the heavy-hex code, assume that the two logical qubits of the CZ state are encoded on two copies of the [[4, 1, 2]] code, shown in Extended Data Fig. ##FIG##5##2## (right). In the following section, we show how to encode the magic state prepared on the error-detecting code onto two copies of the [[4, 1, 2]] code, in a fault-tolerant way such that any single error can be detected. For the remainder of this section, we assume the magic state has been prepared over two copies of the [[4, 1, 2]] code.</p>", "<p id=\"Par38\">To distinguish the two small error-detecting codes of interest consistently, throughout the <xref rid=\"Sec5\" ref-type=\"sec\">Methods</xref> we will refer to the error-detecting code used in the main text as the [[4, 2, 2]] code to contrast this code with the [[4, 1, 2]] code. Specifically, we label the codes by their encoding parameters [[<italic>n</italic>, <italic>k</italic>, <italic>d</italic>]]. Both of these codes have a distance <italic>d</italic> = 2 using <italic>n</italic> = 4 physical qubits. The two codes differ by the number of logical qubits they each encode. The [[4, 2, 2]] code encodes <italic>k</italic> = 2 logical qubits and the [[4, 1, 2]] code encodes <italic>k</italic> = 1 logical qubit.</p>", "<title>The theory of code deformations</title>", "<p id=\"Par39\">We inject a state into a larger code<sup>##UREF##4##11##,##UREF##11##19##–##UREF##18##26##,##UREF##29##41##,##UREF##36##52##</sup> using code deformation<sup>##UREF##11##19##,##UREF##34##46##,##UREF##37##53##</sup>. In what follows, we describe the theory of code deformations using the stabilizer formalism. We remark that more general theories of code deformations can be found elsewhere in the literature<sup>##UREF##34##46##,##UREF##37##53##</sup>. The theory we present is sufficient to describe the state-injection operations of interest.</p>", "<p id=\"Par40\">We describe code deformations using the stabilizer formalism<sup>##UREF##38##54##</sup>. Quantum error-correcting codes can be described with an Abelian subgroup of Pauli operators called the stabilizer group . The encoded state lies in the common +1 eigenvalue eigenspace of the elements of the stabilizer group. We call this subspace the code space. Stabilizer codes also have associated logical operators that can be generated by a set of mutually anti-commuting pairs with 1 ≤ <italic>j</italic> ≤ <italic>k</italic>. These Pauli operators commute with the stabilizer group but are not themselves stabilizer operators. The distance of the code <italic>d</italic> is the weight of the least-weight logical operator. We can detect any single error if the code has a distance of at least <italic>d</italic> = 2. We give examples of small stabilizer codes, together with their logical operators in Extended Data Fig. ##FIG##5##2##. These examples will be relevant for the following discussion on state injection.</p>", "<p id=\"Par41\">We measure the stabilizer operators to identify the errors. As the encoded state is specified by specific eigenstates of a list of commuting Pauli operators, finding a measurement of one or more of these operators in the incorrect eigenspace indicates that an error has occurred. By arguing that we can detect any single error, we must have a distance of at least <italic>d</italic> = 2.</p>", "<p id=\"Par42\">A code deformation is where we perform a measurement that projects a stabilizer code onto another. Specifically, we assume we have prepared an initial code in which, once prepared, we start measuring the stabilizer operators of a second code that we call the final code. This projects the initial code onto the final code. Let us denote these two codes by their stabilizer group and , respectively. We assume errors may have occurred on the qubits of the initial state that must be detected by measuring the stabilizers of the final code. As such, this operation has an associated code distance, according to the number of local error events that must occur for an undetectable logical error to affect the encoded space.</p>", "<p id=\"Par43\">We detect the errors by comparing repeated readings of stabilizer measurements. Specifically, once we measure the stabilizers , we look to compare their outcomes to stabilizers prepared in the initial code . Variations in the values of these stabilizer measurements indicate that an error has occurred. As such we are interested in code-deformation stabilizersthat is, stabilizers that are prepared in the initial system and checked again after the code deformation is made, when we measure the stabilizer group .</p>", "<p id=\"Par44\">Logical information that is preserved over the code deformation has coinciding logical operators associated with both and . Specifically, the logical operators that are preserved over the code deformation are of the formwhere and are the logical operators for and , respectively.</p>", "<p id=\"Par45\">Ideally, we should maximize the number of stabilizers that coincide in the initial and final codes to maximize the number of errors we detect. In practice, physical constraints imposed by hardware may not allow us to maximize the intersection between and . Here we concentrate on very simple initialization procedures in which the initial stabilizer code is prepared in a product state, or a product state of Bell pairs, together with the small four-qubit codes that initially maintain the encoded magic state.</p>", "<title>Error correction for state injection</title>", "<p id=\"Par46\">In what follows, we will show state injection into the surface code, the heavy-hex code and the colour code. We will also argue that all of these state-injection protocols are tolerant to a single error, thereby maintaining the error suppression achieved in the experiment presented in the main text.</p>", "<p id=\"Par47\">We are interested in the general error model, in which a single error occurs on a circuit element in the stabilizer readout circuit as we deform the initial code onto the final code. However, we argue that for each individual example, we need to study only single-qubit errors that occur immediately before the code deformation takes place.</p>", "<p id=\"Par48\">In addition to the errors that occur on data qubits, we are also interested in errors that occur on the auxiliary measurement qubits we use to perform parity measurements. In essence, these can lead to two types of error: (1) readout errors, in which we obtain the incorrect measurement outcome; and (2) hook errors, in which an error during a stabilizer readout circuit is copied to several other qubits, thereby creating a correlated error. Let us mention how we treat these types of error in the following discussion.</p>", "<p id=\"Par49\">First of all, we neglect to discuss hook errors, as we assume that measures can be taken to mitigate their effects, by either flag qubits or an appropriate choice of stabilizer readout circuit. These measures are well developed for the codes of interest, see, for example, refs. <sup>##UREF##19##27##,##UREF##39##55##–##UREF##41##57##</sup>. We completed the experiment presented in the main text using a device that is tailored to realize the heavy-hex code using additional flag qubits to mitigate the effects of hook errors.</p>", "<p id=\"Par50\">We can detect a measurement error using a generic method, namely, the repetition of measurements. By repeating the measurements at least once, we can identify a single measurement error if the outcomes of two repetitions of the same measurement do not agree. As this method is applicable to all of the following injection schemes, we will not discuss this error-detection method case by case. Rather, we argue now that by measuring the stabilizer generators of twice we can detect any single error. If the measurements of the two rounds of do not agree, we discard the state we have prepared and repeat the state-preparation procedure. Otherwise, assuming the two rounds of measurement for do agree, we check the outcomes to determine whether any Pauli errors occurred during the preparation of , or immediately before the stabilizer generators are measured. Assuming no error is detected, we continue to conduct standard error correction with the final code.</p>", "<p id=\"Par51\">\n<italic>Surface code</italic>\n</p>", "<p id=\"Par52\">Let us start by discussing the example of the surface code<sup>##UREF##42##58##</sup> (Extended Data Fig. ##FIG##6##3##). The stabilizers of the code are shown by the faces in Extended Data Fig. ##FIG##6##3## (left), in which the light faces mark the support of Pauli-X-type stabilizers and the dark faces mark the support of Pauli-Z-type stabilizers. We also show the support of a Pauli-X logical operator in green and a Pauli-Z logical operator in blue. In the theory for code deformation given above, this is the stabilizer group for .</p>", "<p id=\"Par53\">In Extended Data Fig. ##FIG##6##3## (right), we show . The figure shows the [[4, 1, 2]] code outlined in red in the bottom-left corner of the lattice. The remaining qubits are prepared in a product state, such that the blue qubits are initialized in the \|0〉 state and the green qubits are initialized in the \|+〉 state. These disentangled qubits can be regarded as being in the stabilizer state <italic>Z</italic><sub><italic>v</italic></sub> or <italic>X</italic><sub><italic>v</italic></sub>. The logical operator of the initial state can be supported entirely on the [[4, 1, 2]] code. However, the initial code shares the logical operators of the final code if we multiply the logical operators of the [[4, 1, 2]] code by the product state stabilizers.</p>", "<p id=\"Par54\">Importantly, all the qubits support at least one stabilizer operator of such that a single error can be detected. We note that the qubits that are initialized in a product state need to detect only one type of error, because the other type of error acts trivially on the initial state. For example, a Pauli-X error acts trivially on a green qubit and a Pauli-Z error acts trivially on a blue qubit, whereas, respectively, a Pauli-Z or Pauli-X error on the same qubit will be detected by a stabilizer of . Finally, all qubits of the [[4, 1, 2]] code support one of each type of stabilizer of and as such can also detect both types of Pauli error. By inspection then, we see that we can detect any single-qubit error that occurs at the initialization step.</p>", "<p id=\"Par55\">\n<italic>Heavy-hex code</italic>\n</p>", "<p id=\"Par56\">We can also inject the [[4, 1, 2]] code into the heavy-hex code. This is particularly relevant with respect to the experiment presented in the main text, as the experiment is implemented on hardware that is tailored to realize the heavy-hex code. The heavy-hex code is a subsystem code closely related to the surface code. However, as the code is a subsystem code, stabilizers are not measured directly. Rather, we have a group of check operators, known as the gauge group, that we measure to infer the values of the stabilizer operators. Nevertheless, we find the arguments given above are sufficient to show that a state can be injected while detecting a single error.</p>", "<p id=\"Par57\">To review, the gauge group of the heavy-hex code includes weight-2 Pauli-Z terms on adjacent pairs of qubits that share a row. We show one such term in Extended Data Fig. ##FIG##7##4## (left). The code also has Pauli-X-type checks. These are identical to the Pauli-X-type stabilizer operators of the surface code (Extended Data Fig. ##FIG##6##3##). These checks are used to infer Pauli-X- and Pauli-Z-type stabilizer operators. The Pauli-X-type stabilizer operators are the product of Pauli-X terms on all of the qubits on two adjacent rows (Extended Data Fig. ##FIG##7##4##, left). The Pauli-Z stabilizer operators are the same as those of the surface code (Extended Data Fig. ##FIG##6##3##). We also show the support of logical Pauli-X and Pauli-Z stabilizer operators in Extended Data Fig. ##FIG##7##4## (left) in green and blue, respectively. Once again, this stabilizer group can be regarded as with respect to the simplified code-deformation theory we have presented. Although we infer their values from measuring the gauge checks, the basic theory of state injection holds for our discussion on error correction.</p>", "<p id=\"Par58\">We show for the heavy-hex code in Extended Data Fig. ##FIG##7##4## (right), in which the [[4, 1, 2]] code, highlighted in red, is prepared on qubits in the bottom-left corner of the lattice, and the green qubits are prepared in the \|+〉<sub><italic>v</italic></sub> state and the blue qubits are prepared in the \|0〉<sub><italic>v</italic></sub> state. These qubits have an associated stabilizer <italic>X</italic><sub><italic>v</italic></sub> or <italic>Z</italic><sub><italic>v</italic></sub>. Once again, similar to the case of surface code, the logical operators that are completely supported on the [[4, 1, 2]] code can be multiplied by elements of the stabilizer group of such that they are equivalent to those of shown in Extended Data Fig. ##FIG##7##4## (left). As such, the encoded logical information is preserved over the state-injection procedure, as these logical operators are members of .</p>", "<p id=\"Par59\">As with the case of the surface code, we argue that we can tolerate any single-qubit error during the injection procedure. Every single green qubit supports at least one Pauli-X-type stabilizer and every single blue qubit supports at least one Pauli-Z-type stabilizer of . As such, we can detect a single Pauli-Z error on the green qubits and a single Pauli-X error on the blue qubits that occurs up to the point the code deformation takes place. We are not concerned with Pauli-X errors acting on the green qubits and the Pauli-Z errors acting on the blue qubits as these errors act trivially on the initial state. Finally, all of the qubits of the [[4, 1, 2]] code support both a Pauli-X- and a Pauli-Z-type stabilizer of , and as such, they can all detect both types of error. This accounts for single-qubit errors occurring on all of the qubits of the system during the state-injection process with the heavy-hex code.</p>", "<p id=\"Par60\">\n<italic>Colour code</italic>\n</p>", "<p id=\"Par61\">Let us finally discuss the colour code<sup>##REF##17155532##59##</sup>. This is a particularly interesting example as the [[4, 2, 2]] code can be injected directly into the colour code. We show the colour-code lattice in Extended Data Fig. ##FIG##8##5##. For , each lattice face supports both a Pauli-X- and Pauli-Z-type stabilizer. The code supports two logical operators, where is the product of Pauli-X terms supported on all the qubits along the bottom boundary of the lattice and is the product of Pauli-Z terms supported on all the qubits along the left boundary of the lattice. Likewise is the product of Pauli-X terms supported on all the qubits along the left boundary of the lattice and is the product of Pauli-Z terms supported on all the qubits along the bottom boundary of the lattice. We highlight the support of the logical operators on the left and bottom boundaries in blue and green, respectively, in Extended Data Fig. ##FIG##8##5##.</p>", "<p id=\"Par62\">We define the stabilizer group for in the caption of Extended Data Fig. ##FIG##8##5##, in which the [[4, 2, 2]] code is placed on a four-qubit face of the lattice, and all of the other qubits are prepared in Bell pairs, with stabilizer operators <italic>X</italic><sub><italic>a</italic></sub><italic>X</italic><sub><italic>b</italic></sub> and <italic>Z</italic><sub><italic>a</italic></sub><italic>Z</italic><sub><italic>b</italic></sub>, marked by highlighted edges in the figure. We colour the edges either blue or green according to the colouring convention for edges used in ref. <sup>##REF##17155532##59##</sup>. Nevertheless, all highlighted edges, of both colours, support the same Bell pair.</p>", "<p id=\"Par63\">We can multiply the logical operators of the [[4, 2, 2]] code by elements of such that we obtain the logical operators of . As such, the logical qubits encoded on the error-detecting code are preserved over the state-injection process.</p>", "<p id=\"Par64\">We finally argue that we can detect any single-qubit error during the state injection process. Extended Data Fig. ##FIG##8##5## shows the support of the stabilizer operators of with coloured faces. Specifically, there is both a Pauli-X- and Pauli-Z-type stabilizer on each of the coloured faces. By inspection, we see that every qubit supports at least one coloured face and, therefore, supports both a Pauli-X- and a Pauli-Z-type stabilizer. We note also that the error-detecting code also supports both a Pauli-X- and a Pauli-Z-type stabilizer on its respective face. As such, we can detect any single-qubit error over the state-injection process.</p>", "<title>Some remarks on state-injection procedures</title>", "<p id=\"Par65\">We have presented state-injection protocols for several different codes for the error-suppressed magic state we discussed in the main text. We argued that we can detect a single error that may occur in any of these protocols, such that we maintain the error suppression we have demonstrated in our experiment. The injection protocols we have presented can be improved by combining them with other methods presented in the literature to improve the performance and yield of state injection. For instance, in refs. <sup>##UREF##12##20##,##UREF##16##24##</sup>, two-step preparation procedures are proposed, in which a magic state is injected onto an intermediate-sized code, where error detection is used to suppress errors, before injecting the intermediate-sized code onto a larger code. This method is compatible with the injection protocols we have presented here. We might also adopt the method presented in ref. <sup>##UREF##17##25##</sup>, in which the authors propose estimating the logical error on the injected state in the decoding step of state injection.</p>", "<p id=\"Par66\">It is worth remarking that these error-detection protocols can be improved by increasing the fraction of error events that can be detected. We might, for example, consider better choices of that can be prepared before the state-injection procedure begins. In the case of subsystem codes, we might also look for additional error-detection checks that can be made between intermediate gauge measurements we make to infer the values of the stabilizers, and the stabilizers of the initial code, during the preparation procedure.</p>", "<title>Encoding the CZ state on two [[4, 1, 2]] codes using the heavy-hex lattice geometry</title>", "<p id=\"Par67\">Two of our state-injection protocols described above require that the CZ state is encoded on copies of the [[4, 1, 2]] code. Here we show how to transform the encoded CZ state prepared on the [[4, 2, 2]] code as we have described in the main text onto two copies of the [[4, 1, 2]] code. This transformation is made using measurements. In this sense, it can be understood as a code deformation similar to that discussed in the previous section. We argue that we can detect any one single error over the code deformation process, thereby maintaining the error suppression obtained in the main text. We also show how this process can be mapped onto the heavy-hex lattice geometry. The protocol is outlined in Extended Data Fig. ##FIG##9##6##, and we show how the outline is mapped onto the heavy-hex geometry in Extended Data Fig. ##FIG##10##7##.</p>", "<p id=\"Par68\">Before discussing the transformation, we first briefly review the ideas behind state teleportation abstractly. We can view the transformation as a small instance of a lattice surgery operation<sup>##UREF##28##38##</sup> in which the gates are performed between logical qubits by measuring appropriate logical degrees of freedom. Furthermore, in this particular instance, we can view the operation as a lattice surgery operation between a small colour code and a small surface code<sup>##UREF##35##48##,##REF##29109426##60##,##UREF##43##61##</sup>, in which we interpret the [[4, 2, 2]] code and the [[4, 1, 2]] code as a small colour code and surface code, respectively. After performing a logical parity measurement between the two codes, the transformation is completed with a partial condensation operation of the small colour code, as described in ref. <sup>##UREF##35##48##</sup>.</p>", "<p id=\"Par69\">To explain the operation, we consider the evolution of the stabilizers and logical operators of the code at each step of the measurement pattern shown in Extended Data Fig. ##FIG##9##6## independently from the implementation of the code. We have three logical qubits indexed <italic>A</italic>, <italic>B</italic> and <italic>C</italic>, where, initially, <italic>A</italic> and <italic>B</italic> are encoded on the [[4, 2, 2]] code and <italic>C</italic> is encoded on the [[4, 1, 2]] code. In essence, the operation teleports the logical state encoded on qubit <italic>B</italic> onto qubit <italic>C</italic>, up to a Clifford operation. Logical qubit <italic>A</italic> is not involved in the operation, so we concentrate on qubits <italic>B</italic> and <italic>C</italic>.</p>", "<p id=\"Par70\">The teleportation operation proceeds as follows:<list list-type=\"order\"><list-item><p id=\"Par71\">Prepare ,</p></list-item><list-item><p id=\"Par72\">Measure <italic>X</italic><sub><italic>B</italic></sub><italic>Z</italic><sub><italic>C</italic></sub>,</p></list-item><list-item><p id=\"Par73\">Measure <italic>Z</italic><sub><italic>B</italic></sub>,</p></list-item><list-item><p id=\"Par74\">Apply Pauli correction.</p></list-item></list></p>", "<p id=\"Par75\">The operation functions with <italic>A</italic> and <italic>B</italic> prepared in some arbitrary logical state, but to illustrate the operation we assume they are in a product state with . We omit qubit <italic>A</italic> from the discussion, as it is unchanged by the transformation, and we leave it as an exercise to the reader to verify the general case.</p>", "<p id=\"Par76\">Initially, an arbitrary state in the <italic>B</italic> subsystem along with a logical state on the <italic>C</italic> subsystem can be described by the following vector state: , in which we have chosen a convenient basis for the vectors on <italic>B</italic>. Upon measuring the joint logical operator <italic>X</italic><sub><italic>B</italic></sub><italic>Z</italic><sub><italic>C</italic></sub> and obtaining measurement outcome <italic>m</italic><sub>2</sub>, the resulting state of the joint system is . Finally, upon measuring <italic>Z</italic><sub><italic>B</italic></sub> and obtaining the measurement outcome <italic>m</italic><sub>3</sub>, the resulting state is . An appropriate Pauli correction depending on the measurement outcomes <italic>m</italic><sub>2</sub> and <italic>m</italic><sub>3</sub> enables us to recover the state . As such, we see the logical information that was originally encoded on the <italic>B</italic> subsystem in the form of the coefficients <italic>a</italic> and <italic>b</italic> now lies entirely on the <italic>C</italic> subsystem. Finally, we note that, with this operation, the basis of the logical information has been rotated by a Hadamard operation. This can be corrected at a later step. Extended Data Fig. ##FIG##9##6## shows how this transformation is conducted between an encoded qubit of the [[4, 2, 2]] code and the logical qubit of the [[4, 1, 2]] code by performing logical measurements.</p>", "<p id=\"Par77\">We now discuss how to implement the described state teleportation on a heavy-hex lattice (Extended Data Fig. ##FIG##10##7##). We begin by preparing the encoded CZ state as explained in the main text, together with an encoded [[4, 1, 2]] code. The [[4, 1, 2]] code is prepared in the logical state \|+〉. We can prepare this state using qubits outlined in the orange box shown in Extended Data Fig. ##FIG##10##7## (top), in which the four qubits, 4, 6, 15 and 17 are the data qubits of the code and qubits 5, 10 and 16 are used to perform weight-4 parity checks with qubits 5 and 16 used as flag qubits. The [[4, 1, 2]] code is prepared in a fault-tolerant manner by initializing the data qubits in the \|+〉 state and then measuring each of the Pauli-Z-type stabilizer operators <italic>Z</italic><sub>4</sub><italic>Z</italic><sub>6</sub> and <italic>Z</italic><sub>15</sub><italic>Z</italic><sub>17</sub>. These measurements can be facilitated with the ancillary qubits 5 and 16, respectively. Each of these operators is measured twice such that we can detect a single measurement error during preparation (see also ref. <sup>##REF##35362994##4##</sup>).</p>", "<p id=\"Par78\">We transfer a single logical qubit of the [[4, 2, 2]] code onto the [[4, 1, 2]] code using logical measurements. In step 3, we perform a weight-4 measurement that measures the parity of two logical qubits over the two codes. To do this using the heavy-hexagonal lattice geometry, we first transport the codes. This can be performed in two rounds of swap gates or teleportation operations, as shown by the arrows in Extended Data Fig. ##FIG##10##7## (top), in which the blue arrows are performed first, in parallel, and the green arrows are performed in parallel afterwards. It should be noted that these rounds of parallel swap gates are fault-tolerant because all individual swap operations involve a single data qubit as well as an ancillary qubit. Thus, any potential two-qubit gate error is effectively a single-qubit error on the code that would be detected. After the swap operation, we facilitate the logical parity measurement with qubits 5, 10 and 16, shown in the green box in Extended Data Fig. ##FIG##10##7##. The logical measurement is performed twice to identify a measurement error that may occur in this step. The outcomes of both of these measurements should agree. An odd parity in measurement outcomes indicates that a measurement error has occurred.</p>", "<p id=\"Par79\">Finally, we measure the logical operator to complete the teleportation operation. We measure this operator on both of its two-qubit supports. Specifically, these are and , in which <italic>S</italic><sup><italic>Z</italic></sup> = <italic>Z</italic><sub>2</sub><italic>Z</italic><sub>4</sub><italic>Z</italic><sub>13</sub><italic>Z</italic><sub>15</sub> is the weight-4 Pauli-Z stabilizer of the [[4, 2, 2]] code. Measuring both of these weight-2 logical operators enables us to detect a single error, as the product of their outcomes should agree with the value of the Pauli-Z stabilizer <italic>S</italic><sup><italic>Z</italic></sup>. This final measurement completes the teleportation operation and, moreover, projects the error-detecting code onto a second copy of the [[4, 1, 2]] code. Finally, we remark that projecting into a known eigenstate enables us to regard this logical operator as a weight-2 stabilizer. As such, we can now regard the [[4, 2, 2]] code that we prepared initially as a [[4, 1, 2]] code. We, therefore, have the state encoded on the logical space of two [[4, 1, 2]] codes shown in the purple and orange boxes shown in Extended Data Fig. ##FIG##10##7##(bottom).</p>", "<title>Analysis in terms of single-gate errors</title>", "<p id=\"Par80\">All circuits considered, both for magic-state preparation and logical tomography, use redundancy to detect errors. For the mid-circuit syndrome measurements, performed with the circuit shown in Fig. ##FIG##0##1##, this redundancy comes, in part, by using flag qubits. These yield an outcome of 0 unless an error has occurred. These outcomes are, therefore, error-sensitive events, allowing errors to be detected.</p>", "<p id=\"Par81\">Additional error-sensitive events come from the results of the syndrome measurements themselves. For the circuit shown in Fig. ##FIG##1##2b##, these events are as follows:<list list-type=\"order\"><list-item><p id=\"Par82\">The results of the two measurements should agree.</p></list-item><list-item><p id=\"Par83\"><italic>S</italic><sup><italic>X</italic></sup> should yield 0, because the system is prepared in a +1 eigenstate of this operator.</p></list-item><list-item><p id=\"Par84\">Although the first <italic>S</italic><sup><italic>Z</italic></sup> will yield a random outcome, the following feedforward means that the resulting state is in the +1 eigenspace of <italic>S</italic><sup><italic>Z</italic></sup>. This will then be the expected outcome for the value of final <italic>S</italic><sup><italic>Z</italic></sup> measurement.</p></list-item></list></p>", "<p id=\"Par85\">For concreteness, we will consider the measurement of logical <italic>Z</italic><italic>Z</italic>, for which the final <italic>S</italic><sup><italic>Z</italic></sup> measurement is achieved through the final measurement of data qubits. The circuit, then, has eight flag results in addition to the above three conditions for syndrome measurements. This gives 11 error-sensitive events in all. To analyse how errors in the circuit are detected, we consider all the possible ways in which Pauli errors can be inserted around each gate. Specifically, we consider the insertion of <italic>X</italic>, <italic>Y</italic> and <italic>Z</italic> before any single-qubit gate, and all possible single- and two-qubit Paulis before any two-qubit gate, on the qubits that support the gate. We then simulate each of these circuits to determine how the error is detected.</p>", "<p id=\"Par86\">This analysis has two important uses. First, it can be used to verify the fault tolerance of the scheme, by confirming that all Pauli errors with non-trivial effect are in some way detected by the error-sensitive events. Second, it can be used to determine the specific combination of error-sensitive events, <italic>s</italic>, that detect each error. This information can then be used to infer the corresponding probabilities <italic>ε</italic><sub><italic>s</italic></sub> that such errors occurred, by looking at how often the corresponding error signature occurs within the outcomes measured.</p>", "<p id=\"Par87\">After performing this analysis, it was found that the circuit is fault-tolerant. The only cases in which an error was not detected are those where the system was in an eigenstate of the error operator, or where its application was immediately followed by a measurement in an eigenbasis of the Pauli error. In both of these cases, the error will have a trivial effect on the circuit output.</p>", "<p id=\"Par88\">When calculating the <italic>ε</italic><sub><italic>s</italic></sub>, it is important to note that the error signatures, <italic>s</italic>, are not necessarily unique for each type of error. For example, <italic>X</italic> and <italic>Y</italic> Paulis inserted immediately before any measurement will yield the identical effect of a measurement error. We, therefore, also determine the degeneracy, <italic>N</italic><sub><italic>s</italic></sub>, for each error signature. This is the number of unique errors that gives rise to the same error signature. With this information, we can then analyse the syndrome outcomes from experimental data, looking for these signatures and determining the probabilities with which they occur<sup>##UREF##24##32##</sup>.</p>", "<p id=\"Par89\">Owing to the limited number of error-sensitive events used in this experiment, these probabilities can be calculated directly. The combined probability, <italic>ε</italic><sub><italic>s</italic></sub>, for all forms of error that lead to a particular signature is determined using the number of shots for which that signature occurs, <italic>n</italic><sub><italic>s</italic></sub>, and the number of shots for which no error is detected, <italic>n</italic><sub>0</sub>. The ratio of these numbers of shots will be the ratio of the probability that the error occurs with the probability that it does not:Simply rearranging this relation gives us the value of <italic>ε</italic><sub><italic>s</italic></sub> (ref. <sup>##UREF##44##62##</sup>). We then use the degeneracy to obtain the average probability for each possible single-qubit Pauli error with this signature: <italic>ε</italic><sub><italic>s</italic></sub>/<italic>N</italic><sub><italic>s</italic></sub>.</p>", "<title>Standard magic-state preparation circuits</title>", "<p id=\"Par90\">Here we describe magic-state preparation circuits with no error suppression that are compared with our error-suppressed scheme described in the main text.</p>", "<p id=\"Par91\">In Extended Data Fig. ##FIG##11##8a##, we show a circuit that prepares an encoded CZ state by, first, preparing a CZ state on two physical qubits and, then, encoding the state such that the Pauli observables of the two qubits of the CZ state can be represented as logical operators of the error-detecting code we encode. Finally, we measure the stabilizer operators of the code to encode the state, assuming we obtain the correct stabilizer measurement outcomes. The circuit used for the preparation step is shown in Extended Data Fig. ##FIG##11##8b##.</p>", "<p id=\"Par92\">We can make use of the stabilizer operators of the CZ state to simplify the preparation circuit shown in Extended Data Fig. ##FIG##11##8b##. We define a stabilizer operator <italic>U</italic>, with respect to state , as an operator for which the action is trivial on its respective state, that is, . We can check that the CZ state is invariant under the action of a controlled-not gate conditioned on the control qubit in the zero stateThis unitary gate is equivalent to a standard controlled-not gate, , followed by a bit flip on the target qubit, that is,This observation enables us to simplify the preparation circuit. Once the CZ state is prepared, we add the gate in the dashed box in Extended Data Fig. ##FIG##11##8##, as the state we have prepared at this stage is invariant under this inclusion. The inclusion of this operator enables us to simplify the circuit, as the repeated application of the two Pauli-X rotations and the repeated application of two controlled-not operations used in the circuit act like an identity operation. This trivial step in the circuit is marked on the figure between vertical dashed lines. We can, therefore, omit all of the controlled-not operations and the bit-flip operations from the circuit shown in our implementation of this method of state preparation. As such, this preparation step includes only two entangling gates: a controlled Hadamard gate and a swap gate. We perform logical tomography by appending the circuits shown in Fig. ##FIG##1##2b,c## to the end of the circuit shown in Extended Data Fig. ##FIG##11##8a##. Likewise, we can perform physical tomography on the output of the circuit shown in Extended Data Fig. ##FIG##11##8a##.</p>", "<p id=\"Par93\">Moreover, we note that the CZ state is also stabilized by the swap gate:and <italic>C</italic><italic>Z</italic> as defined in the main text. The CZ state is uniquely stabilized by the Abelian stabilizer group generated by the set .</p>", "<p id=\"Par94\">Finally, we also compare our error-suppressed magic-state preparation scheme to a circuit that prepares the same magic state on two physical qubits (Extended Data Fig. ##FIG##11##8c##). We prepare the state on two physical qubits using a single entangling gate, together with single-qubit rotations, before measuring the state in varying single-qubit Pauli bases, <italic>P</italic> and <italic>Q</italic>, to conduct state tomography on the circuit output.</p>", "<title>Device overview</title>", "<p id=\"Par95\">Encoded state data collection on ibm_peekskill v.2.4.0 spanned several days over a single region. During this time, monitoring experiments were interleaved with tomography data collection trials. Device coherence times for all qubits exceed about 100 μs and two-qubit errors per gate was found to range from 0.35% to 0.59%. Detailed monitoring of readout errors are provided in Extended Data Fig. ##FIG##12##9f,g## and time-averaged readout fidelities ranged from 98.1% to 99.6% for all qubits. Average device characterization data are summarized in Extended Data Tables ##TAB##0##1## and ##TAB##1##2##. Unencoded magic-state data were collected over a single 24-h period on ibm_peekskill v.2.5.4 on all physical pairs and the best-performing edge is reported in Extended Data Table ##TAB##1##2##. Although the unencoded magic-state data were not interleaved with encoded-state tomography, the best-performing pair of physical qubits was found to have a low two-qubit error per gate of 0.38%, and this error is comparable with the lowest two-qubit error per gate for edges used in the encoded magic-state experiments.</p>", "<title>Real-time feedforward control of qubits</title>", "<p id=\"Par96\">In the past decade, several experiments were performed that exploit fast feedback or real-time control within the execution of a quantum program. Fast feedback has been used for conditional reset<sup>##REF##23368293##63##–##UREF##46##66##</sup>, state and gate teleportation<sup>##REF##15201904##67##–##REF##23955231##69##</sup> with low branching complexity and in more demanding algorithms such as the iterative-phase estimation protocol<sup>##REF##34533358##70##</sup>, to name a few. More recently, there have been demonstrations of quantum error correction using real-time control in various systems<sup>##UREF##0##2##,##UREF##1##6##,##REF##27146630##71##,##REF##36949196##72##</sup>. There have also been examples of work toward classical-control microarchitectures that enable the seamless integration of qubits and classical operations with tens of qubits.</p>", "<p id=\"Par97\">Our work was performed with the first-generation real-time control system of IBM Quantum, in which we use centralized processing of mid-circuit measurement outcomes to classically condition a quantum circuit. The control system architecture is based on a hierarchical heterogeneous system of field-programmable gate array controllers with computing elements for concurrent real-time processing, microwave control and qubit readout. These are synchronized through a global clock and linked with a real-time communication network to enable synchronized collective operations such as control flow. Branching incurs a constant latency penalty to execute the branch (of the order of 500 ns). Real-time computations will incur a variable latency overhead depending on the complexity of the decision. The system provides specialized fast-path control-flow capabilities for rapid and deterministic conditional reset operations. Collective control of the system requires orchestration through a proprietary heterogeneous hardware compiler and code generator. We use an open-access platform that is programmable through Qiskit and OpenQASM 3—an open-source imperative C-style real-time quantum programming language<sup>##UREF##47##73##</sup>. All experiments were performed through Qiskit and IBM Quantum Services<sup>##UREF##48##74##,##UREF##49##75##</sup>.</p>", "<title>Estimates for magic-state yield</title>", "<p id=\"Par98\">Let us attempt to model the error rate of the components of the device using the yield we have evaluated experimentally. The yield is a helpful figure of merit as it tells us precisely how often a single-error event occurs to leading order in the error rate. We first try to model the yield using simple three-parameter models that we derive below. We also compare the yield to numerical simulations of our circuits. We show the estimated yield for different experiments in Extended Data Table ##TAB##2##3##, in comparison with our analytical model and numerical results.</p>", "<p id=\"Par99\">Both of our analyses have good agreement with the experiment if we assume a two-qubit gate-error rate and a measurement error rate of the order of 2%. This is a high error rate compared with those measured in Extended Data Tables ##TAB##0##1## and ##TAB##1##2##. However, we remark that neither our analytical model nor our simulations account for common error processes such as leakage, cross talk, two-level systems and idling errors that may occur during slow-circuit processes that will introduce additional noise to the system. We suggest discrepancies in our modelling, and the experimentally observed yields can be attributed to these details that are difficult to model analytically or numerically.</p>", "<p id=\"Par100\">Let us present our analytical model to evaluate the yield. We can estimate the magic-state yield as <italic>Q</italic><italic>R</italic>, where <italic>Q</italic> is the probability that the random measurement outcomes we obtain throughout our experiment yield the values we need to complete the magic-state preparation scheme and <italic>R</italic> is the probability that the experiment does not experience a single error.</p>", "<p id=\"Par101\">If we have that <italic>ε</italic><sub><italic>P</italic></sub> is the probability that a single parity measurement introduces an error and <italic>D</italic> is the number of parity measurements that are conducted in an experiment, that is, the depth, we can write , thereby giving the equationWe note that <italic>Q</italic> and <italic>D</italic> vary for different experiments.</p>", "<p id=\"Par102\">For our rough calculation, we find reasonably good agreement with the experimental data if we take <italic>ε</italic><sub><italic>P</italic></sub> ≈ 22%. This equates, approximately, to a two-qubit gate-error rate and a measurement error rate of about 2%. Each parity measurement we perform uses eight entangling gates and three mid-circuit measurements. Therefore, neglecting higher-order terms, we obtain the probability that a parity measurement introduces a single error iswhere <italic>ε</italic><sub>2<italic>Q</italic></sub> is the two-qubit gate-error rate and <italic>ε</italic><sub><italic>M</italic></sub> is the probability of a measurement error. If we set <italic>ε</italic><sub>2<italic>Q</italic></sub> = <italic>ε</italic><sub><italic>M</italic></sub> = 2%, we find that <italic>ε</italic><sub><italic>P</italic></sub> = 22%.</p>", "<p id=\"Par103\">We also need to predict <italic>Q</italic> for different experiments. Let us begin with the error-suppressed experiment in which we use feedforward. Here, in the noiseless case, we have one random measurement outcome, in which we initially measure . It is readily checked that the probability that we project the \|++⟩ state onto the +1 eigenvalue eigenspace of the <italic>C</italic><italic>Z</italic> operator is <italic>Q</italic><sub>FF</sub> = ⟨++∣(1 + <italic>C</italic><italic>Z</italic>)∣++⟩/2 = 3/4. In the case that we do not use feedforward, in addition to obtaining the correct outcome for the measurement, we must also post-select on obtaining the correct outcome of the initial measurement of <italic>S</italic><sup><italic>Z</italic></sup>. We obtain the +1 eigenvalue subspace of this operator with probability 1/2. We, therefore, have <italic>Q</italic><sub>PS</sub> = 3/4 × 1/2 = 3/8. Finally, in the standard preparation procedure, we measure both <italic>S</italic><sup><italic>Z</italic></sup> and <italic>S</italic><sup><italic>X</italic></sup>, and we require that both give the +1 outcome. Each measurement gives the correct outcome with probability 1/2. We, therefore, have <italic>Q</italic><sub>STND</sub> = 1/2 × 1/2 = 1/4.</p>", "<p id=\"Par104\">Let us comment on the features of this model that agree with the experiment. First of all, we observe that the error-suppressed scheme using feedforward has a consistently better yield than the other two schemes, both the error-suppressed scheme using post-selection and the standard preparation scheme. Furthermore, we observe that the error-suppressed post-selection scheme and the standard scheme have comparable yields, for both tomography circuits shown in Fig. ##FIG##1##2##.</p>", "<p id=\"Par105\">Furthermore, our model explains the difference in yield between different tomography experiments conducted using the two different circuits shown in Fig. ##FIG##1##2##. The tomography circuit in Fig. ##FIG##1##2c## uses two additional parity measurements than that shown in Fig. ##FIG##1##2b##. As such the tomography circuit in Fig. ##FIG##1##2c## is inherently more noisy than that in Fig. ##FIG##1##2b##. This is reflected in Fig. ##FIG##3##4## in which the yield for tomography circuits shown in Fig. ##FIG##1##2b,c## are shown in Fig. ##FIG##3##4## (left, right).</p>", "<p id=\"Par106\">Our rudimentary analytical model correctly predicts several qualitative features of our experimental data. However, it neglects many details of the circuit. As we might expect, we find better agreement with the experimentally observed yield if we simulate our circuit. We assume an error rate for each of the two-qubit entangling gates and a measurement error rate of 2%. These results are also shown in Extended Data Table ##TAB##2##3##. Again, the physical error rate of these circuit elements is considerably higher than the observed error rates of these components. As mentioned at the beginning of this section, we attribute this to noise processes that are not captured by either our analytical model or our numerical simulations. In practice, it is extremely difficult to capture all of the physical details that occur in an experiment.</p>", "<title>State tomography with readout error mitigation using noisy positive-operator-valued measurements</title>", "<p id=\"Par107\">The state tomography in the main text uses the Qiskit Experiments implementation of state tomography<sup>##UREF##23##31##</sup>. A notable change from the previous works is that we do not use readout error mitigation in the main text. Instead, we perform tomographic fitting assuming ideal measurements, which attributes any undetectable measurement errors to errors in the reconstructed quantum state. For physical tomography, we use the cvxpy_gaussian_lstsq fitter with measurement data using the default Pauli-measurement basis on each physical qubit to obtain a weighted maximum-likelihood estimate, constrained to the space of positive, semi-definite, unit trace density matrices. For logical tomography, we use the cvxpy_linea_lstsq fitter with a custom measurement basis using Pauli expectation values, rather than Pauli eigenstate probabilities. In this case, the custom fitter weights are calculated from the inverse of the standard error in the Pauli expectation value estimates for each post-selected logical Pauli operator measurement.</p>", "<p id=\"Par108\">Susceptibility to measurement error is a common issue in tomographic methods. In general, tomographic tools are only as good as the noise model of the measurement apparatus, that is, our ability to calculate the likelihood representing the conditional probability of obtaining a dataset given some test density matrix. In this section, we discuss an alternative approach combining readout error characterization with tomographic reconstruction. Although the dominant measurement error source in tomography experiments is because of qubit readout, it is a common practice to assume local, uncorrelated readout errors in the <italic>Z</italic> basis. A set of noisy positive operator-valued measurements (POVMs) on a single-qubit is,where <italic>p</italic> is the probability of assigning outcome 1 to a state initially prepared as \|0⟩ and <italic>q</italic> is the probability of assigning outcome 0 to astate initially prepared as \|1⟩; that is, <italic>p</italic> = <italic>P</italic>(1∣0) and <italic>q</italic> = <italic>P</italic>(0∣1). We can also construct noisy POVMs for measurements in the Pauli-X or Pauli-Y eigenbases by rotating the noisy POVMs shown in equation (##FORMU##127##12##) by an appropriate angle assuming ideal unitaries, because the measurement error is typically several orders of magnitude greater than the one-qubit gate error.</p>", "<p id=\"Par109\">By interleaving small batches of experimental data collection with readout calibration experiments, one can construct noisy POVMs for each data qubit applicable to a small duration of data collection to be used in fitting procedures discussed above. In Extended Data Fig. ##FIG##12##9a##, state infidelities from fitting with noisy POVMs can be compared with fitting with ideal projectors (<italic>p</italic>, <italic>q</italic> ≡ 0), in which the latter is reported in the main text. Using readout mitigation, the fault-tolerant tomography routines far outperform both unencoded tomography and the physical tomography of the encoded state. As the terminating measurements in logical tomography are very similar to those in physical tomography, we would expect both of these experiments to demonstrate similar infidelities. Resolving this discrepancy remains an open research question.</p>", "<p id=\"Par110\">Furthermore, it is unclear if our assumed construction of noisy POVMs, or the measured readout error calibrations, collectively reflect the true measurement errors experienced by data qubits. We, therefore, test the sensitivity of the outcomes of state tomography to the choice of measurement compensation in Extended Data Fig. ##FIG##12##9b–d##. State infidelity is calculated by fitting experimental tomography data to POVMs parameterized by <italic>p</italic> and <italic>q</italic>. To simplify, these readout error probabilities are set to be constant for all qubits and time. Dark-blue regions of low infidelity (with the minima marked with a red star) do not coincide with the state infidelity calculated using the global average of experimentally measured readout calibrations (marked by a black dot). This disparity suggests that either the target experiments experienced initialization or measurement errors at a higher rate than measured by simpler calibrations and/or fitting with potentially incorrect <italic>A</italic>-matrices yields a highly non-positive state that is mapped to a high-fidelity physical state under constrained optimization.</p>", "<p id=\"Par111\">Combining readout mitigation with tomography thus remains an open question for further work, and the results of the main text are limited by unaddressed readout error on terminal measurements. We expect that state tomography experiments in Extended Data Fig. ##FIG##12##9b–e## at <italic>p</italic> = <italic>q</italic> = 0 provide a reasonable upper bound on the error of the underlying magic state.</p>" ]	[ "<title>Experimental results</title>", "<p id=\"Par13\">We performed our experiments using the first-generation real-time control system architecture of IBM Quantum deployed on ibm_peekskill; one of the IBM Quantum Falcon Processors (<ext-link ext-link-type=\"uri\" xlink:href=\"https://quantum.ibm.com/\">https://quantum.ibm.com/</ext-link>). Device characterization can be found in the section ‘<xref rid=\"Sec15\" ref-type=\"sec\">Device overview</xref>’. The control system architectures give access to dynamic circuit operations, such as real-time adaptive circuit operations that depend on the outcomes of mid-circuit measurements, that is, feedforward (see section ‘<xref rid=\"Sec16\" ref-type=\"sec\">Real-time feedforward control of qubits</xref>’).</p>", "<p id=\"Par14\">Our results are shown in Fig. ##FIG##2##3##, in which we present state infidelities for various state-preparation schemes calculated using both logical tomography and physical tomography. For results provided in the main text, we model the reconstructed state assuming that the readout is conducted with projective measurements. We also present an alternative analysis in the section ‘<xref rid=\"Sec18\" ref-type=\"sec\">State tomography with readout error mitigation using noisy positive-operator-valued measurements</xref>’, in which we combine the readout error characterization with tomographic reconstruction using noisy positive-operator-valued measurements.</p>", "<p id=\"Par15\">To accommodate drift in device parameters over the data collection period, a complete set of tomography circuits was interleaved and submitted in batches of about 10<sup>4</sup> shots until a total of about 10<sup>6</sup> shots were collected over several days. The resulting counts database is uniformly sampled with a replacement for 10 bootstrap trials with a batch size limited to 20% of the total database before post-selection. The standard deviation, σ, of these bootstrapped trials is plotted as an error bar in all data figures.</p>", "<p id=\"Par16\">The tomographic fitting was done using positive semi-definite constrained weight-least-squares convex optimization using the Qiskit Experiments tomography module<sup>##UREF##23##31##</sup>. For logical tomography, the fitting weights were set proportional to the inverse of the standard errors for each logical Pauli expectation value estimate. These weights accommodate the different logical yield rates for each logical Pauli measurement. The logical yield for each basis measurement is shown in Fig. ##FIG##3##4## and discussed in more detail below.</p>", "<p id=\"Par17\">We first compare the state-preparation scheme using dynamic circuits with the same preparation scheme executed with static circuits and post-selection. This comparison is conducted using logical tomography. These are the left and middle data points shown in blue in Fig. ##FIG##2##3##. We find that the infidelities are commensurate in these two experiments. Using dynamic circuits with feedforward operations, we encode a two-qubit error-suppressed input magic state with a logical infidelity (1.87 ± 0.16) × 10<sup>−2</sup>. In the post-selection experiment, we obtain an infidelity of (1.23 ± 0.11) × 10<sup>−2</sup>. The feedforward operations in our experiment can introduce idling periods, of the order of hundreds of nanoseconds, during which additional errors can accumulate. To leading order we attribute the difference in fidelity between these preparation schemes to errors that occur while the control system is occupied performing the dynamical feedforward operation. In return for this loss in fidelity, we find that the use of dynamical circuits significantly increases the yield of magic states (Fig. ##FIG##3##4##).</p>", "<p id=\"Par18\">We can analyse the commonly occurring errors in fault-tolerant circuits using syndrome outcomes to infer the events that are likely to have caused them<sup>##UREF##24##32##</sup>. This is done using the method detailed in the section ‘<xref rid=\"Sec13\" ref-type=\"sec\">Analysis in terms of single-gate errors</xref>’ using the results of the error-suppressed scheme without any post-selection. Assuming an uncorrelated error model, we find that the average probability per single-error event is 0.19% with a standard deviation of 0.11%. The single most-likely error event occurs with probability 1.2%. This event corresponds to an error occurring during the <italic>X</italic> stabilizer measurement that spreads to and is detected by a flag qubit. Similar errors in other stabilizer measurements show the probability increasing from 0.35% for the initial <italic>Z</italic> measurement to 0.41% and 0.45% for the two measurements. This suggests that, rather than being caused by Pauli errors, these results might be caused by other effects such as an accumulation of leakage on the flag qubits.</p>", "<p id=\"Par19\">We verify the performance of our logical tomography procedure by comparing our results with the infidelity obtained using physical tomography for the magic-state preparation procedure, in which we obtain the <italic>S</italic><sup><italic>Z</italic></sup> = +1 eigenspace with post-selection. The fitter weights in this case are the standard Gaussian weights based on the observed frequencies of each projective measurement outcome of each basis element. In physical tomography, the yield after post-selection is constant in all 81 measurement bases. We find an acceptance rate of 14.9 ± 0.1% for the error-suppressed scheme using physical tomography, in which the standard deviation represents variation over 81 physical Pauli directions.</p>", "<p id=\"Par20\">To compare the infidelity obtained with physical tomography even-handedly with that obtained using logical tomography, we reconstruct the logical subspace from the density matrix obtained from physical tomography on the data qubits of the code, <italic>ρ</italic><sub>phys</sub>. The logical subspace is obtained by projecting <italic>ρ</italic><sub>phys</sub> onto the logical subspace<sup>##REF##29219563##33##,##UREF##25##34##</sup>. We obtain the elements of the density matrix of the logical subspace <italic>ρ</italic> using the equationwhere <italic>k</italic>, <italic>l</italic>, <italic>m</italic>, <italic>n</italic> = 0, 1 specify orthogonal vectors in the logical subspace and is the probability that the state we prepare is in the logical subspace. Using this method, we obtain the projected logical infidelity for the error-suppressed procedure as (1.70 ± 0.35) × 10<sup>−2</sup> with the probability of finding <italic>ρ</italic><sub>phys</sub> in the logical subspace <italic>P</italic><sub><italic>L</italic></sub> = 0.898 ± 0.008. An average post-selection acceptance rate over all physical Pauli directions is found to be 14.9 ± 0.1%. This projected logical infidelity is shown as the rightmost blue data point in Fig. ##FIG##2##3## to be compared with the central blue data point. This comparison demonstrates the consistency between logical tomography and physical tomography. For reference, raw state fidelities from physical tomography before logical projection are reported in the section ‘<xref rid=\"Sec18\" ref-type=\"sec\">State tomography with readout error mitigation using noisy positive-operator-valued measurements</xref>’.</p>", "<p id=\"Par21\">We compare our error-suppressed magic-state preparation procedure with a standard static circuit that encodes a physical copy of the magic state into the four-qubit code. We show infidelity data points for the standard scheme in Fig. ##FIG##2##3## with orange markers. Our experiments consistently demonstrate that our error-suppressed encoding scheme has an infidelity at least four times smaller than a standard scheme to encode magic states. We show yields using different logical tomography experiments for the standard preparation scheme with orange markers in Fig. ##FIG##3##4##. In the case of physical tomography, the encoded state on the four data qubits has a post-selection acceptance rate of 20.9 ± 0.1%, and the reconstructed density matrix is found in the code space with probability <italic>P</italic><sub><italic>L</italic></sub> = 0.789 ± 0.004.</p>", "<p id=\"Par22\">Finally, we compare our error-suppressed preparation procedure with a state-preparation experiment performed using physical qubits. We mark the lowest infidelity obtained over all of the adjacent pairs of physical qubits on the 27 qubit device, (2.4 ± 0.3) × 10<sup>−2</sup>, with a red line in Fig. ##FIG##2##3##. Remarkably, all fidelities for all of our error-suppressed magic-state preparation schemes exceed the fidelity of a simple experiment to prepare the CZ state with physical qubits.</p>" ]	[ "<title>Discussion</title>", "<p id=\"Par23\">We have presented a scheme that encodes an input magic state with a fidelity higher than we can achieve with any pair of physical qubits on the same device using basic entangling operations. This improvement in fidelity, which is beyond the break-even point set by basic physical qubit operations, can be attributed to quantum error correction that suppresses the noise that accumulates during state preparation.</p>", "<p id=\"Par24\">The yield of magic states benefited from the use of dynamic circuits in which mid-circuit measurements condition gate operations in real time. Remarkably, we find that the operation is sufficiently rapid that its execution came at only a small cost in output state fidelity on the superconducting device. These dynamic circuits are essential to future quantum-computing architectures as they will be needed, for example, to perform magic-state distillation circuits<sup>##UREF##2##9##–##UREF##4##11##</sup> and gate teleportation<sup>##REF##15744292##35##,##UREF##26##36##</sup>, as well as many other measurement-based methods<sup>##UREF##6##13##,##UREF##10##17##–##UREF##11##19##,##UREF##27##37##–##UREF##35##48##</sup> that have been proposed to complete a universal set of logic gates.</p>", "<p id=\"Par25\">We have shown that experimental progress has reached a point at which we can make prototype gadgets that can affect the resource cost of large-scale quantum computers. In the <xref rid=\"Sec5\" ref-type=\"sec\">Methods</xref>, we explain how our prototype can be used together with magic-state distillation. It will be interesting to continue to design, develop and test new gadgets with real hardware that will improve the performance of the key subroutines needed for fault-tolerant quantum computing. Further developments in the theory of pieceable fault tolerance<sup>##UREF##32##44##</sup> might show us ways of producing better magic states with small devices. Error-suppressed magic states could improve the time cost of recent proposals<sup>##REF##34860063##49##,##REF##34860056##50##</sup> for error-corrected circuits that are supplemented by error-mitigation techniques to complete non-Clifford operations. Ultimately, experimental progress that we make to this end in the near term can benefit large-scale quantum-computing architectures.</p>" ]	[]	[ "<p id=\"Par1\">To run large-scale algorithms on a quantum computer, error-correcting codes must be able to perform a fundamental set of operations, called logic gates, while isolating the encoded information from noise<sup>##REF##30932564##1##–##REF##36813892##8##</sup>. We can complete a universal set of logic gates by producing special resources called magic states<sup>##UREF##2##9##–##UREF##4##11##</sup>. It is therefore important to produce high-fidelity magic states to conduct algorithms while introducing a minimal amount of noise to the computation. Here we propose and implement a scheme to prepare a magic state on a superconducting qubit array using error correction. We find that our scheme produces better magic states than those that can be prepared using the individual qubits of the device. This demonstrates a fundamental principle of fault-tolerant quantum computing<sup>##UREF##5##12##</sup>, namely, that we can use error correction to improve the quality of logic gates with noisy qubits. Moreover, we show that the yield of magic states can be increased using adaptive circuits, in which the circuit elements are changed depending on the outcome of mid-circuit measurements. This demonstrates an essential capability needed for many error-correction subroutines. We believe that our prototype will be invaluable in the future as it can reduce the number of physical qubits needed to produce high-fidelity magic states in large-scale quantum-computing architectures.</p>", "<p id=\"Par2\">A scheme to prepare a magic state, an important ingredient for quantum computers, on a superconducting qubit array using error correction is proposed that produces better magic states than those that can be prepared using the individual qubits of the device.</p>", "<title>Subject terms</title>" ]	[ "<title>Main</title>", "<p id=\"Par3\">We distil magic states to complete a universal set of fault-tolerant logic gates that is needed for large-scale quantum computing with low-density parity-check code architectures<sup>##UREF##6##13##–##REF##35594359##18##</sup>. High-fidelity magic states are produced<sup>##UREF##2##9##–##UREF##4##11##</sup> by processing noisy input magic states with fault-tolerant distillation circuits; experimental progress in preparing input magic states using trapped-ion architectures is described in refs. <sup>##REF##34608286##3##,##REF##35614250##7##</sup>. It is expected that a considerable number of the qubits of a quantum computer will be occupied performing magic-state distillation schemes and, as such, it is valuable to find ways of reducing its cost. One way to reduce the cost is to improve the fidelity of input states<sup>##UREF##4##11##,##UREF##11##19##–##UREF##18##26##</sup>, such that magic states can be distilled with less resource-intensive circuits.</p>", "<p id=\"Par4\">Here we propose and implement an error-suppressed encoding circuit to prepare a state that is input to magic-state distillation using a heavy-hexagonal lattice of superconducting qubits<sup>##REF##35362994##4##,##REF##37202409##5##,##UREF##19##27##</sup>. Our circuit prepares an input magic state, which we call a CZ state, encoded on a four-qubit error-detecting code. We explain how our encoded magic state can be used in large-scale quantum-computing architectures<sup>##UREF##4##11##,##UREF##20##28##</sup> in the section ‘<xref rid=\"Sec6\" ref-type=\"sec\">Using CZ states in large-scale quantum-computing architectures</xref>’. Our circuit is capable of detecting any single error during state preparation, as such, the infidelity of the encoded state is suppressed as , where <italic>ε</italic> is the probability that a circuit element experiences an error. By contrast, a standard encoding circuit prepares an input state with infidelity . Furthermore, we can improve the yield of the prepared magic states with the error-suppressed circuit using adaptive circuits that are conditioned in real time on the outcomes of mid-circuit measurements. We propose several tomographical experiments to interrogate the preparation of the magic state, including a complete set of fault-tolerant projective logical Pauli measurements that can also tolerate the occurrence of a single error during readout.</p>", "<title>Magic-state preparation and logical tomography</title>", "<p id=\"Par5\">We prepare the CZ state as follows:encoded on a distance-2 error-detecting code, in which the distinct bit strings label orthogonal computational basis states over two qubits. We can achieve the CZ state by, first, preparing the state and, then, projecting it onto the <italic>C</italic><italic>Z</italic> = +1 eigenspace of the controlled-phase (CZ) operator <italic>CZ</italic> = diag(1, 1, 1, −1), that is, with the projector . We can perform both these operations with the four-qubit code. Specifically, it has a fault-tolerant preparation of the \|++⟩ state and, as we will show, we can make a fault-tolerant measurement of the logical <italic>C</italic><italic>Z</italic> operator to prepare an encoded CZ state.</p>", "<p id=\"Par6\">Encoded states of the four-qubit code lie in the common +1 eigenvalue eigenspace of its stabilizer operators <italic>S</italic><sup><italic>X</italic></sup> = <italic>X</italic> ⊗ <italic>X</italic> ⊗ <italic>X</italic> ⊗ <italic>X</italic>, <italic>S</italic><sup><italic>Z</italic></sup> = <italic>Z</italic> ⊗ <italic>Z</italic> ⊗ <italic>Z</italic> ⊗ <italic>Z</italic> and <italic>S</italic><sup><italic>Y</italic></sup> = <italic>S</italic><sup><italic>Z</italic></sup><italic>S</italic><sup><italic>X</italic></sup>, where <italic>X</italic> and <italic>Z</italic> are the standard Pauli matrices. The four-qubit code encodes two logical qubits that are readily prepared in a logical state by initializing four data qubits in the superposition state, \|+⟩ ∝ \|0⟩ + \|1⟩, and measuring <italic>S</italic><sup><italic>Z</italic></sup>. We note that we use bars to indicate we are describing states and operations in the logical subspace. We prepare the state with <italic>S</italic><sup><italic>Z</italic></sup> = +1 using either post-selection or, alternatively, an adaptive Pauli-X rotation on a single-qubit given a random −1 outcome from the <italic>S</italic><sup><italic>Z</italic></sup> measurement.</p>", "<p id=\"Par7\">The four-qubit code has a transversal implementation of the CZ gate on its encoded subspace, , where . We can measure this operator as follows. We note that conjugating <italic>S</italic><sup><italic>X</italic></sup> with the unitary rotation , where , gives the Hermitian operator:Given that we prepare the code with <italic>S</italic><sup><italic>X</italic></sup> = +1, measuring effectively gives a reading of .</p>", "<p id=\"Par8\">It is essential to our scheme that we reach the <italic>S</italic><sup><italic>Z</italic></sup> = +1 eigenspace. This is because of the non-trivial commutation relations of with the stabilizer operators of the code<sup>##UREF##21##29##,##UREF##22##30##</sup>; . This commutator shows that only commutes with <italic>S</italic><sup><italic>X</italic></sup> in the <italic>S</italic><sup><italic>Z</italic></sup> = +1 subspace. If <italic>S</italic><sup><italic>Z</italic></sup> = −1, we can check that and <italic>S</italic><sup><italic>X</italic></sup> anti-commute, and are therefore incompatible observables in this subspace.</p>", "<p id=\"Par9\">We can perform all of the aforementioned measurements, <italic>S</italic><sup><italic>X</italic></sup>, and <italic>S</italic><sup><italic>Z</italic></sup>, on the heavy-hexagon lattice geometry<sup>##UREF##19##27##</sup>. Figure ##FIG##0##1## shows one such setup. The circuit is fault-tolerant in the sense that a Pauli error introduced by a circuit element, on the support of the circuit element, is always detected by a flag qubit or a stabilizer measurement. The verification of this is detailed in the section ‘<xref rid=\"Sec13\" ref-type=\"sec\">Analysis in terms of single-gate errors</xref>’.</p>", "<p id=\"Par10\">We, therefore, present a sequence of measurements that prepare the input magic state and, in tandem, identify a single error that may have occurred during the preparation procedure. Figure ##FIG##1##2## shows the sequence and describes its function. As we can detect a single error, we expect the infidelity of the output state to be . We compare our error-suppressed magic-state preparation scheme to a standard scheme for encoding a two-qubit magic state, as well as a circuit that prepares the magic state on two physical qubits. Both of these schemes are described in the section ‘<xref rid=\"Sec14\" ref-type=\"sec\">Standard magic-state preparation circuits</xref>’.</p>", "<p id=\"Par11\">We verify our state-preparation schemes by performing two variants of quantum-state tomography to reconstruct the logical state. The first method uses fault-tolerant circuits that directly measure the logical operators; we refer to this tomographical method as ‘logical tomography’. For the second method, which we refer to as ‘physical tomography’, we perform standard state tomography on the full state of the four data qubits of the system and then project the reconstructed state onto the logical subspace. Logical tomography with the four-qubit code is shown in Fig. ##FIG##1##2b,c##. All of our logical tomography circuits can tolerate a single error at the readout stage, by repeating the measurement of logical operators and by comparing measurement outcomes to earlier readings of stabilizer measurements.</p>", "<p id=\"Par12\">Logical tomography is more efficient than physical tomography because we are directly measuring and reconstructing the encoded logical state, rather than the physical state. In the case of the four-qubit code, this requires only 7 distinct circuits, whereas physical tomography requires 81 different measurement circuits.</p>", "<title>Online content</title>", "<p id=\"Par112\">Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41586-023-06846-3.</p>", "<title>Supplementary information</title>", "<p>\n\n\n</p>" ]	[ "<title>Extended data figures and tables</title>", "<p id=\"Par115\">\n\n</p>", "<p id=\"Par116\">\n\n</p>", "<p id=\"Par117\">\n\n</p>", "<p id=\"Par118\">\n\n</p>", "<p id=\"Par119\">\n\n</p>", "<p id=\"Par120\">\n\n</p>", "<p id=\"Par121\">\n\n</p>", "<p id=\"Par122\">\n\n</p>", "<p id=\"Par123\">\n\n</p>", "<p id=\"Par124\">\n\n</p>", "<p id=\"Par125\">\n\n</p>", "<p id=\"Par126\">\n\n</p>", "<title>Extended data</title>", "<p>is available for this paper at 10.1038/s41586-023-06846-3.</p>", "<title>Supplementary information</title>", "<p>The online version contains supplementary material available at 10.1038/s41586-023-06846-3.</p>", "<title>Acknowledgements</title>", "<p>We acknowledge the use of IBM Quantum services for this work. These system capabilities are available as open access to device users. We also acknowledge the work of the IBM Quantum software and hardware teams that enabled this project. The views expressed are ours and do not reflect the official policy or position of IBM or the IBM Quantum team. B.J.B. is grateful for the hospitality of the Center for Quantum Devices at the University of Copenhagen. J.R.W. acknowledges support from the NCCR SPIN, a National Centre of Competence in Research, funded by the Swiss National Science Foundation (grant no. 51NF40-180604). R.S.G. and S.T.M. acknowledge support from the Army Research Office under QCISS (W911NF-21-1-0002). T.A., M.H., and M.B.H. acknowledge support from IARPA under LogiQ (contract W911NF-16-1-0114) on real-time control software work. All statements of fact, opinion or conclusions contained herein are ours and should not be construed as representing the official views or policies of the US government.</p>", "<title>Author contributions</title>", "<p>R.S.G., N.S., T.A., M.H. and M.B.H. enabled experimental execution using real-time control flow; S.T.M. performed unencoded magic-state tomography experiments; R.S.G. performed encoded magic-state tomography experiments and simulations; C.J.W. and S.T.M. conceived and developed tomographic fitting procedures, with and without error mitigation, implemented by R.S.G; J.R.W. conducted numerical simulations to test the fault-tolerant properties of the preparation circuits and for analysis of the experimental results; R.S.G., C.J.W., S.T.M., J.R.W., M.T. and B.J.B. performed data analysis; T.J.-O., T.J.Y., A.W.C. and B.J.B. developed the fault-tolerant magic-state preparation circuits; R.S.G. assumed primary responsibility for experimental execution, analysis, codebase and data management; M.T. and B.J.B supervised the project; R.S.G., N.S., T.A., C.J.W., S.T.M., J.R.W., M.T. and B.J.B. wrote the paper with input from all the authors.</p>", "<title>Peer review</title>", "<title>Peer review information</title>", "<p id=\"Par113\"><italic>Nature</italic> thanks Leonid Pryadko and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. ##SUPPL##1##Peer reviewer reports## are available.</p>", "<title>Data availability</title>", "<p>The datasets generated and analysed during this study are available at 10.6084/m9.figshare.23535237.</p>", "<title>Code availability</title>", "<p>The codebase used for data analysis and figure generation is available at 10.6084/m9.figshare.23535237; other supporting codes are available upon reasonable request.</p>", "<title>Competing interests</title>", "<p id=\"Par114\">A patent (application no. 18/053087) was filed on 7 November 2022 with listed inventors B.J.B., A.W.C., R.S.G., T.J.-O. and T.J.Y. The authors declare no other competing financial or non-financial interests.</p>" ]	[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>A fault-tolerant circuit to make parity measurements.</title><p><bold>a</bold>, A circuit that measures <italic>S</italic><sup><italic>X</italic></sup>, <italic>S</italic><sup><italic>Z</italic></sup> and using flag qubits on the heavy-hexagonal lattice architecture. <bold>b</bold>, The four-qubit code is encoded on qubits with even indices and the other qubits are used to make the fault-tolerant parity measurement. The circuit measures <italic>S</italic><sup><italic>X</italic></sup> by setting and <italic>S</italic><sup><italic>Z</italic></sup> by setting <italic>U</italic> = <italic>H</italic>, where <italic>H</italic> is the Hadamard gate. The circuit measures if we set <italic>U</italic> = <italic>T</italic>. The measurement outcome <italic>M</italic> gives the reading of the parity measurement. Essential to the fault-tolerant procedure are flag fault-tolerant readout circuits<sup>##REF##35362994##4##,##REF##37202409##5##,##UREF##19##27##,##REF##30118291##51##</sup> that identify errors that occur during the parity measurement. Outcomes <italic>f</italic> and <italic>g</italic> are flag qubit readings that indicate that the circuit may have introduced a logical error to the data qubits.</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>Fault-tolerant schemes for magic-state preparation and logical tomography.</title><p><bold>a</bold>, Preparation of a CZ state on a four-qubit code in three steps. In the code-preparation step, the four-qubit code is prepared in the logical state by measuring with the <italic>S</italic><sup><italic>Z</italic></sup> operator. We can use adaptive circuits or post-selection to correct for <italic>S</italic><sup><italic>Z</italic></sup> = −1 outcomes. In the magic-state initialization step, we measure the operator and post-select on the +1 outcome. In the final error-detection step, we identify the errors that may have occurred during preparation. We measure a second time to identify if a measurement error occurred during the magic-state initialization step. We finally measure <italic>S</italic><sup><italic>X</italic></sup> and <italic>S</italic><sup><italic>Z</italic></sup> a second time to identify Pauli errors that may have occurred, and to determine if the initial <italic>S</italic><sup><italic>Z</italic></sup> measurement gave a readout error. <bold>b</bold>,<bold>c</bold>, We replace the parity measurements in the dashed box of <bold>a</bold> with circuits <bold>b</bold> and <bold>c</bold> to make logical tomographic measurements and, at the same time, infer a complete set of stabilizer data for error detection. For example, if we set <italic>S</italic><sup><italic>Q</italic></sup> = <italic>S</italic><sup><italic>X</italic></sup> and measure qubits in the <italic>R</italic> = <italic>Z</italic> basis, we infer the value of <italic>S</italic><sup><italic>Z</italic></sup>, as in <bold>a</bold>, and we also obtain readings of the logical , and . Likewise, we can set <italic>S</italic><sup><italic>Q</italic></sup> = <italic>S</italic><sup><italic>Z</italic></sup> with either <italic>R</italic> = <italic>X</italic> to infer <italic>S</italic><sup><italic>X</italic></sup> as well as logical Pauli operators , and , or <italic>R</italic> = <italic>Y</italic> to infer <italic>S</italic><sup><italic>Y</italic></sup> as well as logical Pauli operators , and . In <bold>c</bold>, we include a measurement for logical qubit <italic>j</italic> = 1, 2 to measure logical operators of the form , and with <italic>k</italic> ≠ <italic>j</italic> and <italic>k</italic> = 1, 2, where we take an appropriate choice of <italic>R</italic>. The operator is measured twice to identify the occurrence of measurement errors. Operators are supported on three of the data qubits and can therefore be read out with an appropriate modification of the circuit shown in Fig. ##FIG##0##1##.</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><title>Infidelities measured in magic-state preparation experiments.</title><p>State infidelity for error-suppressed (error supp.) and standard schemes are shown in blue and orange, respectively. On the <italic>x</italic>-axis, a state is reconstructed with either logical or physical tomography. The correction for the initial <italic>S</italic><sup><italic>Z</italic></sup> measurement in Fig. ##FIG##1##2a## is implemented using either real-time feedforward (FF) or post-selection (PS). For the physical data points, the state from physical tomography is projected onto the logical subspace before computing the infidelity by fitting to ideal projectors. Error bars represent 1σ from bootstrapping. For all tomographic methods, the error-suppressed scheme achieves a lower state infidelity compared with the standard scheme. The unencoded magic state prepared directly on two physical qubits gives an average (avg.) infidelity across 28 qubit pairs as approximately 6.2 × 10<sup>−2</sup> (green dashed line) using 18 repetitions over a 24-h period with 10<sup>5</sup> shots per circuit. Of these, the best-performing pair yields a minimum (min.) infidelity of (2.354 ± 0.271) × 10<sup>−2</sup> (red solid line) found over all repetitions for all qubit pairs. In all cases, the error-suppressed scheme exceeds the fidelity of the best two-qubit unencoded magic state.</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><title>Magic-state yield for feedforward versus post-selection.</title><p>Yield is calculated for logical tomography circuits shown in Fig. ##FIG##1##2b,c## for the error-suppressed (error supp.) scheme with feedforward (blue circles) versus post-selection (blue squares); a standard scheme is shown for reference (orange squares). All rates use datasets reported in Fig. ##FIG##2##3## where error bars represent 1σ from bootstrapping. The shaded area of the graph shows the increase in yield for the error-suppressed scheme using feedforward (FF) compared with the post-selection (PS) scheme or the standard scheme. The optimal acceptance rate assuming no noise is 75% for the feedforward scheme, 37.5% for the post-selection scheme and 25% for the standard scheme. The observed acceptance rates are because of the additional detection of errors. We estimate the yield in the presence of noise in the section ‘<xref rid=\"Sec17\" ref-type=\"sec\">Estimates for magic-state yield</xref>’. We observe a stark difference in yields between experiments conducted with the logical tomography circuit shown in Fig. ##FIG##1##2b,c##, shown to the left and right of the dashed line, respectively. We can attribute this to the depth of the logical tomography circuit, in which deeper circuits, such as those shown in Fig. ##FIG##1##2c##, are more likely to introduce detectable errors. This is discussed in the section ‘<xref rid=\"Sec17\" ref-type=\"sec\">Estimates for magic-state yield</xref>’.</p></caption></fig>", "<fig id=\"Fig5\"><label>Extended Data Fig. 1</label><caption><title>A generic magic-state distillation protocol.</title><p>Encoded input magic states are combined such that higher fidelity magic states are produced with some probability. For a single use of a magic-state distillation protocol, the error of an input magic state <italic>ϵ</italic> is suppressed like <italic>ϵ</italic> → <italic>ϵ</italic><sup><italic>d</italic></sup> where <italic>d</italic> is a constant determined by the magic-state distillation protocol. Applying distillation recursively allows us to produce magic states with an arbitrarily high fidelity. By initializing error-suppressed magic states in the first step, where the error is suppressed as <italic>ϵ</italic><sup>2</sup> we obtain a quadratic improvement in the fidelity of the output magic state.</p></caption></fig>", "<fig id=\"Fig6\"><label>Extended Data Fig. 2</label><caption><title>Small codes.</title><p>We describe how to encode these codes into higher distance codes. (left) The error-detecting code prepared in the main text. We refer to this code as the [[4, 2, 2]] code to distinguish it from the [[4, 1, 2]] code shown to the right of the figure. The [[4, 2, 2]] code has stabilizer generators <italic>S</italic><sup><italic>X</italic></sup> = <italic>X</italic><sub>1</sub><italic>X</italic><sub>2</sub><italic>X</italic><sub>3</sub><italic>X</italic><sub>4</sub> and <italic>S</italic><sup><italic>Z</italic></sup> = <italic>Z</italic><sub>1</sub><italic>Z</italic><sub>2</sub><italic>Z</italic><sub>3</sub><italic>Z</italic><sub>4</sub> and logical operators , , and for logical operators indexed <italic>A</italic> and <italic>B</italic>. (right). The [[4, 1, 2]] code is an error detecting code that encodes a single logical qubit. It is closely related to the error detecting code shown (left). It has stabilizer generators <italic>S</italic><sup><italic>X</italic></sup> = <italic>X</italic><sub>1</sub><italic>X</italic><sub>2</sub><italic>X</italic><sub>3</sub><italic>X</italic><sub>4</sub>, and , and logical operators , .</p></caption></fig>", "<fig id=\"Fig7\"><label>Extended Data Fig. 3</label><caption><title>Injecting an encoded magic state into the surface code.</title><p>The magic state is initially encoded on a [[4, 1, 2]] code. (left) The standard surface code with physical qubits on the vertices of a square lattice and standard Pauli-X and Pauli-Z type stabilizers marked by lattice faces. Supports for the logical Pauli-X and Pauli-Z operators are shown in green and blue, respectively. (right) We show the initial state that is injected into the surface code. The [[4, 1, 2]] code is shown in red in the bottom-left corner. The remaining qubits of the surface code lattice are prepared in a product state, where blue (green) qubits are prepared in the () state. We show the code deformation stabilizers, i.e. , shaded on the right lattice.</p></caption></fig>", "<fig id=\"Fig8\"><label>Extended Data Fig. 4</label><caption><title>Injecting an encoded state into the heavy-hex code.</title><p>The injected state is initially encoded on the [[4, 1, 2]] code. (left) A lattice with qubits on the vertices. We show the support of a single Pauli-Z gauge check and a Pauli-X stabilizer operator. The support of the Pauli-Z gauge check is shown in dark gray. The Pauli-X stabilizer operator is shaded grey towards the top of the lattice. We also show the support of a Pauli-X- and Pauli-Z-type stabilizer in green and blue, respectively. (right) The stabilizer group for . The [[4, 1, 2]] code is outlined in red in the bottom-left corner of the lattice. The other qubits are initialized in a product state with blue (green) qubits initialized in the () state. Stabilizer operators are shaded in the figure.</p></caption></fig>", "<fig id=\"Fig9\"><label>Extended Data Fig. 5</label><caption><title>Injecting an encoded two-qubit state into the color code.</title><p>The state is initially encoded with the [[4, 2, 2]] code. A qubit is supported on each of the vertices of the lattice. We initialize the system such that the [[4, 2, 2]] code, shaded in red, is supported on a weight-four face in the bottom left corner of the lattice. The other qubits are prepared in Bell pairs on the highlighted blue and green edge terms. As such, we shade the faces of where both a Pauli-X and Pauli-Z stabilizer is supported. The support of the logical operators on the left and bottom boundaries are highlighted in blue and green, respectively.</p></caption></fig>", "<fig id=\"Fig10\"><label>Extended Data Fig. 6</label><caption><title>Preparing a CZ state over two [[4, 1, 2]]-codes.</title><p>At step 1 the codes are prepared. The [[4, 2, 2]] code that encodes the two-qubit CZ-state is represented by the red square where its four qubits lie at the vertices of the square. This preparation is described in the main text. The code is prepared adjacent to a [[4, 1, 2]]-code that is initialized in an eigenstate of the state. The qubits in the figure are indexed according to the qubit-map shown in Extended Data Fig. ##FIG##10##7##. At step 2 the qubits are transported in order to perform a logical parity measurement in step 3 using the heavy-hex lattice geometry. Note that the qubit indices have changed. This step can be performed with swaps, for instance, as shown in Extended Data Fig. ##FIG##10##7##(top). At step 3 a logical parity measurement is made. It can be performed in a fault-tolerant manner using qubits 5, 10, and 16, as shown in the green box in Extended Data Fig. ##FIG##10##7##(bottom). We complete the operation by measuring the logical operator in step 4. This weight-two measurement can be repeated in two locations on the [[4, 2, 2]] code such that a single measurement error can be detected. This final measurement projects the [[4, 2, 2]] code onto the [[4, 1, 2]]-code by reassigning the logical operators as stabilizers of the system.</p></caption></fig>", "<fig id=\"Fig11\"><label>Extended Data Fig. 7</label><caption><title>Mapping the encoding onto the heavy-hexagonal lattice geometry.</title><p>We encode the CZ-state onto two copies of the [[4, 1, 2]]-code. (top) We prepare the encoded CZ-state as defined in the main text using the qubits outlined in the purple box. We additionally prepare a [[4, 1, 2]]-code in the logical state using the qubits outlined in the orange box. To perform step 3, as shown in Extended Data Fig. ##FIG##9##6##, we first move the codes, as in step 2. This can be performed using swap gates between adjacent qubits. Swap gates are performed, first, between pairs of qubits marked by a blue arrow, and then between pairs of qubits marked with green arrows. Each set of swap gates, the blue set and the green set, can be performed in parallel. Completing the swap operations moves the codes over the qubit map. We show the locations of the codes after the swap operations by outlining their supporting qubits with a purple and orange box, respectively, in the bottom figure. In their new locations, the logical parity measurement of step 3 can be performed using ancillary qubits 5,10 and 16, outlined in the green box in the bottom figure. At the final step we facilitate the measurement of <italic>Z</italic><sub>4</sub><italic>Z</italic><sub>6</sub> and <italic>Z</italic><sub>15</sub><italic>Z</italic><sub>17</sub> using ancillary qubits 5 and 16, respectively.</p></caption></fig>", "<fig id=\"Fig12\"><label>Extended Data Fig. 8</label><caption><title>Magic-state preparation without error suppression.</title><p>We can encode a physical CZ state using the circuit outlined in (a), where the preparation step, Prep., is shown in (b). The magic state is then encoded using stabilizer measurements <italic>S</italic><sup><italic>X</italic></sup> and <italic>S</italic><sup><italic>Z</italic></sup>. The preparation circuit, (b), first prepares a CZ state on two physical qubits before preparing the state to encode it in the four-qubit code by stabilizer measurements. The circuit makes use of a Pauli-Y rotation with , a controlled-Hadamard gate and a bitflip. We find that we can simplify the circuit once the CZ state is prepared by making use of the stabilizer operators of the CZ state. As discussed in the main text we observe that the circuit element in the box with a dotted outline acts trivially on the CZ-state. The inclusion of this stabilizer operator allows us to remove all of the Pauli-X and controlled-not operations shown in the circuit, as the circuit elements in the box negate their adjacent self-inverse gates. Indeed, the circuit elements that lie in between the vertical dashed lines act like the identity operator. (c) The CZ state is prepared on two physical qubits, where the circuit elements are defined above. We perform state tomography on this state by making different choices of single-qubit Pauli measurements, <italic>P</italic> and <italic>Q</italic>, on the output of this circuit.</p></caption></fig>", "<fig id=\"Fig13\"><label>Extended Data Fig. 9</label><caption><title>Combining readout-error mitigation with state tomography methods.</title><p>(a) State infidelity for the standard (orange) vs. error-suppressed (blue) schemes using different tomographic methods; error-bars represent 1<italic>σ</italic> std. dev. from bootstrapping. On the <italic>x</italic>-axis, a state is reconstructed with either logical tomography (Logical) or physical tomography after logical projection (Physical); tomography assumes either ideal projectors, as in the main text, or noisy POVMs representing uncorrelated, local readout errors (RO) on terminal data qubit measurements. Raw physical tomography (Raw Phys.) refers to the state on four physical qubits prior to logical projection. Red dotted (green dot-dashed) lines show lowest (average) state infidelities of the two-qubit unencoded magic state prepared with RO mitigation. With RO mitigation, logical tomography outperforms the min. unencoded state supporting conclusions in the main text. (b)-(e) Heatmap of state infidelity vs. avg. measurement error, <italic>p</italic> ≡ <italic>P</italic>(1∣0), <italic>q</italic> ≡ <italic>P</italic>(0∣1). Experimental tomography data is fit to noisy POVMs using a parameterized <italic>A</italic>-matrix, <italic>A</italic> ≔ [[1 − <italic>p</italic>, <italic>q</italic>], [<italic>p</italic>, 1 − <italic>q</italic>]], where <italic>p</italic>, <italic>q</italic> are constant for all qubits and time. Experimental readout calibrations data are averaged over time and qubits, and correspond to a single state infidelity in (b)-(e) (black dots). These state infidelities (black dots) do not coincide with local minima (red stars) or even high-fidelity regions. (f)-(g) Readout calibration measurements of <italic>p</italic>, <italic>q</italic> vs. time for all four data qubits over several days; average rates (black solid) are used in (b)-(e) for state fidelities marked by black dots.</p></caption></fig>" ]	[ "<table-wrap id=\"Tab1\"><label>Extended Data Table 1</label><caption><p>Average single-qubit benchmarks</p></caption></table-wrap>", "<table-wrap id=\"Tab2\"><label>Extended Data Table 2</label><caption><p>Average two-qubit gate benchmarks</p></caption></table-wrap>", "<table-wrap id=\"Tab3\"><label>Extended Data Table 3</label><caption><p>Estimated magic-state yield compared with experiment</p></caption></table-wrap>" ]	[ "<inline-formula id=\"IEq1\"><alternatives><tex-math id=\"M1\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} 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\n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\mathcal{O}}({\\varepsilon })$$\\end{document}</tex-math><mml:math id=\"M4\"><mml:mrow><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">O</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi mathvariant=\"italic\">ε</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:math></alternatives></inline-formula>", "<disp-formula id=\"Equa\"><alternatives><tex-math id=\"M5\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\left\|CZ\\right\\rangle \\equiv \\frac{\\left\|00\\right\\rangle +\\left\|01\\right\\rangle +\\left\|10\\right\\rangle }{\\sqrt{3}},$$\\end{document}</tex-math><mml:math id=\"M6\" display=\"block\"><mml:mrow><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mi>C</mml:mi><mml:mi>Z</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced><mml:mo>≡</mml:mo><mml:mfrac><mml:mrow><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mn>00</mml:mn></mml:mrow></mml:mrow></mml:mrow></mml:mfenced><mml:mo>+</mml:mo><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mn>01</mml:mn></mml:mrow></mml:mrow></mml:mrow></mml:mfenced><mml:mo>+</mml:mo><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mn>10</mml:mn></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow><mml:mrow><mml:msqrt><mml:mrow><mml:mn>3</mml:mn></mml:mrow></mml:msqrt></mml:mrow></mml:mfrac><mml:mo>,</mml:mo></mml:mrow></mml:math></alternatives></disp-formula>", "<inline-formula id=\"IEq3\"><alternatives><tex-math id=\"M7\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\left\|++\\right\\rangle ={\\sum }_{a,b=0,1}\\left\|ab\\right\\rangle /2$$\\end{document}</tex-math><mml:math id=\"M8\"><mml:mrow><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mo>+</mml:mo><mml:mo>+</mml:mo></mml:mrow></mml:mrow></mml:mrow></mml:mfenced><mml:mo>=</mml:mo><mml:msub><mml:mrow><mml:mo>∑</mml:mo></mml:mrow><mml:mrow><mml:mi>a</mml:mi><mml:mo>,</mml:mo><mml:mi>b</mml:mi><mml:mo>=</mml:mo><mml:mn>0</mml:mn><mml:mo>,</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mi>a</mml:mi><mml:mi>b</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced><mml:mo>/</mml:mo><mml:mn>2</mml:mn></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq4\"><alternatives><tex-math id=\"M9\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\left\|CZ\\right\\rangle \\propto {\\Pi }^{+}\\left\|++\\right\\rangle $$\\end{document}</tex-math><mml:math id=\"M10\"><mml:mrow><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mi>C</mml:mi><mml:mi>Z</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced><mml:mo>∝</mml:mo><mml:msup><mml:mrow><mml:mi mathvariant=\"normal\">Π</mml:mi></mml:mrow><mml:mrow><mml:mo>+</mml:mo></mml:mrow></mml:msup><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mo>+</mml:mo><mml:mo>+</mml:mo></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq5\"><alternatives><tex-math id=\"M11\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\Pi }^{+}=({\\mathbb{1}}+CZ)/2$$\\end{document}</tex-math><mml:math id=\"M12\"><mml:mrow><mml:msup><mml:mrow><mml:mi mathvariant=\"normal\">Π</mml:mi></mml:mrow><mml:mrow><mml:mo>+</mml:mo></mml:mrow></mml:msup><mml:mo>=</mml:mo><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi mathvariant=\"double-struck\">1</mml:mi><mml:mo>+</mml:mo><mml:mi>C</mml:mi><mml:mi>Z</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>/</mml:mo><mml:mn>2</mml:mn></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq7\"><alternatives><tex-math id=\"M13\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\left\|\\overline{+}\\,\\overline{+}\\right\\rangle $$\\end{document}</tex-math><mml:math id=\"M14\"><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mover accent=\"true\"><mml:mrow><mml:mo>+</mml:mo></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover><mml:mspace width=\"0.25em\"/><mml:mover accent=\"true\"><mml:mrow><mml:mo>+</mml:mo></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq8\"><alternatives><tex-math id=\"M15\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\overline{CZ}\\simeq \\sqrt{Z}\\otimes {\\sqrt{Z}}^{\\dagger }\\otimes {\\sqrt{Z}}^{\\dagger }\\otimes \\sqrt{Z}$$\\end{document}</tex-math><mml:math id=\"M16\"><mml:mrow><mml:mover accent=\"true\"><mml:mrow><mml:mi>C</mml:mi><mml:mi>Z</mml:mi></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover><mml:mo>≃</mml:mo><mml:msqrt><mml:mrow><mml:mi>Z</mml:mi></mml:mrow></mml:msqrt><mml:mo>⊗</mml:mo><mml:msup><mml:mrow><mml:msqrt><mml:mrow><mml:mi>Z</mml:mi></mml:mrow></mml:msqrt></mml:mrow><mml:mrow><mml:mo>†</mml:mo></mml:mrow></mml:msup><mml:mo>⊗</mml:mo><mml:msup><mml:mrow><mml:msqrt><mml:mrow><mml:mi>Z</mml:mi></mml:mrow></mml:msqrt></mml:mrow><mml:mrow><mml:mo>†</mml:mo></mml:mrow></mml:msup><mml:mo>⊗</mml:mo><mml:msqrt><mml:mrow><mml:mi>Z</mml:mi></mml:mrow></mml:msqrt></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq9\"><alternatives><tex-math id=\"M17\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\sqrt{Z}={\\rm{diag}}(1,i)$$\\end{document}</tex-math><mml:math id=\"M18\"><mml:mrow><mml:msqrt><mml:mrow><mml:mi>Z</mml:mi></mml:mrow></mml:msqrt><mml:mo>=</mml:mo><mml:mi mathvariant=\"normal\">diag</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mi>i</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq10\"><alternatives><tex-math id=\"M19\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\widetilde{T}=T\\otimes {T}^{\\dagger }\\otimes {T}^{\\dagger }\\otimes T$$\\end{document}</tex-math><mml:math id=\"M20\"><mml:mrow><mml:mover accent=\"true\"><mml:mrow><mml:mi>T</mml:mi></mml:mrow><mml:mrow><mml:mo>~</mml:mo></mml:mrow></mml:mover><mml:mo>=</mml:mo><mml:mi>T</mml:mi><mml:mo>⊗</mml:mo><mml:msup><mml:mrow><mml:mi>T</mml:mi></mml:mrow><mml:mrow><mml:mo>†</mml:mo></mml:mrow></mml:msup><mml:mo>⊗</mml:mo><mml:msup><mml:mrow><mml:mi>T</mml:mi></mml:mrow><mml:mrow><mml:mo>†</mml:mo></mml:mrow></mml:msup><mml:mo>⊗</mml:mo><mml:mi>T</mml:mi></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq11\"><alternatives><tex-math id=\"M21\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$T={\\rm{diag}}(1,\\sqrt{i})$$\\end{document}</tex-math><mml:math id=\"M22\"><mml:mrow><mml:mi>T</mml:mi><mml:mo>=</mml:mo><mml:mi mathvariant=\"normal\">diag</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:msqrt><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msqrt></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:math></alternatives></inline-formula>", "<disp-formula id=\"Equ1\"><label>1</label><alternatives><tex-math id=\"M23\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\overline{W}\\equiv \\widetilde{T}{S}^{X}{\\widetilde{T}}^{\\dagger }\\propto \\overline{CZ}{S}^{X}.$$\\end{document}</tex-math><mml:math id=\"M24\" display=\"block\"><mml:mrow><mml:mover accent=\"true\"><mml:mrow><mml:mi>W</mml:mi></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover><mml:mo>≡</mml:mo><mml:mover accent=\"true\"><mml:mrow><mml:mi>T</mml:mi></mml:mrow><mml:mrow><mml:mo>~</mml:mo></mml:mrow></mml:mover><mml:msup><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mi>X</mml:mi></mml:mrow></mml:msup><mml:msup><mml:mrow><mml:mover accent=\"true\"><mml:mrow><mml:mi>T</mml:mi></mml:mrow><mml:mrow><mml:mo>~</mml:mo></mml:mrow></mml:mover></mml:mrow><mml:mrow><mml:mo>†</mml:mo></mml:mrow></mml:msup><mml:mo>∝</mml:mo><mml:mover accent=\"true\"><mml:mrow><mml:mi>C</mml:mi><mml:mi>Z</mml:mi></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover><mml:msup><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mi>X</mml:mi></mml:mrow></mml:msup><mml:mo>.</mml:mo></mml:mrow></mml:math></alternatives></disp-formula>", "<inline-formula id=\"IEq12\"><alternatives><tex-math id=\"M25\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\overline{W}$$\\end{document}</tex-math><mml:math id=\"M26\"><mml:mover accent=\"true\"><mml:mrow><mml:mi>W</mml:mi></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq13\"><alternatives><tex-math id=\"M27\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\overline{CZ}$$\\end{document}</tex-math><mml:math id=\"M28\"><mml:mover accent=\"true\"><mml:mrow><mml:mi>C</mml:mi><mml:mi>Z</mml:mi></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq14\"><alternatives><tex-math id=\"M29\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\overline{W}$$\\end{document}</tex-math><mml:math id=\"M30\"><mml:mover accent=\"true\"><mml:mrow><mml:mi>W</mml:mi></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq15\"><alternatives><tex-math id=\"M31\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$[{S}^{X},\\overline{W}]=(1-{S}^{Z}){S}^{X}\\overline{W}$$\\end{document}</tex-math><mml:math id=\"M32\"><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:msup><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mi>X</mml:mi></mml:mrow></mml:msup><mml:mo>,</mml:mo><mml:mover accent=\"true\"><mml:mrow><mml:mi>W</mml:mi></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover></mml:mrow><mml:mo>]</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:msup><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mi>Z</mml:mi></mml:mrow></mml:msup></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:msup><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mi>X</mml:mi></mml:mrow></mml:msup><mml:mover accent=\"true\"><mml:mrow><mml:mi>W</mml:mi></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq16\"><alternatives><tex-math id=\"M33\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\overline{W}$$\\end{document}</tex-math><mml:math id=\"M34\"><mml:mover accent=\"true\"><mml:mrow><mml:mi>W</mml:mi></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq17\"><alternatives><tex-math id=\"M35\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\overline{W}$$\\end{document}</tex-math><mml:math id=\"M36\"><mml:mover accent=\"true\"><mml:mrow><mml:mi>W</mml:mi></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq18\"><alternatives><tex-math id=\"M37\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\overline{W}$$\\end{document}</tex-math><mml:math id=\"M38\"><mml:mover accent=\"true\"><mml:mrow><mml:mi>W</mml:mi></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq19\"><alternatives><tex-math id=\"M39\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\overline{W}$$\\end{document}</tex-math><mml:math id=\"M40\"><mml:mover accent=\"true\"><mml:mrow><mml:mi>W</mml:mi></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq20\"><alternatives><tex-math id=\"M41\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$U={\\mathbb{1}}$$\\end{document}</tex-math><mml:math id=\"M42\"><mml:mrow><mml:mi>U</mml:mi><mml:mo>=</mml:mo><mml:mi mathvariant=\"double-struck\">1</mml:mi></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq21\"><alternatives><tex-math id=\"M43\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\overline{W}$$\\end{document}</tex-math><mml:math id=\"M44\"><mml:mover accent=\"true\"><mml:mrow><mml:mi>W</mml:mi></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq22\"><alternatives><tex-math id=\"M45\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\mathcal{O}}({{\\varepsilon }}^{2})$$\\end{document}</tex-math><mml:math id=\"M46\"><mml:mrow><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">O</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msup><mml:mrow><mml:mi mathvariant=\"italic\">ε</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq23\"><alternatives><tex-math id=\"M47\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\left\|\\overline{+}\\,\\overline{+}\\right\\rangle $$\\end{document}</tex-math><mml:math id=\"M48\"><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mover accent=\"true\"><mml:mrow><mml:mo>+</mml:mo></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover><mml:mspace width=\"0.25em\"/><mml:mover accent=\"true\"><mml:mrow><mml:mo>+</mml:mo></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq24\"><alternatives><tex-math id=\"M49\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\left\|+\\right\\rangle }^{\\otimes 4}$$\\end{document}</tex-math><mml:math id=\"M50\"><mml:msup><mml:mrow><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mo>+</mml:mo></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow><mml:mrow><mml:mo>⊗</mml:mo><mml:mn>4</mml:mn></mml:mrow></mml:msup></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq25\"><alternatives><tex-math id=\"M51\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\overline{W}$$\\end{document}</tex-math><mml:math id=\"M52\"><mml:mover accent=\"true\"><mml:mrow><mml:mi>W</mml:mi></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq26\"><alternatives><tex-math id=\"M53\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\overline{W}$$\\end{document}</tex-math><mml:math id=\"M54\"><mml:mover accent=\"true\"><mml:mrow><mml:mi>W</mml:mi></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq27\"><alternatives><tex-math id=\"M55\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\overline{Z}}_{1}$$\\end{document}</tex-math><mml:math id=\"M56\"><mml:msub><mml:mrow><mml:mover accent=\"true\"><mml:mrow><mml:mi>Z</mml:mi></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq28\"><alternatives><tex-math id=\"M57\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\overline{Z}}_{2}$$\\end{document}</tex-math><mml:math id=\"M58\"><mml:msub><mml:mrow><mml:mover accent=\"true\"><mml:mrow><mml:mi>Z</mml:mi></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq29\"><alternatives><tex-math id=\"M59\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\overline{Z}}_{1}{\\overline{Z}}_{2}$$\\end{document}</tex-math><mml:math id=\"M60\"><mml:mrow><mml:msub><mml:mrow><mml:mover accent=\"true\"><mml:mrow><mml:mi>Z</mml:mi></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mover accent=\"true\"><mml:mrow><mml:mi>Z</mml:mi></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq30\"><alternatives><tex-math id=\"M61\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\overline{{X}_{1}}$$\\end{document}</tex-math><mml:math id=\"M62\"><mml:mover accent=\"true\"><mml:mrow><mml:msub><mml:mrow><mml:mi>X</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq31\"><alternatives><tex-math id=\"M63\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\overline{{X}_{2}}$$\\end{document}</tex-math><mml:math id=\"M64\"><mml:mover accent=\"true\"><mml:mrow><mml:msub><mml:mrow><mml:mi>X</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq32\"><alternatives><tex-math id=\"M65\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\overline{{X}_{1}}\\overline{{X}_{2}}$$\\end{document}</tex-math><mml:math id=\"M66\"><mml:mrow><mml:mover accent=\"true\"><mml:mrow><mml:msub><mml:mrow><mml:mi>X</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover><mml:mover accent=\"true\"><mml:mrow><mml:msub><mml:mrow><mml:mi>X</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq33\"><alternatives><tex-math id=\"M67\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\overline{{X}_{1}}\\overline{{Z}_{2}}$$\\end{document}</tex-math><mml:math id=\"M68\"><mml:mrow><mml:mover accent=\"true\"><mml:mrow><mml:msub><mml:mrow><mml:mi>X</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover><mml:mover accent=\"true\"><mml:mrow><mml:msub><mml:mrow><mml:mi>Z</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq34\"><alternatives><tex-math id=\"M69\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\overline{{Z}_{1}}\\overline{{X}_{2}}$$\\end{document}</tex-math><mml:math id=\"M70\"><mml:mrow><mml:mover accent=\"true\"><mml:mrow><mml:msub><mml:mrow><mml:mi>Z</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover><mml:mover accent=\"true\"><mml:mrow><mml:msub><mml:mrow><mml:mi>X</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq35\"><alternatives><tex-math id=\"M71\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\overline{{Y}_{1}}\\overline{{Y}_{2}}$$\\end{document}</tex-math><mml:math id=\"M72\"><mml:mrow><mml:mover accent=\"true\"><mml:mrow><mml:msub><mml:mrow><mml:mi>Y</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover><mml:mover accent=\"true\"><mml:mrow><mml:msub><mml:mrow><mml:mi>Y</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq36\"><alternatives><tex-math id=\"M73\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\overline{Y}}_{j}$$\\end{document}</tex-math><mml:math id=\"M74\"><mml:msub><mml:mrow><mml:mover accent=\"true\"><mml:mrow><mml:mi>Y</mml:mi></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover></mml:mrow><mml:mrow><mml:mi>j</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq37\"><alternatives><tex-math id=\"M75\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\overline{Y}}_{j}$$\\end{document}</tex-math><mml:math id=\"M76\"><mml:msub><mml:mrow><mml:mover accent=\"true\"><mml:mrow><mml:mi>Y</mml:mi></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover></mml:mrow><mml:mrow><mml:mi>j</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq38\"><alternatives><tex-math id=\"M77\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\overline{Y}}_{j}\\overline{{X}_{k}}$$\\end{document}</tex-math><mml:math id=\"M78\"><mml:mrow><mml:msub><mml:mrow><mml:mover accent=\"true\"><mml:mrow><mml:mi>Y</mml:mi></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover></mml:mrow><mml:mrow><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mover accent=\"true\"><mml:mrow><mml:msub><mml:mrow><mml:mi>X</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq39\"><alternatives><tex-math id=\"M79\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\overline{Y}}_{j}\\overline{{Z}_{k}}$$\\end{document}</tex-math><mml:math id=\"M80\"><mml:mrow><mml:msub><mml:mrow><mml:mover accent=\"true\"><mml:mrow><mml:mi>Y</mml:mi></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover></mml:mrow><mml:mrow><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mover accent=\"true\"><mml:mrow><mml:msub><mml:mrow><mml:mi>Z</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq40\"><alternatives><tex-math id=\"M81\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\overline{Y}}_{j}$$\\end{document}</tex-math><mml:math id=\"M82\"><mml:msub><mml:mrow><mml:mover accent=\"true\"><mml:mrow><mml:mi>Y</mml:mi></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover></mml:mrow><mml:mrow><mml:mi>j</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq41\"><alternatives><tex-math id=\"M83\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\overline{Y}}_{j}$$\\end{document}</tex-math><mml:math id=\"M84\"><mml:msub><mml:mrow><mml:mover accent=\"true\"><mml:mrow><mml:mi>Y</mml:mi></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover></mml:mrow><mml:mrow><mml:mi>j</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq42\"><alternatives><tex-math id=\"M85\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\overline{W}$$\\end{document}</tex-math><mml:math id=\"M86\"><mml:mover accent=\"true\"><mml:mrow><mml:mi>W</mml:mi></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover></mml:math></alternatives></inline-formula>", "<disp-formula id=\"Equ2\"><label>2</label><alternatives><tex-math id=\"M87\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\rho }_{kl,mn}=\\frac{\\langle \\overline{k}\\,\\overline{l}\| {\\rho }_{{\\rm{phys}}}\| \\overline{m}\\,\\overline{n}\\rangle }{{P}_{L}},$$\\end{document}</tex-math><mml:math id=\"M88\" display=\"block\"><mml:mrow><mml:msub><mml:mrow><mml:mi>ρ</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi><mml:mi>l</mml:mi><mml:mo>,</mml:mo><mml:mi>m</mml:mi><mml:mi>n</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mo>⟨</mml:mo><mml:mover accent=\"true\"><mml:mrow><mml:mi>k</mml:mi></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover><mml:mspace width=\"0.25em\"/><mml:mover accent=\"true\"><mml:mrow><mml:mi>l</mml:mi></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover><mml:mo>∣</mml:mo><mml:msub><mml:mrow><mml:mi>ρ</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">phys</mml:mi></mml:mrow></mml:msub><mml:mo>∣</mml:mo><mml:mover accent=\"true\"><mml:mrow><mml:mi>m</mml:mi></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover><mml:mspace width=\"0.25em\"/><mml:mover accent=\"true\"><mml:mrow><mml:mi>n</mml:mi></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover><mml:mo>⟩</mml:mo></mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mi>P</mml:mi></mml:mrow><mml:mrow><mml:mi>L</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfrac><mml:mo>,</mml:mo></mml:mrow></mml:math></alternatives></disp-formula>", "<inline-formula id=\"IEq43\"><alternatives><tex-math id=\"M89\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${P}_{L}={\\sum }_{k,l}\\langle \\overline{k}\\,\\overline{l}\| \\rho \| \\overline{k}\\,\\overline{l}\\rangle $$\\end{document}</tex-math><mml:math id=\"M90\"><mml:mrow><mml:msub><mml:mrow><mml:mi>P</mml:mi></mml:mrow><mml:mrow><mml:mi>L</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mrow><mml:mo>∑</mml:mo></mml:mrow><mml:mrow><mml:mi>k</mml:mi><mml:mo>,</mml:mo><mml:mi>l</mml:mi></mml:mrow></mml:msub><mml:mo>⟨</mml:mo><mml:mover accent=\"true\"><mml:mrow><mml:mi>k</mml:mi></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover><mml:mspace width=\"0.25em\"/><mml:mover accent=\"true\"><mml:mrow><mml:mi>l</mml:mi></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover><mml:mo>∣</mml:mo><mml:mi>ρ</mml:mi><mml:mo>∣</mml:mo><mml:mover accent=\"true\"><mml:mrow><mml:mi>k</mml:mi></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover><mml:mspace width=\"0.25em\"/><mml:mover accent=\"true\"><mml:mrow><mml:mi>l</mml:mi></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover><mml:mo>⟩</mml:mo></mml:mrow></mml:math></alternatives></inline-formula>", "<disp-formula id=\"Equ3\"><label>3</label><alternatives><tex-math id=\"M91\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\| {\\rm{TOF}}\\rangle \\propto \\sum _{j,k}\| \\,j\\rangle \| k\\rangle \| \\,jk\\rangle =\| 000\\rangle +\| 010\\rangle +\| 100\\rangle +\| 111\\rangle ,$$\\end{document}</tex-math><mml:math id=\"M92\" display=\"block\"><mml:mrow><mml:mrow><mml:mo>∣</mml:mo><mml:mrow><mml:mi mathvariant=\"normal\">TOF</mml:mi></mml:mrow><mml:mo>⟩</mml:mo></mml:mrow><mml:mo>∝</mml:mo><mml:munder><mml:mrow><mml:mo>∑</mml:mo></mml:mrow><mml:mrow><mml:mi>j</mml:mi><mml:mo>,</mml:mo><mml:mi>k</mml:mi></mml:mrow></mml:munder><mml:mrow><mml:mo>∣</mml:mo><mml:mspace width=\"0.15em\"/><mml:mrow><mml:mi>j</mml:mi></mml:mrow><mml:mo>⟩</mml:mo></mml:mrow><mml:mrow><mml:mo>∣</mml:mo><mml:mrow><mml:mi>k</mml:mi></mml:mrow><mml:mo>⟩</mml:mo></mml:mrow><mml:mrow><mml:mo>∣</mml:mo><mml:mspace width=\"0.15em\"/><mml:mrow><mml:mi>j</mml:mi><mml:mi>k</mml:mi></mml:mrow><mml:mo>⟩</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mrow><mml:mo>∣</mml:mo><mml:mrow><mml:mn>000</mml:mn></mml:mrow><mml:mo>⟩</mml:mo></mml:mrow><mml:mo>+</mml:mo><mml:mrow><mml:mo>∣</mml:mo><mml:mrow><mml:mn>010</mml:mn></mml:mrow><mml:mo>⟩</mml:mo></mml:mrow><mml:mo>+</mml:mo><mml:mrow><mml:mo>∣</mml:mo><mml:mrow><mml:mn>100</mml:mn></mml:mrow><mml:mo>⟩</mml:mo></mml:mrow><mml:mo>+</mml:mo><mml:mrow><mml:mo>∣</mml:mo><mml:mrow><mml:mn>111</mml:mn></mml:mrow><mml:mo>⟩</mml:mo></mml:mrow><mml:mo>,</mml:mo></mml:mrow></mml:math></alternatives></disp-formula>", "<inline-formula id=\"IEq44\"><alternatives><tex-math id=\"M93\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\left\|CZ\\right\\rangle }_{1,2}{\\left\|CZ\\right\\rangle }_{3,4}$$\\end{document}</tex-math><mml:math id=\"M94\"><mml:mrow><mml:msub><mml:mrow><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mi>C</mml:mi><mml:mi>Z</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow><mml:mrow><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mn>2</mml:mn></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mi>C</mml:mi><mml:mi>Z</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow><mml:mrow><mml:mn>3</mml:mn><mml:mo>,</mml:mo><mml:mn>4</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math></alternatives></inline-formula>", "<disp-formula id=\"Equ4\"><label>4</label><alternatives><tex-math id=\"M95\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\left\|\\xi \\right\\rangle =(\\left\|0010\\right\\rangle +\\left\|1010\\right\\rangle +\\left\|0100\\right\\rangle +\\left\|0101\\right\\rangle )/2.$$\\end{document}</tex-math><mml:math id=\"M96\" display=\"block\"><mml:mrow><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mi>ξ</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced><mml:mo>=</mml:mo><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mn>0010</mml:mn></mml:mrow></mml:mrow></mml:mrow></mml:mfenced><mml:mo>+</mml:mo><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mn>1010</mml:mn></mml:mrow></mml:mrow></mml:mrow></mml:mfenced><mml:mo>+</mml:mo><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mn>0100</mml:mn></mml:mrow></mml:mrow></mml:mrow></mml:mfenced><mml:mo>+</mml:mo><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mn>0101</mml:mn></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>/</mml:mo><mml:mn>2</mml:mn><mml:mo>.</mml:mo></mml:mrow></mml:math></alternatives></disp-formula>", "<inline-formula id=\"IEq45\"><alternatives><tex-math id=\"M97\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\|1\\rangle \|{\\rm{T}}{\\rm{O}}{\\rm{F}}\\rangle $$\\end{document}</tex-math><mml:math id=\"M98\"><mml:mrow><mml:mo>\|</mml:mo><mml:mn>1</mml:mn><mml:mo>⟩</mml:mo></mml:mrow><mml:mrow><mml:mo>\|</mml:mo><mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">T</mml:mi><mml:mi mathvariant=\"normal\">O</mml:mi><mml:mi mathvariant=\"normal\">F</mml:mi></mml:mrow></mml:mrow><mml:mo>⟩</mml:mo></mml:mrow></mml:math></alternatives></inline-formula>", "<disp-formula id=\"Equ5\"><label>5</label><alternatives><tex-math id=\"M99\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\left\|1\\right\\rangle \\left\|{\\rm{TOF}}\\right\\rangle =C{X}_{4,3}C{X}_{3,1}C{X}_{2,1}C{X}_{1,3}\\left\|\\xi \\right\\rangle ,$$\\end{document}</tex-math><mml:math id=\"M100\" display=\"block\"><mml:mrow><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:mrow></mml:mrow></mml:mfenced><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">TOF</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced><mml:mo>=</mml:mo><mml:mi>C</mml:mi><mml:msub><mml:mrow><mml:mi>X</mml:mi></mml:mrow><mml:mrow><mml:mn>4</mml:mn><mml:mo>,</mml:mo><mml:mn>3</mml:mn></mml:mrow></mml:msub><mml:mi>C</mml:mi><mml:msub><mml:mrow><mml:mi>X</mml:mi></mml:mrow><mml:mrow><mml:mn>3</mml:mn><mml:mo>,</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:mi>C</mml:mi><mml:msub><mml:mrow><mml:mi>X</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn><mml:mo>,</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:mi>C</mml:mi><mml:msub><mml:mrow><mml:mi>X</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mn>3</mml:mn></mml:mrow></mml:msub><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mi>ξ</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced><mml:mo>,</mml:mo></mml:mrow></mml:math></alternatives></disp-formula>", "<inline-formula id=\"IEq46\"><alternatives><tex-math id=\"M101\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\left\|CZ\\right\\rangle \\left\|CZ\\right\\rangle $$\\end{document}</tex-math><mml:math id=\"M102\"><mml:mrow><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mi>C</mml:mi><mml:mi>Z</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mi>C</mml:mi><mml:mi>Z</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow></mml:math></alternatives></inline-formula>", "<disp-formula id=\"Equ6\"><label>6</label><alternatives><tex-math id=\"M103\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\left\|\\chi \\right\\rangle =\\left\|0000\\right\\rangle +\\left\|0001\\right\\rangle +\\left\|1000\\right\\rangle +\\left\|1001\\right\\rangle +\\left\|0110\\right\\rangle .$$\\end{document}</tex-math><mml:math id=\"M104\" display=\"block\"><mml:mrow><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mi>χ</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced><mml:mo>=</mml:mo><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mn>0000</mml:mn></mml:mrow></mml:mrow></mml:mrow></mml:mfenced><mml:mo>+</mml:mo><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mn>0001</mml:mn></mml:mrow></mml:mrow></mml:mrow></mml:mfenced><mml:mo>+</mml:mo><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mn>1000</mml:mn></mml:mrow></mml:mrow></mml:mrow></mml:mfenced><mml:mo>+</mml:mo><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mn>1001</mml:mn></mml:mrow></mml:mrow></mml:mrow></mml:mfenced><mml:mo>+</mml:mo><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mn>0110</mml:mn></mml:mrow></mml:mrow></mml:mrow></mml:mfenced><mml:mo>.</mml:mo></mml:mrow></mml:math></alternatives></disp-formula>", "<inline-formula id=\"IEq47\"><alternatives><tex-math id=\"M105\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$C{X}_{2,3}C{X}_{2,4}\\left\|\\chi \\right\\rangle $$\\end{document}</tex-math><mml:math id=\"M106\"><mml:mrow><mml:mi>C</mml:mi><mml:msub><mml:mrow><mml:mi>X</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn><mml:mo>,</mml:mo><mml:mn>3</mml:mn></mml:mrow></mml:msub><mml:mi>C</mml:mi><mml:msub><mml:mrow><mml:mi>X</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn><mml:mo>,</mml:mo><mml:mn>4</mml:mn></mml:mrow></mml:msub><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mi>χ</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq48\"><alternatives><tex-math id=\"M107\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\left\|CZ\\right\\rangle \\left\|01\\right\\rangle $$\\end{document}</tex-math><mml:math id=\"M108\"><mml:mrow><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mi>C</mml:mi><mml:mi>Z</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mn>01</mml:mn></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq49\"><alternatives><tex-math id=\"M109\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\mathcal{S}}$$\\end{document}</tex-math><mml:math id=\"M110\"><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">S</mml:mi></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq50\"><alternatives><tex-math id=\"M111\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\mathcal{L}}$$\\end{document}</tex-math><mml:math id=\"M112\"><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">L</mml:mi></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq51\"><alternatives><tex-math id=\"M113\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\overline{X}}_{j},{\\overline{Z}}_{j}\\in {\\mathcal{L}}$$\\end{document}</tex-math><mml:math id=\"M114\"><mml:mrow><mml:msub><mml:mrow><mml:mover accent=\"true\"><mml:mrow><mml:mi>X</mml:mi></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover></mml:mrow><mml:mrow><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mrow><mml:mover accent=\"true\"><mml:mrow><mml:mi>Z</mml:mi></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover></mml:mrow><mml:mrow><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mo>∈</mml:mo><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">L</mml:mi></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq52\"><alternatives><tex-math id=\"M115\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{\\mathcal{S}}}_{{\\rm{init}}}$$\\end{document}</tex-math><mml:math id=\"M116\"><mml:msub><mml:mrow><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">S</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">init</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq53\"><alternatives><tex-math id=\"M117\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{\\mathcal{S}}}_{{\\rm{fin}}}$$\\end{document}</tex-math><mml:math id=\"M118\"><mml:msub><mml:mrow><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">S</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">fin</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq54\"><alternatives><tex-math id=\"M119\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{\\mathcal{S}}}_{{\\rm{fin}}}$$\\end{document}</tex-math><mml:math id=\"M120\"><mml:msub><mml:mrow><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">S</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">fin</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq55\"><alternatives><tex-math id=\"M121\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{\\mathcal{S}}}_{{\\rm{init}}}$$\\end{document}</tex-math><mml:math id=\"M122\"><mml:msub><mml:mrow><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">S</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">init</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<disp-formula id=\"Equ7\"><label>7</label><alternatives><tex-math id=\"M123\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{\\mathcal{S}}}_{{\\rm{def}}}={{\\mathcal{S}}}_{{\\rm{init}}}\\cap {{\\mathcal{S}}}_{{\\rm{fin}}},$$\\end{document}</tex-math><mml:math id=\"M124\" display=\"block\"><mml:mrow><mml:msub><mml:mrow><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">S</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">def</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mrow><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">S</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">init</mml:mi></mml:mrow></mml:msub><mml:mo>∩</mml:mo><mml:msub><mml:mrow><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">S</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">fin</mml:mi></mml:mrow></mml:msub><mml:mo>,</mml:mo></mml:mrow></mml:math></alternatives></disp-formula>", "<inline-formula id=\"IEq56\"><alternatives><tex-math id=\"M125\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{\\mathcal{S}}}_{{\\rm{fin}}}$$\\end{document}</tex-math><mml:math id=\"M126\"><mml:msub><mml:mrow><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">S</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">fin</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq57\"><alternatives><tex-math id=\"M127\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{\\mathcal{S}}}_{{\\rm{init}}}$$\\end{document}</tex-math><mml:math id=\"M128\"><mml:msub><mml:mrow><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">S</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">init</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq58\"><alternatives><tex-math id=\"M129\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{\\mathcal{S}}}_{{\\rm{fin}}}$$\\end{document}</tex-math><mml:math id=\"M130\"><mml:msub><mml:mrow><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">S</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">fin</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq59\"><alternatives><tex-math id=\"M131\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\mathcal{L}}$$\\end{document}</tex-math><mml:math id=\"M132\"><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">L</mml:mi></mml:math></alternatives></inline-formula>", "<disp-formula id=\"Equ8\"><label>8</label><alternatives><tex-math id=\"M133\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\mathcal{L}}={{\\mathcal{L}}}_{{\\rm{init}}}\\cap {{\\mathcal{L}}}_{{\\rm{fin}}},$$\\end{document}</tex-math><mml:math id=\"M134\" display=\"block\"><mml:mrow><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">L</mml:mi><mml:mo>=</mml:mo><mml:msub><mml:mrow><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">L</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">init</mml:mi></mml:mrow></mml:msub><mml:mo>∩</mml:mo><mml:msub><mml:mrow><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">L</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">fin</mml:mi></mml:mrow></mml:msub><mml:mo>,</mml:mo></mml:mrow></mml:math></alternatives></disp-formula>", "<inline-formula id=\"IEq60\"><alternatives><tex-math id=\"M135\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{\\mathcal{L}}}_{{\\rm{init}}}$$\\end{document}</tex-math><mml:math id=\"M136\"><mml:msub><mml:mrow><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">L</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">init</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq61\"><alternatives><tex-math id=\"M137\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{\\mathcal{L}}}_{{\\rm{fin}}}$$\\end{document}</tex-math><mml:math id=\"M138\"><mml:msub><mml:mrow><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">L</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">fin</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq62\"><alternatives><tex-math id=\"M139\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{\\mathcal{S}}}_{{\\rm{init}}}$$\\end{document}</tex-math><mml:math id=\"M140\"><mml:msub><mml:mrow><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">S</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">init</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq63\"><alternatives><tex-math id=\"M141\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{\\mathcal{S}}}_{{\\rm{fin}}}$$\\end{document}</tex-math><mml:math id=\"M142\"><mml:msub><mml:mrow><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">S</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">fin</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq64\"><alternatives><tex-math id=\"M143\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{\\mathcal{S}}}_{{\\rm{init}}}$$\\end{document}</tex-math><mml:math id=\"M144\"><mml:msub><mml:mrow><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">S</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">init</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq65\"><alternatives><tex-math id=\"M145\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{\\mathcal{S}}}_{{\\rm{fin}}}$$\\end{document}</tex-math><mml:math id=\"M146\"><mml:msub><mml:mrow><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">S</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">fin</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq66\"><alternatives><tex-math id=\"M147\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{\\mathcal{S}}}_{{\\rm{fin}}}$$\\end{document}</tex-math><mml:math id=\"M148\"><mml:msub><mml:mrow><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">S</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">fin</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq67\"><alternatives><tex-math id=\"M149\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{\\mathcal{S}}}_{{\\rm{fin}}}$$\\end{document}</tex-math><mml:math id=\"M150\"><mml:msub><mml:mrow><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">S</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">fin</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq68\"><alternatives><tex-math 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id=\"M167\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{\\mathcal{S}}}_{{\\rm{fin}}}$$\\end{document}</tex-math><mml:math id=\"M168\"><mml:msub><mml:mrow><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">S</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">fin</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq77\"><alternatives><tex-math id=\"M169\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} 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id=\"M175\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{\\mathcal{L}}}_{{\\rm{def}}}$$\\end{document}</tex-math><mml:math id=\"M176\"><mml:msub><mml:mrow><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">L</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">def</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq81\"><alternatives><tex-math id=\"M177\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} 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id=\"M183\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\overline{X}}_{A}$$\\end{document}</tex-math><mml:math id=\"M184\"><mml:msub><mml:mrow><mml:mover accent=\"true\"><mml:mrow><mml:mi>X</mml:mi></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover></mml:mrow><mml:mrow><mml:mi>A</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq85\"><alternatives><tex-math id=\"M185\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} 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id=\"M191\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{\\mathcal{S}}}_{{\\rm{init}}}$$\\end{document}</tex-math><mml:math id=\"M192\"><mml:msub><mml:mrow><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">S</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">init</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq89\"><alternatives><tex-math id=\"M193\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{\\mathcal{S}}}_{{\\rm{init}}}$$\\end{document}</tex-math><mml:math id=\"M194\"><mml:msub><mml:mrow><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">S</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">init</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq90\"><alternatives><tex-math id=\"M195\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{\\mathcal{S}}}_{{\\rm{fin}}}$$\\end{document}</tex-math><mml:math id=\"M196\"><mml:msub><mml:mrow><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">S</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">fin</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq91\"><alternatives><tex-math id=\"M197\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{\\mathcal{S}}}_{{\\rm{def}}}$$\\end{document}</tex-math><mml:math id=\"M198\"><mml:msub><mml:mrow><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">S</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">def</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq92\"><alternatives><tex-math id=\"M199\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{\\mathcal{S}}}_{{\\rm{init}}}$$\\end{document}</tex-math><mml:math id=\"M200\"><mml:msub><mml:mrow><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">S</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">init</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq93\"><alternatives><tex-math id=\"M201\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\left\|+\\right\\rangle }_{C}$$\\end{document}</tex-math><mml:math id=\"M202\"><mml:msub><mml:mrow><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mo>+</mml:mo></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow><mml:mrow><mml:mi>C</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq94\"><alternatives><tex-math id=\"M203\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\left\|\\psi \\right\\rangle }_{B}=a{\\left\|+\\right\\rangle }_{B}+b{\\left\|-\\right\\rangle }_{B}$$\\end{document}</tex-math><mml:math id=\"M204\"><mml:mrow><mml:msub><mml:mrow><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mi>ψ</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow><mml:mrow><mml:mi>B</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mi>a</mml:mi><mml:msub><mml:mrow><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mo>+</mml:mo></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow><mml:mrow><mml:mi>B</mml:mi></mml:mrow></mml:msub><mml:mo>+</mml:mo><mml:mi>b</mml:mi><mml:msub><mml:mrow><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mo>−</mml:mo></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow><mml:mrow><mml:mi>B</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq95\"><alternatives><tex-math id=\"M205\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\left\|+\\right\\rangle $$\\end{document}</tex-math><mml:math id=\"M206\"><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mo>+</mml:mo></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq96\"><alternatives><tex-math id=\"M207\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${(a\\left\|+\\right\\rangle +b\\left\|-\\right\\rangle )}_{B}\\otimes {\\left\|+\\right\\rangle }_{C}$$\\end{document}</tex-math><mml:math id=\"M208\"><mml:mrow><mml:msub><mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>a</mml:mi><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mo>+</mml:mo></mml:mrow></mml:mrow></mml:mrow></mml:mfenced><mml:mo>+</mml:mo><mml:mi>b</mml:mi><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mo>−</mml:mo></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:mi>B</mml:mi></mml:mrow></mml:msub><mml:mo>⊗</mml:mo><mml:msub><mml:mrow><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mo>+</mml:mo></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow><mml:mrow><mml:mi>C</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq97\"><alternatives><tex-math id=\"M209\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$a{\\left\|+\\right\\rangle }_{B}{\\left\|{m}_{2}\\right\\rangle }_{C}+b{\\left\|-\\right\\rangle }_{B}{\\left\|1\\oplus {m}_{2}\\right\\rangle }_{C}$$\\end{document}</tex-math><mml:math id=\"M210\"><mml:mrow><mml:mi>a</mml:mi><mml:msub><mml:mrow><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mo>+</mml:mo></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow><mml:mrow><mml:mi>B</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mi>m</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow><mml:mrow><mml:mi>C</mml:mi></mml:mrow></mml:msub><mml:mo>+</mml:mo><mml:mi>b</mml:mi><mml:msub><mml:mrow><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mo>−</mml:mo></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow><mml:mrow><mml:mi>B</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mn>1</mml:mn><mml:mo>⊕</mml:mo><mml:msub><mml:mrow><mml:mi>m</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow><mml:mrow><mml:mi>C</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq98\"><alternatives><tex-math id=\"M211\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\left\|{m}_{3}\\right\\rangle }_{B}\\otimes \\left(a\\right.{\\left\|{m}_{2}\\right\\rangle }_{C}+{(-1)}^{{m}_{3}}b{\\left\|1\\oplus {m}_{2}\\right\\rangle }_{C}$$\\end{document}</tex-math><mml:math id=\"M212\"><mml:mrow><mml:msub><mml:mrow><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mi>m</mml:mi></mml:mrow><mml:mrow><mml:mn>3</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow><mml:mrow><mml:mi>B</mml:mi></mml:mrow></mml:msub><mml:mo>⊗</mml:mo><mml:mfenced open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:mi>a</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced><mml:msub><mml:mrow><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mi>m</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow><mml:mrow><mml:mi>C</mml:mi></mml:mrow></mml:msub><mml:mo>+</mml:mo><mml:msup><mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mo>−</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mi>m</mml:mi></mml:mrow><mml:mrow><mml:mn>3</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:msup><mml:mi>b</mml:mi><mml:msub><mml:mrow><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mn>1</mml:mn><mml:mo>⊕</mml:mo><mml:msub><mml:mrow><mml:mi>m</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow><mml:mrow><mml:mi>C</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq99\"><alternatives><tex-math id=\"M213\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\left\|0\\right\\rangle }_{B}\\otimes (a{\\left\|0\\right\\rangle }_{C}+b{\\left\|1\\right\\rangle }_{C})$$\\end{document}</tex-math><mml:math id=\"M214\"><mml:mrow><mml:msub><mml:mrow><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mn>0</mml:mn></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow><mml:mrow><mml:mi>B</mml:mi></mml:mrow></mml:msub><mml:mo>⊗</mml:mo><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>a</mml:mi><mml:msub><mml:mrow><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mn>0</mml:mn></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow><mml:mrow><mml:mi>C</mml:mi></mml:mrow></mml:msub><mml:mo>+</mml:mo><mml:mi>b</mml:mi><mml:msub><mml:mrow><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow><mml:mrow><mml:mi>C</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq100\"><alternatives><tex-math id=\"M215\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\overline{Z}}_{B}$$\\end{document}</tex-math><mml:math id=\"M216\"><mml:msub><mml:mrow><mml:mover accent=\"true\"><mml:mrow><mml:mi>Z</mml:mi></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover></mml:mrow><mml:mrow><mml:mi>B</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq101\"><alternatives><tex-math id=\"M217\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\overline{Z}}_{B}={Z}_{2}{Z}_{4}$$\\end{document}</tex-math><mml:math id=\"M218\"><mml:mrow><mml:msub><mml:mrow><mml:mover accent=\"true\"><mml:mrow><mml:mi>Z</mml:mi></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover></mml:mrow><mml:mrow><mml:mi>B</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mrow><mml:mi>Z</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mi>Z</mml:mi></mml:mrow><mml:mrow><mml:mn>4</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq102\"><alternatives><tex-math id=\"M219\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${S}^{Z}{\\overline{Z}}_{B}={Z}_{13}{Z}_{15}$$\\end{document}</tex-math><mml:math id=\"M220\"><mml:mrow><mml:msup><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mi>Z</mml:mi></mml:mrow></mml:msup><mml:msub><mml:mrow><mml:mover accent=\"true\"><mml:mrow><mml:mi>Z</mml:mi></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover></mml:mrow><mml:mrow><mml:mi>B</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mrow><mml:mi>Z</mml:mi></mml:mrow><mml:mrow><mml:mn>13</mml:mn></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mi>Z</mml:mi></mml:mrow><mml:mrow><mml:mn>15</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq103\"><alternatives><tex-math id=\"M221\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\overline{Z}}_{B}$$\\end{document}</tex-math><mml:math id=\"M222\"><mml:msub><mml:mrow><mml:mover accent=\"true\"><mml:mrow><mml:mi>Z</mml:mi></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover></mml:mrow><mml:mrow><mml:mi>B</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq104\"><alternatives><tex-math id=\"M223\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\mathbb{1}}\\otimes H\\left\|CZ\\right\\rangle $$\\end{document}</tex-math><mml:math id=\"M224\"><mml:mrow><mml:mi mathvariant=\"double-struck\">1</mml:mi><mml:mo>⊗</mml:mo><mml:mi>H</mml:mi><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mi>C</mml:mi><mml:mi>Z</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq105\"><alternatives><tex-math id=\"M225\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\overline{W}$$\\end{document}</tex-math><mml:math id=\"M226\"><mml:mover accent=\"true\"><mml:mrow><mml:mi>W</mml:mi></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover></mml:math></alternatives></inline-formula>", "<disp-formula id=\"Equ9\"><label>9</label><alternatives><tex-math id=\"M227\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\frac{{n}_{s}}{{n}_{0}}\\approx \\frac{{\\varepsilon }_{s}}{1-{\\varepsilon }_{s}}.$$\\end{document}</tex-math><mml:math id=\"M228\" display=\"block\"><mml:mrow><mml:mfrac><mml:mrow><mml:msub><mml:mrow><mml:mi>n</mml:mi></mml:mrow><mml:mrow><mml:mi>s</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mi>n</mml:mi></mml:mrow><mml:mrow><mml:mn>0</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:mfrac><mml:mo>≈</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mrow><mml:mi>ε</mml:mi></mml:mrow><mml:mrow><mml:mi>s</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:msub><mml:mrow><mml:mi>ε</mml:mi></mml:mrow><mml:mrow><mml:mi>s</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfrac><mml:mo>.</mml:mo></mml:mrow></mml:math></alternatives></disp-formula>", "<inline-formula id=\"IEq106\"><alternatives><tex-math id=\"M229\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\left\|\\psi \\right\\rangle $$\\end{document}</tex-math><mml:math id=\"M230\"><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mi>ψ</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq107\"><alternatives><tex-math id=\"M231\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$U\\left\|\\psi \\right\\rangle =\\left\|\\psi \\right\\rangle $$\\end{document}</tex-math><mml:math id=\"M232\"><mml:mrow><mml:mi>U</mml:mi><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mi>ψ</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced><mml:mo>=</mml:mo><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mi>ψ</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow></mml:math></alternatives></inline-formula>", "<disp-formula id=\"Equb\"><alternatives><tex-math id=\"M233\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$C{X}^{{\\prime} }=\\left\|1\\right\\rangle \\left\\langle 1\\right\|\\otimes {\\mathbb{1}}+\\left\|0\\right\\rangle \\left\\langle 0\\right\|\\otimes X.$$\\end{document}</tex-math><mml:math id=\"M234\" display=\"block\"><mml:mrow><mml:mi>C</mml:mi><mml:msup><mml:mrow><mml:mi>X</mml:mi></mml:mrow><mml:mrow><mml:mo>′</mml:mo></mml:mrow></mml:msup><mml:mo>=</mml:mo><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:mrow></mml:mrow></mml:mfenced><mml:mfenced close=\"∣\" open=\"⟨\"><mml:mrow><mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:mrow></mml:mrow></mml:mfenced><mml:mo>⊗</mml:mo><mml:mi mathvariant=\"double-struck\">1</mml:mi><mml:mo>+</mml:mo><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mn>0</mml:mn></mml:mrow></mml:mrow></mml:mrow></mml:mfenced><mml:mfenced close=\"∣\" open=\"⟨\"><mml:mrow><mml:mrow><mml:mrow><mml:mn>0</mml:mn></mml:mrow></mml:mrow></mml:mrow></mml:mfenced><mml:mo>⊗</mml:mo><mml:mi>X</mml:mi><mml:mo>.</mml:mo></mml:mrow></mml:math></alternatives></disp-formula>", "<inline-formula id=\"IEq108\"><alternatives><tex-math id=\"M235\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$CX=\\left\|0\\right\\rangle \\left\\langle 0\\right\|\\otimes {\\mathbb{1}}+\\left\|1\\right\\rangle \\left\\langle 1\\right\|\\otimes X$$\\end{document}</tex-math><mml:math id=\"M236\"><mml:mrow><mml:mi>C</mml:mi><mml:mi>X</mml:mi><mml:mo>=</mml:mo><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mn>0</mml:mn></mml:mrow></mml:mrow></mml:mrow></mml:mfenced><mml:mfenced close=\"∣\" open=\"⟨\"><mml:mrow><mml:mrow><mml:mrow><mml:mn>0</mml:mn></mml:mrow></mml:mrow></mml:mrow></mml:mfenced><mml:mo>⊗</mml:mo><mml:mi mathvariant=\"double-struck\">1</mml:mi><mml:mo>+</mml:mo><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:mrow></mml:mrow></mml:mfenced><mml:mfenced close=\"∣\" open=\"⟨\"><mml:mrow><mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:mrow></mml:mrow></mml:mfenced><mml:mo>⊗</mml:mo><mml:mi>X</mml:mi></mml:mrow></mml:math></alternatives></inline-formula>", "<disp-formula id=\"Equc\"><alternatives><tex-math id=\"M237\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$C{X}^{{\\prime} }=({\\mathbb{1}}\\otimes X)CX.$$\\end{document}</tex-math><mml:math id=\"M238\" display=\"block\"><mml:mrow><mml:mi>C</mml:mi><mml:msup><mml:mrow><mml:mi>X</mml:mi></mml:mrow><mml:mrow><mml:mo>′</mml:mo></mml:mrow></mml:msup><mml:mo>=</mml:mo><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi mathvariant=\"double-struck\">1</mml:mi><mml:mo>⊗</mml:mo><mml:mi>X</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mi>C</mml:mi><mml:mi>X</mml:mi><mml:mo>.</mml:mo></mml:mrow></mml:math></alternatives></disp-formula>", "<inline-formula id=\"IEq109\"><alternatives><tex-math id=\"M239\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$C{X}^{{\\prime} }$$\\end{document}</tex-math><mml:math id=\"M240\"><mml:mrow><mml:mi>C</mml:mi><mml:msup><mml:mrow><mml:mi>X</mml:mi></mml:mrow><mml:mrow><mml:mo>′</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math></alternatives></inline-formula>", "<disp-formula id=\"Equd\"><alternatives><tex-math id=\"M241\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\rm{swap}}=({\\mathbb{1}}+X\\otimes X+Y\\otimes Y+Z\\otimes Z)/2,$$\\end{document}</tex-math><mml:math id=\"M242\" display=\"block\"><mml:mrow><mml:mi mathvariant=\"normal\">swap</mml:mi><mml:mo>=</mml:mo><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi mathvariant=\"double-struck\">1</mml:mi><mml:mo>+</mml:mo><mml:mi>X</mml:mi><mml:mo>⊗</mml:mo><mml:mi>X</mml:mi><mml:mo>+</mml:mo><mml:mi>Y</mml:mi><mml:mo>⊗</mml:mo><mml:mi>Y</mml:mi><mml:mo>+</mml:mo><mml:mi>Z</mml:mi><mml:mo>⊗</mml:mo><mml:mi>Z</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>/</mml:mo><mml:mn>2</mml:mn><mml:mo>,</mml:mo></mml:mrow></mml:math></alternatives></disp-formula>", "<inline-formula id=\"IEq110\"><alternatives><tex-math id=\"M243\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\langle CZ,\\,C{X}^{{\\prime} }{\\rm{swap}}\\rangle $$\\end{document}</tex-math><mml:math id=\"M244\"><mml:mrow><mml:mo>⟨</mml:mo><mml:mi>C</mml:mi><mml:mi>Z</mml:mi><mml:mo>,</mml:mo><mml:mspace width=\"0.10em\"/><mml:mi>C</mml:mi><mml:msup><mml:mrow><mml:mi>X</mml:mi></mml:mrow><mml:mrow><mml:mo>′</mml:mo></mml:mrow></mml:msup><mml:mi mathvariant=\"normal\">swap</mml:mi><mml:mo>⟩</mml:mo></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq111\"><alternatives><tex-math id=\"M245\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$R={(1-{\\varepsilon }_{P})}^{D}$$\\end{document}</tex-math><mml:math id=\"M246\"><mml:mrow><mml:mi>R</mml:mi><mml:mo>=</mml:mo><mml:msup><mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:msub><mml:mrow><mml:mi>ε</mml:mi></mml:mrow><mml:mrow><mml:mi>P</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:mi>D</mml:mi></mml:mrow></mml:msup></mml:mrow></mml:math></alternatives></inline-formula>", "<disp-formula id=\"Equ10\"><label>10</label><alternatives><tex-math id=\"M247\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\,{\\rm{logical\\; yield}}\\,=Q{(1-{\\varepsilon }_{P})}^{D}.$$\\end{document}</tex-math><mml:math id=\"M248\" display=\"block\"><mml:mrow><mml:mspace width=\"0.25em\"/><mml:mi mathvariant=\"normal\">logical yield</mml:mi><mml:mspace width=\"0.25em\"/><mml:mo>=</mml:mo><mml:mi>Q</mml:mi><mml:msup><mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:msub><mml:mrow><mml:mi>ε</mml:mi></mml:mrow><mml:mrow><mml:mi>P</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:mi>D</mml:mi></mml:mrow></mml:msup><mml:mo>.</mml:mo></mml:mrow></mml:math></alternatives></disp-formula>", "<disp-formula id=\"Equ11\"><label>11</label><alternatives><tex-math id=\"M249\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\varepsilon }_{P}\\approx 8{\\varepsilon }_{2Q}+3{\\varepsilon }_{M},$$\\end{document}</tex-math><mml:math id=\"M250\" display=\"block\"><mml:mrow><mml:msub><mml:mrow><mml:mi>ε</mml:mi></mml:mrow><mml:mrow><mml:mi>P</mml:mi></mml:mrow></mml:msub><mml:mo>≈</mml:mo><mml:mn>8</mml:mn><mml:msub><mml:mrow><mml:mi>ε</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn><mml:mi>Q</mml:mi></mml:mrow></mml:msub><mml:mo>+</mml:mo><mml:mn>3</mml:mn><mml:msub><mml:mrow><mml:mi>ε</mml:mi></mml:mrow><mml:mrow><mml:mi>M</mml:mi></mml:mrow></mml:msub><mml:mo>,</mml:mo></mml:mrow></mml:math></alternatives></disp-formula>", "<inline-formula id=\"IEq112\"><alternatives><tex-math id=\"M251\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\overline{W}$$\\end{document}</tex-math><mml:math id=\"M252\"><mml:mover accent=\"true\"><mml:mrow><mml:mi>W</mml:mi></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq113\"><alternatives><tex-math id=\"M253\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\overline{W}$$\\end{document}</tex-math><mml:math id=\"M254\"><mml:mover accent=\"true\"><mml:mrow><mml:mi>W</mml:mi></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover></mml:math></alternatives></inline-formula>", "<disp-formula id=\"Equ12\"><label>12</label><alternatives><tex-math id=\"M255\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${Z}_{0}^{{\\prime} }:=\\left[\\begin{array}{cc}1-p & 0\\\\ 0 & q\\end{array}\\right],{Z}_{1}^{{\\prime} }:=\\left[\\begin{array}{cc}p & 0\\\\ 0 & 1-q\\end{array}\\right],$$\\end{document}</tex-math><mml:math id=\"M256\" display=\"block\"><mml:mrow><mml:msubsup><mml:mrow><mml:mi>Z</mml:mi></mml:mrow><mml:mrow><mml:mn>0</mml:mn></mml:mrow><mml:mrow><mml:mo>′</mml:mo></mml:mrow></mml:msubsup><mml:mo>:</mml:mo><mml:mo>=</mml:mo><mml:mrow><mml:mrow><mml:mfenced close=\"]\" open=\"[\"><mml:mrow><mml:mtable><mml:mtr><mml:mtd columnalign=\"center\"><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:mi>p</mml:mi></mml:mtd><mml:mtd columnalign=\"center\"><mml:mn>0</mml:mn></mml:mtd></mml:mtr><mml:mtr><mml:mtd columnalign=\"center\"><mml:mn>0</mml:mn></mml:mtd><mml:mtd columnalign=\"center\"><mml:mi>q</mml:mi></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:mfenced></mml:mrow></mml:mrow><mml:mo>,</mml:mo><mml:msubsup><mml:mrow><mml:mi>Z</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mo>′</mml:mo></mml:mrow></mml:msubsup><mml:mo>:</mml:mo><mml:mo>=</mml:mo><mml:mrow><mml:mrow><mml:mfenced close=\"]\" open=\"[\"><mml:mrow><mml:mtable><mml:mtr><mml:mtd columnalign=\"center\"><mml:mi>p</mml:mi></mml:mtd><mml:mtd columnalign=\"center\"><mml:mn>0</mml:mn></mml:mtd></mml:mtr><mml:mtr><mml:mtd columnalign=\"center\"><mml:mn>0</mml:mn></mml:mtd><mml:mtd columnalign=\"center\"><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:mi>q</mml:mi></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:mfenced></mml:mrow></mml:mrow><mml:mo>,</mml:mo></mml:mrow></mml:math></alternatives></disp-formula>", "<inline-formula id=\"IEq114\"><alternatives><tex-math id=\"M257\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\overline{X}}_{A}={X}_{1}{X}_{2}$$\\end{document}</tex-math><mml:math id=\"M258\"><mml:mrow><mml:msub><mml:mrow><mml:mover accent=\"true\"><mml:mrow><mml:mi>X</mml:mi></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover></mml:mrow><mml:mrow><mml:mi>A</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mrow><mml:mi>X</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mi>X</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq115\"><alternatives><tex-math id=\"M259\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\overline{Z}}_{A}={Z}_{1}{Z}_{3}$$\\end{document}</tex-math><mml:math id=\"M260\"><mml:mrow><mml:msub><mml:mrow><mml:mover accent=\"true\"><mml:mrow><mml:mi>Z</mml:mi></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover></mml:mrow><mml:mrow><mml:mi>A</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mrow><mml:mi>Z</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mi>Z</mml:mi></mml:mrow><mml:mrow><mml:mn>3</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq116\"><alternatives><tex-math id=\"M261\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\overline{X}}_{B}={X}_{1}{X}_{3}$$\\end{document}</tex-math><mml:math id=\"M262\"><mml:mrow><mml:msub><mml:mrow><mml:mover accent=\"true\"><mml:mrow><mml:mi>X</mml:mi></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover></mml:mrow><mml:mrow><mml:mi>B</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mrow><mml:mi>X</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mi>X</mml:mi></mml:mrow><mml:mrow><mml:mn>3</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq117\"><alternatives><tex-math id=\"M263\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\overline{Z}}_{B}={Z}_{1}{Z}_{2}$$\\end{document}</tex-math><mml:math id=\"M264\"><mml:mrow><mml:msub><mml:mrow><mml:mover accent=\"true\"><mml:mrow><mml:mi>Z</mml:mi></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover></mml:mrow><mml:mrow><mml:mi>B</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mrow><mml:mi>Z</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mi>Z</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq118\"><alternatives><tex-math id=\"M265\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${S}_{{\\rm{T}}}^{Z}={Z}_{1}{Z}_{2}$$\\end{document}</tex-math><mml:math id=\"M266\"><mml:mrow><mml:msubsup><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">T</mml:mi></mml:mrow><mml:mrow><mml:mi>Z</mml:mi></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:msub><mml:mrow><mml:mi>Z</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mi>Z</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq119\"><alternatives><tex-math id=\"M267\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${S}_{{\\rm{B}}}^{Z}={Z}_{3}{Z}_{4}$$\\end{document}</tex-math><mml:math id=\"M268\"><mml:mrow><mml:msubsup><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">B</mml:mi></mml:mrow><mml:mrow><mml:mi>Z</mml:mi></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:msub><mml:mrow><mml:mi>Z</mml:mi></mml:mrow><mml:mrow><mml:mn>3</mml:mn></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mi>Z</mml:mi></mml:mrow><mml:mrow><mml:mn>4</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq120\"><alternatives><tex-math id=\"M269\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\overline{X}={X}_{1}{X}_{2}$$\\end{document}</tex-math><mml:math id=\"M270\"><mml:mrow><mml:mover accent=\"true\"><mml:mrow><mml:mi>X</mml:mi></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover><mml:mo>=</mml:mo><mml:msub><mml:mrow><mml:mi>X</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mi>X</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq121\"><alternatives><tex-math id=\"M271\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\overline{Z}={Z}_{1}{Z}_{3}$$\\end{document}</tex-math><mml:math id=\"M272\"><mml:mrow><mml:mover accent=\"true\"><mml:mrow><mml:mi>Z</mml:mi></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover><mml:mo>=</mml:mo><mml:msub><mml:mrow><mml:mi>Z</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mi>Z</mml:mi></mml:mrow><mml:mrow><mml:mn>3</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq122\"><alternatives><tex-math id=\"M273\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\left\|0\\right\\rangle }_{v}$$\\end{document}</tex-math><mml:math id=\"M274\"><mml:msub><mml:mrow><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mn>0</mml:mn></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow><mml:mrow><mml:mi>v</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq123\"><alternatives><tex-math id=\"M275\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\left\|+\\right\\rangle }_{v}$$\\end{document}</tex-math><mml:math id=\"M276\"><mml:msub><mml:mrow><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mo>+</mml:mo></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow><mml:mrow><mml:mi>v</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq124\"><alternatives><tex-math id=\"M277\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{\\mathcal{S}}}_{{\\rm{def.}}}={{\\mathcal{S}}}_{{\\rm{init.}}}\\cap {{\\mathcal{S}}}_{{\\rm{fin.}}}$$\\end{document}</tex-math><mml:math id=\"M278\"><mml:mrow><mml:msub><mml:mrow><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">S</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">def.</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mrow><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">S</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">init.</mml:mi></mml:mrow></mml:msub><mml:mo>∩</mml:mo><mml:msub><mml:mrow><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">S</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">fin.</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq125\"><alternatives><tex-math id=\"M279\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{\\mathcal{S}}}_{{\\rm{init.}}}$$\\end{document}</tex-math><mml:math id=\"M280\"><mml:msub><mml:mrow><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">S</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">init.</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq126\"><alternatives><tex-math id=\"M281\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\left\|0\\right\\rangle $$\\end{document}</tex-math><mml:math id=\"M282\"><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mn>0</mml:mn></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq127\"><alternatives><tex-math id=\"M283\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\left\|+\\right\\rangle $$\\end{document}</tex-math><mml:math id=\"M284\"><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mo>+</mml:mo></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq128\"><alternatives><tex-math id=\"M285\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{\\mathcal{S}}}_{{\\rm{def.}}}={{\\mathcal{S}}}_{{\\rm{init.}}}\\cap {{\\mathcal{S}}}_{{\\rm{fin.}}}$$\\end{document}</tex-math><mml:math id=\"M286\"><mml:mrow><mml:msub><mml:mrow><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">S</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">def.</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mrow><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">S</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">init.</mml:mi></mml:mrow></mml:msub><mml:mo>∩</mml:mo><mml:msub><mml:mrow><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">S</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">fin.</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq129\"><alternatives><tex-math id=\"M287\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{\\mathcal{S}}}_{{\\rm{init.}}}$$\\end{document}</tex-math><mml:math id=\"M288\"><mml:msub><mml:mrow><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">S</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">init.</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq130\"><alternatives><tex-math id=\"M289\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{\\mathcal{S}}}_{{\\rm{def.}}}$$\\end{document}</tex-math><mml:math id=\"M290\"><mml:msub><mml:mrow><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">S</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">def.</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq131\"><alternatives><tex-math id=\"M291\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\left\|+\\right\\rangle $$\\end{document}</tex-math><mml:math id=\"M292\"><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mo>+</mml:mo></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq132\"><alternatives><tex-math id=\"M293\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\overline{Z}}_{2}$$\\end{document}</tex-math><mml:math id=\"M294\"><mml:msub><mml:mrow><mml:mover accent=\"true\"><mml:mrow><mml:mi>Z</mml:mi></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq133\"><alternatives><tex-math id=\"M295\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\overline{Z}}_{B}$$\\end{document}</tex-math><mml:math id=\"M296\"><mml:msub><mml:mrow><mml:mover accent=\"true\"><mml:mrow><mml:mi>Z</mml:mi></mml:mrow><mml:mo accent=\"true\">¯</mml:mo></mml:mover></mml:mrow><mml:mrow><mml:mi>B</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq134\"><alternatives><tex-math id=\"M297\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\left\|+\\right\\rangle $$\\end{document}</tex-math><mml:math id=\"M298\"><mml:mfenced close=\"⟩\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mo>+</mml:mo></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq135\"><alternatives><tex-math id=\"M299\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$V=\\exp (i\\theta Y)$$\\end{document}</tex-math><mml:math id=\"M300\"><mml:mrow><mml:mi>V</mml:mi><mml:mo>=</mml:mo><mml:mi>exp</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mi>θ</mml:mi><mml:mi>Y</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq136\"><alternatives><tex-math id=\"M301\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\tan \\theta =\\sqrt{2}$$\\end{document}</tex-math><mml:math id=\"M302\"><mml:mrow><mml:mi>tan</mml:mi><mml:mi>θ</mml:mi><mml:mo>=</mml:mo><mml:msqrt><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msqrt></mml:mrow></mml:math></alternatives></inline-formula>" ]	[]	[]	[]	[]	[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>" ]	[ "<table-wrap-foot><p>Data shown is for qubits of ibm_peekskill used in this work.</p></table-wrap-foot>", "<table-wrap-foot><p>Data shown are for the qubits of ibm_peekskill used in this work. CX gates, constructed from echoed cross-resonance pulse sequences, are specified in one direction, with the reverse directions accessed by the addition of single-qubit gates. Error per gate (EPG) is extracted from isolated two-qubit randomized benchmarking (spectator qubits idling). The notation * denotes error rates for the best performing physical qubit pair on ibm_peekskill during unencoded magic state preparation experiments defining the minimum (red line) in Fig. ##FIG##2##3##.</p></table-wrap-foot>", "<table-wrap-foot><p>We compare our analytical model, Eqn. (##FORMU##123##10##), and numerics to the experimental data. We calculate the yield for the error-suppressed preparation experiment using feedforward (FF) and the error-suppressed preparation experiment using (PS). We also estimate acceptance rates for the standard experiment. The depth of the circuits <italic>D</italic> vary depending on the different tomography experiment we run, so we treat them separately. We append 2(b) and 2(c) to the different experiments depending on the tomography circuit we used, in reference to the circuits shown in Fig. ##FIG##1##2(b)## and ##FIG##1##(c)## in the main text.</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]	[ "<inline-graphic xlink:href=\"41586_2023_6846_Article_IEq1.gif\"/>", "<inline-graphic xlink:href=\"41586_2023_6846_Article_IEq2.gif\"/>", "<graphic xlink:href=\"41586_2023_6846_Article_Equa.gif\" position=\"anchor\"/>", "<inline-graphic xlink:href=\"41586_2023_6846_Article_IEq3.gif\"/>", "<inline-graphic xlink:href=\"41586_2023_6846_Article_IEq4.gif\"/>", "<inline-graphic xlink:href=\"41586_2023_6846_Article_IEq5.gif\"/>", "<inline-graphic xlink:href=\"41586_2023_6846_Article_IEq7.gif\"/>", "<inline-graphic xlink:href=\"41586_2023_6846_Article_IEq8.gif\"/>", "<inline-graphic 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xlink:href=\"41586_2023_6846_MOESM1_ESM.xlsx\"><label>Supplementary Data 1</label><caption><p>State tomography results by state-preparation scheme and readout-error characterization methods as shown in Fig. 3 and Extended Data Fig. 9.</p></caption></media>", "<media xlink:href=\"41586_2023_6846_MOESM2_ESM.pdf\"><caption><p>Peer Review File</p></caption></media>" ]	[{"label": ["2."], "surname": ["Ryan-Anderson"], "given-names": ["C"], "article-title": ["Realization of real-time fault-tolerant quantum error correction"], "source": ["Phys. 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PMC10781629	37857841	[]	[]	[]	[]	[]	[ "<title>Subject terms</title>" ]	[ "<title>To the Editor:</title>", "<p id=\"Par1\">Cytomegalovirus (CMV) reactivation is observed in 40–70% of the patients following allogeneic stem cell transplantation (allo-SCT) and, together with donor CMV positive serostatus, it has been associated with impaired outcome in terms of overall survival (OS) [##REF##24041869##1##, ##REF##2997951##2##], and also graft versus host disease (GVHD) in several studies [##REF##9753343##3##, ##REF##2164434##4##].</p>", "<p id=\"Par2\">Letermovir (LET) is the first anti-viral drug licensed for CMV prophylaxis, that has proved to be very effective in reducing the incidence of CMV clinically significant infections (CMV-csi) and CMV disease, together with an excellent safety profile. The results of the registrative study [##REF##29211658##5##] have been confirmed in several single and multicentric experiences [##REF##36271650##6##–##UREF##0##9##] and a recent metanalysis showed that LET significantly reduces both CMV-related events, but also mortality for all causes and non relapse mortality (NRM) [##UREF##0##9##]. On the other hand, the potential role of LET prophylaxis in reducing acute and/or chronic GVHD is still a matter of debate.</p>", "<p id=\"Par3\">In this retrospective study, we analyzed 480 transplants consecutively performed in our Institution (NO LET ERA: <italic>n</italic> = 325 - March 2006/November 2018 and LET ERA: <italic>n</italic> = 155 - December 2018/September 2022). The management of CMV at our center was conducted as previously published [##UREF##0##9##]. Moving from the NO LET ERA to the LET ERA, we observed an increase in the transplants for primary myelofibrosis (4% vs 19%; <italic>p</italic> = 0.002), in haploidentical donors (11% vs 30%; <italic>p</italic> = 0.007) which was counterbalanced by a reduction in sibling donors (40% vs 19%; <italic>p</italic> = 0.008), an increase in the use of peripheral blood stem cells (PBSC) (74% vs 90%; <italic>p</italic> = 0.0002), and an increase in the use of myeloablative conditioning regimens (MAC) (43% vs 63%; <italic>p</italic> = 0.04). Moreover, we registered a significant increase in patients with HCT-CI ≥ 3 (34% vs 48%; <italic>p</italic> = 0.007) and a corresponding reduction in patients with HCT-CI = 0 (36% vs 24%; <italic>p</italic> = 0.01).</p>", "<p id=\"Par4\">As expected, the cumulative incidence of CMV-csi was lower in the LET ERA vs the NO LET ERA, both at day + 100 (8% vs 43%; <italic>p</italic> < 0.001) and at day + 180 (26% vs 47%; <italic>p</italic> < 0.001) (Fig. ##FIG##0##1a##). Similarly, the incidence of CMV disease declined from 6 and 8% to 1 and 3% in the two ERAs, by day + 100 (<italic>p</italic> = 0.02) and by day +180 (<italic>p</italic> = 0.02), respectively. Interestingly, in the LET ERA we registered a significant reduction in the incidence of grade II/IV aGVHD (37% vs 28%; <italic>p</italic> = 0.005), overall cGVHD (30% vs 12%; <italic>p</italic> = 0.04) and moderate/severe cGVHD (21% vs 10%; <italic>p</italic> = 0.003). Comparing the NO LET vs LET ERA, the 1-year NRM, the 1-year cumulative incidence of relapse (CIR) and the 1-year overall survival (OS) were 19% vs 15%; (<italic>p</italic> = 0.28), 31% vs 23% (<italic>p</italic> = 0.12), and 65% vs 73% (<italic>p</italic> = 0.47), respectively. The 1-, 2-, and 3-year graft and relapse free survival (GRFS) in the LET vs NO LET ERA were 41% vs 49%, 29% vs 39%, and 26% vs 39%, respectively (<italic>p</italic> = 0,0034; Fig. ##FIG##0##1b##). By multivariate analysis, and focusing on aGVHD the factors associated with reduced risk were: RIC regimen (HR 0.71; <italic>p</italic> = 0.01), sibling donor (HR 0.70; <italic>p</italic> = 0.02), and LET administration (HR 0.68; <italic>p</italic> = 0.02). In the case of cGVHD, we observed that sibling donor (HR 1.60; <italic>p</italic> = 0.01) was associated with increased risk, whereas LET administration was protective (HR 0.54; <italic>p</italic> = 0.02). When we looked at the NRM, two factors were significantly associated with reduced risk of death for causes other than relapse: low HCT-CI (HR 0.52; <italic>p</italic> = 0.01) and KPS 90–100 (HR 0.46; <italic>p</italic> = 0.02). As concerns relapse risk, the only factor that proved to protect against relapse was having received LET prophylaxis (HR 0.67; <italic>p</italic> = 0.05). Finally, focusing on OS, age as a continuous variable was associated with impaired outcome (HR 1.02; <italic>p</italic> = 0.002), whereas low-risk HCT-CI category (HR 0.66; <italic>p</italic> = 0.005) and a KPS 90–100 (HR 0.54; <italic>p</italic> = 0.01) were associated with improved long-term survival.</p>", "<p id=\"Par5\">In this manuscript, we confirm that LET significantly reduces the incidence of CMV-csi (<italic>p</italic> < 0.0001; Fig. ##FIG##0##1a##). We also observed a significant reduction in the incidence of grade II/IV aGVHD, of overall cGVHD and of moderate/severe cGVHD. Thus, the use of LET was associated with a significant improvement in the 1-, 2- and 3-years graft and relapse free survival (GRFS) (<italic>p</italic> = 0.0034; Fig. ##FIG##0##1b##). This is of note, because the 155 patients in the LET ERA were at higher risk of developing GVHD and also CMV-related complications as compared to the patients in the NO LET ERA, because of the greater number of haploidentical transplants and greater use of PBSC [##REF##26735993##10##]. If it is true that LET was independently associated with a reduced risk of cGVHD by multivariate analysis, it should be considered that two other factors may have contributed to the reduction of cGVHD in the LET ERA: the extensive use of ATG in sibling transplants with PBSC after 2016 and of post-transplant cyclophosphamide (PTCY) from 2018 onwards for haploidentical transplants. In particular, the routinely use of ATG in our center for sibling transplants with PBSC was implemented only after the results of the study by Kroger and Colleagues, in which we also enrolled patients [##REF##26735993##10##]. This means that, before 2016, patients transplanted from a sibling donor did not receive any lymphodepletion and this may explain the paradoxic result of the multivariate analysis, showing that sibling donor was an independent risk factor for cGVHD.</p>", "<p id=\"Par6\">In order to identify some indirect effect of LET use, we also compared the incidence of fever of undetermined origin (FUO), bacterial infections, proven/probable invasive fungal infections (IFIs) and re-hospitalization in the two ERAs. Although we observed a reduction in these events following the use of LET, these differences did not reach the statistical significance as in other reports [##REF##3029908##11##]. Notably, in the more recent LET ERA, patients were at higher risk of developing infections (older age, more primary myelofibrosis, higher HCT-CI, more haploidentical donors, fewer sibling donors, more cases of PBSC and MAC regimens). From this viewpoint, the slight reduction in infections and re-hospitalization in the LET ERA is clinically highly significant, in particular considering the higher prevalence of high HCT-CI. We are conscious of the fact that these data are the results of a retrospective analysis, covering a long period of time, but these patients are all consecutive, and this gives a clear picture of the impact of LET prophylaxis in real-life.</p>", "<p id=\"Par7\">Focusing on the costs of an extensive prophylaxis with LET, there are recently published data suggesting that LET is cost-effective compared with PET alone in terms of quality-adjusted life-years [##REF##36840894##12##]. We calculated the costs of 1st year hospital-readmissions in the two ERAs due to transplant-related complications (namely GVHD and/or infections). In our series we did not observe any difference comparing the LET ERA and NO LET ERA. The main reason for this finding is probably represented by the fact that patients in the LET ERA were at higher risk of transplant-related complications, due to higher age and comorbidity index. This may have increased their management costs, and, may have mitigated the advantages of LET prophylaxis in costs reduction due to CMV-csi reduction.</p>", "<p id=\"Par8\">In conclusion, we confirmed that LET prophylaxis significantly reduces the incidence of CMV-csi (Fig. ##FIG##0##1a##). Following LET introduction, we also observed a reduction of the incidence of aGVHD, overall cGVHD and moderate/severe cGVHD, and, moreover and for the first time, a significant improvement of the composite transplant outcome of GRFS (Fig. ##FIG##0##1b##).</p>" ]	[ "<title>Acknowledgements</title>", "<p>This study was conducted with no specific funding. Special thanks to Studio Moretto for English revision.</p>", "<title>Author contributions</title>", "<p>MM, VR and DR were responsible for designing the study and wrote the Manuscript; MF, FS, EM, EAB, SB, FR, AL, LS, AC extracted and analyzed data, updated reference lists and created figures and tables; SP and NP performed the statistical analysis; LS and AC were involved in results’ interpretation. All the authors gave their final approval before submission.</p>", "<title>Data availability</title>", "<p>The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.</p>", "<title>Competing interests</title>", "<p id=\"Par9\">The authors declare no competing interests.</p>" ]	[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>Cumulative incidence of clinically significant CMV infections (CMV-csi) and graft and relapse-free survival (GRFS) in the NO LET ERA vs LET ERA.</title><p><bold>a</bold> Cumulative incidence of CMVcsi at 100 days: 43% vs 8%; at 180 days: 47% vs 26%. <bold>b</bold> GRFS at 1 year: 49% vs. 41%.</p></caption></fig>" ]	[]	[]	[]	[]	[]	[]	[]	[ "<fn-group><fn><p>The original online version of this article was revised: The order of the addresses has been corrected.</p></fn><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p><bold>Change history</bold></p><p>11/29/2023</p><p>A Correction to this paper has been published: 10.1038/s41409-023-02158-2</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"41409_2023_2124_Fig1_HTML\" id=\"d32e415\"/>" ]	[]	[{"label": ["9."], "surname": ["Vyas", "Raval", "Kamat", "LaPlante", "Tang", "Chemaly"], "given-names": ["A", "AD", "S", "K", "Y", "RF"], "article-title": ["Real-world outcomes associated with letermovir use for cytomegalovirus primary prophylaxis in allogeneic hematopoietic cell transplant recipients: a systematic review and meta-analysis of observational studies"], "source": ["Open Forum Infect Dis"], "year": ["2022"], "volume": ["10"], "fpage": ["687"], "pub-id": ["10.1093/ofid/ofac687"]}]	{ "acronym": [], "definition": [] }	12	CC BY	no	2024-01-13 00:02:19	Bone Marrow Transplant. 2024 Oct 19; 59(1):138-140	oa_package/29/c3/PMC10781629.tar.gz
PMC10781630	37992757	[]	[ "<title>Methods</title>", "<title>Bacterial strains and phages</title>", "<p id=\"Par12\"><italic>B. subtilis</italic> BEST7003 was grown in MMB (LB supplemented with 0.1 mM MnCl<sub>2</sub> and 5 mM MgCl<sub>2</sub>) with or without 0.5% agar at 37 °C or 30 °C respectively. Whenever applicable, media were supplemented with ampicillin (100 μg ml<sup>−1</sup>), chloramphenicol (34 μg ml<sup>−1</sup>) or kanamycin (50 μg ml<sup>−1</sup>) to ensure the maintenance of plasmids. <italic>B. subtilis</italic> phages phi3T (BGSCID 1L1) and SPβ (BGSCID 1L5) were obtained from the Bacillus Genetic Stock Center (BGSC). Prophages were induced using Mitomycin C (Sigma, M0503).</p>", "<p id=\"Par13\">Phage titre was determined using the small-drop plaque assay method<sup>##REF##19066813##41##</sup>. Four hundred microlitres of overnight culture of bacteria was mixed with 0.5% agar and 30 ml MMB and poured into a 10-cm<sup>2</sup> plate followed by incubation for 1 h at room temperature. In cases of bacteria expressing Gad1 homologue and Gad1 mutations, 0.1–1 mM IPTG was added to the medium. Tenfold serial dilutions in MMB were performed for each of the tested phages and 10-µl drops were put on the bacterial layer. After the drops had dried up, the plates were inverted and incubated at room temperature overnight. Plaque-forming units (PFUs) were determined by counting the derived plaques after overnight incubation, and lysate titre was determined by calculating PFU ml<sup>−1</sup>. When no individual plaques could be identified, a faint lysis zone across the drop area was considered to be ten plaques. Efficiency of plating was measured by comparing plaque assay results on control bacteria and bacteria containing the defence system and/or a candidate anti-defence gene.</p>", "<title>Plasmid construction</title>", "<p id=\"Par14\">For protein purification and biochemistry, <italic>B. cereus</italic> VD045 <italic>GajA</italic> (IMG ID 2519684552) and <italic>GajB</italic> (IMG ID 2519684553) genes were codon-optimized for expression in <italic>E. coli</italic>, synthesized as gBlocks (Integrated DNA Technologies) and cloned into custom pET vectors with an N-terminal 6×His-SUMO2 fusion tag (GajB alone) or a C-terminal 6×His tag (GajA alone). GajA and GajB proteins were co-expressed using a custom pET vector with an N-terminal 6×His-SUMO2 or N-terminal 6×His-SUMO2-5×GS tag on GajA and a ribosome-binding site between GajA and GajB. Phi3T and <italic>Shewanella sp</italic>. phage 1/4 Gad1 (IMG ID 2708680195) gBlocks were cloned into a custom pBAD vector containing a chloramphenicol resistance gene and an IPTG-inducible promoter. For Gad1 pull-down assays, <italic>Shewanella sp</italic>. phage 1/4 Gad1 was cloned with a ribosome-binding site after the GajB gene in the N-terminal 6×His-SUMO2-5×GS GajAB plasmid.</p>", "<p id=\"Par15\">For plaque assays, the DNA of Gad1 was amplified from the phage phi3T genome using KAPA HiFi HotStart ReadyMix (Roche, KK2601). Because Gad1 was toxic in <italic>B. subtilis</italic> cells containing Gabija, <italic>Shewanella</italic> sp. phage 1/4 Gad1 was used and synthesized by GenScript. Gad1 and related homologues were cloned into the pSG-thrC-Phspank vector<sup>##REF##36174646##42##</sup> and transformed to DH5α competent cells. The cloned vector and the vector containing Gad1 substitution and truncation mutants were subsequently transformed into <italic>B. subtilis</italic> BEST7003 cells containing Gabija integrated into the <italic>amyE</italic> locus<sup>##REF##29371424##1##</sup>, resulting in cultures expressing both Gabija and a Gad1 homologue. As a negative control, a transformant with an identical plasmid containing GFP instead of the anti-defence gene was used. Transformation in <italic>B. subtilis</italic> was performed using MC medium as previously described<sup>##REF##29371424##1##</sup>. Sanger sequencing was then applied to verify the integrity of the inserts and the mutations. The pSG1 plasmids containing point mutations in Gabija were constructed by subcloning the Gabija sequence into pGEM9Z using restriction enzymes, site-directed mutagenesis as previously described<sup>##REF##19055817##43##</sup> and Gibson assembly back into pSG1, and the plasmids were transformed into <italic>B. subtilis</italic> BEST7003 cells. Sanger sequencing of the mutations regions was applied to verify the mutations in Gabija.</p>", "<title>Protein expression and purification</title>", "<p id=\"Par16\">Recombinant GajAB and GajAB–Gad1 complexes were purified from <italic>E. coli</italic> as previously described<sup>##REF##30007416##44##</sup>. In brief, the expression plasmids described above were transformed into BL21(DE3), BL21(DE3)-RIL (Agilent) or LOBSTR-BL21(DE3)-RIL cells (Kerafast), plated on MDG medium plates (1.5% Bacto agar, 0.5% glucose, 25 mM Na<sub>2</sub>HPO<sub>4</sub>, 25 mM KH<sub>2</sub>PO<sub>4</sub>, 50 mM NH<sub>4</sub>Cl, 5 mM Na<sub>2</sub>SO<sub>4</sub>, 0.25% aspartic acid, 2–50 μM trace metals, 100 μg ml<sup>−1</sup> ampicillin and 34 μg ml<sup>−1</sup> chloramphenicol) and grown overnight at 37 °C. Five colonies were used to inoculate 30 ml of MDG starter overnight cultures (37 °C, 230 rpm). Ten millilitres of MDG starter cultures were then inoculated in 1 l M9ZB expression cultures (47.8 mM Na<sub>2</sub>HPO<sub>4</sub>, 22 mM KH<sub>2</sub>PO<sub>4</sub>, 18.7 mM NH<sub>4</sub>Cl, 85.6 mM NaCl, 1% Cas-Amino acids, 0.5% glycerol, 2 mM MgSO<sub>4</sub>, 2–50 μM trace metals, 100 μg ml<sup>−1</sup> ampicillin and 34 μg ml<sup>−1</sup> chloramphenicol) and induced with 0.5 mM IPTG after reaching an optical density at 600 nm (OD<sub>600 nm</sub>) of 1.5 or higher (overnight, 16 °C, 230 rpm).</p>", "<p id=\"Par17\">After overnight induction, cells were pelleted by centrifugation, resuspended and lysed by sonication in 60 ml lysis buffer (20 mM HEPES pH 7.5, 400 mM NaCl, 10% glycerol, 20 mM imidazole and 1 mM DTT). Lysate was clarified by centrifugation, and supernatant was poured over Ni-NTA resin (Qiagen). Resin was then washed with lysis buffer, lysis buffer supplemented with 1 M NaCl and lysis buffer again, and was finally eluted with lysis buffer supplemented with 300 mM imidazole. Samples were then dialysed overnight in 14-kDa MWCO dialysis tubing (Ward’s Science) with SUMO2 cleavage by hSENP2 as previously described<sup>##REF##36848932##29##,##UREF##1##30##</sup>. hSENP2 did not efficiently cleave N-terminal 6×His-SUMO2-GajAB and the complex was therefore purified with an additional 5×GS linker. Proteins for crystallography and cryo-EM were dialysed in dialysis buffer (20 mM HEPES-KOH pH 7.5, 250 mM KCl and 1 mM DTT), purified by size-exclusion chromatography using a 16/600 Superdex 200 column (Cytiva) and stored in gel filtration buffer (20 mM HEPES-KOH pH 7.5, 20 mM KCl and 1 mM TCEP-KOH). Proteins for biochemical assays were dialysed in dialysis buffer, purified by size-exclusion chromatography using a 16/600 Superdex 200 column (Cytiva) or 16/600 Sephacryl 300 column (Cytiva) and stored in gel filtration buffer with 10% glycerol. Purified proteins were concentrated to more than 10 mg ml<sup>−1</sup> using a 30-kDa MWCO centrifugal filter (Millipore Sigma), aliquoted, flash-frozen in liquid nitrogen and stored at −80 °C.</p>", "<p id=\"Par18\">Co-expression of Gabija with Phi3T Gad1 results in mild toxicity in <italic>E. coli</italic> grown on MDG medium plates. No toxicity was observed using a closely related Gad1 homologue from the <italic>Shewanella</italic> phage 1/4. Biochemical analysis of Gabija–Gad1 interactions was therefore conducted with <italic>Shewanella</italic> phage 1/4 Gad1. Notably, all Gad1 residues analysed are 100% conserved between Phi3T Gad1 and <italic>Shewanella</italic> phage 1/4 Gad1. For <italic>Shewanella</italic> phage 1/4 Gad1 pull-down assays, SUMO2-5×GS-GajA-GajB-Gad1 point-mutant plasmids were transformed and expressed in BL21(DE3)-RIL or LOBSTR-BL21(DE3)-RIL cells, and subjected to Ni-NTA column chromatography and SUMO2 cleavage with SENP2. Gad1 pull-down was analysed by SDS–PAGE and Coomassie Blue staining.</p>", "<title>Crystallization and X-ray structure determination</title>", "<p id=\"Par19\">Crystals were grown in hanging drop format using EasyXtal 15-well trays (NeXtal). Native GajAB crystals were grown at 18 °C in 2-μl drops mixed 1:1 with purified protein (10 mg ml<sup>−1</sup>, 20 mM HEPES, 250 mM KCl and 1 mM TCEP-KOH) and reservoir solution (100 mM HEPES-NaOH pH 7.5, 2.4% PEG-400 and 2.2 M ammonium sulfate). Crystals were grown for seven days before cryo-protection with reservoir solution supplemented with 25% glycerol, and were collected by plunging in liquid nitrogen. X-ray diffraction data were collected at the Advanced Photon Source (beamlines 24-ID-C and 24-ID-E). Data were processed using the SSRL autoxds script (A. Gonzalez, Stanford SSRL). Experimental phase information was determined by molecular replacement using monomeric GajA and GajB AlphaFold2-predicted structures<sup>##REF##34265844##31##,##REF##35637307##32##</sup> in PHENIX<sup>##UREF##2##45##</sup>. Model building was completed in Coot<sup>##REF##33885789##22##</sup> and then refined in PHENIX. The final structure was refined to stereochemistry statistics as reported in Extended Data Table ##TAB##0##1##. Structure images and figures were prepared in PyMOL.</p>", "<title>Electrophoretic mobility shift assay</title>", "<p id=\"Par20\">56-bp sequence-specific motif target dsDNA (5′ TTTTTTTTTTTTTTTTTAATAACCCGGTTATTTTTTTTTTTTTTTTTTTTTTTTTT 3′) (ref. <sup>##REF##33885789##22##</sup>) or scrambled dsDNA (5′ TTTTTTTTTTTTTTTTTGACATTACATTCAGTTTTTTTTTTTTTTTTTTTTTTTTT 3′) was incubated with a final concentration of 2 µM, 5 µM or 10 µM purified GajAB, GajA[E379A]–GajB or GajAB–Gad1 complexes in 20 µl gel shift reactions containing 1 µM dsDNA, 5 mM CaCl<sub>2</sub> and 20 mM Tris-HCl pH 8.0 for 30 min at 4 °C. Ten microlitres was then mixed with 2 μl of 50% glycerol and separated on a 2% TB (Tris-borate) agarose gel. The gel was then run at 250 V for 45 min, post-stained with TB containing 10 µg ml<sup>−1</sup> ethidium bromide while rocking at room temperature, de-stained in TB buffer for 40 min and imaged on a ChemiDoc MP Imaging System.</p>", "<title>DNA cleavage assay</title>", "<p id=\"Par21\">The same 56-bp dsDNA substrates as above were incubated with GajAB, GajA[E379A]–GajB or GajAB–Gad1 complexes in a 20-μl DNA cleavage reaction buffer containing 1 µM dsDNA, 1 µM GajAB, GajA[E379A]–GajB or GajAB–Gad1, 1 mM MgCl<sub>2</sub> and 20 mM Tris-HCl pH 9.0 for 20 min at 37 °C. After incubation, reactions were stopped with DNA loading buffer containing 60 mM EDTA, and 10 µl was analysed on a 2% TB agarose gel, which was run at 250 V for 45 min. The gel was then post-stained while rocking at room temperature with TB buffer containing 10 µg ml<sup>−1</sup> ethidium bromide, de-stained in TB buffer alone for 40 min and imaged on a ChemiDoc MP Imaging System.</p>", "<title>Cryo-EM sample preparation and data collection</title>", "<p id=\"Par22\">For the SUMO2-GajAB–Gad1 co-complex sample, 3 μl of 1 mg ml<sup>−1</sup> was vitrified using a Mark IV Vitrobot (Thermo Fisher Scientific). Before sample vitrification, 2/1 Carbon Quantfoil grids were glow-discharged using an easiGlow (Pelco). Grids were then double-sided blotted for 9 s, with a constant force of 0, 100% relative humidity chamber at 4 °C and a 10-s wait time before plunging into liquid ethane and storing in liquid nitrogen.</p>", "<p id=\"Par23\">GajAB–Gad1 co-complex cryo-EM grids were screened using a Talos Arctica microscope (Thermo Fisher Scientific) operating at 200 kV, and the final map was collected on a Titan Krios microscope (Thermo Fisher Scientific) operating at 300 kV. Both microscopes operated with a K3 direct electron detector (Gatan). SerialEM software v.3.8.6 was used for all data collection. For final data collection, a total of 9,243 movies were taken at a pixel size of 0.3115 Å, a total dose of 41.1 e<sup>−</sup> per Å<sup>2</sup> and a dose per frame of 0.63 e<sup>−</sup> per Å<sup>2</sup> at a defocus range of −0.8 to −1.9 µm.</p>", "<title>Cryo-EM data processing</title>", "<p id=\"Par24\">SBGrid Consortium provided data-processing software<sup>##REF##24040512##46##</sup>. Movies were imported into cryoSPARC<sup>##REF##28165473##47##</sup> for patch-based motion correction, patch-based CTF estimation, two-dimensional and three-dimensional particle classification and non-uniform refinement. The cryoSPARC data-processing procedure is outlined in Extended Data Fig. ##FIG##9##6##. In brief, after patch-based CTF estimation, 500 micrographs were selected and autopicked using Blob Picker, which resulted in 625,295 particles after extracting from micrographs. Two-dimensional classifications were then used to generate five templates for Template Picker, from which 110,654 particles were picked from 500 micrographs. After three more rounds of 2D classification, 648,298 particles from all 9,243 micrographs were used in ab initios (<italic>K</italic> = 3), followed by heterogenous refinement. The best class with 573,410 particles was then used to go back and extract from all micrographs, which resulted in 570,485 particles used in a final 2D classification and ab initio. A total of 351,193 particles from one ab-initio class were used in non-uniform refinement along with defocus and global CTF refinement, resulting in a 2.84 Å <italic>C</italic><sub>1</sub> symmetry and 2.57 Å <italic>D</italic><sub>2</sub> symmetry map, which was then used for model building.</p>", "<title>Cryo-EM model building</title>", "<p id=\"Par25\">Model building was performed in Coot<sup>##REF##15572765##48##</sup> by manually docking AlphaFold2-predicted structures<sup>##REF##34265844##31##,##REF##35637307##32##</sup> as starting models and then manually completing refinement and model correction. To model the Gad1 fist domain, an AlphaFold2 model of the Gad1 arm–fist region was superimposed on the cryo-EM density of the manually built shoulder–arm region and then fit into density in Coot<sup>##REF##15572765##48##</sup>. To complete the model for the sparse GajB density, the X-ray GajB structure was superimposed on the cryo-EM density. GajAB–Gad1 model was refined in PHENIX<sup>##UREF##2##45##</sup>, and the structure stereochemistry statistics are reported in Extended Data Table ##TAB##1##2##. Figures were prepared in PyMOL and UCSF ChimeraX<sup>##REF##32881101##49##</sup>.</p>", "<title>Statistics and reproducibility</title>", "<p id=\"Par26\">Experimental details about replicates are found in the figure legends.</p>", "<title>Reporting summary</title>", "<p id=\"Par27\">Further information on research design is available in the ##SUPPL##1##Nature Portfolio Reporting Summary## linked to this article.</p>" ]	[]	[]	[]	[ "<p id=\"Par1\">Bacteria encode hundreds of diverse defence systems that protect them from viral infection and inhibit phage propagation<sup>##REF##29371424##1##–##REF##36123438##5##</sup>. Gabija is one of the most prevalent anti-phage defence systems, occurring in more than 15% of all sequenced bacterial and archaeal genomes<sup>##REF##29371424##1##,##REF##35538097##6##,##REF##35639517##7##</sup>, but the molecular basis of how Gabija defends cells from viral infection remains poorly understood. Here we use X-ray crystallography and cryo-electron microscopy (cryo-EM) to define how Gabija proteins assemble into a supramolecular complex of around 500 kDa that degrades phage DNA. Gabija protein A (GajA) is a DNA endonuclease that tetramerizes to form the core of the anti-phage defence complex. Two sets of Gabija protein B (GajB) dimers dock at opposite sides of the complex and create a 4:4 GajA–GajB assembly (hereafter, GajAB) that is essential for phage resistance in vivo. We show that a phage-encoded protein, Gabija anti-defence 1 (Gad1), directly binds to the Gabija GajAB complex and inactivates defence. A cryo-EM structure of the virally inhibited state shows that Gad1 forms an octameric web that encases the GajAB complex and inhibits DNA recognition and cleavage. Our results reveal the structural basis of assembly of the Gabija anti-phage defence complex and define a unique mechanism of viral immune evasion.</p>", "<p id=\"Par2\">X-ray crystallography, cryo-EM and biochemical analysis provide insight into the assembly of the bacterial Gabija complex, an anti-phage system, and reveal how viruses can evade this defence mechanism.</p>", "<title>Subject terms</title>" ]	[ "<title>Main</title>", "<p id=\"Par3\">Bacterial Gabija defence operons encode the proteins GajA and GajB, which together protect cells against diverse phages<sup>##REF##29371424##1##</sup>. To define the structural basis of Gabija anti-phage defence, we co-expressed <italic>Bacillus cereus</italic> VD045 GajA and GajB and determined a 3.0 Å X-ray crystal structure of the protein complex (Fig. ##FIG##0##1a,b##, Extended Data Fig. ##FIG##4##1a,b## and Extended Data Table ##TAB##0##1##). The structure of the GajAB complex reveals an intricate 4:4 assembly with a tetrameric core of GajA subunits braced on either end by dimers of GajB (Fig. ##FIG##0##1b##). We focused our analysis first on individual Gabija protein subunits. GajA contains an N-terminal ATPase domain that is divided into two halves by the insertion of a protein dimerization interface (discussed further below) (Fig. ##FIG##0##1c##). The GajA ATPase domain consists of an eleven-stranded β-sheet (β1<sup>ABC</sup>, β2<sup>ABC</sup>, β4–6<sup>ABC</sup> and β3<sup>ABC</sup>, β7–11<sup>ABC</sup>) that folds around the central α1<sup>ABC</sup> helix (Fig. ##FIG##0##1c## and Extended Data Fig. ##FIG##5##2a##). Sequence analysis of diverse GajA homologues shows that the GajA ATPase domain contains a highly conserved ATP-binding site that is shared with canonical ABC ATPase proteins<sup>##REF##27037766##8##</sup> (Extended Data Fig. ##FIG##5##2a##). The GajA C terminus contains a four-stranded parallel β-sheet β1–β4<sup>T</sup> surrounded by three α-helices α3<sup>T</sup>, α4<sup>T</sup> and α12<sup>T</sup> that form a Toprim (topoisomerase-primase) domain associated with proteins that catalyse double-stranded DNA (dsDNA) breaks<sup>##REF##9722641##9##,##REF##8538787##10##</sup> (Fig. ##FIG##0##1c## and Extended Data Fig. ##FIG##5##2a##). Consistent with a role in dsDNA cleavage, the structure of GajA confirms previous predictions of overall shared homology between GajA and a class of DNA endonucleases named OLD (overcoming lysogenization defect) nucleases<sup>##REF##31400118##11##,##REF##32009148##12##</sup>. Discovered at first as an <italic>Escherichia coli</italic> phage P2 protein responsible for cell toxicity in <italic>recB</italic> and <italic>recC</italic> mutant cells<sup>##REF##4884707##13##–##REF##4944857##15##</sup>, OLD nucleases occur in diverse bacterial genomes, either as single proteins (class 1) or associated with partner UvrD/PcrA/Rep-like helicase proteins (class 2), but the specific function of most OLD nuclease proteins is unknown<sup>##REF##31400118##11##,##REF##32009148##12##</sup>. GajA is a class 2 OLD nuclease, with the Toprim domain containing a complete active site composed of DxD after β3<sup>T</sup> (D432 and D434), an invariant glutamate after β2<sup>T</sup> (E379) and an invariant glycine between α1<sup>T</sup> and β1<sup>T</sup> (G409). This is similar to the active site of <italic>Burkholderia pseudomallei</italic> (<italic>Bp</italic>OLD), which was previously shown to be essential for a two-metal-dependent mechanism of DNA cleavage<sup>##REF##31400118##11##</sup> (Fig. ##FIG##0##1d## and Extended Data Fig. ##FIG##5##2a##).</p>", "<p id=\"Par4\">The structure of GajB reveals a superfamily 1A DNA helicase domain. Bacterial DNA helicases belonging to this superfamily typically have a role in DNA repair<sup>##REF##23161005##16##</sup> (Fig. ##FIG##0##1e##). Superfamily 1A helicase proteins such as UvrD, Rep and PcrA translocate along single-stranded DNA in the 3′ to 5′ direction, and are architecturally divided into four subdomains—1A, 1B, 2A and 2B—that reposition relative to each other during helicase function<sup>##REF##23161005##16##</sup>. GajB contains all of the conserved helicase motifs that are required for ATP hydrolysis and nucleic acid unwinding, including a Walker A motif Gx(4)GK-[TT] and a UvrD-like DEXQD-box Walker B motif that is responsible for the hydrolysis of nucleoside triphosphate<sup>##REF##23161005##16##–##REF##11545728##18##</sup> (Fig. ##FIG##0##1f## and Extended Data Fig. ##FIG##6##3a##). Activation of superfamily 1A DNA helicase proteins such as UvrD and Rep is known to require protein dimerization and the rotation of the 2B subdomain<sup>##REF##9288744##19##–##REF##31363055##21##</sup>. Comparisons with UvrD and Rep show that GajB protomers in the GajAB complex exhibit a partial rotation of the 2B domain relative to 2A–1A–1B, consistent with a partially active conformation that is poised to interact with phage DNA (Extended Data Fig. ##FIG##4##1e##).</p>", "<title>Gabija forms a supramolecular complex</title>", "<p id=\"Par5\">To define the mechanism by which the Gabija complex assembles, we analysed oligomerization interfaces within the GajAB structure. Purification of individual Gabija proteins shows that GajA alone is sufficient to oligomerize into a homo-tetrameric assembly (Extended Data Fig. ##FIG##4##1a##). GajB migrates as a monomer on size-exclusion chromatography, supporting a stepwise model of GajAB assembly (Fig. ##FIG##1##2a## and Extended Data Fig. ##FIG##4##1a##). GajA tetramers form through two highly conserved oligomerization interfaces (Fig. ##FIG##1##2b,c## and Extended Data Fig. ##FIG##5##2##). First, the GajA N-terminal ATPase domain contains an insertion between β7<sup>ABC</sup> and β8<sup>ABC</sup> that consists of four α-helices (α1–α4<sup>D</sup>) that zip up against a partnering GajA protomer to form a hydrophobic interface along the α2<sup>D</sup> helix (Fig. ##FIG##1##2b##). A similar α1–α4<sup>D</sup> dimerization interface exists in the structure of the <italic>Thermus scotoductus</italic> class 1 OLD (<italic>Ts</italic>OLD) protein, which shows that this interface is conserved within divergent OLD nucleases<sup>##REF##32009148##12##</sup> (Figs. ##FIG##0##1c## and ##FIG##1##2c##). The GajA ATPase domain contains a second oligomerization interface in a loop between β6<sup>ABC</sup> and α6<sup>ABC</sup>, in which hydrogen-bond contacts between D135 and R139 interlock two GajA dimers to form the tetrameric core assembly (Fig. ##FIG##1##2c##). Compared to GajA, the GajB–GajB dimerization interface is minimal and consists of a hydrophobic surface in the GajB helicase 1B domain centred at Y119 and I122 (Fig. ##FIG##1##2d##). Major GajA–GajB contacts also occur in the GajB helicase 1B domain, in which GajA R97 in a loop between α4<sup>ABC</sup> and β5<sup>ABC</sup> forms hydrogen-bond contacts with Q150 in GajB α7 along with hydrophobic packing interactions centred at GajB V147 (Fig. ##FIG##1##2e## and Extended Data Fig. ##FIG##6##3a##). Notably, the GajAB structure shows that the GajB helicase 1A subdomain, which includes the DEXQD-box active site, is positioned adjacent to the GajA ATPase domain, suggesting that GajB ATP hydrolysis and DNA-unwinding activity might regulate the activation of the GajA ATPase domain (Fig. ##FIG##1##2e##). In addition to the major GajAB interface contacts, Gabija supramolecular complex assembly is driven by extensive protomer interactions that result in around 31,000 Å<sup>2</sup> of surface area buried for the GajA tetramer and around 1,800 Å<sup>2</sup> of surface area buried for each GajB subunit.</p>", "<p id=\"Par6\">We reconstituted Gabija activity in vitro and observed that the GajAB complex binds to and rapidly cleaves a previously characterized 56-bp dsDNA substrate that contains a sequence-specific motif derived from phage lambda DNA<sup>##REF##33885789##22##</sup> (Extended Data Fig. ##FIG##4##1c##). The GajAB complex can interact with a scrambled DNA sequence but is unable to cleave this target DNA (Extended Data Fig. ##FIG##4##1c,d##). GajA and GajB proteins are each essential for phage defence in vivo<sup>##REF##29371424##1##,##REF##33885789##22##</sup>, but we observed, in agreement with previous results, that GajA is alone sufficient to cleave target DNA and does not require GajB in vitro<sup>##REF##33885789##22##,##REF##37480847##23##</sup> (Extended Data Fig. ##FIG##4##1c##). These results suggest that GajAB complex formation could have a specific role in controlling substrate recognition or nuclease activation during phage infection. To confirm these findings, we analysed protein interaction interfaces in the GajAB complex structure and tested the effects of a panel of mutations on the assembly of the Gabija complex in vitro and the ability of Gabija to defend <italic>Bacillus subtilis</italic> cells from phage SPβ infection in vivo. Mutations to the GajA–GajB hetero-oligomerization interface, including GajA K94E and R97A and GajB V147E and Q150R, disrupted complex formation, indicating that these regions are crucial for Gabija complex assembly (Extended Data Fig. ##FIG##4##1f##). Likewise, these substitutions to the GajA–GajB interface markedly reduced the ability of Gabija to inhibit phage replication in <italic>B. subtilis</italic>. Substitutions to the GajA–GajA dimerization interface including I199E, I212E and K229E also resulted in the complete loss of phage resistance (Fig. ##FIG##1##2f##). By contrast, phage resistance was tolerant to mutations in the GajB–GajB interface, which suggests that this minimal interaction surface is not strictly essential for anti-phage defence. Together, these results define the structural basis of GajA and GajB interaction and show that the formation of the GajAB supramolecular complex is crucial for Gabija anti-phage defence.</p>", "<title>Structural basis of Gabija viral evasion</title>", "<p id=\"Par7\">To overcome host immunity, phages encode evasion proteins that specifically inactivate anti-phage defence<sup>##REF##23242138##24##–##REF##36848932##29##</sup>. In a companion study, Yirmiya et al. report the discovery of a viral inhibitor of Gabija anti-phage defence<sup>##UREF##1##30##</sup>, and we reasoned that defining the mechanism of immune evasion would provide further insight into the function of the Gabija complex. Gad1 is a <italic>Bacillus</italic> phage Phi3T protein that is atypically large (35 kDa) compared to other characterized phage immune-evasion proteins (Extended Data Fig. ##FIG##7##4a##). Protein interaction analysis showed that Gad1 binds directly to GajAB (Extended Data Fig. ##FIG##7##4b,c##), and we used cryo-EM to determine a 2.6 Å structure of the GajAB–Gad1 co-complex assembly (Fig. ##FIG##2##3a,b##, Extended Data Figs. ##FIG##8##5## and ##FIG##9##6a–g## and Extended Data Table ##TAB##1##2##). The GajAB–Gad1 co-complex structure reveals a mechanism of inhibition in which Gad1 proteins form an oligomeric web that wraps 360° around the host defence complex. Eight copies of phage Gad1 encircle the GajAB assembly, forming a 4:4:8 GajAB–Gad1 complex that is around 775 kDa in size (Fig. ##FIG##2##3b,c##). Gad1 mainly recognizes the GajA nuclease core, forming extensive contacts along the surface of the GajA dimerization domain (Fig. ##FIG##2##3c,d##). Key GajAB–Gad1 contacts include hydrogen-bond interactions from a Gad1 positively charged loop located between β5 and β6 with GajA α2<sup>D</sup> (Fig. ##FIG##2##3e## and Extended Data Fig. ##FIG##10##7a–c##), and hydrophobic packing interactions between Gad1 Y190 and F192 with GajA α2<sup>D</sup> (Fig. ##FIG##2##3f## and Extended Data Fig. ##FIG##10##7a##). Although the contacts between Gad1 and GajB are limited, both GajA and GajB are necessary for Gad1 interaction, indicating that Gad1 specifically targets the fully assembled GajAB complex to inactivate host anti-phage defence (Extended Data Fig. ##FIG##7##4d##).</p>", "<p id=\"Par8\">Gad1 wraps around the GajAB complex using a network of homo-oligomeric interactions and notable conformational flexibility. On either side of the GajAB complex, four copies of Gad1 interlock into a tetrameric interface along the primary GajA-binding site (Fig. ##FIG##2##3d##). The Gad1 tetrameric interface is formed by hydrogen-bond interactions between the C-terminal ‘shoulder’ domain of each Gad1 monomer and a highly conserved set of three cysteine residues, C282, C284 and C285, which form disulfide interactions at an inter-subunit interface (Fig. ##FIG##2##3d,g## and Extended Data Fig. ##FIG##10##7a,d##). The N terminus of each Gad1 monomer forms an ‘arm’ domain that extends out from the shoulder and reaches around the GajA nuclease active site to connect to a partnering Gad1 protomer from the opposite side of the complex. At the end of the Gad1 arm is an N-terminal ‘fist’ domain that allows two partnering Gad1 protomers to interact and complete the octameric web assembly (Fig. ##FIG##2##3c,h##). Structural flexibility limits resolution in this portion of the cryo-EM map, but AlphaFold2 modelling<sup>##REF##34265844##31##,##REF##35637307##32##</sup> and rigid-body placement of the Gad1 N-terminal fist domain suggests that conserved hydrophobic residues around the Gad1 α1 helix mediate the fist–fist interactions (Fig. ##FIG##2##3h## and Extended Data Fig. ##FIG##10##7a##). To fully encircle GajAB, Gad1 adopts two distinct structural conformations. Each pair of Gad1 proteins that wrap around and connect at the edge of the GajAB complex are formed by one Gad1 protomer reaching out from the shoulder with an arm domain extended straight down and one Gad1 protomer reaching out with an arm domain bent around 35° to the left (Fig. ##FIG##2##3i## and Extended Data Fig. ##FIG##9##6h##). Sequence analysis of Gad1 proteins from phylogenetically diverse phages shows that the Gad1 N-terminal arm domain is highly variable in length (Extended Data Fig. ##FIG##10##7a##), providing further evidence that conformational flexibility in this region is crucial to inhibit host Gabija defence.</p>", "<p id=\"Par9\">To test the importance of individual GajAB–Gad1 interfaces, we next analysed a series of Gad1 substitution and truncation mutants for their ability to interact with GajAB and inhibit Gabija anti-phage defence. The Gad1 residue F192 is located between β4 and β5 and is part of a highly conserved region that forms the centre of the primary GajA–Gad1 interface (Extended Data Fig. ##FIG##10##7a##). The Gad1 substitution F192R blocked the ability of Gad1 to interact with GajAB in vitro and inhibit Gabjia anti-phage defence in vivo (Fig. ##FIG##2##3j## and Extended Data Fig. ##FIG##10##7a,e##). However, individual mutations throughout the periphery were insufficient to disrupt Gad1 inhibition of Gabjia anti-phage defence. This shows that the large footprint of Gad1 is tolerant to small perturbations that might enable host resistance. Similarly, mutations to the conserved Gad1 cysteine residues in the tetrameric shoulder interface greatly reduced the stability of GajAB–Gad1 complex formation in vitro but only exhibited a threefold decrease and mostly still permitted Gad1 to block phage defence in <italic>B. subtilis</italic> cells (Fig. ##FIG##2##3j## and Extended Data Fig. ##FIG##10##7e##). The formation of the GajAB–Gad1 complex was also disrupted in vitro by a Y103R mutation in the Gad1 fist–fist interface (Fig. ##FIG##2##3h## and Extended Data Fig. ##FIG##10##7f##). Finally, in contrast to wild-type Gad1, expression of the Gad1 N-terminal fist–arm or C-terminal shoulder domains alone was unable to inhibit Gabija, providing evidence that full wrapping of Gad1 around the GajAB complex is necessary to enable phage evasion of anti-phage defence (Fig. ##FIG##2##3j## and Extended Data Fig. ##FIG##10##7e##).</p>", "<title>Gad1 blocks Gabija DNA cleavage</title>", "<p id=\"Par10\">Superposition of the GajAB–Gad1 and GajAB complexes shows that Gad1 binding does not induce a notable conformational change in GajAB, and suggests that Gad1 instead functions through steric hindrance of Gabija anti-phage defence (Extended Data Fig. ##FIG##10##7h##). To define the mechanism of Gad1 inhibition of Gabija anti-phage defence, we modelled interactions between GajAB and target DNA. The GajA Toprim domain is structurally homologous to the <italic>E. coli</italic> protein MutS, which is involved in DNA repair<sup>##REF##20167596##33##</sup>. Superimposing the MutS–DNA structure revealed positively charged patches lining a groove in the GajA Toprim domain that dips into the nuclease active site (Extended Data Fig. ##FIG##11##8##). Notably, the Gad1 arm domain directly occupies this putative DNA-binding surface, supporting a model in which the phage protein directly clashes with the path of target dsDNA (Fig. ##FIG##3##4a,b##). To determine the effect of viral inhibition on GajAB catalytic function, we tested the role of Gad1 in individual steps of DNA binding and target DNA cleavage. Gad1 prevented GajAB from binding to target DNA and abolished all nuclease activity in vitro (Fig. ##FIG##3##4c,d## and Supplementary Fig. ##SUPPL##0##1##). Gad1 proteins with F192R or C282E mutations were no longer able to inhibit DNA cleavage, in agreement with the inability of F192R-mutant proteins and the reduced ability of C282E-mutant proteins to block Gabija defence in vivo and form stable GajAB–Gad1 complexes in vitro (Extended Data Fig. ##FIG##10##7g##). Together, these results show that phage Gad1 binds to and wraps around the GajAB complex to block target DNA degradation. Our findings reveal a complete mechanism by which phages evade the Gabija defence system of the host (Fig. ##FIG##3##4e##).</p>", "<p id=\"Par11\">Our study defines the structural basis of the formation of the Gabija supramolecular complex, and explains how phages block DNA cleavage to overcome this type of host immunity. The approximately 500-kDa GajAB complex expands an emerging theme in anti-phage defence, whereby protein subunits assemble into large machines to resist phage infection—similarly to the supramolecular complexes in CRISPR<sup>##REF##35562427##34##</sup>, CBASS<sup>##REF##35859168##35##,##REF##35948638##36##</sup> and RADAR<sup>##REF##36764290##37##,##REF##36764292##38##</sup> immunity. These results parallel human innate immunity, in which key effectors in inflammasome, Toll-like receptor, RIG-I-like receptor and cGAS–STING signalling pathways also oligomerize into large assemblies to block viral replication<sup>##REF##25359439##39##,##REF##30846571##40##</sup>. In contrast to the exceptionally large defence complexes of the host, phage evasion proteins are typically small, 5–20-kDa proteins that sterically occlude key protein binding and active-site motifs<sup>##REF##30208287##25##,##REF##31942051##26##</sup>. Breaking this rule, the 35-kDa anti-Gabija protein Gad1 is one of the largest described viral protein–protein inhibitors of host immune signalling (Extended Data Fig. ##FIG##7##4##). Whereas most viral evasion proteins that are larger than 20 kDa are enzymatic domains that catalytically modify target host factors or signalling molecules, the large size of Gad1 is necessary to bind to, oligomerize and encircle the entire host GajAB complex. Resistance to small phage proteins that simply block the GajA active site could explain why Gabija is a highly prevalent defence system in diverse bacterial phyla. A key question opened by our structures of the Gabija complex is how GajB helicase activity is linked to the activation of the GajA nuclease domain to control the cleavage of target DNA. Gad1 encasing the GajAB complex to trap it in an inactive state is a new mechanism by which phages evade host defences, and this finding provides a template to understand how viruses disrupt the complex mechanisms of activation of diverse anti-phage defence systems in bacteria.</p>", "<title>Online content</title>", "<p id=\"Par28\">Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41586-023-06855-2.</p>", "<title>Supplementary information</title>", "<p>\n\n\n\n</p>", "<title>Source data</title>", "<p>\n\n\n</p>" ]	[ "<title>Extended data figures and tables</title>", "<p id=\"Par31\">\n\n</p>", "<p id=\"Par32\">\n\n</p>", "<p id=\"Par33\">\n\n</p>", "<p id=\"Par34\">\n\n</p>", "<p id=\"Par35\">\n\n</p>", "<p id=\"Par36\">\n\n</p>", "<p id=\"Par37\">\n\n</p>", "<p id=\"Par38\">\n\n</p>", "<p id=\"Par39\">\n\n</p>", "<p id=\"Par40\">\n\n</p>", "<title>Extended data</title>", "<p>is available for this paper at 10.1038/s41586-023-06855-2.</p>", "<title>Supplementary information</title>", "<p>The online version contains supplementary material available at 10.1038/s41586-023-06855-2.</p>", "<title>Acknowledgements</title>", "<p>We thank J. Asnes, J. Grippen and members of the P.J.K. and R.S. laboratories for comments and discussion, and A. Lu for assistance with X-ray data collection. The work was funded by grants to P.J.K. from the Pew Biomedical Scholars program, the Burroughs Wellcome Fund PATH program, the Mathers Foundation, the Mark Foundation for Cancer Research, the Cancer Research Institute, the Parker Institute for Cancer Immunotherapy and the National Institutes of Health (1DP2GM146250-01), and by grants to R.S. from the European Research Council (ERC-AdG GA 101018520), the Israel Science Foundation (MAPATS grant 2720/22), the Ernest and Bonnie Beutler Research Program of Excellence in Genomic Medicine, the Deutsche Forschungsgemeinschaft (SPP 2330, grant 464312965) and the Knell Family Center for Microbiology. E.Y. is supported by the Clore Scholars Program and in part by the Israeli Council for Higher Education (CHE) via the Weizmann Data Science Research Center. A.G.J. is supported by a Life Science Research Foundation postdoctoral fellowship of the Open Philanthropy Project. X-ray data were collected at the Northeastern Collaborative Access Team beamlines 24-ID-C and 24-ID-E (P30 GM124165), and used a Pilatus detector (S10RR029205), an Eiger detector (S10OD021527) and the Argonne National Laboratory Advanced Photon Source (DE-AC02-06CH11357). Cryo-EM data were collected at the Harvard Cryo-EM Center for Structural Biology at Harvard Medical School. We thank T. Humphreys for help with cryo-EM data collection. Part of this research was supported by the NIH grant U24GM129547 and was performed at the Pacific Northwest Center for Cryo-EM at Oregon Health & Science University, with access through EMSL (grid.436923.9), a DOE Office of Science User Facility sponsored by the Office of Biological and Environmental Research.</p>", "<title>Author contributions</title>", "<p>The study was designed and conceived by S.P.A. and P.J.K. All protein purification and biochemical assays were performed by S.P.A. and S.E.M. Crystallography structural analysis was performed by S.P.A. Cryo-EM structural analysis was performed by S.P.A., A.G.J. and M.L.M. Model building and analysis were performed by S.P.A. and P.J.K. Bioinformatics and protein sequence analysis were performed by E.Y., A.L., G.A. and R.S. Phage challenge assays were performed by A.L. and R.S. Figures were prepared by S.P.A. with assistance from S.E.M. The manuscript was written by S.P.A. and P.J.K. All authors contributed to editing the manuscript, and support its conclusions.</p>", "<title>Peer review</title>", "<title>Peer review information</title>", "<p id=\"Par29\"><italic>Nature</italic> thanks David Taylor and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. ##SUPPL##2##Peer reviewer reports## are available.</p>", "<title>Data availability</title>", "<p>Coordinates and structure factors of the Gabija GajAB complex have been deposited in the PDB under the accession code <ext-link ext-link-type=\"uri\" xlink:href=\"https://doi.org/10.2210/pdb8SM3/pdb\">8SM3</ext-link>. Coordinates and density maps of the GajAB–Gad1 co-complex are deposited with the PDB and the Electron Microscopy Data Bank (EMDB) under the accession codes <ext-link ext-link-type=\"uri\" xlink:href=\"http://doi.org/10.2210/pdb8U7I/pdb\">8U7I</ext-link> and <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ebi.ac.uk/pdbe/entry/EMD-41983\">EMD-41983</ext-link>. All other data are available in the manuscript or Supplementary Fig. ##SUPPL##0##1##. <xref ref-type=\"sec\" rid=\"Sec20\">Source data</xref> are provided with this paper.</p>", "<title>Competing interests</title>", "<p id=\"Par30\">R.S. is a scientific cofounder and advisor of BiomX and EcoPhage. The remaining authors declare no competing interests.</p>" ]	[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>Structure of the Gabija anti-phage defence complex.</title><p><bold>a</bold>, Schematic of <italic>B. cereus</italic> (<italic>Bc</italic>) Gabija defence operon and domain organization of GajA and GajB. <bold>b</bold>, Overview of the GajAB X-ray crystal structure shown in three orientations. GajA protomers are depicted in two shades of blue and GajB protomers are in red. <bold>c</bold>, Isolated GajA monomer (top) and comparison with a <italic>Ts</italic>OLD nuclease monomer (bottom) (Protein Data Bank (PDB) ID:<ext-link ext-link-type=\"uri\" xlink:href=\"https://doi.org/10.2210/pdb6P74/pdb\">6P74</ext-link>)<sup>##REF##32009148##12##</sup>. <bold>d</bold>, Magnified views of Toprim catalytic residues in GajA (left) and <italic>Bp</italic>OLD (right) (PDB ID: <ext-link ext-link-type=\"uri\" xlink:href=\"https://doi.org/10.2210/pdb6NK8/pdb\">6NK8</ext-link>)<sup>##REF##31400118##11##</sup>. The location of the GajA cut-away image is indicated with a box in <bold>c</bold> and magnesium ions are depicted as grey spheres. <bold>e</bold>, Isolated GajB monomer (top) and comparison with <italic>E. coli</italic> (<italic>Ec</italic>) UvrD (bottom) (PDB ID: <ext-link ext-link-type=\"uri\" xlink:href=\"https://doi.org/10.2210/pdb2IS2/pdb\">2IS2</ext-link>)<sup>##REF##17190599##20##</sup>. <bold>f</bold>, Magnified views of the DEXQD-box motif in GajB (left) and <italic>Ec</italic>UvrD (right). The locations of the GajB and UvrD cut-away images are indicated with boxes in <bold>e</bold>.</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>Mechanism of Gabija supramolecular complex assembly.</title><p><bold>a</bold>, Schematic model of GajAB complex formation by GajA tetramerization and GajB docking. <bold>b</bold>, Overview of the GajA α2<sup>D</sup>–α2<sup>D</sup> dimerization interface and detailed view of interacting residues. For clarity, each GajA monomer is depicted in two shades of blue. <bold>c</bold>, Overview of the GajA–GajA ATPase interaction and detailed view of the inter-subunit D135–R139 interaction. <bold>d</bold>, Overview of the minimal GajB–GajB dimer interface and detailed view of GajB–GajB hydrophobic interactions centred around Y119, N121 and I122. <bold>e</bold>, Left, overview of the GajA–GajB interface, highlighting the proximity of GajA ABC ATPase and GajB helicase active-site residues. Right, the box indicates the location of GajA R97 and GajB V147 and Q150 interaction. <bold>f</bold>, Analysis of mutations in the GajA–GajB (A–B), GajA–GajA (A–A), and GajB–GajB (B–B) multimerization interfaces. GajA and GajB mutations were selected by identifying central residues with well-defined protein–protein contacts within each multimerization interface, and were tested to determine their effects on the ability of the <italic>B. cereus</italic> Gabija operon to defend cells against phage infection. Data represent the phage SPβ average plaque-forming units (PFU) ml<sup>−1</sup> of three biological replicates, with individual data points shown. WT, wild type.</p><p>##SUPPL##3##Source Data##</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><title>Structural basis of viral evasion of Gabija defence.</title><p><bold>a</bold>, Schematic model of GajAB–Gad1 co-complex formation and domain organization of phage Phi3T Gad1. <bold>b</bold>, Cryo-EM density map of <italic>Bc</italic>GajAB in complex with Phi3T Gad1, shown in three different orientations. The map is coloured by the model, with Gad1 monomers depicted in two shades of green. <bold>c</bold>,<bold>d</bold>, Side view of the complete Gad1 octameric complex (<bold>c</bold>) and top-down view of the Gad1 tetrameric interface (<bold>d</bold>), with boxes highlighting views that are magnified in <bold>e</bold>–<bold>h</bold>. <bold>e</bold>,<bold>f</bold>, Magnified views of major Gad1–GajA interface contacts including a Gad1 positively charged loop (<bold>e</bold>) and hydrophobic interactions with GajA α2<sup>D</sup> (<bold>f</bold>). <bold>g</bold>,<bold>h</bold>, Magnified views of major Gad1–Gad1 oligomerization interactions including disulfide bonds in the C-terminal shoulder domain (<bold>g</bold>) and fist–fist domain contacts modelled by rigid-body placement of an AlphaFold2 fist-domain structure prediction into the cryo-EM map (<bold>h</bold>). <bold>i</bold>, Two distinct conformations of Gad1 observed in the GajAB–Gad1 co-complex structure. Differences in the rotation of the Gad1 arm domain are highlighted on the right. <bold>j</bold>, Analysis of the effect of Gad1 mutations in the GajA–Gad1 and Gad1–Gad1 multimerization interfaces on the ability of Gad1 to enable evasion of Gabija defence. Data represent PFU ml<sup>−1</sup> of phage SPβ infecting cells expressing <italic>Bc</italic>Gabija and <italic>Shewanella</italic> sp. phage 1/4 Gad1, or negative control (NC) cells expressing empty vector for either plasmid. <italic>Shewanella</italic> sp. phage 1/4 Gad1 residues are numbered according to the Phi3T Gad1 structure. <italic>Shewanella</italic> sp. phage 1/4 Gad1 N-terminal and C-terminal truncations (N-term and C-term, respectively) are M1–L152 and V153–E348, respectively. Data are the average of three biological replicates, with individual data points shown.</p><p>##SUPPL##4##Source Data##</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><title>Inhibition of Gabija DNA binding and cleavage enables viral evasion.</title><p><bold>a</bold>, Cartoon representation of the GajAB–Gad1 co-complex structure with modelled DNA (orange), based on structural homology with <italic>E. coli</italic> MutS (PDB ID <ext-link ext-link-type=\"uri\" xlink:href=\"https://doi.org/10.2210/pdb3K0S/pdb\">3K0S</ext-link>)<sup>##REF##20167596##33##</sup>. <bold>b</bold>, Top, isolated GajA protomer with modelled DNA (orange) bound to the Toprim domain. Bottom, the same GajA promoter with Gad1, showing substantial steric clashes between Gad1 and the path of the DNA. <bold>c</bold>,<bold>d</bold>, Biochemical analysis of GajAB 56-bp target DNA binding (<bold>c</bold>) and target cleavage (<bold>d</bold>) shows that Gad1 potently inhibits the activity of GajAB. Data are representative of three independent experiments. <bold>e</bold>, Model of Gabija anti-phage defence and mechanism of Gad1 immune evasion.</p></caption></fig>", "<fig id=\"Fig5\"><label>Extended Data Fig. 1</label><caption><title>GajA and GajB form a supramolecular complex that cleaves phage lambda DNA in vitro.</title><p><bold>a</bold>, Size-exclusion chromatography (16/600 S200) analysis of recombinant <italic>Bc</italic>GajA and <italic>Bc</italic>GajB proteins, and the co-expressed <italic>Bc</italic>GajAB complex. Brackets indicate fractions collected for biochemical and structural analysis with A<sub>260/280</sub> of the final purified proteins listed above. <bold>b</bold>, SDS–PAGE analysis of purified GajA, GajB, and GajAB. Asterisk indicates minor contamination with the <italic>E. coli</italic> protein ArnA. Data are representative of at least 3 independent experiments. <bold>c</bold>, Agarose gel analysis of the ability of GajA, GajB, and GajAB to cleave a 56-bp target and scrambled dsDNA demonstrates that GajA alone and the GajAB complex can cleave target DNA only. The sequence-specific GajA target dsDNA with cleavage site described in Cheng et al.<sup>##REF##33885789##22##</sup> and the scrambled 15-bp sequence are shown below. <bold>d</bold>, Catalytic dead GajA[E379A]–GajB complex binding to target dsDNA (left) and scrambled dsDNA (right). <bold>e</bold>, Structural comparison of GajB and <italic>Ec</italic>Rep (PDB ID 1UAA)<sup>##REF##9288744##19##</sup> demonstrates the GajB 2B domain is rotated in a partially active intermediate position in the GajAB complex structure. <bold>f</bold>, SDS–PAGE analysis of <italic>Bc</italic>GajAB mutant protein complex formation after co-expression and Ni-NTA pull-down demonstrates that mutations to the GajA–GajB interface disrupt complex formation. The GajA and GajB homo-oligomerization interfaces are not required for GajA–GajB interaction, but it is not known if these mutants remain competent at forming the wild-type 4:4 complex. Data in <bold>c</bold>,<bold>d</bold>,<bold>f</bold> are representative of 3 independent experiments.</p></caption></fig>", "<fig id=\"Fig6\"><label>Extended Data Fig. 2</label><caption><title>Structural characterization of GajA.</title><p><bold>a</bold>, Structure-guided alignment of GajA proteins from indicated bacteria coloured according to amino acid conservation. The determined <italic>Bacillus cereus</italic> VD045 GajA secondary structure is displayed, and active-site and oligomerization interface residues are annotated according to the key below. Secondary structure abbreviations include ABC ATPase domain (ABC), dimerization domain (D), and Toprim domain (T). <bold>b</bold>,<bold>c</bold>, Zoomed-in views of GajA–GajA oligomerization interactions including dimerization domain interactions (<bold>b</bold>) and ABC ATPase domain interactions (<bold>c</bold>).</p></caption></fig>", "<fig id=\"Fig7\"><label>Extended Data Fig. 3</label><caption><title>Structural characterization of GajB.</title><p><bold>a</bold>, Structure-guided alignment of GajB proteins from indicated bacteria coloured according to amino acid conservation. The determined <italic>Bacillus cereus</italic> VD045 GajB secondary structure is displayed, and active-site and oligomerization interface residues are annotated according to the key below.</p></caption></fig>", "<fig id=\"Fig8\"><label>Extended Data Fig. 4</label><caption><title>Size comparison of Gad1 with known phage immune-evasion proteins and biochemical characterization of Gad1 for binding to the GajAB complex.</title><p><bold>a</bold>, Analysis of known phage immune-evasion proteins according to function and molecular weight demonstrates that Gad1 is atypically large for an evasion protein that functions through protein–protein interactions with a host anti-phage defence system. Phage immune-evasion proteins are categorized and exhibited as coloured dots coloured according to the key below. Notable evasion proteins are indicated with text labels<sup>##REF##23242138##24##–##REF##36750095##28##,##REF##36174646##42##,##REF##36754343##50##–##REF##31942067##53##</sup>. <bold>b</bold>, Top, size-exclusion chromatography analysis (16/600 S200) of SUMO2-tagged <italic>Bc</italic>GajAB with or without phage Phi3T Gad1 used for cryo-EM structural studies. Bottom, size-exclusion chromatography analysis (16/600 S300) of <italic>Bc</italic>GajAB with or without <italic>Shewanella</italic> phage 1/4 Gad1 used for biochemical studies. Brackets indicate fractions collected and the A<sub>260/280</sub> of the final purified proteins is indicated above. <italic>Shewanella</italic> phage 1/4 Gad1 was used preferentially for biochemical studies due to less toxicity during <italic>E. coli</italic> expression. <bold>c</bold>, SDS–PAGE analysis of purified SUMO2-tagged GajAB, SUMO2-tagged GajAB in complex with phage Phi3T Gad1, untagged GajAB, and untagged GajB in complex with <italic>Shewanella</italic> phage 1/4 Gad1. <bold>d</bold>, SDS–PAGE analysis of Ni-NTA co-purified GajA, GajB, and GajAB with <italic>Shewanella</italic> phage 1/4 Gad1 indicates that Gad1 only binds the fully assembled GajAB complex. Asterisk indicates minor contamination with the <italic>E. coli</italic> protein ArnA. Data in <bold>b</bold>–<bold>d</bold> are representative of 3 independent experiments.</p></caption></fig>", "<fig id=\"Fig9\"><label>Extended Data Fig. 5</label><caption><title>Cryo-EM data processing for the GajAB–Gad1 co-complex.</title><p><bold>a</bold>, Section of a representative electron micrograph (<italic>n</italic> = 9,243) of SUMO2–GajAB in complex with phage Phi3T Gad1. Scale bar, 50 nm. <bold>b</bold>, Data-processing scheme used to generate the final 2.57-Å map.</p></caption></fig>", "<fig id=\"Fig10\"><label>Extended Data Fig. 6</label><caption><title>Cryo-EM map quality of the GajAB–Gad1 co-complex and model to map fitting.</title><p><bold>a</bold>, Reconstruction of the GajAB–Gad1 co-complex coloured by local resolution. <bold>b</bold>, Fourier shell correlation (FSC) of the EM map. <bold>c</bold>, GajA, GajB, and Gad1 map to model fit for designated regions. <bold>d</bold>–<bold>f</bold>, Isolated GajA (<bold>d</bold>), GajB (<bold>e</bold>) and Gad1 (<bold>f</bold>) density maps with model fitting. <bold>g</bold>, GajAB–Gad1 model that was used for refining the cryo-EM map for Extended Data Table ##TAB##1##2##. <bold>h</bold>, Left, sections of Gad1 chains that were built de novo from the cryo-EM density and built using rigid-body placement of AlphaFold2 modelled residues. Right, cryo-EM density used to fit placement of Gad1 fist–fist domain contacts that complete protomer interactions.</p></caption></fig>", "<fig id=\"Fig11\"><label>Extended Data Fig. 7</label><caption><title>Biochemical and structural characterization of the GajAB–Gad1 co-complex.</title><p><bold>a</bold>, Structure-guided alignment of Gad1 proteins from indicated phage or prophage genomes coloured according to amino acid conservation. The <italic>Bacillus</italic> phage Phi3T Gad1 secondary structure is displayed according to the two different conformations observed in the GajAB–Gad1 co-complex structure. Oligomerization interface residues are annotated according to the key below. <bold>b</bold>,<bold>c</bold>, Magnified views of Gad1–GajA interface contacts including hydrophobic interactions in AlphaFold2 arm domain structure of Gad1 and the Toprim domain of GajA (<bold>b</bold>) and Gad1 shoulder domain residue Q244 interaction with GajA dimerization domain residue E277 (<bold>c</bold>). <bold>d</bold>, Magnified view of Gad1–Gad1 oligomerization interactions between shoulder domains of Gad1 protomers. <bold>e</bold>, SDS–PAGE analysis of the ability of <italic>Shewanella</italic> phage 1/4 Gad1 mutant proteins to interact with the GajAB complex. <italic>Shewanella</italic> phage 1/4 Gad1 mutant proteins were co-expressed with SUMO2-tagged GajAB (GajA-tagged) and co-purified by Ni-NTA pull-down. <italic>Shewanella sp</italic>. phage 1/4 Gad1 residues are numbered according to the Phi3T Gad1 structure. To measure high stringency of GajAB–Gad1 interactions, complexes were washed with a 1 M NaCl buffer prior to elution and co-purification. Notably, the Gad1 mutant C282E is no longer able to interact with GajAB in vitro under these stringent conditions, but retains the ability to disrupt Gabija defence in vivo, suggesting that lower-affinity interactions still occur. <bold>f</bold>, SDS–PAGE analysis of the ability of <italic>Shewanella</italic> phage 1/4 Gad1 fist–fist interface mutant proteins to interact with the GajAB complex. <italic>Shewanella</italic> phage 1/4 Gad1 mutant proteins were co-expressed with SUMO2-tagged GajAB (GajA-tagged), co-purified by Ni-NTA pull-down, and treated with SENP2 to cleave the SUMO2 tag prior to SDS–PAGE gel loading. <italic>Shewanella sp</italic>. phage 1/4 Gad1 residues are numbered according to the Phi3T Gad1 structure. <bold>g</bold>, Agarose gel analysis of the ability of GajAB–Gad1 mutant complexes to cleave target 56-bp dsDNA after a 1 min or 20 min incubation. <bold>h</bold>, Superposition of the GajAB crystal structure and GajAB from the GajAB–Gad1 cryo-EM structure demonstrates no significant conformational change after Gad1 binding. Data in <bold>e</bold>–<bold>g</bold> are representative of 3 independent experiments.</p></caption></fig>", "<fig id=\"Fig12\"><label>Extended Data Fig. 8</label><caption><title>Modelling DNA-bound GajA.</title><p><bold>a</bold>,<bold>b</bold>, Isolated GajA protomer modelled with DNA bound to the Toprim domain shown with surface electrostatic potential (<bold>a</bold>) and in cartoon format (<bold>b</bold>). DNA modelling was performed using structural homology with the <italic>E. coli</italic> MutS–DNA complex (PDB ID 3K0S)<sup>##REF##20167596##33##</sup>. <bold>c</bold>, Magnified view of the GajA Toprim active site with modelled DNA.</p></caption></fig>" ]	[ "<table-wrap id=\"Tab1\"><label>Extended Data Table 1</label><caption><p>Summary of X-ray data collection, phasing and refinement statistics</p></caption></table-wrap>", "<table-wrap id=\"Tab2\"><label>Extended Data Table 2</label><caption><p>Cryo-EM data collection, refinement and validation statistics</p></caption></table-wrap>" ]	[]	[]	[]	[]	[]	[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM3\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM4\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM5\"></supplementary-material>" ]	[ "<table-wrap-foot><p>Dataset was collected from an individual crystal. Values in parentheses are for the highest resolution shell.</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"41586_2023_6855_Fig1_HTML\" id=\"d32e479\"/>", "<graphic xlink:href=\"41586_2023_6855_Fig2_HTML\" id=\"d32e634\"/>", "<graphic xlink:href=\"41586_2023_6855_Fig3_HTML\" id=\"d32e828\"/>", "<graphic xlink:href=\"41586_2023_6855_Fig4_HTML\" id=\"d32e972\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6855_Fig5_ESM\" id=\"d32e1460\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6855_Fig6_ESM\" id=\"d32e1489\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6855_Fig7_ESM\" id=\"d32e1506\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6855_Fig8_ESM\" id=\"d32e1579\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6855_Fig9_ESM\" id=\"d32e1599\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6855_Fig10_ESM\" id=\"d32e1644\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6855_Fig11_ESM\" id=\"d32e1715\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6855_Fig12_ESM\" id=\"d32e1753\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6855_Tab1_ESM\" id=\"d32e1763\"><caption><p>Summary of X-ray data collection, phasing and refinement statistics</p></caption></graphic>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6855_Tab2_ESM\" id=\"d32e1778\"><caption><p>Cryo-EM data collection, refinement and validation statistics</p></caption></graphic>" ]	[ "<media xlink:href=\"41586_2023_6855_MOESM1_ESM.pdf\"><label>Supplementary Figure 1</label><caption><p>Uncropped gels.</p></caption></media>", "<media xlink:href=\"41586_2023_6855_MOESM2_ESM.pdf\"><caption><p>Reporting Summary</p></caption></media>", "<media xlink:href=\"41586_2023_6855_MOESM3_ESM.pdf\"><caption><p>Peer Review File</p></caption></media>", "<media xlink:href=\"41586_2023_6855_MOESM4_ESM.xlsx\"><caption><p>Source Data Fig. 2</p></caption></media>", "<media xlink:href=\"41586_2023_6855_MOESM5_ESM.xlsx\"><caption><p>Source Data Fig. 3</p></caption></media>" ]	[{"label": ["17."], "surname": ["Gorbalenya", "Koonin"], "given-names": ["AE", "EV"], "article-title": ["Helicases: amino acid sequence comparisons and structure\u2013function relationships"], "source": ["Curr. Opin. Struct. Biol."], "year": ["1993"], "volume": ["3"], "fpage": ["419"], "lpage": ["429"], "pub-id": ["10.1016/S0959-440X(05)80116-2"]}, {"label": ["30."], "mixed-citation": ["Yirmiya, E. et al. Phages overcome bacterial immunity via diverse anti-defence proteins. "], "italic": ["Nature"]}, {"label": ["45."], "surname": ["Liebschner"], "given-names": ["D"], "article-title": ["Macromolecular structure determination using X-rays, neutrons and electrons: recent developments in Phenix"], "source": ["Acta Crystallogr. D"], "year": ["2019"], "volume": ["75"], "fpage": ["861"], "lpage": ["877"], "pub-id": ["10.1107/S2059798319011471"]}]	{ "acronym": [], "definition": [] }	53	CC BY	no	2024-01-13 00:11:06	Nature. 2024 Nov 22; 625(7994):360-365	oa_package/26/76/PMC10781630.tar.gz
PMC10781631	37932418	[]	[]	[]	[]	[]	[ "<title>Subject terms</title>" ]	[ "<title>To the Editor:</title>", "<p id=\"Par1\">The fundamental therapeutic mechanism of allogeneic hematopoietic stem cell transplantation (HCT) in hematologic malignancies is the graft-versus-tumor reactivity mediated by donor T cells [##REF##11357147##1##, ##REF##19029455##2##]. While severe acute graft-versus-host disease (aGVHD) is a clinical situation to be avoided, patients affected tend to show fewer relapses [##REF##29795428##3##]. Even so, aGVHD is the most relevant complication of allogeneic HCT and the mortality of steroid-refractory grade III and IV aGVHD is unacceptably high. The response to therapy after first-line treatment with glucocorticoids is still limited despite the advantage of recently introduced ruxolitinib [##REF##32160294##4##, ##REF##32756949##5##].</p>", "<p id=\"Par2\">Previously, we and others have demonstrated that the pan-lymphoid CD52 antibody alemtuzumab is able to significantly improve steroid-refractory aGVHD [##REF##18158956##6##–##REF##20348971##8##]. However, this treatment usually causes complete lymphocyte depletion at least in the blood compartment [##REF##20348971##8##, ##REF##6438435##9##]. Severe immunosuppression may lead to infectious complications, particularly reactivation of cytomegalovirus. Furthermore, there are concerns that profound loss of immunosurveillance may facilitate an increased rate of relapse of the malignant disease that initially led to transplantation [##REF##3332120##10##]. For prophylactic alemtuzumab application during conditioning, even a dose dependency regarding relapse and infection rate was suggested [##REF##20587785##11##].</p>", "<p id=\"Par3\">In this single-center analysis, the role of immunosurveillance early after HCT was evaluated in 54 consecutive patients transplanted for various hematologic malignancies who received alemtuzumab between June 2005 and October 2018 for steroid-refractory aGVHD of grade III or IV. Steroid refractoriness of aGVHD was defined as progress after 7 days of high-dose intravenous methyl-prednisolone equivalent. Patients had been diagnosed with acute myelogenous leukemia, myelodysplastic syndrome, myeloproliferative neoplasm, chronic myelomonocytic leukemia, chronic myelogenous leukemia, acute lymphoblastic leukemia, B- or T-cell non-Hodgkins lymphomas or multiple myeloma (Supplementary Table ##SUPPL##0##S1## provides patient details). The median age at the time of HCT was 55 years (range of 13–68 years). Sixteen patients (30%) received transplants from matched related donors (MRD), 22 patients (41%) of HLA-identical (10/10) matched unrelated donors (MUD) and 16 patients (30%) received non-HLA-identical (<10/10) stem cells from mismatched unrelated donors (MMUD). Twelve patients (22%) received a myeloablative conditioning treatment, and 39 patients (72%) received a reduced intensity conditioning regimen. Three patients (6%) were treated with a non-myeloablative conditioning regimen. While 7 (13%) patients (all with MRD) received no T-cell depletion as part of the conditioning regimen, in 42 (78%) patients anti-thymocyte globuline (ATG Fresenius, later Neovii Biotech, Gräfelfing, Germany) and in 4 (7%) patients alemtuzumab (Sanofi-Aventis, Frankfurt, previously Bayer (Schering), Leverkusen, Germany) were added. In one patient, post-transplantation cyclophosphamide was applied when transplanted from an HLA-B MMUD. Standard GVHD prophylaxis consisted of CsA and mycophenolat mofetil. A single patient received GVHD prophylaxis with methotrexate and CsA.</p>", "<p id=\"Par4\">Treatment with alemtuzumab for steroid-refractory aGVHD asked for an absolute dose of 5–10 mg given on 1 or 2 days, followed by repeated doses alemtuzumab approximately every 14 days, usually for 6–8 weeks. As reported in detail [##REF##20348971##8##], this dosing schedule has been established after initially patients received higher doses. It became apparent that due to limited lymphatic tissue expressing the CD52 antigen in these immunosuppressed patients in the early post-transplantation phase this dosing was sufficient. Once alemtuzumab was initiated, concurrent systemic immunosuppressive therapy was reduced step-wise, generally to CsA or tacrolimus only.</p>", "<p id=\"Par5\">With extensive evaluation as required for gastrointestinal complications [##REF##34938988##12##], in 50 of 54 patients, aGVHD was histologically confirmed by gastrointestinal biopsy and in 2 patients by liver histology. The median time between HCT and the first application of alemtuzumab for steroid-refractory aGVHD grade III and IV was 50 days (range of 20–180). The median total dose of alemtuzumab given was 27.5 mg (range of 2–191 mg). Thirty-five patients (65%) showed a meaningful response that led to clinical improvement, allowing discharge from the hospital after a median time of 65 days (range 8–405 days) after the first alemtuzumab (Fig. ##FIG##0##1a##). Only two patients (6%) needed a re-admission related to their GVHD soon after becoming an outpatient. Continuous immunosuppression, usually with a calcineurin inhibitor, was necessary for at least a month in a half of the cohort (18 patients, 50%) after discharge from the hospital. During follow-up, only 17 patients (49%) of the 35 outpatients that improved developed later symptoms of chronic GVHD (cGVHD) requiring medication.</p>", "<p id=\"Par6\">Median follow-up time of surviving patients was 81 months. Fifteen patients (28%) were alive at the time of last follow-up. Median overall survival from the day of transplant for the whole patient cohort was 14 months (95% confidence interval (CI) 5.8–22.2) (Fig. ##FIG##0##1b##) or 12 months (95% CI 3.9–10.1) after start of alemtuzumab treatment (Fig. ##FIG##0##1c##). The probability of overall survival at 5 years after transplant was 34% (16 patients were still alive at this time point). Seventeen individuals died while still being in-patients due to their severe aGVHD due to non-relapse complications, mainly infections. Overall, 34 patients (65%) died because of non-relapse mortality (Fig. ##FIG##0##1d##). Only five patients relapsed (9%) (Fig. ##FIG##0##1e##). One patient with AML relapsed early and died 74 days after the first application of alemtuzumab given for steroid-refractory aGVHD. Four additional patients developed a late relapse, namely 17, 43, 71 and 143 months after the start of alemtuzumab.</p>", "<p id=\"Par7\">One known side effect of clinical importance is the reactivation of CMV after alemtuzumab application. Thirty-five patients (65%) had CMV status positive for the donor and/or recipient. Three of these patients developed histologically confirmed CMV-mediated colitis and one patient a CMV-pneumonia.</p>", "<p id=\"Par8\">To prevent severe aGVHD, many different strategies, such as graft selection and modifications, prophylactic immunosuppression as well as biologic markers for early detection and intervention, were developed. However, even with recently introduced drugs, particularly kinase-inhibitors such as ruxolitinib, in grade III and IV aGVHD the therapeutic effect is limited [##REF##32160294##4##]. Alemtuzumab has long been used as part of the conditioning regimen to reduce alloreactivity. We and others have applied alemtuzumab treatment to diminish the cellular immune reactivity when needed in aGVHD [##REF##18158956##6##–##REF##20348971##8##] or cGVHD since it leads to almost complete depletion of lymphocytes in the blood and may alter the remaining lymphocyte subset composition thereafter [##REF##20348971##8##, ##REF##6438435##9##, ##REF##23416855##13##]. Importantly, due to this intervention with alemtuzumab aGVHD could be reverted in approximately two thirds of the patients in the present study. Interestingly, in a significant number of patients, “re-booting” of the immune system apparently allowed them to regain appropriate immune functions without little or no signs of ongoing GVHD and required surprisingly limited ongoing immunosuppression.</p>", "<p id=\"Par9\">The use of alemtuzumab in aGVHD rapidly diminishes immune cells and immune activity. This approach may lead to virus reactivation and clinically requires particular attention in situations where the patient and/or donor is CMV positive. It could be argued that with such an approach early after transplantation due to lack of immunosurveillance relapse may become a problem. However, we saw few relapses even in patients with unfavorable prognostic factors and without significant subsequent cGVHD. This would suggest that intense aGVHD can help to eradicate remaining tumor cells fast and completely. Thus, these findings provide evidence that tumor responses seen in severe aGVHD may be so profound that even the deep immunosuppression by alemtuzumab therapy would not be deleterious for tumor control. Then a second attempt of a more compatible immunoreconstitution may be a second chance for some of these severely ill patients.</p>", "<title>Supplementary information</title>", "<p>\n\n</p>" ]	[ "<title>Supplementary information</title>", "<p>The online version contains supplementary material available at 10.1038/s41409-023-02144-8.</p>", "<title>Author contributions</title>", "<p>Study conception and design: MG, AG, RR, NS. Material preparation, data collection and analysis: LP, NS. Writing the first draft of the manuscript: LP, MG. Critical input and review of the manuscript: all authors. Approval of the final manuscript: all authors.</p>", "<title>Funding</title>", "<p>Open Access funding enabled and organized by Projekt DEAL.</p>", "<title>Data availability</title>", "<p>Data of this work are available from the corresponding author upon reasonable request.</p>", "<title>Competing interests</title>", "<p id=\"Par10\">The authors declare no competing interests.</p>" ]	[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>Clinical course and survival outcomes in patients (<italic>n</italic> = 54) treated by alemtuzumab for steroid-refractory grade III or IV aGVHD.</title><p><bold>a</bold> Percentage of patients that could be discharged from the hospital after start of alemtuzumab. <bold>b</bold> Kaplan–Meier survival curves are shown for overall survival from start of HCT or <bold>c</bold> from start of alemtuzumab. <bold>d</bold> Time-dependent description of patient events after HCT for non-relapse mortality (NRM) (<italic>N</italic> = 34) or <bold>e</bold> relapse (<italic>N</italic> = 5).</p></caption></fig>" ]	[]	[]	[]	[]	[]	[]	[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>" ]	[ "<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"41409_2023_2144_Fig1_HTML\" id=\"d32e282\"/>" ]	[ "<media xlink:href=\"41409_2023_2144_MOESM1_ESM.docx\"><caption><p>Table 1</p></caption></media>" ]	[]	{ "acronym": [], "definition": [] }	13	CC BY	no	2024-01-13 00:02:19	Bone Marrow Transplant. 2024 Nov 6; 59(1):153-155	oa_package/02/8d/PMC10781631.tar.gz
PMC10781632	38200298	[]	[]	[]	[]	[]	[ "<p id=\"Par1\">Asymmetric catalysis is an advanced area of chemical synthesis, but the handling of abundantly available, purely aliphatic hydrocarbons has proven to be challenging. Typically, heteroatoms or aromatic substructures are required in the substrates and reagents to facilitate an efficient interaction with the chiral catalyst. Confined acids have recently been introduced as tools for homogenous asymmetric catalysis, specifically to enable the processing of small unbiased substrates<sup>##UREF##0##1##</sup>. However, asymmetric reactions in which both substrate and product are purely aliphatic hydrocarbons have not previously been catalysed by such super strong and confined acids. We describe here an imidodiphosphorimidate-catalysed asymmetric Wagner–Meerwein shift of aliphatic alkenyl cycloalkanes to cycloalkenes with excellent regio- and enantioselectivity. Despite their long history and high relevance for chemical synthesis and biosynthesis, Wagner–Meerwein reactions utilizing purely aliphatic hydrocarbons, such as those originally reported by Wagner and Meerwein, had previously eluded asymmetric catalysis.</p>", "<p id=\"Par2\">We describe an imidodiphosphorimidate-catalysed asymmetric Wagner–Meerwein shift of aliphatic alkenyl cycloalkanes to cycloalkenes with excellent regio- and enantioselectivity.</p>", "<title>Subject terms</title>" ]	[ "<title>Main</title>", "<p id=\"Par3\">Since Bredig and Fiske<sup>##UREF##1##2##</sup> discovered a nonenzymatic, modestly enantioselective cinchona alkaloid-catalysed cyanohydrin synthesis in 1912<sup>##UREF##2##3##</sup>, asymmetric chemical catalysis has evolved extensively and currently encompasses reactions with transition metals<sup>##UREF##3##4##–##UREF##5##6##</sup>, enzymes<sup>##UREF##6##7##,##UREF##7##8##</sup> and organocatalysts<sup>##UREF##8##9##–##UREF##10##11##</sup>. However, the catalytic and enantioselective processing of purely aliphatic hydrocarbons is still extremely challenging. Typical substrates and reagents used in asymmetric catalysis feature heteroatoms or aromatic groups, which enhance reactivity and enable enantiodifferentiation by providing functional groups that engage in specific interactions with the catalyst. Indeed, to the best of our knowledge, Pfaltz’s landmark iridium-catalysed asymmetric hydrogenation of a purely alkyl-substituted olefin stands out as the only example in which an only-aliphatic hydrocarbon is catalytically processed to give an enantiopure only-aliphatic hydrocarbon<sup>##REF##16339409##12##–##REF##27548029##14##</sup>. We became interested in advancing hydrocarbon chemistry by investigating cationic shifts as a fundamental class of chemical and biochemical transformations. The most prominent example in this regard is the cationic Wagner–Meerwein rearrangement<sup>##UREF##11##15##,##UREF##12##16##</sup>. Catalytic enantioselective Wagner–Meerwein reactions that proceed through purely aliphatic hydrocarbon-based cations, such as those originally reported by Wagner and Meerwein, have not previously been described (Fig. ##FIG##0##1a##,##FIG##0##b##)<sup>##REF##23819438##17##–##UREF##13##21##</sup>.Notable examples of enantioselective cationic shifts were reported by Trost and Yasukata<sup>##REF##11459498##22##</sup>, Trost and Xie<sup>##REF##16669667##23##</sup> and Jacobsen and coworkers<sup>##REF##32845619##24##,##REF##37428959##25##</sup>, and feature π-allyl palladium intermediates to afford enantioenriched α-vinyl cycloketones (Fig. ##FIG##0##1c##) or a hypervalent iodine-catalysed, 1,3-difluorinative reaction of β-substituted styrenes (Fig. ##FIG##0##1d##).</p>", "<p id=\"Par4\">We have recently introduced strong and chiral confined imidodiphosphorimidate (IDPi) Brønsted acids as a new and general tool for asymmetric catalysis<sup>##UREF##14##26##,##UREF##15##27##</sup>. IDPi catalysts have activated unbiased olefins in hydroalkoxylations<sup>##REF##29599238##28##</sup>, hydroarylations<sup>##REF##33399449##29##</sup> and hydrolactonizations<sup>##REF##37043821##30##</sup> and even enabled control over purely hydrocarbon-based non-classical carbocations<sup>##REF##32989271##31##,##REF##36480623##32##</sup>. Encouraged by these results, we became tempted to challenge our IDPi motif with purely aliphatic hydrocarbon-based substrates toward highly enantioselective Wagner–Meerwein shifts and describe here the results of these investigations (Fig. ##FIG##0##1e##).</p>", "<p id=\"Par5\">At the onset of our studies, we subjected olefin <bold>1a</bold> to 5 mol% of various confined chiral Brønsted acid catalysts covering a broad p<italic>K</italic><sub>a</sub>(negative logarithm of the acid dissociation constant <italic>K</italic><sub>a</sub>) range at room temperature for 24 h to obtain ring-expanded cycloalkene <bold>3a</bold> (Fig. ##FIG##1##2##). As expected, weaker acids, such as imidodiphosphoric acid <bold>2a</bold>, failed to give any reactivity. Similarly, iminoimidodiphosphoric acid <bold>2b</bold> gave only poor conversion of substrate <bold>1a</bold> to furnish olefin isomerization product <bold>6a</bold>. By contrast, our highly acidic and confined IDPi catalyst <bold>2c</bold> gave product <bold>3a</bold> in moderate conversion and a high enantiomeric ratio of 96:4. A substantial amount of exoproduct <bold>4a</bold> is also formed in similar enantioselectivity and is slowly converted into the corresponding endoproduct <bold>3a</bold>, already suggesting that deprotonation cannot be enantiodetermining. We further observed trace amounts of product <bold>5a</bold> (less than 0.5%) in which a methyl group has migrated. Our highly acidic and more confined IDPi catalyst apparently suppresses olefin isomerization of the starting material to give hydrocarbon <bold>6a</bold> (less than 2%). Notably, a change in the sulfonamide inner core of the catalyst from pentafluorophenylsulfonyl to perfluoronaphthylsulfonyl (IDPi <bold>2d</bold>) delivered a drastic increase in reactivity and gave full conversion with excellent yield and enantioselectivity. We hypothesized that the high chemoselectivity, enantiocontrol and overall reactivity were enabled by the highly confined microenvironment offered by the IDPi catalysts. To visually assess this confinement effect, buried volumes were calculated using a model ion pair of the catalysts<sup>##UREF##16##33##,##REF##21082623##34##</sup> (Fig. ##FIG##1##2##). A higher percent buried volume, which corresponds to a narrower pocket, may indicate that the carbocation is stabilized within the IDPi anion pocket, possibly compensating for the absence of traditional heteroatom or resonance stabilization. Indeed, consistent with these expectations, the sulfonylimidophosphoryl groups not only modulate the overall acidity but also contribute to the confinement of the catalytic active site. Notably, when compared with IDPi <bold>2d</bold> (53% buried volume), the even more acidic IDPi <bold>2e</bold>, which provides a relatively larger pocket (45% buried volume), afforded product <bold>3a</bold> in only poor yield. At 46% consumption of substrate <bold>1a</bold>, only 31% yield of product <bold>3a</bold> was obtained, with moderate enantioselectivity along with more olefin isomerization product <bold>6a</bold>. These results suggest that a fine-tuned balance between acidity and confinement is crucial to achieve high chemoselectivity, enantiocontrol and overall reactivity. After a brief screening of catalysts and reaction conditions (Supplementary Table ##SUPPL##0##1##), we selected catalyst <bold>2d</bold>, CHCl<sub>3</sub> (4 M) and room temperature for 24 h and found that product <bold>3a</bold> can be obtained in both excellent yield and enantioselectivity (91%, 97:3 enantiomeric ratio).</p>", "<p id=\"Par6\">The substrate scope of the Wagner–Meerwein shift was assessed using a variety of alkenyl cycloalkanes that were readily converted into the corresponding ring-expanded cycloalkene products (Fig. ##FIG##2##3##). Linear and longer alkyl chains attached to the olefin gave products <bold>3a</bold>–<bold>c</bold> in both excellent yields and enantioselectivities. Shortening the alkyl chain to <italic>n</italic>-propyl (<bold>3d</bold>) retained the high enantioselectivity, but moderate enantioselectivity was obtained with an ethyl group (<bold>3e</bold>, 84:16 enantiomeric ratio). The incremental reduction in enantiomeric ratio (<bold>3b</bold> → <bold>3a</bold> → <bold>3c</bold> → <bold>3d</bold> → <bold>3e</bold>) is consistent with either simple steric repulsion or dispersive interactions of the <italic>n</italic>-alkyl groups contributing to the enantioselectivity. To our delight, branched alkyl substituents at the olefin exhibited high yields and excellent enantioselectivities (<bold>3f</bold>–<bold>h</bold>). A substrate bearing an alkene substituent was also tolerated, providing product <bold>3i</bold> with an enantiomeric ratio of 95.5:4.5 in 83% yield. <italic>O</italic>-benzyl and aryl group functionalities at the alkyl chain can be utilized successfully (<bold>3j</bold> and <bold>3k</bold>). We also investigated the analogous four- to five-membered ring expansion using IDPi <bold>2f</bold>, and both linear and branched alkyl substituents at the olefin gave the desired products in high yields and good to high enantioselectivities (<bold>3l</bold>–<bold>n</bold>). Apart from aliphatic hydrocarbons, aryl-substituted substrates also react. For example, five- to six-membered ring expansion with IDPi <bold>2g</bold> provided the corresponding products <bold>3o</bold>–<bold>r</bold> with different stereoelectronic properties in both high yields and enantioselectivities. Aryl-substituted substrates in the four- to five-membered ring expansion reacted with moderate yields and enantioselectivities (<bold>3s</bold> and <bold>3t</bold>). As expected, both the three- and six-membered ring substrates under the optimal condition are unreactive (Supplementary Fig. ##SUPPL##0##1##). To illustrate the synthetic utility of this asymmetric transformation, a short enantioselective total synthesis of (−)-herbertene<sup>##UREF##17##35##</sup> was developed. First, product <bold>3t</bold> was converted into cyclopropane <bold>10</bold> by a Simmons–Smith reaction in 74% yield, and hydrogenolysis delivered (−)-herbertene in 89% yield. X-ray diffraction analysis of osmate esters derived from ring expansion products <bold>3g</bold>, <bold>3n</bold> and <bold>3o</bold> allowed the unambiguous determination of the absolute configurations of these products<sup>##REF##31820648##36##</sup>. The stereochemistry of all other products has been assigned by analogy.</p>", "<p id=\"Par7\">To gain insight into the reaction mechanism, we synthesized enantiopure (greater than 99:1 enantiomeric ratio), <sup>13</sup>C-labelled exoproduct <bold>11</bold> and subjected it to the reaction conditions (Fig. ##FIG##3##4a##). Olefin isomerization of olefin <bold>11</bold> with IDPi <bold>2d</bold> gave endoproduct <bold>12</bold> in a greater than 99:1 enantiomeric ratio. Similarly, its enantiomer, ent-<bold>11</bold> (98.3:1.7 enantiomeric ratio), reacted at slightly slower rate (mismatched case) and gave product ent-<bold>12</bold> in 98.4:1.6 enantiomeric ratio (Supplementary Fig. ##SUPPL##0##2##). In both cases, we did not observe side products resulting from [1,2]-methyl or [1,2]-<italic>n</italic>-hexyl shifts. Although these results could be interpreted as implying irreversible ring expansion, our computational results indicate that this is not the case (see below). We also subjected the corresponding olefin isomer <bold>6a</bold> and alcohol <bold>13</bold> to the reaction conditions (Fig. ##FIG##3##4b##). In contrast to substrate <bold>1a</bold>, which afforded the desired product <bold>3a</bold> in excellent yield and enantioselectivity, both substrates <bold>6a</bold> and <bold>13</bold> gave product <bold>3a</bold> in only moderate yield and slightly lower enantioselectivity. In both cases, the substrate is less reactive, and an alternative pathway (for example, the reaction proceeding through protonated alcohol or a concerted pathway that is less enantioselective) might occur. Alternatively, the means by which the carbocation is generated may affect the subsequent rearrangement through a non-equilibrium process (see below). Furthermore, we investigated the reaction progression of <sup>13</sup>C-labelled substrate <bold>1a</bold>′ with IDPi <bold>2d</bold> by <sup>13</sup>C nuclear magnetic resonance (NMR) spectroscopy (Fig. ##FIG##3##4c##). <sup>13</sup>C NMR data acquired at the beginning of the reaction show the formation of endoproduct <bold>3a</bold>′ and exoproduct <bold>4a</bold>′ in almost equimolar amount. After approximately 2 h, product <bold>4a</bold>′ slowly converts into the thermodynamically favoured product <bold>3a</bold>′. Although the formation of product <bold>3a″</bold> through an [1,2]-<italic>n</italic>-hexyl group migration after the ring expansion was not detected, traces of both a methyl-migrated side product <bold>5a</bold>′ (less than 0.5%) and the olefin isomerization product <bold>6a</bold>′ (less than 2%) were detected. The consumption of starting material <bold>1a</bold>′ shows a characteristic first-order exponential decay, which has been observed for other intramolecular IDPi-catalysed reactions, for which only one substrate molecule is involved in the rate-limiting step of the reaction<sup>##REF##37043821##30##,##REF##30768254##37##</sup>. Additionally, the catalyst concentration remained constant, and no substantial chemical shift changes were observed during the reaction in the <sup>1</sup>H and <sup>13</sup>C NMR spectra. The reaction order of catalyst <bold>2d</bold> was determined with variable time normalization analysis<sup>##UREF##18##38##,##REF##30746083##39##</sup> from <sup>1</sup>H NMR concentration profiles and was found to be first order as well (##SUPPL##0##Supplementary Information## has further details). Reactions performed at five different temperatures ranging from 15 °C to 55 °C enabled us to determine the thermodynamic parameters of the reaction with the Eyring equation. An enthalpy of activation = 12.5 ± 0.5 kcal mol<sup>−1</sup>, a negative entropy of activation = −30.3 ± 1.6 kcal mol<sup>−1</sup> K<sup>−1</sup> and a free energy of activation <sub>298K</sub> = 21.5 ± 0.7 kcal mol<sup>−1</sup> were determined (Fig. ##FIG##3##4d##).</p>", "<p id=\"Par8\">On the basis of these experiments, we can propose a plausible reaction mechanism (Fig. ##FIG##3##4e##). Accordingly, the catalytic cycle is initiated with the protonation of olefin <bold>1a</bold>′ by IDPi <bold>2d</bold> to provide an alkyl-carbocation that is highly confined within the IDPi anion cavity as a contact ion pair (<bold>I</bold>), followed by an enantiodetermining five- to six-membered ring expansion, which affords ion pair <bold>II</bold>. We assume that the protonation of the substrate is the overall rate-limiting step of the reaction as covalent adducts have not been observed by <sup>31</sup>P NMR spectroscopy during the reaction monitoring. Additionally, the negative activation entropy is in the range of various reported literature values for protonation of olefins<sup>##REF##37043821##30##,##UREF##19##40##</sup>. Kinetic deprotonation gives an isolable exocyclic product <bold>4a</bold>′ that can reversibly be protonated to regenerate ion pair <bold>II</bold>. Finally, deprotonation from ion pair <bold>II</bold> furnishes the thermodynamically preferred endocyclic product <bold>3a</bold>′ and regenerates catalyst <bold>2d</bold>. Before the ring expansion, ion pair <bold>I</bold> could isomerize to furnish trisubstituted <bold>6a</bold>′ or convert to methyl-migrated ion pair <bold>III</bold>, which upon deprotonation, leads to side product <bold>5a</bold>′. Alternatively, ion pair <bold>II</bold> could undergo a [1,2]-<italic>n</italic>-hexyl shift to provide ion pair <bold>IV</bold>, which upon deprotonation, would lead to side product <bold>3a</bold>″. Similarly, ion pair <bold>II</bold> could undergo a [1,2]-methyl shift followed by deprotonation to provide the side products <bold>14a</bold>′ and <bold>15a</bold>′ (Supplementary Fig. ##SUPPL##0##3##). Although these hypothetical side reactions did not occur in the five- to six-membered ring expansion, in the corresponding four- to five-membered ring expansion, we did observe the [1,2]-methyl shift and [1,2]-<italic>n</italic>-hexyl shift side products.</p>", "<p id=\"Par9\">Density functional theory calculations support the proposed mechanism and lead to a model for the origin of enantioselectivity. Our calculations (CPCM(CHCl<sub>3</sub>)-ωB97X-D4/def2-TZVPP//CPCM(CHCl<sub>3</sub>)-B3LYP-D3(BJ)/def2-SVP) (##SUPPL##0##Supplementary Information## has details and references) on the conversion of substrate <bold>1c</bold> suggest that the rate-determining step is protonation of the olefin, which has a predicted free energy barrier of 17.2 kcal mol<sup>−1</sup> (Fig. ##FIG##3##4f##). The enantioselectivity-determining step is the subsequent five- to six-membered ring expansion, and the origin of enantioselectivity emerges out of a balance of non-covalent interactions within the confined chiral pocket, consistent with an induced-fit model<sup>##REF##35975151##41##,##REF##31984734##42##</sup>. Specifically, favourable dispersion interactions and C–H···O hydrogen bonds seem to play key roles (Fig. ##FIG##3##4g##; ##SUPPL##0##Supplementary Information## has additional details)<sup>##UREF##20##43##,##REF##23824256##44##</sup>. The lowest-energy transition state structures leading to <italic>R</italic> (TS<sub>GH</sub>, red path) and <italic>S</italic> (TS<sub>CD</sub>, blue path) products differ in free energy by 3.0 kcal mol<sup>−1</sup>, which is in reasonable agreement with the experimental enantiomeric ratio of 96:4 (which equates to a difference in free energy barriers ∆∆G<sup>‡</sup> of approximately 1.9 kcal mol<sup>−1</sup>) (##SUPPL##0##Supplementary Information## has further discussion and other levels of theory). Note that although the barrier for the favoured ring expansion step is very small, the overall barrier from predecessor conformer <bold>B</bold> is slightly larger. Although we cannot rule out the inhibition of conformational equilibration owing to the catalyst architecture or non-statistical dynamic effects here (the differences in enantioselectivity in Fig. ##FIG##3##4b## are at least consistent with such effects), we note that transition structures connected directly to carbocation conformer <bold>B</bold> lead to a prediction of the same sense of enantioselectivity (##SUPPL##0##Supplementary Information## has additional discussion). Ring expansion is followed by a facile deprotonation of the cation and energetically downhill dissociation to form the final product <bold>3c</bold>. These results are consistent with an overall mechanism of the IDPi-catalysed rearrangement consisting of the following steps: (1) rate-determining protonation; (2) subsequent enantioselectivity-determining ring expansion; and (3) facile deprotonation to afford the final product after catalyst–substrate dissociation—a mechanistic scenario not unlike that found for terpene synthase enzymes for which the difficult chemical step is carbocation generation and stereoselectivity is controlled by the shape of the carbocation binding site<sup>##REF##21541432##45##</sup>.</p>", "<p id=\"Par10\">The high-acidity and confined chiral microenvironment of our IDPi catalysts enables the handling of purely aliphatic hydrocarbons in catalytic asymmetric Wagner–Meerwein shifts that proceed through unstabilized ‘classical cations’. We believe that the presented approach bears great potential for related reactions of aliphatic hydrocarbons.</p>", "<title>Online content</title>", "<p id=\"Par11\">Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41586-023-06826-7.</p>", "<title>Supplementary information</title>", "<p>\n\n</p>" ]	[ "<title>Supplementary information</title>", "<p>The online version contains supplementary material available at 10.1038/s41586-023-06826-7.</p>", "<title>Acknowledgements</title>", "<p>Generous support was received from the Max Planck Society, the Deutsche Forschungsgemeinschaft (German Research Foundation), the Leibniz Award (to B.L.), Germany’s Excellence Strategy (Grant EXC 2033–390677874–RESOLV) and the European Research Council (Early-stage organocatalysis; to B.L.). We thank the technicians of our group and the members of our gas chromatography, mass spectrometry, high-performance liquid chromatography and nuclear magnetic resonance service departments. For acid dissociation constant measurements, we thank I. Leito, K. Kaupmees and M. Lõkov of Tartu University, Estonia as well as M. Lindner and H. van Thienen. H. Schucht, J. Rust and the entire X-ray crystallography department are acknowledged for X-ray crystallographic analysis. Computational work was supported by the US National Science Foundation (CHE-2154083 and XSEDE/ACCESS Programs). This work was also financially supported by the Institute for Chemical Reaction Design and Discovery, which was established by the World Premier International Research Initiative; the Ministry of Education, Culture, Sports, Science and Technology, Japan; the List Sustainable Digital Transformation Catalyst Collaboration Research Platform offered by Hokkaido University and by the Japan Society for the Promotion of Science Grants-in-Aid for Scientific Research (Grants 21H01925 and 22K14672).</p>", "<title>Author contributions</title>", "<p>B.L. designed and oversaw the project. V.N.W. developed and optimized the Wagner–Meerwein shift reaction. M.L. performed the kinetic studies using nuclear magnetic resonance spectroscopy. N.T. performed the computational studies on the percent buried volume. W.D., C.J.L. and D.J.T. performed the computational studies. V.N.W. and B.L. wrote the manuscript with contributions from all authors.</p>", "<title>Peer review</title>", "<title>Peer review information</title>", "<p id=\"Par12\"><italic>Nature</italic> thanks the anonymous reviewers for their contribution to the peer review of this work.</p>", "<title>Funding</title>", "<p>Open access funding provided by Max Planck Society.</p>", "<title>Data availability</title>", "<p>We declare that the experimental procedures and analytical data supporting the findings of this study are available in the article and ##SUPPL##0##Supplementary Information##. Raw and unprocessed nuclear magnetic resonance data are available from the corresponding author on reasonable request. Crystallographic data for compounds <bold>7</bold>, <bold>8</bold> and <bold>9</bold> are available free of charge from the Cambridge Crystallographic Data Centre under deposition numbers <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ccdc.cam.ac.uk/structures/Search?Ccdcid=2248971\">CCDC 2248971</ext-link>, <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ccdc.cam.ac.uk/structures/Search?Ccdcid=2248972\">CCDC 2248972</ext-link> and <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ccdc.cam.ac.uk/structures/Search?Ccdcid=2248970\">CCDC 2248970</ext-link>. All computed and reported structures in the article and ##SUPPL##0##Supplementary Information## can be found on ioChem-BD at 10.19061/iochem-bd-6-262.</p>", "<title>Competing interests</title>", "<p id=\"Par13\">A patent on the synthesis of imino-imidodiphosphates catalysts has been filed (patent no. WO 2017/037141 A1, EP 3 138 845 A1). Furthermore, a patent on an improved synthesis of imidodiphosphoryl-derived catalysts using hexachlorophosphazonium salts has been filed (patent no. EP 3 981 775 A1).</p>" ]	[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>Carbocation rearrangements.</title><p><bold>a</bold>, Borneol to camphene rearrangement (Wagner 1899)<sup>##UREF##21##46##</sup>. <bold>b</bold>, Camphene to isobornyl chloride rearrangement (Meerwein 1922)<sup>##UREF##22##47##,##UREF##23##48##</sup>. <bold>c</bold>, Palladium-catalysed semipinacol rearrangement/Wagner–Meerwein shift. <bold>d</bold>, Aryl iodide-catalysed enantioselective 1,3-difluorination. <bold>e</bold>, Catalytic asymmetric Wagner–Meerwein shifts of aliphatic hydrocarbons (this work). HX*, Brønsted acid.</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>Investigation of acidity, confinement and chiral pocket size of catalyst in the catalytic Wagner–Meerwein shift.</title><p>Yields and conversion were determined by <sup>1</sup>H NMR spectroscopy using 1,3,5-trimethoxybenzene as the internal standard. Side product distribution ratios were determined by crude <sup>1</sup>H NMR spectroscopy. The enantiomeric ratio was determined by gas chromatography analysis (##SUPPL##0##Supplementary Information## has details). <bold>a</bold>, Estimated p<italic>K</italic><sub>a</sub> values in CH<sub>3</sub>CN based on the literature report for a similar catalyst<sup>##UREF##15##27##,##REF##29988150##49##</sup>. <bold>b</bold>, Calculated steric map of simplified substrate visualized by SambVca 2.1 (ref. <sup>##REF##31477851##50##</sup>). The map is viewed from the centre of the substrate and directed toward the active site of each catalyst. The colour indicates depth along the <italic>z</italic> axis; the red zone is closer to the substrate, whereas the blue zone is farther away. For the ion pair of <bold>2b</bold>, one of the C–H bond lengths is fixed (Supplementary Fig. ##SUPPL##0##21##). conv., conversion; e.r., enantiomeric ratio; IDP, imidodiphosphoric acid; <italic>i</italic>IDP, iminoimidodiphosphoric acid; ND, not determined; <sup><italic>n</italic></sup>Hex, <italic>n</italic>-hexyl; <sup><italic>n</italic></sup>Pen, <italic>n</italic>-pentyl; 2-Np<sup>F</sup>, 2-perfluoronaphthyl; Ph<sup>F</sup>, pentafluorophenyl; p<italic>K</italic><sub>a</sub>, negative logarithm of the acid dissociation constant; RT, room temperature; <sup><italic>t</italic></sup>Bu, <italic>tert</italic>-butyl; %<italic>V</italic><sub>Bur</sub>, percent buried volume.</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><title>Scope of the catalytic asymmetric Wagner–Meerwein shift.</title><p>Reactions were performed at 0.25 mmol scale. Isolated yields after chromatographic purification. The enantiomeric ratio was determined by gas chromatography analysis (##SUPPL##0##Supplementary Information## has details). <sup>a</sup>At 50 °C for 24 h. <sup>b</sup>With catalyst <bold>2f</bold> in <italic>n-</italic>hexane at room temperature for 36 h. <sup>c</sup>With catalyst <bold>2g</bold> in methyl cyclohexane at 60 °C for 48 h. <sup>d</sup>With catalyst <bold>2g</bold> in methyl cyclohexane at 60 °C for 72 h. <sup>e</sup>With catalyst <bold>2g</bold> in methyl cyclohexane at 80 °C for 96 h. <sup>f</sup>With catalyst <bold>2g</bold> in methyl cyclohexane at 90 °C for 6 days. <sup>g</sup>With catalyst <bold>2d</bold> in methyl cyclohexane at 50 °C for 24 h. The structure of osmate ester <bold>7</bold> derived from product <bold>3g</bold>, H atoms and the disordered atoms are omitted for clarity. The structure of one of the two independent molecules of osmate ester <bold>8</bold> contained in the unit cell derived from product <bold>3n</bold> and H atoms are omitted for clarity. The structure of osmate ester <bold>9</bold> derived from product <bold>3o</bold>, H atoms, water molecule and the disordered atoms are omitted for clarity (Supplementary Figs. ##SUPPL##0##10–16##). Bn, benzyl; TFA, trifluoroacetic acid; TMEDA, <italic>N</italic>,<italic>N</italic>,<italic>N</italic>′,<italic>N</italic>′-tetramethylethylenediamine.</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><title>Mechanistic and computational studies.</title><p><bold>a</bold>, <sup>13</sup>C-labelled exocyclic olefin isomerization. <bold>b</bold>, Wagner–Meerwein shift with olefin isomers and corresponding alcohol. Yields and conversion were determined by <sup>1</sup>H NMR spectroscopy using 1,3,5-trimethoxybenzene as the internal standard. Olefin isomer <bold>6a</bold> contains 3% inseparable side product <bold>5a</bold> (##SUPPL##0##Supplementary Information## has details). <bold>c</bold>, Reaction of <sup>13</sup>C-labelled substrate <bold>1a</bold>′ catalysed by IDPi <bold>2d</bold>. Reaction profile monitored by <sup>13</sup>C NMR spectroscopy. <bold>d</bold>, Eyring plot obtained for the reaction of IDPi <bold>2d</bold> with substrate <bold>1a</bold>. <bold>e</bold>, A plausible catalytic cycle. <bold>f</bold>, Density functional theory-derived free energy profile (CPCM(CHCl<sub>3</sub>)-ωB97X-D4/def2-TZVPP/CPCM(CHCl<sub>3</sub>)-B3LYP-D3(BJ)/def2-SVP). The red path denotes the path to <italic>R</italic> enantiomer, and the blue path denotes the path to <italic>S</italic>. HX = <bold>2d</bold>. Structures B, C and G are different conformations. B is connected directly to TS<sub>AB</sub>, C is connected directly to TS<sub>CD</sub> and G is connected directly to TS<sub>GH</sub> (##SUPPL##0##Supplementary Information## has additional details on the conformational landscape of this complex system). <bold>g</bold>, Computed TS<sub>CD</sub> (major (<italic>S</italic>) enantiomer). Bond lengths shown are in angstrom (Å).</p></caption></fig>" ]	[]	[ "<inline-formula id=\"IEq1\"><alternatives><tex-math id=\"M1\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\triangle {H}^{\\ddagger }$$\\end{document}</tex-math><mml:math id=\"M2\"><mml:mrow><mml:mo>∆</mml:mo><mml:msup><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mrow><mml:mo>‡</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq2\"><alternatives><tex-math id=\"M3\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\triangle {S}^{\\ddagger }$$\\end{document}</tex-math><mml:math id=\"M4\"><mml:mrow><mml:mo>∆</mml:mo><mml:msup><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mo>‡</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq3\"><alternatives><tex-math id=\"M5\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\triangle {G}^{\\ddagger }$$\\end{document}</tex-math><mml:math id=\"M6\"><mml:mrow><mml:mo>∆</mml:mo><mml:msup><mml:mrow><mml:mi>G</mml:mi></mml:mrow><mml:mrow><mml:mo>‡</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:math></alternatives></inline-formula>" ]	[]	[]	[]	[]	[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>" ]	[ "<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"41586_2023_6826_Fig1_HTML\" id=\"d32e330\"/>", "<graphic xlink:href=\"41586_2023_6826_Fig2_HTML\" id=\"d32e546\"/>", "<graphic xlink:href=\"41586_2023_6826_Fig3_HTML\" id=\"d32e745\"/>", "<inline-graphic xlink:href=\"41586_2023_6826_Article_IEq1.gif\"/>", "<inline-graphic xlink:href=\"41586_2023_6826_Article_IEq2.gif\"/>", "<inline-graphic xlink:href=\"41586_2023_6826_Article_IEq3.gif\"/>", "<graphic xlink:href=\"41586_2023_6826_Fig4_HTML\" id=\"d32e1022\"/>" ]	[ "<media xlink:href=\"41586_2023_6826_MOESM1_ESM.pdf\"><label>Supplementary Information</label><caption><p>Supporting Information.</p></caption></media>" ]	[{"label": ["1."], "surname": ["Mitschke", "Turberg", "List"], "given-names": ["B", "M", "B"], "article-title": ["Confinement as a unifying element in selective catalysis"], "source": ["Chem"], "year": ["2020"], "volume": ["6"], "fpage": ["2515"], "lpage": ["2532"], "pub-id": ["10.1016/j.chempr.2020.09.007"]}, {"label": ["2."], "surname": ["Bredig", "Fiske"], "given-names": ["G", "PS"], "article-title": ["Durch Katalysatoren bewirkte asymmetrische Synthese"], "source": ["Biochem. 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PMC10781633	37898726	[ "<title>Introduction</title>", "<p id=\"Par2\">Many studies investigating the treatment of graft versus host disease (GvHD) use overall response per 2014 NIH consensus criteria as the primary efficacy endpoint [##UREF##0##1##]. The overall response rate (ORR) is defined as the proportion of patients who achieve a complete response (CR) or a partial response (PR). The overall response rate (ORR) may be reported at a fixed time point or derived from the best overall response (BOR) achieved at any time point during treatment.</p>", "<p id=\"Par3\">ORR at week 24 was used as primary endpoint in REACH3, a randomized phase 3 study comparing ruxolitinib versus best available therapy (BAT) for glucocorticoid-refractory chronic GvHD (cGvHD), and BOR at any time point up to week 24 was used as one of the secondary endpoints [##REF##34260836##2##]. Both ORR at week 24 and BOR were higher for ruxolitinib than in the control group (BAT). To investigate how long the response to treatment is maintained, the duration of response (DOR) was calculated for all patients who achieved BOR = CR or PR, results were also reported in Zeiser et al. [##REF##34260836##2##]. As DOR is computed for responders only, a formal statistical test to compare DOR between the two treatment arms was not performed since such a comparison would not be based on all randomized patients. Another measure of potential clinical interest is the time to the first response, which can be either calculated for responders only (e.g., all patients who achieved BOR = CR or PR) or for all randomized patients.</p>", "<p id=\"Par4\">In this paper, we illustrate the application of the so-called probability of being in response function (PBR function), an extension of Kaplan–Meier estimation which facilitates the simultaneous graphical representation of the time to first response and subsequent failure, i.e., combining time to first response, response rates, and DOR into one easily interpretable measure using all randomized patients. PBR was introduced by Temkin et al as a non-parametric method to estimate the response probability as a function of time [##REF##571291##3##]. Begg and Larson and Ellis et al suggested estimating the PBR from parametric models [##REF##7044436##4##, ##REF##18187370##5##].</p>" ]	[ "<title>Materials and methods</title>", "<title>REACH3 study and efficacy endpoints</title>", "<p id=\"Par5\">REACH3 was an open-label randomized controlled study investigating the efficacy and safety of ruxolitinib versus BAT in patients 12 years or older with glucocorticoid-refractory or dependent cGvHD. Overall, 329 patients were randomized; 165 and 164 patients were assigned to the ruxolitinib and BAT arm, respectively. All patients (or their guardian) provided informed consent. Response evaluation was performed according to the 2014 NIH consensus criteria, response assessments were made regularly (e.g., every 4 weeks up to week 24) as per study protocol [##UREF##0##1##]. ORR at week 24, defined as the proportion of patients who achieved a CR or a PR 24 weeks after randomization, was used as primary endpoint. Other efficacy endpoints included the BOR defined as proportion of patients who achieved overall response (CR or PR) at any time point up to and including week 24, the DOR which was derived for the subset of patients with BOR = CR or PR only and failure-free survival (FFS), defined as time from randomization to recurrence of underlying disease, start of new systemic treatment for cGvHD, or death, whichever came first. An overview of selected efficacy endpoints is given in supplementary material ##SUPPL##0##S1##. More details on the REACH3 study design and results can be found in Zeiser et al. [##REF##34260836##2##].</p>", "<title>Probability of being in response function</title>", "<p id=\"Par6\">The probability of being in response function (PBR function) suggested by Temkin can be derived from a multistate model (Fig. ##FIG##0##1##) [##REF##571291##3##]. As illustrated for seven hypothetical patients in Fig. ##FIG##1##2##, all patients are in state 0 (<italic>not in response</italic>) at baseline (=randomization date). A patient responding to treatment enters state 1 = <italic>in response</italic> at the time of the first documented response. Such patients may lose their response at a later time point and enter the <italic>absorbing state</italic>, state 2, (Pat-ID 2, 3, and 6) or remain <italic>in response</italic> at the time of the statistical analysis (Pat-ID 1), in which case they are censored in state 1. Patients who die or progress or start a new systemic cGvHD treatment or do not achieve response within 24 weeks switch from state 0 to state 2 (Pat-ID 4 and 5). For example, Pat-ID 5 might have died without response and Pat-ID 4 may not have achieved PR up to week 24. Any patient who neither reached state 1 nor state 2 (i.e., drop out before week 24 without response and without any of the events defined as state 2) would have been censored in state 0 (e.g., Pat-ID 7).</p>", "<p id=\"Par7\">Considering the day of randomization as baseline (time=0), the probability that a randomized patient is in response at a time point <italic>t</italic> can be obtained by scrolling over the time axis and assessing the state at that time point for each patient. Thus, the PBR can be calculated as a function of time by applying time-to-event methodology similar to the well-known Kaplan–Meier plot. While the Kaplan–Meier plot estimates one right-censored time-to-event variable (e.g., overall survival or duration of response) from a fixed time point (e.g., time of randomization or time of first response) for all patients, the PBR function aggregates two time-to-event variables, namely time from randomization to first response and time from first response to subsequent failure [##UREF##1##6##], more details and an illustration using the hypothetical data from Fig. ##FIG##1##2## are provided in supplementary material ##SUPPL##0##S2##. In order to compare PBR between treatment arms, we calculated the difference of PBR curves (ruxolitinib minus BAT) with pointwise 95% confidence intervals. All calculations were performed using R-4.1.0.</p>" ]	[ "<title>Results</title>", "<p id=\"Par8\">In REACH3, greater efficacy was observed on ruxolitinib versus BAT for most efficacy endpoints [##REF##34260836##2##]. Summary results of the efficacy parameters are displayed in Table ##TAB##0##1##, more detailed information on the outcome of the pre-planned study endpoints is published in Zeiser et al. [##REF##34260836##2##]. In addition, not reported previously, the median time to first response for all randomized patients (with non-responders censored) was 29 days (95% CI: 24 to 31 days) for ruxolitinib and 50 days (95% CI: 29 to 57 days) for BAT. A slightly earlier time to first response, a higher probability of being in response at all time points and a longer response duration for ruxolitinib than BAT is apparent from the PBR curves (Fig. ##FIG##2##3##). The higher clinical benefit of ruxolitinib is also visualized by a larger area under the curve.</p>", "<p id=\"Par9\">As per definition of PBR, the maximum of the curves is lower than the reported BOR (76.4% vs 60.4% for ruxolitinib and BAT, respectively) because PBR is estimated from the percentage of patients in response at the same point in time, whereas BOR is calculated from the best response, ignoring when this best response occurred and how long it was sustained. Calculating the cumulative number of patients with BOR = CR or PR up to week 24 reaches exactly the BOR rates at week 24 (dashed lines in Fig. ##FIG##2##3##). However, in contrast to PBR this naive cumulation ignores completely that patients may have lost their response before week 24 (e.g., Pat-ID 6 in Fig. ##FIG##1##2##) and is shown for comparative purpose only.</p>", "<p id=\"Par10\">The difference between PBR curves with confidence intervals, which provides a more formal comparison than the visual inspection of Fig. ##FIG##2##3##, clearly shows that superiority of ruxolitinib is achieved within very few weeks after randomization and is maintained over the entire study period (Fig. ##FIG##3##4##).</p>" ]	[ "<title>Discussion</title>", "<p id=\"Par11\">Although probability of being in response was considered as a useful method to assess response over time in the statistical literature during the last decades, very few applications can be found in clinical research. In one recent paper, Huang et al. used PBR (referred to as PBIR by Huang et al.) to compare different treatments for renal cell carcinoma [##REF##32628533##7##].</p>", "<p id=\"Par12\">In this post-hoc analysis, we applied PBR to the REACH3 study data to show the benefit of this method when assessing efficacy of cGvHD treatments. PBR provides easily interpretable curves presenting simultaneously the time from treatment start to first response and subsequent failure based on all randomized patients. Results obtained in REACH3 clearly illustrate the superiority of ruxolitinib versus BAT, further confirming the results reported in the original study publication [##REF##34260836##2##]. PBR offers a visual comparison of efficacy over time between treatment arms based on all patients and the entire study period. In contrast, DOR estimates time from first response and visualizes duration of response for the subgroup of patients who responded to treatment only, which can result in a biased assessment of treatment benefit (for example, if a higher percentage of patients reaches the response state in the experimental than in the control arm).</p>", "<p id=\"Par13\">The multistate model and the resulting estimated PBR function considered in this paper were defined in alignment with the definition of efficacy endpoints as pre-specified in the REACH3 study protocol. The design of this multistate model (PBR function), allowing transition from state 0 = <italic>not in response</italic> to state 1 = <italic>in response</italic> but NOT state 1 to state 0, as well as the definition of events for the end of response are based on exactly the same criteria as REACH3 efficacy endpoints.</p>", "<p id=\"Par14\">In future work or for other studies, one could extend the model, or alternatively define events differently than done here for REACH3. In particular, as determined in the study protocol, patients were counted as responders only if the first response occurred up to week 24. However, first response to treatment may occur after week 24, i.e., patients who did not respond up to week 24 do not necessarily need to enter the absorbing state 2 at week 24. Furthermore, loss of response was aligned with the definition of DOR, i.e., once a patient enters state 1 = <italic>in response</italic>, the patient can either stay in that state until the analysis cut-off date or can lose the “in response” status by entering the absorbing state 2, thus ending the duration of response. However, in diseases such as cGvHD, it may also be meaningful to extend the model by allowing transitions from “in response” (state 1) back to “not in response” (state 0) before entering the absorbing state 2. If for instance a patient achieves an overall response of PR by improvement of cGvHD symptoms in several organs at week 8, but subsequently one organ worsened at week 12 (with response maintained in the other organs) and improved again at week 16 without having changed systematic cGvHD treatment, it would be reasonable to assign state 0 <italic>= not in response</italic>, state 1 = <italic>in response</italic>, state 0 <italic>= not in response</italic> and state 1 = <italic>in response</italic> at study start, week 8, week 12 and week 16, respectively. PBR could be applied to such model extensions.</p>", "<p id=\"Par15\">One of the reasons why applications of PBR can hardly be found in the clinical literature may be the lack of statistical software to perform these analyses. Recently, Xiadong et al. provided the R-package PBIR which can be easily applied within the open-source software R [##UREF##2##8##]. Due to the special situation that cGvHD first response to treatment in REACH3 was counted up to week 24, we have generated our own R-codes and used PBIR R-package for validation purposes only (PBIR would have cut the curves at week 24).</p>", "<p id=\"Par16\">It would also be useful to have a formal statistical test for comparing the treatments with respect to PBR. We do not elaborate on this topic here for two reasons. Firstly, the difference of PBR curves including pointwise 95% confidence intervals (Fig. ##FIG##3##4##) allows a good visual comparison between treatment arms, and provides sufficient evidence that the difference between the curves is statistically significant. Secondly, to our knowledge, a statistical test providing a direct generalization of the usual log-rank test is not yet available for the situation described in this paper; its development is subject of forthcoming work. Considering a parametric estimation of PBR curves using the exponential distribution, Ellis et al. proposed a statistical test by comparing the area under the PBR curves (referred to as expected duration of response, EDoR) between treatment arms [##REF##18187370##5##]. Other alternatives (such as a log-rank test of time in response from entering the response state until leaving it for the absorbing state, potentially setting time in response to 0 for those patients who went from state 0 straight to state 2) are conceivable, but a thorough discussion of their properties, interpretational restrictions and precise relation with the PBR curve is beyond the scope of this paper.</p>", "<p id=\"Par17\">As illustrated with the data of REACH3 we strongly believe that PBR can serve as a meaningful efficacy endpoint for the assessment of cGvHD treatments, in addition to ORR/BOR and failure-free survival (FFS) which are recommended as endpoints for clinical trials by the NIH clinical design working group [##UREF##3##9##]. Compared to these established endpoints PBR provides a more comprehensive summary of treatment efficacy because it integrates several aspects of the treatment benefit (time-to-response, duration of response) into a single measure. Whereas time is not taken into account for ORR/BOR, FFS describes the time to treatment failure only but does neither assess if patients respond to treatment nor the time to response. Further clinical input would be required to include PBR into an updated cGvHD response guideline, also to ensure that consistent criteria are applied across future clinical trials. For example, a clear definition of response duration and end of response would be required. The current guidelines postulate to ‘<italic>document durability of response and to determine whether continued treatment is needed to maintain response</italic>’ and state that ‘<italic>Efforts to document the durability of response are strongly encouraged</italic>’, but do not provide clear definitions of response durability [##UREF##0##1##, ##UREF##3##9##]. Finally, PBR represents a useful endpoint measure which could be applied for all diseases and indications, for which clinical benefit is assessed by response to treatment in the context of time, demonstrating further utility outside of the cGvHD treatment landscape.</p>" ]	[]	[ "<p id=\"Par1\">Overall response rate (ORR) is commonly used as key endpoint to assess treatment efficacy of chronic graft versus host disease (cGvHD), either as ORR at week 24 or as best overall response rate (BOR) at any time point up to week 24 or beyond. Both endpoints as well as duration of response (DOR) were previously reported for the REACH3 study, a phase 3 open-label, randomized study comparing ruxolitinib (RUX) versus best available therapy (BAT). The comparison between RUX and BAT was performed on ORR and BOR using all randomized patients, while DOR was derived for the subgroup of responders only. Here we illustrate the application of the probability of being in response (PBR), a graphical method presenting simultaneously the time to first response and subsequent failure using all randomized patients. In REACH3, PBR showed an earlier time to first response, a higher probability of being in response and a longer duration of response for RUX compared to BAT. PBR is a clinically easily interpretable measurement and can serve as a novel efficacy endpoint to assess treatments for chronic graft versus host disease.</p>", "<title>Subject terms</title>" ]	[ "<title>Supplementary information</title>", "<p>\n\n</p>" ]	[ "<title>Supplementary information</title>", "<p>The online version contains supplementary material available at 10.1038/s41409-023-02128-8.</p>", "<title>Acknowledgements</title>", "<p>This study was sponsored by Novartis Pharma AG. The authors would like to thank the patients, their families and investigators and staff at participating study sites. We are also grateful to one reviewer whose comments motivated the generation of Supplementary Material ##SUPPL##0##S2##. Editorial assistance was provided by Soracha Ward, PhD of Novartis Ireland, in accordance with Good Publication Practice (GPP3) guidelines (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ismpp.org/gpp3\">http://www.ismpp.org/gpp3</ext-link>). These data were presented in part at the American Society of Hematology Congress, New Orleans, 10–13 December 2022. These data are reproduced in part from Hollaender N, Glimm E, Gauvin J, Stefanelli T, and Zeiser R. The Probability of Being in Response (PBR): A Novel Efficacy Endpoint for Chronic Graft Versus Host Disease (GvHD) Applied to the Reach-3 Study of Ruxolitinib Versus BAT, Blood (2022) 140 (supplement 1): 10521–10522 with permission from Elsevier.</p>", "<title>Author contributions</title>", "<p>All authors made substantial contributions to the conception and design or analysis and interpretation of the data, and the drafting and critical review of the manuscript. All authors approved the final version of the manuscript for publication.</p>", "<title>Data availability</title>", "<p>Individual data sharing to third parties will not be possible. Access to aggregated data might be granted following review. Such requests can be submitted to the corresponding author for consideration.</p>", "<title>Competing interests</title>", "<p id=\"Par18\">NH and TS are current employees of Novartis. EG and JG hold equity in and are current employees of Novartis. RZ received honoraria from and is on the speaker’s bureau for Incyte, Mallinckrodt, and Novartis, and has consulted for Incyte and Novartis.</p>" ]	[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>Multistate model for the response status.</title><p>Illustration of multistate model for response status. Absorbing events were either death, start of new systemic cGvHD therapy, underlying malignancy relapse, not having achieved a response up to week 24 (non-responders only) or cGvHD progression (responders only). *Patient cannot achieve a response anymore after entering State 2.</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>Illustration of response states over time and the concept of the PBR.</title><p>Graphical illustration of response states overtime for seven hypothetical patients. 0, State 0, not in response; 1, State 1, in response; 2, State 2, absorbing; Pat-ID, patient ID.</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><title>Probability of being in response (PBR) function of ruxolitinib versus BAT in REACH3.</title><p>Comparison of PBR for ruxolitinib (blue) versus BAT (orange) in REACH3. Solid lines show PBR curves, dotted lines show cumulative number of patients with PR/CR up to week 24.</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><title>Difference of PBR with pointwise 95% confidence interval (difference refer to ruxolitinib − BAT).</title><p>Difference in PBR (black line) with 95% CI (blue lines) between ruxolitinib and BAT.</p></caption></fig>" ]	[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Summary of efficacy results in REACH3.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th>Endpoint</th><th>Population</th><th/><th>Rux</th><th>BAT</th></tr></thead><tbody><tr><td>ORR at Cycle 7 Day 1</td><td>All patients</td><td><italic>n</italic> (%)</td><td>82 (49.7)</td><td>42 (25.6)</td></tr><tr><td rowspan=\"4\">Failure-free survival (FFS)</td><td rowspan=\"4\">All patients</td><td>Probability (95% CI) at</td><td/><td/></tr><tr><td>month 3 from randomization</td><td>83.6 (77.1, 88.5)</td><td>71.1 (63.3, 77.5)</td></tr><tr><td>month 6 from randomization</td><td>74.9 (67.5, 80.9)</td><td>44.5 (36.5, 55.1)</td></tr><tr><td>month 12 from randomization</td><td>64.0 (55.8, 71.1)</td><td>29.6 (22.3, 37.2)</td></tr><tr><td>Best overall response (BOR)</td><td>All patients</td><td><italic>n</italic> (%)</td><td>126 (76.4)</td><td>99 (60.4)</td></tr><tr><td rowspan=\"3\">Duration of response (DOR)</td><td rowspan=\"3\">Responders</td><td>Probability (95% CI) at</td><td/><td/></tr><tr><td>month 6 from first response</td><td>76.6 (67.5, 83.2)</td><td>52.1 (41.8, 61.5)</td></tr><tr><td>month 12 from first response</td><td>68.5 (58.9, 76.3)</td><td>40.3 (30.3, 50.5)</td></tr><tr><td rowspan=\"3\">Time to first response (TTFR)</td><td/><td>Days since randomization</td><td/><td/></tr><tr><td>(a) Responders</td><td>median (range)</td><td>20 (13, 170)</td><td>28 (13, 171)</td></tr><tr><td>(b) All patients</td><td>median (95% CI)</td><td>29 (24, 31)</td><td>50 (29, 57)</td></tr><tr><td rowspan=\"4\">Probability of being in response function (PBR)</td><td rowspan=\"4\">All patients</td><td>Probability (95% CI) at</td><td/><td/></tr><tr><td>month 3 from randomization</td><td>67.9 (56.0, 79.8)</td><td>47.6 (38.0, 57.2)</td></tr><tr><td>month 6 from randomization</td><td>62.0 (51.4, 72.7)</td><td>35.4 (28.2, 42.6)</td></tr><tr><td>month 12 from randomization</td><td>53.3 (44.0, 62.6)</td><td>26.0 (20.6, 31.5)</td></tr></tbody></table></table-wrap>" ]	[]	[]	[]	[]	[]	[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>" ]	[ "<table-wrap-foot><p>All patients include all randomized subjects (<italic>N</italic> = 165 for ruxolitinib, <italic>N</italic> = 164 for BAT). Responders include all patients with BOR = CR or PR (<italic>N</italic> = 126 for ruxolitinib, <italic>N</italic> = 99 for BAT).</p><p><italic>BAT</italic> best available therapy, <italic>BOR</italic> best overall response, <italic>CI</italic> confidence interval, <italic>DOR</italic> duration of response, <italic>PBR</italic> probability of being in response function, <italic>TTFR</italic> time to first response.</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"41409_2023_2128_Fig1_HTML\" id=\"d32e267\"/>", "<graphic xlink:href=\"41409_2023_2128_Fig2_HTML\" id=\"d32e275\"/>", "<graphic xlink:href=\"41409_2023_2128_Fig3_HTML\" id=\"d32e508\"/>", "<graphic xlink:href=\"41409_2023_2128_Fig4_HTML\" id=\"d32e532\"/>" ]	[ "<media xlink:href=\"41409_2023_2128_MOESM1_ESM.docx\"><caption><p>Supplementary Material</p></caption></media>" ]	[{"label": ["1."], "surname": ["Lee", "Wolff", "Kitko", "Koreth", "Inamoto", "Jagasia"], "given-names": ["SJ", "D", "C", "J", "Y", "M"], "article-title": ["Measuring therapeutic response in chronic graft-versus-host disease. National Institutes of Health consensus development project on criteria for clinical trials in chronic graft-versus-host disease: IV. The 2014 Response Criteria Working Group report"], "source": ["Biol Blood Marrow Transpl"], "year": ["2015"], "volume": ["21"], "fpage": ["984"], "lpage": ["99"], "pub-id": ["10.1016/j.bbmt.2015.02.025"]}, {"label": ["6."], "surname": ["Tsai", "Luo", "Crowley", "Matsui", "Crowley"], "given-names": ["WY", "X", "J", "S", "J"], "article-title": ["The probability of being in response function and its applications"], "source": ["Frontiers of biostatistical methods and applications in clinical oncology"], "year": ["2017"], "publisher-loc": ["Singapore"], "publisher-name": ["Springer Singapore"], "fpage": ["151"], "lpage": ["64"]}, {"label": ["8."], "mixed-citation": ["Xiaodong L, Huang B, Tian L. PBIR: estimating the probability of being in response and related outcomes. R package version 0.1-0 (2020) 2020. Available from: "], "ext-link": ["https://CRAN.R-project.org/package=PBIR"]}, {"label": ["9."], "surname": ["Martin", "Lee", "Przepiorka", "Horowitz", "Koreth", "Vogelsang"], "given-names": ["PJ", "SJ", "D", "MM", "J", "GB"], "article-title": ["National Institutes of Health consensus development project on criteria for clinical trials in chronic graft-versus-host disease: VI. The 2014 Clinical Trial Design Working Group Report"], "source": ["Biol Blood Marrow Transpl"], "year": ["2015"], "volume": ["21"], "fpage": ["1343"], "lpage": ["59"], "pub-id": ["10.1016/j.bbmt.2015.05.004"]}]	{ "acronym": [], "definition": [] }	9	CC BY	no	2024-01-13 00:02:19	Bone Marrow Transplant. 2024 Oct 28; 59(1):12-16	oa_package/4f/54/PMC10781633.tar.gz
PMC10781634	37865719	[ "<title>Introduction</title>", "<p id=\"Par2\">Post-transplant lymphoproliferative disease (PTLD) is an aggressive and potentially fatal hematologic malignancy that can occur following transplantation due to immunosuppression. Nearly all PTLD cases following hematopoietic stem cell transplant (HCT) are Epstein–Barr virus (EBV)-positive (EBV<sup>+</sup>) and occur as a result of EBV activation in EBV-negative patients who receive a transplant from EBV<sup>+</sup> donors or due to EBV reactivation in previously infected patients following transplantation [##REF##29414277##1##, ##REF##22570658##2##].</p>", "<p id=\"Par3\">EBV<sup>+</sup> PTLD is an ultra-rare disease, with an incidence of 1.1–1.7% within the first year after allogeneic HCT [##REF##30828868##3##, ##REF##23442063##4##]. In the USA, there were about 8200 HCTs in 2021, thus resulting in fewer than 150 new PTLD cases per year, and in Europe there were 19,806 HCTs, thus resulting in approximately 275 new cases [##UREF##0##5##, ##UREF##1##6##]. The median time to PTLD from HCT is about 2–4 months, with the majority of cases occurring within the first year following transplant, corresponding to recovery of the immune system [##REF##30828868##3##, ##REF##23771985##7##, ##REF##24056821##8##]. The most frequently and consistently identified risk factors for developing EBV<sup>+</sup> PTLD are prior HCT, post-transplant EBV DNAemia, T-cell depletion ex vivo or in vivo, histocompatibility or EBV serology mismatch between the donor and the recipient, and the use of cord blood [##REF##24056821##8##–##REF##34607072##16##].</p>", "<p id=\"Par4\">Clinical practice treatment guidelines recommend rituximab with or without reduction in immunosuppression (RIS) as pre-emptive therapy for EBV reactivation (based on EBV viral load) and for treatment of EBV<sup>+</sup> PTLD following HCT [##REF##27365460##17##]. Patients who fail rituximab have poor outcomes with limited treatment options. Although results vary according to protocol, up to 50% of patients with EBV<sup>+</sup> PTLD post-HCT may experience failure to rituximab-containing treatment [##REF##30828868##3##, ##REF##34607072##16##]. Factors associated with a poor response to rituximab include acute graft-versus-host disease (GvHD) with immunosuppressive drugs, extranodal involvement, the inability to tolerate RIS, and the use of bone marrow graft [##REF##23771985##7##, ##REF##24212561##18##]. The 3-year overall survival (OS) for allogeneic HCT recipients with EBV<sup>+</sup> PTLD treated with rituximab-containing therapies ranges from 20% to 48% [##REF##24056821##8##, ##REF##32024048##19##, ##REF##21057556##20##], and patients with multiple risk factors experience the worst OS rates [##REF##23771985##7##].</p>", "<p id=\"Par5\">Alternative treatment options for patients with EBV<sup>+</sup> PTLD post-HCT after failure of initial therapy represent a significant unmet clinical need. Guidelines for subsequent treatment options in patients with relapsed or refractory (R/R) EBV<sup>+</sup> PTLD post-HCT are based on a limited body of evidence [##REF##27365460##17##], and outcomes following rituximab ± chemotherapy failure are usually poor, with a reported median OS of 33 days [##REF##24212561##18##]. Further, chemotherapy is usually ineffective, with a high treatment-related mortality rate in patients with R/R EBV<sup>+</sup> PTLD post-HCT [##REF##23771985##7##, ##REF##24212561##18##], which limits treatment options following failure of rituximab. Little information is available regarding the clinical characteristics, treatment patterns, and survival of patients with R/R EBV<sup>+</sup> PTLD following HCT in a real-world setting. Collation of such data may help inform future treatment decisions and guide how physicians manage these patients in the absence of well-defined, global treatment guidelines.</p>", "<p id=\"Par6\">To address the knowledge gap, we conducted a retrospective chart review at multiple stem cell transplant centers to describe the clinical characteristics and survival of HCT recipients with R/R EBV<sup>+</sup> PTLD following rituximab ± chemotherapy failure.</p>" ]	[ "<title>Methods</title>", "<title>Study design and conduct</title>", "<p id=\"Par7\">A multicenter, non-interventional, retrospective chart review of allogeneic HCT recipients with R/R EBV<sup>+</sup> PTLD following rituximab ± chemotherapy failure was performed. The study was approved by an independent ethics committee, research ethics board, or institutional review board at each center and complied with the Declaration of Helsinki, the International Council for Harmonisation Tripartite Guideline for Good Clinical Practice, and local laws.</p>", "<title>Selection of the study population</title>", "<p id=\"Par8\">A total of 22 sites in Europe (Austria, Belgium, Germany, France, Italy, Spain, and Sweden) and North America (Canada and the USA) contributed data to the study.</p>", "<p id=\"Par9\">Inclusion and exclusion criteria were aligned with the multicenter, open-label, phase III ALLELE trial assessing tabelecleucel in patients with R/R EBV<sup>+</sup> PTLD following rituximab ± chemotherapy [##UREF##3##21##]. Eligible patients were HCT recipients who were diagnosed with R/R EBV<sup>+</sup> PTLD following rituximab ± chemotherapy failure, of any age, and with data records available. PTLD was locally assessed using confirmatory histology or high EBV viremia with clinical and/or radiologic assessment via computed tomography or positron emission tomography. Patients were excluded if they had received cytotoxic T-lymphocytes (CTL), donor lymphocyte infusion (DLI), or had specific PTLD histology of Burkitt, Hodgkin, or T-cell lymphoma.</p>", "<p id=\"Par10\">Existing chart data on patients diagnosed with EBV<sup>+</sup> PTLD following HCT who received rituximab or rituximab plus chemotherapy between January 2000 and December 2018 and in whom disease was refractory (failed to achieve complete response or partial response) or had relapsed at any point after such therapy were collected. A comprehensive data collection form was developed to capture the heterogeneity of the disease, and electronic case report forms (eCRFs) were developed and utilized through a secured website for study site personnel to submit information. The conduct of the study was standardized, and rigorous procedures to ensure accuracy were followed throughout the data collection process. Data management procedures were implemented following good clinical practice guidelines and included validation and skip patterns to minimize data entry errors, development of guidelines for completion of the eCRFs, and extensive training of study site personnel.</p>", "<p id=\"Par11\">The collected data were entered into a validated database. The data were reviewed manually by trained personnel to ensure data quality, and any data issues identified were addressed through queries and communicated to the sites for resolution. An extensive effort was undertaken to ensure data quality with multiple rounds of medical review to reach resolution. Data management procedures were implemented following Good Clinical Practice guidelines.</p>", "<title>Patient characteristics and outcomes</title>", "<p id=\"Par12\">Demographic information, HCT characteristics, PTLD characteristics, treatment history, and OS data were evaluated. Demographic information included patients’ age (years) and sex (male/female). HCT characteristics included age at HCT, initial diagnosis leading to HCT, the type of allograft used, the stem cell source, and the conditioning regimen used. PTLD characteristics included the time from transplant to PTLD, pre-emptive use of rituximab for PTLD, PTLD histology type, PTLD stage, extranodal sites of PTLD, CD20 marker, Eastern Cooperative Oncology Group (ECOG)/Karnofsky/Lansky score, and the incidence of secondary central nervous system involvement. OS was defined as the time from the index date to the date of death from any cause. OS was assessed using the date of failure to rituximab-containing therapy as the index date, unless otherwise stated. Patients who were lost to follow-up or still alive were censored at the last reported contact or recorded visit date. Cause of death was reported as recorded by the physicians in the case report form.</p>", "<title>Statistical analyses</title>", "<p id=\"Par13\">All continuous variables were summarized using descriptive statistics and all categorical variables were summarized using frequencies and percentages. OS was summarized using the Kaplan–Meier method. Association between several important clinical and demographic variables and mortality was evaluated using Cox proportional hazards multivariate regression analysis. These variables included age (years) at initial PTLD diagnosis, sex, time from transplantation to PTLD diagnosis (days), baseline lactate dehydrogenase (LDH), stage at initial PTLD diagnosis, ECOG performance status, PTLD histology at initial diagnosis, extranodal PTLD sites, pre-emptive use of rituximab for EBV viremia, and response to initial rituximab-containing treatment.</p>" ]	[ "<title>Results</title>", "<title>Patient demographics and disease characteristics</title>", "<p id=\"Par14\">Medical chart data from 81 patients with R/R EBV<sup>+</sup> PTLD following rituximab ± chemotherapy failure were analyzed.</p>", "<p id=\"Par15\">Of the 81 included patients, 37 (45.7%) underwent HCT between 2000 and 2010; 44 (54.3%) patients underwent HCT after 2010 (Table ##TAB##0##1##). The median (minimum–maximum) age at HCT was 48.7 (2–75) years. The most common primary disease leading to HCT was acute myeloid leukemia (32.1%), followed by acute lymphoblastic leukemia (16.0%) and myelodysplastic syndromes (8.6%). Conditioning regimens used prior to HCT included myeloablative conditioning (59.3%) and reduced intensity conditioning (37.0%). Patients received transplants from human leukocyte antigen (HLA)-matched unrelated donors (40.7%), mismatched unrelated donors (33.3%), or matched related donors (12.3%). Stem cells were obtained from peripheral blood mononuclear cells (53.1%), cord blood (25.9%), or bone marrow (11.1%). At the time of HCT, 53 (65.4%) patients were in remission from their primary disease and 26 (32.1%) patients had relapsed disease (data not shown). A total of 17 (21.0%) patients received anti-thymocyte globulin.</p>", "<p id=\"Par16\">Patient PTLD disease characteristics are described in Table ##TAB##1##2##. EBV<sup>+</sup> viremia was detected in the majority (95.1%) of patients at a median time from HCT of 1.9 months. Seventeen (22.0%) patients were treated pre-emptively with rituximab to prevent PTLD. The median time from HCT to initial PTLD diagnosis was 3.0 months and median age at initial PTLD diagnosis was 49.0 years. Most (74.1%) patients had a baseline LDH of ≥250 U/L. PTLD was diagnosed at an advanced stage (III or IV) in 63 (77.8%) patients. The most common histologic subtype was monomorphic PTLD, which was observed in 52 (64.2%) patients; 18 (22.2%) patients presented with polymorphic PTLD. The most common sites of PTLD involvement were the lymph nodes (62 patients [76.5%]), liver (29 patients [35.8%]), spleen (23 patients [28.4%]), lung (17 patients [21.0%]), and gastrointestinal tract (14 patients [17.3%]) (data not shown). Overall, PTLD involved extranodal sites in 69.1% of patients. CD20 positivity was observed in 52 of 67 patients with available data.</p>", "<title>Treatments for PTLD</title>", "<p id=\"Par17\">The median time (minimum–maximum) from PTLD diagnosis to initial treatment was 0.1 (0.0–3.1) months. After diagnosis of PTLD, RIS was reported for 54 (66.7%) patients. Sixty-eight (84%) patients received rituximab alone and 13 (16.0%) received rituximab combined with chemotherapy as their initial treatment for PTLD. Of the 68 patients who received rituximab alone, the median (minimum–maximum) number of doses was 2 (1–9). Thirty-six (44.4%) patients received next-line therapy, with a chemotherapy-containing regimen being most common (32/36). Only four (11.1%) patients who received next-line therapy achieved a durable response of >6 months from the treatment end date; two of these patients subsequently relapsed.</p>", "<title>Overall survival</title>", "<p id=\"Par18\">At the time of chart review, 74 (91.4%) patients had died (Table ##TAB##2##3##). The most common cause of death was PTLD (56.8%), followed by GvHD (13.5%) and treatment-related mortality (10.8%).</p>", "<p id=\"Par19\">From the date of R/R to rituximab-containing therapy, median (range) follow-up was 0.7 (0.03–107.1) months with a median OS (95% confidence interval [CI]) of 0.7 (0.3–1.0) months. OS (95% CI) at 12 months was 14.7% (8.0–23.3) (Table ##TAB##3##4##, Fig. ##FIG##0##1##). In patients who received next-line therapy, median (minimum–maximum) follow-up was 2.0 (0.1–107.1) months with a median OS (95% CI) of 2.0 (1.1–5.5) months from the start date of the next line.</p>", "<title>Risk factors associated with mortality</title>", "<p id=\"Par20\">A multivariate analysis using the Cox proportional hazards ratio regression model was conducted to determine if key baseline characteristics were associated with mortality (Table ##TAB##4##5##). Early PTLD onset (defined as ≤100 days after HCT; hazard ratio (HR) [95% CI]: 2.33 [1.25–4.37]) and a best overall response of stable or progressive disease (i.e., non-responders) following initial therapy (HR [95% CI]: 3.74 [1.81–7.70]) were significantly associated with mortality.</p>" ]	[ "<title>Discussion</title>", "<p id=\"Par21\">Medical literature describing clinical outcomes in patients with R/R EBV<sup>+</sup> PTLD is limited; available data reporting the experience of a few patients who receive a subsequent treatment after rituximab indicate very poor outcomes [##UREF##4##22##, ##REF##18836488##23##]. This retrospective chart review is the first to describe the survival of HCT recipients with EBV<sup>+</sup> PTLD following rituximab ± chemotherapy failure. We observed that patients with R/R EBV<sup>+</sup> PTLD post-HCT experience poor survival, with a median OS of 0.7 months from the time of initial treatment failure, and only 14.7% of patients surviving at 12 months, with the majority dying because of PTLD-related mortality (56.8%) and treatment-related mortality (10.8%), while patients who received next-line therapy had a median OS of 2.0 months from the initiation of the next line, thus demonstrating an urgent unmet medical need in this patient population. These data can be used as a benchmark for future interventional studies in this disease setting.</p>", "<p id=\"Par22\">Given such poor outcomes, we sought to identify factors associated with mortality in patients with R/R EBV<sup>+</sup> PTLD. Identification of such factors may help delineate high-risk patients and ultimately improve early detection and treatment options for patients with R/R EBV<sup>+</sup> PTLD. Our multivariate analysis evaluated whether age at initial PTLD diagnosis, sex, region, baseline LDH, stage at initial PTLD diagnosis, PTLD histology at initial diagnosis, time from HCT procedure to initial PTLD diagnosis, extranodal PTLD sites, pre-emptive use of rituximab for EBV viremia, response to initial rituximab-containing treatment, number of systemic treatments, receipt of next-line therapy and ECOG/Karnofsky/Lansky score were associated with survival in patients with R/R EBV<sup>+</sup> PTLD. In this multivariate analysis, two factors were significantly associated with mortality: early PTLD onset (≤100 days after HCT) and the lack of response to initial therapy. To our knowledge, this is the first time early PTLD onset and a lack of response to initial therapy has been associated with an elevated risk of mortality. There was a suggestion of an association between elevated baseline LDH (≥250 U/L) and mortality also observed, which is unsurprising as previous analyses in patients with PTLD following HCT or solid organ transplant have reported that elevated LDH was associated with a lack of response to initial treatment and reduced OS [##REF##23442063##4##].</p>", "<p id=\"Par23\">Our study is the first to assess patients with R/R EBV<sup>+</sup> PTLD in the HCT setting, albeit using a retrospective study design. Limitations associated with retrospective observational studies are that they may be difficult to establish causality and they may also be subject to certain biases. However, in the setting of a rare disease requiring urgent care, a prospective cohort design is likely to be impractical. We focused on OS as the outcome of interest, given that it can be assessed accurately in a real-world setting, whereas other outcomes such as response rate have limitations in real-world settings, such as the lack of standardized modalities for evaluating response to treatment, temporal changes in treatment and technology, variable evaluation frequencies, and variability in physician practice. Patients who received DLI or EBV- or multivirus-specific CTL therapy after PTLD diagnosis (an option available for several years) or those with a history of Burkitt, Hodgkin, or T-cell lymphoma were excluded in our study protocol in order to align with the phase III ALLELE trial; thus, our results are only representative of patients with PTLD for whom such therapy is not available. Given these considerations, our study provides significant insights for this high unmet need population.</p>", "<p id=\"Par24\">A key strength of this study is that it is the largest and most comprehensive multinational chart review of patients with R/R EBV<sup>+</sup> PTLD following failure of rituximab-containing therapy. A further strength is that careful thought was given to the identification of important prognostic factors and to minimization of missing data. Our study evaluated charts recorded between 2000 and 2018, during which time no novel therapies were approved that may have impacted the study findings. Data were not available after this time period.</p>", "<p id=\"Par25\">Our study confirmed the lack of adequate treatment options that target the underlying pathology of PTLD. As there were no therapies approved for the management of PTLD from 2000 to 2018, rituximab-containing therapy became an established treatment option, although not all patients respond. Survival outcomes are worse for patients without a response to rituximab. HCT patients may also be frail and require a subsequent therapy that has a tolerable safety profile after failure of rituximab. Lack of a standard of care may contribute to differences in treatment patterns, which further limits comparability and generalizability between small studies and hinders research advances urgently needed by this subset of patients. The outcomes associated with the use of rituximab ± chemotherapy for patients with R/R EBV<sup>+</sup> PTLD post-HCT described in this retrospective chart review underline the unmet need for new treatment options that are safe and effective in this patient group.</p>", "<p id=\"Par26\">In summary, this retrospective chart review has demonstrated that patients with R/R EBV<sup>+</sup> PTLD have limited therapy options, resulting in poor outcomes. Our analysis confirms the high unmet medical need in such patients post-HCT in whom EBV<sup>+</sup> PTLD relapses or becomes refractory to initial rituximab-containing treatment.</p>" ]	[]	[ "<p id=\"Par1\">Epstein–Barr virus-positive (EBV<sup>+</sup>) post-transplant lymphoproliferative disease (PTLD) is an ultra-rare and aggressive condition that may occur following allogeneic hematopoietic cell transplant (HCT) due to immunosuppression. Approximately half of EBV<sup>+</sup> PTLD cases are relapsed or refractory (R/R) to initial rituximab-containing therapy. There are limited treatment options and no standard of care for patients with R/R EBV<sup>+</sup> PTLD, and little is known about their treatment history and outcomes. We performed a multinational, multicenter, retrospective chart review of patients with R/R EBV<sup>+</sup> PTLD following HCT to describe patients’ demographic and disease characteristics, treatment history, and overall survival (OS) from rituximab failure. Among 81 patients who received initial treatment with rituximab as monotherapy (84.0%) or in combination with chemotherapy (16.0%), median time from HCT to PTLD diagnosis was 3.0 months and median OS was 0.7 months. Thirty-six patients received a subsequent line of treatment. The most frequent causes of death were PTLD (56.8%), graft-versus-host disease (13.5%) and treatment-related mortality (10.8%). In multivariate analysis, early PTLD onset and lack of response to initial treatment were associated with mortality. This real-world study demonstrates that the prognosis of patients with R/R EBV<sup>+</sup> PTLD following HCT remains poor, highlighting the urgent unmet medical need in this population.</p>", "<title>Subject terms</title>" ]	[]	[ "<title>Acknowledgements</title>", "<p>The authors gratefully acknowledge Benedetto Bruno, Federica Cavallo, Paul Chauvet, Sylvain Choquet, Vikas Dharnidharka, Daan Dierickx, Ulrich Jaeger, Charles Herbaux, Howard Huang, Periana Minga, Pietro Merli, Anthea Peters, Loretta Nastoupil, Josea Pérez Simón, John Reitan, Guillermo Rodríguez, Montserrat Rovira, Ahmed Sawas, Francesca Sismondi, Erin Sundaram, Ralph Ulrich Trappe, Jamie Wenke, and Heiner Zimmermann for their assistance with manuscript development. This study was funded by Atara Biotherapeutics. Medical writing assistance was provided by Folabomi Oladosu PhD, and Lee Blackburn MSc, from AMICULUM USA, funded by Atara Biotherapeutics.</p>", "<title>Author contributions</title>", "<p>GC, PB, AB, JSC, IGC, PC, FF, NG, DK, PL, AP, NS, LYSS, MS, JS, DT, BX and MM contributed to data interpretation and the writing, reviewing, and amending of the manuscript; the first draft was prepared by the academic authors and a medical writer funded by Atara. GC, PB, AB, JSC, IGC, PC, FF, NG, DK, PL, AP, NS, LYSS, MS, JS, DT, BX and MM made the decision to submit the manuscript for publication and vouch for the accuracy and completeness of the data and for the fidelity of the study to the protocol.</p>", "<title>Data availability</title>", "<p>Aggregate data analyses generated during this study are included in this published article. Patient-level data are owned by individual sites, but due to the rare nature of EBV<sup>+</sup> PTLD, will not be shared to ensure patient confidentiality.</p>", "<title>Competing interests</title>", "<p id=\"Par27\">PB has received compensation as a member of a scientific advisory boards for Allogene, Amgen, B.M.S., Kite/Gilead, Incyte, Novartis and Pierre Fabre, and consulting fees from Jazz Pharmaceuticals, Milteny Biomed., Nektar and Novartis. DK has received research grants from Atara Biotherapeutics and GSK, and consulting fees from GSK, Merck, Takeda, Roche and Allovir. LYSS has received compensation for consulting/advisory roles for Abbvie, AstraZeneca, Beigene, BMS and Janssen in the past 2 years. AB, NG and BX are employees and shareholders of Atara Biotherapeutics. NS and DT were employees and shareholders of Atara Biotherapeutics at the time of the study. The other authors declare no competing interests.</p>" ]	[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>Kaplan–Meier plot of OS from date of R/R to rituximab ± chemotherapy.</title><p>OS is from the R/R date to the end of follow-up. <italic>OS</italic> Overall survival, <italic>R/R</italic> Relapsed or refractory.</p></caption></fig>" ]	[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>HCT characteristics.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th>Characteristics</th><th>R/R to rituximab ± chemotherapy<break/>(<italic>N</italic> = 81)</th></tr></thead><tbody><tr><td>Sex, <italic>n</italic> (%)</td><td/></tr><tr><td> Male</td><td>49 (60.5)</td></tr><tr><td> Female</td><td>32 (39.5)</td></tr><tr><td>Year of HCT, <italic>n</italic> (%)</td><td/></tr><tr><td> 2000–2010</td><td>37 (45.7)</td></tr><tr><td> 2010–2018</td><td>44 (54.3)</td></tr><tr><td>Age at HCT, years</td><td/></tr><tr><td> Median (minimum–maximum)</td><td>48.7 (2–75)</td></tr><tr><td>Initial diagnosis leading to HCT, <italic>n</italic> (%)</td><td/></tr><tr><td> Acute myeloid leukemia</td><td>26 (32.1)</td></tr><tr><td> Myelodysplastic syndromes</td><td>7 (8.6)</td></tr><tr><td> Acute lymphocytic leukemia</td><td>13 (16.0)</td></tr><tr><td> Non-Hodgkin lymphoma</td><td>4 (4.9)</td></tr><tr><td> Aplastic anemia</td><td>5 (6.2)</td></tr><tr><td> Chronic lymphocytic leukemia</td><td>4 (4.9)</td></tr><tr><td> Chronic myeloid leukemia</td><td>4 (4.9)</td></tr><tr><td> Multiple myeloma</td><td>1 (1.2)</td></tr><tr><td> Other</td><td>16 (19.8)</td></tr><tr><td> Missing</td><td>1 (1.2)</td></tr><tr><td>Type of allograft, <italic>n</italic> (%)</td><td/></tr><tr><td> Matched related donor</td><td>10 (12.3)</td></tr><tr><td> Matched unrelated donor</td><td>33 (40.7)</td></tr><tr><td> Haploidentical</td><td>5 (6.2)</td></tr><tr><td> Mismatched related donor</td><td>3 (3.7)</td></tr><tr><td> Mismatched unrelated donor</td><td>27 (33.3)</td></tr><tr><td> Unknown</td><td>2 (2.5)</td></tr><tr><td>Stem cell source, <italic>n</italic> (%)</td><td/></tr><tr><td> PBMCs</td><td>43 (53.1)</td></tr><tr><td> Cord blood</td><td>21 (25.9)</td></tr><tr><td> Bone marrow</td><td>9 (11.1)</td></tr><tr><td> Unknown</td><td>7 (8.6)</td></tr><tr><td>Conditioning regimen used, <italic>n</italic> (%)</td><td/></tr><tr><td> Reduced intensity conditioning</td><td>30 (37.0)</td></tr><tr><td> Myeloablative conditioning</td><td>48 (59.3)</td></tr><tr><td> Unknown</td><td>2 (2.5)</td></tr><tr><td>Anti-T-cell antibody treatment received, <italic>n</italic> (%)</td><td/></tr><tr><td> Yes</td><td>17 (21.0)</td></tr><tr><td> No</td><td>64 (79.0)</td></tr><tr><td>Type of anti-T-cell antibody treatment received, <italic>n</italic> (%)</td><td/></tr><tr><td> Anti-thymocyte globulin</td><td>17 (100.0)</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>PTLD disease characteristics.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th>Characteristics</th><th>R/R to rituximab ± chemotherapy (<italic>N</italic> = 81)</th></tr></thead><tbody><tr><td>Time from HCT to EBV viremia, months</td><td/></tr><tr><td> Median (minimum–maximum)</td><td>1.9 (0.0–102.6)</td></tr><tr><td>Pre-emptive use of rituximab for PTLD, <italic>n</italic> (%)</td><td/></tr><tr><td> Yes</td><td>17 (22.1)</td></tr><tr><td> No</td><td>60 (77.9)</td></tr><tr><td>Age at initial PTLD diagnosis, years</td><td/></tr><tr><td> Median (minimum–maximum)</td><td>49.0 (2–75)</td></tr><tr><td>Baseline ECOG performance score (only for subjects ≥ 16 years old),<sup>a</sup>\n<italic>n</italic> (%)</td><td/></tr><tr><td> <2</td><td>8 (22.9)</td></tr><tr><td> ≥2</td><td>27 (77.1)</td></tr><tr><td>Baseline elevated LDH,<sup>b</sup>\n<italic>n</italic> (%)</td><td>60 (74.1)</td></tr><tr><td>Time from HCT to PTLD, months</td><td/></tr><tr><td> Median (minimum–maximum)</td><td>3.0 (0.8–100.8)</td></tr><tr><td>PTLD histology type, <italic>n</italic> (%)</td><td/></tr><tr><td> Early lesions</td><td>2 (2.5)</td></tr><tr><td> Polymorphic</td><td>18 (22.2)</td></tr><tr><td> Monomorphic</td><td>52 (64.2)</td></tr><tr><td> DLBCL</td><td>46 (56.8)</td></tr><tr><td> Unknown</td><td>9 (11.1)</td></tr><tr><td>PTLD stage, <italic>n</italic> (%)</td><td/></tr><tr><td> Stage I/II</td><td>8 (9.8)</td></tr><tr><td> Stage III</td><td>17 (21.0)</td></tr><tr><td> Stage IV</td><td>46 (56.8)</td></tr><tr><td> Unknown</td><td>10 (12.3)</td></tr><tr><td>Extranodal sites of PTLD, <italic>n</italic> (%)</td><td/></tr><tr><td> Yes</td><td>56 (69.1)</td></tr><tr><td> No</td><td>24 (29.6)</td></tr><tr><td> Unknown</td><td>1 (1.2)</td></tr><tr><td>CD20 marker at diagnosis, <italic>n</italic> (%)</td><td/></tr><tr><td> Positive</td><td>52 (64.2)</td></tr><tr><td> Negative</td><td>15 (18.5)</td></tr><tr><td> Unknown</td><td>14 (17.3)</td></tr><tr><td>Secondary CNS involvement, <italic>n</italic> (%)</td><td>7 (8.6)</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab3\"><label>Table 3</label><caption><p>Treatment-related mortality.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th/><th>R/R to rituximab ± chemotherapy<break/>(<italic>N</italic> = 81)<break/><italic>n</italic> (%)</th></tr></thead><tbody><tr><td>Total deaths</td><td>74 (91.4)</td></tr><tr><td>Cause of death</td><td/></tr><tr><td> PTLD</td><td>42 (56.8)</td></tr><tr><td> GvHD</td><td>10 (13.5)</td></tr><tr><td> Treatment-related mortality</td><td>8 (10.8)</td></tr><tr><td> Sepsis infection</td><td>5 (6.8)</td></tr><tr><td> Relapsed primary disease leading to HCT</td><td>3 (4.1)</td></tr><tr><td> Organ rejection/failure</td><td>3 (4.1)</td></tr><tr><td> Unknown</td><td>2 (2.7)</td></tr><tr><td> Graft failure</td><td>1 (1.4)</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab4\"><label>Table 4</label><caption><p>Overall survival.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th/><th>R/R to rituximab ± chemotherapy<break/>(<italic>N</italic> = 81)</th></tr></thead><tbody><tr><td>Median follow-up, months (minimum–maximum)</td><td>0.7 (0.03–107.1)</td></tr><tr><td>Median OS,<sup>a</sup> months (95% CI)</td><td>0.7 (0.3–1.0)</td></tr><tr><td>OS rate,<sup>a</sup> % (95% CI)</td><td/></tr><tr><td> 3 months</td><td>22.2 (13.9–31.8)</td></tr><tr><td> 6 months</td><td>16.0 (9.1–24.8)</td></tr><tr><td> 12 months</td><td>14.7 (8.0–23.3)</td></tr><tr><td> 24 months</td><td>9.4 (4.2–17.0)</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab5\"><label>Table 5</label><caption><p>Multivariate analysis of potential factors associated with mortality.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th/><th>R/R to rituximab ± chemotherapy, <italic>N</italic></th><th>HR (95% CI)</th><th><italic>p-</italic>value</th></tr></thead><tbody><tr><td>Age at initial PTLD diagnosis</td><td/><td/><td/></tr><tr><td> <60 years (low risk)</td><td>69</td><td>ref</td><td/></tr><tr><td> ≥60 years (high risk)</td><td>12</td><td>1.22 (0.59–2.51)</td><td>0.5943</td></tr><tr><td>Sex</td><td/><td/><td/></tr><tr><td> Male</td><td>49</td><td>ref</td><td/></tr><tr><td> Female</td><td>32</td><td>1.10 (0.61–1.99)</td><td>0.7566</td></tr><tr><td colspan=\"2\">Elevated baseline LDH (≥250 U/L)</td><td/><td/></tr><tr><td> No</td><td>11</td><td>ref</td><td/></tr><tr><td> Yes</td><td>60</td><td>2.51 (0.93–6.82)</td><td>0.0706</td></tr><tr><td> Missing</td><td>10</td><td>2.56 (0.75–8.76)</td><td>0.1329</td></tr><tr><td>Region</td><td/><td/><td/></tr><tr><td> North America</td><td>24</td><td>ref</td><td/></tr><tr><td> Europe</td><td>57</td><td>0.99 (0.45–2.21)</td><td>0.9852</td></tr><tr><td colspan=\"4\">PTLD stage at initial diagnosis</td></tr><tr><td> Stage 1 or 2</td><td>8</td><td>ref</td><td/></tr><tr><td> Stage 3 or 4</td><td>63</td><td>0.86 (0.34–2.19)</td><td>0.7563</td></tr><tr><td> Missing</td><td>10</td><td>0.69 (0.21–2.26)</td><td>0.5414</td></tr><tr><td colspan=\"2\">PTLD histology at initial diagnosis</td><td/><td/></tr><tr><td> All other types</td><td>29</td><td>ref</td><td/></tr><tr><td> Monomorphic</td><td>52</td><td>0.72 (0.42–1.23)</td><td>0.2322</td></tr><tr><td>Time from HCT procedure to initial PTLD diagnosis</td><td>81</td><td>0.99 (0.96–1.02)</td><td>0.5952</td></tr><tr><td>PTLD onset<sup>a</sup></td><td/><td/><td/></tr><tr><td> Late</td><td>37</td><td>ref</td><td/></tr><tr><td> Early</td><td>44</td><td>2.33 (1.25–4.37)</td><td>0.0081</td></tr><tr><td>Extranodal sites of PTLD</td><td/><td/><td/></tr><tr><td> No or unknown</td><td>25</td><td>ref</td><td/></tr><tr><td> Yes</td><td>56</td><td>1.00 (0.52–1.92)</td><td>0.9986</td></tr><tr><td colspan=\"2\">Pre-emptive use of rituximab for PTLD</td><td/><td/></tr><tr><td> No or unknown</td><td>64</td><td>ref</td><td/></tr><tr><td> Yes</td><td>17</td><td>0.85 (0.41–1.75)</td><td>0.6551</td></tr><tr><td>Response to initial therapy<sup>b</sup></td><td/><td/><td/></tr><tr><td> Responders</td><td>15</td><td>ref</td><td/></tr><tr><td> Non-responders</td><td>66</td><td>3.74 (1.81–7.70)</td><td>0.0004</td></tr><tr><td>Total number of systemic treatments</td><td/><td/><td/></tr><tr><td> 1</td><td>43</td><td>ref</td><td/></tr><tr><td> 2</td><td>29</td><td>0.41 (0.07–2.55)</td><td>0.3409</td></tr><tr><td> 3</td><td>9</td><td>0.36 (0.05–2.75)</td><td>0.3237</td></tr><tr><td>Received next line of therapy</td><td/><td/><td/></tr><tr><td> No</td><td>45</td><td>ref</td><td/></tr><tr><td> Yes</td><td>36</td><td>0.53 (0.09–3.18)</td><td>0.4832</td></tr><tr><td colspan=\"4\">ECOG/Karnofsky/Lansky score</td></tr><tr><td> <2/≥70/≥70 (low risk)</td><td>13</td><td/><td/></tr><tr><td> ≥2/<70/<70 (high risk)</td><td>34</td><td>1.57 (0.70–3.51)</td><td>0.2755</td></tr><tr><td> Missing</td><td>34</td><td>0.72 (0.31–1.70)</td><td>0.4519</td></tr></tbody></table></table-wrap>" ]	[]	[]	[]	[]	[]	[]	[ "<table-wrap-foot><p><italic>HCT</italic> Hematopoietic stem cell transplant, <italic>PBMC</italic> Peripheral blood mononuclear cell, <italic>R/R</italic> Relapsed or refractory.</p></table-wrap-foot>", "<table-wrap-foot><p><italic>CNS</italic> Central nervous system, <italic>DLBCL</italic> Diffuse large B-cell lymphoma, <italic>EBV</italic> Epstein–Barr virus, <italic>ECOG</italic> Eastern Cooperative Oncology Group, <italic>HCT</italic> Hematopoietic stem cell transplant, <italic>LDH</italic> Lactate dehydrogenase, <italic>PTLD</italic> Post-transplant lymphoproliferative disease, <italic>R/R</italic> Relapsed or refractory.</p><p><sup>a</sup>Data reported in 35 patients.</p><p><sup>b</sup>LDH levels ≥250 U/L were considered elevated.</p></table-wrap-foot>", "<table-wrap-foot><p><italic>GvHD</italic> Graft-versus-host disease, <italic>HCT</italic> Hematopoietic stem cell transplant, <italic>PTLD</italic> Post-transplant lymphoproliferative disease, <italic>R/R</italic> Relapsed or refractory.</p></table-wrap-foot>", "<table-wrap-foot><p><italic>CI</italic> Confidence interval, <italic>OS</italic> Overall survival, <italic>R/R</italic> Relapsed/refractory.</p><p><sup>a</sup>From the time of rituximab ± chemotherapy failure leading to R/R Epstein–Barr virus-positive post-transplant lymphoproliferative disease following hematopoietic stem cell transplant.</p></table-wrap-foot>", "<table-wrap-foot><p><italic>CI</italic> Confidence interval, <italic>ECOG</italic> Eastern Cooperative Oncology Group, <italic>HCT</italic> Hematopoietic stem cell transplant, <italic>HR</italic> Hazard ratio, <italic>LDH</italic> Lactate dehydrogenase, <italic>PTLD</italic> Post-transplant lymphoproliferative disease, <italic>ref</italic> Reference, <italic>R/R</italic> Relapsed or refractory.</p><p><sup>a</sup>Early PTLD onset is defined as ≤100 days after HCT, whereas late PTLD onset is defined as >100 days after HCT.</p><p><sup>b</sup>Responders were patients who achieved a complete or partial response to initial therapy. Non-responders were patients who had stable disease or progressive disease following initial therapy.</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"41409_2023_2127_Fig1_HTML\" id=\"d32e1297\"/>" ]	[]	[{"label": ["5."], "mixed-citation": ["Passweg JR, Baldomero H, Ciceri F, Corbacioglu S, de la C\u00e1mara R, Dolstra H, et al. Hematopoietic cell transplantation and cellular therapies in Europe 2021. The second year of the SARS-CoV-2 pandemic. A report from the EBMT Activity Survey. Bone Marrow Transplant. 2023;58:647\u201358."]}, {"label": ["6."], "mixed-citation": ["Bolon YT, Atshan R, Allbee-Johnson M, Estrada-Merly N, Lee SJ. Current use and outcome of hematopoietic stem cell transplantation: CIBMTR US summary slides. 2022. "], "ext-link": ["https://cibmtr.org/CIBMTR/Resources/Summary-Slides-Reports"]}, {"label": ["12."], "surname": ["Kalra", "Roessner", "Jupp", "Williamson", "Tellier", "Chaudhry"], "given-names": ["A", "C", "J", "T", "R", "A"], "article-title": ["Risk factors for post-transplant lymphoproliferative disorder after thymoglobulin-conditioned hematopoietic cell transplantation"], "source": ["Clin Transplant"], "year": ["2018"], "volume": ["32"], "fpage": ["e13150"], "pub-id": ["10.1111/ctr.13150"]}, {"label": ["21."], "mixed-citation": ["ClinicalTrials.gov. NCT03394365. Tabelecleucel for solid organ or allogeneic hematopoietic cell transplant participants with Epstein-Barr virus-associated post-transplant lymphoproliferative disease (EBV+ PTLD) after failure of rituximab or rituximab and chemotherapy. 2017. "], "ext-link": ["https://ClinicalTrials.gov/show/NCT03394365"]}, {"label": ["22."], "surname": ["Xu", "Forsythe", "Barlev", "Rashid", "Watson"], "given-names": ["H", "A", "A", "N", "C"], "article-title": ["A systematic literature review of real-world evidence in post-transplant lymphoproliferative disorder"], "source": ["Blood."], "year": ["2018"], "volume": ["132"], "fpage": ["5839"], "pub-id": ["10.1182/blood-2018-99-113583"]}]	{ "acronym": [], "definition": [] }	23	CC BY	no	2024-01-13 00:02:19	Bone Marrow Transplant. 2024 Oct 21; 59(1):52-58	oa_package/a2/4c/PMC10781634.tar.gz
PMC10781635	38177689	[]	[ "<title>Methods</title>", "<title>Road transect studies</title>", "<p id=\"Par32\">We collated published results from road transect studies conducted in Burkina Faso, Niger and Mali (West Africa)<sup>##UREF##29##40##</sup>, Kenya (East Africa)<sup>##UREF##31##42##,##UREF##58##82##</sup> and northern Botswana (southern Africa)<sup>##UREF##14##20##,##UREF##32##43##</sup>, together with published and unpublished survey results from northern Cameroon (Central Africa)<sup>##UREF##30##41##</sup> (R.B. and B.M.C, unpublished data). These studies covered a combined survey distance of 94,151 km (Supplementary Table ##SUPPL##0##1##), yielding 53,209 sightings of the 42 study species. In each study, routes were surveyed during an ‘early’ and ‘recent’ period, separated by an interval of ca. 20–40 yr. For each raptor species in each study and survey period, we calculated an average encounter rate (individuals seen per 100 km) separately for routes lying within PAs and UPAs (Extended Data Fig. ##FIG##5##1##). Protected areas were defined by the authors of the original studies, who excluded site categories affording little or no meaningful protection for wildlife, or where the degree of protection provided was uncertain (##SUPPL##0##Supplementary Information##: Survey routes and protected areas). In the absence of historical digital maps, contemporary PA boundaries<sup>##UREF##12##17##</sup> were used when estimating land areas during early and recent survey periods. Where insufficient detail had been provided, PA types were confirmed subsequently by the lead author of the study in question (Supplementary Table ##SUPPL##0##4##). To minimize chance effects, we restricted our analyses to species for which at least 20 individuals had been recorded in a given study area during the early survey period, with at least five sightings each in PAs and UPAs. Potential effects of excluding cases with smaller sample sizes are considered in ##SUPPL##0##Supplementary Information##: Case selection.</p>", "<title>Estimating change in encounter rates</title>", "<p id=\"Par33\">We used the following protocol to estimate each species’ annual rate of change within a given study area. First, we averaged its encounter rates within PAs and UPAs separately during the early (E) and the recent period (R). We weighted each average by the extent of land within PAs and UPAs within the species’ range in the study area in question, extracted from the African Raptor Databank<sup>##UREF##59##83##</sup> (Extended Data Fig. ##FIG##6##2##). Since not all of the selected PAs had been surveyed within a given study area, we estimated each species’ overall encounter rate under two scenarios, in which the average encounter rate within unsurveyed PAs was assumed to have either been (1) the same as in surveyed PAs or (2) the same as in UPAs. These scenarios respectively yielded a high and low estimate of the species’ average encounter rate in each study area and survey period, and hence produced four estimates of change (<italic>C</italic>) between the two periods. These corresponded to E1→R1, E1→R2, E2→R1 and E2→R2. We converted these to annual rates of change using the formula AC = −(1 − (1 + <italic>C</italic>)ˆ(1/<italic>t</italic>)), where ‘AC’ is the annual rate of change, ‘<italic>C</italic>’ is the overall change between the two periods (replaced by each of the four change estimates in turn), and ‘<italic>t</italic>’ is the time (in years) separating the midpoints of the two survey periods. This provided four estimates of the annual rate of change of each species in each study.</p>", "<p id=\"Par34\">Fifteen species had been surveyed adequately in just a single study area. For these, we calculated a median annual rate of change from the four estimates. For each of the remaining 27 species, surveyed in multiple studies, we calculated a median annual rate of change by combining one of the <italic>n</italic> change estimates in turn from each of the relevant studies (Extended Data Fig. ##FIG##6##2##). Importantly, we weighted each change estimate in accordance with the species’ range size in the study area in question so that extreme changes within a relatively small area (for example, northern Cameroon) did not disproportionately influence the median value. Thus, for species surveyed in two, three or four studies, we calculated a weighted median annual rate of change (AR) from 16, 64 or 256 permutations, respectively. We projected this value (plus quartiles) over three generation lengths (GLs) (ref. <sup>##REF##32058610##51##</sup>, R. Martin, personal communication, 2021; Supplementary Table ##SUPPL##0##2##) using the formula −(1 − (1 + AR)ˆ(3 × GL)).</p>", "<p id=\"Par35\">In the approach described above, we extrapolated mean encounter rates from surveyed PAs and UPAs to unsurveyed PAs, on the assumption that encounter rates within the latter were likely to be similar to those recorded on surveyed land. To test the effects of these extrapolations, we also estimated rates of change when unsurveyed PAs were excluded from the analyses. Change estimates derived from these two approaches typically differed by just 1–2 percentage points over three generation lengths (median = 1.0; range = 0.1–7.4; <italic>n</italic> = 42), supporting our decision to use extrapolated values for unsurveyed PAs (Supplementary Table ##SUPPL##0##5## and Fig. ##FIG##0##1##). Notably, the exclusion of unsurveyed PAs typically yielded decline rates that were slightly more pronounced than those presented in Table ##TAB##0##1## and Fig. ##FIG##1##2##, suggesting that our decline estimates are slightly conservative.</p>", "<p id=\"Par36\">When combining PA and UPA data from multiple studies, we thus weighted annual change rates by the land area surveyed in each study to produce a composite estimate of each species’ rate of change (Extended Data Fig. ##FIG##6##2##). It was not possible to apply a similar weighting when comparing PA and UPA rates of change due to differences in the relative area of protected and unprotected land present in each study area. For example, most of the protected and unprotected land surveyed occurred in northern Botswana and West Africa, respectively. Had we applied a weighting based on land area, change rates within PAs would have more strongly reflected conditions in northern Botswana, while those in UPAs would have reflected conditions in West Africa. Since declines were significantly more severe in West Africa, this approach would have exaggerated the apparent benefits of site protection. To avoid this potential bias, we compared PA and UPA change rates using unweighted values.</p>", "<p id=\"Par37\">As a measure of each species’ dependency on protected areas, we compared its encounter rates within PAs and UPAs by subtracting the UPA value from the PA value and dividing by the higher value. Thus, if a species’ mean encounter rate within PAs was higher than in UPAs, we calculated its PA dependency index as: (PA rate − UPA rate)/PA rate. Index values potentially ranged from −1.0 (recorded only in UPAs) to +1.0 (recorded only in PAs).</p>", "<p id=\"Par38\">Median body mass values were extracted from ref. <sup>##UREF##60##84##</sup>. In recent African raptor studies, species have been classified as ‘large’ on the basis of a body mass threshold typically set at 1,000–1,400 g<sup>##UREF##15##21##,##UREF##17##23##,##UREF##31##42##,##UREF##32##43##</sup>. Following ref. <sup>##UREF##31##42##</sup>, we adopted 1,300 g as the threshold separating these two size groups, partly reflecting their prey requirements, extracted from ref. <sup>##UREF##61##85##</sup>. Among the 42 species surveyed, those weighing ≤1,300 g prey mainly on small mammals, birds, lizards or invertebrates, while the heavier species prey mainly on larger reptiles (particularly snakes), medium-sized birds or mammals, or else scavenge on carcasses (Supplementary Table ##SUPPL##0##2##).</p>", "<p id=\"Par39\">We used general linear models (GLMs) and non-parametric tests in R (v.4.1.3)<sup>##UREF##62##86##</sup> to examine changes in species encounter rates in relation to survey period, study area, body mass, protected area status and PA dependency. GLMs were run using the ‘lme4’ package. Where the same species or studies were sampled multiple times, the variables ‘Species’ and/or ‘Study’ were included as random terms. Otherwise, measurements were taken from distinct samples. To avoid over-parameterization, we limited the combined number of explanatory and random variables to two (where <italic>n</italic> ≥ 42) or three (<italic>n</italic> ≥ 60). Where appropriate, we compared model variants in which the explanatory variables were either entered separately or as an interaction term. We selected a top model by applying the Akaike information criterion, corrected for small sample sizes (AICc), using ‘AICctab’ in the package ‘bblme’. We used the ‘Anova’ function to calculate Chi-squared and (two-tailed) <italic>P</italic> values for each explanatory term, and applied the functions ‘testUniformity’, ‘testDispersion’, ‘testOutliers’ and ‘testQuantiles’ in the package ‘DHARMa’ to check that the data complied with model assumptions. Where diagnostics indicated a poor model fit, we instead used a paired Wilcoxon signed-rank test or a Kruskal–Wallis test, as appropriate. Analyses are referred to in the results section as models 1 to 17 in Extended Data Table ##TAB##1##1##, where each model is summarized.</p>", "<title>Determining direction of change from SABAP2 reporting rates</title>", "<p id=\"Par40\">To examine trends among raptors in South Africa, we measured variation in reporting rates during SABAP2 (2007–2021)<sup>##UREF##35##46##</sup> using survey data downloaded from ref. <sup>##UREF##63##87##</sup>, each entry recording the outcome of one visit to one 5’ × 5’ grid cell (pentad). However, interpreting change in SABAP2 reporting rates (the proportion of pentad survey visits yielding at least one sighting of the target species) is problematic, as rates vary in a nonlinear manner in relation to abundance<sup>##UREF##64##88##</sup>. We therefore limited our analysis to determining the direction of change. Since relatively few data were collected during 2007, we restricted the dataset to the years 2008–2021. We established that reporting rates tended to increase in relation to visit duration, and decided to limit the dataset to visits of 2–5 h (Supplementary Fig. ##SUPPL##0##2##). To ensure adequate survey coverage, we selected pentads that had been surveyed at least 20 times, with a minimum of five visits each in 2008–2014 and 2015–2021. We further limited the dataset to pentads in which the target species was recorded at least twice during the 14-yr period, as confirmation of occupancy. Of the 42 species examined, 30 met these selection criteria within at least 30 pentads in South Africa (Extended Data Table ##TAB##2##2##).</p>", "<p id=\"Par41\">We used the ‘glmer’ function in R to determine, for each species in turn, whether SABAP2 reporting rates varied significantly in relation to year. We specified the target species’ detection during pentad visits as the dependent variable (binary: positive, negative) and ‘Year’ (numeric: 08–21) as a fixed effect, fitting each model with a binomial error distribution. Since reporting rates tend to vary seasonally, we also entered ‘Seasonal interval’ as a fixed effect, dividing the calendar year into 6, 4, 3, 2 or 1-month intervals in separate model variants. Since each pentad was sampled multiple times, ‘Pentad ID’ was entered as a random effect. We selected a top model on the basis of the minimum AICc value. Where the AICc values for model variants differed by no more than 2 points we selected the variant in which ‘Seasonal interval’ was more finely resolved, for example, into 12 calendar months rather than six 2-month intervals. The direction of change in reporting rates was determined from the slope coefficient, and Chi-squared and <italic>P</italic> values were calculated using the ‘Anova’ function (Extended Data Table ##TAB##2##2##).</p>", "<title>Reporting summary</title>", "<p id=\"Par42\">Further information on research design is available in the ##SUPPL##1##Nature Portfolio Reporting Summary## linked to this article.</p>" ]	[ "<title>Results</title>", "<p id=\"Par7\">We found strong evidence of widespread declines among African raptor species spanning up to 40 yr (Table ##TAB##0##1##). Overall, 37 (88%) of the 42 species examined had declined, 29 (69%) by at least 30% over three generation lengths—a criterion used by IUCN to identify species at risk of global extinction<sup>##UREF##33##44##</sup>. Of 27 species surveyed in multiple regions, 24 (89%) had exceeded this decline threshold (Fig. ##FIG##1##2##), 13 of which are currently classified as Least Concern<sup>##UREF##34##45##</sup>. While 7 of these 13 species have extensive global ranges outside of Africa, where trends may differ from those reported here, the remaining 6 are African endemics or near-endemics (Fig. ##FIG##1##2##).</p>", "<title>Large raptors showed more rapid declines</title>", "<p id=\"Par8\">The annual rate of change in encounter rates within the four regions combined was inversely related to body mass, with larger species showing significantly steeper declines (effect size = –0.016 × sqrt(mass, kg), <italic>R</italic><sup>2</sup> = 0.109, <italic>P</italic> = 0.0185; model 1 in Extended Data Table ##TAB##1##1##). This relationship was amplified when projected over three generation lengths (effect size = –0.351 × sqrt(mass, kg), <italic>R</italic><sup>2</sup> = 0.254, <italic>P</italic> = 0.0004; model 2 in Extended Data Table ##TAB##1##1##), since generation length itself is positively correlated with body mass (effect size = 2.555 × log(mass, g), <italic>R</italic><sup>2</sup> = 0.830, <italic>P</italic> < 0.0001; model 3 in Extended Data Table ##TAB##1##1##). We note, however, that this pattern was strongly influenced by the 10 heaviest species, all of which had declined at rates exceeding 60% over three generation lengths (Fig. ##FIG##2##3##). Thus, larger, apex raptors and scavengers had declined more rapidly per annum than smaller species, and since larger species tend to live longer, this relationship was more pronounced when projected over three generation lengths.</p>", "<title>Rates of change varied between regions</title>", "<p id=\"Par9\">Raptor population decline rates were significantly more severe in West Africa than elsewhere. In Central, East and southern Africa, there was no significant regional variation in encounter rate trends (<italic>χ</italic><sup>2</sup><sub>2</sub> = 0.2113, <italic>P</italic> = 0.8997; model 4 in Extended Data Table ##TAB##1##1##), and the median annual rate of change was −2.3%. In West Africa, encounter rates for the same species had declined more than twice as rapidly, at a median of −5.4% per annum (<italic>χ</italic><sup>2</sup><sub>1</sub> = 13.288, <italic>P</italic> = 0.0003; model 5 in Extended Data Table ##TAB##1##1##).</p>", "<p id=\"Par10\">To extend our geographical coverage within southern Africa, we determined the direction of change in atlas reporting rates in South Africa for 30 of the 42 species, using data from the Southern African Bird Atlas Project (SABAP2) spanning 2008–2021<sup>##UREF##35##46##</sup>. Reporting rates for 15 species had changed significantly (Bonferroni correction applied), of which 9 (60%) had suffered declines. Ten of the 15 species showed the same direction of change in South Africa as was evident from road transect surveys elsewhere (Table ##TAB##0##1##; concordance no greater than chance: <italic>χ</italic><sup>2</sup><sub>1</sub> = 1.666, <italic>P</italic> = 0.1967).</p>", "<p id=\"Par11\">Decline rates derived from road transect surveys showed a negative but non-significant association with migratory status, after controlling for body-mass effects. The mean annual rate of decline among 14 species that are either migratory or have both migratory and sedentary populations in Africa was 52% higher than among 28 wholly sedentary species (effect size = 0.015, <italic>R</italic><sup>2</sup> = 0.170, <italic>P</italic> = 0.0989; model 6 in Extended Data Table ##TAB##1##1##).</p>", "<title>Raptor declines were less severe within PAs than elsewhere</title>", "<p id=\"Par12\">In each region, the median annual decline rate was greater in unprotected areas (UPAs) than within the protected area types assessed here (Fig. ##FIG##3##4a##), significantly so in the case of West Africa (Wilcoxon signed-ranks test: <italic>V</italic> = 349, <italic>P</italic> = 0.0005; model 7 in Extended Data Table ##TAB##1##1##) and Kenya (<italic>V</italic> = 229, <italic>P</italic> = 0.0004; model 8 in Extended Data Table ##TAB##1##1##). Overall, 33 (79%) of the 42 species had declined more rapidly in UPAs, as had 24 (89%) of the 27 species surveyed in multiple regions. The median annual rate of decline among the 42 species assessed was 2.3 times higher in UPAs (−2.66%, quartiles: −1.74% to −5.25%) than in PAs (−1.15%, quartiles: +0.06% to −2.18%) (<italic>V</italic> = 792, <italic>P</italic> < 0.0001; model 9 in Extended Data Table ##TAB##1##1##). Similarly, the median rate of decline over three generation lengths was 2.5 times higher in UPAs (−48%, quartiles: −27% to −78%) than in PAs (−19%, quartiles: +1% to −49%) (<italic>V</italic> = 765, <italic>P</italic> < 0.0001; model 10 in Extended Data Table ##TAB##1##1##). Thus, while many species had declined in both protected and unprotected areas, annual rates of decline were more than twice as high in the latter.</p>", "<p id=\"Par13\">When PA effects were controlled for, large raptors (>1,300 g; Supplementary Table ##SUPPL##0##2##) continued to show steeper annual declines than smaller species (<italic>χ</italic><sup>2</sup><sub>1</sub> = 5.781, <italic>P</italic> = 0.0162; model 11 in Extended Data Table ##TAB##1##1##). Projected over three generation lengths, decline rates of large raptors were substantially higher than those of smaller species, within PAs (median change: −50.5% vs −13.5%) as well as UPAs (−80.7% vs −31.9%) (<italic>χ</italic><sup>2</sup><sub>1</sub> = 20.942, <italic>P</italic> < 0.0001; model 12 in Extended Data Table ##TAB##1##1##). The influence of body mass on decline rate was thus greater on unprotected land (a difference of 49 percentage points) than on protected land (37 percentage points) (<italic>χ</italic><sup>2</sup><sub>1</sub> = 10.491, <italic>P</italic> = 0.0012; model 12 in Extended Data Table ##TAB##1##1##) (Fig. ##FIG##3##4b##). Notably, even within PAs, decline rates of most large species had exceeded the IUCN Vulnerable threshold (−30% over three generation lengths) (Fig. ##FIG##3##4c##; model 13 in Extended Data Table ##TAB##1##1##). Indeed, 17 (40%) of the 42 species had declined within PAs at rates exceeding the Vulnerable, Endangered or Critically Endangered threshold, compared with 27 species (64%) in UPAs. Thus, although population declines within the PA types assessed were lower than elsewhere, particularly for large raptor species, in some cases they still exceeded IUCN thresholds classifying species at risk of extinction.</p>", "<title>Reliance on protected areas had increased significantly</title>", "<p id=\"Par14\">To further examine the role of protected areas as potential refugia for raptor populations, we measured the disparity between each species’ encounter rates within the PA types we assessed and in UPAs, as an index of its dependence on the former. A positive index value indicated a higher encounter rate within PAs, and values potentially ranged from +1.0 (recorded only in PAs) to −1.0 (recorded only in UPAs). In each survey period, large raptors were significantly more dependent on PAs than were smaller species (<italic>χ</italic><sup>2</sup><sub>1</sub> = 4.461, <italic>P</italic> = 0.0346, <italic>n</italic> = 84; model 14 in Extended Data Table ##TAB##1##1##). Between the two periods, 29 (69%) of the 42 species had become more dependent on PAs, with the median dependency score rising from 0.56 to 0.83 for large raptors and from 0.15 to 0.44 for smaller species (<italic>χ</italic><sup>2</sup><sub>1</sub> = 12.151, <italic>P</italic> = 0.0005, <italic>n</italic> = 84; model 14 in Extended Data Table ##TAB##1##1##) (Fig. ##FIG##4##5a##).</p>", "<p id=\"Par15\">The widening disparity between raptor abundance levels in PAs and UPAs was driven by differences in decline rates. While encounter rates in UPAs fell by a median of 54% (Wilcoxon signed-rank test: <italic>V</italic> = 849.0, <italic>P</italic> < 0.0001; model 15 in Extended Data Table ##TAB##1##1##), in PAs they fell by a median of 19% (<italic>V</italic> = 633.5, <italic>P</italic> < 0.0232; model 15 in Extended Data Table ##TAB##1##1##). Thus, raptors had become less abundant both within PAs and UPAs, indicating that the growing disparity arose more from a rapid deterioration in conditions outside of protected areas than from improving or stable conditions within.</p>", "<title>Rapidly declining species had become more PA-dependent</title>", "<p id=\"Par16\">Interestingly, the rate of change in abundance was correlated with change in a species’ dependence on protected areas (effect size = −0.033, <italic>R</italic><sup>2</sup> = 0.189, <italic>P</italic> = 0.0024; model 16 in Extended Data Table ##TAB##1##1##). However, since both measures were derived from encounter rate values, we caution that the nature of this relationship may have been influenced by a high level of endogeneity within the model. Nevertheless, our findings indicate that species suffering the sharpest drop in abundance had become more dependent on protected areas than those showing little or no change (Fig. ##FIG##4##5b##).</p>" ]	[ "<title>Discussion</title>", "<p id=\"Par17\">Over periods of ca. 20–40 yr, many of the 42 African raptor species examined had endured a double jeopardy – of precipitous population declines coupled with an increasing reliance on protected areas. While declines on a similar geographic scale have been reported previously for African vultures<sup>##UREF##36##47##</sup>, this study encompasses a much larger, more ecologically diverse group of savanna predators and scavengers, whose trajectories are more likely to reflect the broad range of pressures now facing African raptor populations.</p>", "<p id=\"Par18\">Our trend analyses leveraged published road transect studies, whose key findings were in broad agreement with those of single-species studies employing more tailored survey methods<sup>##UREF##16##22##,##REF##24816839##48##–##UREF##38##50##</sup>. They indicate that as a group, Africa’s diurnal raptors are facing an extinction crisis, with more than two-thirds of the species examined potentially qualifying as globally threatened. Notably, 13 of those surveyed in multiple regions are currently listed by IUCN as Least Concern (Table ##TAB##0##1##). A further 6 species recognized as globally threatened (secretarybird <italic>Sagittarius serpentarius</italic>, lappet-faced vulture <italic>Torgos tracheliotos</italic>, bateleur, tawny eagle <italic>Aquila rapax</italic>, steppe eagle <italic>A. nipalensis</italic> and martial eagle <italic>Polemaetus bellicosus</italic>) had declined more rapidly than the threshold rates used to define their current threat status. Our findings thus highlight the need to reassess their status at the earliest opportunity.</p>", "<p id=\"Par19\">In contrast, our decline rate for hooded vulture (−67% over three generations) was much lower than that estimated in 2016<sup>##UREF##36##47##</sup> (−83%) and on which the species’ current threat status (Critically Endangered) was initially based. This follows a recent review<sup>##REF##32058610##51##</sup> in which the species’ generation length estimate was substantially shortened, reducing the apparent scale of its decline over three generation lengths. Hooded vulture remains Critically Endangered, however, following a surge in demand for vulture body parts in West Africa, its stronghold region<sup>##UREF##25##32##,##UREF##34##45##</sup>. Three additional species showing steep declines are augur buzzard <italic>Buteo augur</italic>, Dickinson’s kestrel <italic>Falco dickinsoni</italic> and Beaudouin’s snake-eagle <italic>Circaetus beaudouini</italic>. The latter is of particular concern, having declined by 80–85% over three generation lengths within a large (and probably representative) portion of its global breeding range<sup>##UREF##39##52##</sup>. The plight of these African endemics illustrates the pressing need for research into raptors with restricted breeding ranges.</p>", "<p id=\"Par20\">We show that large African raptors have suffered steeper annual declines than smaller species, mirroring the pattern of extinction risk observed among terrestrial mammalian predators<sup>##REF##24408439##33##</sup>. The risks to large-bodied species are compounded both by their biological traits (for example, low population density, delayed maturity and low annual fecundity<sup>##REF##24408439##33##,##REF##16037416##53##</sup>) and environmental factors (home ranges requiring extensive tracts of scarce, suitable habitat, thereby increasing the species’ exposure to human impacts). Furthermore, the loss of large-bodied species has a disproportionate effect on the resilience and functioning of ecosystems, as well as on human-centric values, such as revenue from tourism<sup>##REF##25187922##54##,##REF##26601172##55##</sup>.</p>", "<title>Declines were more pronounced in West Africa</title>", "<p id=\"Par21\">Decline rates reported from West Africa<sup>##UREF##29##40##,##UREF##40##56##,##UREF##41##57##</sup> were significantly more pronounced than those recorded elsewhere, consistent with the severity of threats documented in the region<sup>##UREF##24##31##,##UREF##25##32##,##UREF##29##40##,##UREF##40##56##–##UREF##42##58##</sup>, many being substantially worse there than elsewhere in sub-Saharan Africa<sup>##UREF##13##19##,##UREF##43##59##</sup>. Protected areas in West and Central Africa are particularly underfunded and mismanaged<sup>##UREF##13##19##</sup>, and high regional levels of poverty and corruption have been linked to adverse conservation outcomes for charismatic mammal species<sup>##UREF##43##59##,##UREF##44##60##</sup>. Furthermore, the rate of agricultural expansion in West Africa during the 1970s–2000s was more than three times that of Africa as a whole (##SUPPL##0##Supplementary Information##: Anthropogenic pressures). Hence, raptor declines seem likely to have continued in the region since road transect surveys were last conducted in the early 2000s, highlighting the need for repeat surveys. In contrast, SABAP2 reporting rates suggest that proportionately fewer species had declined in South Africa than elsewhere, albeit over a shorter, more recent timeframe (2008–2021).</p>", "<p id=\"Par22\">Migrant species appear to have suffered steeper declines than residents, although this effect was statistically non-significant. Similarly, there was no significant relationship between the direction of change evident among Palaearctic migrants in Africa and in Europe<sup>##UREF##45##61##</sup>, perhaps reflecting disparities between the populations surveyed, or shifts in the over-wintering distributions of some Palaearctic migrant species<sup>##UREF##46##62##</sup>.</p>", "<title>Decline rates were often high within protected areas</title>", "<p id=\"Par23\">Raptors of all sizes lead an increasingly perilous existence in African savannas, where food supplies and breeding sites have been drastically reduced and persecution by humans is now widespread<sup>##UREF##29##40##,##UREF##31##42##,##UREF##40##56##,##UREF##41##57##</sup>. While annual declines on unprotected land were thus often substantially higher than within the PAs we assessed, there is now widespread acknowledgement that many African PAs are also losing their ecological integrity<sup>##REF##25469888##18##,##UREF##47##63##,##REF##34385386##64##</sup>, thereby depriving threatened species of effective refugia. Indeed, the scale of this deterioration has been assessed in a recent study<sup>##UREF##13##19##</sup>, which showed that over 82% of land encompassed within 516 African conservation areas was considered to be failing or deteriorating. Moreover, vulture and eagle species can range widely across protected area boundaries, exposing them to retaliatory and sentinel poisoning by pastoralists and poachers, respectively<sup>##UREF##48##65##</sup>, and to persecution by livestock farmers. Consequently, levels of attrition were high even within the PA types we assessed, where 40% of species had declined at rates exceeding the IUCN Vulnerable threshold. Clearly, the size, connectivity and/or management of these PAs has failed to safeguard such highly mobile species, reflecting concerns that many African PAs are too small to protect large raptors adequately<sup>##REF##26941945##66##</sup>.</p>", "<title>Study limitations</title>", "<p id=\"Par24\">While our sample accounted for 40% of Africa’s 106 diurnal raptor species<sup>##UREF##39##52##</sup>, their trajectories may not be representative of trends among the remaining species, many of which are forest dependent. Globally, tropical forest raptors are at greater risk of extinction than those associated with savannas<sup>##UREF##3##8##</sup>, perhaps especially so in Africa, where net forest loss during 2010–2020 exceeded that of all other continents<sup>##UREF##49##67##</sup>. Geographically, North Africa represents a further, notable gap in our coverage. Here, many of the same threats prevail as elsewhere in Africa, and the limited evidence available<sup>##UREF##20##27##–##UREF##22##29##</sup> suggests that raptor population trends in the region may be similar to those south of the Sahara.</p>", "<p id=\"Par25\">Differing trends within PAs and UPAs could result from factors other than site protection, including the possibility that land encompassed within PAs was initially more favourable for raptors than land left unprotected, as indicated by disparities between PA and UPA encounter rates during early survey periods (Supplementary Table ##SUPPL##0##3##). To investigate this possibility we re-examined survey data from northern Botswana, demonstrating that PA and UPA encounter rates within the same 1° × 1° grid cells were higher than those from grid cells where PAs were absent, suggesting that high PA encounter rates were due in part to more favourable initial conditions (##SUPPL##0##Supplementary Information##: Comparing protected and unprotected areas). However, separating the effects of site protection from other factors would require a more rigorous counterfactual study design<sup>##REF##35444280##68##</sup> involving a before–after control–intervention (BACI) approach<sup>##REF##33309331##69##</sup>, or the careful matching of ecologically similar transects from PAs and UPAs<sup>##REF##32528022##70##</sup>. The application of a counterfactual approach thus remains the ‘gold standard’ for future analyses of PA effects, and we recommend caution when interpreting PA–UPA disparities.</p>", "<p id=\"Par26\">Shrub encroachment within savanna habitats since the 1980s could have adversely affected raptor detectability, potentially contributing to the disparities observed between early and recent encounter rates. Since vegetation structure in the vicinity of survey transects was not assessed, we were unable to test whether changes in woody cover had occurred along the routes surveyed. Although widespread changes in shrub encroachment have been reported<sup>##UREF##9##14##,##UREF##50##71##</sup>, their effects are likely to have been small in comparison with many of the declines reported here. Moreover, although shrub encroachment would seem less likely to impede the detection of large soaring raptors, these species had shown some of the steepest declines (##SUPPL##0##Supplementary Information##: Detectability).</p>", "<title>Mitigating raptor declines</title>", "<p id=\"Par27\">While ongoing efforts to protect Africa’s charismatic megafauna, including elephants <italic>Loxodonta</italic> spp.<sup>##REF##25136107##72##</sup> and lions <italic>Panthera leo</italic><sup>##UREF##13##19##,##UREF##41##57##</sup>, help safeguard critical raptor habitats, raptors have distinct management requirements differing from those of large mammals. These include the protection of nesting trees and cliffs, the global adoption of bio-pesticides for locust control<sup>##UREF##51##73##</sup>, more effective management of <italic>Quelea</italic> control operations, and an improved understanding of the corridors and habitats required by migrant raptors. Mitigation is urgently required to end the extensive mortality caused by powerlines and windfarms<sup>##UREF##19##26##–##UREF##22##29##</sup>, particularly along migratory flyways. Innovation is needed to reduce mortalities caused by lethal pole and turbine designs, and better enforcement of regulations is required to prevent energy infrastructure from being built within protected and sensitive areas<sup>##REF##36691259##74##</sup>.</p>", "<p id=\"Par28\">The future of Africa’s raptors also rests on (1) effective legislation for species protection, (2) enhanced management of PAs, particularly in relation to tree loss, disturbance at nest sites, poaching and poisoning, (3) tighter coordination between government and conservation stakeholders<sup>##UREF##8##13##</sup> and (4) both improved law enforcement and innovative economic incentives to counter persecution<sup>##UREF##18##24##</sup>, sentinel poisoning<sup>##UREF##48##65##</sup> and the harvesting of raptors for food and belief-based use<sup>##UREF##23##30##–##UREF##25##32##</sup>. Better coordination is also required between range states encompassing migratory routes<sup>##UREF##52##75##</sup>, facilitated by frameworks such as the Convention on Migratory Species (CMS) Memorandum of Understanding (MOU) on the conservation of birds of prey in Africa and Eurasia.</p>", "<p id=\"Par29\">To address the need for long-term raptor monitoring and expanded research and conservation programmes, we have developed the African Raptor Leadership Grant, which supports educational and mentoring opportunities, boosting local conservation initiatives and knowledge of raptors across the continent. Furthermore, we recommend increased stakeholder engagement in raptor conservation to develop regional raptor Red Lists, monitoring schemes and species action plans, with guidance from the CMS Raptor MOU Technical Advisory Group and relevant IUCN Species Specialist Groups.</p>", "<p id=\"Par30\">The evidence we present here of a significant shift in the reliance of African raptor species on protected areas substantiates recent calls to expand the global protected area network<sup>##UREF##53##76##,##UREF##54##77##</sup> and demonstrates the importance of proposals agreed at the Convention on Biological Diversity COP15 in 2022: to effectively conserve and manage at least 30% of the world’s surface by 2030<sup>##UREF##55##78##</sup>. Furthermore, our results underscore the need to substantially improve PA management throughout Africa, to meet the ‘green list standard’ set by the IUCN World Commission on Protected Areas<sup>##UREF##56##79##</sup>. In this regard, a recent African-driven initiative—APACT—may prove pivotal in leveraging the finances needed to effectively manage new and existing conserved areas<sup>##UREF##47##63##</sup>.</p>", "<p id=\"Par31\">While raptors also extensively utilize unprotected areas, particularly during migration<sup>##UREF##57##80##</sup> and seasonal stays<sup>##REF##28821735##81##</sup>, human population projections for sub-Saharan Africa<sup>##UREF##5##10##</sup> point to further, widespread conversion and degradation of natural habitats, particularly on unprotected land. Well-established links between land conversion and biodiversity loss<sup>##REF##16040698##1##–##REF##22535780##4##,##UREF##4##9##,##UREF##6##11##</sup>, together with the patterns of decline documented here, give cause to doubt whether large raptors will persist over much of Africa’s unprotected land in the latter half of this century. Broad-scale interventions and collaborations are thus urgently required to address the multitude of threats facing raptors in unprotected areas, thereby also helping to protect other wildlife species. Furthermore, there is a pressing need to substantially improve the connectivity, management and coverage of PAs in Africa, in line with global aspirations<sup>##UREF##54##77##–##UREF##56##79##</sup>—a transition considered fundamental to safeguarding biodiversity, ecosystem functioning and climate resilience<sup>##UREF##53##76##</sup>.</p>" ]	[]	[ "<p id=\"Par1\">The conversion of natural habitats to farmland is a major cause of biodiversity loss and poses the greatest extinction risk to birds worldwide. Tropical raptors are of particular concern, being relatively slow-breeding apex predators and scavengers, whose disappearance can trigger extensive cascading effects. Many of Africa’s raptors are at considerable risk from habitat conversion, prey-base depletion and persecution, driven principally by human population expansion. Here we describe multiregional trends among 42 African raptor species, 88% of which have declined over a ca. 20–40-yr period, with 69% exceeding the International Union for Conservation of Nature criteria classifying species at risk of extinction. Large raptors had experienced significantly steeper declines than smaller species, and this disparity was more pronounced on unprotected land. Declines were greater in West Africa than elsewhere, and more than twice as severe outside of protected areas (PAs) than within. Worryingly, species suffering the steepest declines had become significantly more dependent on PAs, demonstrating the importance of expanding conservation areas to cover 30% of land by 2030—a key target agreed at the UN Convention on Biological Diversity COP15. Our findings also highlight the significance of a recent African-led proposal to strengthen PA management—initiatives considered fundamental to safeguarding global biodiversity, ecosystem functioning and climate resilience.</p>", "<p id=\"Par2\">A compilation of survey data from pre- and post-2000 for 42 raptor species across parts of West, Central, East and southern Africa shows 88% of species in population decline and reveals trends across regions, protected areas and species size.</p>", "<title>Subject terms</title>" ]	[ "<title>Main</title>", "<p id=\"Par3\">The conversion of wooded habitats to agricultural land is more damaging to biodiversity than any other human activity<sup>##REF##16040698##1##–##REF##22535780##4##</sup> and poses the greatest extinction risk to birds worldwide<sup>##REF##15618485##2##,##REF##17550306##3##</sup>. Tropical raptors are especially vulnerable, being particularly slow-breeding<sup>##UREF##0##5##,##UREF##1##6##</sup> and subject to a wide range of threats linked to rapid human population growth, farmland expansion<sup>##UREF##2##7##–##UREF##5##10##</sup> and habitat fragmentation<sup>##UREF##6##11##</sup>. While resident tropical raptors thus have great potential as a model system for investigating land-use change impacts, trends in their abundance have been little studied so far, reflecting the paucity of suitable long-term survey data and a limited capacity for conservation research in most developing countries<sup>##UREF##7##12##</sup>. Here we present a multiregional assessment of trends among many of Africa’s widespread, diurnal raptor species, and compare rates of change in their abundance within protected and unprotected areas.</p>", "<p id=\"Par4\">Africa is exceptionally important for global raptor conservation, supporting high numbers of threatened species<sup>##UREF##8##13##</sup>. Over the past ca. 60 yr, however, the continent’s human population has expanded rapidly<sup>##UREF##5##10##</sup>, driving widespread land conversion and habitat degradation, and creating areas where cumulative human impacts on threatened raptors are especially acute<sup>##UREF##4##9##</sup>. Sub-Saharan Africa lost almost 5 million ha of forest and non-forest natural vegetation per annum during 1975–2000 alone<sup>##UREF##9##14##</sup> and now experiences the most severe rate of land degradation in the world<sup>##UREF##10##15##</sup>. With its human population projected to double by 2058, demands for grazing, arable land and energy are expected to rise substantially<sup>##UREF##5##10##,##UREF##11##16##</sup>. These trends will amplify existing pressures on Africa’s protected areas (PAs), which currently account for just 14% of its land and inland waters<sup>##UREF##12##17##</sup>. Although many PAs are considered to be failing or deteriorating<sup>##REF##25469888##18##,##UREF##13##19##</sup>, well-managed sites form a critical refuge for the continent’s declining raptor populations<sup>##UREF##14##20##–##UREF##17##23##</sup>.</p>", "<p id=\"Par5\">Additional threats to Africa’s avian apex predators, meso-predators and scavengers include prey-base depletion<sup>##UREF##8##13##</sup>, persecution (shooting, trapping, poisoning)<sup>##UREF##18##24##</sup>, unintentional poisoning<sup>##REF##35305837##25##</sup>, electrocution/collision with energy infrastructure<sup>##UREF##19##26##–##UREF##22##29##</sup> and killing for food and belief-based uses<sup>##UREF##23##30##–##UREF##25##32##</sup>. These pressures are typically more acute within unprotected land and have probably impacted larger raptor species more severely, reflecting global patterns of extinction risk among terrestrial mammalian predators<sup>##REF##24408439##33##</sup>. Importantly, the loss and depletion of predator populations not only affects the species concerned, but can also trigger extensive cascading effects among their prey populations, disrupting ecosystem functioning<sup>##UREF##4##9##,##UREF##26##34##–##REF##29348647##37##</sup>. Ecosystem services provided by raptors include the rapid removal of carcasses, potentially limiting the transmission of zoonotic diseases to human populations<sup>##REF##29348647##37##–##REF##22443166##39##</sup>.</p>", "<p id=\"Par6\">Despite these pressures, and the keystone role played by many raptor species, attempts to measure trends in their abundance have been hindered by the absence of systematic, pan-African bird monitoring programmes, generating robust, long-term trend data for this species group. Here, based on repeated raptor road transect surveys undertaken in four African regions, we examine changes in encounter rates (individuals recorded per 100 km) among 42 species dependent mainly on savanna habitats. To determine rates of change, we combined published and unpublished road transect data from surveys conducted during 1969–1995 and 2000–2020 in West Africa (Burkina Faso, Niger and Mali)<sup>##UREF##29##40##</sup>, Central Africa (northern Cameroon)<sup>##UREF##30##41##</sup>, East Africa (Kenya)<sup>##UREF##31##42##</sup> and southern Africa (northern Botswana)<sup>##UREF##14##20##,##UREF##32##43##</sup> (Fig. ##FIG##0##1##, Supplementary Table ##SUPPL##0##1## and Extended Data Fig. ##FIG##5##1##). Pooling these data has provided unprecedented insights into trends in the abundance of Africa’s savanna raptors, enabling us to identify species whose composite decline estimates exceed the limits defining their current International Union for Conservation of Nature (IUCN) threat status. We also determine the extent to which decline rates differed between selected PA categories and unprotected land, and investigate potential links between abundance change, body size and protected area dependency.</p>", "<title>Supplementary information</title>", "<p>\n\n\n</p>", "<title>Source data</title>", "<p>\n\n\n\n\n\n</p>" ]	[ "<title>Extended data</title>", "<p id=\"Par47\">\n\n</p>", "<p id=\"Par48\">\n\n</p>", "<p id=\"Par49\">\n\n</p>", "<p id=\"Par50\">\n\n</p>", "<title>Extended data</title>", "<p id=\"Par43\">is available for this paper at 10.1038/s41559-023-02236-0.</p>", "<title>Supplementary information</title>", "<p id=\"Par44\">The online version contains supplementary material available at 10.1038/s41559-023-02236-0.</p>", "<title>Acknowledgements</title>", "<p>This study would not have been possible without the survey work undertaken by the late J. M. Thiollay and C. Smeenk in West and East Africa, respectively. Funding sources for the four road transect studies have been acknowledged in the relevant publications<sup>##UREF##14##20##,##UREF##29##40##–##UREF##32##43##,##UREF##58##82##</sup>. Additional data were incorporated into the present study from surveys conducted in Cameroon by R.B. and B.M.C.; these were financially and logistically supported by the Institute of Environmental Sciences (CML) of the University of Leiden, the Netherlands, through its collaborative programme with the University of Dschang, Cameroon, at the Centre for Environment and Development Studies in Cameroon (CEDC). In addition, D.O. acknowledges logistical support from the National Geographic Society and San Diego Zoo Wildlife Alliance. P.S. gratefully acknowledges support received from the University of St Andrews, at which he is an Honorary Research Fellow. We also thank R. Davies and his team at Habitat Info for providing up-to-date range maps for African raptors; R. Patchett for advice on modelling road transect data; R. Camp for advice on weighting methods; the many citizen scientists who have contributed to SABAP2; and M. Brooks for guidance on accessing SABAP2 data.</p>", "<title>Author contributions</title>", "<p>P.S. and D.O. conceived the study and collated published and unpublished road transect data. D.O., R.B., R.G., M.H., M.Z.V., C.J.K., B.M.C., M.O., S.K., P.W., G.M. and S.T. collected data. L.D. performed the analysis of species and PA distributions. P.S. analysed the road transect data. A.A. and P.S. formulated the analysis of SABAP2 data, which P.S. performed. P.S. and D.O. wrote the paper, with contributions from C.R., A.A., R.B., M.H., A.B., M.Z.V., U.G.-O., C.M. and C.J.K., who helped finalize the text.</p>", "<title>Peer review</title>", "<title>Peer review information</title>", "<p id=\"Par45\"><italic>Nature Ecology & Evolution</italic> thanks Chevonne Reynolds and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.</p>", "<title>Data availability</title>", "<p>Survey data used in statistical analyses are available in Figshare, with the identifier 10.6084/m9.figshare.23727030. Additional background data used in the study are available in the ##SUPPL##0##Supplementary Information##. <xref ref-type=\"sec\" rid=\"Sec20\">Source data</xref> are provided with this paper.</p>", "<title>Code availability</title>", "<p>Statistical analyses were conducted using open-source packages and functions in R. Copies of the code used to reformat data and perform analyses are available in Figshare, with the identifier 10.6084/m9.figshare.23727030.</p>", "<title>Competing interests</title>", "<p id=\"Par46\">The authors declare no competing interests.</p>" ]	[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>Trend estimates were derived from four road transect studies and a bird atlas project, located in West, Central, East and southern Africa.</title><p>Road transects were conducted in West Africa, northern Cameroon and Kenya in 1969–1977 and 2000–2020, and in northern Botswana in 1991–1995 and 2015–2016. Here, orange shading indicates parts of the global range of bateleur <italic>Terathopius ecaudatus</italic> that lie within road transect countries and overlap with areas where climatic conditions match those of the routes surveyed in that country. Grey shading indicates the rest of the species’ range within surveyed and unsurveyed countries alike. Bar charts show percentage change in the number of individuals encountered per 100 km within protected and unprotected areas (PAs and UPAs), projected over three generation lengths; 44 yr in this instance. The species’ trajectory within its South African range (mauve) was derived from SABAP2 reporting rates during 2008–2021. Photograph: © André Botha.</p><p>##SUPPL##2##Source data##</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>Percentage change in the number of individuals encountered per 100 km during road transect surveys, projected over three generation lengths.</title><p>Fifteen species were surveyed adequately in single regions only (grey bars). The remaining 27 were each surveyed in two regions (lighter green bars) or 3–4 regions (dark green). Bar length shows a given species’ median rate of change in abundance, estimated under two scenarios, in which average encounter rates in unsurveyed PAs were assumed to have been the same as in surveyed PAs, or the same as in UPAs (<xref rid=\"Sec13\" ref-type=\"sec\">Methods</xref>). Points overlaid on bars show individual change estimates, where the sample size (<italic>n</italic> = 4, 16, 64 or 256) reflects the number of studies in which the species was surveyed (1, 2, 3 or 4 studies); error bars show the Q1–Q3 range. Twenty-nine species had declined at rates exceeding the IUCN Vulnerable threshold; 24 had exceeded the limits defining their current threat category. Fifteen of these are African endemics or near-endemics, 6 of which (illustrated) were surveyed in multiple regions and are currently listed as Least Concern. Silhouettes drawn from photographs: © André Botha.</p><p>##SUPPL##3##Source data##</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><title>Encounter rate changes projected over three generation lengths in relation to body mass.</title><p>Each point represents one species (<italic>n</italic> = 42), grouped taxonomically as in Supplementary Table ##SUPPL##0##2##. Circle size indicates the number of regions in which the species was surveyed (<italic>n</italic> = 1–4). Rates of change were more variable among small–medium raptors than among large species (≥1,300 g; dashed line). Most large raptors had declined by at least 80% over three generation lengths, partly reflecting the positive relationship between body mass and longevity.</p><p>##SUPPL##4##Source data##</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><title>The effects of protected area status and body mass on rates of change.</title><p>Results from protected and unprotected areas are shown in orange and grey, respectively. <bold>a</bold>, In all four road transect studies, median annual decline rates in UPAs exceeded those within the PAs assessed, significantly so in West Africa and Kenya. Boxplots show the median, first and third quartiles of the change in abundance within protected and unprotected areas in each of the four regions. Whiskers extend to ±1.5× the interquartile range. Each point represents one species; <italic>n</italic> = 28 (West Africa), 15 (N. Cameroon), 22 (Kenya) and 25 (N. Botswana). <bold>b</bold>, The effects of site protection were more pronounced among large (≥1,300 g) than among small–medium raptors. Median decline rates in PAs vs UPAs differed by 30 percentage points among large raptors and by 18 percentage points among small–medium species. Boxplots show the median, first and third quartiles of the rate of change in abundance of large vs small–medium raptor species, inside vs outside of protected areas. Whiskers extend to ±1.5× the interquartile range. Each point represents one species; <italic>n</italic> = 15 large, 27 small–medium species. <bold>c</bold>, Modelled relationship between the rate of change in abundance, body mass and protected area status (PAs vs UPAs). Notably, declines over three generation lengths exceeded the IUCN Vulnerable threshold (−30%, blue line) for the bulk of species in UPAs and for most large raptors within the PA types assessed. Fitted lines and shading indicate modelled change rates ±1 s.e.m. (model 13 in Extended Data Table ##TAB##1##1##).</p><p>##SUPPL##5##Source data##</p></caption></fig>", "<fig id=\"Fig5\"><label>Fig. 5</label><caption><title>The number of individuals encountered per 100 km in protected versus unprotected areas, as an index of each species’ dependence on PAs.</title><p>Index values potentially ranged from +1.0 (recorded only within PAs) to −1.0 (recorded only in UPAs). <bold>a</bold>, Boxplot showing PA dependency scores in relation to survey period (green, 1969–1995; blue, 2000–2020) and body size class. In each period, large raptors were significantly more dependent on PAs than small–medium species. Notably, for species in both size classes, PA dependency increased significantly between 1969–1995 and 2000–2020. Boxplots show the median, first and third quartiles. Whiskers extend to ±1.5× the interquartile range. Each point represents one species; <italic>n</italic> = 15 large, 27 small–medium. <bold>b</bold>, Scatterplot showing annual change in abundance vs change in dependency on protected areas.The extent to which a species’ dependence on PAs changed between the two periods was significantly correlated with change in abundance. Species whose encounter rates had declined sharply had become more dependent on PAs than those showing a moderate decline or increase. Each point represents one species (<italic>n</italic> = 42); the fitted line and shading show modelled change rates ±1 s.e.m. (model 16 in Extended Data Table ##TAB##1##1##).</p><p>##SUPPL##6##Source data##</p></caption></fig>", "<fig id=\"Fig6\"><label>Extended Data Fig. 1</label><caption><title>Routes surveyed during four road transect studies.</title><p>These were conducted during 1969–1977 and 2000–2020 in northern Cameroon, Kenya, Burkina Faso, Niger and Mali, and during 1991–1995 and 2015–2016 in northern Botswana. Panels adapted with permission from: Burkina Faso, Niger and Mali, ref. <sup>##UREF##29##40##</sup>, Wiley; northern Cameroon, ref. <sup>##UREF##30##41##</sup>, Allen Press; Kenya, ref. <sup>##UREF##31##42##</sup>, Elsevier; northern Botswana, ref. <sup>##UREF##32##43##</sup>, Elsevier.</p></caption></fig>", "<fig id=\"Fig7\"><label>Extended Data Fig. 2</label><caption><title>Method used to produce a composite estimate of the rate of change in abundance.</title><p>The survey data used here are for bateleur <italic>Terathopius ecaudatus</italic>, and were drawn from all four road transect studies. Average encounter rates within PAs and UPAs are shown for early (E) and recent (R) survey periods. For each period, we combined these to produce a weighted average for the study area in question, based on two scenarios, in which the average encounter rate within unsurveyed PAs was assumed either to be (1.) the same as in surveyed PAs, or (2.) the same as in UPAs. The land area to which the PA encounter rate was assumed to apply thus differed between these two scenarios, as indicated by the relative sizes of the green (PA) and red (UPA) boxes shown, exaggerated here for illustrative effect. These encounter rate values yielded four estimates of change for each study area between survey periods, corresponding to E1– > R1, E1– > R2, E2– > R1 and E2– > R2, as illustrated. We converted these estimates to annual rates of change for each study area, and multiplied them by the species’ range size within each area. We used the weighted values to calculate an average annual rate of change for each of the 256 permutations, derived from the four change estimates and four study areas. Finally, we calculated the median plus quartiles 1 and 3 from these permutations, and projected these over three generation lengths.</p></caption></fig>" ]	[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Changes in the number of individuals encountered per 100 km during road transect studies</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\">Species<sup>a</sup></th><th rowspan=\"2\">Migratory status<sup>b</sup></th><th rowspan=\"2\">Current IUCN status<sup>c</sup></th><th colspan=\"7\">Change over three generation lengths (%)<sup>d</sup></th></tr><tr><th>Median</th><th>Quartiles</th><th>West Africa</th><th>Northern Cameroon</th><th>Kenya</th><th>Northern Botswana</th><th>South Africa<sup>e</sup></th></tr></thead><tbody><tr><td><p><bold>Secretarybird</bold></p><p><bold><italic>Sagittarius serpentarius</italic></bold></p></td><td>AS</td><td>EN</td><td>−85</td><td><p>−85.1</p><p>−85.7</p></td><td>−</td><td>−</td><td>−89</td><td>−82</td><td><bold>⇩</bold></td></tr><tr><td><p>Black-winged kite</p><p><italic>Elanus caeruleus</italic></p></td><td>AS</td><td>LC</td><td>−32</td><td><p>−31.7</p><p>−32.6</p></td><td>−18</td><td>+74</td><td>−36</td><td>−58</td><td><bold>⇩</bold></td></tr><tr><td><p>Scissor-tailed kite</p><p><italic>Chelictinia riocourii</italic></p></td><td>AM</td><td>VU</td><td>−48</td><td><p>−47.1</p><p>−48.1</p></td><td>−48</td><td>+24</td><td>−</td><td>−</td><td>−</td></tr><tr><td><p>Black kite</p><p><italic>Milvus migrans</italic></p></td><td>PMAM</td><td>LC</td><td>−60</td><td><p>−59.6</p><p>−61.0</p></td><td>−64</td><td>−41</td><td>−40</td><td>−64</td><td>⇩</td></tr><tr><td><p>Hooded vulture</p><p><italic>Necrosyrtes monachus</italic></p></td><td>AS</td><td>CR</td><td>−67</td><td><p>−66.2</p><p>−68.0</p></td><td>−51</td><td>−51</td><td>−88</td><td>−80</td><td>⇧</td></tr><tr><td><p>White-backed vulture</p><p><italic>Gyps africanus</italic></p></td><td>AS</td><td>CR</td><td>−86</td><td><p>−81.8</p><p>−89.6</p></td><td>−95</td><td>−71</td><td>−74</td><td>−20</td><td>⇩</td></tr><tr><td><p>Rüppell’s vulture</p><p><italic>Gyps rueppelli</italic></p></td><td>AS</td><td>CR</td><td>−97</td><td><p>−97.3</p><p>−97.6</p></td><td>−98</td><td>−88</td><td>−21</td><td>−</td><td>−</td></tr><tr><td><p><bold>Lappet-faced vulture</bold></p><p><bold><italic>Torgos tracheliotos</italic></bold></p></td><td>AS</td><td>EN</td><td>−90</td><td><p>−88.0</p><p>−92.0</p></td><td>−97</td><td>−</td><td>−69</td><td>−76</td><td>⇩</td></tr><tr><td><p>White-headed vulture</p><p><italic>Trigonoceps occipitalis</italic></p></td><td>AS</td><td>CR</td><td>−90</td><td><p>−85.6</p><p>−93.0</p></td><td>−94</td><td>−</td><td>−</td><td>−77</td><td><bold>⇩</bold></td></tr><tr><td><p>Short-toed snake-eagle</p><p><italic>Circaetus gallicus</italic></p></td><td>PM</td><td>LC</td><td>−25</td><td><p>−24.4</p><p>−25.6</p></td><td>−</td><td>−25</td><td>−</td><td>−</td><td>−</td></tr><tr><td><p><bold>Beaudouin’s snake-eagle</bold></p><p><bold><italic>Circaetus beaudouini</italic></bold></p></td><td>AM</td><td>VU</td><td>−83</td><td><p>−80.4</p><p>−85.3</p></td><td>−83</td><td>−</td><td>−</td><td>−</td><td>−</td></tr><tr><td><p>Black-chested snake-eagle</p><p><italic>Circaetus pectoralis</italic></p></td><td>AS</td><td>LC</td><td>+15</td><td><p>+14.3</p><p>+15.6</p></td><td>−</td><td>−</td><td>−29</td><td>+77</td><td>⇧</td></tr><tr><td><p><bold>Brown snake-eagle</bold></p><p><bold><italic>Circaetus cinereus</italic></bold></p></td><td>AS</td><td>LC</td><td>−55</td><td><p>−52.3</p><p>−56.9</p></td><td>−78</td><td>−71</td><td>−15</td><td>+67</td><td>⇧</td></tr><tr><td><p><bold>Bateleur</bold></p><p><bold><italic>Terathopius ecaudatus</italic></bold></p></td><td>AS</td><td>EN</td><td>−87</td><td><p>−76.9</p><p>−92.8</p></td><td>−91</td><td>−89</td><td>−50</td><td>−75</td><td><bold>⇩</bold></td></tr><tr><td><p>Western marsh-harrier</p><p><italic>Circus aeruginosus</italic></p></td><td>PM</td><td>LC</td><td>−4</td><td><p>−2.6</p><p>−5.3</p></td><td>−4</td><td>−</td><td>−</td><td>−</td><td>−</td></tr><tr><td><p>Montagu’s harrier</p><p><italic>Circus pygargus</italic></p></td><td>PM</td><td>LC</td><td>−51</td><td><p>−50.2</p><p>−51.9</p></td><td>−50</td><td>+12</td><td>−59</td><td>−</td><td><bold>⇩</bold></td></tr><tr><td><p><bold>African harrier-hawk</bold></p><p><bold><italic>Polyboroides typus</italic></bold></p></td><td>AS</td><td>LC</td><td>−58</td><td><p>−53.3</p><p>−61.5</p></td><td>−64</td><td>−</td><td>−</td><td>−34</td><td><bold>⇧</bold></td></tr><tr><td><p><bold>Dark chanting-goshawk</bold></p><p><bold><italic>Melierax metabates</italic></bold></p></td><td>AS</td><td>LC</td><td>−41</td><td><p>−40.0</p><p>−42.3</p></td><td>−44</td><td>−41</td><td>−</td><td>−23</td><td><bold>⇩</bold></td></tr><tr><td><p>Eastern chanting-goshawk</p><p><italic>Melierax poliopterus</italic></p></td><td>AS</td><td>LC</td><td>+116</td><td><p>+106.8</p><p>+125.3</p></td><td>−</td><td>−</td><td> + 116</td><td>−</td><td>−</td></tr><tr><td><p>Pale chanting-goshawk</p><p><italic>Melierax canorus</italic></p></td><td>AS</td><td>LC</td><td>+40</td><td><p>+39.3</p><p>+40.2</p></td><td>−</td><td>−</td><td>−</td><td> + 40</td><td><bold>⇩</bold></td></tr><tr><td><p>Gabar goshawk</p><p><italic>Micronisus gabar</italic></p></td><td>AS</td><td>LC</td><td>−21</td><td><p>−19.3</p><p>−22.5</p></td><td>−23</td><td>−</td><td>−</td><td>−14</td><td><bold>⇧</bold></td></tr><tr><td><p>Lizard buzzard</p><p><italic>Kaupifalco monogrammicus</italic></p></td><td>AS</td><td>LC</td><td>−21</td><td><p>−18.7</p><p>−22.6</p></td><td>−21</td><td>−</td><td>−</td><td>−</td><td><bold>⇩</bold></td></tr><tr><td><p>Shikra</p><p><italic>Accipiter badius</italic></p></td><td>AM</td><td>LC</td><td>−45</td><td><p>−39.8</p><p>−49.3</p></td><td>−32</td><td>−</td><td>−</td><td>−65</td><td><bold>⇩</bold></td></tr><tr><td><p><bold>Grasshopper buzzard</bold></p><p><bold><italic>Butastur rufipennis</italic></bold></p></td><td>AM</td><td>LC</td><td>−32</td><td><p>−28.7</p><p>−34.4</p></td><td>−32</td><td>−</td><td>−</td><td>−</td><td>−</td></tr><tr><td><p>Eurasian buzzard</p><p><italic>Buteo buteo</italic></p></td><td>PM</td><td>LC</td><td>−31</td><td><p>−30.3</p><p>−31.4</p></td><td>−</td><td>−</td><td> + 36</td><td>−54</td><td><bold>⇩</bold></td></tr><tr><td><p><bold>Augur buzzard</bold></p><p><bold><italic>Buteo augur</italic></bold></p></td><td>AS</td><td>LC</td><td>−78</td><td><p>−78.0</p><p>−78.7</p></td><td>−</td><td>−</td><td>−78</td><td>−</td><td>−</td></tr><tr><td><p>Tawny eagle</p><p><italic>Aquila rapax</italic></p></td><td>AS</td><td>VU</td><td>−66</td><td><p>−62.7</p><p>−69.6</p></td><td>−91</td><td>−71</td><td>−7</td><td>+93</td><td><bold>⇧</bold></td></tr><tr><td><p>Steppe eagle</p><p><italic>Aquila nipalensis</italic></p></td><td>PM</td><td>EN</td><td>−91</td><td><p>−90.2</p><p>−91.3</p></td><td>−</td><td>−56</td><td>−78</td><td>−96</td><td><bold>⇩</bold></td></tr><tr><td><p><bold>African hawk-eagle</bold></p><p><bold><italic>Aquila spilogaster</italic></bold></p></td><td>AS</td><td>LC</td><td>−91</td><td><p>−91.1</p><p>−91.8</p></td><td>−84</td><td>−</td><td>−</td><td>−97</td><td><bold>⇧</bold></td></tr><tr><td><p><bold>Wahlberg’s eagle</bold></p><p><bold><italic>Hieraaetus wahlbergi</italic></bold></p></td><td>AM</td><td>LC</td><td>−74</td><td><p>−62.2</p><p>−81.9</p></td><td>−81</td><td>−</td><td>−32</td><td>−48</td><td>⇧</td></tr><tr><td><p>Booted eagle</p><p><italic>Hieraaetus pennatus</italic></p></td><td>PMAM</td><td>LC</td><td>+3</td><td><p>+2.1</p><p>+4.4</p></td><td>+3</td><td>−</td><td>−</td><td>−</td><td><bold>⇧</bold></td></tr><tr><td><p><bold>Martial eagle</bold></p><p><bold><italic>Polemaetus bellicosus</italic></bold></p></td><td>AS</td><td>EN</td><td>−90</td><td><p>−84.0</p><p>−93.6</p></td><td>−97</td><td>−</td><td>−23</td><td>−56</td><td>⇩</td></tr><tr><td><p><bold>Long-crested eagle</bold></p><p><bold><italic>Lophaetus occipitalis</italic></bold></p></td><td>AS</td><td>LC</td><td>−79</td><td><p>−78.4</p><p>−79.1</p></td><td>−</td><td>−66</td><td>−80</td><td>−</td><td><bold>⇧</bold></td></tr><tr><td><p>African pygmy-falcon</p><p><italic>Polihierax semitorquatus</italic></p></td><td>AS</td><td>LC</td><td>+44</td><td><p>+41.3</p><p>+46.7</p></td><td>−</td><td>−</td><td> + 44</td><td>−</td><td><bold>⇩</bold></td></tr><tr><td><p>Lesser kestrel</p><p><italic>Falco naumanni</italic></p></td><td>PM</td><td>LC</td><td>−65</td><td><p>−64.7</p><p>−66.0</p></td><td>−</td><td>−</td><td>−53</td><td>−74</td><td><bold>⇩</bold></td></tr><tr><td><p>Common kestrel</p><p><italic>Falco tinnunculus</italic></p></td><td>PMAS</td><td>LC</td><td>−70</td><td><p>−68.8</p><p>−72.0</p></td><td>−</td><td>−65</td><td>−71</td><td>−</td><td><bold>⇩</bold></td></tr><tr><td><p>Greater kestrel</p><p><italic>Falco rupicoloides</italic></p></td><td>AS</td><td>LC</td><td>−11</td><td><p>−10.5</p><p>−10.6</p></td><td>−</td><td>−</td><td>−</td><td>−11</td><td><bold>⇩</bold></td></tr><tr><td><p><bold>Fox kestrel</bold></p><p><bold><italic>Falco alopex</italic></bold></p></td><td>AS</td><td>LC</td><td>−33</td><td><p>−32.1</p><p>−33.9</p></td><td>−33</td><td>−</td><td>−</td><td>−</td><td>−</td></tr><tr><td><p>Grey kestrel</p><p><italic>Falco ardosiaceus</italic></p></td><td>AS</td><td>LC</td><td>−25</td><td><p>−19.8</p><p>−29.0</p></td><td>−25</td><td>−</td><td>−</td><td>−</td><td>−</td></tr><tr><td><p><bold>Dickinson’s kestrel</bold></p><p><bold><italic>Falco dickinsoni</italic></bold></p></td><td>AS</td><td>LC</td><td>−53</td><td><p>−53.1</p><p>−53.3</p></td><td>−</td><td>−</td><td>−</td><td>−53</td><td>−</td></tr><tr><td><p>Red-necked falcon</p><p><italic>Falco ruficollis</italic></p></td><td>AS</td><td>LC</td><td>−27</td><td><p>−26.4</p><p>−27.5</p></td><td>−27</td><td>−</td><td>−</td><td>−</td><td>−</td></tr><tr><td><p>Lanner falcon</p><p><italic>Falco biarmicus</italic></p></td><td>AS</td><td>LC</td><td>−20</td><td><p>−19.6</p><p>−20.2</p></td><td>−19</td><td>−</td><td>−</td><td>−22</td><td><bold>⇧</bold></td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Extended Data Table 1</label><caption><p>Details of statistical models referred to in the Results section</p></caption></table-wrap>", "<table-wrap id=\"Tab3\"><label>Extended Data Table 2</label><caption><p>Analysis of SABAP2 reporting rates for 30 raptor species in South Africa during 2008–2021</p></caption></table-wrap>" ]	[]	[]	[]	[]	[]	[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM3\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM4\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM5\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM6\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM7\"></supplementary-material>" ]	[ "<table-wrap-foot><p>Encounter rate changes for each species were estimated from studies conducted in West Africa, northern Cameroon and Kenya (1969–1977 to 2000–2020) and in northern Botswana (1991–1995 to 2015–2016). These were annualized, averaged across studies (weighted by the species’ range size in each study area) and projected over three generation lengths. Fifteen species shown in bold are African endemics or near-endemics whose decline estimates exceed the limits defining their current IUCN threat status. Trends among 30 of the 42 species were also determined in South Africa, from SABAP2 reporting rates recorded during 2008–2021; bold arrows indicate <italic>P</italic> < 0.05.</p><p><sup>a</sup>Species are listed in taxonomic order, following ref. <sup>##UREF##39##52##</sup>.</p><p><sup>b</sup>Migratory status: AS, Afrotropical sedentary; AM, Afrotropical migrant; PM, Palaearctic migrant. Sources: refs. <sup>##UREF##15##21##,##UREF##39##52##</sup>.</p><p><sup>c</sup>IUCN global threat status: LC, Least Concern; VU, Vulnerable; EN, Endangered; CR, Critically Endangered<sup>##UREF##34##45##</sup>.</p><p><sup>d</sup>Median, Q1 and Q3 rates of change over three generation lengths were derived from two scenarios, in which average encounter rates in unsurveyed PAs were assumed to have either been the same as in surveyed PAs, or the same as in UPAs, respectively (<xref rid=\"Sec13\" ref-type=\"sec\">Methods</xref>).</p><p><sup>e</sup>Species meeting data selection criteria in fewer than 30 SABAP2 pentads (‘−’) were excluded from the analysis (<xref rid=\"Sec13\" ref-type=\"sec\">Methods</xref>).</p></table-wrap-foot>", "<table-wrap-foot><p>All models were run in R and produced two-tailed <italic>P</italic> values.</p></table-wrap-foot>", "<table-wrap-foot><p>For data selection criteria and model structure see Methods and model 17 in Extended Data Table ##TAB##1##1##. The direction of change in reporting rate is given by the slope coefficient. Significant changes are shown in bold (Bonferroni correction applied). Species are ordered taxonomically, as in Table ##TAB##0##1##.</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>These authors contributed equally: Phil Shaw, Darcy Ogada.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"41559_2023_2236_Fig1_HTML\" id=\"d32e592\"/>", "<graphic xlink:href=\"41559_2023_2236_Fig2_HTML\" id=\"d32e1854\"/>", "<graphic xlink:href=\"41559_2023_2236_Fig3_HTML\" id=\"d32e1914\"/>", "<graphic xlink:href=\"41559_2023_2236_Fig4_HTML\" id=\"d32e2046\"/>", "<graphic xlink:href=\"41559_2023_2236_Fig5_HTML\" id=\"d32e2159\"/>", "<graphic position=\"anchor\" xlink:href=\"41559_2023_2236_Fig6_ESM\" id=\"d32e2877\"/>", "<graphic position=\"anchor\" xlink:href=\"41559_2023_2236_Fig7_ESM\" id=\"d32e2892\"/>", "<graphic position=\"anchor\" xlink:href=\"41559_2023_2236_Tab1_ESM\" id=\"d32e2902\"><caption><p>Details of statistical models referred to in the Results section.</p></caption></graphic>", "<graphic position=\"anchor\" xlink:href=\"41559_2023_2236_Tab2_ESM\" id=\"d32e2921\"><caption><p>Analysis of SABAP2 reporting rates for 30 raptor species in South Africa during 2008–2021.</p></caption></graphic>" ]	[ "<media xlink:href=\"41559_2023_2236_MOESM1_ESM.pdf\"><label>Supplementary Information</label><caption><p>Extended Methods, Supplementary Tables 1–9, Figs. 1–3 and References.</p></caption></media>", "<media xlink:href=\"41559_2023_2236_MOESM2_ESM.pdf\"><caption><p>Reporting Summary</p></caption></media>", "<media xlink:href=\"41559_2023_2236_MOESM3_ESM.csv\"><label>Source Data Fig. 1</label><caption><p>Source data for regional bar charts, showing percentage decline in bateleur encounter rates within protected and unprotected areas, projected over three generation lengths.</p></caption></media>", "<media xlink:href=\"41559_2023_2236_MOESM4_ESM.csv\"><label>Source Data Fig. 2</label><caption><p>Source data for horizontal bar chart, showing percentage change in encounter rates for 42 species, projected over three generation lengths.</p></caption></media>", "<media xlink:href=\"41559_2023_2236_MOESM5_ESM.csv\"><label>Source Data Fig. 3</label><caption><p>Source data for bubble chart, showing change rates over three generation lengths, in relation to body mass.</p></caption></media>", "<media xlink:href=\"41559_2023_2236_MOESM6_ESM.xlsx\"><label>Source Data Fig. 4</label><caption><p>Source data for Fig 4 showing: decline rates within regions, in relation to PA status; change rates in relation to body mass and PA status; modelled change rates within PAs and UPAs, in relation to body mass.</p></caption></media>", "<media xlink:href=\"41559_2023_2236_MOESM7_ESM.xlsx\"><label>Source Data Fig. 5</label><caption><p>Source data for Fig. 5 showing change in PA dependency values in relation to body size class, and change in PA dependency in relation to change in abundance.</p></caption></media>" ]	[{"label": ["5."], "mixed-citation": ["Newton, I. 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PMC10781636	38093021	[]	[ "<title>Methods</title>", "<title>Identification of <italic>TAS1R</italic> genes from genome and RNA sequencing data</title>", "<p id=\"Par26\">We used genome and transcriptome data as well as related raw sequence reads for a broad range of vertebrates (Supplementary Table ##SUPPL##2##1##). First, a tblastn search was conducted against the 33 genomes using amino acid sequences of exon 6 of the <italic>TAS1R</italic>s of human, chicken and zebrafish as queries. Hit sequences meeting the E value threshold of 1,110<sup>–40</sup> were used to construct a phylogenetic tree using RAxML v.8.2.12 with the JTT + G (CAT approximation) model. The G protein-coupled receptor family C group 6 member A (GPRC6A) genes, which are the closest relative of T1Rs<sup>##REF##16966606##40##</sup>, were used as the outgroup. Identified valid <italic>TAS1R</italic> sequences were used for subsequent iterations of the tblastn search. RNA sequencing data were assembled using Bridger v.r2014-12-01 with default parameters and were used as a database for the tblastn search<sup>##REF##25723335##41##</sup>.</p>", "<p id=\"Par27\">We also conducted a NCBI tblastn search against all reference genomes of Deuterostomia excluding jawed vertebrates (Gnathostomata), and did not find any <italic>TAS1R</italic> orthologues. In addition, an NCBI blastp search against the nr database, excluding Gnathostomata, yielded no hits for <italic>TAS1R</italic> orthologues. Subsequently, we performed comprehensive annotation of <italic>TAS1R</italic> exons in 21 organisms, including model organisms and species that were presumed to possess novel/unclassified <italic>TAS1R</italic> members, as identified via the procedure above.</p>", "<p id=\"Par28\">The exon regions were predicted using AUGUSTUS v.3.2.3 (ref. <sup>##REF##15980513##42##</sup>). followed by an evaluation of the exon–intron boundaries by aligning the genome sequences with the human and zebrafish <italic>TAS1R</italic> sequences and by the GT/AG rule. Because a certain degree of base errors was observed in the genome assembly for axolotl, sequence correction was needed for our <italic>TAS1R</italic> identification. We retrieved the raw reads of the public genome data and RNA sequencing data corresponding to the <italic>TAS1R</italic> exons using bowtie2 (ref. <sup>##REF##22388286##43##</sup>) and blastn and used that data to correct the <italic>TAS1R</italic> sequences by checking the alignment. The <italic>TAS1R</italic> amino acid sequences identified for axolotl, coelacanth and bichir were used as queries for an additional tblastn search of other vertebrates.</p>", "<title>Phylogenetic analysis</title>", "<p id=\"Par29\">For the full-length amino acid sequences, non-homologous residues were masked using PREQUAL<sup>##REF##29868763##44##</sup> and the sequences were aligned using MAFFT v.7.427 with the ginsi option<sup>##REF##23329690##45##</sup>. The phylogenetic tree was constructed using RAxML as described above. In addition, a maximum-likelihood tree was constructed under the posterior mean site frequency approximation<sup>##REF##28950365##46##</sup> of the JTT + C20 + F + Γ model with 1,000 bootstrap replicates using IQ-TREE v.2.2.2.6 (ref. <sup>##REF##32011700##47##</sup>). Bayesian tree inference was conducted with MrBayes 3.2.6 with the JTT-F + Γ<sub>4</sub> model<sup>##REF##22357727##48##</sup>. Two simultaneous runs were carried out with 10,000,000 generations, of which 2,500,000 were discarded as burn-in, and convergence was assessed with Tracer<sup>##REF##29718447##49##</sup>. Trees were visualized with iTOL<sup>##REF##33885785##50##</sup>. Alternative tree topologies were evaluated with the approximately unbiased test with 100,000 replicates using CONSEL v.0.20 (ref. <sup>##REF##11751242##51##</sup>).</p>", "<title>Synteny analysis</title>", "<p id=\"Par30\">The synteny of genes proximal to the novel T1Rs was analysed using annotations available in Ensembl 97 (ref. <sup>##REF##34791404##52##</sup>) for human (GRCh38), chicken (GRCg6a), anole lizard (AnoCar2.0), coelacanth (LatCha1), zebrafish (GRCz11) and spotted gar (LepOcu1). For bichir, annotations were conducted using Cufflinks on a draft assembly. The gene annotation for axolotl was obtained from the Axolotl-omics website (AmexG_v6.0-DD)<sup>##REF##33827918##53##</sup>. NCBI annotation was referred to for the West African lungfish (PAN1.0) and elephant fish (Callorhinchus_milii-6.1.3). Novel <italic>TAS1R</italic>s were added to the gene list in our synteny analysis if they were not accurately identified in the public annotation data.</p>", "<title>Conserved motifs in the sequence upstream of <italic>TAS1R4</italic></title>", "<p id=\"Par31\">Sequences up to 300 bp upstream of the <italic>TAS1R4</italic> open reading frames were collected for whale shark, bamboo shark, cloudy catshark, elephant fish, bichir, coelacanth, axolotl, two-lined caecilian, Japanese gecko, anole lizard and central bearded dragon. The sequences were aligned using MAFFT<sup>##REF##23329690##45##</sup> and then used for MEME analysis<sup>##REF##25953851##19##</sup> to search for a maximum of three conserved sequence motifs. The motifs discovered by MEME were then used for comparison with known transcription-factor binding motifs in TRANSFAC v.11.3 using STAMP<sup>##REF##17478497##54##</sup>. The known Oct-11/Pou2f3 motif was obtained from JASPAR<sup>##REF##34850907##55##</sup>.</p>", "<title>Experimental animals</title>", "<p id=\"Par32\">This study was carried out in accordance with the National Institutes of Health guide for the care and use of laboratory animals (NIH Publication No. 8023, revised 1978). Both male and female bichir (<italic>Polypterus senegalus</italic>), ~5–7 cm body length, were purchased from a local commercial source. We found no differences in the expression of genes encoding T1Rs or downstream signal-transduction molecules, such as TRPM5, Gαia1 and Gα14, between male and female bichir by in situ hybridization.</p>", "<title>Cloning <italic>TAS1R</italic>s of bichir and elephant fish</title>", "<p id=\"Par33\"><italic>TAS1R1</italic>, <italic>TAS1R2A</italic>, <italic>TAS1R2B</italic>, <italic>TAS1R3B</italic>, <italic>TAS1R4</italic> and <italic>TAS1R8</italic> were amplified by PCR from the genomic DNA or cDNA of bichir. <italic>TAS1R6-1</italic>, <italic>TAS1R6-2</italic>, <italic>TAS1R6-3</italic>, <italic>TAS1R3C</italic> and <italic>TAS1R4</italic> were amplified by PCR from the genomic DNA of elephant fish (<italic>Callorhinchus milii</italic>). PCR and Sanger sequencing for the coding sequences of their <italic>TAS1R</italic> genes were performed using specific primers designed based on the annotation from the whole genome assemblies. The PCR products of the exons were assembled into one full-length sequence using overlapping PCR (In-fusion cloning; Clontech) for each <italic>TAS1R</italic> and were then subcloned into the pEAK10 expression vector (Edge Biosystems).</p>", "<title>Functional analysis of T1Rs</title>", "<p id=\"Par34\">Responses of the T1Rs to various taste-associated stimulants were measured using a cell-based luminescence assay, as described previously<sup>##REF##21981007##22##,##REF##24214976##23##</sup>. Briefly, HEK293T cells were transiently co-transfected with an expression vector for an individual T1R along with a chimeric rat G protein (rG15i2) and a calcium-binding photoprotein (mt-apoclytin-II). Cells were seeded in 96-well plates and assayed 2 days after transfection. Cells were exposed to each taste stimulant individually, and luminescence intensity was measured using a FlexStation 3 microplate reader (Molecular Devices). The response in each well was calculated based on the area under the curve and expressed as RLU. Data were collected from three independent experiments, each carried out with duplicate samples. We adapted a strict definition for the positive response as >10,000 RLU along with a statistically significant difference against control (buffer) with a false discovery rate (<italic>q</italic>) of <0.01 (one-sided <italic>t</italic>-test). A limitation of this assay is that concentrations of amino acids and sugars were presented at a maximum of 50 mM or 100 mM to avoid receptor-independent calcium increases, caused for instance by changes in osmolarity<sup>##REF##24214976##23##</sup>, which can prevent the accurate assessment of responses to higher ligand concentrations. The osmotic pressure of each of the Arg and His solutions was higher than those of the other amino acid solutions because large amounts of HCl or NaOH were required for pH adjustment; this may have caused the higher response to 50 mM His of bichir T1R2B/T1R3B (Fig. ##FIG##3##4c##).</p>", "<title>In situ hybridization</title>", "<p id=\"Par35\">In situ hybridization was performed as described previously<sup>##REF##16274966##9##</sup>. In brief, fresh-frozen sections (10 μm thick) of bichir jaw tissue were placed on MAS-coated glass slides (Matsunami Glass) and fixed with 4% paraformaldehyde in phosphate-buffered saline. Prehybridization (58 °C, 1 h), hybridization (58 °C, two overnight sessions), washing (58 °C, 0.2× saline–sodium citrate) and development (nitroblue tetrazolium/ 5-bromo-4-chloro-3-indolyl phosphate; NBT-BCIP) were performed using digoxigenin-labelled probes. Images of stained sections were obtained using a fluorescence microscope (DM6 B; Leica) equipped with a cooled CCD digital camera (DFC7000 T; Leica). Double-label fluorescence in situ hybridization was performed using digoxigenin- and fluorescein-labelled RNA probes. Each labelled probe was detected sequentially by incubation with a peroxidase-conjugated antibody against digoxigenin and peroxidase-conjugated anti-fluorescein (Roche) followed by incubation with tyramide signal amplification (TSA)–Alexa Fluor 555 and TSA–Alexa Fluor 488 (Invitrogen) using the tyramide signal amplification method. Images of stained sections were obtained using a confocal laser-scanning microscope (LSM 800; ZEISS). The entire coding regions for the six T1Rs and two G protein α subunits as well as the partial coding region for Trpm5, all of which were amplified from bichir cDNA synthesized from lip tissue, were used as probes for in situ hybridization.</p>", "<title>Reporting summary</title>", "<p id=\"Par36\">Further information on research design is available in the ##SUPPL##1##Nature Portfolio Reporting Summary## linked to this article.</p>" ]	[ "<title>Results</title>", "<title>Identification of novel <italic>TAS1R</italic> family members</title>", "<p id=\"Par6\">We identified homologues of <italic>TAS1R</italic> genes that are included in public genome/transcriptome databases for diverse taxa of jawed vertebrates (Supplementary Table ##SUPPL##2##1##). Except for jawed vertebrates, <italic>TAS1R</italic> genes were not identified in any Deuterostomia reference genomes (lampreys, hagfishes, tunicates, lancelets, sea urchins, starfish, hemichordate, etc.) or the nr database, suggesting that the <italic>TAS1R</italic>/T1R family exists only in jawed vertebrates. All phylogenetic trees, as estimated using different methods and datasets, consistently revealed the existence of many <italic>TAS1R</italic>s that had not been categorized into any of the three known clades: <italic>TAS1R1</italic>, <italic>TAS1R2</italic> and <italic>TAS1R3</italic>. These previously undocumented <italic>TAS1R</italic>s were found in lizards, amphibians, lungfishes, coelacanth, bichir and cartilaginous fishes (Fig. ##FIG##0##1a## and Extended Data Figs. ##FIG##5##1##–##FIG##7##3##). The novel <italic>TAS1R</italic>s could be classified into five new clades. One clade, which is the sister clade of <italic>TAS1R3</italic>, was named <italic>TAS1R4</italic> and contains genes from all jawed vertebrates investigated except mammals, birds, crocodilians, turtles, frog, sterlets or neopterygians (Fig. ##FIG##0##1b## and Extended Data Fig. ##FIG##8##4##). Another novel <italic>TAS1R</italic>, named <italic>TAS1R5</italic>, exists in axolotl, lungfishes and coelacanth and is close to the clade comprising <italic>TAS1R1</italic> and <italic>TAS1R2</italic> (Fig. ##FIG##0##1a##).</p>", "<p id=\"Par7\">The sister clade to <italic>TAS1R1</italic> + <italic>TAS1R2</italic> + <italic>TAS1R5</italic>, which was named <italic>TAS1R6</italic>, was identified exclusively in cartilaginous fishes. <italic>TAS1R6</italic> could be further divided into three subclades, namely <italic>TAS1R6-1</italic>, <italic>TAS1R6-2</italic> and <italic>TAS1R6-3</italic>, all of which were found to be present in elephant fish (also called elephant shark), belonging to the taxon Holocephali of cartilaginous fishes (Extended Data Figs. ##FIG##5##1##–##FIG##7##3##). Therefore, the three <italic>TAS1R6</italic> subclades probably emerged in the common ancestor of extant cartilaginous fishes. A thorough search of the genomes and transcriptomes of the four cartilaginous fish species identified only <italic>TAS1R3</italic>, <italic>TAS1R4</italic> and <italic>TAS1R6</italic>, but no orthologues of <italic>TAS1R1</italic>, <italic>TAS1R2</italic> or <italic>TAS1R5</italic> (Fig. ##FIG##0##1b## and Extended Data Fig. ##FIG##8##4##), suggesting that the <italic>TAS1R1</italic>, <italic>TAS1R2</italic> and <italic>TAS1R5</italic> genes in bony vertebrates are co-orthologues of the <italic>TAS1R6</italic> genes in cartilaginous fish.</p>", "<p id=\"Par8\">Another novel <italic>TAS1R</italic> clade, <italic>TAS1R7</italic>, was found exclusively in axolotl and lizards. Yet another new clade, <italic>TAS1R8</italic>, was identified only in bichir and lungfishes, and its monophyly was robustly supported (Fig. ##FIG##0##1## and Extended Data Figs. ##FIG##5##1##–##FIG##7##3##), suggesting that <italic>TAS1R8</italic> emerged in the common ancestor of bichir and lungfishes. Indeed, the likelihood of an alternative relationship, in which <italic>TAS1R7</italic> and <italic>TAS1R8</italic> form an exclusive cluster and represent a species tree, was rejected statistically based on the approximately unbiased test (<italic>P</italic> < 10<sup>–4</sup>; Extended Data Fig. ##FIG##9##5##), suggesting that <italic>TAS1R7</italic> and <italic>TAS1R8</italic> are distinct groups. Among the vertebrates we investigated, the axolotl was found to possess <italic>TAS1R</italic>s from the greatest number (seven) of clades (Fig. ##FIG##0##1b## and Supplementary Table ##SUPPL##2##2##).</p>", "<title>Each of <italic>TAS1R3</italic> and <italic>TAS1R2</italic> consists of two paralogous clades</title>", "<p id=\"Par9\">Remarkably, the phylogenetic analysis also revealed that <italic>TAS1R3</italic> of bony vertebrates could be divided into two clades, named <italic>TAS1R3A</italic> and <italic>TAS1R3B</italic>, with high branch support (Fig. ##FIG##0##1## and Extended Data Figs. ##FIG##5##1##–##FIG##7##3##). <italic>TAS1R3A</italic> was found to be present in tetrapods and lungfishes but not other vertebrates, whereas <italic>TAS1R3B</italic> was identified only in amphibians, lungfishes, coelacanth and ray-finned fishes. The sister clade to <italic>TAS1R3A</italic> + <italic>TAS1R3B</italic> was identified exclusively in cartilaginous fishes and named <italic>TAS1R3C</italic>. This distribution suggested that an ancestral <italic>TAS1R3</italic> gene was duplicated in the common ancestor of bony vertebrates, with subsequent independent loss of <italic>TAS1R3A</italic> in certain lineages such as coelacanth and ray-finned fishes, whereas <italic>TAS1R3B</italic> was lost in Amniota (mammals and sauropsids). Therefore, the <italic>TAS1R3</italic> genes in mammals and teleost fishes are paralogues. Axolotl and Australian lungfish retained both <italic>TAS1R3A</italic> and <italic>TAS1R3B</italic> although the lungfish <italic>TAS1R3B</italic> has been pseudogenized. Furthermore, the amphibians possess two groups of <italic>TAS1R3B</italic>, named <italic>TAS1R3B1</italic> and <italic>TAS1R3B2</italic> (Fig. ##FIG##0##1a##), suggesting that <italic>TAS1R3B</italic> was again duplicated—at the latest—before the common ancestor of amphibians.</p>", "<p id=\"Par10\">A distinguishing feature of <italic>TAS1R3B</italic> in ray-finned fishes is the presence of additional introns. In contrast to other <italic>TAS1R</italic>s, which consist of six exons, exon 3 of <italic>TAS1R3B</italic> in ray-finned fishes has been altered during evolution such that it now comprises two exons, suggesting the acquisition of an intron in the common ancestor of ray-finned fishes (Extended Data Fig. ##FIG##10##6##). Furthermore, exon 6 of <italic>TAS1R3B</italic> in non-bichir ray-finned fishes acquired an additional intron, resulting in a total of eight exons of the gene. Thus, this intron is likely to have been inserted after the divergence of bichir. Except for these two instances, the exon–intron structure is conserved among the <italic>TAS1R</italic> genes we investigated.</p>", "<p id=\"Par11\">Also, <italic>TAS1R2</italic> does not form a single clade in the tree (Fig. ##FIG##0##1##). The <italic>TAS1R2</italic> genes in ray-finned fishes form a clade with <italic>TAS1R1</italic>, and the other <italic>TAS1R2</italic> group from tetrapods, lungfish, coelacanth, bowfin and bichir forms a sister group to the clade comprising <italic>TAS1R1</italic> and the ray-finned fish <italic>TAS1R2</italic>. The paraphyletic relationship of the two <italic>TAS1R2</italic> groups is concordant with previous reports<sup>##UREF##1##13##</sup>. Hereafter, we refer to the major vertebrate group as <italic>TAS1R2A</italic> and the ray-finned fish group as <italic>TAS1R2B</italic> (Fig. ##FIG##0##1##). Notably, we found that the anciently diverged ray-finned fishes such as bowfin and bichir retained both <italic>TAS1R2A</italic> and <italic>TAS1R2B</italic> as well as <italic>TAS1R1</italic>. We assessed the likelihood of other phylogenetic relationships in which <italic>TAS1R2</italic>s have a single origin, and the hypotheses were significantly rejected (<italic>P</italic> < 10<sup>–6</sup>, approximately unbiased test; Extended Data Fig. ##FIG##9##5##). These results suggested that the <italic>TAS1R2</italic> genes in mammals and teleost fishes are paralogues. Thus, the <italic>TAS1R</italic> phylogenetic tree comprised a total of 11 <italic>TAS1R</italic> clades: <italic>TAS1R1</italic>, <italic>TAS1R2A</italic>, <italic>TAS1R2B</italic>, <italic>TAS1R3A</italic>, <italic>TAS1R3B</italic>, <italic>TAS1R3C</italic>, <italic>TAS1R4</italic>, <italic>TAS1R5</italic>, <italic>TAS1R6</italic>, <italic>TAS1R7</italic> and <italic>TAS1R8</italic>. This unexpected gene diversity challenges conventional conceptions about the evolution of the genetic basis for umami and sweet receptors.</p>", "<title>Birth-and-death evolution of the <italic>TAS1R</italic> family</title>", "<p id=\"Par12\">Some of the higher-level relationships among the <italic>TAS1R</italic> clades were supported with relatively high branch support, as exemplified by the exclusive cluster of <italic>TAS1R3</italic> + <italic>TAS1R4</italic>, the clade of the other <italic>TAS1R</italic>s, the clade of <italic>TAS1R1</italic> + <italic>TAS1R2B</italic> + <italic>TAS1R2A</italic> + <italic>TAS1R5</italic>, and the sister relationship of this latter clade to <italic>TAS1R6</italic> (Fig. ##FIG##0##1##). Based on the phylogenetic relationships and the distribution of all <italic>TAS1R</italic> members (Fig. ##FIG##0##1b##), the most parsimonious evolutionary scenario could be deduced as follows (Fig. ##FIG##1##2##). The first <italic>TAS1R</italic> gene emerged in the ancestral lineage of jawed vertebrates during the period 615–473 million years ago (Ma) according to TimeTree<sup>##UREF##2##16##</sup>. This ancestral <italic>TAS1R</italic> underwent multiple duplications to produce at least five <italic>TAS1R</italic> genes: <italic>TAS1R3C</italic> (the ancestral gene of <italic>TAS1R3A</italic> and <italic>TAS1R3B</italic>), <italic>TAS1R4</italic>, <italic>TAS1R7</italic>, <italic>TAS1R8</italic> and <italic>TAS1R6</italic> (the ancestral gene of <italic>TAS1R1</italic>, <italic>TAS1R2B</italic>, <italic>TAS1R2A</italic> and <italic>TAS1R5</italic>). Owing to speciation between cartilaginous fishes and bony vertebrates ~473 Ma, <italic>TAS1R6</italic> and the ancestral gene of clade <italic>TAS1R1</italic> + <italic>TAS1R2B</italic> + <italic>TAS1R2A</italic> + <italic>TAS1R5</italic> diverged. This speciation probably also led to the split between <italic>TAS1R3C</italic> and clade <italic>TAS1R3A</italic> + <italic>TAS1R3B</italic>. In the stem lineage of bony vertebrates (473–435 Ma), <italic>TAS1R1</italic>, <italic>TAS1R2A</italic>, <italic>TAS1R2B</italic> and <italic>TAS1R5</italic> were generated via additional gene duplication events. Simultaneously, <italic>TAS1R3A</italic> and <italic>TAS1R3B</italic> were generated by gene duplication, resulting in a total of nine <italic>TAS1R</italic>s in the common ancestor of bony vertebrates (Fig. ##FIG##1##2##). After the divergence of ray-finned and lobe-finned fishes ~435 Ma, a portion of the expanded <italic>TAS1R</italic>s began to be differentially lost during vertebrate evolution. For example, <italic>TAS1R8</italic> was lost in the tetrapod ancestor, <italic>TAS1R3B</italic> and <italic>TAS1R5</italic> were lost in the amniote ancestor, and <italic>TAS1R4</italic> and <italic>TAS1R7</italic> were lost in the mammalian ancestor (Fig. ##FIG##1##2##). Thus, gene expansion before the common ancestor of bony vertebrates as well as the subsequent loss of a subset of genes have resulted in the rather dispersed distribution of <italic>TAS1R</italic>s in extant species (Fig. ##FIG##0##1b##).</p>", "<title><italic>TAS1R</italic> gene cluster revealed by scanning understudied genomes</title>", "<p id=\"Par13\">The simplest model for gene amplification is a tandem duplication that produces multiple genes located side-by-side<sup>##UREF##3##17##,##REF##103000##18##</sup>. However, <italic>TAS1R1</italic>, <italic>TAS1R2</italic> and <italic>TAS1R3</italic> are located far from each other in both mammalian and teleost genomes. In human chromosome 1, for example, <italic>TAS1R1</italic> is 12 Mb distant from <italic>TAS1R2A</italic> and 5 Mb distant from <italic>TAS1R3A</italic>, with many intervening genes in each case. In zebrafish, each of <italic>TAS1R1</italic> and <italic>TAS1R3B</italic> is located on a different chromosome from the two copies of <italic>TAS1R2B</italic>, prompting us to hypothesize that <italic>TAS1R</italic> members may have undergone expansion by tandem duplications in the ancestral genome, followed by subsequent translocation to distant regions during evolution. To address this possibility, the synteny of <italic>TAS1R3</italic> and <italic>TAS1R4</italic> was investigated among vertebrates, particularly those having the novel <italic>TAS1R</italic>s (Fig. ##FIG##2##3## and Extended Data Fig. ##FIG##11##7##). Indeed, the novel <italic>TAS1R</italic>s were found to be located side-by-side in anole lizard, axolotl, lungfish, coelacanth and elephant fish (Fig. ##FIG##2##3a##). Even <italic>TAS1R2A</italic> and <italic>TAS1R3B</italic> are located next to each other in axolotl and bichir. This result suggested that a <italic>TAS1R</italic> gene cluster had formed in the common ancestor of jawed vertebrates.</p>", "<p id=\"Par14\">A comparison of neighbouring genes revealed that the <italic>TAS1R</italic> cluster is flanked by two genes, namely <italic>DVL1</italic> and <italic>MXRA8</italic>, in the genomes of human, chicken, axolotl, lungfish, coelacanth, bichir and elephant fish (Fig. ##FIG##2##3a##), suggesting that these two genes were adjacent to the <italic>TAS1R</italic> cluster in the common ancestor of jawed vertebrates. On the opposite end of the <italic>TAS1R</italic> cluster, the gene order of <italic>ACAP3</italic>–<italic>PUSl1</italic>–<italic>LPAR6</italic>–<italic>INTS11</italic>–<italic>CPTP</italic> may have been established in the sarcopterygian ancestor based on conservation among coelacanth, axolotl, chicken and partly in lizard. Furthermore, the presence of other <italic>TAS1R</italic>-proximal genes is also conserved even across distant chromosomal regions (Extended Data Fig. ##FIG##11##7##). This suggested that a chromosomal region containing both <italic>TAS1R</italic> and multiple neighbouring genes—rather than the <italic>TAS1R</italic> gene alone—had translocated to a different region in each lineage. Based on the inferred ancestral gene order, the unique distribution of <italic>TAS1R</italic>s among present-day mammals and teleost fishes may have been a consequence of a combination of several events (Fig. ##FIG##2##3b##): (1) tandem duplication that produced a <italic>TAS1R</italic> cluster in the ancestor of jawed vertebrates; (2) local translocation of a subset of <italic>TAS1R</italic>s within a chromosome, as seen in multiple clusters observed in axolotl and coelacanth (Extended Data Fig. ##FIG##11##7##); (3) translocation of entire <italic>TAS1R</italic>-containing regions to different chromosomes, as observed in zebrafish; and (4) gene loss(es) in each lineage, as partly observed as the presence of pseudogenes (Fig. ##FIG##0##1a##). Moreover, lineage-specific duplication events have occurred such as <italic>TAS1R2B</italic> in zebrafish and fugu and <italic>TAS1R2A</italic> in coelacanth (Fig. ##FIG##0##1a## and Extended Data Fig. ##FIG##11##7##)<sup>##REF##23886383##12##,##UREF##1##13##</sup>. Finally, we found that some of the <italic>TAS1R</italic>s identified have been pseudogenized; for example, the whale shark <italic>TAS1R3C</italic> and the lungfish <italic>TAS1R3B</italic> (Fig. ##FIG##0##1##). These observations also support the evolutionary model of the <italic>TAS1R</italic> family presented in Fig. ##FIG##2##3b##.</p>", "<title>Conservation of a possible Oct-binding site in <italic>TAS1R4</italic></title>", "<p id=\"Par15\">Because <italic>TAS1R4</italic> is shared among a wide variety of vertebrates in contrast to the other novel <italic>TAS1R</italic>s, we expected that a transcriptional regulatory mechanism might be conserved among the species. To explore the existence of a possible regulatory element, sequences upstream of the open reading frames of <italic>TAS1R4</italic> from various species were aligned, and MEME<sup>##REF##25953851##19##</sup> was used to search for transcription-factor binding motifs conserved among the species. The most significant hit was the binding motif for the Oct family (<italic>P</italic> < 10<sup>–12</sup> for Oct-4, <italic>P</italic> < 10<sup>–7</sup> for Oct-1). At least one sequence of the known Oct-binding motif ‘ATGCAAAT’ is conserved among cartilaginous fishes, coelacanth, bichir and lizards in the region upstream of <italic>TAS1R4</italic> (Fig. ##FIG##2##3c,d##). Although little is known about the transcriptional regulatory network in taste-receptor cells (TRCs), one known transcription factor responsible for TRC differentiation is Skn-1a, which is an Oct factor also known as Oct-11, Epoc-1 or Pou2f3 (ref. <sup>##REF##21572433##20##</sup>). In mammals, Skn-1a is exclusively expressed in umami, sweet and bitter TRCs, and loss of Skn-1a results in the complete absence of these TRCs<sup>##REF##21572433##20##,##REF##29216297##21##</sup>. This finding suggested that <italic>TAS1R4</italic> expression is governed by a conserved regulatory mechanism involving an Oct transcription factor, possibly Skn-1a. Although Oct-binding sites were not observed in the other novel <italic>TAS1R</italic>s, these findings may help to elucidate the molecular mechanisms underlying the conserved and/or lineage-specific expression of a variety of <italic>TAS1R</italic>s in TRCs, which will enhance our understanding of the evolutionary origin of TRCs.</p>", "<title>T1R diversity expands the range of taste sensation</title>", "<p id=\"Par16\">Because receptor responses cannot be predicted from sequence analysis alone, functional tests using cultured cells heterologously expressing the target receptor are useful. We previously established a high-throughput screening system for the T1R receptors using a luminescence-based assay<sup>##REF##21981007##22##</sup> and have used it to identify ligands for both mammalian<sup>##REF##34450087##7##,##REF##24214976##23##</sup> and non-mammalian<sup>##REF##25146290##24##–##REF##36473437##26##</sup> T1R receptors. To examine which T1R receptors can form heterodimers and which ligands they respond to, we performed the functional analysis for the T1Rs of bichir, which possesses two newly discovered T1R groups (T1R4 and T1R8) and four known T1R groups (T1R1, T1R2A, T1R2B and T1R3B). Because <italic>TAS1R4</italic> is the sister clade of <italic>TAS1R3</italic> and is present in all vertebrates that harbour the other novel <italic>TAS1R</italic>s (Fig. ##FIG##0##1b##), T1R4 could be assumed to form a heterodimer with another T1R. We combined either T1R3B or T1R4 with another T1R (T1R1, T1R2A, T1R2B or T1R8) in the functional analysis (Fig. ##FIG##3##4a##). Among these receptor pairs, strong responses to amino acids were detected for T1R1/T1R3B, T1R2B/T1R3B and T1R8/ T1R4 (Fig. ##FIG##3##4b## and Extended Data Fig. ##FIG##12##8##). For bichir T1R2A, its combination with T1R3B or T1R4 did not yield a response to any of the tastants examined (Extended Data Fig. ##FIG##12##8a##). Responses were not observed when T1R4 or T1R8 alone was used (Extended Data Fig. ##FIG##12##8a##), suggesting that these newly discovered T1Rs function as obligate heterodimers in bichir.</p>", "<p id=\"Par17\">The bichir T1R8/T1R4 responded strongly to Phe and to branched-chain amino acids (BCAA; Ile, Val and Leu), whereas T1R1/T1R3B and T1R2B/T1R3B responded strongly to basic amino acids (Arg and His) (Fig. ##FIG##3##4b,c##). Fishes have 12 nutritionally essential amino acids (Cys, His, Ile, Leu, Lys, Met, Phe, Arg, Thr, Trp, Tyr and Val)<sup>##REF##30239556##27##</sup>, 9 of which are included in the 17 amino acids that were tested in the T1R functional analysis. Notably, all six amino acids to which the bichir T1Rs responded are essential amino acids (<italic>P</italic> < 0.05; one-sided Fisher’s exact test), suggesting that the bichir T1Rs may sense essential amino acids in foods by taking advantage of the ability to perceive BCAA via the T1R4-related receptor.</p>", "<p id=\"Par18\">Bichir T1R1/T1R3B also responded to sucralose, a structural analogue of sucrose. Although only T1R2A/T1R3A is responsible for sugar perception in mammals and lizards<sup>##REF##36473437##26##</sup>, we previously demonstrated that T1R1/T1R3A of birds has gained the ability to detect sugars<sup>##REF##25146290##24##,##REF##34244416##25##</sup>. Also, T1R2B/T1R3B of two teleost fishes, namely carp<sup>##REF##32046636##28##</sup> and gilthead seabream<sup>##REF##33086689##29##</sup>, can detect sugars at high concentrations (100–200 mM). Our assay was unable to analyse sugars at concentrations greater than 100 mM because of non-specific responses caused by changes in osmolarity. Although the sucrose response at 100 mM was not significantly higher than the thresholds we set in this study (>10,000 relative light units (RLU) with a false discovery rate (<italic>q</italic>) of <0.01), combined with the fact that its structural analogue, sucralose, could elicit a clear response, higher concentrations of sucrose may be able to activate bichir T1R1/T1R3B. In addition, we found that bichir T1R8/T1R4 could respond to GMP, although a previous study reported that neither T1R1/T1R3B nor T1R2B/T1R3B of medaka fish nor T1R2B/T1R3B of zebrafish could be activated by 5′-ribonucleotides<sup>##REF##17522303##10##</sup>. Therefore, the origin and evolution of sugar and nucleotide taste perception may need to be reconsidered based on results from future genetic and functional analyses of T1Rs.</p>", "<p id=\"Par19\">We also performed a functional analysis of elephant fish T1Rs. Three genes of the T1R6 clade, namely T1R6-1, T1R6-2 and T1R6-3, were tested in combination with T1R3C and T1R4, and only the response of the T1R6-2/T1R4 pair could be detected (Fig. ##FIG##3##4d–f## and Extended Data Fig. ##FIG##12##8b##). This combination responded to a relatively broad range of amino acids, including both BCAA (Val, Leu) and basic amino acids (Arg, Lys). The T1Rs of mammals and teleosts have little or no response to BCAA but can respond to basic amino acids<sup>##REF##11894099##5##,##REF##17522303##10##,##REF##24214976##23##</sup>. The observed strong response of bichir T1R8/T1R4 and elephant fish T1R6-2/T1R4 to BCAA may reflect functional characteristics of the novel T1Rs involving T1R4 and possibly that of ancient T1Rs in the vertebrate ancestor.</p>", "<title>Expression of the novel T1Rs in TRCs</title>", "<p id=\"Par20\">To investigate whether the novel T1Rs are indeed expressed in TRCs, we performed in situ hybridization with sections of the lips and gill rakers of bichir (Fig. ##FIG##4##5a##). T1R1, T1R2A, T1R2B, T1R3B, T1R4 and T1R8 were expressed in subsets of TRCs. Genes encoding downstream signal-transduction molecules, such as TRPM5, Gαia1 and Gα14, were also highly expressed in subsets of TRCs in the lips and gill rakers. The signal frequencies for TRPM5, Gαia1 and Gα14 were higher than those for T1Rs.</p>", "<p id=\"Par21\">To examine the localization of T1Rs in TRCs, we next performed double-label fluorescence in situ hybridization. This analysis confirmed the overlap of the signal for T1R1 with that of T1R3B, T1R2B with T1R3B and T1R8 with T1R4 (Fig. ##FIG##4##5b##). These results suggested that T1R1/T1R3B, T1R2B/T1R3B and T1R8/T1R4 function as heterodimers, in accordance with the results of our functional assays.</p>" ]	[ "<title>Discussion</title>", "<p id=\"Par22\">The complex evolutionary history of the T1R/TAS1R family includes ancient gene expansions followed by independent lineage-specific losses, which contrasts with conventional wisdom that essentially only three members were retained during evolution<sup>##REF##16207936##11##,##REF##19002141##30##</sup>. The evolution of certain other chemoreceptors, such as the T2R (or TAS2R) bitter-taste receptor family and olfactory receptors, followed a birth-and-death process<sup>##REF##16285855##31##</sup>. In this mode of evolution, tens or hundreds of the receptor family/superfamily genes have undergone extensive lineage-specific duplication followed by frequent gene loss by deletion/inactivation<sup>##REF##19002141##30##</sup>. Our results suggest that a similar process—although less extensive than what occurred for other chemoreceptors—contributed to the phylogenetic and functional expansion of the T1R family early during vertebrate evolution. <italic>TAS1R</italic>s were not subjected to extensive birth-and-death evolution, possibly because T1R ligands are limited to amino acids, sugars and nucleotides in contrast to T2Rs and olfactory receptors that respond to a wider range of ligands/stimulants. In line with our discovery, many chemoreceptors, including <italic>TAS1R</italic>s in teleost fishes, have recently been reported to have undergone dynamic evolution including lineage-specific expansion and gene losses<sup>##UREF##4##32##</sup>. It is also possible that the ancient expansion might have contributed to an alternate use of T1Rs in tissues other than the sensory organs because certain G protein-coupled receptors (including T1Rs) are expressed in the gut of mammals and teleost fishes<sup>##REF##17724330##33##,##REF##33654150##34##</sup> although their functions remain unresolved.</p>", "<p id=\"Par23\">The functional combinations of the bichir T1R8/T1R4 and the elephant fish T1R6-2/T1R4 suggest that T1R4 may have a similar role to T1R3 by forming a functional heterodimer with another novel T1R such as T1R5, T1R6, T1R7 or T1R8. This model is supported by the fact that species with either <italic>TAS1R5</italic>, <italic>TAS1R6</italic>, <italic>TAS1R7</italic> or <italic>TAS1R8</italic> also have <italic>TAS1R4</italic> (Fig. ##FIG##0##1b##) and that <italic>TAS1R4</italic> is phylogenetically the sister group of <italic>TAS1R3</italic> (Fig. ##FIG##0##1a##). Therefore, the common ancestor of bony vertebrates, which had at least nine T1Rs, probably had two types of heterodimeric T1R receptors, namely T1R3- and T1R4-dependent receptors. This relatively wide variety of possible T1R combinations involving two duplicated genes of T1R2 (A and B) and T1R3 (A and B) might have contributed to the diversification of taste sensation.</p>", "<p id=\"Par24\">Our findings provoke new questions, one of which is why many <italic>TAS1R</italic> genes—particularly the T1R4-related receptors—have become unnecessary in each lineage independently, and many species have come to rely predominantly on T1R3-dependent receptors (Fig. ##FIG##1##2##). One possible explanation is that dietary changes could have rendered one or more T1Rs unnecessary, and therefore, gene loss might have had little or no effect on survival. This is plausible because previous studies reported losses of <italic>TAS1R</italic>s and <italic>TAS2R</italic>s in many land vertebrates, presumably in association with specific dietary shifts<sup>##UREF##4##32##,##REF##29234667##35##–##REF##25389445##37##</sup>. Also, the behaviour of swallowing foods whole, without mastication, could have diminished the essentiality of taste sense in certain vertebrates, as previously discussed with respect to mammals<sup>##REF##22411809##36##,##REF##24803572##38##</sup> and squamates<sup>##REF##30155374##39##</sup>. Alternatively, it is possible that T1R3-dependent receptors have acquired greater functional flexibility and/or evolvability than other T1Rs; various tastants might have been detected via the evolutionary tuning of sequences and structures of the T1R3-dependent receptors rather than additional gene duplication. Such cases are indeed known for land vertebrates such as primates<sup>##REF##34450087##7##</sup> and birds<sup>##REF##25146290##24##,##REF##34244416##25##</sup>. To address these issues, it will be essential to carry out functional analyses of the newly discovered T1Rs in addition to the known T1R1/T1R3 and T1R2/T1R3 for a broad range of vertebrates, as our current results demonstrate. For example, the response to BCAA is a previously unreported characteristic shared between the bichir T1R8/T1R4 and elephant fish T1R6-2/T1R4 (Fig. ##FIG##3##4##). This type of result provides insight into the sensory characteristics of an ancestor of vertebrates. We also found that bichir T1Rs responded to other essential amino acids, a sucrose analogue and a nucleotide. Future analysis will resolve whether the functions indeed reflect the characteristics of the ancestral species.</p>", "<p id=\"Par25\">Thus, by demonstrating the unexpected diversity and unique evolutionary process of the T1R family, our results set the stage for understanding the evolutionary-scale changes in taste sense in vertebrates. The remarkably broad range of tastants detected by the T1Rs reflects the latent diversity of taste senses in vertebrates, and this may explain their successful expansion across diverse feeding habitats on Earth. Our understanding of taste sense will be further enhanced by clarifying T1R repertoires in each species, their tissue-specific expression, transcriptional regulatory mechanisms and protein structures. Revealing the functional and structural diversity of the novel T1Rs will also help us elucidate the molecular mechanisms by which human T1Rs recognize palatable tastes.</p>" ]	[]	[ "<p id=\"Par1\">Taste is a vital chemical sense for feeding behaviour. In mammals, the umami and sweet taste receptors comprise three members of the taste receptor type 1 (T1R/<italic>TAS1R</italic>) family: T1R1, T1R2 and T1R3. Because their functional homologues exist in teleosts, only three <italic>TAS1R</italic> genes generated by gene duplication are believed to have been inherited from the common ancestor of bony vertebrates. Here, we report five previously uncharacterized <italic>TAS1R</italic> members in vertebrates, <italic>TAS1R4</italic>, <italic>TAS1R5</italic>, <italic>TAS1R</italic>6, <italic>TAS1R7</italic> and <italic>TAS1R8</italic>, based on genome-wide survey of diverse taxa. We show that mammalian and teleost fish <italic>TAS1R2</italic> and <italic>TAS1R3</italic> genes are paralogues. Our phylogenetic analysis suggests that the bony vertebrate ancestor had nine <italic>TAS1R</italic>s resulting from multiple gene duplications. Some <italic>TAS1R</italic>s were lost independently in descendent lineages resulting in retention of only three <italic>TAS1R</italic>s in mammals and teleosts. Combining functional assays and expression analysis of non-teleost fishes we show that the novel T1Rs form heterodimers in taste-receptor cells and recognize a broad range of ligands such as essential amino acids, including branched-chain amino acids, which have not been previously considered as T1R ligands. This study reveals diversity of taste sensations in both modern vertebrates and their ancestors, which might have enabled vertebrates to adapt to diverse habitats on Earth.</p>", "<p id=\"Par2\">A combination of phylogenetic analysis and functional assays reveals surprising diversity of taste receptors in the ancestors of vertebrates and their complex evolutionary history.</p>", "<title>Subject terms</title>" ]	[ "<title>Main</title>", "<p id=\"Par3\">Taste is one of the most important senses that govern the feeding behaviour of animals. It is widely accepted that mammals have five basic tastes: umami (savoury), sweet, bitter, salty and sour<sup>##REF##22717400##1##,##REF##19837029##2##</sup>. Taste receptor type 1 (T1R, encoded by <italic>TAS1R</italic>), a G protein-coupled receptor family, consists of three members, namely T1R1, T1R2 and T1R3, which are encoded by the genes <italic>TAS1R1</italic>, <italic>TAS1R2</italic> and <italic>TAS1R3</italic>, respectively, and act as umami or sweet receptors<sup>##REF##11917125##3##,##UREF##0##4##</sup>. The T1R1/T1R3 heterodimer functions as an umami taste receptor in mammals and detects <sc>l</sc>-amino acids and 5′-ribonucleotides<sup>##REF##11894099##5##–##REF##34450087##7##</sup>. The mammalian T1R2/T1R3 heterodimer acts as a sweet sensor<sup>##REF##14636554##6##,##REF##11509186##8##</sup>. Likewise, homologues of <italic>TAS1R</italic> family genes have been identified in teleost fishes<sup>##REF##16274966##9##</sup>, and each of the heterodimers T1R1/T1R3 and T1R2/T1R3 can sense several amino acids in teleosts<sup>##REF##17522303##10##</sup>.</p>", "<p id=\"Par4\">A previous phylogenetic analysis revealed that all mammalian and teleost <italic>TAS1R</italic>s can be grouped into the <italic>TAS1R1</italic>, <italic>TAS1R2</italic> and <italic>TAS1R3</italic> clades<sup>##REF##16207936##11##</sup>, suggesting that their common ancestor had only three T1R members derived from gene duplications that have been retained in present-day species. Lineage-specific duplications and losses of <italic>TAS1R</italic> genes have occurred within each of the <italic>TAS1R1</italic>, <italic>TAS1R2</italic> and <italic>TAS1R3</italic> clades, as exemplified by multiple <italic>TAS1R2</italic> genes in zebrafish and fugu, and loss of <italic>TAS1R2</italic> in birds<sup>##REF##23886383##12##</sup>. A few genomic studies of vertebrates such as squamates, coelacanth and sharks have suggested the existence of taxonomically unplaced <italic>TAS1R</italic>s that may not be included in the aforementioned three clades<sup>##UREF##1##13##–##REF##30649300##15##</sup>. However, the lack of comprehensive characterization and systematic classification has limited our understanding of the evolutionary history of <italic>TAS1R</italic> genes, the functional diversity of T1Rs, and the molecular basis of taste sense in vertebrates.</p>", "<p id=\"Par5\">Here, we present an evolutionary analysis of diverse <italic>TAS1R</italic>s in jawed vertebrates, with an exhaustive taxon sampling encompassing all major ‘fish’ lineages. In addition to clades <italic>TAS1R1</italic>, <italic>TAS1R2</italic> and <italic>TAS1R3</italic>, we identified five novel <italic>TAS1R</italic> clades. The results suggest that the vertebrate ancestor possessed more T1Rs than most modern vertebrates, challenging the paradigm that only three T1R family members have been retained during evolution. Functional analyses suggest that the novel T1Rs have shaped the diversity of taste sense. We propose that the T1R family has undergone an ancient birth-and-death evolution that accelerated their functional differentiation, which may have led to the diversification of feeding habitats among vertebrates.</p>", "<title>Supplementary information</title>", "<p>\n\n\n\n</p>" ]	[ "<title>Extended data</title>", "<p id=\"Par41\">\n\n</p>", "<p id=\"Par42\">\n\n</p>", "<p id=\"Par43\">\n\n</p>", "<p id=\"Par44\">\n\n</p>", "<p id=\"Par45\">\n\n</p>", "<p id=\"Par46\">\n\n</p>", "<p id=\"Par47\">\n\n</p>", "<p id=\"Par48\">\n\n</p>", "<title>Extended data</title>", "<p id=\"Par37\">is available for this paper at 10.1038/s41559-023-02258-8.</p>", "<title>Supplementary information</title>", "<p id=\"Par38\">The online version contains supplementary material available at 10.1038/s41559-023-02258-8.</p>", "<title>Acknowledgements</title>", "<p>We thank S. Hyodo (The University of Tokyo) for providing the <italic>Callorhinchus milii</italic> sample. We also thank E. Kamiya (School of Life Science and Technology, Tokyo Institute of Technology) for technical assistance. The authors acknowledge Open Facility Center, Tokyo Institute of Technology, for sequencing assistance. Computations were partially performed on the supercomputer systems at the Research Organization of Information and Systems National Institute of Genetics and the Institute of Statistical Mathematics. This study was supported by Japan Society for the Promotion of Science KAKENHI grant nos. 19H03272 (to H.N.), 18K14427, 20H02941 and 23H02168 (to Y.T.), Research Project Grant(B) from the Institute of Science and Technology, Meiji University (to Y.I.), and the Lotte Shigemitsu Prize (to Y.T. and Y.I.).</p>", "<title>Author contributions</title>", "<p>H.N., Y.T. and Y.I. conceived and supervised the study. H.N., T.K., S.K. and M.O. analysed the vertebrate genomes. H.N. performed the phylogenetic and synteny analyses. Y.T. performed the functional assay. K.K., A.G., K.H., S.O. and Y.I. performed in situ hybridization experiments. H.N., Y.T. and Y.I. wrote the original draft of the manuscript. H.N., Y.T., Y.I., S.K. and M.O. edited the manuscript.</p>", "<title>Peer review</title>", "<title>Peer review information</title>", "<p id=\"Par39\"><italic>Nature Ecology & Evolution</italic> thanks Iker Irisarri and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.</p>", "<title>Data availability</title>", "<p>The <italic>TAS1R</italic> sequences and phylogenetic trees are provided in Supplementary Data ##SUPPL##0##1## and ##SUPPL##0##2##, respectively.</p>", "<title>Code availability</title>", "<p>No code was generated in this study.</p>", "<title>Competing interests</title>", "<p id=\"Par40\">The authors declare no competing interests.</p>" ]	[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>Phylogenetic tree and the revised classification of <italic>TAS1R</italic> members.</title><p><bold>a</bold>, Maximum-likelihood tree for amino acid sequences inferred from <italic>TAS1R</italic>s for 21 jawed vertebrates constructed with the JTT + G (CAT approximation) model in RAxML. Coloured circles in each node represent bootstrap values calculated with 1,000 replications, whereas those with low bootstrap support (<60) have no circles. Species classification is represented with coloured highlighting at the tips of the tree. GPRC6A was used as an outgroup (not shown), Afr, African; Aust, Australian. <bold>b</bold>, Distribution of <italic>TAS1R</italic> members among chordates. The colour of circles corresponds to the coloured highlighting in <bold>a</bold> and indicates the presence of <italic>TAS1R</italic> members in the genome assemblies of the various chordates. Phylogenetic relationships among species and among <italic>TAS1R</italic>s are shown on the left and top, respectively. <italic>TAS1R6</italic> of cartilaginous fishes is the orthologue of the <italic>TAS1R1</italic>/<italic>2A</italic>/<italic>2B</italic>/<italic>5</italic> clade and is shown as a circle with assorted colours. Similarly, <italic>TAS1R3C</italic> of cartilaginous fishes is shown with two shades of green that represent <italic>TAS1R3A</italic> and <italic>TAS1R3B</italic>. Circles with asterisks denote putative pseudogenes.</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>Birth-and-death history of the <italic>TAS1R</italic> family genes during vertebrate evolution.</title><p>The colour key indicates the names of the various <italic>TAS1R</italic> members. Filled, coloured circles on the branches indicate the presence of <italic>TAS1R</italic> members, whereas open circles indicate their absence, as estimated based on the phylogenetic tree (Fig. ##FIG##0##1a##) and distribution among vertebrates (Fig. ##FIG##0##1a##). Arrowheads above open circles indicate that the <italic>TAS1R</italic> member was lost at the branch. Geological periods and ages (Ma) taken from TimeTree<sup>##UREF##2##16##</sup> are shown at the bottom. Taxon names are shown below branches. Species-specific gene duplication events for each <italic>TAS1R</italic> were ignored. Illustrations of the species, including humans (Kikunae Ikeda, the discoverer of umami), are shown on the right.</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><title>Synteny around <italic>TAS1R</italic>s and conserved Oct-like motifs in the <italic>TAS1R4</italic> upstream regions across vertebrates.</title><p><bold>a</bold>, Synteny around each <italic>TAS1R</italic> gene cluster is partly conserved across representative vertebrates. <italic>TAS1R</italic>s are represented by black polygons, and those with asterisks are putative pseudogenes. Coloured polygons indicate genes shared among species, and grey colour represents genes not shared among the species or unknown. The species tree is shown on the left. The deduced gene orders in common ancestors of Sarcopterygii and jawed vertebrates are shown at the bottom. <bold>b</bold>, Proposed model for the expansion of <italic>TAS1R</italic> genes across distant chromosomal regions during evolution. <bold>c</bold>, Conserved motifs located upstream of <italic>TAS1R4</italic>. Sequence alignment of the upstream region of the <italic>TAS1R4</italic> open reading frame revealed two conserved Oct-like transcription-factor binding motifs (blue shading). Numbers represent nucleotide positions from the <italic>TAS1R4</italic> start codon site. The asterisk indicates one of the motifs that significantly resembles the Oct factor binding motif. <bold>d</bold>, Sequence logo for the conserved motif denoted with the asterisk in <bold>c</bold>. Known binding motifs of Oct-1 (retrieved from TRANSFAC) and Oct-11/Pou2f3/Skn-1a/Epoc-1 (retrieved from JASPAR) are compared.</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><title>Functional analysis of T1Rs from bichir and elephant fish.</title><p><bold>a</bold>, T1R repertoire in bichir and their combinations used for the functional analysis. ND, not detected for any ligands tested. <bold>b</bold>, Responses of three combinations of T1R1/T1R3B (upper), T1R2B/T1R3B (middle) and T1R8/T1R4 (lower) to each of 17 amino acids (50 mM), nucleic acids (10 mM), sugars and sucralose (100 mM). Values represent the mean ± s.e.m. of six independent experiments performed with duplicate samples. , >10,000 RLU with <italic>q</italic> < 0.01; *, >10,000 RLU with <italic>q</italic> < 0.001 by one-sided <italic>t</italic>-test (T1R1/T1R3B Arg, <italic>P</italic> = 0.0012; sucralose, <italic>P</italic> = 0.000094; T1R2B/T1R3B His, <italic>P</italic> = 0.00015; T1R8/T1R4 Phe, <italic>P</italic> = 0.00063; Val, <italic>P</italic> = 0.00030; Leu, <italic>P</italic> = 0.00057; Ile, <italic>P</italic> = 0.000013; GMP, <italic>P</italic> = 0.00047). Amino acids that are essential in fishes are highlighted in yellow. AUC, area under the curve. <bold>c</bold>, Dose–response curves for T1R1/T1R3B (upper), T1R2B/T1R3B (middle) and T1R8/T1R4 (lower) to three basic amino acids (Arg, His and Lys; blue), two BCAAs (Ile and Val; light blue) and an artificial sweetener (sucralose; orange). Values represent the mean ± s.e.m. of six independent experiments performed with duplicate samples. <bold>d</bold>–<bold>f</bold>, Same as <bold>a</bold>–<bold>c</bold>, respectively, for elephant fish and the functional analysis of T1R6-2/T1R4 (Ala, <italic>P</italic> = 0.00015; Arg, <italic>P</italic> = 0.00013; Lys, <italic>P</italic> = 0.000094; Val, <italic>P</italic> = 0.000011; Leu, <italic>P</italic> = 0.000045; Ala + IMP, <italic>P</italic> = 0.000069).</p></caption></fig>", "<fig id=\"Fig5\"><label>Fig. 5</label><caption><title>In situ hybridization of T1Rs in TRCs of bichir.</title><p><bold>a</bold>, Expression of six T1Rs and three marker genes in sagittal sections of the lips. Yellow arrowheads indicate TRCs that expressed the various genes. Scale bar, 50 μm. The experiments were repeated at least three times. <bold>b</bold>, Double-label fluorescence in situ hybridization for the combinations of T1R1/T1R3B (upper), T1R2B/T1R3B (middle) and T1R8/T1R4 (lower) in the sections. White arrowheads indicate co-expressing cells. Scale bar, 50 μm. The experiments were repeated at least twice.</p></caption></fig>", "<fig id=\"Fig6\"><label>Extended Data Fig. 1</label><caption><title>Maximum-likelihood tree of <italic>TAS1R</italic> members identified for 21 vertebrates.</title><p>A maximum-likelihood tree was constructed from the amino acid sequences encoded by <italic>TAS1R</italic>s using RAxML with the JTT + G (CAT approximation) model. Branch supports represent bootstrap values calculated with 1,000 replications. <italic>TAS1R</italic> clade names are shown on the right.</p></caption></fig>", "<fig id=\"Fig7\"><label>Extended Data Fig. 2</label><caption><title>Maximum-likelihood tree with the mixture model for the <italic>TAS1R</italic> members identified for 21 vertebrates.</title><p>A maximum-likelihood tree was constructed from the amino acid sequences encoded by <italic>TAS1R</italic>s using IQ-tree under the posterior mean site frequency approximation of the JTT + C20 + F + Γ model. Branch supports represent bootstrap values calculated with 1,000 replications. <italic>TAS1R</italic> clade names are shown on the right.</p></caption></fig>", "<fig id=\"Fig8\"><label>Extended Data Fig. 3</label><caption><title>Bayesian tree of <italic>TAS1R</italic> members identified for 21 vertebrates.</title><p>Bayesian tree inference was performed for the amino acid sequences encoded by <italic>TAS1R</italic>s using MrBayes with the JTT-F + Γ<sub>4</sub> model. Branch supports represent Bayesian posterior probabilities, and asterisks indicate a posterior probability of 1.00. <italic>TAS1R</italic> clade names are shown on the right.</p></caption></fig>", "<fig id=\"Fig9\"><label>Extended Data Fig. 4</label><caption><title>Maximum-likelihood tree for exon 6 sequences of <italic>TAS1R</italic>s identified for 33 vertebrates.</title><p>A maximum-likelihood tree was constructed from the amino acid sequences of <italic>TAS1R</italic> exon 6 using RAxML with the JTT + G (CAT approximation) model. Branch supports represent bootstrap values calculated with 1,000 replications. <italic>TAS1R</italic> clade names are shown on the right.</p></caption></fig>", "<fig id=\"Fig10\"><label>Extended Data Fig. 5</label><caption><title>Tree topologies examined for the approximately unbiased test.</title><p><bold>a</bold>, Tree topology assuming the grouping of <italic>TAS1R7</italic> and <italic>TAS1R8</italic> and showing a species tree. <bold>b</bold>, Tree topologies for various relationships among <italic>TAS1R2A</italic> and <italic>TAS1R2B</italic> genes from amphibians, coelacanth, and ray-finned fishes. The <italic>p</italic>-values for the approximately unbiased test calculated with CONSEL are shown above each tree. Relationships within each collapsed group were fixed to be the same as in the maximum-likelihood tree (Fig. ##FIG##0##1##).</p></caption></fig>", "<fig id=\"Fig11\"><label>Extended Data Fig. 6</label><caption><title>The history of intron acquisitions for the two exons of <italic>TAS1R3B</italic> in ray-finned fishes.</title><p>Filled boxes represent exons, and lines represent introns. The phylogenetic relationship among <italic>TAS1R3B</italic>-containing species is shown on the left. The closed and open arrowheads indicates the deduced timing (<italic>that is</italic>, the common ancestors of Actinopterygii and Actinopteri) for the acquisitions of introns in exons 3 and 6 of <italic>TAS1R3B</italic>, respectively. The total number of exons of the <italic>TAS1R3B</italic> genes is shown in parentheses.</p></caption></fig>", "<fig id=\"Fig12\"><label>Extended Data Fig. 7</label><caption><title>Comparison of synteny among vertebrates.</title><p><italic>TAS1R</italic>s and non-<italic>TAS1R</italic> genes are represented by colored and grey polygons, respectively, with each pointed end indicating the direction of transcription. Genomic regions are categorized according to the <italic>TAS1R1</italic>-, <italic>TAS1R2</italic>-, <italic>TAS1R3</italic>-containing regions as well as the other <italic>TAS1R</italic>-containing regions. Orthologous non-<italic>TAS1R</italic> genes between the closely represented species are connected by light-blue lines.</p></caption></fig>", "<fig id=\"Fig13\"><label>Extended Data Fig. 8</label><caption><title>No significant response of various combinations of T1Rs.</title><p><bold>a</bold>, Five T1R combinations, T1R4-only, and T1R8-only from the bichir were coexpressed in HEK293T cells, and their responses to each of the 17 amino acids (50 mM), nucleic acids (10 mM), sugars and sucralose (100 mM) were tested. Values represent the mean ± standard error of six independent experiments performed with duplicate samples. <bold>b</bold>, Same as (<bold>a</bold>) except using five T1R combinations from the elephant fish. A lack of response may be due to technical issues with the heterologous expression system, or there is a possibility that they were functional and could respond to ligands not used in this study.</p></caption></fig>" ]	[]	[]	[]	[]	[]	[]	[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM3\"></supplementary-material>" ]	[ "<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>These authors contributed equally: Hidenori Nishihara, Yasuka Toda.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"41559_2023_2258_Fig1_HTML\" id=\"d32e623\"/>", "<graphic xlink:href=\"41559_2023_2258_Fig2_HTML\" id=\"d32e1174\"/>", "<graphic xlink:href=\"41559_2023_2258_Fig3_HTML\" id=\"d32e1300\"/>", "<graphic xlink:href=\"41559_2023_2258_Fig4_HTML\" id=\"d32e1602\"/>", "<graphic xlink:href=\"41559_2023_2258_Fig5_HTML\" id=\"d32e1682\"/>", "<graphic position=\"anchor\" xlink:href=\"41559_2023_2258_Fig6_ESM\" id=\"d32e2140\"/>", "<graphic position=\"anchor\" xlink:href=\"41559_2023_2258_Fig7_ESM\" id=\"d32e2162\"/>", "<graphic position=\"anchor\" xlink:href=\"41559_2023_2258_Fig8_ESM\" id=\"d32e2186\"/>", "<graphic position=\"anchor\" xlink:href=\"41559_2023_2258_Fig9_ESM\" id=\"d32e2208\"/>", "<graphic position=\"anchor\" xlink:href=\"41559_2023_2258_Fig10_ESM\" id=\"d32e2244\"/>", "<graphic position=\"anchor\" xlink:href=\"41559_2023_2258_Fig11_ESM\" id=\"d32e2272\"/>", "<graphic position=\"anchor\" xlink:href=\"41559_2023_2258_Fig12_ESM\" id=\"d32e2305\"/>", "<graphic position=\"anchor\" xlink:href=\"41559_2023_2258_Fig13_ESM\" id=\"d32e2325\"/>" ]	[ "<media xlink:href=\"41559_2023_2258_MOESM1_ESM.pdf\"><label>Supplementary Information</label><caption><p>Supplementary Data 1 and 2.</p></caption></media>", "<media xlink:href=\"41559_2023_2258_MOESM2_ESM.pdf\"><caption><p>Reporting Summary</p></caption></media>", "<media xlink:href=\"41559_2023_2258_MOESM3_ESM.xlsx\"><label>Supplementary Tables</label><caption><p>Supplementary Tables 1 and 2.</p></caption></media>" ]	[{"label": ["4."], "mixed-citation": ["Hummel, T. & Welge-L\u00fcssen, A. "], "italic": ["Taste and Smell: An Update"]}, {"label": ["13."], "surname": ["Picone"], "given-names": ["B"], "article-title": ["Taste and odorant receptors of the coelacanth\u2013a gene repertoire in transition"], "source": ["J. Exp. Zool. B"], "year": ["2014"], "volume": ["322"], "fpage": ["403"], "lpage": ["414"], "pub-id": ["10.1002/jez.b.22531"]}, {"label": ["16."], "surname": ["Kumar"], "given-names": ["S"], "article-title": ["TimeTree 5: an expanded resource for species divergence times."], "source": ["Mol. Biol. Evol."], "year": ["2022"], "volume": ["39"], "fpage": ["masc174"], "pub-id": ["10.1093/molbev/msac174"]}, {"label": ["17."], "mixed-citation": ["Ohno, S. "], "italic": ["Evolution by Gene Editing"]}, {"label": ["32."], "mixed-citation": ["Policarpo, M., Baldwin, M., Casane, D. & Salzburger, W. Diversity and evolution of the vertebrate chemoreceptor gene repertoire. Preprint at 10.21203/rs.3.rs-2922188/v1 (2023)."]}]	{ "acronym": [], "definition": [] }	55	CC BY	no	2024-01-13 00:02:19	Nat Ecol Evol. 2024 Dec 13; 8(1):111-120	oa_package/3a/ce/PMC10781636.tar.gz
PMC10781637	37925531	[ "<title>Introduction</title>", "<p id=\"Par2\">Allogeneic hematopoietic stem cell transplantation (HSCT) provides a curative treatment for pediatric patients affected with non-malignant diseases like primary immunodeficiencies (PID), haemoglobinopathies (HBP), bone marrow failure (BMF) syndromes, or inborn errors of metabolism (IEM) [##REF##34781360##1##–##REF##29458734##7##]. For these non-malignant diseases a variety of mainly chemotherapy based conditioning regimens are applied. They include cytotoxic agents as busulfan, treosulfan, cyclophosphamide, thiotepa or melphalan. Significant morbidity and mortality risks exist for children undergoing allogeneic HSCT [##REF##35671392##8##, ##REF##31235684##9##]. The use of reduced intensity or reduced toxicity conditioning regimens to decrease risks of conditioning-related morbidities is restricted by the need of sustained engraftment with a sufficient percentage of donor-type chimerism to ensure disease-free survival.</p>", "<p id=\"Par3\">Treosulfan’s (L-threitol-1,4-bis-methanesulfonate) potential for myeloablative conditioning with low toxicity was first demonstrated in adults [##REF##34514593##10##–##REF##21659356##13##] and then in children with malignancies [##REF##33332189##14##–##REF##23085832##18##]. It is approved in combination with fludarabine in the EU, Switzerland, Australia, and Canada [##UREF##3##19##]. However, in essentially all non-malignant transplant indications, extensive experience already exists with treosulfan based conditioning in the form of case series [##REF##23085832##18##, ##REF##33740322##20##, ##UREF##4##21##], single-arm prospective studies [##UREF##4##21##–##UREF##7##24##], or retrospective registry analyses [##REF##34094045##5##, ##REF##34999929##25##, ##REF##35100336##26##].</p>", "<p id=\"Par4\">We prospectively compared safety and efficacy of treosulfan/fludarabine with busulfan/fludarabine myeloablative conditioning in children with non-malignant disease. The trial was conducted in accordance with the approved European pediatric investigational plan for treosulfan (PIP; EMEA-000883-PIP01-10) including a pharmacokinetic (PK) sub-study.</p>" ]	[ "<title>Materials and methods</title>", "<title>Study design</title>", "<p id=\"Par5\">A prospective, randomized (1:1), open-label, multicenter, active-controlled, parallel-group phase 2 clinical trial (MC-FludT.16/NM) was conducted across 4 European countries between April 2015 to June 2021. Each treatment arm also administered fludarabine whereas thiotepa could be added for intensification of the regimen at the treating physicians’ discretion before randomization. Pharmacokinetic analyses on treosulfan were conducted to contribute to a final population pharmacokinetics (Pop-PK) model. The study was conducted in accordance with the ethical principles of the Declaration of Helsinki and the applicable national laws of the participating countries (Czech Republic, Germany, Italy, Poland). All patients and/or their parents/legal guardians provided written consent prior to the participation in the study.</p>", "<p id=\"Par6\">Randomization (1:1 ratio using a permuted block technique) for either treosulfan or busulfan was performed centrally by the sponsor’s clinical research department using a computer-generated randomization list and was stratified by the 2 regimens. Treosulfan was administered IV over 2 hours consecutively on Day -6, -5, and -4. Based on an initially evaluated Pop-PK model, the individual total dose of treosulfan was adapted on the actual body surface area (BSA) [##UREF##8##27##]. Accordingly, 10, 12, or 14 g/m² treosulfan was administered to patients with BSA ≤ 0.5 m², >0.5 to ≤1.0 m², >1.0 m², respectively. Busulfan was given IV on 4 consecutive days (4.8 to 3.2 mg/kg/day on Days -7, -6, -5, and -4, according to the actual body weight) and thiotepa (2 × 5 mg/kg on Day -2). All patients were observed until Day +100 after HSCT for acute toxicity and freedom from transplantation (treatment)-related mortality. The follow-up was continued for each patient until at least 12 months after HSCT.</p>", "<p id=\"Par7\">Further information for actual administration of the preparative regimens is provided (Supplementary Section ##SUPPL##0##1.3##).</p>", "<title>Study participants</title>", "<p id=\"Par8\">Pediatric patients 28 days to less than 18 years of age with nonmalignant disease including IEM, PID, HBP, and BMF were eligible. Only patients with an indication for a first allogeneic HSCTs were enrolled if a matched sibling donor, matched family donor, matched unrelated donor, or umbilical cord blood was available. Lansky Performance Scores or Karnofsky Performance Scores for those ≥16 years of age had to be at least 70%. Main exclusion criteria included obese pediatric patients with body mass index > 30 kg/m<sup>2</sup>, patients with Fanconi anemia and other chromosomal breakage or radiosensitivity disorders, trisomy 21, and Dyskeratosis Congenita.</p>", "<title>Study objectives</title>", "<p id=\"Par9\">The primary objective was to compare freedom from transplantation (treatment)-related mortality (freedom from TRM), defined as death from any transplantation (treatment)-related cause from start of conditioning treatment (Day -7) until Day +100 after allogeneic HSCT. Toxicity was documented using the Common Terminology Criteria for Adverse Events (CTCAE, version 4.03) until Day +100 and serious adverse reactions until the end of follow-up.</p>", "<p id=\"Par10\">Comparative exploratory analyses also included engraftment, primary or secondary graft failure, complete ( ≥ 95%) or mixed ( ≥ 20%) donor-type chimerism, transplantation-related mortality (TRM), overall survival (OS), acute [##UREF##9##28##, ##UREF##10##29##] and chronic [##UREF##11##30##] graft versus host disease (GVHD), and GVHD-free survival as previously described [##UREF##2##16##]. A more detailed description of the secondary endpoints is provided (Supplementary Section ##SUPPL##0##1.1## and ##SUPPL##0##1.2##).</p>", "<title>Statistical analysis plan</title>", "<p id=\"Par11\">The trial was not powered for confirmatory statistical testing of any pre-specified hypotheses. Following the approved PIP, at least 100 evaluable children had to be enrolled. Descriptive statistics including 95% confidence intervals (CI) was applied to summarize all endpoints, including baseline characteristics and covariates used in multivariate analyses. Three (2.9%) umbilical cord blood transplanted patients were included in the matched unrelated donor (2) and matched family donor (1) subgroup (Supplementary Table ##SUPPL##0##3##). The following analyses were done to compare endpoints between treatment arms. Fisher’s exact test was used for rate of hepatic sinusoidal obstruction syndrome. Freedom from TRM, complete and mixed donor-type chimerism was analyzed using Cochran-Mantel-Haenszel tests. Duration of neutropenia and leukopenia was evaluated with Wilcoxon-Mann-Whitney tests.</p>", "<p id=\"Par12\">All time-to-event endpoints were measured from the day of HSCT (except for chronic GVHD [cGVHD] from 100 days after HSCT) to the event or competing event (if applicable). The probability of event over time for freedom from TRM, TRM, OS, and GVHD-free survival was estimated by Kaplan-Meier estimator, and for engraftment, primary and secondary graft failure until 12 months after HSCT, incidence of acute GVHD (aGVHD) and cGVHD was estimated by cumulative incidence functions due to competing risks. For comparisons, Pepe-Mori tests for engraftment were performed. Cox models for freedom from TRM, TRM, OS, and GVHD-free survival, and Fine and Gray models for engraftment, graft failure, and incidence of aGVHD and cGVHD were applied to adjust for covariates in multivariate analyses. The following covariates are additionally considered to examine efficacy and safety in prespecified subgroups or in multivariate analyses (disease groups, age group, donor type, thiotepa, and serotherapy). All analyses were predefined and SAS software (Version 9.4) was used.</p>", "<title>Pharmacokinetic assessment (treosulfan)</title>", "<p id=\"Par13\">Patients of the PIP pre-specified age groups were included in the PK sub-study for both pediatric allogeneic HSCT trials MC-FludT.16/NM and MC-FludT.17/M [##UREF##2##16##]. Blood samples were taken by limited sampling procedure as previously described [##UREF##2##16##]. The non-compartmental analysis was applied based on the individual plasma concentration-time- data. The following pharmacokinetic parameters were determined as previously described [##UREF##2##16##]: maximum observed concentration, time to reach maximum plasma concentration, area under the time-concentration curve or from time zero to infinity, apparent terminal elimination half-life, clearance, and volume of distribution. PK parameters were also stratified by BSA. Further details of bioanalytical methods and the model-based PK parameter calculation have been previously described [##UREF##2##16##, ##UREF##12##31##].</p>", "<p id=\"Par14\">Pharmacokinetic analyses used the Phoenix™ WinNonlin<sup>®</sup> (version 6.2.1). Non-compartmental analysis model 202 (constant infusion input, plasma data) was applied.</p>" ]	[ "<title>Results</title>", "<title>Patient characteristics</title>", "<p id=\"Par15\">A total of 106 patients were randomized of which 101 patients received the study drug, underwent allogeneic HSCT, and were included in the efficacy and safety analyses (Fig. ##FIG##0##1##). More than half of the patients were male (66.3%) and mean age of all patients was 6.0 ( ± 5.3) years. Underlying diseases were PID (n = 53), HBP (n = 35), BMF (n = 11), and IEM (n = 7) (Table ##TAB##0##1##). Among patients with HBPs, only 5 (38.5%) beta-thalssaemia patients were in the busulfan arm and 16 (76.2%) in the treosulfan arm. In the busulfan arm 72.0% of patients had a Lansky Performance Score of 100% compared to 82.4% in the treosulfan arm (Table ##TAB##0##1##). Depending on their individual BSA, patients received treosulfan at a dose of 10 g/m<sup>2</sup>/day (17.3%), 12 g/m<sup>2</sup>/day (61.5%), or 14 g/m<sup>2</sup>/day (21.2%) on three consecutive days.</p>", "<title>Efficacy results</title>", "<p id=\"Par16\">The incidence of freedom from TRM until Day +100 was 90.0% (95% CI: 78.2%, 96.7%) and 100.0% (95% CI: 93.0%, 100.0%) in the busulfan and treosulfan arm (difference of incidences –10.0% [95% CI: –21.8%, –2.0%]; <italic>P</italic> = 0.0528) (Table ##TAB##1##2##). Until Day +100, five patients (10.0%) had died from transplantation or a treatment-related cause in the busulfan arm. No death was reported in the treosulfan arm. A beneficial outcome for treosulfan regarding the primary endpoint was evident across all predefined subgroups including disease group, age group, donor type, thiotepa and serotherapy (Supplementary Fig. ##SUPPL##0##3##).</p>", "<p id=\"Par17\">The Kaplan-Meier estimate of TRM at 12 months was 12.0% (95% CI: 5.6%, 24.8%) and 3.9% (95% CI: 1.0%, 14.8%) in the busulfan and treosulfan arm (HR: 0.29 [95% CI: 0.06, 1.41]). Estimate of TRM at 12 months in the disease subgroup HBPs was 7.7% for busulfan and 0% for treosulfan. After a median follow-up of 25 months (busulfan range: 11.7-63.3 months; treosulfan range: 10.7-60.9 months) the 12-month estimate of OS was 88.0% (95% CI: 75.2%, 94.4%) in the busulfan arm versus 96.1% (95% CI: 85.2%, 99.0%) in the treosulfan arm (HR: 0.29 [95% CI: 0.06, 1.41]; Fig. ##FIG##1##2##, Table ##TAB##1##2##). OS estimate in the subgroup of HBPs was 92.3% for busulfan and 100% for treosulfan. Infection-related deaths were more frequently observed in the busulfan arm (10.0%) than in the treosulfan arm (2.0%) (Supplementary Table ##SUPPL##0##2##).</p>", "<p id=\"Par18\">The conditional cumulative incidence of neutrophil engraftment was comparable between the treatment arms (busulfan: 100.0% [95% CI: 93.0%, 100.0%] and treosulfan: 97.3% [95% CI: 87.0%, 100.0%]) (Table ##TAB##1##2##). The median duration of CTCAE Grade IV neutropenia was significantly shorter in the busulfan arm (busulfan: 14.5 days [interquartile range {IQR}: 10.0, 21.0]) compared to treosulfan (20.0 days [IQR: 12.0, 22.0], <italic>P</italic> = 0.0108). Similar results were seen for the median duration of CTCAE Grade IV leukopenia (busulfan: 14.5 days [IQR: 12.0, 20.0] and treosulfan: 19.0 days [IQR: 13.0, 21.0], <italic>P</italic> = 0.0087).</p>", "<p id=\"Par19\">Primary graft failure was noted in 2 patients each in the busulfan arm (4.0%) and the treosulfan arm (3.9%). However, none of the patients (0%) in the busulfan arm experienced a secondary graft failure as compared to 9 patients (18.4%) in the treosulfan arm (Table ##TAB##1##2## and Supplementary Table ##SUPPL##0##3##). Overall, cumulative incidences of primary and secondary graft failures at 12 months were 4.0% (95% CI: 0.0%, 9.4%) versus 15.8% (95% CI: 5.8%, 25.9%) respectively (<italic>P</italic> = 0.0366) (Supplementary Fig. ##SUPPL##0##4##). Cumulative incidence of graft failures in the subgroup of HBPs reached 0% after busulfan and 9.5% in the treosulfan treatment group.</p>", "<p id=\"Par20\">The fraction of patients with complete donor-type chimerism decreased between Day +28 and Month 12 in both treatment arms (busulfan: from 82.0% to 76.7%; treosulfan: from 84.3% to 49.0%) (Table ##TAB##1##2##). The odds ratio at Month 12 was 0.5429 (95% CI: 0.20, 1.51). Incidence of complete donor-type chimerism at month 12 in the subgroup of HBPs was 66.7% after busulfan and 42.9% after treosulfan. The fraction of all patients with mixed donor-type chimerism of ≥20% between Day +28 and Month 12 remained nearly unchanged in the busulfan arm (from 98.0 to 97.7) whereas it declined in the treosulfan arm from 94.1% to 75.5%. Two patients (4.0%) in the busulfan arm and 5 patients (9.8%) in the treosulfan arm received donor lymphocyte infusions.</p>", "<p id=\"Par21\">Acute GVHD of at least Grade III was noted in 4 patients (8.0%) in the busulfan arm as compared to 7 patients (13.7%) after treosulfan (Table ##TAB##2##3##). However, moderate/severe cGVHD was observed more frequently in patients treated with busulfan (7 [14.0%]) compared to treosulfan (1 [2.0%]). Fifteen patients (30.0%) in the busulfan arm and 8 patients (15.7%) in the treosulfan arm experienced either death, aGVHD of at least Grade III, or moderate / severe cGVHD. The corresponding Kaplan-Meier estimate of GVHD-free survival at 12 months was 69.4% (95% CI: 54.4%, 80.3%) in the busulfan arm and 82.9% (95% CI: 68.7%, 91.1%) in the treosulfan arm (HR: 0.58 [95% CI: 0.24, 1.38]) (Table ##TAB##1##2## and Supplementary Fig. ##SUPPL##0##1##). Chronic GVHD-free survival at Month 12 was 69.4% (95% CI: 54.4%, 80.3%) after busulfan and 89.3% (95% CI: 76.2%, 95.4%) after treosulfan (difference <italic>P</italic> = 0.0308), being statistically significant in favor of treosulfan (Supplementary Fig. ##SUPPL##0##2##).</p>", "<title>Pharmacokinetic results</title>", "<p id=\"Par22\">Due to the PIP requirements, pharmacokinetic analyses included this trial and the simultaneously performed trial for malignant hematological diseases (MC-FludT.17/M [##UREF##2##16##]). Treatment with 10, 12, or 14 g/m² treosulfan per day resulted in comparable mean maximum observed concentration and AUC values of treosulfan in plasma. A trend for increase of treosulfan exposure in the higher BSA categories was observed (Table ##TAB##3##4##).</p>", "<title>Safety</title>", "<p id=\"Par23\">The incidences of total treatment-emergent adverse events and treatment-emergent serious adverse events were similar in the two treatment arms (Table ##TAB##2##3##). Most common treatment-emergent adverse events were oral mucositis (busulfan: 80.0%; treosulfan: 70.6%), fever (busulfan: 72.0%; treosulfan: 70.6%) and vomiting (busulfan: 64.0%; treosulfan: 66.7%) (Supplementary Table ##SUPPL##0##1##). The incidence of hepatic sinusoidal obstruction syndrome was higher in the busulfan arm (all grades: busulfan: 10.0%, treosulfan: 2.0%, <italic>P</italic> = 0.1120; ≥ Grade III according to Jones: busulfan 4.0%, treosulfan 0.0%, <italic>P</italic> = 0.2426). No unknown risks were identified in the trial.</p>", "<p id=\"Par24\">Nine patients (8.9%) died until data cut-off; 7 of 50 patients (14.0%) in the busulfan arm and 2 of 51 patients (3.9%) in the treosulfan arm. All deaths were transplantation related. In both arms, most common causes were infection and GVHD associated multiple organ failure (Supplementary Table ##SUPPL##0##2##).</p>" ]	[ "<title>Discussion</title>", "<p id=\"Par25\">In this study, treosulfan-based conditioning showed a clinically meaningful trend towards improved freedom from TRM on Day +100 as well as reduced TRM at 12 months after transplantation. Also, OS and GvHD-free survival were increased, when compared to busulfan-based conditioning treatment. However, incidence of complete donor-type chimerism declined over time and an increased risk of secondary graft failure was observed after treosulfan. Accordingly, more treosulfan than busulfan treated children received second transplant procedures, donor lymphocyte infusions or stem cell boosts. Finally, all 9 patients who experienced secondary graft failure in the treosulfan arm were rescued and survived.</p>", "<p id=\"Par26\">Meanwhile, there already is ample published clinical experience with treosulfan-based conditioning in pediatric HSCT [##REF##34781360##1##, ##REF##35288820##3##, ##REF##34094045##5##, ##UREF##4##21##, ##UREF##5##22##, ##UREF##7##24##–##REF##35100336##26##, ##REF##35088211##32##–##REF##22645178##39##]. Accordingly, in its guideline for HSCT for inborn errors of immunity, the EBMT Inborn Error Working Party offers treosulfan-based alternatives for conditioning treatment [##UREF##13##40##].</p>", "<p id=\"Par27\">However, the question of the optimal preparative regimen for a patient with a particular non-malignant disease is usually answered by retrospective registry analyses of populations with a single non-malignant condition. Due to the given heterogeneity of such rare diseases the conduct of prospectively randomized trials for single specific syndromes is not considered feasible. Nevertheless, most recently published, large retrospective analyses are in line with the findings reported in our prospective study.</p>", "<p id=\"Par28\">Albert et al. [##REF##35100336##26##] analyzed 197 patients with Wiskott-Aldrich Syndrome. The 3-year OS was 88.7% and cGVHD-free survival (events include death, graft failure, and severe cGVHD) was 81.7%. OS and cGVHD-free survival were not significantly affected by the conditioning regimen (busulfan vs treosulfan-based). Patients receiving a treosulfan-based conditioning had a higher incidence of graft failure and mixed donor chimerism and more frequently underwent second procedures. The overall cumulative incidence of primary and secondary graft failure was 8.3% at 3 years. It was higher in the treosulfan (14.3%) than in the busulfan (2.9%) group, comparable to our results.</p>", "<p id=\"Par29\">Chiesa et al. [##REF##32614953##2##] retrospectively analyzed 635 children and 77 adults with chronic granulomatous disease. In this disease, the preparative regimen (busulfan vs. treosulfan) did not influence OS or event-free survival. However, univariate analysis revealed a significant impact of conditioning regimen on the overall rate of graft failures at 3 years with 10% after the treosulfan/fludarabine/thiotepa, 13% after busulfan/fludarabine, 22% after treosulfan/fludarabine and only 3% after busulfan/cyclophosphamide.</p>", "<p id=\"Par30\">For beta-thalassemia major, Lüftinger et al. [##REF##34999929##25##] performed a retrospective EBMT analysis of 772 patients, 410 of whom received busulfan/fludarabine and 362 treosulfan/fludarabine based conditioning. Two-year OS was 92.7% (95% CI: 89.3%, 95.1%) after busulfan and 94.7% (95% CI: 91.7%, 96.6%) after treosulfan. The incidence of second HSCT procedure at 2 years was 4.6% in the busulfan vs. 9.0% in the treosulfan group, representing a significant difference in the multivariate analysis. There were high cure rates in both arms of the study.</p>", "<p id=\"Par31\">In summary, these retrospective analyses suggest that outcome differences between treosulfan or busulfan based conditioning regimens partly depend on the specific disease entity. The results of our prospective randomized study are in line with these observations regarding an improved survival, lower toxicity and cGVHD incidence, but a potentially higher rate of mixed chimerism and graft failure after treosulfan-based conditioning. For instance, in our subgroup of 21 patients with beta-thalassemia major 0 out of 5 and 3 out of 16 patients experienced a graft failure after treatment with busulfan or treosulfan, respectively. However, 100% versus 93.8% engrafted and survived at least 12 months after transplant. In our small subgroup of 13 patients with chronic granulomatous disease (CGD) 0 out of 7 and 2 out of 6 patients experienced a graft failure after treatment with busulfan or treosulfan, respectively. Finally, 6 out of 7 versus 4 out of 6 patients engrafted and survived at least 12 months after transplant. As discussed below, the patient numbers with a disease-specific indication within our trial are too small for any firm safety or efficacy conclusion. Further well-designed comparative disease-specific real world data analyses are, therefore, highly warranted as referenced above.</p>", "<p id=\"Par32\">The PK sub-study on treosulfan included in our trials MC-FludT.16/NM and MC-FludT.17/M applied a BSA-adapted dose calculation. This was based on a Pop-PK model aiming at a comparative treosulfan exposure to all pediatric age (BSA) groups starting at 4 weeks of age [##UREF##8##27##]. Noncompartmental analysis revealed that the BSA-adapted dosing resulted in comparable exposure through the different BSA categories (Table ##TAB##3##4##). Meanwhile, several Pop-PK models have been published based on pediatric treosulfan PK data collected by various groups [##UREF##14##41##–##REF##24487253##49##]. All models revealed the need for adaptation of treosulfan dose in children of less than 1 or 2 years of age. However, individualized dosing based on therapeutic drug monitoring has so far not been shown to be superior to BSA adapted dosing [##UREF##15##50##].</p>", "<p id=\"Par33\">Despite the beneficial survival results of treosulfan based conditioning therapy as suggested by our prospective comparative trial, several limitations exist. Heterogeneity of the non-malignant transplant indications and the limited sample size affect treatment arm comparability. Randomized allocation was not stratified for underlying disease and resulted in an increased number of beta-thalassemia major in the treosulfan vs. the busulfan arm. Also, the overall study population consisted primarily of patients with PIDs and HBPs while IEMs and BMFs were underrepresented. The inclusion of patients with specific disease entities and selection of the conditioning intensity was at the investigators’ discretion. This resulted in 84% patients received the intensified treatment with thiotepa. Moreover, inclusion and exclusion criteria limited study recruitment by age, weight, body surface area, and organ function. For patients outside of these criteria, e.g., with obesity, anorexia or limited organ function the risk estimates may differ and potentially favor treosulfan. Patient numbers were too small for any potential analysis of conditioning drug exposure in subgroups.</p>", "<p id=\"Par34\">Treating physicians may prefer treosulfan over busulfan in patients with increased risk of TRM related to e.g., concomitant infections or pre-existing organ dysfunction. Although secondary graft failures were more common in the treosulfan group, these patients were rescued by second procedures. Moreover, there is strong evidence suggesting a reduced risk for impairment of gonadal function, acute and chronic GVHD, and other early and late adverse effects after treosulfan based conditioning [##UREF##16##51##–##UREF##20##56##]. In summary, our study provides important additional evidence enabling physicians to choose the most appropriate conditioning regimen for children with non-malignant transplant indications.</p>" ]	[]	[ "<p id=\"Par1\">Optimal conditioning prior to allogeneic hematopoietic stem cell transplantation for children with non-malignant diseases is subject of ongoing research. This prospective, randomized, phase 2 trial compared safety and efficacy of busulfan with treosulfan based preparative regimens. Children with non-malignant diseases received fludarabine and either intravenous (IV) busulfan (4.8 to 3.2 mg/kg/day) or IV treosulfan (10, 12, or 14 g/m<sup>2</sup>/day). Thiotepa administration (2 × 5 mg/kg) was at the investigator’s discretion. Primary endpoint was freedom from transplantation (treatment)-related mortality (freedom from TRM), defined as death between Days -7 and +100. Overall, 101 patients (busulfan 50, treosulfan 51) with at least 12 months follow-up were analyzed. Freedom from TRM was 90.0% (95% CI: 78.2%, 96.7%) after busulfan and 100.0% (95% CI: 93.0%, 100.0%) after treosulfan. Secondary outcomes (transplantation-related mortality [12.0% versus 3.9%]) and overall survival (88.0% versus 96.1%) favored treosulfan. Graft failure was more common after treosulfan (n = 11), than after busulfan (n = 2) while all patients were rescued by second procedures except one busulfan patient. CTCAE Grade III adverse events were similar in both groups. This study confirmed treosulfan to be an excellent alternative to busulfan and can be safely used for conditioning treatment in children with non-malignant disease.</p>", "<title>Subject terms</title>" ]	[ "<title>Supplementary information</title>", "<p>\n\n</p>" ]	[ "<title>Supplementary information</title>", "<p>The online version contains supplementary material available at 10.1038/s41409-023-02135-9.</p>", "<title>Acknowledgements</title>", "<p>We would like to thank the participating study patients, physicians, nurses, trial coordinators, and data managers who were involved in the MC-FludT.16/NM trial. Medical writing and editorial assistance were provided by Imran Khan (medical writer employed and funded by medac GmbH). This work was supported by an unrestricted grant from medac GmbH, Wedel, Germany, which was the clinical trial sponsor.</p>", "<title>Author contributions</title>", "<p>Contribution: KWS, KK designed the study, contributed patients, and were primarily responsible for writing of the manuscript; KWS and RB contributed equally. They had access to full trial data; PB, JS, AS, JG, DR, J.S., FF, BG, FL, SP, PS, SB, MZ, MC, SC, MM contributed patients and helped with data cleaning, and edited the manuscript; XL provided all bio-statistical support and contributed to data analysis; JB, JK contributed to the study design and contributed to the manuscript.</p>", "<title>Funding</title>", "<p>This study was part of the European Medicines Agency (EMA) approved pediatric development plan for treosulfan and was sponsored and fully financed by medac GmbH, 22880 Wedel, Germany. Open Access funding enabled and organized by Projekt DEAL.</p>", "<title>Data availability</title>", "<p>The datasets generated during and/or analysed during the current study are not publicly available.</p>", "<title>Competing interests</title>", "<p id=\"Par35\">K-WS: speaker fees Jazz, research and travel grants medac, travel grant Neovii. RB: research and travel grants medac, travel grant Neovii. PB: no relevant disclosures. JS: no relevant disclosures. Ansgar Schulz: no relevant disclosures. JG: no relevant disclosures. JG: no relevant disclosures. DR: no relevant disclosures. JS: no relevant disclosures. FF: no relevant disclosures. BG: Honoraria: Amgen GmbH, EUSA Pharma GmbH, medac GmbH, Novartis Pharma GmbH; Membership of advisory committee of Amgen GmbH, EUSA Pharma GmbH. FL: no relevant disclosures. Simona Piras: no relevant disclosures. PS: no relevant disclosures. SB: no relevant disclosures. MZ: no relevant disclosures. MC: no relevant disclosures. SC: no relevant disclosures. XL: employee of medac GmbH. JB: employee of medac GmbH. JK: employee of medac GmbH. MM: no relevant disclosures. KK: Speaker’s bureau: JazzPharma, medac, Novartis.</p>" ]	[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>Consort diagramm.</title><p>Patient disposition, for In- and Exclusion Criteria see Supplementary Information (1.4).</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>Kaplan-Meier curves and forest plots for transplantation-related mortality and overall survival.</title><p>All Death Events in Overall Survival are Attributed to Events of Transplantation-related Mortality <bold>a</bold> Kaplan-Meier estimate of transplantation-related mortality of children with non-malignant disease randomized to treosulfan or busulfan based conditioning prior to allogeneic transplantation (FAS). <bold>b</bold> Forest plot for transplantation-related mortality displaying 12-month rates by subgroups (FAS) <bold>c</bold> Kaplan-Meier estimate of overall survival of children with non-malignant disease randomized to treosulfan or busulfan based conditioning prior to allogeneic transplantation (FAS). <bold>d</bold> Forest plot for overall survival displaying 12-month rates by subgroups (FAS).</p></caption></fig>" ]	[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Baseline demographics and disease characteristics.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th/><th>Busulfan (N = 50)</th><th>Treosulfan (N = 51)</th></tr></thead><tbody><tr><td colspan=\"3\">Gender, n (%)</td></tr><tr><td> Female</td><td>19 (38.0)</td><td>15 (29.4)</td></tr><tr><td> Male</td><td>31 (62.0)</td><td>36 (70.6)</td></tr><tr><td>Age, years; mean (SD)</td><td>6.0 (5.3)</td><td>5.0 (4.4)</td></tr><tr><td colspan=\"3\">ICH age group, n (%)</td></tr><tr><td> 28 days to 23 months</td><td>14 (28.0)</td><td>14 (27.5)</td></tr><tr><td> 2 to 11 years</td><td>26 (52.0)</td><td>31 (60.8)</td></tr><tr><td> 12 to 17 years</td><td>10 (20.0)</td><td>6 (11.8)</td></tr><tr><td colspan=\"3\">Race, n (%)</td></tr><tr><td> White</td><td>43 (86.0)</td><td>41 (80.4)</td></tr><tr><td> Black or African American</td><td>2 (4.0)</td><td>2 (3.9)</td></tr><tr><td> Asian</td><td>1 (2.0)</td><td>6 (11.8)</td></tr><tr><td> Other</td><td>4 (8.0)</td><td>2 (3.9)</td></tr><tr><td>Weight, kg; mean (SD)</td><td>23.6 (15.9)</td><td>19.7 (11.3)</td></tr><tr><td>BMI, kg/m2; mean (SD)</td><td>16.97 (3.07)</td><td>16.77 (2.20)</td></tr><tr><td>Body surface area, m2; mean (SD)</td><td>0.836 (0.396)</td><td>0.746 (0.297)</td></tr><tr><td colspan=\"3\">Median time between diagnosis and HSCT (months)</td></tr><tr><td> Primary immunodeficiencies</td><td>7.85</td><td>8.38</td></tr><tr><td> Inborn error of metabolism</td><td>5.75</td><td>6.34</td></tr><tr><td> Haemoglobinopathies</td><td>89.91</td><td>67.61</td></tr><tr><td> Bone marrow failure syndromes</td><td>37.62</td><td>27.24</td></tr><tr><td colspan=\"3\">Disease groups, n (%)</td></tr><tr><td> Primary immunodeficiency</td><td>28 (56.0)</td><td>23 (45.1)</td></tr><tr><td> Inborn error of metabolism</td><td>4 (8.0)</td><td>2 (3.9)</td></tr><tr><td> Haemoglobinopathy</td><td>13 (26.0)</td><td>21 (41.2)</td></tr><tr><td> Beta-thalassemia major</td><td>5 (10.0)</td><td>16 (31.4)</td></tr><tr><td> Sickle cell disease</td><td>8 (16.0)</td><td>5 (9.8)</td></tr><tr><td> Bone marrow failure syndrome</td><td>5 (10.0)</td><td>5 (9.8)</td></tr><tr><td colspan=\"3\">Donor type, n (%)</td></tr><tr><td> MRD</td><td>17 (34.0)</td><td>14 (27.5)</td></tr><tr><td> MUD</td><td>33 (66.0)</td><td>37 (72.5)</td></tr><tr><td colspan=\"3\">Applied performance score, n (%)</td></tr><tr><td> Lansky performance score</td><td>48 (96.0)</td><td>50 (98.0)</td></tr><tr><td> 70</td><td>2 (4.0)</td><td>1 (2.0)</td></tr><tr><td> 80</td><td>1 (2.0)</td><td>0 (0.0)</td></tr><tr><td> 90</td><td>9 (18.0)</td><td>7 (13.7)</td></tr><tr><td> 100</td><td>36 (72.0)</td><td>42 (82.4)</td></tr><tr><td> Karnofsky performance score</td><td>2 (4.0)</td><td>1 (2.0)</td></tr><tr><td> 100</td><td>2 (4.0)</td><td>1 (2.0)</td></tr><tr><td colspan=\"3\">Thiotepa, n (%)</td></tr><tr><td> No</td><td>8 (16.0)</td><td>8 (15.7)</td></tr><tr><td> Yes</td><td>42 (84.0)</td><td>43 (84.3)</td></tr><tr><td colspan=\"3\">Serotherapy, n (%)</td></tr><tr><td> No</td><td>18 (36.0)</td><td>13 (25.5)</td></tr><tr><td> Yes</td><td>32 (64.0)</td><td>38 (74.5)</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Freedom from Transplantation (treatment)-related Mortality and Secondary Outcomes (FAS).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th/><th>Busulfan</th><th>Treosulfan</th></tr><tr><th/><th>(N = 50)</th><th>(N = 51)</th></tr></thead><tbody><tr><td colspan=\"3\">Freedom from transplantation (treatment)-related mortality until Day + 100</td></tr><tr><td> Patients without event, n (%)</td><td>45 (90.0%)</td><td>51 (100.0%)</td></tr><tr><td> Incidence, % (95% CI)</td><td>90.0 (78.2, 96.7)</td><td>100.0 (93.0, 100.0)</td></tr><tr><td> Difference in incidences, % (95% CI)</td><td>–10.0 (–21.8, –2.0)</td><td/></tr><tr><td> P †</td><td colspan=\"2\">0.0528</td></tr><tr><td colspan=\"3\">TRM</td></tr><tr><td> Patients with event, n (%)</td><td>7 (14.0)</td><td>2 (3.9)</td></tr><tr><td> TRM at 12 months‡, % (95% CI)</td><td>12.0 (5.6, 24.8)</td><td>3.9 (1.0, 14.8)</td></tr><tr><td> Hazard ratio (Treosulfan/Busulfan)§ (95% CI)</td><td>0.29 (0.08, 1.09)</td><td/></tr><tr><td> P §</td><td colspan=\"2\">0.1244</td></tr><tr><td colspan=\"3\">OS</td></tr><tr><td> Patients without event, n (%)</td><td>43 (86.0)</td><td>49 (96.1)</td></tr><tr><td> OS at 12 months‡, % (95% CI)</td><td>88.0 (75.2, 94.4)</td><td>96.1 (85.2, 99.0)</td></tr><tr><td> Hazard ratio (Treosulfan/Busulfan)§ (95% CI)</td><td>0.29 (0.06, 1.41)</td><td/></tr><tr><td> P §</td><td colspan=\"2\">0.1244</td></tr><tr><td colspan=\"3\">Engraftment</td></tr><tr><td colspan=\"3\"> Reconstitution of granulopoiesis, n (%)</td></tr><tr><td> Patients with event</td><td>36 (72.0)</td><td>40 (78.4)</td></tr><tr><td> Patients without event (censored) or with competing event</td><td>14 (28.0)</td><td>11 (21.6)</td></tr><tr><td> Censored</td><td>2 (4.0)</td><td>2 (3.9%)</td></tr><tr><td> Death \| \|</td><td>0 (0.0%)</td><td>0 (0.0%)</td></tr><tr><td> Rescue therapy \| \|</td><td>12 (24.0%)</td><td>9 (17.6%)</td></tr><tr><td> Conditional cumulative incidence at 28 days, % (95% CI)</td><td>88.5 (75.9, 100.0)</td><td>81.0 (65.8, 96.1)</td></tr><tr><td> Maximum conditional cumulative incidence reached, % (95% CI)</td><td>100.0 (93.0, 100.0)</td><td>97.3 (87.0, 100.0)</td></tr><tr><td> P</td><td colspan=\"2\">0.0521</td></tr><tr><td colspan=\"3\"> Neutropenia</td></tr><tr><td> Yes#</td><td>50 (100.0%)</td><td>51 (100.0%)</td></tr><tr><td colspan=\"3\"> Duration of neutropenia, days*</td></tr><tr><td> n</td><td>48</td><td>49</td></tr><tr><td> Median (Q1, Q3)</td><td>14.5 (10.0, 21.0)</td><td>20.0 (15.0, 25.0)</td></tr><tr><td> P ††</td><td colspan=\"2\">0.0108</td></tr><tr><td colspan=\"3\"> Reconstitution of leukopoiesis, n (%)</td></tr><tr><td> Patients with event</td><td>36 (72.0)</td><td>40 (78.4)</td></tr><tr><td> Patients without event (censored) or with competing event</td><td>14 (28.0)</td><td>11 (21.6)</td></tr><tr><td> Censored</td><td>2 (4.0)</td><td>2 (3.9)</td></tr><tr><td> Death \| \|</td><td>0 (0.0)</td><td>0 (0.0)</td></tr><tr><td> Rescue therapy \| \|</td><td>12 (24.0)</td><td>9 (17.6)</td></tr><tr><td> Conditional cumulative incidence at 28 days, % (95% CI)</td><td>88.5 (75.7, 100.0)</td><td>90.5 (82.2, 98.8)</td></tr><tr><td> Maximum conditional cumulative incidence reached, % (95% CI)</td><td>100.0 (93.0, 100.0)</td><td>96.8 (85.3, 100.0)</td></tr><tr><td> P</td><td colspan=\"2\">0.2469</td></tr><tr><td colspan=\"3\"> Leukopenia</td></tr><tr><td> Yes‡‡</td><td>50 (100.0%)</td><td>51 (100.0%)</td></tr><tr><td colspan=\"3\"> Duration of leukopenia, days a</td></tr><tr><td> n</td><td>48</td><td>49</td></tr><tr><td> Median (Q1, Q3)</td><td>14.5 (12.0, 20.0)</td><td>19.0 (16.0, 22.0)</td></tr><tr><td> P ††</td><td>0.0087</td><td/></tr><tr><td colspan=\"3\"> Reconstitution of thrombopoiesis > 20 × 109/L, n (%)</td></tr><tr><td> Patients with event</td><td>35 (70.0)</td><td>40 (78.4)</td></tr><tr><td> Patients without event (censored) or with competing event</td><td>15 (30.0)</td><td>11 (21.6)</td></tr><tr><td> Censored</td><td>3 (6.0)</td><td>2 (3.9)</td></tr><tr><td> Death \| \|</td><td>0 (0.0)</td><td>0 (0.0)</td></tr><tr><td> Rescue therapy \| \|</td><td>12 (24.0)</td><td>9 (17.6)</td></tr><tr><td> Conditional cumulative incidence at 28 days, % (95% CI)</td><td>77.6 (61.4, 93.9)</td><td>85.7 (75.6, 95.8)</td></tr><tr><td> Maximum conditional cumulative incidence reached, % (95% CI)</td><td>96.8 (84.6, 100.0)</td><td>100.0 (92.7, 100.0)</td></tr><tr><td> P</td><td colspan=\"2\">0.8595</td></tr><tr><td colspan=\"3\">Graft failure</td></tr><tr><td> Patients with event b, n (%)</td><td>2 (4.0)</td><td>11 (21.6)</td></tr><tr><td> Primary graft failure</td><td>2 (4.0)</td><td>2 (3.9)</td></tr><tr><td> Secondary graft failure</td><td>0 (0.0)</td><td>9 (18.4)</td></tr><tr><td> Cumulative incidence at 12 months, % (95% CI)</td><td>4.0 (0.0, 9.4)</td><td>15.8 (5.8, 25.9)</td></tr><tr><td> Hazard ratio (Treosulfan/Busulfan) c (95% CI)</td><td colspan=\"2\">5.48 (1.11, 27.03)</td></tr><tr><td> P c</td><td colspan=\"2\">0.0366</td></tr><tr><td colspan=\"3\">Incidence of complete donor type chimerism until Month 12</td></tr><tr><td> Patients at risk at Day +28 d</td><td>50</td><td>51</td></tr><tr><td> Patients with complete chimerism, n (%)</td><td>41 (82.0)</td><td>43 (84.3)</td></tr><tr><td> Patients without information, n (%)</td><td>0 (0.0)</td><td>1 (2.0)</td></tr><tr><td> Odds ratio e (95% CI)</td><td colspan=\"2\">1.5824 (0.51, 4.89)</td></tr><tr><td> P e†</td><td colspan=\"2\">0.425</td></tr><tr><td> Patients at risk at Day +100 d</td><td>46</td><td>51</td></tr><tr><td> Patients with complete chimerism, n (%)</td><td>39 (84.8)</td><td>34 (66.7)</td></tr><tr><td> Patients without information, n (%)</td><td>2 (4.3)</td><td>3 (5.9)</td></tr><tr><td> Odds ratio e (95% CI)</td><td colspan=\"2\">0.3972 (0.12, 1.28)</td></tr><tr><td> P e†</td><td colspan=\"2\">0.1196</td></tr><tr><td> Patients at risk at Month 12 d</td><td>43</td><td>49</td></tr><tr><td> Patients with complete chimerism, n (%)</td><td>33 (76.7)</td><td>24 (49.0)</td></tr><tr><td> Patients without information, n (%)</td><td>1 (2.3)</td><td>11 (22.4)</td></tr><tr><td> Odds ratio e (95% CI)</td><td colspan=\"2\">0.5429 (0.20, 1.51)</td></tr><tr><td> P e†</td><td colspan=\"2\">0.2445</td></tr><tr><td colspan=\"3\">Incidence of mixed donor type chimerism (with at least 20% chimerism) until Month 12</td></tr><tr><td> Patients at risk at Day +28 d</td><td>50</td><td>51</td></tr><tr><td> Patients with ≥20% chimerism, n (%)</td><td>49 (98.0)</td><td>48 (94.1)</td></tr><tr><td> Patients without information, n (%)</td><td>0 (0.0)</td><td>1 (2.0)</td></tr><tr><td> Odds ratio e (95% CI)</td><td colspan=\"2\">0.3041 (0.02, 4.32)</td></tr><tr><td> P e†</td><td colspan=\"2\">0.3679</td></tr><tr><td> Patients at risk at Day +100 d</td><td>46</td><td>51</td></tr><tr><td> Patients with ≥ 20% chimerism, n (%)</td><td>44 (95.7)</td><td>46 (90.2)</td></tr><tr><td> Patients without information, n (%)</td><td>2 (4.3)</td><td>3 (5.9)</td></tr><tr><td> Odds ratio e (95% CI)</td><td colspan=\"2\"><0.0001 (NE)</td></tr><tr><td> P e†</td><td colspan=\"2\">0.3173</td></tr><tr><td> Patients at risk at Month 12 d</td><td>43</td><td>49</td></tr><tr><td> Patients with ≥ 20% chimerism, n (%)</td><td>42 (97.7%)</td><td>37 (75.5%)</td></tr><tr><td> Patients without information, n (%)</td><td>1 (2.3)</td><td>11 (22.4)</td></tr><tr><td> Odds ratio e (95% CI)</td><td colspan=\"2\"><0.0001 (NE)</td></tr><tr><td> P e†</td><td colspan=\"2\">0.4142</td></tr><tr><td colspan=\"3\">GVHD-free survival</td></tr><tr><td> Patients with event, n (%)</td><td>15 (30.0)</td><td>8 (15.7)</td></tr><tr><td> Death \| \|</td><td>4 (8.0)</td><td>0 (0.0)</td></tr><tr><td> Acute GVHD of at least Grade III</td><td>4 (8.0)</td><td>7 (13.7)</td></tr><tr><td> Moderate/severe chronic GVHD</td><td>7 (14.0)</td><td>1 (2.0)</td></tr><tr><td> GVHD-free survival at 12 months‡, % (95% CI)</td><td>69.4 (54.4, 80.3)</td><td>82.9 (68.7, 91.1)</td></tr><tr><td> Hazard Ratio (Treosulfan/Busulfan)§ (95% CI)</td><td colspan=\"2\">0.58 (0.24, 1.38)</td></tr><tr><td> P §</td><td colspan=\"2\">0.2178</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab3\"><label>Table 3</label><caption><p>Graft-versus-host-disease and treatment-emergent adverse events (FAS).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th/><th>Busulfan</th><th>Treosulfan</th></tr><tr><th/><th>(N = 50)</th><th>(N = 51)</th></tr></thead><tbody><tr><td colspan=\"3\">Acute GVHD</td></tr><tr><td colspan=\"3\"> GVHD Grade I-IV</td></tr><tr><td> Patients with event, n (%)</td><td>21 (42.0)</td><td>28 (54.9)</td></tr><tr><td> Cumulative incidence at 100 days, % (95% CI)</td><td>42.0 (28.3, 55.7)</td><td>54.9 (41.2, 68.6)</td></tr><tr><td> Hazard ratio (treosulfan/busulfan), % (95% CI)</td><td colspan=\"2\">1.65 (0.93, 2.93)</td></tr><tr><td> P </td><td colspan=\"2\">0.0889</td></tr><tr><td colspan=\"3\"> GVHD Grade III-IV</td></tr><tr><td> Patients with event, n (%)</td><td>4 (8.0)</td><td>7 (13.7)</td></tr><tr><td> Cumulative incidence at 100 days, % (95% CI)</td><td>8.0 (0.5, 15.5)</td><td>13.7 (4.3, 23.2)</td></tr><tr><td> Hazard ratio (treosulfan/busulfan), % (95% CI)</td><td colspan=\"2\">1.63 (0.45, 5.92)</td></tr><tr><td> P </td><td colspan=\"2\">0.4598</td></tr><tr><td colspan=\"3\">Chronic GVHD</td></tr><tr><td> Moderate/Severe†</td><td>44</td><td>47</td></tr><tr><td> Patients with event, n (%)</td><td>10 (22.7)</td><td>5 (10.6)</td></tr><tr><td> Cumulative incidence at 24 months, % (95% CI)</td><td>22.7 (10.3, 35.1)</td><td>10.6 (1.8, 19.5)</td></tr><tr><td> Hazard ratio (treosulfan/busulfan), % (95% CI)</td><td colspan=\"2\">0.46 (0.15, 1.37)</td></tr><tr><td> P </td><td colspan=\"2\">0.1611</td></tr><tr><td colspan=\"3\">Treatment-emergent adverse events</td></tr><tr><td> Subjects with any adverse event, n (%)</td><td>48 (96.0)</td><td>49 (96.1)</td></tr><tr><td> P §</td><td colspan=\"2\">1.0000</td></tr><tr><td> Subjects with AEs of at least CTCAE Grade III, n (%)</td><td>41 (82.0)</td><td>41 (80.4)</td></tr><tr><td> P §</td><td colspan=\"2\">1.0000</td></tr><tr><td> CTCAE Grades I/II, n (%)</td><td>7 (14.0)</td><td>8 (15.7)</td></tr><tr><td> P §</td><td colspan=\"2\">1.0000</td></tr><tr><td> CTCAE Grade III, n (%)</td><td>30 (60.0)</td><td>34 (66.7)</td></tr><tr><td> P §</td><td colspan=\"2\">0.5393</td></tr><tr><td> CTCAE Grade IV, n (%)</td><td>8 (16.0)</td><td>7 (13.7)</td></tr><tr><td> P §</td><td colspan=\"2\">0.7862</td></tr><tr><td> CTCAE Grade V, n (%)</td><td>3 (6.0)</td><td>0 (0.0)</td></tr><tr><td> P §</td><td colspan=\"2\">0.1176</td></tr><tr><td> Serious adverse events, n (%)</td><td>16 (32.0)</td><td>18 (35.3)</td></tr><tr><td> P §</td><td colspan=\"2\">0.8338</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab4\"><label>Table 4</label><caption><p>Pharmacokinetic Results of Treosulfan by BSA Group: Pooled Analysis of MC-FludT.16/NM and MC-FludT.17/M.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th>PK of Treosulfan corrected (mean ± SD, tmax: median [range])</th><th>BSA Group ≤ 0.5 m² (10 g/m² dose group)</th><th>BSA Group ≤ 0.5 - ≤ 1.0 m² (12 g/m² dose group)</th></tr></thead><tbody><tr><td>N</td><td>15</td><td>37†</td></tr><tr><td>Cmax, µg/mL</td><td>608 ± 209</td><td>662 ± 286</td></tr><tr><td>tmax, h</td><td>2.08 (2.00-2.50)</td><td>2.02 (2.00-2.42)</td></tr><tr><td>AUClast, μg.h/mL</td><td>1551 ± 474</td><td>1629 ± 402</td></tr><tr><td>AUC ∞ , μg.h/mL</td><td>1570 ± 482</td><td>1672 ± 401</td></tr><tr><td>t1/2term, h</td><td>1.27 ± 0.178</td><td>1.40 ± 0.173</td></tr><tr><td>CL, L/h</td><td>3.44 ± 2.78</td><td>5.32 ± 1.46</td></tr><tr><td>Vd, L</td><td>6.22 ± 5.26</td><td>10.7 ± 3.68</td></tr></tbody></table></table-wrap>" ]	[]	[]	[]	[]	[]	[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>" ]	[ "<table-wrap-foot><p>Lansky score if age <16 years at registration; Karnofsky score if age ≥16 years at registration.</p><p><italic>BMI</italic> body mass index, <italic>HSCT</italic> hematopoietic stem cell transplantation, <italic>ICH</italic> International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use, <italic>MRD</italic> matched related donor, <italic>MUD</italic> matched unrelated donor, <italic>SD</italic> standard deviation.</p></table-wrap-foot>", "<table-wrap-foot><p>Adjusted for thiotepa and disease.</p><p>†Stratified Cochran-Mantel-Haenszel test.</p><p>‡Based on Kaplan-Meier estimates.</p><p>§Adjusted for thiotepa and disease as factors using Cox regression model.</p><p>\|\|Only if this event occurred first.</p><p>¶Based on Pepe-Mori test.</p><p>#Neutrophilic granulocytes ≤ 0.5 g/L at least once between Day -7 and Day +28.</p><p>∗∗First date with neutropenia until date of engraftment (patients at risk = patients with neutropenia and neutrophilic granulopoiesis).</p><p>††Based on the Wilcoxon-Mann-Whitney test.</p><p>‡‡Leukocytes ≤ 1 g/L at least once between Day -7 and Day +28.</p><p>aFirst date with leukopenia until date of engraftment (patients at risk = patients with leukopenia and leukopoiesis).</p><p>bRate of primary/secondary graft failure calculated as number of patients with graft failure by the number of patients at risk.</p><p>-At risk for primary graft failure: Patients with HSCT.</p><p>-At risk for secondary graft failure: Patients whose neutrophilic granulocytes engrafted after HSCT or were never below the required level.</p><p>cAdjusted for thiotepa and disease as factors using Fine and Gray model.</p><p>dPatients are at risk if they have an examination at the Day +28, Day +100, Month 12 or if they have survived day +30, +107, +379, respectively.</p><p>eMissing values are excluded for odds ratio calculation and tests.</p><p>CI, confidence interval; FAS, full analysis set; GVHD, Graft‑versus-host disease; N, total number of patients; NE, not estimated; OS, overall survival; Q1, first quartile; Q3, third quartile; TRM, transplantation related mortality.</p></table-wrap-foot>", "<table-wrap-foot><p>* Adjusted for thiotepa and disease as factors using Fine and Gray model.</p><p>† Patients are at risk if they have survived 100 days after end of HSCT without graft failure.</p><p>§ Fisher’s exact test.</p><p><italic>CI</italic> confidence interval, <italic>CTCAE</italic> Common Terminology Criteria for Adverse Events, <italic>FAS</italic> full analysis set, <italic>GVHD</italic> Graft-versus-host disease, <italic>N</italic> total number of patients.</p></table-wrap-foot>", "<table-wrap-foot><p>*N = 16 for t1/2term.</p><p>†N = 38 for Cmax, tmax and AUClast.</p><p><italic>AUC</italic> area under the time-concentration curve, <italic>AUC</italic>∞, AUC from time 0 to infinite time, <italic>AUClast</italic> AUC from time 0 to the time of the last measurable plasma concentration, <italic>BSA</italic> body surface area, <italic>CL</italic> total clearance, <italic>Cmax</italic> maximum plasma concentration, <italic>N</italic> total number of patients, <italic>PK</italic> pharmacokinetic, <italic>SD</italic> standard deviation, <italic>t1/2term</italic> apparent terminal elimination half-life, <italic>tmax</italic> time to reach maximum plasma concentration, <italic>Vd</italic> volume of distribution.</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>These authors contributed equally: Karl-Walter Sykora, Rita Beier.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"41409_2023_2135_Fig1_HTML\" id=\"d32e608\"/>", "<graphic xlink:href=\"41409_2023_2135_Fig2_HTML\" id=\"d32e1630\"/>" ]	[ "<media xlink:href=\"41409_2023_2135_MOESM1_ESM.docx\"><caption><p>Supplemental Data</p></caption></media>" ]	[{"label": ["12."], "surname": ["Casper", "Holowiecki", "Trenschel", "Wandt", "Schaefer-Eckart", "Ruutu"], "given-names": ["J", "J", "R", "H", "K", "T"], "article-title": ["Allogeneic hematopoietic SCT in patients with AML following treosulfan/fludarabine conditioning"], "source": ["Bone Marrow Transpl"], "year": ["2012"], "volume": ["47"], "fpage": ["1171"], "lpage": ["7"], "pub-id": ["10.1038/bmt.2011.242"]}, {"label": ["15."], "surname": ["Nemecek", "Hilger", "Adams", "Shaw", "Kiefer", "Le-Rademacher"], "given-names": ["ER", "RA", "A", "BE", "D", "J"], "article-title": ["Treosulfan, fludarabine, and low-dose total body irradiation for children and young adults with acute myeloid leukemia or myelodysplastic syndrome undergoing allogeneic hematopoietic cell transplantation: prospective phase II trial of the pediatric blood and marrow transplant consortium"], "source": ["Biol Blood Marrow Transpl"], "year": ["2018"], "volume": ["24"], "fpage": ["1651"], "lpage": ["6"], "pub-id": ["10.1016/j.bbmt.2018.04.025"]}, {"label": ["16."], "surname": ["Kalwak", "Mielcarek", "Patrick", "Styczynski", "Bader", "Corbacioglu"], "given-names": ["K", "M", "K", "J", "P", "S"], "article-title": ["Treosulfan-fludarabine-thiotepa-based conditioning treatment before allogeneic hematopoietic stem cell transplantation for pediatric patients with hematological malignancies"], "source": ["Bone Marrow Transpl"], "year": ["2020"], "volume": ["55"], "fpage": ["1996"], "lpage": ["2007"], "pub-id": ["10.1038/s41409-020-0869-6"]}, {"label": ["19."], "mixed-citation": ["European Medicines Agency. "], "italic": ["Union Register of medicinal products: Marketing Authorisation. 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PMC10781638	38123678	[]	[ "<title>Methods</title>", "<title>Embryo collection and culture</title>", "<p id=\"Par21\">All experiments were performed under the authorization of the authorities from Upper Bavaria (Tierversuchsantrag von Regierung von Oberbayern). The temperature, humidity and light cycle of mouse cages were maintained at 20–24 °C, 45–65% and 12/12 h dark/light, respectively. F<sub>1</sub> female mice (C57BL/6J × CBA) under 10 weeks of age were superovulated by intraperitoneal injection of 10 U of pregnant mare serum gonadotropin, followed by 10 U of hCG 48 h later, and were then mated with DBA/2J male mice. Zygotes were collected from the oviduct and cumulus cells removed following brief incubation in M2 medium containing hyaluronidase (Sigma-Aldrich). Zygotes were placed in drops of KSOM (potassium simplex optimized medium) and cultured at 37 °C with 5% CO<sub>2</sub> as previously described. For induction of parthenogenetic embryos, MII-stage oocytes were collected, as described above, from superovulated females without mating. Following removal of cumulus cells, oocytes were treated with 10 mM Sr<sup>2+</sup> for 2 h in Ca<sup>2+</sup>-free CZB medium and then incubated in KSOM. For generation of IVF-derived zygotes, MII oocytes from F<sub>1</sub> female mice (C57BL/6J × CBA) were inseminated with activated spermatozoa obtained from the caudal epididymides of adult DBA/2 J male mice.</p>", "<title>Detection of 5-ethynyl-2′-deoxyuridine incorporation</title>", "<p id=\"Par22\">Cells were incubated with 50 μM 5-ethynyl-2′-deoxyuridine (EdU) for 1 h for each time window, as indicated, and processed for quantification of signal intensity. Incorporated EdU was visualized by Click-iT chemistry (Thermo Fisher Scientific) followed by permeabilization as described in the manufacturer’s instructions. Images were acquired on a SP8 confocal laser-scanning microscope (Leica). EdU was coupled to Alexa 594 and images acquired with a Plan-Apochromat ×63/1.4 numerical aperture 1.4 oil-immersion objective (Leica) at 561 nm excitation.</p>", "<title>Analysis of EdU incorporation</title>", "<p id=\"Par23\">To quantify EdU incorporation we manually cropped confocal stacks containing several embryos so that each image contained only one single embryo. Only embryos that looked fertilized and with normal pronuclei following visual inspection were included in this analysis. From embryo images we then automatically obtained the maximum intensity value in the EdU channel of the whole stack by ImageJ (v.1.53k) with a custom-made ImageJ macro. We plotted and analysed the resulting EdU intensity values for each time bin with R.</p>", "<title>Inhibition of ZGA</title>", "<p id=\"Par24\">For inhibition of both minor and major ZGA, embryos were treated with either 0.1 mg ml<sup>−1</sup> α-amanitin or 100 μM DRB from the zygote stage at 17 h after hCG injection until their collection for single-cell Repli-seq at the 2-cell stage. Validation of the α-amanitin effect on transcriptional silencing was done using a Click-iT RNA Alexa Fluor 594 Imaging Kit (Thermo Fisher Scientific) at the 2-cell stage (at 40 h after hCG injection).</p>", "<title>Gene expression analyses following treatment with α-amanitin and DRB</title>", "<p id=\"Par25\">Twelve embryos were treated with either 0.1 mg ml<sup>−1</sup> α-amanitin or 100 μM DRB from 17 to 40 h after hCG to inhibit both minor and major ZGA, then flash-frozen in liquid nitrogen in 5 μl of 2× reaction buffer (CellsDirect One-Step qRT–PCR kit, no. 11753100, Thermo Fisher). Next, 0.5 μl of a 1:200 dilution of ERCC spike-in mix (Thermo Fisher) was added to each group and TaqMan Gene Expression assays were performed according to previous work<sup>##REF##32601371##38##</sup>. Complementary DNA was diluted tenfold before analysis with Universal PCR Master Mix and TaqMan Gene Expression assays (Applied Biosystems). All raw <italic>C</italic><sub>t</sub> values were normalized by those acquired from the ERCC spike-in specific primer set, and relative expression levels of each gene were determined by the ddCt method. We assigned <italic>C</italic><sub>t</sub> values below the detection range as expression level 0. Primers and probes for ribosomal DNA (<italic>Hsa1</italic>) were produced by TIB MolBiol (custom design)<sup>##REF##28846101##45##</sup>. Primers and probes for Zscan4 cluster and ERCC spike-in were purchased from Applied Biosystems.</p>", "<title>Immunostaining following either treatment by α-amanitin and DRB or expression of KDM5B</title>", "<p id=\"Par26\">Embryos were treated with either 0.1 mg ml<sup>−1</sup> α-amanitin<sup>##REF##9013938##55##,##REF##6875454##56##</sup> or 100 μM DRB from 17 to 40 h after hCG and fixed with 4% paraformaldehyde (PFA) for 20 min at room temperature. For KDM5B expression, 2 μg μl<sup>−1</sup> KDM5B of in vitro synthesized messenger RNA was microinjected into zygotes at 18 h after hCG and fixed with 4% PFA for 20 min at room temperature at 48 h after hCG, similar to previous experiments<sup>##REF##31118510##13##,##REF##27626377##33##</sup>. Embryos were then permeabilized with 0.5% Triton X-100 containing PBS for 20 min. For immunostaining following Triton pre-extraction, embryos were first permeabilized with pre-extraction buffer (50 mM NaCl, 3 mM MgCl<sub>2</sub>, 300 mM sucrose, 25 mM HEPES, pH adjusted to 7.4) with 0.5% Triton X-100 for 10 min on ice and washed three times in pre-extraction buffer before fixing in 4% PFA at room temperature for 20 min. Following blocking for 1 h at room temperature in blocking solution (5% normal goat serum in PBS), embryos were incubated with either anti-RNA polymerase II (no. sc-899, 1:100), anti-RNA polymerase II CTD repeat YSPTSPS (phospho S2, no. ab5095, 1:1,000) or anti-H3K4me3 (Diagenode, no. C15410003, 1:250) antibody in blocking solution overnight at 4 °C. Embryos were incubated for 1.5 h at room temperature in blocking solution containing goat anti-rabbit IgG highly cross-adsorbed secondary antibody, Alexa Fluor 488 (Thermo Fisher Scientific, no. A11034, 1:1,000). After washing, embryos were mounted in Vectashield (Vector Laboratories). Confocal microscopy was performed using a ×40 oil objective on an SP8 confocal microscope (Leica) and images acquired with LAS X software.</p>", "<title>Repli-seq</title>", "<p id=\"Par27\">Single-cell Repli-seq was performed as previously described<sup>##REF##34986273##19##</sup> based on ref. <sup>##REF##29382831##5##</sup>. In brief, early-stage zygotes were collected and cultured until they reached the S phase at each developmental stage, based on their time following hCG injection. Embryos were collected at different time points at each developmental stage to achieve sampling over the entire S phase. Collection times are indicated in Supplementary Table ##SUPPL##0##1##. For parthenogenetic embryos and IVF-derived zygotes, the timing of S phase was calculated based on the time elapsed since activation and insemination, respectively. For KDM5B experiments, 2 μg μl<sup>−1</sup> KDM5B of in vitro synthesized mRNA was microinjected into zygotes at 18 h after hCG as previously described<sup>##REF##31118510##13##</sup>. For each developmental stage, embryos were obtained from several litters and embryos from different litters were collected across different dates to ensure robust data collection. The number of mice used for collection of samples for each developmental stage is indicated in parentheses, as follows: zygote (20), 2-cell (30), 4-cell (27), 8-cell (20), 16-cell (15), morula (16), ICM (19), parthenotes (14), IVF zygotes (14), 2-cell + α-amanitin (14), 2-cell + DRB (24) and 2-cell + KDM5B (24). Zona pellucida was removed by exposure to acid Tyrode, and each blastomere was dissociated by gentle pipetting following trypsin treatment. For Repli-seq with physically isolated pronuclei we distinguished maternal and paternal pronuclei based on their size and relative position to the second polar body, and isolated them using micromanipulation. The remaining zygote containing a single pronucleus was also collected following removal of the polar body so that both pronuclei from the same zygote were further processed for Repli-seq. ICM cells were collected following trypsin digestion as previously described<sup>##REF##24183668##57##</sup>, with repeated oral pipetting in 0.5% trypsin and 1 mM EDTA; collection times are indicated in Supplementary Table ##SUPPL##0##1##. To distinguish ICM from trophectoderm cells, blastocysts were labelled with Fluoresbrite YG Microspheres (0.2 μm, Polysciences) before incubation with trypsin, and individual cells were sorted according to either positive (trophectoderm) or negative (ICM) fluorescence under a fluorescence microscope following disaggregation. Individual blastomeres or pronuclei were placed in eight-strip PCR tubes containing lysis buffer, and extracted DNA was fragmented by heat incubation. Fragmented DNA was tagged by the universal primer 5′-TGTGTTGGGTGTGTTTGGKKKKKKKKKKNN-3′ and amplified with whole-gene amplification primer sets, which have individual barcodes. This whole-genome amplification procedure was successfully used for single-cell Repli-seq in cell culture<sup>##REF##33888635##4##,##REF##29382831##5##</sup>. Amplified DNA was purified using the QIAquick 96 PCR Purification Kit (QIAGEN), and concentration determined by NanoDrop (Thermo Scientific). Equal amounts of DNA from each sample (up to 96 samples) were pooled and 1 μg of each was ligated with Illumina adaptors using the NEBNext Ultra II DNA Library Prep Kit (NEB). Illumina sequences (NEBNext Multiplex Oligos for Illumina, NEB) were added to adaptor-ligated samples by PCR. Clean-up and size selection of the PCR product was done using SPRIselect (Beckman Coulter), and the quality of the library was confirmed using a 2100 Bioanalyzer with the High Sensitivity DNA Kit (Agilent).</p>", "<title>Single-cell Repli-seq read alignment and quality control filtering</title>", "<p id=\"Par28\">An overview of sample collection, mapping statistics and quality control is included in Supplementary Table ##SUPPL##0##1##. The quality control parameters we used were (1) the number of reads, which we set as 750,000 aligned reads as minimum; and (2) a coefficient of variation, which we established as a measure of equal/balanced coverage between chromosomes, thus filtering out potential cells with aneuploidy. At early stages, the reason for failure was equally the low number of reads or a high coefficient of variation (typically due to either lack of reads on a complete chromosome or in fragments of the genome; for example, zygotes 13 and 8 were excluded due to low number of reads and zygote 56 to a high coefficient of variation). At later stages, chromosome imbalances were the most common reason for failure (59 cells with high coefficient of variation versus three with low reads in the blastocyst stage), which reflects the known aneuploidy of cells at this embryonic stage. Sequencing reads were aligned to the mm10 genome using bowtie2 (v.2.3.5)<sup>##REF##22388286##58##</sup> with the ‘--local’ option. Duplicates were marked using SAMtools (v.1.9) ‘markdup’ as described by SAMtools<sup>##REF##33590861##59##</sup> documentation (the commands ‘fixmate’ and ‘sort samtools’ were used for this purpose accordingly). Using SAMtools view, reads were filtered by retaining only properly paired reads, removing duplicates and selecting those whose mapping quality was higher than or equal to 20. BED files of the read coordinates were generated with the BEDtools<sup>##REF##20110278##60##</sup> (v.2.29.0) command ‘bamtobed’. Using BEDtools intersect, read counts were obtained for contiguous 50 kb genomic bins. For each cell the average of the bin counts was calculated for chromosomes 1–19; these 19 values were then next used to calculate the coefficient of variation as standard deviation divided by the mean. Cells with a coefficient of variation greater than 0.1 were removed from analyses due to chromosome imbalance. To maximize the number of samples used, the coefficient of variation was recalculated, excluding chromosomes one at a time. Cells were considered for further analysis if they passed the threshold when only one specific chromosome was removed. This chromosome was subsequently masked in downstream analyses; this filter removes abnormal genotypes and cells with aneuploidy.</p>", "<title>Assignment of replication status</title>", "<p id=\"Par29\">Using the read counts obtained for contiguous 50 kb genomic bins, we used the single-cell Repli-seq bioinformatic pipeline previously described<sup>##REF##29382831##5##</sup>, which we followed with some modifications for each embryonic stage as summarized below. Window counts were first normalized to reads per million, and then each bin by its respective average of all samples within the same stage, aiming to correct for mappability biases intrinsic to genomic regions. Outlier regions were then masked, specifically the windows of the lower fifth percentile and upper first percentile values. To correct for low mappability, windows were segmented with the R package copy number (v.1.28.0, R v.4.0.0)<sup>##REF##23442169##61##</sup> to retain segments with the highest 95% of values. We did not perform the G1/G2 normalization described previously<sup>##REF##29382831##5##</sup>, but we verified that this did not impact the results of these analyses. In brief, we used the validated mouse ES cell scRepli-seq datasets in ref. <sup>##REF##29382831##5##</sup> and ran the analysis pipeline as described in their methods section with and without G1 control cells. Subsequently we compared the generated matrix of ones and zeros (that is, bins replicated and not replicated, respectively) by determining the percentage of windows that remained the same (for example, their 1 or 0 replication state did not change) after running the pipeline versus without G1 control. These analyses showed a high concordance between the two pipelines, with over 91% identity of genomic bins with zeros and ones on average across cells (Extended Data Fig. ##FIG##5##1b##). Importantly, those cells classified as outliers based on our analysis correspond to those that were removed in the original publication<sup>##REF##29382831##5##</sup> based on their ‘Removing outlier cells’, and were not considered for further analyses. Data were centred by the mean, scaled by the IQR for each cell and smoothed using a median filter with a running width of 15 windows, followed by segmentation with the R package copynumber. Finally, using the function normalmixEM in the R package mixtools (v.1.2.0)<sup>##UREF##1##62##</sup>, segmented values were used to fit a mixture model with two components to identify replicated and non-replicated window populations. To do this, two normal distribution functions were used to select a cutting threshold that better separated distributions; this value is located where the two individual normal distribution functions intersect. If no intersection was found between the means of the two normal distribution functions, the mid-point of the means was used as a threshold.</p>", "<title>Computing replication scores, RT values and variability scores</title>", "<p id=\"Par30\">Genome-wide replication score was defined as the percentage of replicated genomic bins for each cell. Throughout the manuscript we have used a 50 kb bin size, but we obtained similar results when using 25 and 100 kb bin size. Cells with a replication score greater than 90% and less than 10% were excluded from downstream analyses. We used the replication score to rank cells by S-phase progression for visualization of their replication status on heatmaps (Fig. ##FIG##0##1c##). Next we calculated raw RT values as the fraction of cells that replicated the given genomic bin for each stage, respectively. A RT value indicates earlier RT, because a higher proportion of cells replicated the bin. To correct for potential sampling bias of cells, we calculated the fraction of replicated cells in overlapping intervals of the genome-wide replication score with interval size of 35% and increment of 4.33% (for example, 0–35%, 4.33–39.33% and so on) for each genomic bin. The average of these 16 intervals served as the interval RT value that was used for both visualization of RT profiles (Fig. ##FIG##0##1e##) and downstream analyses. Raw and interval-averaged RT values looked similar overall (Extended Data Fig. ##FIG##5##1c##; RT raw versus interval), except for some stages in which the number of cells within replication score intervals showed a different distribution. Variability score was calculated using the following formula: score = 1 − (abs(<italic>p</italic> − 0.5)/0.5), where <italic>p</italic> is the fraction of replicated cells (ones) for the given bin; note that <italic>p</italic> is corrected for sampling (as described above). The variability score is therefore a measure of variation in the RT programme across cells, because it represents the number of cells that either replicated or did not replicate a given bin. A value of 1 means that one-half of the cells replicated a given bin and corresponds to the highest variance; likewise, a value of 0 means that either all cells replicated or did not replicate a given bin, which corresponds to the lowest variance and/or no variance.</p>", "<title>Identification of initiation zones (referred to as RT peaks), TTRs and termination zones (referred to as RT troughs)</title>", "<p id=\"Par31\">To distinguish the features of RT, initiation zones, TTRs and termination zones were defined based on RT values. Genomic bins were grouped into 15 clusters by their RT values using the Mclust function from the R package mclust (v.5.4.10, R v.4.1.2). Clusters were ranked by their average RT values following analysis similar to that described previously<sup>##REF##32209126##10##</sup>, except that we used RT values for clustering as opposed to the 16 Repli-seq fractions. Initiation zones and termination zones were defined as consecutive bins with local maxima or minima of their cluster ranks, respectively, in sliding windows of 21 genomic bins using the rollappy function from the R package zoo (v.1.8-10). Regions between initiation zones and termination zones were defined as TTRs (Extended Data Fig. ##FIG##7##3b##). The number of initiation zones, which we refer to as RT peaks, recorded previously<sup>##REF##32209126##10##</sup> (approximtely 2,200 in neuronal progenitor cells) is similar to that reported here. To determine the significance of the changes in the number or region size of initiation zones, TTRs and termination zones throughout development, a linear model was fitted using the lm function in R (v.4.1.2). The rank of the developmental stages (that is, 1–7) served as the independent variable. The dependent variable was either the number of regions or the upper quartile of region sizes (75th percentile) for each region type. The <italic>P</italic> value of the coefficient corresponding to the slope indicates the significance of the linear trend. For composite plots, RT values were centred at the middle point of RT peak coordinates in 2 Mb windows and the median of RT values was calculated per position (Fig. ##FIG##0##1h##). To visualize relative RT compared with the neighbouring region, the minimum value of the 2 Mb window was subtracted for each stage.</p>", "<title>Analysis of RT heterogeneity</title>", "<p id=\"Par32\">Heterogeneity analysis was performed using the sigmoidal model formula as described previously<sup>##REF##29382831##5##,##REF##30804559##63##</sup>. A sigmoidal curve was fitted for each genomic bin by the nls function from the R package stats (v.4.1.2), such that nls(<italic>y</italic> ~ 100/(1 + exp(−<italic>g</italic> × (<italic>x</italic> − <italic>M</italic>))), start = list(<italic>g</italic> = 0.1, <italic>M</italic> = <italic>m</italic>0)) (Extended Data Fig. ##FIG##10##6a##). The average genome-wide replication score of each of the 16 overlapping intervals (see above) served as the independent variable (<italic>x</italic>), with the percentage of cells that replicated the bin within the same replication score interval as dependent variable (<italic>y</italic>). Model parameters were <italic>M</italic> = mid-point, <italic>g</italic> = slope (gain) and <italic>m</italic>0 = initial value for <italic>M</italic> (100 minus the mean of <italic>y</italic> values). By this method, the replication status of the given genomic bin was related to the overall S-phase progression of cells (measured in intervals of replication score). To anchor the start and end points of the curve, 16 data points of 0 and 100 values were added to the <italic>x</italic> and <italic>y</italic> variable, respectively. Two parameters were calculated from the curve fitting, <italic>M</italic>-value and <italic>T</italic><sub>width</sub>. The <italic>M</italic>-value (RT mid-point, sometimes also referred to as <italic>T</italic><sub>rep</sub> in the literature<sup>##REF##32209126##10##</sup>) is the replication score (roughly S-phase time) at which 50% of the cells replicated the given bin. A higher <italic>M</italic>-value indicates later RT. <italic>T</italic><sub>width</sub> is a measure of RT heterogeneity and is defined as the replication score difference (approximate S-phase time difference) of between 25 and 75% of the cells that replicated the given genomic bin. A higher <italic>T</italic><sub>width</sub> value indicates higher heterogeneity, because the transition from non-replicated to replicated status is greater.</p>", "<title>Allele-specific analyses</title>", "<p id=\"Par33\">To address any bias that could have been caused by SNPs during alignment, reads were realigned to a SNP-masked genome sequence containing an ‘N’ anywhere in which a SNP between any of the paternal (DBA) or maternal genomes (C57BL/6 × CBA) is located. The bam files were subsequently divided into paternal and maternal reads. Importantly, not all potential SNPs between strains were used. Splitting considered only SNPs that were different for the three genomes or those whose nucleotide was the same for both maternal genomes but different compared with the paternal one. Both reference preparation and splitting were performed with SNPsplit<sup>##REF##27429743##64##</sup> (v.0.5.0). Reads were filtered using the same tools and thresholds as described above for non-allelic analyses—that is, taking into account read duplication, properly paired criteria and a mapping quality filter. Finally, as previously described, BEDtools intersect was used to count the number of reads for each contiguous 50 kb window. All subsequent analyses were performed on genomic bins, with at least five reads assigned either to the maternal or paternal genome of the same sample.</p>", "<p id=\"Par34\">To determine allelic bias, the log<sub>2</sub> ratio of maternal:paternal read counts was calculated for each bin. The majority of physically separated maternal or paternal pronuclei showed a high positive (over +2) or negative (below −2) log<sub>2</sub> ratio, respectively. Pronuclei with a log<sub>2</sub> ratio of the opposite sign were exchanged for downstream analyses. We identified several parthenogenic examples among IVF zygotes (log<sub>2</sub> ratio above 1), which were excluded from further analyses. Finally we calculated Spearman’s correlation coefficients on log<sub>2</sub> maternal:paternal ratios pairwise across single zygotes and visualized these as a correlation heatmap (Extended Data Fig. ##FIG##8##4f##). A high correlation value between two zygotes indicates that, if a genomic bin has a high allelic bias in one of the zygotes it also has a high bias in the other.</p>", "<title>Analysis of imprinted genes</title>", "<p id=\"Par35\">Lists of maternally and paternally imprinted genes were downloaded from the Geneimprint database (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.geneimprint.com/site/genes-by-species.Mus+musculus\">https://www.geneimprint.com/site/genes-by-species.Mus+musculus</ext-link>). RT values were extracted for genomic bins overlapping imprinted genes. If multiple bins overlapped the same gene, RT values were averaged. For expression level and allelic bias analysis, supplementary data were downloaded from Gene Expression Omnibus (GEO) (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE38495\">GSE38495</ext-link> and <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE45719\">GSE45719</ext-link>)<sup>##REF##24408435##65##</sup>. A gene was considered expressed when its average fragments per kilobase exon per million mapped reads value in the given stage was greater than zero. Allelic bias was calculated as the log<sub>2</sub>-transfomed ratio between read counts assigned to Cast or C57BL/6 genomes. A gene was considered maternally biased if the average log<sup>2</sup> allelic ratio was greater than zero, and paternally biased if less than zero. RT values at imprinted genes were visualized on heatmaps and ordered by their expression and allelic bias status. In total we analysed 49 maternally and 37 paternally imprinted genes, corresponding to 98 and 100 genomic bins, respectively.</p>", "<title>Analysis of transposable elements</title>", "<p id=\"Par36\">Transposable element annotation for the mm10 genome was obtained from Hammell’s laboratory repository (<ext-link ext-link-type=\"uri\" xlink:href=\"https://labshare.cshl.edu/shares/mhammelllab/www-data/TEtranscripts/TE_GTF/mm10_rmsk_TE.gtf.gz\">https://labshare.cshl.edu/shares/mhammelllab/www-data/TEtranscripts/TE_GTF/mm10_rmsk_TE.gtf.gz</ext-link>).</p>", "<p id=\"Par37\">Enrichment of transposable elements in RT peaks, TTRs or RT troughs was estimated by calculating the log<sub>2</sub> ratio of the number of transposable elements of the given type overlapping with RT peaks, TTRs or RT troughs relative to the overlap of randomly shifted transposable elements with RT peaks, TTRs or RT troughs, respectively. The final enrichment value was the average of 1,000 iterations.</p>", "<title>Statistical and genome-wide enrichment analysis</title>", "<p id=\"Par38\">For statistical analyses of single-cell RT data we established a bootstrapping approach and calculated 95% confidence intervals to judge statistical significance<sup>##UREF##2##66##</sup>. We chose this method to avoid the inflation of <italic>P</italic> values when <italic>n</italic> is large due to a large number of genomic bins (<italic>n</italic> = approximately 49,000) and thus we applied bootstrapping to samples, in this case single cells (<italic>n</italic> = approximately 30–70), rather than to genomic bins. Namely, we iteratively resampled individual cells with replacement 1,000 times for each stage or condition. For each iteration we recalculated RT values and any subsequent statistic—for example, Spearman’s correlation coefficient or ΔRT between conditions, as described above. We constructed confidence intervals from the bootstrap distribution using the percentile method. The 95% confidence interval is the interval between the 2.5th and 97.5th percentiles of the distribution; when 95% confidence intervals do not include zero or two intervals do not overlap, they are significantly different from zero or different from each other, respectively. For enrichment analysis of overlapping regions or gene classes, genomic bins were grouped by significantly differential RT values to increasing (earlier), decreasing (later) or non-significant (no change) bins. Enrichments were visualized on heatmaps by calculating the ratio of the observed number of overlapping bins relative to the expected value, which is the product of the row and column sums divided by the total number of bins in the corresponding contingency table.</p>", "<title>Analysis of public chromatin datasets</title>", "<p id=\"Par39\">Published datasets were downloaded from GEO with accession numbers <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE66581\">GSE66581</ext-link>, <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE101571\">GSE101571</ext-link> (ATAC-seq<sup>##REF##27309802##36##</sup>), <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE71434\">GSE71434</ext-link> (H3K4me3 chromatin immunoprecipitation sequencing (ChIP)<sup>##REF##27626382##34##</sup>), <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE112834\">GSE112834</ext-link> (H3K36me3 ChIP<sup>##REF##31040401##67##</sup>), <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE98149\">GSE98149</ext-link> (H3K9me3 ChIP<sup>##REF##29686265##68##</sup>), <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE73952\">GSE73952</ext-link> (H3K27me3ChIP<sup>##REF##27626379##39##</sup>) <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE76687\">GSE76687</ext-link> (H3K27me3 ChIP<sup>##REF##27635762##69##</sup>) and <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE135457\">GSE135457</ext-link> (Pol2 Stacc-seq<sup>##REF##33116310##52##</sup>) and<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE76642\">GSE76642</ext-link> (DNase I hypersensitive sites sequencing<sup>##REF##27259149##70##</sup>). Paired-end reads were trimmed by cutadapt (v.3.4) with parameters -a CTGTCTCTTATA -A CTGTCTCTTATA -a AGATCGGAAGAGC -A AGATCGGAAGAGC --minimum-length=20. Following trimming, reads were aligned to the mouse reference (GRCm38) using bowtie2 (v.2.3.5) with parameters --end-to-end --very-sensitive --no-unal --no-mixed --no-discordant -I 10 -X 500. Reads were filtered by mapping quality score using SAMtools (v.1.3) with the parameter -q 12. Read pairs were read into R (v.3.6.3) using the readGAlignmentPairs function from the GenomicAlignment package (v.1.22.0) and were filtered for unique fragments. Fragments aligned to the mitochondrial genome or small scaffolds were not considered in analyses. Fragments were counted in 50 kb consecutive genomic bins (same bins as for RT profiles), normalized by the sum of fragment counts and multiplied by 1 million. Finally, normalized counts were log<sub>2</sub> transformed following the addition of a pseudocount of 1. Note that, for the analysis of H3K27me3 in Extended Data Fig. ##FIG##14##10b,c## the dataset used was that of Liu et al. (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE73952\">GSE73952</ext-link>)<sup>##REF##27626379##39##</sup> whereas in Fig. ##FIG##4##5f## the dataset used was that of Zheng et al.<sup>##REF##27635762##69##</sup> (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE76687\">GSE76687</ext-link>). For the correlation analysis shown in Fig. ##FIG##4##5f## we used the following stages when the actual stage was not available: early 2-cell ATAC-seq for zygote, morula DNase I hypersensitive sites sequencing for ICM and ES cell LmnB1 DamID for ICM. Differential genomic bins between conditions (for example, ATAC-seq following α-amanitin treatment) were called by DESeq2 (v.1.34.0) with an adjusted <italic>P</italic> value cutoff of 0.05. For ATAC-seq analysis in α-amanitin-treated embryos, 2-cell-stage embryos administered α-amanitin treatment by Wu et al.<sup>##REF##29720659##37##</sup> (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE101571\">GSE101571</ext-link>) were compared with untreated 2-cell-stage embryos derived from Wu et al.<sup>##REF##27309802##36##</sup> (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE66581\">GSE66581</ext-link>).</p>", "<title>Analysis of public HiC and LAD datasets</title>", "<p id=\"Par40\">HiC compartment coordinates and scores (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE82185\">GSE82185</ext-link>)<sup>##REF##28703188##16##</sup>, as well as LAD coordinates (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE112551\">GSE112551</ext-link>)<sup>##REF##31118510##13##</sup>, were analysed as previously described<sup>##REF##31118510##13##</sup>.</p>", "<title>Reporting summary</title>", "<p id=\"Par41\">Further information on research design is available in the ##SUPPL##0##Nature Portfolio Reporting Summary## linked to this article.</p>" ]	[]	[ "<title>Discussion</title>", "<p id=\"Par18\">Our data indicate that the establishment of RT occurs progressively following fertilization, hand-in-hand with the gradual acquisition of distinctive chromatin features and similarly to other epigenomic features (Fig. ##FIG##4##5f##). The less well-defined, more heterogeneous RT programme in zygotes and 2-cell embryos may reflect a higher plasticity in the chromatin structure in general and could also be related to changes in histone deposition occurring at these stages<sup>##REF##33169018##50##</sup>. RNA Pol II in zygotes and 2-cell-stage embryos contributes to the definition of RT. The comparatively milder effects on RT elicited by DRB compared with α-amanitin suggest that RNA Pol II itself influences the RT programme in 2-cell-stage embryos to a greater extent than transcriptional elongation. Although further investigation is warranted to determine whether additional, non-transcription-related effects contribute to these observations—for example via structural proteins<sup>##REF##34678064##51##</sup>—our findings align with work showing that ZGA transcription may be less affected by DRB than by α-amanitin<sup>##REF##27626382##34##,##REF##33116310##52##</sup>.</p>", "<p id=\"Par19\">The correlation between transcriptional activity and RT emerges after the 2-cell stage, coinciding with progressive lengthening of the G1 phase<sup>##REF##3944805##53##</sup>, known to be important in the definition of RT<sup>##UREF##0##6##</sup>. Although we observed large-scale changes in RT, for example, with around 20% of the genome switching from early to late RT during preimplantation development, fine-scale changes through the gradual acquisition of histone modifications are also likely to contribute to tuning of RT as cell types emerge. Remarkably, our data indicate that transcription and RNA Pol II function contribute to the definition of the epigenetic features of compartments, in this case their RT (Fig. ##FIG##4##5g##), but not to their segregation<sup>##REF##28709003##14##</sup>. Our observations that the genome structuring into LADs and iLADs precedes the partitioning of RT at later developmental stages establishes an exciting temporal dependency between these two pillars of the epigenome.</p>", "<p id=\"Par20\">Our work lays the foundations for understanding how genome replication is regulated during development and sheds light on how the epigenome is remodelled at the beginning of mammalian development.</p>" ]	[]	[ "<p id=\"Par1\">DNA replication enables genetic inheritance across the kingdoms of life. Replication occurs with a defined temporal order known as the replication timing (RT) programme, leading to organization of the genome into early- or late-replicating regions. RT is cell-type specific, is tightly linked to the three-dimensional nuclear organization of the genome<sup>##REF##35676475##1##,##REF##25409831##2##</sup> and is considered an epigenetic fingerprint<sup>##REF##22028635##3##</sup>. In spite of its importance in maintaining the epigenome<sup>##REF##33888635##4##</sup>, the developmental regulation of RT in mammals in vivo has not been explored. Here, using single-cell Repli-seq<sup>##REF##29382831##5##</sup>, we generated genome-wide RT maps of mouse embryos from the zygote to the blastocyst stage. Our data show that RT is initially not well defined but becomes defined progressively from the 4-cell stage, coinciding with strengthening of the A and B compartments. We show that transcription contributes to the precision of the RT programme and that the difference in RT between the A and B compartments depends on RNA polymerase II at zygotic genome activation. Our data indicate that the establishment of nuclear organization precedes the acquisition of defined RT features and primes the partitioning of the genome into early- and late-replicating domains. Our work sheds light on the establishment of the epigenome at the beginning of mammalian development and reveals the organizing principles of genome organization.</p>", "<p id=\"Par2\">Genome-wide replication timing maps of mouse embryos from the zygote to the blastocyst stage were generated using single-cell Repli-seq, shedding light on the establishment of the epigenome at the beginning of mammalian development.</p>", "<title>Subject terms</title>" ]	[ "<title>Main</title>", "<p id=\"Par3\">Replication timing (RT) is a fundamental epigenetic feature<sup>##UREF##0##6##</sup>, yet how and when RT is established during mammalian development is unknown. During S phase the genome must replicate once and only once. Replication occurs through a coordinated programme whereby origins of replication fire in a temporally defined order, giving rise to replication patterns characteristic of each cell type<sup>##REF##18842067##7##,##REF##25999062##8##</sup>. Early- and late-replication domains correlate with accessible, actively transcribed euchromatin and silent heterochromatin, respectively<sup>##REF##18669478##9##</sup>. RT is interconnected with other epigenetic features, although their temporal and functional dependency has not been fully established. For example, RT is tightly associated with three-dimensional genome organization, with lamina-associated domains (LADs) and B-type compartments typically corresponding to late-replication domains. Whereas mammalian cells do not possess strongly defined genetic sequences specifying replication origins, replication commences within initiation zones, which are regions of about 40 kb that comprise one or more sites of stochastic origin firing<sup>##REF##32209126##10##,##REF##26751768##11##</sup>. Generally, initiation zones of high efficiency tend to replicate early whereas low-efficiency initiation zones replicate late during S phase. Thus, RT is primarily driven by the probability of initiation within initiation zones. How initiation zones are specified at the beginning of development, and whether cells of the early embryo share a similar structure and features of the RT programme with differentiated cells, remain to be established.</p>", "<p id=\"Par4\">Mammalian development begins with fertilization and is followed by an intense period of chromatin remodelling<sup>##REF##25303116##12##</sup>. Major epigenome features are defined for the first time during this developmental time window: LADs are established de novo in mouse zygotes and the A and B compartments, although detectable in zygotes, gradually become more defined as development progresses towards the blastocyst<sup>##REF##31118510##13##</sup>. Topological-associating domains (TADs) are barely detectable before the 8-cell stage and emerge only at late cleavage stages<sup>##REF##28709003##14##–##REF##28703188##16##</sup>. In mice, zygotic genome activation (ZGA) occurs during this time with minor ZGA occurring in zygotes and the major wave of ZGA in late-2-cell-stage embryos<sup>##REF##8135766##17##</sup>. However, when RT programmes first emerge is unknown. In <italic>Drosophila</italic>, microscopy studies indicate that the onset of late replication emerges after ZGA<sup>##REF##29746464##18##</sup> but our understanding of this process—and how and when RT is first established in mammals—is unknown.</p>", "<title>RT emerges gradually during preimplantation development</title>", "<p id=\"Par5\">To understand when and how RT emerges during development, we used single-cell Repli-seq<sup>##REF##29382831##5##,##REF##34986273##19##</sup> in preimplantation mouse embryos (Fig. ##FIG##0##1a,b##). We collected 529 individual cells of which 53, 54, 50, 49, 34, 44 and 55 passed quality control for zygotes, 2-cell, 4-cell, 8-cell, 16-cell, morula and blastocyst-stage inner cell mass (ICM), respectively (Extended Data Fig. ##FIG##5##1a,b##, Supplementary Table ##SUPPL##0##1## and <xref rid=\"Sec9\" ref-type=\"sec\">Methods</xref>). Plotting individual cells based on their replication score, which reflects the percentage of their replicated genome (Fig. ##FIG##0##1c##), showed a clear replication domain structure consistent with progression of replication, with typical early–late transitions across most stages (Fig. ##FIG##0##1c## and Extended Data Fig. ##FIG##5##1c##). Zygotes and 2-cell embryos were an exception and showed a less defined replication pattern across cells and throughout the genome, suggesting a more variable and less coordinated programme (Fig. ##FIG##0##1c##). This was due to neither absence of DNA synthesis nor embryonic heterogeneity in the progression of DNA synthesis, because we verified microscopically that zygotes showed an expected and consistent spatial pattern of DNA synthesis through S phase (Extended Data Fig. ##FIG##5##1d,e##). To provide a quantitative metric of the RT programme we computed a variability score, which measures the variance of the replication programme across cells. RT variability score was highest in zygotes and 2-cell and 4-cell embryos but decreased progressively from the 4-cell stage (Fig. ##FIG##0##1d##). RT of the ICM appeared more variable compared with morula, which may reflect the ICM undergoing cell fate decisions towards epiblast and primitive endoderm<sup>##REF##16678776##20##</sup>, and thus greater heterogeneity in cell identity is likely to be present therein. Overall, the RT programme at the earliest stages of development is less well defined.</p>", "<p id=\"Par6\">Embryonic RT profiles showed both early- and late-replication domains, visible as valleys and plateaus (Fig. ##FIG##0##1e##). Visual inspection showed a progressive delineation of replication domains as development proceeds (Fig. ##FIG##0##1e## and Extended Data Fig. ##FIG##6##2a##). This is independent of S-phase length because length is relatively constant until the blastocyst stage<sup>##REF##7363300##21##</sup>. To address whether and how RT changes during development, we compared ‘early’ (RT ≥ 0.5) and ‘late’ (RT ≤ 0.5) RT values from the zygote to the blastocyst ICM. In general, RT values increased towards earlier or later (Extended Data Fig. ##FIG##6##2b##; increase), indicating definition of the early and late RT programme during development. A portion of the genome showed constant early or late RT throughout (33.1% of the genome replicates early and 16.0% replicates late in all seven stages; Extended Data Fig. ##FIG##6##2b##; constant). However, some regions shift from early to late RT values and vice versa (Extended Data Fig. ##FIG##6##2b##; shuffle). For example, 20.9% of the genome switches from early to late RT from 2-cell to morula and 11.1% does so between 8-cell and 16-cell. Likewise, 3.1% changes from late to early RT between 8-cell and morula. This analysis also showed that, whereas some genomic regions do shift RT between early and late values, the most common trend is a progressive definition of RT values towards more early and more late (Extended Data Fig. ##FIG##6##2b##). Indeed, whereas most of the genome in zygotes and 2-cell embryos (73 and 77%, respectively) shows intermediate RT values (0.4 ≤ RT ≤ 0.8), the genome partitions into RT values spanning the complete S phase as development progresses, resulting in stratification into more extreme early and late RT values after the 2-cell stage (Extended Data Fig. ##FIG##7##3a##). This behaviour resembles A and B compartments<sup>##REF##28709003##14##</sup>, which undergo progressive increase in compartment strength during cleavage stages<sup>##REF##28709003##14##,##REF##28703188##16##</sup>, suggesting that preimplantation serves as period of gradual establishment of three-dimensional nuclear architecture and RT. We conclude that, although approximately half of the genome preserves its RT, the remaining half undergoes changes in RT as development proceeds and becomes more defined over time.</p>", "<p id=\"Par7\">Next we characterized embryonic RT features by extracting initiation zones, but also zones in which opposing replication forks convene (termination zones) and timing transition regions (TTRs), which are regions located between initiation zones and termination zones<sup>##REF##29382831##5##,##REF##18842067##7##</sup>. Because of the resolution of scRepli-seq. and to distinguish these features from those in methods such as OK-seq and EdU-seq<sup>##REF##30078706##22##,##REF##30115746##23##</sup>, we refer to initiation zones as ‘RT peaks’ and to termination zones as ‘RT troughs’. We defined RT peaks as consecutive bins of local maxima and RT troughs as consecutive bins of local minima of RT values (Extended Data Fig. ##FIG##7##3b##)<sup>##REF##32209126##10##</sup>. Globally, RT peaks increase in size (<italic>P</italic> = 0.01) with more, smaller RT peaks at early cleavage stages compared with later stages (Fig. ##FIG##0##1f,g##). Similarly, albeit to a lesser extent, TTRs increase in size (<italic>P</italic> = 0.01; Fig. ##FIG##0##1f##). The size of RT troughs remains overall stable (<italic>P</italic> = 0.19; Fig. ##FIG##0##1f##) and, similar to embryonic stem (ES) cells; RT troughs have higher AT content than RT peaks and TTRs (Extended Data Fig. ##FIG##7##3c##). RT peaks can reshuffle into TTRs and TTRs into RT peaks during each cell division (Extended Data Fig. ##FIG##7##3d##). Similarly, RT troughs converted into TTRs and TTRs into RT troughs but changes from RT peaks into RT troughs and vice versa are extremely rare (Extended Data Fig. ##FIG##7##3d##). Approximately half of RT peaks and RT troughs changed into TTRs at the subsequent developmental stage, suggesting remodelling of replication features between each stage following cell division. Because TTRs are regions in which potential changes in RT occur<sup>##REF##26590169##24##,##REF##31406346##25##</sup>, such remodelling may provide the basis for the gradual developmental progression of the RT programme. In addition, the concomitant decrease in the number of RT peaks and their increase in size suggests a progressive consolidation of the RT programme<sup>##REF##18842067##7##</sup> whereby more adjacent regions with similar RT merge. Indeed, RT peaks become progressively larger and acquire more distinct, earlier relative RT values compared with their genomic surrounding from the 4-cell stage (Fig. ##FIG##0##1h##). Our data support a gradual consolidation of RT features during preimplantation development and suggest that the shaping of RT occurs at the level of RT peaks and TTRs.</p>", "<title>RT in zygote and 2-cell-stage embryos is distinct from later stages</title>", "<p id=\"Par8\">Genome-wide correlation analysis of RT across all stages established that zygotes and 2-cell embryos cluster apart from all other stages (Fig. ##FIG##1##2a##), suggesting that, despite a similar variability score, the 4-cell-stage RT programme differs from zygotes and 2-cell embryos in other features. To determine the basis of the differences in RT behaviour in zygotes and 2-cell embryos we investigated three alternative explanations. First, to determine whether the unusual RT patterns resulted from asynchrony due to different fertilization times, we performed Repli-seq in zygotes produced by in vitro fertilization (IVF), allowing timely control of fertilization. IVF zygotes showed RT profiles similar to those of zygotes arising from natural fertilization (Extended Data Fig. ##FIG##8##4a,b##). Second, we considered whether unusual RT patterns result from disparate RT of maternal and paternal genomes, which are thought to replicate asynchronously<sup>##REF##9344600##26##</sup>, are physically separated as two pronuclei during the first cell cycle and remain topologically segregated in 2-cell-stage nuclei<sup>##REF##11751680##27##</sup>. To address this we performed Repli-seq in parthenogenetic zygotes containing only one copy of the maternal genome. The replication profiles in parthenotes and normal zygotes were similar (Fig. ##FIG##1##2b,c##). Genome-wide correlations of RT values confirmed that RT values in parthenogenetic and naturally fertilized zygotes were comparable, and also with IVF zygotes (Fig. ##FIG##1##2d## and Extended Data Fig. ##FIG##8##4c,d##). This analysis confirmed that RT separates into two major groups containing zygotes and 2-cell embryos versus all other stages (Extended Data Fig. ##FIG##8##4d##). We further generated Repli-seq from physically isolated pronuclei (Extended Data Fig. ##FIG##8##4e##), which showed overall similar RT profiles in maternal and paternal pronuclei (Fig. ##FIG##1##2e,f##). Both pronuclei exhibited genome-wide correlations similar to natural zygotes (Spearman’s <italic>R</italic> = 0.65 and 0.67 for maternal and paternal, respectively; Fig. ##FIG##1##2g##) and to IVF zygotes (Extended Data Fig. ##FIG##8##4c##). Maternal RT values correlated slightly better with parthenotes than paternal RT values (Spearman’s <italic>R</italic> = 0.62 and 0.49, respectively; Fig. ##FIG##1##2h##) suggesting that, while highly similar, differences exist between the RT profiles of parental genomes. Finally we investigated whether allele-specific differences can bias RT patterns by performing single-nucleotide polymorphism (SNP)-based analysis of RT in zygotes from hybrid (F<sub>1</sub> × DBA) crosses. Specifically we asked whether the subtle RT differences between parental genomes are consistent across individual embryos. We find that overall there is no consistent allelic-specific bias in zygotes (Extended Data Fig. ##FIG##8##4f,g##). This indicates that, although maternal and paternal genomes differ slightly in their RT profiles, these differences do not bias zygotic RT. In agreement, RT peaks, TTRs and RT troughs from both genomes have similar RT behaviour (Fig. ##FIG##1##2i## and Extended Data Fig. ##FIG##8##4h,i##). In addition, analysis of imprinted genes indicated no replication asynchrony, in line with findings from ES cells<sup>##REF##29735606##28##</sup> (Extended Data Fig. ##FIG##9##5##). We conclude that RT profiles in zygotes are not due to parental asynchrony but rather reflect inherent properties of RT in both genomes at early developmental stages. Therefore, early embryos show a RT programme that is initially less well defined and becomes progressively more defined from the 4-cell stage.</p>", "<title>Segregation between early and late RT increases as development proceeds</title>", "<p id=\"Par9\">Next, we investigated whether the robustness of RT (cell-to-cell heterogeneity) changes during development. We asked whether and how RT heterogeneity fluctuates throughout S phase. We generated a sigmoid model<sup>##REF##34551299##29##</sup> and computed the relationship between RT values and <italic>T</italic><sub>width</sub> (Extended Data Fig. ##FIG##10##6a##), which quantifies the time difference at which 25–75% of cells replicated a given genomic bin<sup>##REF##32209126##10##,##REF##20739926##30##</sup>, for each stage. The <italic>T</italic><sub>width</sub> value thus reflects the variation in RT across cells within the same stage. <italic>T</italic><sub>width</sub> values decreased during development, indicating an overall more coordinated RT programme (Fig. ##FIG##2##3a##). However, <italic>T</italic><sub>width</sub> increased again for ICM, reflecting the heterogenous nature of the ICM preceding its segregation into epiblast and primitive endoderm lineages (Fig. ##FIG##2##3a##). Regions replicating early and late were relatively homogenous (Extended Data Fig. ##FIG##10##6b##). Overlapping of RT features onto <italic>T</italic><sub>width</sub> values indicated that RT peaks and RT troughs are less heterogeneous compared with TTRs (Fig. ##FIG##2##3b##). In addition, RT peaks and RT troughs are remarkably uniform across cells of the same stage. We also calculated <italic>M</italic>, which is the replication score at which 50% of cells have replicated a given genomic bin. Thus, the distribution of <italic>M</italic>-values indicates how well partitioned into early and late are RT values across the genome. <italic>M</italic> values for mouse ES cells depicted a clear bimodal distribution, reflecting well-defined early and late RT patterns (Fig. ##FIG##2##3c##). This was not the case for early embryonic stages (Fig. ##FIG##2##3c##). Instead, a bimodal distribution became apparent after the 2-cell stage, reflecting the emergence of a RT programme that separates the genome towards early (earlier) and late (later) RT values (Fig. ##FIG##2##3c## and Extended Data Fig. ##FIG##10##6c##). We conclude that RT heterogeneity fluctuates during S phase within each developmental stage in the same manner as it does in all previously studied systems, and that segregation between early and late RT values increases as development proceeds.</p>", "<title>Consolidation of RT is characterized by specific changes in histone modifications</title>", "<p id=\"Par10\">The relationship between RT and transcription remains unclear, with often contradictory reports on RT instructing transcription or vice versa<sup>##REF##18669478##9##,##REF##17333233##31##</sup>. Because the embryo starts transcription de novo following a period of transcriptional silence in the germline, the embryo provides an outstanding opportunity to disentangle the role of transcriptional activation in the establishment of RT. Our above results indicate that the RT programme becomes progressively more defined, particularly after the 2-cell stage (Fig. ##FIG##0##1d,h##), which corresponds to the time of ZGA<sup>##REF##8135766##17##</sup>. Thus we first asked whether chromatin features of active transcription relate to the progressive definition of RT. H3K36me3 became enriched at RT peaks from the 8-cell stage (Fig. ##FIG##3##4a##) (no available data for H3K36me3 at the 4-cell stage), indicating that H3K36me3 marks emerging RT peaks (Extended Data Fig. ##FIG##11##7a##). Whereas H3K36me3 is associated with gene bodies and is thus typically excluded from replication origins in other cells<sup>##REF##30115746##23##</sup>, H3K36me3 does not necessarily reflect transcription elongation kinetics during development<sup>##REF##36577375##32##</sup> and thus our findings may reflect specific embryonic chromatin features. H3K4me3 levels were relatively stable across RT peaks, TTRs and RT troughs, with slightly higher levels at RT peaks and a depletion in RT troughs in zygotes and 2-cell embryos compared with later stages (Fig. ##FIG##3##4b## and Extended Data Fig. ##FIG##11##7a##). Because oocytes have distinctive broad H3K4me3 domains, which are remodelled by demethylases KDM5A/5B upon ZGA<sup>##REF##27626377##33##,##REF##27626382##34##</sup>, we asked whether H3K4me3 inheritance is linked to RT in embryos. For this we expressed KDM5B<sup>##REF##28709003##14##</sup>, known to remove H3K4me3 broad domains<sup>##REF##27626382##34##</sup>, in mouse zygotes and performed scRepli-seq at the 2-cell stage (Extended Data Fig. ##FIG##11##7b##). RT profiles following KDM5B expression showed a similar global pattern in control of 2-cell embryos (Extended Data Fig. ##FIG##11##7c,d##). In addition, KDM5B expression did not affect RT of major ZGA genes, nor of genes expressed in oocytes (Extended Data Fig. ##FIG##11##7e,f##), indicating that removal of H3K4me3 following fertilization does not majorly impact RT at regions containing major ZGA genes.</p>", "<p id=\"Par11\">Next we examined whether RT relates to gene expression levels. Genome-wide correlation of RT values and steady-state transcript abundance were low in zygotes and 2-cell embryos (Spearman’s correlation, <italic>R</italic><sub>s</sub>; Fig. ##FIG##3##4c##). In fact, RT in zygotes and 2-cell embryos correlated similarly with the transcriptome of non-fertilized oocytes and zygotes (Extended Data Fig. ##FIG##11##7g##). This suggests that either the presence of maternally inherited transcripts from oocytes, which dominates the early transcriptome, overrides a possible relationship with RT or that transcriptional activity does not correlate strongly with RT at these stages. We favour the latter interpretation because 2-cell embryos, which undergo massive transcriptional activation and degradation of maternal transcripts, show a similar correlation between their RT and transcriptome to zygotes (Fig. ##FIG##3##4c##). Both transcript abundance and RT values change significantly during developmental progression and thus the increasing correlation between RT and transcription during development stems from changes in both transcript abundance and RT (Extended Data Fig. ##FIG##11##7h,i##). From the 4-cell stage, the correlation between RT and transcript levels increases and the typical relationship between transcription and early replication emerges, with genes expressed at high levels replicating early (Fig. ##FIG##3##4c## and Extended Data Fig. ##FIG##11##7i##). Indeed, the correlation between transcript abundance and RT values is significantly greater from the 4-cell stage onwards (Extended Data Fig. ##FIG##11##7j##). This correlation is similar to ES cells, albeit at a lower extent (Extended Data Fig. ##FIG##11##7k##). These data show that the known correlation between RT and gene expression emerges gradually from the 4-cell stage, with genes showing the highest expression replicating early during S phase.</p>", "<title>RNA polymerase II at ZGA contributes to fine-tuning of the RT programme</title>", "<p id=\"Par12\">We next addressed directly whether transcription regulates the establishment of RT. We incubated zygotes with α-amanitin under conditions that prevent minor and major ZGA but do not affect RNA polymerase (Pol) I transcription, and performed scRepli-seq at the 2-cell stage (Extended Data Fig. ##FIG##12##8a,b##). Evaluation of RT at later stages is not feasible because inhibition of ZGA prevents development beyond the 2-cell stage<sup>##REF##8135766##17##</sup>. RT values in α-amanitin-treated embryos showed a moderate correlation with control embryos (Fig. ##FIG##3##4d##), suggesting that prevention of ZGA with α-amanitin may affect RT at the 2-cell stage. Indeed, we observed changes in RT towards earlier and later following α-amanitin treatment (Extended Data Fig. ##FIG##12##8c##). Further examination showed localized RT changes in α-amanitin-treated embryos (Fig. ##FIG##3##4e##), with a statistically significant delay in RT of genomic bins overlapping with major ZGA genes but not of regions containing genes expressed in oocytes (maternal genes) or control regions (Fig. ##FIG##3##4f## and Extended Data Fig. ##FIG##12##8d##). To better understand how transcription at ZGA affects RT, we sought to distinguish the effects of general transcription inhibition versus transcription elongation. We took advantage of another RNA Pol II inhibitor, 5,6-dichlorobenzimidazone-1-β-<sc>d</sc>-ribofuranoside (DRB), which inhibits transcriptional elongation by inhibition of RNA Pol II Ser2 phosphorylation, whereas α-amanitin results in full transcriptional inhibition<sup>##REF##21922053##35##</sup>, including via RNA Pol II degradation (Extended Data Fig. ##FIG##12##8e,f##). DRB treatment during the same period as α-amanitin led to milder changes in RT compared with α-amanitin (Fig. ##FIG##3##4d,e##). Interestingly, DRB did not significantly change RT of genomic bins containing ZGA genes (Extended Data Fig. ##FIG##12##8g,h##), suggesting that transcriptional elongation of ZGA genes does not affect their RT. However, DRB and α-amanitin led to similar changes in RT of regions without genes expressed at the 2-cell stage (Fig. ##FIG##3##4e## and Extended Data Fig. ##FIG##12##8i##). Thus, we next explored whether other chromatin features relate to the RT phenotype following ZGA inhibition. Prevention of ZGA with α-amanitin alters accessibility in 2-cell embryos<sup>##REF##27309802##36##,##REF##29720659##37##</sup>. Analysis of assay for transposase-accessible chromatin using sequencing (ATAC-seq) datasets showed a significant, positive correlation with RT in 2-cell embryos, indicating that regions replicating early are, in general, more accessible than those that replicate late (Extended Data Fig. ##FIG##13##9a,b##). This correlation was lost following α-amanitin treatment (Extended Data Fig. ##FIG##13##9a,b##). Globally, the changes in RT elicited by α-amanitin anticorrelated with sites of genome-wide accessibility in 2-cell control embryos (Extended Data Fig. ##FIG##13##9c##). Indeed, we find that regions that gain ATAC-seq signal following α-amanitin treatment become replicated later; likewise, regions that lose accessibility become replicated earlier (Extended Data Fig. ##FIG##13##9d##).</p>", "<p id=\"Par13\">To further understand how transcription during ZGA influences RT, we examined RT features in 2-cell embryos treated with α-amanitin or DRB. Prevention of transcription at ZGA using α-amanitin, but not DRB, led to more TTRs, RT peaks and RT troughs with a concomitant decrease in the size of RT troughs (Fig. ##FIG##3##4g## and Extended Data Fig. ##FIG##13##9e##). The increase in their number and the smaller RT troughs suggests a more fragmented, less consolidated RT programme after α-amanitin treatment. These data also suggest that replication may initiate and terminate at different locations in the absence of embryonic transcription. In support of this, RT troughs in α-amanitin-treated embryos do not show AT content enrichment, in contrast to controls (Extended Data Fig. ##FIG##13##9f##). In addition, de novo RT peaks in α-amanitin-treated embryos contain fewer genes normally expressed at the 2-cell stage compared with those insensitive to α-amanitin (Extended Data Fig. ##FIG##13##9g##). Thus, perturbation of RNA Pol II globally at ZGA contributes to fine-tuning of initiation and termination sites at the 2-cell stage.</p>", "<p id=\"Par14\">Finally, we characterized silent chromatin features of the embryonic replication programme. RT troughs contain higher levels of H3K9me3 compared with RT peaks and, to a lesser extent, with TTRs, but these differences emerge only from the 2-cell stage and H3K9me3 levels across RT peaks, TTRs and RT troughs are equivalent in zygotes (Extended Data Fig. ##FIG##14##10a##). H3K27me3 levels are lowest at RT peaks at all developmental stages and, similarly to H3K9me3, RT peaks and RT troughs acquire gradually different histone modifications during development, with RT peaks showing a depletion of H3K27me3 compared with TTRs and RT troughs by the morula stage (Extended Data Fig. ##FIG##14##10b,c##). These findings may relate to the progressive heterochromatin maturation of early embryos<sup>##REF##32601371##38##,##REF##27626379##39##</sup>. Overall, maturation of the RT programme is accompanied by a progressive, relative increase in H3K9me3 at RT troughs and a gradual decrease at RT peaks.</p>", "<title>Organization into LADs and inter-LADs precedes partitioning of early and late replication</title>", "<p id=\"Par15\">Finally we investigated the dependency between three-dimensional genome architecture and the establishment of RT. In differentiated and stem cells, early and late replication correlate with the A and B compartments, respectively<sup>##REF##22028635##3##,##REF##22495300##40##</sup>, and TADs tend to correspond to replication domains<sup>##REF##25409831##2##</sup>. However, because TADs are not clearly detected in early cleavage stages<sup>##REF##28709003##14##,##REF##28703188##16##</sup> we focused on compartments and asked whether the A and B compartments already differ in their RT at the earliest developmental stages. A compartments consistently showed an earlier RT profile compared with B compartments (Fig. ##FIG##4##5a## and Extended Data Fig. ##FIG##14##10d##). The distinction between early and late RT values in both compartments was less pronounced in zygotes and became clearer as development proceeds (Fig. ##FIG##4##5a##). In line with only minor differences in the RT of parental genomes (Fig. ##FIG##1##2##), RT values were only slightly different in maternal and paternal A and B compartments (Extended Data Fig. ##FIG##14##10e##). RT differed more between paternal A and B compartments than in maternal compartments, potentially because of the weaker structure of the latter<sup>##REF##28709003##14##–##REF##28703188##16##</sup> (Extended Data Fig. ##FIG##14##10e,f##). The difference in RT values between A and B compartments increased during development due to both better segregation of RT values and increase in compartment score (Fig. ##FIG##4##5b##). Inhibition of ZGA with α-amanitin completely eliminated RT differences between A and B compartments but the compartment score remained similar (Fig. ##FIG##4##5c##)<sup>##REF##28709003##14##</sup>. Globally, A compartments replicated later and B compartments replicated earlier in α-amanitin-treated embryos compared with controls (Extended Data Fig. ##FIG##14##10g##). Because B compartments are less accessible than A compartments (Extended Data Fig. ##FIG##14##10h##), these observations can be explained by our results indicating that α-amanitin leads to a shift towards earlier replication of less accessible regions. We conclude that partitioning of early and late RT during early development coincides with the maturation of A and B compartments. In addition, whereas ZGA does not contribute to compartment strength<sup>##REF##28709003##14##</sup>, transcriptional inhibition equalizes differences in RT between compartments.</p>", "<p id=\"Par16\">The genetic constitution of mammalian A and B compartments is largely demarcated by repetitive elements<sup>##REF##33514913##41##,##REF##25883320##42##</sup>, which are expressed in the mouse embryo<sup>##REF##23353788##43##,##REF##15469847##44##</sup>. Namely, LINE1 are highly transcribed at the 2-cell stage<sup>##REF##23353788##43##,##REF##28846101##45##</sup> and are enriched in LADs and B compartments<sup>##REF##33514913##41##,##REF##25883320##42##,##REF##23124521##46##</sup>. In fact, LINE1 and SINE segregate mostly exclusively into B and A compartments, respectively<sup>##REF##33514913##41##</sup>. Thus we investigated the replication features of major transposable element families. Overall, LINE1 were enriched in RT troughs and depleted in RT peaks (Extended Data Fig. ##FIG##14##10i##). This enrichment was stronger for evolutionarily young LINE1, L1Md_A and L1Md_T, contrasting with older LINE2, which showed depletion from RT troughs (Extended Data Fig. ##FIG##14##10i##). SINE B2 are enriched in RT peaks and depleted in RT troughs, and this tendency became clearer from the 4-cell stage (Extended Data Fig. ##FIG##14##10i##). MERV-L (MT2_Mm), highly transcribed in 2-cell embryos<sup>##REF##15469847##44##,##REF##15237213##47##</sup>, was more homogeneously distributed across RT peaks, TTRs and RT troughs. However, MERV-L enrichment in RT features, albeit low, changed throughout development (Extended Data Fig. ##FIG##14##10i##). Thus the RT of domains containing MERV-L, unlike LINEs, is dynamic (Extended Data Fig. ##FIG##14##10i##). Indeed, a change in RT of MERV-L occurs during reprogramming of 2-cell-like cells (2CLCs)<sup>##REF##35256805##48##</sup>.</p>", "<p id=\"Par17\">Finally we examined the relationship between LADs and RT. LADs are established in zygotes immediately following fertilization and are reorganized during preimplantation development, but a large proportion of LADs remains constant and is similar to ES cell LADs<sup>##REF##31118510##13##</sup>. In general, LADs, unlike inter-LADs (iLADs), replicate late<sup>##REF##25409831##2##,##REF##20513434##49##</sup>. However, and in sharp contrast to ES cells, RT in zygotes is not clearly distinguishable between LADs and iLADs (Fig. ##FIG##4##5d##). Zygotic LADs differ between parental genomes<sup>##REF##31118510##13##</sup> and, accordingly, paternal LADs and iLADs exhibit a slight segregation of RT values and maternal ones to a lesser extent (Extended Data Fig. ##FIG##14##10j##). RT in zygotes did not exhibit a strong bias towards either paternal or maternal LADs/iLADs (Extended Data Fig. ##FIG##14##10k##). The separation of RT values in LADs and iLADs increases as development proceeds, reaching a clear distinction in ES cells (Fig. ##FIG##4##5d##). These observations raise the possibility that nuclear organization into LADs and iLADs temporally precedes establishment of the RT programme. To address this, we asked whether RT in ES cells corresponds to LADs/iLADs in zygotes. Remarkably, RT values in embryonic stem cells plotted against the LAD boundaries of zygotes indicated a clear demarcation of RT in embryonic stem cells according to zygotic LAD boundaries (Fig. ##FIG##4##5e##), indicating that LAD organization in zygotes predisposes RT at later stages of development. In contrast, plotting the RT values of zygotes over ES cell LAD boundaries did not show such a correlation (Fig. ##FIG##4##5e##). We conclude that organization of LADs and iLADs at the beginning of development precedes the partitioning of early- and late-replication dynamics.</p>", "<title>Online content</title>", "<p id=\"Par42\">Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41586-023-06872-1.</p>", "<title>Supplementary information</title>", "<p>\n\n\n</p>" ]	[ "<title>Extended data figures and tables</title>", "<p id=\"Par45\">\n\n</p>", "<p id=\"Par46\">\n\n</p>", "<p id=\"Par47\">\n\n</p>", "<p id=\"Par48\">\n\n</p>", "<p id=\"Par49\">\n\n</p>", "<p id=\"Par50\">\n\n</p>", "<p id=\"Par51\">\n\n</p>", "<p id=\"Par52\">\n\n</p>", "<p id=\"Par53\">\n\n</p>", "<p id=\"Par54\">\n\n</p>", "<title>Extended data</title>", "<p>is available for this paper at 10.1038/s41586-023-06872-1.</p>", "<title>Supplementary information</title>", "<p>The online version contains supplementary material available at 10.1038/s41586-023-06872-1.</p>", "<title>Acknowledgements</title>", "<p>We thank I. de la Rosa Velazquez and the Genomics Facility at Helmholtz Munich for sequencing; A. Burton and S. Hamperl for critical reading of the manuscript; P. Zhao for advice on computational methods; and V. Dileep for helpful discussions during the initial phase of this work. M.-E.T.-P. acknowledges funding from the Helmholtz Association, Helmholtz AI, the NIH 4DNucleome Programme (grant no. 1U01DK127391-01), the German Research Council through SFB CRC1604 ‘Chromatin Dynamicsʼ (Project ID 213249687) and the SPP Priority Programme Genome 3 (Project ID 507647018).</p>", "<title>Author contributions</title>", "<p>T.N. designed, performed and analysed most of the experiments. T.S. and L.A.-P. performed bioinformatics analyses. K.N.K. took part in library preparation of Repli-seq of blastocyst samples under the supervision of D.M.G. K.N.K and D.M.G. provided essential study support. A.E. performed image analyses. M.P. overexpressed KDM5B to the embryo and tested the reduction of H3K4me3 by immunofluorescence. M.-E.T.-P. conceived, designed and supervised the study. All authors contributed to manuscript preparation and read, commented on and approved the manuscript.</p>", "<title>Peer review</title>", "<title>Peer review information</title>", "<p id=\"Par43\"><italic>Nature</italic> thanks Giacomo Cavalli and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.</p>", "<title>Funding</title>", "<p>Open access funding provided by Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH).</p>", "<title>Data availability</title>", "<p>The scRepli-seq data for the present study are available from the GEO database, accession <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE218365\">GSE218365</ext-link>. Previously published RNA sequencing datasets reanalysed in the present study are available under accessions <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE38495\">GSE38495</ext-link>, <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE45719\">GSE45719</ext-link> and <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE98063\">GSE98063</ext-link>. Chromatin datasets reanalysed in the present study are available under accessions. <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE66581\">GSE66581</ext-link>, <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE101571\">GSE101571</ext-link>, <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE71434\">GSE71434</ext-link>, <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE112834\">GSE112834</ext-link>, <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE98149\">GSE98149</ext-link>, <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE73952\">GSE73952</ext-link>, <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE76687\">GSE76687</ext-link>, <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE135457\">GSE135457</ext-link> and <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE76642\">GSE76642</ext-link>. All other data supporting the findings of the present study are available from the corresponding author on reasonable request. HiC and LAD datasets reanalysed in the present study are available under accessions <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE82185\">GSE82185</ext-link> and <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE112551\">GSE112551</ext-link>.</p>", "<title>Code availability</title>", "<p>Next-generation sequencing data were analysed with publicly available programmes and packages, as detailed in <xref rid=\"Sec9\" ref-type=\"sec\">Methods</xref>. Additional details on specific code used to generate scRepli-seq workflows are available on request.</p>", "<title>Competing interests</title>", "<p id=\"Par44\">M.-E.T.-P. is a member of the ethics advisory panel of MERCK. The other authors declare no competing interests.</p>" ]	[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>RT emerges gradually during mouse preimplantation development.</title><p><bold>a</bold>, Overview of single-cell Repli-seq used to generate RT profiles from single cells in mouse preimplantation embryos based on copy number variation. <bold>b</bold>, Schematic of sampling of embryos and corresponding images of dissociated blastomeres at each stage. The numbers of independent blastomere collections for each stage with similar results are as follows: zygote (3), 2-cell (4), 4-cell (3), 8-cell (3), 16-cell (3), morula (2), ICM (4). Scale bar, 50 μm. <bold>c</bold>, Heatmaps of single cells indicating replication status based on binarized copy number during preimplantation embryogenesis (red, replicated; grey, not replicated). Cells are ranked by their percentage of replicated genome (replication score), which indicates progress in S phase and is plotted as a bar plot on the left. <bold>d</bold>, Variability score during embryonic development; the score is 1 when 50% of cells replicated the genomic bin and 0 when all cells are either replicated (100%) or non-replicated (0%). Each violin plot shows the distribution of scores for all genomic bins. <bold>e</bold>, RT profiles of preimplantation embryos over a representative region on chromosome 2, denoted by black rectangle in <bold>c</bold>. Black line indicates RT profiles, calculated as the average of overlapping intervals defined by genome-wide replication score. <bold>f</bold>,<bold>g</bold>, Size (<bold>f</bold>) and number (<bold>g</bold>) of replication features RT peaks (also known as initiation zones), and RT troughs (also known as termination zones) during preimplantation development. Box plots show median and interquartile range (IQR), and whiskers depict the lowest and highest values within 1.5× IQR. bp, base pair. <bold>h</bold>, Relative RT values centred at RT peaks during embryonic development compared with their neighbouring regions. Note that curves for the 2- and 4-cell stages overlap considerably and, to some extent, with that of zygotes.</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>RT in zygotes is less well defined compared with that in later embryonic stages and does not exhibit global allelic differences.</title><p><bold>a</bold>, Correlation of genome-wide RT values between the indicated stages of preimplantation embryos using Spearman’s <italic>R</italic>. <bold>b</bold>,<bold>c</bold>, Characterization of RT in parthenogenetic zygotes. Heatmaps show replication states in parthenogenetic embryos (<bold>b</bold>) and corresponding average RT profiles (<bold>c</bold>). <bold>d</bold>, Smoothed scatterplot of RT values in normal versus parthenogenetic zygotes. Spearman’s correlation (<italic>R</italic><sub>s</sub>) is indicated. <bold>e</bold>,<bold>f</bold>, Characterization of RT in physically isolated maternal and paternal pronuclei (PN). Heatmaps show replication states in each pronucleus (<bold>e</bold>) and corresponding average RT profiles (<bold>f</bold>) of the chromosome 2 region, indicated by the black rectangle. <bold>g</bold>, Smoothed scatterplot of RT values in zygotes compared with isolated maternal and paternal pronuclei. Spearman’s correlation is indicated. <bold>h</bold>, Smoothed scatterplot of RT values comparing maternal or paternal pronucleus and parthenogenetic zygotes, as indicated. Spearman’s correlation is indicated. <bold>i</bold>, RT values of RT peaks, TTRs and RT troughs in isolated maternal and paternal pronuclei. Box plots show median and IQR, whiskers depict the lowest and highest values within 1.5× IQR.</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><title>RT heterogeneity decreases with developmental progression, and segregation between early and late RT values increases.</title><p><bold>a</bold>, Violin plots showing relative RT heterogeneity (<italic>T</italic><sub>width</sub>), which is the replication score difference between 25 and 75% of cells replicating the 50 kb bin, in embryonic development and in mouse embryonic stem (ES) cells. Dots indicate median. <bold>b</bold>, Contour plot showing <italic>T</italic><sub>width</sub> along progression of RT in mouse embryos. RT peaks, TTRs and RT troughs are indicated. <bold>c</bold>, Violin plots showing RT mid-point value (<italic>M-</italic>value), which is the replication score at which 50% of cells replicated the 50 kb bin during embryonic development and in mouse ES cells. Dots indicate median.</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><title>Consolidation of RT is characterized by specific changes in histone modifications at RT troughs and RT peaks and is influenced by ZGA.</title><p><bold>a</bold>, H3K36me3 coverage at the indicated replication features at different embryonic stages. <bold>b</bold>, H3K4me3 coverage at replication zones at each embryonic stage. <bold>c</bold>, Smoothed scatterplots showing correlation between RT values and transcript abundance (log<sub>2</sub>(TPM)) at the indicated embryonic stages. Spearman’s correlation is indicated. Note that Spearman’s <italic>R</italic> measures not only linear, but also monotonic, relationships and is robust to outliers. <bold>d</bold>, Smoothed scatterplots showing correlation of RT values between control 2-cell embryos and those treated with α-amanitin (top) or DRB (bottom). DRB or α-amanitin was applied continuously from the zygote stage (17 h after human chorionic gonadotropin (hCG)) until collection at the 2-cell stage, to block both minor and major ZGA, as indicated in the schematic. Spearman’s correlation is indicated. <bold>e</bold>, RT profiles of 2-cell embryos overlayed with those from α-amanitin- and DRB-treated 2-cell embryos. Genomic positions of indicated gene classes according to DBTMEE<sup>##REF##25336621##54##</sup> are shown as rectangles. <bold>f</bold>, Box plots showing the difference in RT values (ΔRT) between α-amanitin-treated and untreated 2-cell embryos at genomic bins overlapping only major ZGA genes, only maternal RNA genes or both gene classes compared with non-overlapping bins. <bold>g</bold>, Number of replication features in control 2-cell embryos and in embryos treated with α-amanitin or DRB. Box plots show median and IQR, whiskers depict the lowest and highest values within 1.5× IQR. n.d., not determined (data not available).</p></caption></fig>", "<fig id=\"Fig5\"><label>Fig. 5</label><caption><title>The distinctive RT between A and B compartments is dependent on ZGA, and three-dimensional genome organization precedes partitioning of early- and late-replication dynamics.</title><p><bold>a</bold>, Box plots showing RT values in A and B compartments at the indicated stages. Note that, because HiC (high-throughput chromosome conformation capture) data for the 16-cell stage were unavailable, we used the closest developmental stage (ICM) for this comparison. <bold>b</bold>, Smoothed scatterplots showing correlation between RT values and compartment score at the indicated stages. Spearman’s correlation is indicated. <bold>c</bold>, Box plots showing RT values in A and B compartments (left) and correlation between RT values and compartment score (right) in α-amanitin-treated, 2-cell-stage embryos. <bold>d</bold>, Composite plots depicting RT values computed against LADs and iLADs at their corresponding developmental stage. Zero indicates the position of LAD–iLAD boundaries. Because DamID data for the 16-cell stage were not available, we used the closest developmental stage (ICM) for this comparison. <bold>e</bold>, Composite plots depicting RT values of mouse ES cells plotted against zygotic LADs (left) and RT values of zygotes against LADs in ES cells (right). Zero indicates the position of LAD–iLAD boundaries. <bold>d</bold>,<bold>e</bold>, Shading and lines indicate IQR and median, respectively. <bold>f</bold>, Correlation (Spearman’s <italic>R</italic>) heatmap between RT and distinctive chromatin features. When data for the same stage as RT are not available, those of the closest stage are used for analysis. <bold>g</bold>, Model summarizing our findings indicating progressive resolution of RT following the 2-cell stage. Left, RT peaks merge over time, resulting in changes in both number and size. Right, the effect of ZGA inhibition on RT and its relationship to A and B compartments. <bold>a</bold>,<bold>c</bold>, Box plots show median and IQR, whiskers depict the lowest and highest values within 1.5× IQR.</p></caption></fig>", "<fig id=\"Fig6\"><label>Extended Data Fig. 1</label><caption><title>Quality control of scRepli-seq samples.</title><p><bold>a</bold>. Scatter plots comparing the coefficient of variation calculated on the average read counts per chromosomes and the number of reads for each cell at the indicated embryonic stages. Horizontal and vertical lines indicate cutoffs for filtering cells. <bold>b</bold>. High concordance of replication state between with or without normalization by cells in G1. <bold>c</bold>. Comparison between two computational methods to calculate RT profiles. Shown are representative RT profiles derived from either raw or interval averaged replication timing values in the morula stage. <bold>d</bold>., <bold>e</bold>. Analysis of DNA replication in zygotes by EdU incorporation (d). Representative images of incorporated EdU and the corresponding quantifications are shown in e. Female and male pronuclei are indicated; the white dotted line depicts the nuclear periphery; note the EdU incorporation at the characteristic ring-shaped heterochromatic regions surrounding the nucleoli precursors between the 24 h and 26 h time. Approximate early, mid, and late S-phase times are indicated based on earlier work. Box plots show median of maximum intensity value and the interquartile range (IQR), whiskers depict the smallest and largest values within 1.5 ×IQR. n, and N, number of analysed embryos and number of independent biological replicates, respectively. Scale bar, 10 μm.</p></caption></fig>", "<fig id=\"Fig7\"><label>Extended Data Fig. 2</label><caption><title>Heatmaps of replication status and RT profiles of preimplantation embryos over a representative region on Chromosome 5 and 12.</title><p><bold>a</bold>. Cells are ranked by their replication score. The black line indicates RT profiles calculated as the average of overlapping intervals defined by the genome-wide replication score. <bold>b</bold>. Comparison of RT values in bins of 50 kb across embryonic stages. Representative changes in the RT (increase, shuffle, and constant) are indicated. White regions are regions of no coverage in the corresponding sample.</p></caption></fig>", "<fig id=\"Fig8\"><label>Extended Data Fig. 3</label><caption><title>Dynamics of RT peaks, TTRs, and RT troughs during preimplantation development.</title><p><bold>a</bold>. Alluvial plot showing the changes of RT phases. RT values were categorised in 5 groups from the earliest (1.0 ≥ RT > 0.8) to latest RT (0.2 > RT ≥ 0.0) across the genome. <bold>b</bold>. Representative replication timing profile in the morula stage depicting RT peaks, TTRs, and RT troughs. Grey shading represents 15 clusters of the RT values that were used to call local maxima (RT peaks or IZs) or local minima (RT troughs or TZs). Regions in transition between RT peaks and RT troughs were called as TTRs. The line indicates RT values. <bold>c</bold>. Fraction of A + T nucleotides in RT peaks, TTRs, and RT troughs during preimplantation development. Box plots show median and the interquartile range (IQR), whiskers depict the smallest and largest values within 1.5 ×IQR. <bold>d</bold>. Alluvial plot showing the relative changes of RT peaks, TTRs, and RT troughs at each cell division during preimplantation development. Box plots show median and the interquartile range (IQR), whiskers depict the smallest and largest values within 1.5 ×IQR.</p></caption></fig>", "<fig id=\"Fig9\"><label>Extended Data Fig. 4</label><caption><title>Unique pattern of RT in zygote is not due to differences in replication between maternal and paternal alleles.</title><p><bold>a</bold>. Representative heatmap depicting binarized replication status of all single cells in zygotes produced by IVF. Cells are ranked by their percentage of replicated genome (replication score), which is plotted as a bar plot on the left. <bold>b</bold>. Average RT profile of IVF-derived zygotes at the chromosome 2 region indicated by a black rectangle in a. The lines indicate RT profiles calculated as the average of overlapping intervals defined by the genome-wide replication score. <bold>c</bold>. Smoothed scatterplot comparing the RT values in zygotes, parthenogenetic zygotes, and isolated pronuclei (PN) compared to that of IVF-derived zygote. Rs indicate Spearman’s R. <bold>d</bold>. Correlation of genome-wide RT values between normal zygotes, zygotes produced by IVF, parthenogenetic zygotes and isolated maternal and paternal pronucleus (PN) embryos and later developmental stages using Spearman’s R. <bold>e</bold>. Representative brightfield image of isolated paternal pronucleus and remaining maternal pronucleus in the ooplasm. M, P, and PB indicate maternal pronucleus, paternal pronucleus, and polar body, respectively. Pronuclear isolation was repeated twice independently with similar results. Scale bar, 50 μm. <bold>f</bold>. Correlation heatmap of log2 maternal to paternal ratios between individual zygotes after discrimination of parental origins of sequencing reads using SNPs. Allele-specific bias was calculated by computing correlation coefficients of the maternal to paternal ratios across all genomic bins in which SNPs enabled identification of parent-of-origin allele. <bold>g</bold>. Representative genomic tracks of the log2 maternal to paternal ratio in zygotes (magenta) and in physically isolated maternal (red) or paternal pronucleus (blue) samples after assigning parental origin based on SNPs. Regions in which there are no reads (e.g. ~65–85 Mb) correspond to regions with no SNPs. <bold>h</bold>., <bold>i</bold>. Analysis of the size (h) and number (i) of the replication features in normal zygotes compared to zygotes produced by IVF, parthenogenetic zygotes and isolated maternal and paternal pronucleus (PN). Box plots show median and the interquartile range (IQR), whiskers depict the smallest and largest values within 1.5 ×IQR.</p></caption></fig>", "<fig id=\"Fig10\"><label>Extended Data Fig. 5</label><caption><title>Analysis of imprinted genes indicated no replication asynchrony.</title><p><bold>a</bold>. Analysis of RT at maternally (left) or paternally (right) imprinted genes in zygotes and in mechanically isolated paternal and maternal pronuclei (PN). RT values are shown on the top and expression data as ‘yes’ (detected) or ‘no’ (undetectable) is shown on the bottom. Expression data derives from <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE45719\">GSE45719</ext-link> and indicates expression in the indicated stage or maternal and paternal expression bias detected (grey) or undetected/absent (white).</p></caption></fig>", "<fig id=\"Fig11\"><label>Extended Data Fig. 6</label><caption><title>Early and late replicating regions have less heterogeneity than that of mid replicating region.</title><p><bold>a</bold>. Representative genomic bin on chromosome 8 depicting the parameters of sigmoid curve fitting. M-value (M) represents the replication score at which 50% of the cells replicated the bin. T width (Tw) was calculated based on replication score difference between 25% and 75% of cells replicated the bin. <bold>b</bold>. Smoothed scatter plots of T width along progression of replication timing for the indicated developmental stages. <bold>c</bold>. Measure for multimodality of M-values during embryonic development by Hartigans’ dip test. A greater dip value suggests a greater deviation from unimodal distribution (at least bimodal). Asterisks indicate significance levels (***, p-value < 0.001; ns, non-significant, as follows: for zygotes 9.8e − 01; for 2-cell 5.7e − 01; for 4-cell <2.2e − 16; for 8-cell <2.2e − 16; for morula <2.2e − 16; for ICM < 2.2e − 16).</p></caption></fig>", "<fig id=\"Fig12\"><label>Extended Data Fig. 7</label><caption><title>Correlation between RT values in zygotes and maternal transcripts and analysis of histone modifications in RT peaks, TTRs and RT troughs.</title><p><bold>a</bold>. Kinetics of the relative changes in the enrichment of H3K36me3 and H3K4me3 at RT peaks and RT troughs normalised to TTRs from the zygote to the morula stage. <bold>b</bold>. Immunostaining of histone H3 lysine 4 trimethylation (H3K4me3) after overexpression of Kdm5b. Representative maximal projection images are shown. Total number of embryos (n) analysed in each condition from three independent experiments (N) are shown. Scale bar, 25 μm. <bold>c</bold>. RT profiles of 2-cell stage embryos overlayed with those from Kdm5b-overexpressed 2-cell embryos. Genomic positions of indicated gene classes according to DBTMEE<sup>##REF##25336621##54##</sup> are shown as rectangles. <bold>d</bold>. Smoothed scatterplot of RT values in normal 2-cell embryos versus Kdm5b-overexpressed 2-cell embryo. Spearman’s correlation (Rs) is indicated. <bold>e</bold>. Confidence intervals for the changes of RT (ΔRT) between Kdm5b-overexpressed and untreated 2-cell embryos of genomic bins containing maternally expressed genes or Major ZGA genes. ‘Both’ refers to bins containing ZGA genes and maternally expressed genes, whereas ‘None’ does not overlap with any of the two categories. <bold>f</bold>. Enrichment of genomic regions displaying a significant change in RT upon Kdm5b expression in bins containing maternally expressed genes or Major ZGA genes. ‘Both’ refers to bins containing ZGA genes and maternally expressed genes, whereas ‘None’ does not overlap with any of the two categories. Observed over expected number of bins is shown (O/E). <bold>g</bold>. Smoothed scatterplots showing correlations between transcript levels (log2 TPM) of Metaphase II (MII) stage oocytes with the RT values of zygote and 2-cell stage embryos. Rs indicates Spearman’s R. <bold>h</bold>. Confidence intervals for the difference of transcript levels (Δlog2 TPM) between early (E) vs. late (L) replicating genes. Genomic bins with an RT value greater than 0.5 were considered as Early and with RT value lower than 0.5 as Late. <bold>i</bold>. Confidence intervals for the difference of replication timing (ΔRT) between genes with moderate/high vs. no/low transcript levels. Genes with a transcript level (log2 TPM) greater than 1 were considered moderate/high and with a value lower than 1 as no/low expressed. <bold>j</bold>. Confidence intervals for the Spearman’s correlation between RT and transcript abundance. <bold>k</bold>. Smoothed scatterplot showing correlation between transcript levels (log2 TPM) and RT values in mouse ES cells. Rs indicates Spearman’s R. In e, h-j the dot represents the mean of 1000 bootstrapped values. Error bars indicate the 95% bootstrap confidence interval.</p></caption></fig>", "<fig id=\"Fig13\"><label>Extended Data Fig. 8</label><caption><title>Effect of RNA Pol II inhibition by α-amanitin and DRB on the embryonic RT programme.</title><p><bold>a</bold>. Visualisation of global transcription during minor and major ZGA by EU click chemistry and efficient inhibition of ZGA using α-amanitin. Representative embryos of a total of 24 (control), 19 (α-amanitin treated) or 19 non-EU treated embryos (EU-) are shown. Scale bar, 50 μm. <bold>b</bold>. Taqman RT-qPCR analysis for Zscan4 cluster and rDNA after α-amanitin and DRB treatment. Barplots show mean ± s.d and dots indicate the values of independent biological replicates. <bold>c</bold>. Alluvial plot indicating the RT values categorised in 5 groups from the earliest (1.0 ≥ RT > 0.8) to latest RT (0.2 > RT ≥ 0.0) across the genome in control 2-cell embryos and their changes upon α-amanitin treatment. <bold>d</bold>. Confidence intervals for the changes in RT (ΔRT) upon α-amanitin treatment of genomic bins containing maternally expressed genes or major ZGA genes. ‘Both’ refers to bins containing ZGA genes and maternally expressed genes, whereas ‘None’ does not overlap with any of the two categories. <bold>e</bold>. Immunostaining of RNA Pol II using an antibody recognizing all forms of RNA Pol II or an antibody against its CTD Serine 2 phosphorylated form (S2P) after α-amanitin or DRB treatment with (right) and without (left) Triton pre-extraction. Representative single confocal sections are shown. Total number of embryos (n) analysed in each conditions from two independent experiments (N) are shown. Scale bars, 25 μm. We note that α-amanitin leads to degradation of RNA PolII in our experimental conditions. <bold>f</bold>. Visualisation of global transcription during minor and major ZGA by EU click chemistry and efficient inhibition of ZGA upon DRB treatment. Representative embryos from two independent experimetns (N) are shown. Scale bar, 50 μm. <bold>g</bold>. Difference of RT values (ΔRT) between DRB-treated and untreated 2-cell embryos at genomic bins overlapping only major ZGA genes, only maternal RNA genes, or both genes compared to non-overlapping bins (None). Box plots show median and the interquartile range (IQR), whiskers depict the smallest and largest values within 1.5 ×IQR. <bold>h</bold>. Confidence intervals for the changes in RT (ΔRT) upon DRB treatment of genomic bins containing maternally expressed genes or Major ZGA genes. ‘Both’ refers to bins containing ZGA genes and maternally expressed genes, ‘None’ does not overlap with any of the two categories. <bold>i</bold>. Enrichment of genomic regions displaying significant changes in RT upon α-amanitin treatment with bins that display changes in RT upon DRB treatment in 2-cell stage embryos. Observed over expected number of bins is shown (O/E). In d and h, the dot represents the mean of 1000 bootstrapped values. Error bars indicate the 95% bootstrap confidence interval.</p></caption></fig>", "<fig id=\"Fig14\"><label>Extended Data Fig. 9</label><caption><title>Relationship between ATAC-seq and RT changes upon transcriptional inhibition.</title><p><bold>a</bold>. Smoothed scatterplot showing correlation between ATAC-seq signal and RT values in 2-cell stage embryos (left) and in α-amanitin treated 2-cell stage embryos (right). Rs indicates Spearman’s R. <bold>b</bold>. Pairwise Spearman´s correlation coefficients (R) between RT and ATAC-seq signal in untreated and in α-amanitin treated 2-cell stage embryos. Error bars indicate the 95% bootstrap confidence interval. Dot represents the mean of 1000 bootstrapped values. <bold>c</bold>. Smoothed scatterplot depicting the difference of RT values (ΔRT) between α-amanitin treated and untreated 2-cell embryos against ATAC-seq signal in control 2-cell stage embryos. <bold>d</bold>. Difference of RT values (ΔRT) at genomic bins that significantly lose accessibility (down), gain accessibility (up) or remain unchanged (non-significant) upon α-amanitin treatment in 2-cell stage embryos. Box plots show median and the interquartile range (IQR), whiskers depict the smallest and largest values within 1.5 ×IQR. <bold>e</bold>. Size of RT peaks, TTRs and RT troughs in control versus α-amanitin or DRB treated 2-cell embryos. <bold>f</bold>. A + T content in RT peaks, TTRs, and RT troughs in 2-cell and α-amanitin treated 2-cell embryos. Box plots show median and the interquartile range (IQR), whiskers depict the smallest and largest values within 1.5 ×IQR. <bold>g</bold>. Fraction of RT peaks containing genes expressed at the 2-cell stage relative to all genes in RT peaks specific to 2-cell stage embryos upon α-amanitin treatment (de novo), in RT peaks specific to control 2-cell stage embryos (lost) and RT peaks present in both 2-cell control and α -amanitin treated embryos.</p></caption></fig>", "<fig id=\"Fig15\"><label>Extended Data Fig. 10</label><caption><title>Characterisation of silent chromatin features of the embryonic replication programme and of the parental RT differences of LADs and compartments.</title><p><bold>a</bold>., <bold>b</bold>. Box plots depicting H3K9me3 (a) or H3K27me3 (b) coverage at the indicated replication features at different embryonic stages. <bold>c</bold>. Kinetics of the relative changes in the enrichment of histone modifications at RT peaks and RT troughs normalised to TTRs from the zygote to the blastocyst stage ICM. The ‘oocyte/zygote’ time point indicates H3K27me3 data from oocytes, before fertilisation, and RT from zygotes (after fertilisation). <bold>d</bold>. Analysis to determine statistical significance on the RT differences between A and B compartments based on confidence intervals. Confidence intervals for the difference of replication timing (ΔRT) between A and B compartments. Error bars indicate the 95% bootstrap confidence interval. Dot represents the mean of 1000 bootstrapped values. <bold>e</bold>. Box plots of zygote RT values in maternal (left) and paternal (right) A and B compartments. <bold>f</bold>. Smoothed scatterplots showing the correlation between zygote RT values and maternal and paternal compartment scores. <bold>g</bold>. Box plot depicting the difference of RT values (ΔRT) between α-amanitin treated and untreated 2-cell embryos in A- and B-compartments. <bold>h</bold>. Box plot depicting the ATAC-seq signal in A- and B-compartments in untreated 2-cell stage embryos. <bold>i</bold>. Enrichment of the main families of transposable elements across replication features during early development. Color key indicates the number of overlapping TEs relative to randomly shuffled. <bold>j</bold>. Box plots showing RT values of zygotes within the corresponding zygotic maternal (left) and paternal (right) iLADs and LADs. <bold>k</bold>. Composite plot showing RT values of zygotes plotted against maternal and paternal zygotic LADs. The zero indicates the position of the LAD/iLAD boundaries. Shading shows IQR and the line indicates the median. In a, b, e, g, h, j the box plots show median and the interquartile range (IQR), whiskers depict the smallest and largest values within 1.5 ×IQR.</p></caption></fig>" ]	[]	[]	[]	[]	[]	[]	[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>" ]	[ "<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>These authors contributed equally: Tamas Schauer, Luis Altamirano-Pacheco</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"41586_2023_6872_Fig1_HTML\" id=\"d32e399\"/>", "<graphic xlink:href=\"41586_2023_6872_Fig2_HTML\" id=\"d32e635\"/>", "<graphic xlink:href=\"41586_2023_6872_Fig3_HTML\" id=\"d32e738\"/>", "<graphic xlink:href=\"41586_2023_6872_Fig4_HTML\" id=\"d32e841\"/>", "<graphic xlink:href=\"41586_2023_6872_Fig5_HTML\" id=\"d32e1085\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6872_Fig6_ESM\" id=\"d32e1923\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6872_Fig7_ESM\" id=\"d32e1940\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6872_Fig8_ESM\" id=\"d32e1963\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6872_Fig9_ESM\" id=\"d32e2002\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6872_Fig10_ESM\" id=\"d32e2020\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6872_Fig11_ESM\" id=\"d32e2040\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6872_Fig12_ESM\" id=\"d32e2089\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6872_Fig13_ESM\" id=\"d32e2128\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6872_Fig14_ESM\" id=\"d32e2161\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6872_Fig15_ESM\" id=\"d32e2206\"/>" ]	[ "<media xlink:href=\"41586_2023_6872_MOESM1_ESM.pdf\"><caption><p>Reporting Summary</p></caption></media>", "<media xlink:href=\"41586_2023_6872_MOESM2_ESM.xlsx\"><label>Supplementary Table 1</label><caption><p>Metrics and quality control data of single-cell Repli-seq samples. Overview of sample collection, mapping statistics and quality control. Cells with a high coefficient of variation were removed from analyses and the coefficient of variation was recalculated, excluding each chromosome separately. Cells were considered for further analysis if they passed the threshold when only one specific chromosome was removed.</p></caption></media>" ]	[{"label": ["6."], "mixed-citation": ["Gilbert, D. M. & Gasser, S. M. in "], "italic": ["DNA Replication and Human Disease"]}, {"label": ["62."], "mixed-citation": ["Benaglia, T. et al. Mixtools: an R package for analyzing finite mixture models. "], "italic": ["J. Stat. Softw"], "ext-link": ["jstatsoft.org/article/view/v032i06"]}, {"label": ["66."], "mixed-citation": ["Efron, B. The Jackknife, the Bootstrap and other resampling plans. In "], "italic": ["CBMS-NSF Regional Conference Series in Applied Mathematics"]}]	{ "acronym": [], "definition": [] }	70	CC BY	no	2024-01-13 00:02:19	Nature. 2024 Dec 20; 625(7994):401-409	oa_package/46/d7/PMC10781638.tar.gz
PMC10781639	38200296	[]	[ "<title>Methods</title>", "<title>Data generation</title>", "<title>Overview</title>", "<p id=\"Par30\">To examine variants associated with phenotypes backwards in time, we assembled a large ancient DNA dataset. Here we present new genomic data from 86 ancient individuals from Medieval and post-Medieval periods from Denmark (Extended Data Fig. ##FIG##6##2##, Supplementary Note ##SUPPL##0##1## and Supplementary Table ##SUPPL##3##1##). The samples range in age from around the eleventh to the eighteenth century. We extracted ancient DNA from tooth cementum or petrous bone and shotgun sequenced the 86 genomes to a depth of genomic coverage ranging from 0.02× to 1.6× (mean of 0.39× and median of 0.27×). The genomes of the 86 new individuals were imputed using 1000 Genomes phased data as a reference panel by an imputation method designed for low-coverage genomes (GLIMPSE)<sup>##REF##33414550##51##</sup>, and we also imputed 1,664 ancient genomes presented in the accompanying study<sup>##UREF##1##2##</sup>. Depending on the specific data quality requirements for the downstream analyses, we filtered out samples with poor coverage and variant sites with low minor allele frequency (MAF) and low imputation quality (average genotype probability of <0.98). Our dataset of ancient individuals spans approximately 15,000 years across Eurasia (Extended Data Fig. ##FIG##6##2##).</p>", "<p id=\"Par31\">Authorizations for excavating the three sites, Kirkegård, Holbæk and Tjærby, were granted, respectively, to the Aalborg Historiske Museum, the Museum Vestsjælland (previously Museet for Holbæk og Omeg) and the Kulturhistorisk Museum Randers. The current study of samples from these three sites is covered by agreements given to GeoGenetics, Globe Institute, University of Copenhagen, by the Aalborg Historiske Museum, the Museum Vestsjælland and the Kulturhistorisk Museum Randers, respectively.</p>", "<title>Ancient DNA extraction and library preparation</title>", "<p id=\"Par32\">Laboratory work was conducted in the dedicated ancient DNA clean-room facilities at the Lundbeck Foundation GeoGenetics Centre (Globe Institute, University of Copenhagen). A total of 86 Medieval and post-Medieval human samples from Denmark (Supplementary Table ##SUPPL##3##2##) were processed using semi-automated procedures. Samples were processed in parallel. For each extract, non-USER-treated and USER-treated (NEB) libraries were built<sup>##REF##20516186##52##</sup>. All libraries were sequenced on the NovaSeq 6000 instrument at the GeoGenetics Sequencing Core, Copenhagen, using S4 200-cycle kits v1.5. A more detailed description of DNA extraction and library preparation can be found in Supplementary Note ##SUPPL##0##1##.</p>", "<title>Basic bioinformatics</title>", "<p id=\"Par33\">The sequencing data were demultiplexed using the Illumina software BCL Convert (<ext-link ext-link-type=\"uri\" xlink:href=\"https://emea.support.illumina.com/sequencing/sequencing_software/bcl-convert.html\">https://emea.support.illumina.com/sequencing/sequencing_software/bcl-convert.html</ext-link>). Adaptor sequences were trimmed and overlapping reads were collapsed using AdapterRemoval (v2.2.4)<sup>##REF##26868221##53##</sup>. Single-end collapsed reads of at least 30 bp and paired-end reads were mapped to human reference genome build 37 using BWA (v0.7.17)<sup>##REF##19451168##54##</sup> with seeding disabled to allow for higher sensitivity. Paired- and single-end reads for each library and lane were merged, and duplicates were marked using Picard MarkDuplicates (v2.18.26; <ext-link ext-link-type=\"uri\" xlink:href=\"http://picard.sourceforge.net\">http://picard.sourceforge.net</ext-link>) with a pixel distance of 12,000. Read depth and coverage were determined using samtools (v1.10)<sup>##REF##19505943##55##</sup> with all sites used in the calculation (-a). Data were then merged to the sample level and duplicates were marked again.</p>", "<title>DNA authentication</title>", "<p id=\"Par34\">To determine the authenticity of the ancient reads, post-mortem DNA damage patterns were quantified using mapDamage2.0 (ref. <sup>##REF##23613487##56##</sup>). Next, two different methods were used to estimate the levels of contamination. First, we applied ContamMix to quantify the fraction of exogenous reads in the mitochondrial reads by comparing the mitochondrial DNA consensus genome to possible contaminant genomes<sup>##REF##23523248##57##</sup>. The consensus was constructed using an in-house Perl script that used sites with at least 5× coverage, and bases were only called if observed in at least 70% of reads covering the site. Additionally, we applied ANGSD (v0.931)<sup>##REF##25420514##58##</sup> to estimate nuclear contamination by quantifying heterozygosity on the X chromosome in males. Both contamination estimates used only filtered reads with a base quality of ≥20 and mapping quality of ≥30.</p>", "<title>Imputation</title>", "<p id=\"Par35\">We combined the 86 newly sequenced Medieval and post-Medieval Danish individuals with 1,664 previously published ancient genomes<sup>##UREF##1##2##</sup>. We then excluded individuals showing contamination (more than 5%), low autosomal coverage (less than 0.1×) or low genome-wide average imputation genotype probability (less than 0.98), and we chose the higher-quality sample in a close relative pair (first- or second-degree relatives). A total of 1,557 individuals passed all filters and were used in downstream analyses. We restricted the analysis to SNPs with an imputation INFO score of ≥0.5 and MAF of ≥0.05.</p>", "<title>Kinship analysis and uniparental haplogroup inference</title>", "<p id=\"Par36\">READ<sup>##REF##29684051##59##</sup> was used to detect the degree of relatedness for pairs of individuals.</p>", "<p id=\"Par37\">The mitochondrial DNA haplogroups of the Medieval and post-Medieval individuals were assigned using HaploGrep2 (ref. <sup>##REF##27084951##60##</sup>; Supplementary Fig. ##SUPPL##0##3##). Y-chromosome haplogroup assignment was inferred following an already published workflow<sup>##REF##34459341##61##</sup> (Supplementary Fig. ##SUPPL##0##5##). More details can be found in Supplementary Note ##SUPPL##0##2##.</p>", "<title>Standard population genetics analyses</title>", "<p id=\"Par38\">The main population genetics approach on which we based our inference was population-based painting (detailed below). However, to robustly understand population structure, we applied other standard techniques. First, we used principal-component analysis (PCA) (Extended Data Fig. ##FIG##6##2##) to investigate the overall population structure of the dataset. We used PLINK<sup>##REF##17701901##62##</sup>, excluding SNPs with MAF < 0.05 in the imputed panel. On the basis of 1,210 ancient western Eurasian imputed genomes, the Medieval and post-Medieval samples clustered close to each other, displaying a relatively low genetic variability and situated within the genetic variability observed in the post-Bronze Age western Eurasian populations.</p>", "<p id=\"Par39\">We then used two additional standard methods to estimate ancestry components in our ancient samples. First, we used model-based clustering (ADMIXTURE)<sup>##REF##27216439##63##</sup> (Supplementary Note ##SUPPL##0##1## and Supplementary Fig. ##SUPPL##0##1##) on a subset of 826,248 SNPs. Second, we used qpAdm<sup>##REF##22960212##64##</sup> (Supplementary Note ##SUPPL##0##1##, Supplementary Fig. ##SUPPL##0##2## and Supplementary Table ##SUPPL##0##15##) with a reference panel of three genetic ancestries (WHG, ANA and steppe) on the same 826,248 SNPs. We performed qpAdm applying the option ‘allsnps: YES’ and a set of seven outgroups was used as ‘right populations’: Siberia_UpperPaleolithic_UstIshim, Siberia_UpperPaleolithic_Yana, Russia_UpperPaleolithic_Sunghir, Switzerland_Mesolithic, Iran_Neolithic, Siberia_Neolithic and USA_Beringia. We set a minimum threshold of 100,000 SNPs, and only results with <italic>P</italic> < 0.05 were considered.</p>", "<title>Population painting</title>", "<p id=\"Par40\">Our main analysis used chromosome painting<sup>##REF##22291602##65##</sup> with a panel of six ancient ancestries. This allows fine-scale estimation of ancestry as a function of these populations. We ran chromosome painting on all ancient individuals not in the reference panel, using a reference panel of ancient donors grouped into populations to represent specific ancestries: WHG, EHG, CHG, farmer (ANA + Neolithic), steppe and African (method described in ref. <sup>##UREF##3##11##</sup>). Painting followed the pipeline of ref. <sup>##REF##32939067##66##</sup> based on GLOBETROTTER<sup>##REF##24531965##67##</sup>, with admixture proportions estimated using NNLS. NNLS explains the genome-wide haplotype matches of an individual as a mixture of genome-wide haplotype matches of the reference populations. This set-up allows both the reference panel and any additional samples to be described using these six ancestries (Fig. ##FIG##0##1##).</p>", "<p id=\"Par41\">We then painted individuals born in Denmark of a typical ancestry (typical on the basis of density-based clustering of the first 18 principal components<sup>##UREF##3##11##</sup>). The reference panel used for chromosome painting was designed to capture the various components of European ancestry only, and so we urge caution in interpreting these results for non-European samples.</p>", "<p id=\"Par42\">This dataset provides the opportunity to study the population history of Denmark from the Mesolithic to the post-Medieval period, covering around 10,000 years, which can be considered a typical Northern European population. Our results clearly demonstrate the impact of previously described demographic events, including the influx of Neolithic farmer ancestry ~9,000 years ago and steppe ancestry ~5,000 years ago<sup>##REF##25731166##26##,##REF##26062507##27##</sup>. We highlight genetic continuity from the Bronze Age to the post-Medieval period (Supplementary Note ##SUPPL##0##1## and Supplementary Fig. ##SUPPL##0##1##), although qpAdm detected a small increase in the farmer component during the Viking Age (Supplementary Note ##SUPPL##0##1##, Supplementary Fig. ##SUPPL##0##2## and Supplementary Table ##SUPPL##0##15##), while the Medieval period marked a time of increased genetic diversity, probably reflecting increased mobility across Europe. This genetic continuity is further confirmed by the haplogroups identified in the uniparental genetic markers (Supplementary Note ##SUPPL##0##2##). Together, these results indicate that after the steppe migration in the Bronze Age there may have been no other major gene flow into Denmark from populations with significantly different Neolithic and Bronze Age ancestry compositions and therefore no changes in these ancestry components in the Danish population.</p>", "<title>Local ancestry from population painting</title>", "<p id=\"Par43\">Chromosome painting provides an estimate of the probability that an individual from each reference population is the closest match to the target individual at every position in the genome. This provided our first estimate of local ancestry from ref. <sup>##UREF##1##2##</sup>: the population of the first reference individual to coalesce with the target individual, as estimated by Chromopainter<sup>##REF##22291602##65##</sup>. This was estimated for all white British individuals in the UK Biobank, using the population painting reference panel described above. We refer to this as ‘local ancestry’, although we note that the closest relative in the sample may not represent ancestry in the conventional sense.</p>", "<title>Pathway painting</title>", "<p id=\"Par44\">An alternative approach is to identify to which of the four major ancestry pathways (ANA farmer, CHG, EHG and WHG) each position in the genome best matches. This has the advantage of not forcing haplotypes to choose between ‘steppe’ ancestry and its ancestors but the disadvantage of being more complex to interpret. To do this, we modelled ancestry path labels in the GBR, FIN and TSI 1000 Genomes populations<sup>##REF##26432245##68##</sup> and 1,015 ancient genomes generated using a neural network to assign ancestry paths on the basis of a sample’s nearest neighbours at the first five informative nodes of a marginal tree sequence, with an informative node defined as a node that had at least one leaf from the reference set of ancient samples described above (ref. <sup>##UREF##3##11##</sup>; Supplementary Note ##SUPPL##0##1c##). We refer to these as ‘ancestry path labels’.</p>", "<title>SNP associations</title>", "<p id=\"Par45\">We aimed to generate SNP associations from previous studies for each phenotype in a consistent approach. To generate a list of SNPs associated with MS and RA, we used two approaches: in the first, we downloaded fine-mapped SNPs from previous association studies. For each fine-mapped SNP, if the SNP did not have an ancestry path label, we found the SNP with the highest LD that did, with a minimum threshold of <italic>r</italic><sup>2</sup> ≥ 0.7, in the GBR, FIN and TSI 1000 Genomes populations using LDLinkR<sup>##REF##32180801##69##</sup>. The final SNPs used for each phenotype can be found in Supplementary Table ##SUPPL##3##4## (MS) and Supplementary Table ##SUPPL##3##5## (RA).</p>", "<p id=\"Par46\">For MS, we used data from ref. <sup>##REF##31604244##4##</sup>. For non-MHC SNPs, we used the ‘discovery’ SNPs with <italic>P</italic>(joined) and OR(joined) generated in the replication phase. For MHC variants, we searched the literature for the reported HLA alleles and amino acid polymorphisms (Supplementary Table ##SUPPL##3##3##). In total, we generated 205 SNPs that were either fine-mapped or in high LD with a fine-mapped SNP (15 MHC, 190 non-MHC).</p>", "<p id=\"Par47\">For RA, we downloaded 57 genome-wide-significant non-MHC SNPs for seropositive RA in Europeans<sup>##UREF##8##70##</sup>. We retrieved MHC associations separately (ref. <sup>##REF##21592391##71##</sup>; with associated ORs and <italic>P</italic> values from ref. <sup>##REF##22286218##72##</sup>). In total, we generated 51 SNPs that were either fine-mapped or in high LD with a fine-mapped SNP (3 MHC, 48 non-MHC).</p>", "<p id=\"Par48\">Second, because we could not always find LD proxies for fine-mapped SNPs that were present in our ancestry path label dataset, we found that we were losing significant signal from the HLA region; therefore, we generated a second set of SNP associations. We downloaded full summary statistics for each disease (using ref. <sup>##REF##31604244##4##</sup> for MS and ref. <sup>##REF##24390342##73##</sup> for RA), restricted to sites present in the ancestry path label dataset, and ran PLINK’s (v1.90b4.4)<sup>##REF##25722852##74##</sup> clump method (parameters: --clump-p1 5e-8 --clump-r2 0.05 --clump-kb 250; as in ref. <sup>##REF##33443182##75##</sup>) using LD in the GBR, FIN and TSI 1000 Genomes populations<sup>##REF##26432245##68##</sup> to extract genome-wide-significant independent SNPs.</p>", "<p id=\"Par49\">In the main text, we report results for the first set of SNPs (‘fine-mapped’) for analyses involving local ancestry in modern data and the second set of SNPs (‘pruned’) for analyses involving polygenic measures of selection (CLUES and PALM).</p>", "<title>Anomaly score: regions of unusual ancestry</title>", "<p id=\"Par50\">To assess which regions of ancestry were unusual, we converted the ancestry estimates to <italic>Z</italic> scores by standardizing to the genome-wide mean and standard deviation. Specifically, let <italic>A</italic>(<italic>i</italic>,<italic>j</italic>,<italic>k</italic>) denote the probability of the <italic>k</italic>th ancestry (<italic>k</italic> = 1, ..., <italic>K</italic>) at the <italic>j</italic>th SNP (<italic>j</italic> = 1, ..., <italic>J</italic>) of a chromosome for the <italic>i</italic>th individual (<italic>i</italic> = 1, ..., <italic>N</italic>). We first computed the mean painting for each SNP, . From this, we estimated a location parameter <italic>µ</italic><sub><italic>k</italic></sub> and a scale parameter <italic>σ</italic><sub><italic>k</italic></sub> using a block-median approach. Specifically, we partitioned the genome into 0.5-Mb regions and, within each, computed the mean and standard deviation of the ancestry. The parameter estimates were the median values over the whole genome. We then computed an anomaly score for each SNP for each ancestry <italic>Z</italic>(<italic>j</italic>,<italic>k</italic>) = (<italic>A</italic>(<italic>j</italic>,<italic>k</italic>) – <italic>µ</italic><sub><italic>k</italic></sub>)/<italic>σ</italic><sub><italic>k</italic></sub>. This is the normal-distribution approximation to the Poisson binomial score for excess ancestry, for which a detailed simulation study is presented in ref. <sup>##REF##28378497##76##</sup>.</p>", "<p id=\"Par51\">To arrive at an anomaly score for each SNP aggregated over all ancestries, we also had to account for correlations in the ancestry paintings. Instead of scaling each ancestry deviation <italic>A</italic>(<italic>j</italic>,<italic>k</italic>) = <italic>A</italic>(<italic>j</italic>,<italic>k</italic>) – <italic>µ</italic><sub><italic>k</italic></sub> by its standard deviation, we instead ‘whitened’ them, that is, rotated the data to have an independent signal. Let <italic>C</italic> = <italic>A</italic><sup><italic>T</italic></sup><italic>A</italic>* be a <italic>K</italic> × <italic>K</italic> covariance matrix, and let <italic>C</italic><sup>–1</sup> = <italic>UDV</italic><sup><italic>T</italic></sup> be a singular value decomposition. Then, is the whitening matrix from which <italic>Z</italic> = <italic>A</italic><italic>W</italic> is normally distributed with covariance matrix diag(1) under the null hypothesis that <italic>A</italic> is normally distributed with mean 0 and unknown covariance <italic>Σ</italic>. The ancestry anomaly score test statistic for each SNP is , which is chi-squared distributed with <italic>K</italic> degrees of freedom under the null, and we report <italic>P</italic> values from this.</p>", "<p id=\"Par52\">To test for gene enrichment, we formed a list of all SNPs reaching genome-wide significance (<italic>P</italic> < 5 × 10<sup>–8</sup>) and, using the R package gprofiler2 (ref. <sup>##REF##33564394##77##</sup>), converted these to a list of unique genes. We then used <italic>gost</italic> to perform an enrichment test for each Gene Ontology (GO) term, for which we used default <italic>P</italic>-value correction via the g:Profiler SCS method. This is an empirical correction based on performing random lookups of the same number of genes under the null, to control the error rate and ensure that 95% of reported categories (at <italic>P</italic> = 0.05) are correct.</p>", "<title>Allele frequency over time</title>", "<p id=\"Par53\">To investigate how effect allele frequencies have changed over time, we extracted high-effect alleles for each phenotype from the ancient data. We excluded all non-Eurasian samples, grouped them by ‘groupLabel’, excluded any group with fewer than four samples and coloured points by ancestry proportion according to genome-wide NNLS based on chromosome painting (described above).</p>", "<title>Weighted average prevalence</title>", "<p id=\"Par54\">To understand whether risk-conferring haplotypes evolved in the steppe population or in a pre- or post-dating population, we developed a statistic that could account for the origin of risk to be identified with multiple ancestry groups, which do not have to be the same set for each SNP.</p>", "<p id=\"Par55\">We first applied <italic>k</italic>-means clustering to the dosage of each ancestry for each associated SNP and investigated the dosage distribution of clusters with significantly higher MS prevalence. For the target SNPs, the elbow method<sup>##UREF##9##78##</sup> suggested selecting around 5–7 clusters, and we chose 6 clusters. After performing the <italic>k</italic>-means cluster analysis, we calculated the average probability for each ancestry for case individuals. Furthermore, we calculated the prevalence of MS in each cluster and performed a one-sample <italic>t</italic> test to investigate whether it differed from the overall MS prevalence (0.487%). This tested whether any particular combinations of ancestry were associated with the phenotype at a SNP. Clusters with high MS risk ratios had a high proportion of steppe components (Supplementary Fig. ##SUPPL##0##7##), leading to the conclusion that steppe ancestry alone is driving this signal.</p>", "<p id=\"Par56\">We can then compute the WAP, which summarizes these results into the ancestries. For the <italic>j</italic>th SNP, let denote the sum of the <italic>k</italic>th ancestry probabilities of all the individuals in the <italic>m</italic>th cluster (<italic>k</italic>,<italic>m</italic> = 1, ..., 6), where <italic>n</italic><sub><italic>jm</italic></sub> is the cluster size of the <italic>m</italic>th cluster. Letting <italic>π</italic><sub><italic>jm</italic></sub> denote the prevalence of MS in the <italic>m</italic>th cluster, the WAP for the <italic>k</italic>th ancestry is defined aswhere <italic>P</italic><sub><italic>jkm</italic></sub> is defined as the weight for each cluster.</p>", "<p id=\"Par57\">The standard deviation of is computed as s.d. , where , and <italic>s</italic>(<italic>y</italic><sub><italic>jm</italic></sub>) is the standard deviation of the outcome for the individuals in the <italic>m</italic>th cluster. We also tested the hypothesis against and computed the <italic>P</italic> value as .</p>", "<p id=\"Par58\">For each ancestry, WAP measures the association of that ancestry with MS risk across all clusters. To make a clear comparison, we calculated the risk ratio (compared to the overall MS prevalence) for each ancestry at each SNP and assigned a mean and confidence interval for the risk ratio of each ancestry on each chromosome (Fig. ##FIG##2##3## and Extended Data Fig. ##FIG##11##7##).</p>", "<title>PCA and UMAP of WAP and average dosage</title>", "<p id=\"Par59\">To sort risk-associated SNPs into ancestry patterns according to that risk, we performed PCA on the average ancestry probability and WAP at each MS-associated SNP (Supplementary Fig. ##SUPPL##0##8##). The former showed that all of the HLA SNPs except three from the HLA class II and III regions had much larger outgroup components than the other SNPs. The latter analysis indicated a strong association between steppe ancestry and MS risk. Additionally, outgroup ancestry at rs10914539 on chromosome 1 exceptionally reduced the incidence of MS, whereas outgroup ancestry at rs771767 (chromosome 3) and rs137956 (chromosome 22) significantly boosted MS risk.</p>", "<title>Ancestral risk score</title>", "<p id=\"Par60\">To assign risk to ancient ancestries by computing the equivalent of a polygenic score for each, we followed methods developed in ref. <sup>##UREF##3##11##</sup>. We calculated the effect allele painting frequency for a given ancestry <italic>F</italic><sub>{anc,<italic>i</italic>}</sub> for SNP <italic>i</italic> using the formula:where there are <italic>M</italic><sub>effect</sub> individuals homozygous for the effect allele, <italic>M</italic><sub>alt</sub> individuals homozygous for the other allele and \n is the sum of the painting probabilities for that ancestry in individuals homozygous for the effect allele at SNP <italic>i</italic>. This can be interpreted as an estimate of an ancestral contribution to effect allele frequency in a modern population. The per-SNP painting frequencies can be found in Supplementary Tables ##SUPPL##3##4##–##SUPPL##3##6##.</p>", "<p id=\"Par61\">To calculate the ARS, we summed over all <italic>I </italic>pruned SNPs in an additive model:</p>", "<p id=\"Par62\">We then ran a transformation step as in ref. <sup>##REF##25102153##79##</sup>, centring results around the ancestral mean (that is, all ancestries) and reporting as a <italic>Z</italic> score. To obtain 95% confidence intervals, we ran an accelerated bootstrap over loci, which accounts for the skew of data to better estimate confidence intervals<sup>##UREF##10##80##</sup>.</p>", "<title>GWAS of ancestry and genotypes</title>", "<p id=\"Par63\">The total variance of a trait explained by genotypes (SNP values), ancestry and haplotypes (described below) is a measure of how well each captures the causal factors driving that trait. We therefore computed the variance explained for each data type in a ‘head-to-head’ comparison at either specific SNPs or SNP sets. In this section, we describe the model and covariates accounted for.</p>", "<p id=\"Par64\">We used the UK Biobank to fit GWAS models for local ancestry values and genotype values separately, using only SNPs known to be associated with the phenotype (fine-mapped SNPs). We used the following phenotype codes for each phenotype: MS, data field 131043; RA, data field 131849 (seropositive).</p>", "<p id=\"Par65\">Let <italic>Y</italic><sub><italic>i</italic></sub> denote the phenotype status for the <italic>i</italic>th individual (<italic>i</italic> = 1, ..., 399,998), which takes a value of 1 for a case and 0 for a control, and let <italic>π</italic><sub><italic>i</italic></sub> = Pr(<italic>Y</italic><sub><italic>i</italic></sub> = 1) denote the probability that this individual is a case. Let <italic>X</italic><sub><italic>ijk</italic></sub> denote the <italic>k</italic>th ancestry probability (<italic>k</italic> = 1, ..., <italic>K</italic>) for the <italic>j</italic>th SNP (<italic>j</italic> = 1, ..., 205) of the <italic>i</italic>th individual. <italic>C</italic><sub><italic>ic</italic></sub> is the <italic>c</italic>th predictor (<italic>c</italic> = 1, ..., <italic>N</italic><sub><italic>c</italic></sub>) for the <italic>i</italic>th individual. We used the following logistic regression model for GWAS, which assumes the effects of alleles are additive:</p>", "<p id=\"Par66\">We used <italic>N</italic><sub><italic>c</italic></sub> = 20 predictors in the GWAS models, including sex, age and the first 18 principal components, which are sufficient to capture most of the population structure in the UK Biobank<sup>##UREF##11##81##</sup>.</p>", "<p id=\"Par67\">First, we built the model with <italic>K</italic> = 1. By using only one ancestry probability in each model, we aimed to find the statistical significance of each SNP under each ancestry. We then built the model with <italic>K</italic> = 5, that is, using all six local ancestry probabilities, which sum to 1. We calculated the variance explained by each SNP by summing the variance explained by <italic>X</italic><sub><italic>ijk</italic></sub> (<italic>k</italic> = 1, …, 5).</p>", "<p id=\"Par68\">We considered fitting multivariate models by using all the SNPs as covariates. However, the dataset contains only 1,982 cases. Even when only one ancestry is included, the multivariate model has 191 predictors, which could result in overfitting problems. Therefore, the GWAS models were preferred to multivariate models.</p>", "<p id=\"Par69\">We also fitted a logistic regression model for GWAS using the genotype data as follows:where <italic>X</italic><sub><italic>ij</italic></sub> ∈ {0,1,2} denotes the number of copies of the reference allele of the <italic>j</italic>th SNP (<italic>j</italic> = 1, ..., 205) that the <italic>i</italic>th individual has and <italic>C</italic><sub><italic>ic</italic></sub> (<italic>c</italic> = 1, ..., <italic>N</italic><sub><italic>c</italic></sub>) denotes the covariates, including age, sex and the first 18 principal components, for the <italic>i</italic>th individual, where <italic>N</italic><sub><italic>c</italic></sub> = 20. Because the UK Biobank is underpowered compared to the case–control study in which these SNPs were found, the only statistically significant (<italic>P</italic> < 10<sup>–5</sup>) association was for the HLA class II SNP tagging HLA-DRB115:01.</p>", "<title>GWAS comparison for trait-associated SNPs</title>", "<p id=\"Par70\">In this section, we describe how we moved from associations between SNPs (either genotype values or ancestry) and a trait to total variance explained.</p>", "<p id=\"Par71\">We compared the variance explained by SNPs from the GWAS model using the painting data (all six local ancestry probabilities; the seventh was a linear combination of the first six) with that from the GWAS model using the genotype data. McFadden’s pseudo-<italic>R</italic><sup>2</sup> measure<sup>##UREF##12##82##</sup> is widely used for estimating the variance explained by logistic regression models. McFadden’s pseudo-<italic>R</italic><sup>2</sup> is defined aswhere <italic>L</italic><sub><italic>M</italic></sub> and <italic>L</italic><sub>0</sub> are the likelihoods for the fitted and null model, respectively. Taking overfitting into account, we use the adjusted McFadden’s pseudo-<italic>R</italic><sup>2</sup> value by penalizing the number of predictors:where <italic>N</italic> is the sample size and <italic>k</italic> is the number of predictors.</p>", "<p id=\"Par72\">Specifically, <italic>R</italic><sup>2</sup>(SNPs) is calculated as the extra variance in addition to sex, age and the 18 principal components that can be explained by SNPs:</p>", "<p id=\"Par73\">Notably, two SNPs stood out for explaining much more variance than the others when fitting the GWAS model using the genotype data, but overall more SNPs from GWAS painting explained more than 0.1% of the variance, which indicates that the painting data are probably more efficient for estimating the effect sizes of SNPs and detecting significant SNPs. Additionally, some SNPs from GWAS models using painting data explained almost the same amount of variance, suggesting that these SNPs consist of very similar ancestries.</p>", "<title>HTRX</title>", "<p id=\"Par74\">Ancestry is a strong predictor of MS, but we wanted to understand whether it was tagging some causal factor that was not in our genetic data or whether it was tagging either interactions or rare SNPs. To address this, we propose HTRX, which searches for haplotype patterns that include single SNPs and non-contiguous haplotypes. HTRX is an association between a template of <italic>n</italic> SNPs and a phenotype. The template gives a value for each SNP, with values of 0 or 1 reflecting that the reference allele of the SNP is present or absent, respectively, while an ‘X’ means that either value is allowed. For example, haplotype 1X0 corresponds to a three-SNP haplotype in which the first SNP is the alternative allele and the third SNP is the reference allele, while the second SNP can be either the reference or alternative allele. Therefore, haplotype 1X0 is essentially only a two-SNP haplotype.</p>", "<p id=\"Par75\">To examine the association between a haplotype and a binary phenotype, we replace the genotype term with a haplotype in the standard GWAS model:where <italic>H</italic><sub><italic>ij</italic></sub> denotes the <italic>j</italic>th haplotype probability for the <italic>i</italic>th individual:</p>", "<p id=\"Par76\">HTRX can identify gene–gene interactions and is superior to HTR not only because it can extract combinations of significant SNPs within a region, leading to improved predictive performance, but also because the haplotypes are more interpretable as multi-SNP haplotypes are only reported when they lead to increased predictive performance.</p>", "<title>HTRX model selection procedure for shorter haplotypes</title>", "<p id=\"Par77\">Fitting HTRX models directly on the whole dataset can lead to significant overfitting, especially as the number of SNPs increases. When overfitting occurs, the models experience poorer predictive accuracy against unseen data. Further, HTRX introduces an enormous model space, which must be searched.</p>", "<p id=\"Par78\">To address these problems, we implemented a two-step procedure:</p>", "<p id=\"Par79\">Step 1: Select candidate models. This step aims to address the model search problem by obtaining a set of models more diverse than those obtained with traditional bootstrap resampling<sup>##UREF##13##83##</sup>.<list list-type=\"order\"><list-item><p id=\"Par80\">Randomly sample a subset (50%) of data. Specifically, when the outcome is binary, stratified sampling is used to ensure the subset has approximately the same proportion of cases and controls as the whole dataset.</p></list-item><list-item><p id=\"Par81\">Start from a model with fixed covariates (18 principal components, sex and age) and perform forward regression on the subset, that is, iteratively choose a feature (in addition to the fixed covariates) to add whose inclusion enables the model to explain the largest variance, and select <italic>s</italic> models with the lowest Bayesian information criterion (BIC)<sup>##UREF##14##84##</sup> to enter the candidate model pool.</p></list-item><list-item><p id=\"Par82\">Repeat (1) and (2) <italic>B</italic> times and select all the different models in the candidate model pool as the candidate models.</p></list-item></list></p>", "<p id=\"Par83\">Step 2: Select the best model using tenfold cross-validation.<list list-type=\"order\"><list-item><p id=\"Par84\">Randomly split the whole dataset into ten groups with approximately equal size, using stratified sampling when the outcome is binary.</p></list-item><list-item><p id=\"Par85\">In each of the ten folds, use a different group as the test dataset and take the remaining groups as the training dataset. Then, fit all the candidate models on the training dataset and use these fitted models to compute the additional variance explained by features (out-of-sample <italic>R</italic><sup>2</sup>) in the test dataset. Finally, select the candidate model with the highest average out-of-sample <italic>R</italic><sup>2</sup> as the best model.</p></list-item></list></p>", "<title>HTRX model selection procedure for longer haplotypes (cumulative HTRX)</title>", "<p id=\"Par86\">Longer haplotypes are important for discovering interactions. However, there are 3<sup><italic>k</italic></sup> – 1 haplotypes in HTRX if the region contains <italic>k</italic> SNPs, making this unrealistic for regions with large numbers of SNPs. To address this issue, we propose cumulative HTRX to control the number of haplotypes, which is also a two-step procedure.</p>", "<p id=\"Par87\">Step 1: Extend haplotypes and select candidate models.<list list-type=\"order\"><list-item><p id=\"Par88\">Randomly sample a subset (50%) of data, using stratified sampling when the outcome is binary. This subset is used for all the analysis in (2) and (3).</p></list-item><list-item><p id=\"Par89\">Start with <italic>L</italic> randomly chosen SNPs from the entire <italic>k</italic> SNPs and keep the top <italic>M</italic> haplotypes that are chosen from forward regression. Then, add another SNP to the <italic>M</italic> haplotypes to create 3<italic>M</italic> + 2 haplotypes. There are 3<italic>M</italic> haplotypes obtained by adding 0, 1 or X to the previous <italic>M</italic> haplotypes, as well as two bases of the added SNP, that is, ‘XX…X0’ and ‘XX…X1’ (as X was implicitly used in the previous step). The top <italic>M</italic> haplotypes are then selected using forward regression. Repeat this process until <italic>M</italic> haplotypes are obtained that include <italic>k</italic> – 1 SNPs.</p></list-item><list-item><p id=\"Par90\">Add the last SNP to create 3<italic>M</italic> + 2 haplotypes. Afterwards, start from a model with fixed covariates (18 principal components, sex and age), perform forward regression on the training set and select <italic>s</italic> models with the lowest BIC to enter the candidate model pool.</p></list-item><list-item><p id=\"Par91\">Repeat (1)–(3) <italic>B</italic> times and select all the different models in the candidate model pool as the candidate models.</p></list-item></list></p>", "<p id=\"Par92\">Step 2: Select the best model using tenfold cross-validation, as described in ‘HTRX model selection procedure for shorter haplotypes’.</p>", "<p id=\"Par93\">We note that, because the search procedure in step 1(2) may miss some highly predictive haplotypes, cumulative HTRX acts as a lower bound on the variance explainable by HTRX.</p>", "<p id=\"Par94\">As a model criticism, only common and highly predictive haplotypes (that is, those with the greatest adjusted <italic>R</italic><sup>2</sup>) are correctly identified, but the increased complexity of the search space of HTRX leads to haplotype subsets that are not significant on their own but are significant when interacting with other haplotype subsets being missed. This issue would be eased if we increased all the parameters <italic>l</italic>, <italic>M</italic> and <italic>B</italic> but with higher computational cost or improved the search by optimizing the order of adding SNPs. This leads to decreased certainty that the exact haplotypes proposed are ‘correct’ but reinforces the inference that interaction is extremely important.</p>", "<title>Simulation study for HTRX</title>", "<p id=\"Par95\">To investigate how the total variance explained by HTRX compares to that from GWAS and HTR, we used a simulation study comparing<list list-type=\"order\"><list-item><p id=\"Par96\">linear models (denoted by ‘lm’) and generalized linear models with a logit link function (denoted by ‘glm’);</p></list-item><list-item><p id=\"Par97\">models with or without actual interaction effects;</p></list-item><list-item><p id=\"Par98\">models with or without rare SNPs (frequency of less than 5%);</p></list-item><list-item><p id=\"Par99\">removing or retaining rare haplotypes when rare SNPs exist.</p></list-item></list></p>", "<p id=\"Par100\">We started from creating the genotypes for four different SNPs <italic>G</italic><sub><italic>ijq</italic></sub> (where <italic>i</italic> = 1, ..., 100,000 denotes the index of individuals, <italic>j</italic> = 1 (1XXX), 2 (X1XX), 3 (XX1X) and 4 (XXX1) represents the index of SNPs and <italic>q</italic> = 1,2 for the two genomes as individuals are diploid). If no rare SNPs were included, we sampled the frequency <italic>F</italic><sub><italic>j</italic></sub> of these four SNPs from 5% to 95%; otherwise, we sampled the frequency of the first two SNPs from 2% to 5% (in practice, we obtained <italic>F</italic><sub>1</sub> = 2.8% and <italic>F</italic><sub>2</sub> = 3.1% under our seed) while the frequency of the last two SNPs was sampled from 5% to 95%. For the <italic>i</italic>th individual, we sampled <italic>G</italic><sub><italic>ijq</italic></sub> ~ Binomial(1,<italic>F</italic><sub><italic>j</italic></sub>) for the <italic>q</italic>th genome of the <italic>j</italic>th SNP and took the average value of the two genomes as the genotype for the <italic>j</italic>th SNP of the <italic>i</italic>th individual: . On the basis of the genotype data, we obtained the haplotype data for each individual, and we considered removing haplotypes rarer than 0.1% or not when rare SNPs were generated. In addition, we sampled 20 fixed covariates (including sex, age and 18 principal components) <italic>C</italic><sub><italic>ic</italic></sub>, where <italic>c</italic> = 1, ..., 20 from UK Biobank for 100,000 individuals.</p>", "<p id=\"Par101\">Next, we sampled the effect sizes of SNPs and covariates and normalized them by their standard deviations: and for each fixed <italic>j</italic> and <italic>c</italic>, respectively. When an interaction existed, we created a fixed effect size for haplotype 11XX as twice the average absolute SNP effects: where <italic>H</italic><sub>1</sub> refers to 11XX; otherwise, <italic>H</italic><sub>1</sub> = 0. Note that when rare SNPs were included.</p>", "<p id=\"Par102\">Finally, we sampled the outcome on the basis of the outcome score (for the <italic>i</italic>th individual):where <italic>γ</italic> is a scale factor for the effect sizes of SNPs and haplotype 11XX, <italic>e</italic><sub><italic>i</italic></sub> ~ N(0,0.1) is the random error and <italic>w</italic> is a fixed intercept term. For linear models, outcome <italic>Y</italic><sub><italic>i</italic></sub> = 0<sub><italic>i</italic></sub>; for generalized linear models, we sampled the outcome from the binomial distribution <italic>Y</italic><sub><italic>i</italic></sub> ~ Binomial(1,<italic>π</italic><sub><italic>i</italic></sub>), where is the probability that the <italic>i</italic>th individual is a case.</p>", "<p id=\"Par103\">As the simulation was intended to compare the variance explained by HTRX, HTR and SNPs (GWAS) in addition to fixed covariates, we tripled the effect sizes of SNPs and haplotype 11XX (if an interaction existed) by setting <italic>γ</italic> = 3. In ‘glm’, to ensure a reasonable case prevalence (for example, below 5%), we set <italic>w</italic> = –7, which was also applied in ‘lm’.</p>", "<p id=\"Par104\">We applied the procedure described in ‘HTRX model selection procedure for shorter haplotypes’ for HTRX, HTR and GWAS and visualized the distribution of the out-of-sample <italic>R</italic><sup>2</sup> for each of the best models selected by each method in Supplementary Fig. ##SUPPL##0##11##. In both ‘lm’ and ‘glm’, HTRX had equal predictive performance to the true model. It performed as well as GWAS when interaction effects were absent, explained more variance when an interaction was present and was significantly more explanatory than HTR. When rare SNPs are included, the only effective interaction term is rare. In this case, the difference between GWAS and HTRX became smaller, as expected, and removing the rare haplotypes minimally reduced the performance of HTRX.</p>", "<p id=\"Par105\">In conclusion, we demonstrated through simulation that our HTRX implementation (1) searches the haplotype space effectively and (2) protects against overfitting. This makes it a superior approach compared with HTR and GWAS to integrate SNP effects with gene–gene interactions. Its robustness is also retained when there are rare effective SNPs and haplotypes.</p>", "<title>Quantifying selection using historical allele frequencies from pathway painting</title>", "<p id=\"Par106\">The historical trajectory of SNP frequencies is a strong signal of selection when ancient DNA data are available. This is the main purpose of our pathway painting method and can be used to infer selection at individual loci and combined into a polygenic score by analysing sets of SNPs associated with a trait.</p>", "<p id=\"Par107\">First, we inferred allele frequency trajectories and selection coefficients for a set of LD-pruned genome-wide-significant trait-associated variants using a modified version of CLUES (Coalescent Likelihood Under Effects of Selection)<sup>##REF##31518343##19##</sup>. To account for population structure in our samples, we applied a new chromosome painting technique based on inference of a sample’s nearest neighbours in the marginal trees of an ARG that contains labelled individuals<sup>##UREF##3##11##</sup>. We ran CLUES using a time series of imputed ancient DNA genotype probabilities obtained from 1,015 ancient western Eurasian samples that passed all quality-control filters. We produced four additional models for each trait-associated variant by conditioning the analysis on one of the four ancestral path labels from our chromosome painting model: WHG, EHG, CHG or ANA.</p>", "<p id=\"Par108\">Second, we were able to infer polygenic selection gradients (<italic>ω</italic>) and <italic>P</italic> values for each trait, that is, for MS and RA, in all ancestral paths, using PALM (Polygenic Adaptation Likelihood Method)<sup>##REF##33440170##20##</sup>. Full methods and results can be found in Supplementary Note ##SUPPL##0##6##.</p>", "<title>LDA and LDA score</title>", "<p id=\"Par109\">In population genetics, LD is defined as the non-random association of alleles at different loci in a given population<sup>##REF##18427557##85##</sup>. Just like the values of the genotype, ancestries can be correlated along the genome, and, further, deviation from the expected length distribution for a particular ancestry is a signal of selection, dated by the affected ancestry. We propose an ancestry linkage disequilibrium (LDA) approach to measure the association of ancestries between SNPs and an LDA score to quantify deviations from the null hypothesis that ancestry is inherited at random across loci.</p>", "<p id=\"Par110\">LDA is defined in terms of local ancestry. Let <italic>A</italic>(<italic>i</italic>,<italic>j</italic>,<italic>k</italic>) denote the probability of the <italic>k</italic>th ancestry (<italic>k</italic> = 1, ..., <italic>K</italic>) at the <italic>j</italic>th SNP (<italic>j</italic> = 1, ..., <italic>J</italic>) of a chromosome for the <italic>i</italic>th individual (<italic>i</italic> = 1, ..., <italic>N</italic>).</p>", "<p id=\"Par111\">We define the distance between SNPs <italic>l</italic> and <italic>m</italic> as the average <italic>L</italic><sub>2</sub> norm between ancestries at those SNPs. Specifically, we compute the <italic>L</italic><sub>2</sub> norm for the <italic>i</italic>th genome as</p>", "<p id=\"Par112\">We then compute the distance between SNPs <italic>l</italic> and <italic>m</italic> by averaging <italic>D</italic><sub><italic>i</italic></sub>(<italic>l</italic>, <italic>m</italic>):</p>", "<p id=\"Par113\">We define <italic>D</italic>(<italic>l</italic>, <italic>m</italic>) as the theoretical distance between SNPs <italic>l</italic> and <italic>m</italic> if there is no LDA between them. <italic>D</italic>(<italic>l</italic>, <italic>m</italic>) is estimated bywhere <italic>i</italic> ∈ {1, ..., <italic>N</italic>} is resampled without replacement at SNP <italic>l</italic>. Using the empirical distribution of ancestry probabilities accounts for variability in both the average ancestry and its distribution across SNPs. Ancestry assignment can be very precise in regions of the genome where the reference panel matches the data and uncertain in others where only distant relatives of the underlying populations are available.</p>", "<p id=\"Par114\">The LDA between SNPs <italic>l</italic> and <italic>m</italic> is a similarity, defined in terms of the negative distance –<italic>D</italic>(<italic>l</italic>, <italic>m</italic>) normalized by the expected value <italic>D</italic>*(<italic>l</italic>, <italic>m</italic>) under no LD, expressed as</p>", "<p id=\"Par115\">LDA therefore takes an expected value of 0 when haplotypes are randomly assigned at different SNPs and positive values when the ancestries of the haplotypes are correlated.</p>", "<p id=\"Par116\">LDA is a pairwise quantity. To arrive at a per-SNP property, we define the LDA score of SNP <italic>j</italic> as the total LDA of this SNP with the rest of the genome, that is, the integral of the LDA for that SNP. Because this quantity decreases to zero as we move away from the target SNP, this is in practice computed within a window of <italic>X</italic> cM (we use <italic>X</italic> = 5 as LDA is approximately zero outside this region in our data) on both sides of the SNP. Note that we measure this quantity in terms of the genetic distance, and therefore the LDA score is measuring the length of ancestry-specific haplotypes compared to individual-level recombination rates.</p>", "<p id=\"Par117\">As a technical note, when SNPs are present near either end of the chromosome, they no longer have a complete window, which results in a smaller LDA score. This would be appropriate for measuring total ancestry correlations, but to make LDA score useful for detecting anomalous SNPs we use the LDA score of the symmetric side of the SNP to estimate the LDA score within the non-existent window.where gd(<italic>l</italic>) is the genetic distance (that is, position in cM) of SNP <italic>l</italic> and tg is the total genetic distance of a chromosome. We also assume the LDA on either end of the chromosome equals the LDA of the SNP closest to the end: LDA(<italic>j</italic>,gd = 0) = LDA(<italic>j</italic>,<italic>l</italic><sub>min(gd)</sub>) and LDA(<italic>j</italic>,gd = td) = LDA(<italic>j</italic>,<italic>l</italic><sub>max(gd)</sub>), where gd is the genetic distance and <italic>l</italic><sub>min(gd)</sub> and <italic>l</italic><sub>max(gd)</sub> are the indexes of the SNP with the smallest and largest genetic distance, respectively.</p>", "<p id=\"Par118\">The integral is computed assuming linear interpolation of the LDA score between adjacent SNPs.</p>", "<p id=\"Par119\">LDA thus quantifies the correlations between the ancestry of two SNPs, measuring the proportion of individuals who have experienced a recombination leading to a change in ancestry, relative to the genome-wide baseline. LDA score is the total amount of genome in LDA with each SNP (measured in recombination map distance).</p>", "<title>Simulation study for LDA and LDA score</title>", "<p id=\"Par120\">For the simulation in Supplementary Fig. ##SUPPL##0##46##, an ancient population <italic>P</italic><sub>0</sub> evolved for 2,200 generations before splitting into two subpopulations, <italic>P</italic><sub>1</sub> (steppe) and <italic>P</italic><sub>2</sub> (farmer). After evolution for 400 generations, we added mutations <italic>m</italic><sub>1</sub> and <italic>m</italic><sub>2</sub> at different loci in <italic>P</italic><sub>1</sub> and <italic>P</italic><sub>2</sub>. Both added mutations were then positively selected in the following 300 generations, after which we sampled 20 individuals from each of <italic>P</italic><sub>1</sub> and <italic>P</italic><sub>2</sub> as reference samples. At generation 2,900, <italic>P</italic><sub>1</sub> and <italic>P</italic><sub>2</sub> admixed to <italic>P</italic><sub>3</sub>, in which both added mutations experienced strong positive selection for 20 generations. Finally, we sampled 1,000 individuals from <italic>P</italic><sub>3</sub> to compute their ancestry proportions of <italic>P</italic><sub>1</sub> and <italic>P</italic><sub>2</sub> using the chromosome painting technique and calculated the LDA score of the simulated chromosome positions.</p>", "<p id=\"Par121\">We investigated balancing selection at two loci as well. The balancing selection in <italic>P</italic><sub>1</sub> and <italic>P</italic><sub>2</sub> ensured that the mutant allele reached around 50% frequency, while positive selection made the mutant allele become almost the only allele. In <italic>P</italic><sub>3</sub>, if <italic>m</italic><sub>1</sub> or <italic>m</italic><sub>2</sub> was positively selected, its frequency reached greater than 80% regardless of whether the allele experienced balancing or positive selection in <italic>P</italic><sub>1</sub> or <italic>P</italic><sub>2</sub>, because we set strong positive selection. If <italic>m</italic><sub>1</sub> or <italic>m</italic><sub>2</sub> underwent balancing selection in <italic>P</italic><sub>3</sub>, its frequency slightly increased; for example, if <italic>m</italic><sub>1</sub> underwent balancing selection in <italic>P</italic><sub>1</sub>, it had a frequency of 25% when <italic>P</italic><sub>3</sub> was created, and the frequency reached around 37.5% after 20 generations of balancing selection in <italic>P</italic><sub>3</sub>.</p>", "<p id=\"Par122\">As shown in Supplementary Fig. ##SUPPL##0##47##, positive selection in <italic>P</italic><sub>3</sub> resulted in low LDA scores around the selected locus if this allele was not uncommon (that is, if it had a frequency of 50% (balancing selection) or 100% (positive selection) in subpopulation <italic>P</italic><sub>1</sub> or <italic>P</italic><sub>2</sub>). Note that the balancing selection in <italic>P</italic><sub>1</sub> or <italic>P</italic><sub>2</sub> worked the same as ‘weak positive selection’, because <italic>m</italic><sub>1</sub> and <italic>m</italic><sub>2</sub> were rare when they first occurred and were positively selected until they reached a frequency of 50%.</p>", "<p id=\"Par123\">We also performed simulations for selection at a single locus (Supplementary Figs. ##SUPPL##0##47## and ##SUPPL##0##48##).</p>", "<p id=\"Par124\">Stage 1: An ancient population <italic>P</italic><sub>0</sub> evolved for 1,600 generations, and then we added a mutation <italic>m</italic><sub>0</sub>, which underwent balancing selection until generation 2,200, when <italic>P</italic><sub>0</sub> split into <italic>P</italic><sub>1</sub> and <italic>P</italic><sub>2</sub>, where the frequency of <italic>m</italic><sub>0</sub> was around 50%.</p>", "<p id=\"Par125\">Stage 2: We then explored different combinations of positive, balancing and negative selection of <italic>m</italic><sub>0</sub> in <italic>P</italic><sub>1</sub> and <italic>P</italic><sub>2</sub>. The frequency of <italic>m</italic><sub>0</sub> reached 80%, 50% and 20% when it was positively selected, underwent balancing selection or was negatively selected, respectively, until generation 2,899, when we sampled 20 individuals each in <italic>P</italic><sub>1</sub> and <italic>P</italic><sub>2</sub> as the reference samples.</p>", "<p id=\"Par126\">Stage 3: <italic>P</italic><sub>1</sub> and <italic>P</italic><sub>2</sub> then merged into <italic>P</italic><sub>3</sub> in generation 2,900. In <italic>P</italic><sub>3</sub>, for each combination of selection in stage 2, we simulated positive, balancing and negative selection for <italic>m</italic><sub>0</sub>. The selection lasted for 20 generations, and we then sampled 4,000 individuals from <italic>P</italic><sub>3</sub> as the modern population.</p>", "<p id=\"Par127\">When <italic>m</italic><sub>0</sub> was positively selected in at least one of <italic>P</italic><sub>1</sub> and <italic>P</italic><sub>2</sub> and it experienced negative selection in <italic>P</italic><sub>3</sub>, the LDA scores around the loci of <italic>m</italic><sub>0</sub> were low. Otherwise, no abnormal LDA scores were found surrounding <italic>m</italic><sub>0</sub>.</p>", "<title>Reporting summary</title>", "<p id=\"Par128\">Further information on research design is available in the ##SUPPL##1##Nature Portfolio Reporting Summary## linked to this article.</p>" ]	[]	[ "<title>Discussion</title>", "<p id=\"Par25\">The last 10,000 years have seen some of the most extreme global changes in lifestyle, with the emergence of farming in some regions and pastoralism in others. While 5,000 years ago farmer ancestry predominated across Europe, a relatively diverged genetic ancestry arrived with the steppe migrations around this time<sup>##REF##25731166##26##,##REF##26062507##27##</sup>. We have shown that this genetic ancestry contributes the most genetic risk for MS today and that these variants were the result of positive selection coinciding with the emergence of a pastoralist lifestyle on the Pontic-Caspian steppe and continued selection in the subsequent admixed populations in Europe. These results address the long-standing debate around the north–south gradient in MS prevalence in Europe and indicate that the steppe ancestry gradient in modern populations—specifically in the HLA region—across the continent may cause this phenomenon, in combination with environmental factors. Furthermore, although epistasis between MS-associated variants in the HLA region has been demonstrated before<sup>##REF##17006452##28##–##REF##35501860##31##</sup>, we have shown that accounting for this explains more variance than independent SNP effects alone. Many of the haplotypes carrying these risk alleles have ancestry-specific origins, which could be exploited for individual risk prediction and may offer a pathway from genetic ancestry associations to a mechanistic understanding of MS risk. We have compared these findings with results for RA, another HLA class II-associated chronic inflammatory disease, and found that the genetic risk for RA exhibits a contrasting pattern; for RA, genetic risk was highest in Mesolithic hunter–gatherer ancestry and has decreased over time.</p>", "<p id=\"Par26\">Our interpretation of this history is that co-evolution between a range of pathogens and their human hosts may have resulted in massive and divergent genetic ancestry-specific selection on immune response genes according to lifestyle and environment followed by recombinant-favouring selection after these populations merged. Similar examples of pathogen-driven evolution have recently been published<sup>##REF##33667394##32##,##REF##36819665##33##</sup>. The late Neolithic and Bronze Age were a time of massively increased prevalence of infectious diseases in human populations, owing to increased population density as well as contact with, and consumption of, domesticated animals and their products. The most recent common ancestor of many disease-associated pathogens existed in this period<sup>##REF##25141181##34##–##UREF##7##42##</sup>; although these diseases are common today, it is difficult to infer their geographical ranges in the past, which may have been more limited<sup>##REF##27446241##43##</sup>. We have shown that many of the MS- and RA-associated variants under selection confer some resistance to a range of infectious diseases and pathogens (Supplementary Note ##SUPPL##0##8##; for example, HLA-DRB1*15:01 is associated with protection against tuberculosis<sup>##REF##28928442##44##</sup> and increased risk for lepromatous leprosy<sup>##REF##29717136##45##</sup>). We were, however, underpowered to detect specific associations beyond this hypothesis owing to poor knowledge of the distribution and diversity of past diseases, poor preservation of endogenous pathogens in the archaeological record and a lack of well-powered GWAS for many infectious diseases, partly owing to widespread vaccination programmes. Together, these findings indicate that population dispersals, changing lifestyles and increased population density may have resulted in high and sustained transmission of both new and old pathogens, driving selection of variants in immune response genes, which are now associated with autoimmune diseases.</p>", "<p id=\"Par27\">A pattern that repeatedly appears is that of lifestyle change driving changes in risk and phenotypic outcomes. Our data indicate that, in the past, environmental changes driven by lifestyle innovation may have inadvertently driven an increase in genetic risk for MS. Today, with increasing prevalence of MS cases observed over the last five decades<sup>##REF##30770430##46##,##REF##30879893##47##</sup>, we again observe a striking correlation with changes in our environment, including lifestyle choices and improved hygiene, which no longer favours the previous genetic architecture. Instead, the fine balance of genetically driven cell functions within the immune system, which are needed to combat a broad repertoire of pathogens and parasites without harming self-tissue, has been met with new challenges, including a potential absence of requirement. For example, while a population of immune cells, CD4<sup>+</sup> T helper type 1 (T<sub>H</sub>1) cells, direct strong cellular immune responses against intracellular pathogens, T helper type 2 (T<sub>H</sub>2) cells mediate humoral immune responses against extracellular bacteria and parasites and aid tissue homeostasis and repair. We have shown that the majority of selected MS-associated SNPs are associated with protection against a wide range of infectious challenges, in line with selection for strong but balanced T<sub>H</sub>1/T<sub>H</sub>2 immunity in the Bronze Age. The skewed T<sub>H</sub>1/T<sub>H</sub>2 balance observed in MS may partly result from the developed world’s increased sanitation, which has led to a substantially reduced burden of parasites, which the immune system had evolved to efficiently combat<sup>##REF##17315205##48##</sup>.</p>", "<p id=\"Par28\">Similarly, the new pathogenic challenges associated with agriculture, animal domestication, pastoralism and higher population densities might have substantially increased the risk of triggering a systemic RA-associated inflammatory state in genetically predisposed individuals. This could have led to an increased risk of a serious outcome following subsequent infections<sup>##REF##23192911##49##</sup>, years before any potential joint lesions<sup>##REF##14872479##50##</sup>, resulting in negative selection and might thus represent a parallel between RA-associated inflammation in the Bronze Age and MS today, in which lifestyle changes have exposed previously favourable genetic variants as risks for autoimmune disease.</p>", "<p id=\"Par29\">More broadly, it is clear that the late Neolithic and Bronze Age were a critical period in human history during which highly genetically and culturally divergent populations evolved and mixed<sup>##UREF##1##2##</sup>. These separate histories probably dictate the genetic risk and prevalence of several autoimmune diseases today. Unexpectedly, the emergence of the pastoralist steppe lifestyle may have had an impact on immune responses as great as or greater than that of the emergence of farming during the Neolithic transition, which is commonly held to be the greatest lifestyle change in human history.</p>" ]	[]	[ "<p id=\"Par1\">Multiple sclerosis (MS) is a neuro-inflammatory and neurodegenerative disease that is most prevalent in Northern Europe. Although it is known that inherited risk for MS is located within or in close proximity to immune-related genes, it is unknown when, where and how this genetic risk originated<sup>##UREF##0##1##</sup>. Here, by using a large ancient genome dataset from the Mesolithic period to the Bronze Age<sup>##UREF##1##2##</sup>, along with new Medieval and post-Medieval genomes, we show that the genetic risk for MS rose among pastoralists from the Pontic steppe and was brought into Europe by the Yamnaya-related migration approximately 5,000 years ago. We further show that these MS-associated immunogenetic variants underwent positive selection both within the steppe population and later in Europe, probably driven by pathogenic challenges coinciding with changes in diet, lifestyle and population density. This study highlights the critical importance of the Neolithic period and Bronze Age as determinants of modern immune responses and their subsequent effect on the risk of developing MS in a changing environment.</p>", "<p id=\"Par2\">Analysis of a large ancient genome dataset shows that genetic risk for multiple sclerosis rose in steppe pastoralists, providing insight into how genetic ancestry from the Neolithic and Bronze Age has shaped modern immune responses.</p>", "<title>Subject terms</title>" ]	[ "<title>Main</title>", "<p id=\"Par3\">MS is an autoimmune disease of the brain and spinal cord that currently affects more than 2.5 million people worldwide<sup>##UREF##0##1##</sup>. Its prevalence varies markedly with ethnicity and geographical location, with the highest prevalence observed in Europe (142.81 cases per 100,000 people); Northern Europeans are particularly susceptible to developing the disease<sup>##UREF##2##3##</sup>. The origins of and reasons for this geographical variation are poorly understood, yet such biases may hold important clues as to why the prevalence of autoimmune diseases, including MS, has continued to rise during the past 50 years.</p>", "<p id=\"Par4\">Although still elusive, MS aetiology is thought to involve gene–gene and gene–environment interactions. Accumulating evidence suggests that exogenous triggers initiate a cascade of events involving a multitude of cells and immune pathways in genetically vulnerable individuals, which may ultimately lead to MS neuropathology<sup>##UREF##0##1##</sup>.</p>", "<p id=\"Par5\">Genome-wide association studies (GWAS) have identified 233 commonly occurring genetic variants that are associated with MS; 32 variants are located in the human leukocyte antigen (HLA) region and 201 are located outside the HLA region<sup>##REF##31604244##4##</sup>. The strongest MS associations are found in the HLA region, with the most prominent of these, HLA-DRB115:01, conferring an approximately threefold increase in the risk of MS in individuals carrying at least one copy of this allele. Collectively, genetic factors are estimated to explain approximately 30% of the overall disease risk, while environmental and lifestyle factors are considered the major contributors to MS. For instance, although infection with Epstein–Barr virus (EBV) frequently occurs in childhood and usually is symptomless, delayed infection into early adulthood, as typically observed in countries with high standards of hygiene, is associated with a 32-fold-increased risk of MS<sup>##REF##35025605##5##,##REF##35073561##6##</sup>. Lifestyle factors associated with increased MS risk, such as smoking, obesity during adolescence and nutrition or gut health, also vary geographically<sup>##REF##27934854##7##</sup>. Autoimmunity could also result from altered pressure from other pathogens, creating a shift in the delicate balance of pro- and anti-inflammatory pathways<sup>##REF##33408383##8##</sup>.</p>", "<p id=\"Par6\">European genetic ancestry (henceforth ‘ancestry’) has been postulated to explain part of the global difference in MS prevalence in admixed populations<sup>##REF##30653506##9##</sup>. Specifically, African American individuals with MS exhibit increased European ancestry in the HLA region compared with control individuals, with European haplotypes conferring more MS risk for most HLA alleles, including HLA-DRB115:01. Conversely, Asian American individuals with MS have decreased European ancestry in the HLA region compared with control individuals. Although ancient European ancestry and MS risk in Europe are known to be geographically structured (Fig. ##FIG##0##1a,b##), the effect of ancestry variation within Europe on MS prevalence is unknown.</p>", "<p id=\"Par7\">Present-day ancestral variation can be modelled as a mixture of genetic ancestries derived from ancient populations, who can be distinguished by their subsistence lifestyle: western hunter-gatherers (WHG), eastern hunter-gatherers (EHG), Caucasus hunter-gatherers (CHG), farmers (Anatolian (ANA) + Neolithic) and steppe pastoralists (Fig. ##FIG##0##1c,d##). By using a large ancient genome dataset from the Mesolithic to the Bronze Age, presented in an accompanying study<sup>##UREF##1##2##</sup>, coupled with new Medieval and post-Medieval genomes, we quantified present-day European genetic ancestry with respect to these ancestral populations to identify signals of lifestyle-specific evolution. We then determined whether variants associated with an increased risk of MS have undergone positive selection. We asked when selection occurred and whether the targets of selection were specific to lifestyle. Finally, we examined the environmental conditions that may have caused selection for risk variants, including human subsistence practices and exposure to pathogens. An overview of the evidence provided by all methods used can be found in Extended Data Fig. ##FIG##5##1##.</p>", "<p id=\"Par8\">To examine the ancestry patterns within modern genomes, we estimated ancestry at specific loci (‘local ancestry’) for ~410,000 self-identified ‘white British’ individuals in the UK Biobank<sup>##REF##30305743##10##</sup>, using a reference panel of 318 ancient DNA samples (Fig. ##FIG##0##1## and Extended Data Fig. ##FIG##6##2##; ref. <sup>##UREF##3##11##</sup>) from the Mesolithic and Neolithic, including steppe pastoralists (<xref rid=\"Sec3\" ref-type=\"sec\">Methods</xref>). Comparing the ancestry at each labelled single-nucleotide polymorphism (SNP; <italic>n</italic> = 549,323) to genome-wide ancestry in the UK Biobank provided an ‘anomaly score’. Two regions stood out as having the most extreme ancestry compositions (Fig. ##FIG##1##2a##): the <italic>LCT</italic>/<italic>MCM6</italic> region on chromosome 2, which is well established as regulating lactase persistence<sup>##UREF##3##11##,##REF##19714206##12##</sup>, and the HLA region on chromosome 6.</p>", "<p id=\"Par9\">The HLA region is strongly associated with autoimmune diseases<sup>##REF##32243797##13##</sup>, of which we examined MS and rheumatoid arthritis (RA), a common systemic inflammatory disease that characteristically affects the joints. Our dataset (comprising a large ancient genome dataset from the Mesolithic to the Bronze Age<sup>##UREF##1##2##</sup> and 86 new Medieval and post-Medieval genomes from Denmark; Extended Data Fig. ##FIG##6##2##, Supplementary Note ##SUPPL##0##1## and Supplementary Table ##SUPPL##3##1##) includes a total of 1,750 imputed diploid shotgun-sequenced ancient genomes (Supplementary Table ##SUPPL##3##13##), of which 1,509 are from Eurasia; together with modern data<sup>##REF##30305743##10##</sup>, we achieved an almost complete transect from approximately 10,000 years ago to the present.</p>", "<p id=\"Par10\">The frequencies of the alleles conferring the highest risk for MS (odds ratio (OR) > 1.5), all of which are within the HLA class II region, showed striking patterns in our ancient groups. In particular, the tag SNP (rs3135388[T]) for HLA-DRB115:01, which carries the highest risk for MS (OR = 2.9), was first observed in an Italian Neolithic individual (sample R3 from Grotta Continenza, dated with carbon-14 to between 5836 and 5723 <sc>bce</sc> (before common era), 4.05× coverage) and rapidly increased in frequency around the time of the emergence of the Yamnaya culture around 5,300 years ago in steppe and steppe-derived populations (Fig. ##FIG##1##2##). From risk allele frequencies of individuals in the UK Biobank born in, and having a ‘typical ancestral background’ for, a specific country<sup>##UREF##3##11##</sup>, we found that the frequency of HLA-DRB115:01 was highest in modern populations from Finland, Sweden and Iceland and in ancient populations with a high proportion of steppe ancestry (Fig. ##FIG##1##2b##, inset).</p>", "<p id=\"Par11\">To investigate the risk for a particular genetic ancestry, we used the local ancestry dataset to calculate the risk ratio (<xref rid=\"Sec3\" ref-type=\"sec\">Methods</xref>; weighted average prevalence, WAP) for each ancestry at all MS-associated fine-mapped loci present in the UK Biobank imputed dataset (<italic>n</italic> = 205/233; ref. <sup>##REF##31604244##4##</sup> and <xref rid=\"Sec3\" ref-type=\"sec\">Methods</xref>). For MS, steppe ancestry had the highest risk ratio at nearly all HLA SNPs, whereas farmer and outgroup ancestries were often the most protective (Fig. ##FIG##2##3a##), indicating that a steppe-derived haplotype at these positions confers MS risk.</p>", "<p id=\"Par12\">Having shown that some ancestries carry higher risk at particular SNPs, we wanted to calculate an aggregate risk score for each ancestry. We used a statistic, the ancestral risk score (ARS; introduced in ref. <sup>##UREF##3##11##</sup>), which is equivalent to a polygenic risk score (PRS) for a modern individual consisting entirely of one ancestry. ARS offers an improvement over calculating a PRS using ancient genotype calls directly, as it mitigates the effects of low ancient DNA sample numbers and bias<sup>##REF##32313686##14##</sup> while being robust to intervening drift and selection. We used effect size estimates from previous association studies, under an additive model, with confidence intervals obtained via an accelerated bootstrap<sup>##UREF##4##15##</sup> (Supplementary Note ##SUPPL##0##4##). In the ARS for MS (Fig. ##FIG##2##3b##), steppe ancestry had the largest risk, followed by WHG, CHG and EHG ancestry; the farmer and outgroup ancestries had the lowest ARS. Therefore, steppe ancestry contributes the most risk for MS across all associated SNPs. We tested for a genome-wide association by resampling loci and found that steppe risk still clearly exceeded that for farmers (Fig. ##FIG##2##3c##). Although most of the signal was driven by SNPs in the HLA region, this pattern persisted even when we excluded these SNPs (Fig. ##FIG##2##3b##).</p>", "<p id=\"Par13\">The fact that steppe ancestry confers risk at all but two MS-associated HLA SNPs (Fig. ##FIG##2##3a##) implies that these alleles have a common evolutionary history. We therefore investigated whether ancestry could be used for phenotype prediction. We conducted three types of association analysis in the UK Biobank for disease-associated SNPs, controlling for age, sex and the first 18 principal components. The first was a regular SNP-based association analysis, as in a genome-wide association study. The second tested for association with local ancestry probabilities instead of genotype values (Supplementary Note ##SUPPL##0##3##). The third was based on haplotype trend regression (HTR), which is used to detect interactions between SNPs<sup>##REF##12037407##16##</sup> by treating haplotypes as a set of features from which to predict a trait, instead of using SNPs as in a regular genome-wide association study. We developed a new method called HTR with extra flexibility (HTRX; Supplementary Note ##SUPPL##0##5## and more details in ref. <sup>##REF##37033465##17##</sup>) that searches for haplotype patterns that include single SNPs and non-contiguous haplotypes. To evaluate the performance of our models and prevent overfitting, we assessed its ability to predict out-of-sample data, which measures how well the model can generalize to new data. We showed by simulation (Supplementary Fig. ##SUPPL##0##11##) that HTRX explains the same amount of variance as a regular genome-wide association study when interactions are absent and more variance as interaction strength increases.</p>", "<p id=\"Par14\">Although our cohort of self-identified white British individuals is relatively underpowered with respect to MS (1,949 cases and 398,049 controls; prevalence of 0.487%), MS was associated with steppe and farmer ancestry (<italic>P</italic> < 1 × 10<sup>–10</sup>) in the HLA region (Supplementary Fig. ##SUPPL##0##6##). In three of four main linkage disequilibrium (LD) blocks within the HLA region (class I, two subregions of class II determined by LD blocks at 32.41–32.68 Mb and 33.04–33.08 Mb, and class III), local ancestry explained significantly more variation than genotypes (Fig. ##FIG##3##4##; measured by average out-of-sample McFadden’s <italic>R</italic><sup>2</sup> for logistic regression; <xref rid=\"Sec3\" ref-type=\"sec\">Methods</xref>). While the increased performance of local ancestry in some regions compared with regular GWAS can be explained by tagging of SNPs outside the region, the increased performance of HTRX over GWAS quantifies the total effect of a haplotype, including rare SNPs and epistasis. Across the entire HLA region, haplotypes explained more out-of-sample variation than regular GWAS (at least 2.90%, compared to 2.48%). Interaction signals were also observed within the HLA class I region, within the HLA class II region, and between the HLA class I and class III regions.</p>", "<p id=\"Par15\">We further tested whether co-occurring ancestries at each locus were associated with MS (see <xref rid=\"Sec3\" ref-type=\"sec\">Methods</xref> and Supplementary Fig. ##SUPPL##0##7##) but found no evidence that risk was associated with any ancestry other than steppe ancestry.</p>", "<p id=\"Par16\">Having established that steppe ancestry contributes most of the HLA-associated risk for MS, we investigated whether MS risk evolved under selection. We tested for evidence of directional selection across all associated SNPs, decomposed by ancestry, over time. This test used a ‘pathway-based chromosome painting’ technique (see <xref rid=\"Sec3\" ref-type=\"sec\">Methods</xref>) based on inference of a sample’s nearest neighbours in the marginal trees of an ancestral recombination graph (ARG) that contains labelled individuals<sup>##UREF##3##11##</sup>. The resulting ancestral path labels, for haplotypes in both ancient and modern individuals, allowed us to infer allele frequency trajectories for risk-associated variants while controlling for changes in admixture proportions over time. The paths extend backwards from the present day to approximately 15,000 years ago and are labelled with the unique population through which a path travels (ANA, CHG, EHG or WHG). Because it uses distinct pathways, the approach does not use the labels of the relatively recent steppe admixture or outgroup populations, and the path labels are not representative of a continuous population but rather represent a path backwards in time that encompasses the corresponding population. For example, the CHG path originates in the CHG population, before merging with EHG to form the steppe population, and then merges with other ancestries in later European populations (Fig. ##FIG##0##1##).</p>", "<p id=\"Par17\">In our ancestry path analysis, a substantial fraction of the fine-mapped MS-associated variants were not imputed in our ancient dataset, owing to quality-control filtering and the difficulty of accurately inferring HLA alleles in ancient samples<sup>##UREF##5##18##</sup>. To address this, we LD pruned genome-wide-significant summary statistics from the same study<sup>##REF##31604244##4##</sup>, for which we could reliably assign ancestry path labels (<italic>n</italic> = 62; see <xref rid=\"Sec3\" ref-type=\"sec\">Methods</xref>). This allowed us to test for polygenic selection across disease-associated variants using CLUES<sup>##REF##31518343##19##</sup> and PALM<sup>##REF##33440170##20##</sup>.</p>", "<p id=\"Par18\">For MS, we found evidence that disease risk was selectively increased, when considering all ancestries collectively (<italic>P</italic> = 1.02 × 10<sup>–5</sup>, polygenic selection gradient (<italic>ω</italic>) = 0.017), between 5,000 and 2,000 years ago (Fig. ##FIG##4##5##). Conditioning on each of the four long-term ancestral paths (CHG, EHG, WHG and ANA), we found a statistically significant signal of selection in the WHG (<italic>P</italic> = 7.22 × 10<sup>–5</sup>, <italic>ω</italic> = 0.021), EHG (<italic>P</italic> = 2.60 × 10<sup>–3</sup>, <italic>ω</italic> = 0.016) and CHG (<italic>P</italic> = 3.06 × 10<sup>–2</sup>, <italic>ω</italic> = 0.009) paths but not in the ANA path (<italic>P</italic> = 0.64, <italic>ω</italic> = 0.004). Again, it is likely that selection occurred in the pastoralist population of the steppe, as that population consisted of approximately equal proportions of EHG and CHG ancestry<sup>##REF##26567969##21##</sup> (Fig. ##FIG##0##1##). The SNP driving the largest change in genetic risk over time in the pan-ancestry analysis was rs3129934 (<italic>P</italic> = 1.31 × 10<sup>–11</sup>, selection coefficient (<italic>s</italic>) = 0.018), which tags the HLA-DRB115:01 haplotype<sup>##REF##18941528##22##</sup>. We also tested three other SNPs that tag the HLA-DRB115:01 haplotype (rs3129889, rs3135388 and rs3135391) for evidence of selection and found that the ancestry-stratified signal was consistently strongest in CHG (Fig. ##FIG##4##5b##).</p>", "<p id=\"Par19\">To further examine the nature of selection, we developed a new summary statistic: linkage disequilibrium of ancestry (LDA). LDA is the correlation between local ancestries at two SNPs, measuring whether recombination events between ancestries have occurred at a high frequency compared with recombination events within ancestries. We subsequently defined the ‘LDA score’ of a SNP as the total LDA of the SNP with the rest of the genome. A high LDA score indicates that the haplotype inherited from the reference population is longer than expected, whereas a low score indicates that the haplotype is shorter than expected (that is, underwent more recombination). For example, the <italic>LCT</italic>/<italic>MCM6</italic> region exhibited a high LDA score (Extended Data Fig. ##FIG##7##3##), as expected from a relatively recent selective sweep<sup>##REF##15114531##23##</sup>.The HLA region had significantly lower LDA scores than the rest of chromosome 6 (Extended Data Fig. ##FIG##7##3##). Through simulations, we showed that this signal must have been driven by selection favouring haplotypes of mixed ancestry over single-ancestry haplotypes (Supplementary Figs. ##SUPPL##0##46##–##SUPPL##0##48## and <xref rid=\"Sec3\" ref-type=\"sec\">Methods</xref>). Extending multi-SNP selection models<sup>##REF##32826299##24##</sup>, our explanation is that at least two separate loci arose selectively in separate populations that later admixed and remained selected in the HLA region, justifying a new term, ‘recombinant-favouring selection’. This means that there was selection for diverse ancestry in the HLA region, driven by recombination. Unlike other measures of balancing selection such as <italic>F</italic><sub>ST</sub>, LDA describes excess ancestry LD from specific, dated populations and therefore is an independent signal. For the HLA class II region, the selection measures all lined up (LDA score, <italic>F</italic><sub>ST</sub> and <italic>π</italic>; Extended Data Fig. ##FIG##8##4##), but for the HLA class I region the LDA score had an additional non-diverse minimum at 30.8 Mb, implying that here the genome is ancestrally diverse but genetically strongly constrained. The LDA score is thus informative about the type of selection being detected and whether it has been subject to change.</p>", "<p id=\"Par20\">Because MS would not have conferred a fitness advantage on ancient individuals, it is likely that this selection was driven by traits with shared genetic architecture, of which increased risk for MS in the present is a pleiotropic by-product. We therefore looked at LD-pruned MS-associated SNPs that showed statistically significant evidence for selection using CLUES (<italic>n</italic> = 32) in one or more ancestries and which also had a genome-wide-significant trait association (<italic>P</italic> < 5 × 10<sup>–8</sup>) for any of the 4,359 traits from the UK Biobank (ref. <sup>##REF##30305743##10##</sup>; UK Biobank Neale laboratory, round 2; <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.nealelab.is/uk-biobank/\">http://www.nealelab.is/uk-biobank/</ext-link>) and any of the 2,202 traits in the FinnGen study<sup>##REF##36653562##25##</sup>. We observed that all selected SNPs were also associated with multiple other traits (Supplementary Figs. ##SUPPL##0##19##–##SUPPL##0##27##). To determine whether the observed signal of polygenic selection favouring MS risk could be better explained by selection acting on a genetically correlated trait, we performed a systematic analysis of traits in UK Biobank and FinnGen with at least 20% overlap among the MS-associated selected SNPs (<italic>n</italic> = 115 traits). Using a joint test in PALM specifically designed for disentangling polygenic selection on correlated traits, we found no UK Biobank or FinnGen traits for which the selection signal favouring MS risk was significantly attenuated by selection acting on a genetically correlated trait, when accounting for the number of tests (Supplementary Note ##SUPPL##0##6##). This demonstrates that the selection signal for MS could not be explained by selection acting on any genetically correlated trait that we tested.</p>", "<p id=\"Par21\">Because both the UK Biobank and FinnGen are underpowered with respect to many traits and diseases, we also undertook a manual literature search (<xref rid=\"Sec3\" ref-type=\"sec\">Methods</xref>) for all LD-pruned MS-associated SNPs that showed statistically significant evidence for selection using CLUES (<italic>n</italic> = 32, of which 25 (78%) are in the HLA region). We found that most of the alleles under positive selection were associated with protective effects against specific pathogens and/or infectious diseases (disease or pathogen associated/total selected in ancestry path: pan-ancestry, 11/14; ANA, 8/9; CHG, 6/9; EHG, 6/7; WHG, 17/18; Supplementary Note ##SUPPL##0##8##, Supplementary Table ##SUPPL##3##11## and Extended Data Fig. ##FIG##9##5##), although we note that GWAS data are not available for many infectious diseases. We observed that the selected alleles had protective associations with several chronic viruses (EBV, varicella-zoster virus, herpes simplex virus and cytomegalovirus) and with viruses or diseases not associated with transmission in small hunter-gatherer groups (for example, mumps and influenza). Moreover, many selected alleles conferred a reduction of risk for parasites, for skin and subcutaneous tissue, gastrointestinal, respiratory, urinary tract and sexually transmitted infections, or for pathogens associated with these or other infections (for example, <italic>Clostridioides difficile</italic>, <italic>Streptococcus pyogene</italic>s, <italic>Mycobacterium tuberculosis</italic> and coronavirus) (Supplementary Note ##SUPPL##0##8##, Supplementary Table ##SUPPL##3##11## and Extended Data Fig. ##FIG##9##5##). We emphasize that, although this evidence is strongly suggestive, many of these putative associations may not be statistically robust owing to underpowered GWAS and the bias in candidate gene studies.</p>", "<p id=\"Par22\">We compared these findings for MS with results for RA, which in contrast to MS is a systemic inflammatory disease, although it is mostly known for its characteristic joint lesions<sup>##REF##32243797##13##</sup>. Our findings for RA show a strikingly different ancestry risk profile. HLA-DRB1*04:01 is the largest genetic risk factor for RA; in CLUES analysis, the tag SNP for this allele (rs660895) showed evidence of continuous negative selection until approximately 3,000 years ago (<italic>P</italic> = 7.95 × 10<sup>–7</sup>; Extended Data Fig. ##FIG##10##6##). We found that WHG and EHG ancestries often conferred the most risk at SNPs associated with RA (relative risk ratio of RA-associated SNPs based on WAP; see <xref rid=\"Sec3\" ref-type=\"sec\">Methods</xref>), and these ancestries contributed the greatest risk for RA in aggregate, as reflected by a higher ARS for these ancestries (Supplementary Note ##SUPPL##0##4##), while the steppe and outgroup ancestries had the lowest scores (Extended Data Fig. ##FIG##11##7##). These results were recapitulated in a local ancestry GWAS (Supplementary Note ##SUPPL##0##3##).</p>", "<p id=\"Par23\">We found that RA-associated SNPs have undergone negative polygenic selection (<italic>P</italic> = 3.26 × 10<sup>–3</sup>; Extended Data Fig. ##FIG##10##6##) over the last approximately 15,000 years. When decomposing by ancestry path, we found that all paths exhibited a negative selection gradient; none achieved nominal significance, although the CHG path came close (<italic>P</italic> = 6.33 × 10<sup>–2</sup>, <italic>ω</italic> = −0.014).</p>", "<p id=\"Par24\">These results demonstrate that genetic risk for RA was higher in the distant past, in contrast to MS, with RA-associated risk variants present at higher frequencies in European hunter-gatherer populations before the arrival of agriculture. To understand what might underlie the higher genetic risk in hunter-gatherer populations and subsequent negative selection, we again undertook a manual literature search for pleiotropic effects of LD-pruned SNPs that showed statistically significant evidence of selection (<italic>n</italic> = 55, of which 36 (65%) were in the HLA region). We found that the majority of selected SNPs were associated with protection against distinct pathogens and/or infectious diseases across all paths (disease or pathogen associated/total selected in ancestry path: pan-ancestry, 16/20; ANA, 12/16; CHG, 8/13; EHG, 14/20; WHG, 16/21). We found that selected RA risk alleles were typically linked to the same pathogens or diseases as in the MS analysis, although some SNPs were protective against pathogens or diseases not observed in the MS risk analysis (for example, <italic>Entamoeba histolytica</italic>, measles, viral hepatitis, arthropod-borne viral fevers and viral haemorrhagic fevers, and pneumococcal pneumonia; Supplementary Note ##SUPPL##0##8##, Supplementary Table ##SUPPL##3##12## and Extended Data Fig. ##FIG##9##5##).</p>", "<title>Online content</title>", "<p id=\"Par129\">Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41586-023-06618-z.</p>", "<title>Supplementary information</title>", "<p>\n\n\n\n\n</p>" ]	[ "<title>Extended data figures and tables</title>", "<p id=\"Par132\">\n\n</p>", "<p id=\"Par133\">\n\n</p>", "<p id=\"Par134\">\n\n</p>", "<p id=\"Par135\">\n\n</p>", "<p id=\"Par136\">\n\n</p>", "<p id=\"Par137\">\n\n</p>", "<p id=\"Par138\">\n\n</p>", "<title>Extended data</title>", "<p>is available for this paper at 10.1038/s41586-023-06618-z.</p>", "<title>Supplementary information</title>", "<p>The online version contains supplementary material available at 10.1038/s41586-023-06618-z.</p>", "<title>Acknowledgements</title>", "<p>We extend our thanks to all the former and current staff at the Lundbeck Foundation GeoGenetics Centre and the GeoGenetics Sequencing Core and to colleagues across the many institutions detailed below. We are particularly grateful to M. Madrona, L. Hansen and J. Bitz-Thorsen for laboratory assistance; to J. Hansen, S. Mularczyk, K. Thorø Michler and E. Neerup Nielsen for their help with sampling; and to L. Olsen as project manager for the Lundbeck Foundation GeoGenetics Centre project. The Lundbeck Foundation GeoGenetics Centre is supported by grants from the Lundbeck Foundation (R302-2018-2155, R155-2013-16338), the Novo Nordisk Foundation (NNF18SA0035006), the Wellcome Trust (214300), Carlsberg Foundation (CF18-0024), the Danish National Research Foundation (DNRF94, DNRF174), the University of Copenhagen (KU2016 programme), the Rise II project ‘Towards a New European Prehistory’ (M16-0455) and Ferring Pharmaceuticals A/S (to E.W.). We thank UK Biobank for access to the UK Biobank genomic resource. We also thank and acknowledge the participants and investigators of the FinnGen study. We are thankful to Illumina for collaboration. E.W. thanks St John’s College, Cambridge, for providing a stimulating environment of discussion and learning and the Lundbeck Foundation, the Novo Nordisk Foundation, the Wellcome Trust, the Carlsberg Foundation and the Danish National Research Foundation for financial support. R.N. acknowledges US National Institutes of Health grant R01GM138634. K.E.A., A.P.A., A.K.N.I. and L.F. thank the OAK Foundation.</p>", "<title>Author contributions</title>", "<p>W.B., Y.Y., E.K.I.-P., K.E.A., G.S. and L.T.J. contributed equally to this work. A.K.N.I., D.J.L., L.F. and E.W. led the study. W.B., A.R.-M., M.E.A., L.F., R.N. and E.W. conceptualized the study. R.N., K.K., L.F. and E.W. acquired funding for research. A.R., C.G., F.D., M.L.S.J., S.B.M., B.S., L.K., I.M.H., N.W., L.V. and T.S.K. were involved in sample collection and processing. W.B., Y.Y., E.K.I.-P., A.S., A.P., S.R. and D.J.L. were involved in developing and applying methodology. W.B., Y.Y., E.K.I.-P., G.S., A.P.A., A.R., E.A.D., M.S., S.R., A.K.N.I. and D.J.L. undertook formal analyses of data. W.B., Y.Y., E.K.I.-P., K.E.A., L.T.J., A.K.N.I., L.F. and E.W. drafted the main text (W.B. led this). W.B., Y.Y., E.K.I.-P., G.S., L.T.J., E.A.D., A.S., F.D., M.L.S.J., S.B.M., B.S., L.K., I.M.H., N.W., L.V., A.K.N.I. and D.J.L. drafted the supplementary notes and materials. W.B., Y.Y., E.K.I.-P., K.E.A., L.T.J., A.P.A., K.K., R.N., A.K.N.I., D.J.L., L.F. and E.W. were involved in reviewing drafts and editing. All co-authors read, commented on and agreed the submitted manuscript.</p>", "<title>Peer review</title>", "<title>Peer review information</title>", "<p id=\"Par130\"><italic>Nature</italic> thanks Samira Asgari, Luis Barreiro and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. ##SUPPL##2##Peer reviewer reports## are available.</p>", "<title>Data availability</title>", "<p>All collapsed and paired-end sequence data for new samples sequenced in this study are publicly available on the European Nucleotide Archive (accession code <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ebi.ac.uk/ena/data/view/PRJEB65098\">PRJEB65098</ext-link>), together with trimmed sequence alignment map files, aligned using human genome build GRCh37. Previously published ancient genomic data used in this study are detailed in Supplementary Table ##SUPPL##3##13## and are all already publicly available.</p>", "<title>Code availability</title>", "<p>The modified version of CLUES used in this study is available from <ext-link ext-link-type=\"uri\" xlink:href=\"https://github.com/standard-aaron/clues\">https://github.com/standard-aaron/clues</ext-link> (CLUES: 10.5281/zenodo.8228252; PALM: 10.5281/zenodo.8228262). The pipeline and conda environment necessary to replicate the analysis of allele frequency trajectories and polygenic selection in Supplementary Note ##SUPPL##0##6## are available on GitHub at <ext-link ext-link-type=\"uri\" xlink:href=\"https://github.com/ekirving/ms_paper\">https://github.com/ekirving/ms_paper</ext-link> (10.5281/zenodo.8228192). The code to create ancestry anomaly scores based on chromosome painting is on GitHub at <ext-link ext-link-type=\"uri\" xlink:href=\"https://github.com/danjlawson/ms_paper\">https://github.com/danjlawson/ms_paper</ext-link> (10.5281/zenodo.8232688). The code to compute LDA and LDA score is available on GitHub at <ext-link ext-link-type=\"uri\" xlink:href=\"https://github.com/YaolingYang/LDAandLDAscore\">https://github.com/YaolingYang/LDAandLDAscore</ext-link> (10.5281/zenodo.8228298). The code for HTRX is on GitHub at <ext-link ext-link-type=\"uri\" xlink:href=\"https://github.com/YaolingYang/HTRX\">https://github.com/YaolingYang/HTRX</ext-link> (10.5281/zenodo.8228295). The code for ARS calculation is on GitHub at <ext-link ext-link-type=\"uri\" xlink:href=\"https://github.com/will-camb/ms_paper\">https://github.com/will-camb/ms_paper</ext-link> (10.5281/zenodo.8228406).</p>", "<title>Competing interests</title>", "<p id=\"Par131\">The authors declare no competing interests.</p>" ]	[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>The population history of Europe is associated with the modern-day distribution of MS.</title><p><bold>a</bold>, The modern-day geographical distribution of MS in Europe. Prevalence data for MS (cases per 100,000) were obtained from ref. <sup>##UREF##2##3##</sup>. <bold>b</bold>, Steppe ancestry in modern samples as estimated by ref. <sup>##REF##25731166##26##</sup>. <bold>c</bold>,<bold>d</bold>, A model of European prehistory<sup>##REF##26567969##21##</sup> onto which our reference samples were projected using non-negative least squares (NNLS) for population painting (see <xref rid=\"Sec3\" ref-type=\"sec\">Methods</xref>) (<bold>c</bold>) and the same data represented spatially (<bold>d</bold>). Samples are shown as vertical bars representing their ‘admixture estimate’ obtained by NNLS (see <xref rid=\"Sec3\" ref-type=\"sec\">Methods</xref>) from six ancestries: EHG (green), WHG (pink), CHG (yellow), farmer (ANA + Neolithic; blue), steppe (cyan) or an outgroup (represented by ancient Africans; red). Important population expansions are shown as growing bars, and ‘recent’ (post-Bronze Age) non-reference admixed populations are shown for the Denmark time transect (see Extended Data Fig. ##FIG##6##2## for details). Chronologically, WHG and EHG were largely replaced by farmers amid demographic changes during the ‘Neolithic transition’ around 9,000 years ago. Later migrations during the Bronze Age about 5,000 years ago brought a roughly equal steppe ancestry component from the Pontic-Caspian steppe to Europe, an ancestry descended from the EHG from the middle Don River region and the CHG<sup>##UREF##1##2##</sup>. Steppe ancestry has been associated with the Yamnaya culture and then with the expansion westwards of the Corded Ware culture and Bell Beaker culture, with eastward expansion in the form of the Afanasievo culture<sup>##REF##25731166##26##,##REF##26062507##27##</sup>. ka, thousand years ago.</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>Areas of unusual local ancestry in the genome and ancient and modern frequencies of HLA-DRB115:01.</title><p><bold>a</bold>, Local ancestry anomaly score measuring the difference between the local ancestry and the genome-wide average (capped at –log<sub>10</sub>(<italic>P</italic>) = 20; <xref rid=\"Sec3\" ref-type=\"sec\">Methods</xref>). Significant peaks (reaching genome-wide significance <italic>P</italic> < 5 × 10<sup>–8</sup>, two-tailed <italic>t</italic> test before adjustment for multiple testing, as shown by the blue horizontal line) are labelled with chromosome position (build GRCh37/hg19). <bold>b</bold>, HLA-DRB115:01 frequency (<italic>y</italic> axis) in ancient populations over time (<italic>x</italic> axis; yr <sc>bp</sc>, years before the present); this is the highest effect variant for MS risk (calculated using the rs3135388 tag SNP). For each ancestry (CHG, EHG, WHG, farmer, steppe), the five populations with the highest amount of that ancestry are labelled; other populations are shown as grey points. HLA-DRB115:01 was present in one sample before the steppe expansion but rose to high frequency during the Yamnaya formation (approximate time period shaded red). The geographical distribution of HLA-DRB115:01 frequency in modern populations from the UK Biobank<sup>##UREF##3##11##</sup> is also shown (inset; grey represents no data). FBC, funnel beaker culture; LBK, linear pottery culture (Linearbandkeramik); CWC, corded ware culture.</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><title>Associations between local ancestry at fine-mapped MS-associated SNPs and MS in a modern population.</title><p><bold>a</bold>, Risk ratio of SNPs for MS based on WAP (see <xref rid=\"Sec3\" ref-type=\"sec\">Methods</xref>) when decomposed by inferred ancestry. The mean and s.d. were calculated for each ancestry on the basis of bootstrap resampling for each chromosome (<italic>n</italic> = 408,884 individuals). The distribution of risk ratios for each ancestry is shown as a raincloud plot. SNPs significant at the 1% level are shown individually, coloured by chromosome or HLA region, and those with a risk ratio of >1.2 or <0.8 are annotated with their rsID, HLA region and position (build GRCh37/hg19). <bold>b</bold>,<bold>c</bold>, ARS (see <xref rid=\"Sec3\" ref-type=\"sec\">Methods</xref>) for MS. The mean and confidence intervals were estimated by either bootstrapping over individuals (<bold>b</bold>; which can be interpreted as testing the power to reject a null hypothesis of no association between MS and ancestry; <italic>n</italic> = 1,000 bootstrap resamples with replacement over 24,000 individuals) or bootstrapping over SNPs (<bold>c</bold>; which can be interpreted as testing whether ancestry is associated with MS across the genome; <italic>n</italic> = 1,000 bootstrap resamples with replacement over 204 SNPs). We show the results for all associated SNPs (red) and non-HLA SNPs only (blue) when bootstrapping over individuals.</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><title>MS association in the HLA region.</title><p><bold>a</bold>–<bold>g</bold>, Comparison of variance explained in MS within the UK Biobank for all fine-mapped HLA SNPs with an independent contribution<sup>##REF##31604244##4##</sup>. The plots compare GWAS (treating SNPs as having independent effects), local ancestry at the SNPs and HTRX (haplotypes), after accounting for covariates (<xref rid=\"Sec3\" ref-type=\"sec\">Methods</xref>), for fine-mapped MS-associated SNPs in the HLA region (<bold>a</bold>), the HLA class I and class III regions (<bold>b</bold>), the HLA class II region (<bold>c</bold>), the HLA class I region (<bold>d</bold>), the HLA class III region (<bold>e</bold>) and subregions of the HLA class II region chosen from LD (<bold>f</bold>,<bold>g</bold>). Upward-pointing arrows for HTRX indicate where the values are lower bounds (<xref rid=\"Sec3\" ref-type=\"sec\">Methods</xref>). <bold>h</bold>, Genetic correlations in the HLA region at our time depth from ancestry-based LD (LDA; <xref rid=\"Sec3\" ref-type=\"sec\">Methods</xref>; see Supplementary Fig. ##SUPPL##0##50## for LD).</p></caption></fig>", "<fig id=\"Fig5\"><label>Fig. 5</label><caption><title>Evidence for selection on MS-associated SNPs.</title><p><bold>a</bold>, Stacked line plot of the pan-ancestry PALM analysis for MS, showing the contribution of SNPs to disease risk over time. SNPs are shown as stacked lines, with the width of each line proportional to the population frequency of the positive risk allele, weighted by its effect size. When a line widens over time, the positive risk allele has increased in frequency, and vice versa. SNPs are sorted by the magnitude and direction of selection, with positively selected SNPs at the top, negatively selected SNPs at the bottom and neutral SNPs in the middle. SNPs are coloured by their corresponding <italic>P</italic> value in a single-locus selection test. The asterisk marks the Bonferroni-corrected significance threshold, and nominally significant SNPs are shown in yellow and labelled by their rsID. SNPs marked with the dagger symbol are located in the HLA locus. The <italic>y</italic> axis shows the scaled average PRS in the population, ranging from 0 to 1, with 1 corresponding to the maximum possible average PRS (that is, when all individuals in the population are homozygous for all positive risk alleles), and the <italic>x</italic> axis shows time in units of thousands of years before the present. SE, standard error. <bold>b</bold>, Maximum-likelihood trajectories for four SNPs tagging HLA-DRB115:01, for all ancestry paths combined (All) and for each path separately (Extended Data Fig. ##FIG##5##1## and <xref rid=\"Sec3\" ref-type=\"sec\">Methods</xref>). Portions of the trajectories with high uncertainty (that is, posterior density of <0.08) have been masked. The background is shaded for the approximate time period in which the ancestry existed as an actual population. The <italic>y</italic> axis shows the derived allele frequency (DAF), and the <italic>x</italic> axis shows time in units of thousands of years before the present.</p></caption></fig>", "<fig id=\"Fig6\"><label>Extended Data Fig. 1</label><caption><title>Methods map detailing datasets used, methods, and statistics.</title><p>A narrative of the evidence used is provided in the centre, with boxes on each side detailing the methods used. Boxes are coloured by the dataset used.</p></caption></fig>", "<fig id=\"Fig7\"><label>Extended Data Fig. 2</label><caption><title>Ancient sample PCA, map, ancestry proportions through time for samples in Denmark.</title><p>(1) PC1 vs PC2 of the filtered Western Eurasian ancient samples included in this study. Black circled points are Danish Medieval and post-Medieval samples published here for the first time. Major component ancestry locations are labelled. (2) Map of ancient filtered Eurasian and African ancient samples included in this study. (3a) Map of reference data and time transect of Denmark as in Fig. ##FIG##0##1##. (3b) More recent ancient data (samples <4,200 years ago) not used as reference, showing the clines of the main ancestry components from (3a).</p></caption></fig>", "<fig id=\"Fig8\"><label>Extended Data Fig. 3</label><caption><title>LDAS on chromosome 2 and 6.</title><p>LDA score is a) high in the LCT/MCM6 region while it is b) low in the HLA region.</p></caption></fig>", "<fig id=\"Fig9\"><label>Extended Data Fig. 4</label><caption><title>Signatures of selection at the HLA locus showing different regions of the HLA (horizontal coloured bar) and locations of MS-associated SNPs (vertical lines, coloured by the variance explained by 6 ancestries).</title><p>a): Whole Chromosome 6 “local ancestry” decomposition by genetic position. b). HLA “local ancestry” decomposition by genetic position. c): LDA score; low values are indicative of selection for multiple linked loci, while high values indicate positive selection. d): pi scores (nucleotide diversity) for CEU (Northern and Western European ancestry). MS-associated SNPs fall in highly diverse regions of the HLA. e): Fst scores (divergence between two populations) for CEU vs YRI(Yoruba); locally higher scores indicate regions that have undergone differential selection between the two populations.</p></caption></fig>", "<fig id=\"Fig10\"><label>Extended Data Fig. 5</label><caption><title>The number of protective associations with pathogens or infectious diseases for the MS- and RA-associated selected SNPs.</title><p>The number of protective associations to specific pathogens and/or diseases associated with the MS- and RA-SNPs that showed statistically significant evidence for selection using CLUES. One SNP can have a link to more than one pathogen and/or disease (see ##SUPPL##3##ST11## and ##SUPPL##3##ST12## for details on each SNP). Eight and twenty SNPs had no detectable links to any pathogen or infectious disease in the MS and RA SNP sets, respectively.</p></caption></fig>", "<fig id=\"Fig11\"><label>Extended Data Fig. 6</label><caption><title>Evidence for selection on RA-associated SNPs.</title><p>a) Stacked line plot of the pan-ancestry PALM analysis for RA, showing the contribution of SNPs to disease risk over time. SNPs are shown as stacked lines, the width of each line being proportional to the population frequency of the positive risk allele, weighted by its effect size. When a line widens over time the positive risk allele has increased in frequency, and vice versa. SNPs are sorted by the magnitude and direction of selection, with positively selected SNPs at the top, negatively selected SNPs at the bottom, and neutral SNPs in the middle. SNPs are coloured by their corresponding p-value in a single locus selection test. The asterisk marks the Bonferroni corrected significance threshold, and nominally significant SNPs are shown in yellow and labelled by their rsIDs. SNPs marked with the dagger symbol are located in the HLA locus. The Y-axis shows the scaled average polygenic risk score (PRS) in the population, ranging from 0 to 1, with 1 corresponding to the maximum possible average PRS (i.e. when all individuals in the population are homozygous for all positive risk alleles) and the X-axis shows time in units of thousands of years before present (kyr BP). b) Posterior likelihood trajectory for rs660895, tagging HLA-DRB104:01, inferred by CLUES. Statistical significance was assessed by applying a Bonferroni correction for the number of tests performed for each trait.</p></caption></fig>", "<fig id=\"Fig12\"><label>Extended Data Fig. 7</label><caption><title>Associations between local ancestry at fine-mapped RA SNPs and RA in a modern population.</title><p>a) Risk ratio of SNPs for RA based on weighted average prevalence (WAP; see <xref rid=\"Sec3\" ref-type=\"sec\">Methods</xref>), when decomposed by inferred ancestry. A mean and standard deviation are calculated for each ancestry based on bootstrap resampling, for each chromosome (n = 408,884 individuals). The distribution of risk ratios at each ancestry is shown as a raincloud plot. SNPs significant at the 1% level are shown individually, coloured by chromosome or HLA region, and those with risk ratio >1.1 or <0.9 are annotated with rsID, HLA region and position (build GRCh37/hg19). b-c) Genome-wide Ancestral Risk Scores (ARS, see <xref rid=\"Sec3\" ref-type=\"sec\">Methods</xref>) for RA. Mean and confidence intervals are estimated by either bootstrapping over individuals (b, which can be interpreted as testing power to reject a null hypothesis of no association between RA and ancestry; n = 1000 bootstrap resamples with replacement over 24,000 individuals<italic>)</italic> and bootstrapping over SNPs (c, which can be interpreted as testing whether ancestry is associated with RA genome-wide; n = 1000 bootstrap resamples with replacement over 55 SNPs). We show results for all associated SNPs (red) and non-HLA SNPs only (blue) when bootstrapping over individuals.</p></caption></fig>" ]	[]	[ "<inline-formula id=\"IEq1\"><alternatives><tex-math id=\"M1\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$A\\left(\\right)=\\frac{1}{N}{\\sum }_{i=1}^{N}A\\left(i,j,k\\right)$$\\end{document}</tex-math><mml:math id=\"M2\"><mml:mrow><mml:mi>A</mml:mi><mml:mrow><mml:mfenced close=\")\" 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id=\"Equa\"><alternatives><tex-math id=\"M9\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\bar{\\pi }}_{jk}=\\frac{{P}_{jkm}{\\pi }_{jm}}{{\\sum }_{m=1}^{6}{P}_{jkm}},$$\\end{document}</tex-math><mml:math id=\"M10\" display=\"block\"><mml:mrow><mml:msub><mml:mrow><mml:mover accent=\"true\"><mml:mrow><mml:mi>π</mml:mi></mml:mrow><mml:mo>¯</mml:mo></mml:mover></mml:mrow><mml:mrow><mml:mi>j</mml:mi><mml:mi>k</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mrow><mml:mi>P</mml:mi></mml:mrow><mml:mrow><mml:mi>j</mml:mi><mml:mi>k</mml:mi><mml:mi>m</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mi>π</mml:mi></mml:mrow><mml:mrow><mml:mi>j</mml:mi><mml:mi>m</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:msubsup><mml:mrow><mml:mo>∑</mml:mo></mml:mrow><mml:mrow><mml:mi>m</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mn>6</mml:mn></mml:mrow></mml:msubsup><mml:msub><mml:mrow><mml:mi>P</mml:mi></mml:mrow><mml:mrow><mml:mi>j</mml:mi><mml:mi>k</mml:mi><mml:mi>m</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfrac><mml:mo>,</mml:mo></mml:mrow></mml:math></alternatives></disp-formula>", "<inline-formula id=\"IEq5\"><alternatives><tex-math id=\"M11\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\bar{\\pi }}_{jk}$$\\end{document}</tex-math><mml:math id=\"M12\"><mml:msub><mml:mrow><mml:mover accent=\"true\"><mml:mrow><mml:mi>π</mml:mi></mml:mrow><mml:mo>¯</mml:mo></mml:mover></mml:mrow><mml:mrow><mml:mi>j</mml:mi><mml:mi>k</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq6\"><alternatives><tex-math id=\"M13\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$({\\bar{\\pi }}_{jk})=\\sqrt{{\\sum }_{m=1}^{6}{{w}_{jkm}}^{2}{{\\sigma }_{m}}^{2}}$$\\end{document}</tex-math><mml:math id=\"M14\"><mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mover accent=\"true\"><mml:mrow><mml:mi>π</mml:mi></mml:mrow><mml:mo>¯</mml:mo></mml:mover></mml:mrow><mml:mrow><mml:mi>j</mml:mi><mml:mi>k</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:msqrt><mml:mrow><mml:msubsup><mml:mrow><mml:mo mathsize=\"big\">∑</mml:mo></mml:mrow><mml:mrow><mml:mi>m</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mn>6</mml:mn></mml:mrow></mml:msubsup><mml:msup><mml:mrow><mml:msub><mml:mrow><mml:mi>w</mml:mi></mml:mrow><mml:mrow><mml:mi>j</mml:mi><mml:mi>k</mml:mi><mml:mi>m</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup><mml:msup><mml:mrow><mml:msub><mml:mrow><mml:mi>σ</mml:mi></mml:mrow><mml:mrow><mml:mi>m</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:msqrt></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq7\"><alternatives><tex-math id=\"M15\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${w}_{jkm}=\\frac{{P}_{jkm}}{{\\sum }_{m=1}^{6}{P}_{jkm}}$$\\end{document}</tex-math><mml:math id=\"M16\"><mml:mrow><mml:msub><mml:mrow><mml:mi>w</mml:mi></mml:mrow><mml:mrow><mml:mi>j</mml:mi><mml:mi>k</mml:mi><mml:mi>m</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mrow><mml:mi>P</mml:mi></mml:mrow><mml:mrow><mml:mi>j</mml:mi><mml:mi>k</mml:mi><mml:mi>m</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:msubsup><mml:mrow><mml:mo mathsize=\"big\">∑</mml:mo></mml:mrow><mml:mrow><mml:mi>m</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mn>6</mml:mn></mml:mrow></mml:msubsup><mml:msub><mml:mrow><mml:mi>P</mml:mi></mml:mrow><mml:mrow><mml:mi>j</mml:mi><mml:mi>k</mml:mi><mml:mi>m</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfrac></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq8\"><alternatives><tex-math id=\"M17\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\sigma }_{m}=\\frac{s\\left({y}_{{jm}}\\right)}{\\sqrt{{n}_{{jm}}}}$$\\end{document}</tex-math><mml:math id=\"M18\"><mml:mrow><mml:msub><mml:mrow><mml:mi>σ</mml:mi></mml:mrow><mml:mrow><mml:mi>m</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mi>s</mml:mi><mml:mrow><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mspace width=\"0.20em\"/><mml:mi>y</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"italic\">jm</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow></mml:mrow><mml:mrow><mml:msqrt><mml:mrow><mml:msub><mml:mrow><mml:mi>n</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"italic\">jm</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msqrt></mml:mrow></mml:mfrac></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq9\"><alternatives><tex-math id=\"M19\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${H}_{0}:{\\bar{\\pi }}_{{jk}}=\\bar{\\pi }$$\\end{document}</tex-math><mml:math id=\"M20\"><mml:mrow><mml:msub><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mrow><mml:mn>0</mml:mn></mml:mrow></mml:msub><mml:mo>:</mml:mo><mml:msub><mml:mrow><mml:mover accent=\"true\"><mml:mrow><mml:mi>π</mml:mi></mml:mrow><mml:mo>¯</mml:mo></mml:mover></mml:mrow><mml:mrow><mml:mi mathvariant=\"italic\">jk</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mover accent=\"true\"><mml:mrow><mml:mi>π</mml:mi></mml:mrow><mml:mo>¯</mml:mo></mml:mover></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq10\"><alternatives><tex-math id=\"M21\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${H}_{1}:{\\bar{\\pi }}_{{jk}}\\ne \\bar{\\pi }$$\\end{document}</tex-math><mml:math id=\"M22\"><mml:mrow><mml:msub><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:mo>:</mml:mo><mml:msub><mml:mrow><mml:mover accent=\"true\"><mml:mrow><mml:mi>π</mml:mi></mml:mrow><mml:mo>¯</mml:mo></mml:mover></mml:mrow><mml:mrow><mml:mi mathvariant=\"italic\">jk</mml:mi></mml:mrow></mml:msub><mml:mo>≠</mml:mo><mml:mover accent=\"true\"><mml:mrow><mml:mi>π</mml:mi></mml:mrow><mml:mo>¯</mml:mo></mml:mover></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq11\"><alternatives><tex-math id=\"M23\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${p}_{jk}=2\\left(1-\\phi \\left(\\frac{\\left\|\\bar{\\pi }-{\\bar{\\pi }}_{jk}\\right\|}{{\\rm{s.d.}}\\left({\\bar{\\pi }}_{jk}\\right)}\\right)\\right)$$\\end{document}</tex-math><mml:math id=\"M24\"><mml:mrow><mml:msub><mml:mrow><mml:mi>p</mml:mi></mml:mrow><mml:mrow><mml:mi>j</mml:mi><mml:mi>k</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mn>2</mml:mn><mml:mrow><mml:mrow><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:mi>ϕ</mml:mi><mml:mrow><mml:mrow><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mfrac><mml:mrow><mml:mfenced close=\"∣\" open=\"∣\"><mml:mrow><mml:mrow><mml:mrow><mml:mover accent=\"true\"><mml:mrow><mml:mi>π</mml:mi></mml:mrow><mml:mo>¯</mml:mo></mml:mover><mml:mo>−</mml:mo><mml:msub><mml:mrow><mml:mover accent=\"true\"><mml:mrow><mml:mi>π</mml:mi></mml:mrow><mml:mo>¯</mml:mo></mml:mover></mml:mrow><mml:mrow><mml:mi>j</mml:mi><mml:mi>k</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">s.d.</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mover accent=\"true\"><mml:mrow><mml:mi>π</mml:mi></mml:mrow><mml:mo>¯</mml:mo></mml:mover></mml:mrow><mml:mrow><mml:mi>j</mml:mi><mml:mi>k</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow></mml:mfrac></mml:mrow></mml:mfenced></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow></mml:mrow></mml:mrow></mml:math></alternatives></inline-formula>", "<disp-formula id=\"Equb\"><alternatives><tex-math id=\"M25\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${f}_{\\left\\{{\\rm{anc}},i\\right\\}}=\\frac{{\\sum }_{j}^{{M}_{{\\rm{effect}}}}{\\rm{painting}}{{\\rm{certainty}}}_{\\left\\{j,i,{\\rm{anc}}\\right\\}}}{{\\sum }_{j}^{{M}_{{\\rm{alt}}}}{\\rm{painting}}{{\\rm{certainty}}}_{\\left\\{j,i,{\\rm{anc}}\\right\\}}+{\\sum }_{j}^{{M}_{{\\rm{effect}}}}{\\rm{painting}}{{\\rm{certainty}}}_{\\left\\{j,i,{\\rm{anc}}\\right\\}}},$$\\end{document}</tex-math><mml:math id=\"M26\" display=\"block\"><mml:mrow><mml:msub><mml:mrow><mml:mi>f</mml:mi></mml:mrow><mml:mrow><mml:mfenced close=\"}\" open=\"{\"><mml:mrow><mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">anc</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:msubsup><mml:mrow><mml:mo>∑</mml:mo></mml:mrow><mml:mrow><mml:mi>j</mml:mi></mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mi>M</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">effect</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msubsup><mml:mi mathvariant=\"normal\">painting</mml:mi><mml:msub><mml:mrow><mml:mi mathvariant=\"normal\">certainty</mml:mi></mml:mrow><mml:mrow><mml:mfenced close=\"}\" open=\"{\"><mml:mrow><mml:mrow><mml:mrow><mml:mspace width=\"0.20em\"/><mml:mi>j</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi mathvariant=\"normal\">anc</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:msubsup><mml:mrow><mml:mo>∑</mml:mo></mml:mrow><mml:mrow><mml:mi>j</mml:mi></mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mi>M</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">alt</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msubsup><mml:mi mathvariant=\"normal\">painting</mml:mi><mml:msub><mml:mrow><mml:mi mathvariant=\"normal\">certainty</mml:mi></mml:mrow><mml:mrow><mml:mfenced close=\"}\" open=\"{\"><mml:mrow><mml:mrow><mml:mrow><mml:mspace width=\"0.20em\"/><mml:mi>j</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi mathvariant=\"normal\">anc</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow></mml:msub><mml:mo>+</mml:mo><mml:msubsup><mml:mrow><mml:mo>∑</mml:mo></mml:mrow><mml:mrow><mml:mi>j</mml:mi></mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mi>M</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">effect</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msubsup><mml:mi mathvariant=\"normal\">painting</mml:mi><mml:msub><mml:mrow><mml:mi mathvariant=\"normal\">certainty</mml:mi></mml:mrow><mml:mrow><mml:mfenced close=\"}\" open=\"{\"><mml:mrow><mml:mrow><mml:mrow><mml:mspace width=\"0.20em\"/><mml:mi>j</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi mathvariant=\"normal\">anc</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow></mml:msub></mml:mrow></mml:mfrac><mml:mo>,</mml:mo></mml:mrow></mml:math></alternatives></disp-formula>", "<inline-formula id=\"IEq12\"><alternatives><tex-math id=\"M27\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\sum }_{j}^{{M}_{{\\rm{effect}}}}$$\\end{document}</tex-math><mml:math id=\"M28\"><mml:msubsup><mml:mrow><mml:mo>∑</mml:mo></mml:mrow><mml:mrow><mml:mi>j</mml:mi></mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mi>M</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">effect</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msubsup></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq22\"><alternatives><tex-math id=\"M29\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\rm{painting}}{{\\rm{certainty}}}_{\\{j,i,{\\rm{anc}}\\}}$$\\end{document}</tex-math><mml:math id=\"M30\"><mml:mi mathvariant=\"normal\">painting</mml:mi><mml:msub><mml:mrow><mml:mi mathvariant=\"normal\">certainty</mml:mi></mml:mrow><mml:mrow><mml:mrow><mml:mo>{</mml:mo><mml:mrow><mml:mspace width=\"0.20em\"/><mml:mi>j</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi mathvariant=\"normal\">anc</mml:mi></mml:mrow><mml:mo>}</mml:mo></mml:mrow></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<disp-formula id=\"Equc\"><alternatives><tex-math id=\"M31\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{\\rm{ARS}}}_{{\\rm{anc}}}=\\mathop{\\sum }\\limits_{i}^{I}{f}_{\\left\\{{\\rm{anc}},i\\right\\}}\\times {\\beta }_{i}.$$\\end{document}</tex-math><mml:math id=\"M32\" display=\"block\"><mml:mrow><mml:msub><mml:mrow><mml:mi mathvariant=\"normal\">ARS</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">anc</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:munderover accent=\"false\" accentunder=\"false\"><mml:mrow><mml:mo>∑</mml:mo></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow><mml:mrow><mml:mi>I</mml:mi></mml:mrow></mml:munderover><mml:msub><mml:mrow><mml:mi>f</mml:mi></mml:mrow><mml:mrow><mml:mfenced close=\"}\" open=\"{\"><mml:mrow><mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">anc</mml:mi><mml:mo>,</mml:mo><mml:mi>i</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow></mml:msub><mml:mo>×</mml:mo><mml:msub><mml:mrow><mml:mi>β</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>.</mml:mo></mml:mrow></mml:math></alternatives></disp-formula>", "<disp-formula id=\"Equd\"><alternatives><tex-math id=\"M33\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${Y}_{i} \\sim {\\rm{Binomial}}\\left(1,{\\pi }_{i}\\right){\\rm{;}}\\log \\left(\\frac{{\\pi }_{i}}{1-{\\pi }_{i}}\\right)=\\mathop{\\sum }\\limits_{k=1}^{K}{\\beta }_{jk}\\,{X}_{ijk}+\\mathop{\\sum }\\limits_{c=1}^{{N}_{c}}{\\gamma }_{c}{C}_{ic}.$$\\end{document}</tex-math><mml:math id=\"M34\" display=\"block\"><mml:mrow><mml:msub><mml:mrow><mml:mi>Y</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>~</mml:mo><mml:mi mathvariant=\"normal\">Binomial</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:msub><mml:mrow><mml:mi>π</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mrow></mml:mrow></mml:mfenced><mml:mi mathvariant=\"normal\">;</mml:mi><mml:mi>log</mml:mi><mml:mrow><mml:mrow><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mfrac><mml:mrow><mml:msub><mml:mrow><mml:mi>π</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:msub><mml:mrow><mml:mi>π</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfrac></mml:mrow></mml:mfenced></mml:mrow></mml:mrow><mml:mo>=</mml:mo><mml:munderover accent=\"false\" accentunder=\"false\"><mml:mrow><mml:mo>∑</mml:mo></mml:mrow><mml:mrow><mml:mi>k</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mi>K</mml:mi></mml:mrow></mml:munderover><mml:msub><mml:mrow><mml:mi>β</mml:mi></mml:mrow><mml:mrow><mml:mi>j</mml:mi><mml:mi>k</mml:mi></mml:mrow></mml:msub><mml:mspace width=\"0.10em\"/><mml:msub><mml:mrow><mml:mi>X</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi><mml:mi>k</mml:mi></mml:mrow></mml:msub><mml:mo>+</mml:mo><mml:munderover accent=\"false\" accentunder=\"false\"><mml:mrow><mml:mo>∑</mml:mo></mml:mrow><mml:mrow><mml:mi>c</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mi>c</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:munderover><mml:msub><mml:mrow><mml:mi>γ</mml:mi></mml:mrow><mml:mrow><mml:mi>c</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mi>C</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mi>c</mml:mi></mml:mrow></mml:msub><mml:mo>.</mml:mo></mml:mrow></mml:math></alternatives></disp-formula>", "<disp-formula id=\"Eque\"><alternatives><tex-math id=\"M35\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${Y}_{i} \\sim {\\rm{Binomial}}\\left(1,{\\pi }_{i}\\right){\\rm{;}}\\log \\left(\\frac{{\\pi }_{i}}{1-{\\pi }_{i}}\\right)={\\beta }_{j}{X}_{ij}+\\mathop{\\sum }\\limits_{c=1}^{{N}_{c}}{\\gamma }_{c}{C}_{ic},$$\\end{document}</tex-math><mml:math id=\"M36\" display=\"block\"><mml:mrow><mml:msub><mml:mrow><mml:mi>Y</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>~</mml:mo><mml:mi mathvariant=\"normal\">Binomial</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:msub><mml:mrow><mml:mi>π</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mrow></mml:mrow></mml:mfenced><mml:mi mathvariant=\"normal\">;</mml:mi><mml:mi>log</mml:mi><mml:mrow><mml:mrow><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mfrac><mml:mrow><mml:msub><mml:mrow><mml:mi>π</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:msub><mml:mrow><mml:mi>π</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfrac></mml:mrow></mml:mfenced></mml:mrow></mml:mrow><mml:mo>=</mml:mo><mml:msub><mml:mrow><mml:mi>β</mml:mi></mml:mrow><mml:mrow><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mi>X</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mo>+</mml:mo><mml:munderover accent=\"false\" accentunder=\"false\"><mml:mrow><mml:mo>∑</mml:mo></mml:mrow><mml:mrow><mml:mi>c</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mi>c</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:munderover><mml:msub><mml:mrow><mml:mi>γ</mml:mi></mml:mrow><mml:mrow><mml:mi>c</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mi>C</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mi>c</mml:mi></mml:mrow></mml:msub><mml:mo>,</mml:mo></mml:mrow></mml:math></alternatives></disp-formula>", "<disp-formula id=\"Equf\"><alternatives><tex-math id=\"M37\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${R}^{2}=1-\\frac{\\mathrm{ln}\\left({L}_{M}\\right)}{{lm}\\left({L}_{0}\\right)},$$\\end{document}</tex-math><mml:math id=\"M38\" display=\"block\"><mml:mrow><mml:msup><mml:mrow><mml:mi>R</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:mfrac><mml:mrow><mml:mi>ln</mml:mi><mml:mrow><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mi>M</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow></mml:mrow><mml:mrow><mml:mi mathvariant=\"italic\">lm</mml:mi><mml:mrow><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mn>0</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow></mml:mrow></mml:mfrac><mml:mo>,</mml:mo></mml:mrow></mml:math></alternatives></disp-formula>", "<disp-formula id=\"Equg\"><alternatives><tex-math id=\"M39\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\rm{Adjusted}}\\,{R}^{2}=1-\\frac{\\frac{{\\rm{ln}}\\left({L}_{M}\\right)}{N-k}}{\\frac{{\\rm{ln}}\\left({L}_{0}\\right)}{N-1}},$$\\end{document}</tex-math><mml:math id=\"M40\" display=\"block\"><mml:mrow><mml:mi mathvariant=\"normal\">Adjusted</mml:mi><mml:mspace width=\"0.25em\"/><mml:msup><mml:mrow><mml:mi>R</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:mfrac><mml:mrow><mml:mfrac><mml:mrow><mml:mi mathvariant=\"normal\">ln</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mi>M</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow><mml:mrow><mml:mi>N</mml:mi><mml:mo>−</mml:mo><mml:mi>k</mml:mi></mml:mrow></mml:mfrac></mml:mrow><mml:mrow><mml:mfrac><mml:mrow><mml:mi mathvariant=\"normal\">ln</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mi>L</mml:mi></mml:mrow><mml:mrow><mml:mn>0</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow><mml:mrow><mml:mi>N</mml:mi><mml:mo>−</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:mfrac></mml:mrow></mml:mfrac><mml:mo>,</mml:mo></mml:mrow></mml:math></alternatives></disp-formula>", "<disp-formula id=\"Equh\"><alternatives><tex-math id=\"M41\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${R}^{2}\\left({\\rm{SNPs}}\\right)={R}^{2}\\left({\\rm{sex}}+{\\rm{age}}+18{\\rm{PCs}}+{\\rm{SNPs}}\\right)-{R}^{2}\\left({\\rm{sex}}+{\\rm{age}}+18{\\rm{PCs}}\\right).$$\\end{document}</tex-math><mml:math id=\"M42\" display=\"block\"><mml:mrow><mml:msup><mml:mrow><mml:mi>R</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">SNPs</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced><mml:mo>=</mml:mo><mml:msup><mml:mrow><mml:mi>R</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">sex</mml:mi><mml:mo>+</mml:mo><mml:mi mathvariant=\"normal\">age</mml:mi><mml:mo>+</mml:mo><mml:mn>18</mml:mn><mml:mi mathvariant=\"normal\">PCs</mml:mi><mml:mo>+</mml:mo><mml:mi mathvariant=\"normal\">SNPs</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced><mml:mo>−</mml:mo><mml:msup><mml:mrow><mml:mi>R</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">sex</mml:mi><mml:mo>+</mml:mo><mml:mi mathvariant=\"normal\">age</mml:mi><mml:mo>+</mml:mo><mml:mn>18</mml:mn><mml:mi mathvariant=\"normal\">PCs</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced><mml:mo>.</mml:mo></mml:mrow></mml:math></alternatives></disp-formula>", "<disp-formula id=\"Equi\"><alternatives><tex-math id=\"M43\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${Y}_{i} \\sim {\\rm{Binomial}}\\left(1,{\\pi }_{i}\\right){\\rm{;}}\\log \\left(\\frac{{\\pi }_{i}}{1-{\\pi }_{i}}\\right)={\\beta }_{j}{H}_{ij}+\\mathop{\\sum }\\limits_{c=1}^{{N}_{c}}{\\gamma }_{c}{C}_{ic},$$\\end{document}</tex-math><mml:math id=\"M44\" display=\"block\"><mml:mrow><mml:msub><mml:mrow><mml:mi>Y</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>~</mml:mo><mml:mi mathvariant=\"normal\">Binomial</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:msub><mml:mrow><mml:mi>π</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mrow></mml:mrow></mml:mfenced><mml:mi mathvariant=\"normal\">;</mml:mi><mml:mi>log</mml:mi><mml:mrow><mml:mrow><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mfrac><mml:mrow><mml:msub><mml:mrow><mml:mi>π</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:msub><mml:mrow><mml:mi>π</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfrac></mml:mrow></mml:mfenced></mml:mrow></mml:mrow><mml:mo>=</mml:mo><mml:msub><mml:mrow><mml:mi>β</mml:mi></mml:mrow><mml:mrow><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mo>+</mml:mo><mml:munderover><mml:mrow><mml:mo mathsize=\"big\">∑</mml:mo></mml:mrow><mml:mrow><mml:mi>c</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mi>c</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:munderover><mml:msub><mml:mrow><mml:mi>γ</mml:mi></mml:mrow><mml:mrow><mml:mi>c</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mi>C</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mi>c</mml:mi></mml:mrow></mml:msub><mml:mo>,</mml:mo></mml:mrow></mml:math></alternatives></disp-formula>", "<disp-formula id=\"Equj\"><alternatives><tex-math id=\"M45\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${H}_{ij}=\\left\\{\\begin{array}{ll}1, & {\\rm{if}}\\,i{\\rm{th}}\\,{\\rm{individual}}\\,{\\rm{has}}\\,{\\rm{haplotype}}\\,j\\,{\\rm{in}}\\,{\\rm{both}}\\,{\\rm{genomes}},\\\\ \\frac{1}{2}, & {\\rm{if}}\\,i{\\rm{th}}\\,{\\rm{individual}}\\,{\\rm{has}}\\,{\\rm{haplotype}}\\,j\\,{\\rm{in}}\\,{\\rm{one}}\\,{\\rm{of}}\\,{\\rm{the}}\\,{\\rm{two}}\\,{\\rm{genomes}},\\\\ 0, & {\\rm{otherwise}}.\\end{array}\\right.$$\\end{document}</tex-math><mml:math id=\"M46\" display=\"block\"><mml:mrow><mml:msub><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mrow><mml:mrow><mml:mfenced open=\"{\"><mml:mrow><mml:mtable><mml:mtr><mml:mtd columnalign=\"left\"><mml:mn>1</mml:mn><mml:mo>,</mml:mo></mml:mtd><mml:mtd columnalign=\"left\"><mml:mi mathvariant=\"normal\">if</mml:mi><mml:mspace width=\"0.25em\"/><mml:mi>i</mml:mi><mml:mi mathvariant=\"normal\">th</mml:mi><mml:mspace width=\"0.25em\"/><mml:mi mathvariant=\"normal\">individual</mml:mi><mml:mspace width=\"0.25em\"/><mml:mi mathvariant=\"normal\">has</mml:mi><mml:mspace width=\"0.25em\"/><mml:mi mathvariant=\"normal\">haplotype</mml:mi><mml:mspace width=\"0.25em\"/><mml:mi>j</mml:mi><mml:mspace width=\"0.25em\"/><mml:mi mathvariant=\"normal\">in</mml:mi><mml:mspace width=\"0.25em\"/><mml:mi mathvariant=\"normal\">both</mml:mi><mml:mspace width=\"0.25em\"/><mml:mi mathvariant=\"normal\">genomes</mml:mi><mml:mo>,</mml:mo></mml:mtd></mml:mtr><mml:mtr><mml:mtd columnalign=\"left\"><mml:mfrac><mml:mrow><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:mfrac><mml:mo>,</mml:mo></mml:mtd><mml:mtd columnalign=\"left\"><mml:mi mathvariant=\"normal\">if</mml:mi><mml:mspace width=\"0.25em\"/><mml:mi>i</mml:mi><mml:mi mathvariant=\"normal\">th</mml:mi><mml:mspace width=\"0.25em\"/><mml:mi mathvariant=\"normal\">individual</mml:mi><mml:mspace width=\"0.25em\"/><mml:mi mathvariant=\"normal\">has</mml:mi><mml:mspace width=\"0.25em\"/><mml:mi mathvariant=\"normal\">haplotype</mml:mi><mml:mspace width=\"0.25em\"/><mml:mi>j</mml:mi><mml:mspace width=\"0.25em\"/><mml:mi mathvariant=\"normal\">in</mml:mi><mml:mspace width=\"0.25em\"/><mml:mi mathvariant=\"normal\">one</mml:mi><mml:mspace width=\"0.25em\"/><mml:mi mathvariant=\"normal\">of</mml:mi><mml:mspace width=\"0.25em\"/><mml:mi mathvariant=\"normal\">the</mml:mi><mml:mspace width=\"0.25em\"/><mml:mi mathvariant=\"normal\">two</mml:mi><mml:mspace width=\"0.25em\"/><mml:mi mathvariant=\"normal\">genomes</mml:mi><mml:mo>,</mml:mo></mml:mtd></mml:mtr><mml:mtr><mml:mtd columnalign=\"left\"><mml:mn>0</mml:mn><mml:mo>,</mml:mo></mml:mtd><mml:mtd columnalign=\"left\"><mml:mi mathvariant=\"normal\">otherwise</mml:mi><mml:mo>.</mml:mo></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:mfenced></mml:mrow></mml:mrow></mml:mrow></mml:math></alternatives></disp-formula>", "<inline-formula id=\"IEq13\"><alternatives><tex-math id=\"M47\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${G}_{{ij}}=\\frac{{G}_{{ij}1}+{G}_{{ij}2}}{2}$$\\end{document}</tex-math><mml:math id=\"M48\"><mml:mrow><mml:msub><mml:mrow><mml:mi>G</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"italic\">ij</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mrow><mml:mi>G</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"italic\">ij</mml:mi><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mrow><mml:mi>G</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"italic\">ij</mml:mi><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:mfrac></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq14\"><alternatives><tex-math id=\"M49\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\beta }_{{G}_{j}}$$\\end{document}</tex-math><mml:math id=\"M50\"><mml:msub><mml:mrow><mml:mi>β</mml:mi></mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mi>G</mml:mi></mml:mrow><mml:mrow><mml:mi>j</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq15\"><alternatives><tex-math id=\"M51\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\beta }_{{C}_{c}}$$\\end{document}</tex-math><mml:math id=\"M52\"><mml:msub><mml:mrow><mml:mi>β</mml:mi></mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mi>C</mml:mi></mml:mrow><mml:mrow><mml:mi>c</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq16\"><alternatives><tex-math id=\"M53\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\beta }_{{G}_{i}} \\sim \\frac{U\\left(-{\\rm{1,1}}\\right)}{{\\rm{s.d.}}\\left({G}_{j}\\right)}$$\\end{document}</tex-math><mml:math id=\"M54\"><mml:mrow><mml:msub><mml:mrow><mml:mi>β</mml:mi></mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mi>G</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msub><mml:mo>~</mml:mo><mml:mfrac><mml:mrow><mml:mi>U</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:mo>−</mml:mo><mml:mi mathvariant=\"normal\">1,1</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">s.d.</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mi>G</mml:mi></mml:mrow><mml:mrow><mml:mi>j</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow></mml:mfrac></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq17\"><alternatives><tex-math id=\"M55\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\beta }_{{C}_{c}} \\sim \\frac{U\\left(-{\\rm{1,1}}\\right)}{{\\rm{s.d.}}\\left({C}_{c}\\right)}$$\\end{document}</tex-math><mml:math id=\"M56\"><mml:mrow><mml:msub><mml:mrow><mml:mi>β</mml:mi></mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mi>C</mml:mi></mml:mrow><mml:mrow><mml:mi>c</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msub><mml:mo>~</mml:mo><mml:mfrac><mml:mrow><mml:mi>U</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:mo>−</mml:mo><mml:mi mathvariant=\"normal\">1,1</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">s.d.</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mi>C</mml:mi></mml:mrow><mml:mrow><mml:mi>c</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow></mml:mfrac></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq18\"><alternatives><tex-math id=\"M57\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\beta }_{{H}_{1}}=\\frac{1}{2}{\\sum }_{j=1}^{4}\\left\|{\\beta }_{{G}_{j}}\\right\|$$\\end{document}</tex-math><mml:math id=\"M58\"><mml:mrow><mml:msub><mml:mrow><mml:mi>β</mml:mi></mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:mfrac><mml:msubsup><mml:mo>∑</mml:mo><mml:mrow><mml:mi>j</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mn>4</mml:mn></mml:mrow></mml:msubsup><mml:mrow><mml:mfenced close=\"\|\" open=\"\|\"><mml:mrow><mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mi>β</mml:mi></mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mi>G</mml:mi></mml:mrow><mml:mrow><mml:mi>j</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msub></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq19\"><alternatives><tex-math id=\"M59\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${F}_{{H}_{1}}=0.09 \\% $$\\end{document}</tex-math><mml:math id=\"M60\"><mml:mrow><mml:msub><mml:mrow><mml:mi>F</mml:mi></mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mn>0.09</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math></alternatives></inline-formula>", "<disp-formula id=\"Equk\"><alternatives><tex-math id=\"M61\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${O}_{i}=\\mathop{\\sum }\\limits_{c=1}^{20}{\\beta }_{c}{C}_{{ic}}+\\gamma \\left(\\mathop{\\sum }\\limits_{j=1}^{4}{\\beta }_{{G}_{j}}{G}_{{ij}}+{\\beta }_{{H}_{1}}{H}_{1}\\right)+{e}_{i}+w,$$\\end{document}</tex-math><mml:math id=\"M62\" display=\"block\"><mml:mrow><mml:msub><mml:mrow><mml:mi>O</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:munderover><mml:mo>∑</mml:mo><mml:mrow><mml:mi>c</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mn>20</mml:mn></mml:mrow></mml:munderover><mml:mrow><mml:msub><mml:mrow><mml:mi>β</mml:mi></mml:mrow><mml:mrow><mml:mi>c</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mi>C</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"italic\">ic</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo>+</mml:mo><mml:mi>γ</mml:mi><mml:mrow><mml:mrow><mml:mrow><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:munderover><mml:mo>∑</mml:mo><mml:mrow><mml:mi>j</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mn>4</mml:mn></mml:mrow></mml:munderover><mml:mrow><mml:msub><mml:mrow><mml:mi>β</mml:mi></mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mi>G</mml:mi></mml:mrow><mml:mrow><mml:mi>j</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mi>G</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"italic\">ij</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo>+</mml:mo><mml:msub><mml:mrow><mml:mi>β</mml:mi></mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:mrow></mml:mfenced></mml:mrow></mml:mrow></mml:mrow><mml:mo>+</mml:mo><mml:msub><mml:mrow><mml:mi>e</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>+</mml:mo><mml:mi>w</mml:mi><mml:mo>,</mml:mo></mml:mrow></mml:math></alternatives></disp-formula>", "<inline-formula id=\"IEq20\"><alternatives><tex-math id=\"M63\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\pi }_{i}=\\frac{{e}^{{O}_{i}}}{1+{e}^{{O}_{i}}}$$\\end{document}</tex-math><mml:math id=\"M64\"><mml:mrow><mml:msub><mml:mrow><mml:mi>π</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mspace width=\".25em\"/></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:msup><mml:mrow><mml:mi>e</mml:mi></mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mi>O</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msup></mml:mrow><mml:mrow><mml:mn>1</mml:mn><mml:mo>+</mml:mo><mml:msup><mml:mrow><mml:mi>e</mml:mi></mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mi>O</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msup></mml:mrow></mml:mfrac></mml:mrow></mml:math></alternatives></inline-formula>", "<disp-formula id=\"Equl\"><alternatives><tex-math id=\"M65\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${D}_{i}(l,m)={\\parallel A(i,l,\\cdot )-A(i,m,\\cdot )\\parallel }_{2}=\\sqrt{\\frac{1}{K}{\\sum }_{k=1}^{K}{(A(i,l,k)-A(i,m,k))}^{2}}.$$\\end{document}</tex-math><mml:math id=\"M66\" display=\"block\"><mml:mrow><mml:msub><mml:mrow><mml:mi>D</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>l</mml:mi><mml:mo>,</mml:mo><mml:mi>m</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow><mml:mo>=</mml:mo><mml:msub><mml:mrow><mml:mrow><mml:mo>∥</mml:mo><mml:mrow><mml:mrow><mml:mrow><mml:mi>A</mml:mi><mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>l</mml:mi><mml:mo>,</mml:mo><mml:mo>⋅</mml:mo></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow><mml:mo>−</mml:mo><mml:mi>A</mml:mi><mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>m</mml:mi><mml:mo>,</mml:mo><mml:mo>⋅</mml:mo></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:mrow><mml:mo>∥</mml:mo></mml:mrow></mml:mrow></mml:mrow></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:msqrt><mml:mrow><mml:mfrac><mml:mrow><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mi>K</mml:mi></mml:mrow></mml:mfrac><mml:msubsup><mml:mo>∑</mml:mo><mml:mrow><mml:mi>k</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mi>K</mml:mi></mml:mrow></mml:msubsup><mml:mrow><mml:msup><mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>A</mml:mi><mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>l</mml:mi><mml:mo>,</mml:mo><mml:mi>k</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow><mml:mo>−</mml:mo><mml:mi>A</mml:mi><mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>m</mml:mi><mml:mo>,</mml:mo><mml:mi>k</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:mrow></mml:msqrt><mml:mo>.</mml:mo></mml:mrow></mml:math></alternatives></disp-formula>", "<disp-formula id=\"Equm\"><alternatives><tex-math id=\"M67\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$D\\left(l,m\\right)=\\frac{1}{N}\\mathop{\\sum }\\limits_{i=1}^{N}{D}_{i}\\left(l,m\\right).$$\\end{document}</tex-math><mml:math id=\"M68\" display=\"block\"><mml:mrow><mml:mi>D</mml:mi><mml:mrow><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:mi>l</mml:mi><mml:mo>,</mml:mo><mml:mi>m</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mi>N</mml:mi></mml:mrow></mml:mfrac><mml:munderover><mml:mo>∑</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mi>N</mml:mi></mml:mrow></mml:munderover><mml:mrow><mml:msub><mml:mrow><mml:mi>D</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mrow><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:mi>l</mml:mi><mml:mo>,</mml:mo><mml:mi>m</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow></mml:mrow><mml:mo>.</mml:mo></mml:mrow></mml:math></alternatives></disp-formula>", "<disp-formula id=\"Equn\"><alternatives><tex-math id=\"M69\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${D}^{* }(l,m)\\approx \\frac{1}{N}\\mathop{\\sum }\\limits_{{\\rm{i}}=1}^{N}{\\parallel A({i}^{* },l,\\cdot )-A(i,m,\\cdot )\\parallel }_{2},$$\\end{document}</tex-math><mml:math id=\"M70\" display=\"block\"><mml:mrow><mml:msup><mml:mrow><mml:mi>D</mml:mi></mml:mrow><mml:mrow><mml:mo></mml:mo></mml:mrow></mml:msup><mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>l</mml:mi><mml:mo>,</mml:mo><mml:mi>m</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow><mml:mo>≈</mml:mo><mml:mfrac><mml:mrow><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mi>N</mml:mi></mml:mrow></mml:mfrac><mml:munderover><mml:mo>∑</mml:mo><mml:mrow><mml:mi mathvariant=\"normal\">i</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mi>N</mml:mi></mml:mrow></mml:munderover><mml:mrow><mml:msub><mml:mrow><mml:mrow><mml:mrow><mml:mrow><mml:mo>∥</mml:mo><mml:mrow><mml:mi>A</mml:mi><mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msup><mml:mrow><mml:mi>i</mml:mi></mml:mrow><mml:mrow><mml:mo></mml:mo></mml:mrow></mml:msup><mml:mo>,</mml:mo><mml:mi>l</mml:mi><mml:mo>,</mml:mo><mml:mo>⋅</mml:mo></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow><mml:mo>−</mml:mo><mml:mi>A</mml:mi><mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>m</mml:mi><mml:mo>,</mml:mo><mml:mo>⋅</mml:mo></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:mrow><mml:mo>∥</mml:mo></mml:mrow></mml:mrow></mml:mrow></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:mrow><mml:mo>,</mml:mo></mml:mrow></mml:math></alternatives></disp-formula>", "<disp-formula id=\"Equo\"><alternatives><tex-math id=\"M71\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\rm{LDA}}\\left(l,m\\right)=\\frac{{D}^{* }\\left(l,m\\right)-D\\left(l,m\\right)}{{D}^{* }\\left(l,m\\right)}.$$\\end{document}</tex-math><mml:math id=\"M72\" display=\"block\"><mml:mrow><mml:mi mathvariant=\"normal\">LDA</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:mi>l</mml:mi><mml:mo>,</mml:mo><mml:mi>m</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:msup><mml:mrow><mml:mi>D</mml:mi></mml:mrow><mml:mrow><mml:mo></mml:mo></mml:mrow></mml:msup><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:mi>l</mml:mi><mml:mo>,</mml:mo><mml:mi>m</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced><mml:mo>−</mml:mo><mml:mi>D</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:mi>l</mml:mi><mml:mo>,</mml:mo><mml:mi>m</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow><mml:mrow><mml:msup><mml:mrow><mml:mi>D</mml:mi></mml:mrow><mml:mrow><mml:mo></mml:mo></mml:mrow></mml:msup><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:mi>l</mml:mi><mml:mo>,</mml:mo><mml:mi>m</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow></mml:mfrac><mml:mo>.</mml:mo></mml:mrow></mml:math></alternatives></disp-formula>", "<disp-formula id=\"Equp\"><alternatives><tex-math id=\"M73\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\rm{LDAS}}(\\,j{\\rm{;}}X)=\\left\\{\\begin{array}{l}{\\int }_{{\\rm{gd}}(\\,j)-X}^{{\\rm{gd}}(\\,j)+X}{\\rm{LDA}}(\\,j,l)\\,d{\\rm{gd}},{\\rm{if}}\\,X\\le {\\rm{gd}}(\\,j)\\le {\\rm{tg}}-X,\\\\ {\\int }_{0}^{{\\rm{gd}}(\\,j)+X}{\\rm{LDA}}(\\,j,l)\\,d{\\rm{gd}}+{\\int }_{2{\\rm{gd}}(\\,j)}^{{\\rm{gd}}(\\,j)+X}{\\rm{LDA}}(\\,j,l)\\,d{\\rm{gd}},{\\rm{if}}\\,{\\rm{gd}}(\\,j) < X,\\\\ {\\int }_{{\\rm{gd}}(\\,j)-X}^{{\\rm{tg}}}{\\rm{LDA}}(\\,j,l)\\,d{\\rm{gd}}+{\\int }_{{\\rm{gd}}(\\,j)-X}^{2{\\rm{gd}}(\\,j)-{\\rm{tg}}}{\\rm{LDA}}(\\,j,l)\\,d{\\rm{gd}},{\\rm{if}}\\,{\\rm{gd}}(\\,j) > {\\rm{tg}}-X.\\end{array}\\right.$$\\end{document}</tex-math><mml:math id=\"M74\" display=\"block\"><mml:mrow><mml:mi mathvariant=\"normal\">LDAS</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mspace width=\"0.23em\"/><mml:mi>j</mml:mi><mml:mi mathvariant=\"normal\">;</mml:mi><mml:mi>X</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mrow><mml:mrow><mml:mfenced open=\"{\"><mml:mrow><mml:mtable><mml:mtr><mml:mtd columnalign=\"left\"><mml:msubsup><mml:mrow><mml:mo>∫</mml:mo></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">gd</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mspace width=\"0.23em\"/><mml:mi>j</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>−</mml:mo><mml:mi>X</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">gd</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mspace width=\"0.23em\"/><mml:mi>j</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>+</mml:mo><mml:mi>X</mml:mi></mml:mrow></mml:msubsup><mml:mi mathvariant=\"normal\">LDA</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mspace width=\"0.23em\"/><mml:mi>j</mml:mi><mml:mo>,</mml:mo><mml:mi>l</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mspace width=\"0.23em\"/><mml:mi>d</mml:mi><mml:mi mathvariant=\"normal\">gd</mml:mi><mml:mo>,</mml:mo><mml:mi mathvariant=\"normal\">if</mml:mi><mml:mspace width=\"0.23em\"/><mml:mi>X</mml:mi><mml:mo>≤</mml:mo><mml:mi mathvariant=\"normal\">gd</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mspace width=\"0.23em\"/><mml:mi>j</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>≤</mml:mo><mml:mi mathvariant=\"normal\">tg</mml:mi><mml:mo>−</mml:mo><mml:mi>X</mml:mi><mml:mo>,</mml:mo></mml:mtd></mml:mtr><mml:mtr><mml:mtd columnalign=\"left\"><mml:msubsup><mml:mrow><mml:mo>∫</mml:mo></mml:mrow><mml:mrow><mml:mn>0</mml:mn></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">gd</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mspace width=\"0.23em\"/><mml:mi>j</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>+</mml:mo><mml:mi>X</mml:mi></mml:mrow></mml:msubsup><mml:mi mathvariant=\"normal\">LDA</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mspace width=\"0.23em\"/><mml:mi>j</mml:mi><mml:mo>,</mml:mo><mml:mi>l</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mspace width=\"0.23em\"/><mml:mi>d</mml:mi><mml:mi mathvariant=\"normal\">gd</mml:mi><mml:mo>+</mml:mo><mml:msubsup><mml:mrow><mml:mo>∫</mml:mo></mml:mrow><mml:mrow><mml:mn>2</mml:mn><mml:mi mathvariant=\"normal\">gd</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mspace width=\"0.23em\"/><mml:mi>j</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">gd</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mspace width=\"0.23em\"/><mml:mi>j</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>+</mml:mo><mml:mi>X</mml:mi></mml:mrow></mml:msubsup><mml:mi mathvariant=\"normal\">LDA</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mspace width=\"0.23em\"/><mml:mi>j</mml:mi><mml:mo>,</mml:mo><mml:mi>l</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mspace width=\"0.23em\"/><mml:mi>d</mml:mi><mml:mi mathvariant=\"normal\">gd</mml:mi><mml:mo>,</mml:mo><mml:mi mathvariant=\"normal\">if</mml:mi><mml:mspace width=\"0.23em\"/><mml:mi mathvariant=\"normal\">gd</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mspace width=\"0.23em\"/><mml:mi>j</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo><</mml:mo><mml:mi>X</mml:mi><mml:mo>,</mml:mo></mml:mtd></mml:mtr><mml:mtr><mml:mtd columnalign=\"left\"><mml:msubsup><mml:mrow><mml:mo>∫</mml:mo></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">gd</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mspace width=\"0.23em\"/><mml:mi>j</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>−</mml:mo><mml:mi>X</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">tg</mml:mi></mml:mrow></mml:msubsup><mml:mi mathvariant=\"normal\">LDA</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mspace width=\"0.23em\"/><mml:mi>j</mml:mi><mml:mo>,</mml:mo><mml:mi>l</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mspace width=\"0.23em\"/><mml:mi>d</mml:mi><mml:mi mathvariant=\"normal\">gd</mml:mi><mml:mo>+</mml:mo><mml:msubsup><mml:mrow><mml:mo>∫</mml:mo></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">gd</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mspace width=\"0.23em\"/><mml:mi>j</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>−</mml:mo><mml:mi>X</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn><mml:mi mathvariant=\"normal\">gd</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mspace width=\"0.23em\"/><mml:mi>j</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>−</mml:mo><mml:mi mathvariant=\"normal\">tg</mml:mi></mml:mrow></mml:msubsup><mml:mi mathvariant=\"normal\">LDA</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mspace width=\"0.23em\"/><mml:mi>j</mml:mi><mml:mo>,</mml:mo><mml:mi>l</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mspace width=\"0.23em\"/><mml:mi>d</mml:mi><mml:mi mathvariant=\"normal\">gd</mml:mi><mml:mo>,</mml:mo><mml:mi mathvariant=\"normal\">if</mml:mi><mml:mspace width=\"0.23em\"/><mml:mi mathvariant=\"normal\">gd</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mspace width=\"0.23em\"/><mml:mi>j</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>></mml:mo><mml:mi mathvariant=\"normal\">tg</mml:mi><mml:mo>−</mml:mo><mml:mi>X</mml:mi><mml:mo>.</mml:mo></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:mfenced></mml:mrow></mml:mrow></mml:mrow></mml:math></alternatives></disp-formula>", "<inline-formula id=\"IEq21\"><alternatives><tex-math id=\"M75\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\int }_{{\\rm{gd}}\\left(\\,j\\right)-X}^{{\\rm{gd}}\\left(\\,j\\right)+X}{\\rm{LDA}}\\left(\\,j,l\\right)d{\\rm{gd}}$$\\end{document}</tex-math><mml:math id=\"M76\"><mml:mrow><mml:msubsup><mml:mrow><mml:mo>∫</mml:mo></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">gd</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:mspace width=\"0.20em\"/><mml:mi>j</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced><mml:mo>−</mml:mo><mml:mi>X</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">gd</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:mspace width=\"0.20em\"/><mml:mi>j</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced><mml:mo>+</mml:mo><mml:mi>X</mml:mi></mml:mrow></mml:msubsup><mml:mi mathvariant=\"normal\">LDA</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:mspace width=\"0.20em\"/><mml:mi>j</mml:mi><mml:mo>,</mml:mo><mml:mi>l</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced><mml:mi>d</mml:mi><mml:mi mathvariant=\"normal\">gd</mml:mi></mml:mrow></mml:math></alternatives></inline-formula>" ]	[]	[]	[]	[]	[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM3\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM4\"></supplementary-material>" ]	[ "<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>These authors contributed equally: William Barrie, Yaoling Yang, Evan K. Irving-Pease, Kathrine E. Attfield, Gabriele Scorrano, Lise Torp Jensen</p></fn><fn><p>These authors jointly supervised this work: Astrid K. N. Iversen, Daniel J. Lawson, Lars Fugger, Eske Willerslev</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"41586_2023_6618_Fig1_HTML\" id=\"d32e637\"/>", "<graphic xlink:href=\"41586_2023_6618_Fig2_HTML\" id=\"d32e732\"/>", "<graphic xlink:href=\"41586_2023_6618_Fig3_HTML\" id=\"d32e832\"/>", "<graphic xlink:href=\"41586_2023_6618_Fig4_HTML\" id=\"d32e960\"/>", "<graphic xlink:href=\"41586_2023_6618_Fig5_HTML\" id=\"d32e1109\"/>", "<inline-graphic xlink:href=\"41586_2023_6618_Article_IEq1.gif\"/>", "<inline-graphic xlink:href=\"41586_2023_6618_Article_IEq2.gif\"/>", "<inline-graphic xlink:href=\"41586_2023_6618_Article_IEq3.gif\"/>", "<inline-graphic xlink:href=\"41586_2023_6618_Article_IEq4.gif\"/>", "<graphic xlink:href=\"41586_2023_6618_Article_Equa.gif\" position=\"anchor\"/>", "<inline-graphic xlink:href=\"41586_2023_6618_Article_IEq5.gif\"/>", "<inline-graphic xlink:href=\"41586_2023_6618_Article_IEq6.gif\"/>", "<inline-graphic xlink:href=\"41586_2023_6618_Article_IEq7.gif\"/>", "<inline-graphic xlink:href=\"41586_2023_6618_Article_IEq8.gif\"/>", "<inline-graphic xlink:href=\"41586_2023_6618_Article_IEq9.gif\"/>", "<inline-graphic xlink:href=\"41586_2023_6618_Article_IEq10.gif\"/>", "<inline-graphic xlink:href=\"41586_2023_6618_Article_IEq11.gif\"/>", "<graphic xlink:href=\"41586_2023_6618_Article_Equb.gif\" position=\"anchor\"/>", "<inline-graphic xlink:href=\"41586_2023_6618_Article_IEq12.gif\"/>", "<inline-graphic xlink:href=\"41586_2023_6618_Article_IEq22.gif\"/>", "<graphic xlink:href=\"41586_2023_6618_Article_Equc.gif\" position=\"anchor\"/>", "<graphic xlink:href=\"41586_2023_6618_Article_Equd.gif\" position=\"anchor\"/>", "<graphic xlink:href=\"41586_2023_6618_Article_Eque.gif\" position=\"anchor\"/>", "<graphic xlink:href=\"41586_2023_6618_Article_Equf.gif\" position=\"anchor\"/>", "<graphic xlink:href=\"41586_2023_6618_Article_Equg.gif\" position=\"anchor\"/>", "<graphic xlink:href=\"41586_2023_6618_Article_Equh.gif\" position=\"anchor\"/>", "<graphic xlink:href=\"41586_2023_6618_Article_Equi.gif\" position=\"anchor\"/>", "<graphic xlink:href=\"41586_2023_6618_Article_Equj.gif\" position=\"anchor\"/>", "<inline-graphic xlink:href=\"41586_2023_6618_Article_IEq13.gif\"/>", "<inline-graphic xlink:href=\"41586_2023_6618_Article_IEq14.gif\"/>", "<inline-graphic xlink:href=\"41586_2023_6618_Article_IEq15.gif\"/>", "<inline-graphic xlink:href=\"41586_2023_6618_Article_IEq16.gif\"/>", "<inline-graphic xlink:href=\"41586_2023_6618_Article_IEq17.gif\"/>", "<inline-graphic xlink:href=\"41586_2023_6618_Article_IEq18.gif\"/>", "<inline-graphic xlink:href=\"41586_2023_6618_Article_IEq19.gif\"/>", "<graphic xlink:href=\"41586_2023_6618_Article_Equk.gif\" position=\"anchor\"/>", "<inline-graphic xlink:href=\"41586_2023_6618_Article_IEq20.gif\"/>", "<graphic xlink:href=\"41586_2023_6618_Article_Equl.gif\" position=\"anchor\"/>", "<graphic xlink:href=\"41586_2023_6618_Article_Equm.gif\" position=\"anchor\"/>", "<graphic xlink:href=\"41586_2023_6618_Article_Equn.gif\" position=\"anchor\"/>", "<graphic xlink:href=\"41586_2023_6618_Article_Equo.gif\" position=\"anchor\"/>", "<graphic xlink:href=\"41586_2023_6618_Article_Equp.gif\" position=\"anchor\"/>", "<inline-graphic xlink:href=\"41586_2023_6618_Article_IEq21.gif\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6618_Fig6_ESM\" id=\"d32e5449\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6618_Fig7_ESM\" id=\"d32e5464\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6618_Fig8_ESM\" id=\"d32e5475\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6618_Fig9_ESM\" id=\"d32e5486\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6618_Fig10_ESM\" id=\"d32e5504\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6618_Fig11_ESM\" id=\"d32e5515\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6618_Fig12_ESM\" id=\"d32e5536\"/>" ]	[ "<media xlink:href=\"41586_2023_6618_MOESM1_ESM.pdf\"><caption><p>Supplementary Information</p></caption></media>", "<media xlink:href=\"41586_2023_6618_MOESM2_ESM.pdf\"><caption><p>Reporting Summary</p></caption></media>", "<media xlink:href=\"41586_2023_6618_MOESM3_ESM.pdf\"><caption><p>Peer Review File</p></caption></media>", "<media xlink:href=\"41586_2023_6618_MOESM4_ESM.xlsx\"><caption><p>Supplementary Tables</p></caption></media>" ]	[{"label": ["1."], "mixed-citation": ["Attfield, K. 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Preprint at "], "italic": ["bioRxiv"]}, {"label": ["82."], "mixed-citation": ["McFadden, D. in "], "italic": ["Frontiers in Econometrics"]}, {"label": ["83."], "surname": ["Efron"], "given-names": ["B"], "article-title": ["Bootstrap methods: another look at the jackknife"], "source": ["Ann. Stat."], "year": ["1979"], "volume": ["7"], "fpage": ["1"], "lpage": ["26"], "pub-id": ["10.1214/aos/1176344552"]}, {"label": ["84."], "surname": ["Kass", "Wasserman"], "given-names": ["RE", "L"], "article-title": ["A reference Bayesian test for nested hypotheses and its relationship to the Schwarz criterion"], "source": ["J. Am. Stat. Assoc."], "year": ["1995"], "volume": ["90"], "fpage": ["928"], "lpage": ["934"], "pub-id": ["10.1080/01621459.1995.10476592"]}]	{ "acronym": [], "definition": [] }	85	CC BY	no	2024-01-13 00:02:19	Nature. 2024 Jan 10; 625(7994):321-328	oa_package/f5/28/PMC10781639.tar.gz
PMC10781640	37957315	[]	[ "<title>Methods</title>", "<title>Sampling and preprocessing</title>", "<p id=\"Par22\">Floating LAO biomass was sampled from the air–water interface of the anoxic activated sludge tank at the Schifflange wastewater treatment plant (Esch-sur-Alzette, Luxembourg; 49° 30′ 48.29″ N; 6° 1′ 4.53″ E) in the form of a single islet (examples illustrated in Fig. 2 of ref. <sup>##REF##28721231##30##</sup>). The sampling frequency—weekly—was chosen as it is the generation time of the activated sludge in the BWWTP (the average time it remains in the system) and the average doubling time of the dominant <italic>Microthrix</italic> population<sup>##REF##25424998##32##</sup>. For each sampling date, indicated as dates in the format YYYY-MM-DD, one entire ‘islet’ was sampled using a levy cane of 500 ml. Samples were quickly homogenized and collected in 50 ml sterile Falcon tubes and then immediately flash-frozen by immersion in liquid nitrogen and stored at −80 °C to guarantee optimal sample integrity and quality.</p>", "<p id=\"Par23\">For the 51 timepoints of the training set (21 March 2011 to 3 May 2012), samples were treated in 2012 as previously described<sup>##REF##33077707##27##</sup>: 200 mg was subsampled from the collected islet using a sterile metal spatula, at all times guaranteeing that the samples remained in the frozen state, and used for subsequent biomolecular extraction according to a previously published procedure (using the Qiagen AllPrep DNA/RNA/protein mini kit-based method on ‘LAO-enriched mixed microbial community’<sup>##REF##22763648##26##</sup>).</p>", "<p id=\"Par24\">Additional concomitant biomolecular extractions were applied to a total of 21 samples collected during the month of June from 2012 to 2016 and extracted in a separate experiment in 2018. The sample preprocessing protocol was carried out on a customized robotic system owned by the lab (Beckman-Coulter_Platform Biomek 4000 NXP Span8 Gripper) following the same protocol as for the training set sample extraction described above with few differences. The biomolecular extraction was then performed using the commercial AllPrep DNA/RNA/protein mini kit (Qiagen, 80004), conducted on a customized robotic system owned by the lab (Tecan-LU_UNILU_EWS_EXTRACTION_EU-0908- Freedom EVO 200). An RNase treatment followed by DNA precipitation was carried out on the DNA, and the RNA was purified using the commercial kit Zymo RNA Clean & Concentrator-5 (R1013). RNA quality was assessed as in the previous study for the same environment<sup>##REF##28721231##30##</sup>.</p>", "<title>High-throughput meta-omics</title>", "<p id=\"Par25\">DNA (400 ng) was sheared using NGS Bioruptor (Diogenode, UCD300) with 30 s ON and 30 s OFF for 10 cycles. DNA libraries were prepared using TruSeq Nano DNA kit (Illumina, FC-121-4002) employing standard protocol with 8 PCR cycles. The libraries were prepared for a 350 bp average insert size. RNA (1 µg) was depleted of ribosomal RNA using the RiboZero kit (Illumina, MRZB12424). Ribosomal RNA-depleted samples were further processed and prepared using the TruSeq Stranded mRNA library preparation kit (Illumina, RS-122-2101). The fragmentation time was reduced to 3 min. The samples were amplified for 8 PCR cycles. The prepared libraries were quantified using Qubit 4 (Thermo Fisher) and quality checked using Bioanalyzer 2100 (Agilent). Sequencing was performed on a NexSeq 500 instrument using 2 × 150 bp read length at the LCSB sequencing platform (RRID <ext-link ext-link-type=\"uri\" xlink:href=\"https://scicrunch.org/resolver/SCR_021931/\">SCR_021931</ext-link>).</p>", "<title>Collection of environmental variables</title>", "<p id=\"Par26\">The environmental variables were collected on site by the researcher(s) while they were performing the sampling. These include dry matter, phosphate, nitrate, ammonium, oxygen, conductivity, pH, temperature and oxygen (Supplementary Table ##SUPPL##3##5##), following previously established protocol<sup>##REF##33077707##27##</sup>. The other variables were retrieved from the automated data collection routine of the Schifflange BWWTP, which measures these values online and aggregates them as 2 h averages starting at 1:00. These recordings include the same variables for different parts of the plant (inflow, both vats, outflow) with the addition of other measurements such as the in/outflow volume. For simplicity, we used exclusively the variable pertaining to the inflow, both vats and outflow in this study (Supplementary Table 6). The Schifflange plant is depicted in <ext-link ext-link-type=\"uri\" xlink:href=\"https://sivec.lu/installation/station-depuration/\">https://sivec.lu/installation/station-depuration/</ext-link>, with the various components named in German. The variables were screened for collinearity (Extended Data Fig. ##FIG##6##3##) using the Pearson correlation coefficient to allow a rational selection, resulting in 15 variables used from the 59 initial ones. The variables Oxygen_manual, Dry_matter, NH4.N, Vat1_NH4.N and Vat2_NH4.N were transformed using the square root function.</p>", "<title>Co-assembly of metagenomics and metatranscriptomics reads</title>", "<p id=\"Par27\">All the samples from the training and the test datasets followed the same bioinformatic pipeline. Sample-wise preprocessing of the MG and MT data was performed using IMP (v.3.0)<sup>##REF##27986083##34##</sup> (<ext-link ext-link-type=\"uri\" xlink:href=\"https://git-r3lab.uni.lu/IMP/imp3\">https://git-r3lab.uni.lu/IMP/imp3</ext-link>) with custom parameters, that is, (1) Illumina Truseq2 adapters were trimmed, and (2) the step involving the filtering of reads of human origin step was omitted for the preprocessing. The reads were corrected using BayesHammer<sup>##REF##23368723##35##</sup> per sample, per omic. The resulting MG and MT reads were assembled with metaSPAdes (v.3.13.1)<sup>##REF##28298430##36##</sup> and rnaSPAdes (v.3.13.1)<sup>##REF##31494669##37##</sup>, respectively. The MG and MT reads of each sample were re-assembled together using the contigs and ‘highly filtered’ transcripts from the first assemblies as trusted contigs.</p>", "<title>Contig sorting into biological subsets</title>", "<p id=\"Par28\">Contigs longer than 1,000 nt from each sample were retained and sorted into four subsets: eukaryotes, plasmids, viruses and chromosomal prokaryotes. First, the contigs were screened for eukaryotes using EukRep (v.0.6.7)<sup>##REF##29496730##38##</sup>; the resulting non-eukaryotic contigs were searched for plasmidial sequences with Plasflow (v.1.1.0)<sup>##REF##29346586##39##</sup> and cbar (v.1.2)<sup>##REF##20538725##40##</sup> as well as for viral sequences using virsorter (v.1.0.6, categories 1 and 2)<sup>##REF##26038737##41##</sup> and deepvirfinder (v.1.0)<sup>##REF##34084563##42##</sup>. A contig was considered viral or plasmidial if both tools agreed in the prediction; all leftover sequences were considered chromosomal prokaryotic. Later, some contigs of the latter group were moved to the eukaryotic (see ‘Taxonomic and functional annotation’ section).</p>", "<title>Binning and clustering</title>", "<p id=\"Par29\">The chromosomal prokaryotic subsets of each sample were binned using IMP (v.3.0)<sup>##REF##27986083##34##</sup> with MaxBin<sup>##REF##25136443##43##</sup>, MetaBAT<sup>##REF##26336640##44##</sup> and binny<sup>##REF##36239393##45##</sup> plus a refinement step with DAS Tool<sup>##REF##29807988##46##</sup>. The resulting bins were dereplicated along the entire time series with dRep (v.0.5.4)<sup>##REF##28742071##47##</sup> to create rMAGs on the basis of the results of CheckM (v.1.0.7)<sup>##REF##25977477##48##</sup>, such as contamination and completeness (results for the rMAGs are shown in Fig. ##FIG##0##1a##). Similarly, the eukaryotic subsets were binned with MetaBat<sup>##REF##26336640##44##</sup> and dereplicated using dRep (v.0.5.4)<sup>##REF##28742071##47##</sup> without genome quality assessment resulting in rMAGs. All the plasmidial, viral and the unbinned contigs from the eukaryotic and chromosomal prokaryotic subsets were clustered using CD-HIT (v.4.6.8)<sup>##REF##23060610##49##</sup> on each of those subsets. We refer to the subset of the clustered unbinned contigs as rContigs. The collection of the rMAGs and the rContigs constitutes the representative database (rDB) of the system.</p>", "<title>Taxonomic and functional annotation</title>", "<p id=\"Par30\">The rMAGs and the rContigs were annotated taxonomically using the Contig Annotation Tool and Bin Annotation Tool (v.5.1.2)<sup>##REF##31640809##50##</sup>, respectively. The ORFs were predicted from the rDB using AUGUSTUS (c3.3.3)<sup>##UREF##5##51##</sup> for the eukaryotic set and IMP (v.3.0)<sup>##REF##27986083##34##</sup> for all the other sets. The ORFs were annotated using Mantis (v.1.02)<sup>##REF##34076241##52##</sup> with the heuristic approach and using kofam<sup>##REF##31742321##53##</sup>, tigrfam<sup>##UREF##6##54##</sup>, EGGNOG<sup>##REF##30418610##55##</sup>, Pfam-A<sup>##REF##33125078##56##</sup> and NCBIG<sup>##REF##33270901##57##</sup>. Subsequently, only the entries with KO terms assigned by kofam were retained for analysis.</p>", "<title>MG and MT quantification and filtering</title>", "<p id=\"Par31\">The filtered MG and MT reads were aligned to the ORF reference set using bwa<sup>##REF##19451168##58##</sup> and sorted using samtools (v.1.11)<sup>##REF##19505943##59##</sup>. The resulting sorted bam files were processed using bam2hits (v.1.0.9)<sup>##REF##21310039##60##</sup> and the output split with a maximum number of 100,000 ORFs per subset while respecting the bam2hits read groups. Each subset was quantified with mmseq (v.1.0.9)<sup>##REF##21310039##60##</sup> and mmcollapse<sup>##REF##24281695##61##</sup>, then the quantifications per sample were the normalized form of fragments per kilobase million, merged and re-normalized to fragments per kilobase million. Values of gene abundance and expression inferior to 10<sup>−7</sup> were considered equal to 0, and ORFs and transcripts that were not present in at least 20% of the training set were discarded from further analysis.</p>", "<title>MP quantification and filtering</title>", "<p id=\"Par32\">Raw MP data were retrieved from the PRIDE repository with accession number <ext-link ext-link-type=\"uri\" xlink:href=\"https://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD013655\">PXD013655</ext-link> (ref. <sup>##REF##33077707##27##</sup>); samples were processed as previously described<sup>##REF##25424998##32##</sup> and re-analysed. The complete set of predicted ORFs was subsetted to obtain smaller sample-specific databases. The MG alignment files generated in the previous step were processed with featurecounts<sup>##REF##24227677##62##</sup>, and all the ORFs with a count greater than 0 for the given sample were included in the appropriate sample. Each sample-specific database was concatenated with a cRAP database of contaminants (<ext-link ext-link-type=\"uri\" xlink:href=\"https://thegpm.org/cRAP\">https://thegpm.org/cRAP</ext-link>; downloaded in July 2019) and the human UniProtKB Reference Proteome (UniProt Consortium, 2021), and decoys were generated by adding the reversed sequences of all protein entries to the databases for the estimation of false discovery rates. The search was performed using SearchGUI (v.3.3.20)<sup>##REF##29774740##63##</sup> with X!Tandem<sup>##REF##27990826##64##</sup>, MS-GF+<sup>##REF##25358478##65##</sup> and Comet<sup>##REF##23148064##66##</sup> as search engines and the following parameters: trypsin was used as the digestion enzyme, and a maximum of two missed cleavages was allowed. The tolerance levels for matching to the database were 10 ppm for MS1 and 15 ppm for MS2. Carbamidomethylation of cysteine residues and oxidation of methionines were set as fixed and variable modifications, respectively. Peptides with length between 7 and 60 amino acids and with a charge state between +2 and +4 were considered for identification. The results from SearchGUI were merged using PeptideShaker-1.16.45 (ref. <sup>##REF##25574629##67##</sup>), and all identifications were filtered to achieve a protein false discovery rate of 1%. The sample-specific peptide-spectrum matches obtained for each analysis were then used to calculate dataset-wide protein groups using the Occam subgroup method from the Pout2Prot algorithm<sup>##REF##35143215##68##</sup>. The dataset-wide protein group output was then submitted to Prophane<sup>##REF##32859984##69##</sup> with default parameters to retrieve the quantitative values using normalized spectral abundance factor. Values of protein abundance inferior to 10<sup>−3</sup> were considered equal to 0, and only proteins present in at least 20% of the training samples were retained for further analysis.</p>", "<title>Batch effect correction</title>", "<p id=\"Par33\">The whole data analysis was conducted in R 3.4.4. First, we transformed the MG, MT and MP data using the central log ratio with the function ‘clr’<sup>##UREF##7##70##</sup> to overcome the inherent problems of compositional data<sup>##UREF##8##71##,##REF##33575646##72##</sup>. To estimate the batch effect between the train and test samples introduced by the different experimental procedures (mainly the robotic biomolecular extraction in the test samples and the read length), we regressed every entry in the MG and MT matrices with a linear model (with the function ‘lm’) as:where <italic>Y</italic> is the central log ratio (clr)transformed quantification matrix; <italic>α</italic> is the intercept of the model; <italic>X</italic><sub>E</sub> and <italic>X</italic><sub>T</sub> are the environmental and technical variables (number of reads, average length of reads), respectively; <bold>β</bold><sub>E</sub> and <bold>β</bold><sub>T</sub> are the vectors of the environmental and technical coefficients, respectively; and <italic>ε</italic> is the randomly distributed Gaussian error <italic>N</italic> (0, <italic>σ</italic><sup>2</sup>). The non-normality of <bold>β</bold><sub>T</sub> was assessed with the Shapiro test<sup>##UREF##9##73##</sup> (function ‘shapiro.test’), sampling 10 times 5,000 ORFs at random per technical variable for the MG and MT matrices and computing the scores in Supplementary Tables ##SUPPL##3##3## and ##SUPPL##3##4##, respectively. Therefore, we corrected the quantification matrices as:subtracting the estimated batch effect from the quantification matrices. The distributions of <bold>β</bold><sub>T</sub> are shown in Extended Data Fig. ##FIG##5##2a##.</p>", "<title>Eigengenes and their analysis</title>", "<p id=\"Par34\">The EGs for the training set (samples from 21 March 2011 to 3 May 2012) were computed as singular right eigenvectors obtained with the function ‘svd’. The data were normalized according to the basal expression<sup>##REF##10963673##22##</sup> computing the quantification matrices as:where the first element of the eigenvalues vector <bold>Σ</bold> has been replaced by 0. The EGs were recomputed from the normalized matrices and subsequently tested using the Ljung–Box test (‘Box.test’), the augmented Dickey–Fuller test (‘adf.test’) and two Kwiatkowski–Phillips–Schmidt–Shin (‘kpss.tests’) tests with null hypotheses ‘trend’ and ‘level’, respectively. If at least two of the four tests were passed (<italic>P</italic> < 0.05 for Ljung–Box and Kwiatkowski–Phillips–Schmidt–Shin tests; <italic>P</italic> > 0.05 for Dickey–Fuller test) the EG was considered time-dependent. The <italic>i</italic><sup>th</sup> EG was modelled using seasonal ARIMA modelling (where the subtraction of the seasonal effects on the data was not required beforehand). The ARIMA model is described by three non-seasonal parameters: <italic>P</italic> (autoregressive terms), <italic>d</italic> (number of integrations for differencing) and <italic>q</italic> (moving average terms). Considering that the training set did not span two cycles (the hypothetical period of seasonal patterns), we added up to four Fourier transform terms to the model as a proxy for the seasonal component. The Fourier transform can identify in a series of data the sum of sine and cosine waves underlying the data. In this way, if the period of time is correct (in this case, 1 yr), the Fourier terms can explicitly provide the seasonal part of the temporal behaviour. Using multiple terms allows for complex seasonal effects, while limiting the maximum number to 4 prevents overfitting of the data. For this, we used the ‘arima’ function of the package fable (v.0.3.1)<sup>##UREF##4##29##</sup> as:where <italic>X</italic> is the matrix of the environmental variables, and the Fourier term includes a number of sine and cosine components <italic>K</italic>, ranging from 0 to 4. The value of <italic>K</italic> therefore spans from no seasonal effect (<italic>K</italic> = 0) to increasingly complex ones. The best model of the five was selected according to their <italic>R</italic><sup>2</sup> values. The best model thus provided the weights for the environmental variables (<italic>X</italic>) for the parameters <italic>P</italic>, <italic>d</italic> and <italic>q</italic> and for as many sine and cosine terms as the selected <italic>K</italic> parameter. We called the ensemble of those variables the ‘explanatory variables’, and we assessed their significance using analysis of variance (‘anova’ function).</p>", "<title>Eigengenes clustering and Granger causality network</title>", "<p id=\"Par35\">Considering that we required a clustering approach that is independent of scale, we computed the Pearson correlations between pairs of EGs, the output was made absolute and the Minkowski distance was computed. The clusters were retrieved using the ‘cutreeDynamic’ function (deepSplit=0, pamRespectsDendro=FALSE, minClusterSize=3) from the dynamicTreeCut package<sup>##REF##19114008##74##</sup> (because it can accommodate a complex structuring of the data), resulting in 17 groups (Extended Data Fig. ##FIG##5##2d##). From each of the 17 groups, a representative EG was selected according to the following criteria: (1) MG or MT (because MP data do not exist beyond the training set) and (2) smoothest profile (minimal median of the absolute de-trended time series). The resulting EGs are S1–17 in Fig. ##FIG##1##2a##.</p>", "<p id=\"Par36\">The signals were tested two at a time with the Granger causality test (grangertest) from the lmtest package (v.0.9-38)<sup>##UREF##10##75##</sup>, and if <italic>P</italic> < 0.05, the two signals were considered connected. The signals were screened for nonlinearity via empirical dynamic modelling as implemented in the R package rEDM (v.1.14.0)<sup>##UREF##11##76##</sup>. We first identified the best number of lags (embedding value) to analyse the signals using the simplex function and default parameters. The signals were screened with the S-map method<sup>##UREF##12##77##</sup>, and only three signals appeared to be putatively nonlinear: S7, S8 and S17. All the causal links identified with the Granger causality test were also tested with the convergent cross-mapping method (Extended Data Fig. ##FIG##8##5##) using the function ccm with library size=c(20,50,1) and default parameters. If one of the signals connected with the Granger test was one of the putatively nonlinear ones, we verified the link using convergent cross-mapping, again from the rEDM package<sup>##UREF##11##76##</sup>. Visualization of the network was performed with Cytoscape<sup>##REF##14597658##78##</sup> while manually adjusting the edges and directionality arrows to add the empirical dynamic modelling to the Granger causality results.</p>", "<title>Modelling the signals and model selection</title>", "<p id=\"Par37\">For each signal, we trained multiple models using three techniques (ARIMA, Prophet and neural network using the functions ARIMA, Prophet and NNETAR, respectively, all implemented in the R package fable<sup>##UREF##4##29##</sup>), alongside a range of values for the parameters accounting for seasonal components. Each signal was modelled as a separate process whereby the signal itself was the target of the model and the environmental parameters were the only exogenous variables. Therefore, we did not use any information transfer among the signals in the modelling.</p>", "<p id=\"Par38\">We fitted ARIMA with up to four Fourier components (see ‘Eigengenes and their analysis’ section), while the parameters <italic>P</italic>, <italic>d</italic> and <italic>q</italic> were automatically optimized by the function, leading to five ARIMA models (one for each increment of Fourier transform terms, starting with 0). For the Prophet modelling, we specified seasonality (period = ‘year’, type = ‘additive’ and order = from 0 to 4, analogously to the Fourier transform terms of the ARIMA) and growth (type = ‘logistic’), resulting in five Prophet models. The neural network function was used whereby the number of nodes in the hidden layer was set to 10, 20 and 30. The 13 models for each signal were scored according to their RMSE, and the 3 models with the lowest RMSE were combined (weighted by 1 – RMSE), as a 14th ensemble model. The RMSE was calculated for the 14 models as well (Extended Data Fig. ##FIG##12##9##). For each signal, the model with the lowest RMSE was selected for the putative generative process and used to forecast the test set with the function ‘forecast’ of the fable package and supplying environmental parameter readings.</p>", "<title>Forecasting the signals and reconstruction of future samples</title>", "<p id=\"Par39\">The 17 signals were forecast (‘forecast’ function of the fable package<sup>##UREF##4##29##</sup>) for the 5 yr following the training set, using the fitted models and the environmental variables (recorded in the forecasting period) as exogenous variables. The forecast signals were therefore used to ‘reconstruct’ the information in the future samples, that is, predict the actual gene abundance (MG) and expression value (MT) matrices associated with the test samples. This was possible because the matrices used to summarize the LAO community can be expressed using a linear combination of the 17 signals plus a basal gene abundance/expression (that we previously removed in the analysis). We therefore decided to ‘reconstruct’ June 2012–2016 matrices for the reaction, pathway and family summarization of gene abundances (MG) and expression values (MT). We ran linear regression (‘lm’ function) using the six training set matrices for the categories above as target variables and the 17 signals as explanatory variables. We then ‘reconstructed’ the test matrices using a linear combination of the forecast signals over the test set, weighted by the betas and offset by the intercept (basal level) derived from the linear model of the training set while also adding the intercept (basal level). The reconstructed and the real samples were compared on an individual basis (Extended Data Fig. ##FIG##13##10##) and on a month-averaged basis (Fig. ##FIG##3##4##).</p>", "<title>Reporting summary</title>", "<p id=\"Par40\">Further information on research design is available in the ##SUPPL##1##Nature Portfolio Reporting Summary## linked to this article.</p>" ]	[ "<title>Results and Discussion</title>", "<title>Characterization of the microbial community</title>", "<p id=\"Par7\">From the experimental period between 21 March 2011 and 3 May 2012 (ref. <sup>##REF##33077707##27##</sup>), we previously obtained and analysed 51 weekly samples, to which we added 21 samples collected in the month of June during the years 2012–2016. The 72 samples were submitted to the same meta-omic analyses—MG, metatranscriptomics (MT) and metaproteomics (MP)—and processed individually to obtain 72 metagenomic assemblies, collections of metagenome-assembled genomes (MAGs), plasmids, viruses, unbinned prokaryotic chromosomal contigs and the corresponding gene expression at the transcriptional and proteomic levels. The combined datasets of the previous time series alongside the new samples were analysed together with updated bioinformatic workflows to allow a coherent comparison between samples along the time series while addressing the batch effect arising from combining the two sets (see <xref rid=\"Sec9\" ref-type=\"sec\">Methods</xref> and Extended Data Fig. ##FIG##5##2a##). To form coherent sets spanning the whole time series, we individually clustered the bins (prokaryotic and eukaryotic) and the contigs (viral, plasmid and unbinned) according to their sequence (see <xref rid=\"Sec9\" ref-type=\"sec\">Methods</xref>), which led to a total of 144 representative MAGs (rMAGs) and 1,681,736 representative contigs (rContigs), yielding 4,711,952 open reading frames (ORFs) (Supplementary Tables ##SUPPL##3##1## and ##SUPPL##3##2##). A KEGG Orthology group (KO term) was assigned to 55% of the total retrieved ORFs, while taxonomic affiliations were assigned to 38.5%. The number of ORF copies as well as their detected gene expression and protein abundances were determined over the extended dataset (see <xref rid=\"Sec9\" ref-type=\"sec\">Methods</xref>). We found on average 2.2 × 10<sup>6</sup> ± 4.8 × 10<sup>5</sup> (s.d.) ORFs, 9.1 × 10<sup>5</sup> ± 1.7 × 10<sup>5</sup> transcripts and 2.4 × 10<sup>5</sup> ± 2.5 × 10<sup>4</sup> protein groups per sample. However, most of the genes were not found to be expressed over the entire dataset or were only detected in a few samples. This suggests that an important fraction of the gene pool in the LAOs is not specifically required for community function or their expression levels are below the detection limit, hinting that their cumulative functional effort may be compartmentalized. This finding supports previous results<sup>##REF##28721231##30##</sup> showing how a large portion of the community is redundant and only few functions are keystone. Read recruitment (on the ORF level) per sample was on average 59 ± 9% for the MG and 82 ± 3% for the MT, and peptide matching was 27 ± 4%. The recruitment improved from the previous work on the same datasets, which reported that 26 ± 3% and 27 ± 3% of the MG and MT reads mapped against the MAGs, respectively<sup>##REF##33077707##27##</sup>. This is due to an update of the bioinformatic tools used and the inclusion of all the unbinned contigs longer than 1,000 nt in the analysis (see <xref rid=\"Sec9\" ref-type=\"sec\">Methods</xref>).</p>", "<p id=\"Par8\">The rMAGs spanned the expected phyla of the BWWTP community and included members of the Actinobacteria, Bacteroidetes, Chlorobi, Fusobacteria, Nitrospirae, Proteobacteria and Spirochaetes, in addition to <italic>Candidatus</italic> Gracilibacteria (Fig. ##FIG##0##1a##), thereby reproducing previously described results<sup>##REF##33077707##27##</sup>. On a more detailed taxonomic level, we were able to identify three strains of <italic>Microthrix parvicella</italic> and 17 strains of <italic>Moraxella</italic> spp. At no point over the course of the time series did a single rMAG largely dominate the community, but the combined populations of the genera <italic>Microthrix</italic> and <italic>Moraxella</italic> exhibited a percentage abundance with medians of 15.9% and 3.6%, respectively<sup>##REF##33139880##31##,##REF##25424998##32##</sup>. The majority of the contigs were not affiliated with defined MAGs (Fig. ##FIG##0##1b##) and are probably coming from incomplete genomes and alternative regions of the rMAGs, thus encapsulating the within-population diversity of the LAO community.</p>", "<title>The temporal signals underlying the microbial community</title>", "<p id=\"Par9\">Considering that the information necessary to forecast the community dynamics and linked gene expression may be most represented in any biological (for example, taxonomical or functional representation) or environmental data layer, we decided to include multiple layers in our analysis (the whole workflow is depicted in Extended Data Fig. ##FIG##4##1##). Regarding the microbial community, we explored multiple taxonomic and functional levels at once and summarized their temporal characteristics. Thus, the three quantification matrices (MG, MT and MP) were used to compute ‘summary’ matrices according to the ORF descriptors. Hence, we computed one matrix per omic layer for the six formed taxonomic descriptors (phylum, class, order, family, genus and species) and two functional ones (KO terms and pathways) (Extended Data Fig. ##FIG##5##2b##). The resulting 27 matrices (3 original and 24 summary) were used to compute the system’s eigengenes (EGs)<sup>##REF##10963673##22##</sup>. In previous work, the first EG in a time series was shown to represent ‘steady state’ gene expression, encapsulating the largest explained variance (EV). Therefore, the first EG (average EV 50 ± 22% in all the datasets) was removed. We screened the subsequent EGs for time dependency (see <xref rid=\"Sec9\" ref-type=\"sec\">Methods</xref>), selecting a set of 210 EGs, and assessed how much of the data variation they explained (Extended Data Fig. ##FIG##5##2c##).</p>", "<p id=\"Par10\">To reduce potential redundancy associated with the time-resolved EGs identified across multiple data layers and bring together the same temporal behaviours, we clustered the set of 210 EGs into 17 representative EGs (see <xref rid=\"Sec9\" ref-type=\"sec\">Methods</xref>). These are hereafter referred to as signals (S1–17) and shown in Fig. ##FIG##1##2a##. We assumed that the 17 signals were not redundant because they were different enough to not cluster together. Each cluster contained multiple EGs with their associated EV (Extended Data Fig. ##FIG##5##2d##), and we associated the maximum EV of each cluster to its respective signal. In total, signals S1–17 accounted for 91.1% of the ‘temporal’ EV in the system (while the leftover 8.9% represented noise) and covered all temporal information in the training set.</p>", "<p id=\"Par11\">The 17 representative signals (S1–17) were modelled using the environmental parameters as exogenous variables (after collinearity screening, see <xref rid=\"Sec9\" ref-type=\"sec\">Methods</xref> and Extended Data Fig. ##FIG##6##3##) as shown in Fig. ##FIG##1##2b##. Moreover, the model includes predictors derived from the ARIMA, such as the intercept (the basal abundance/expression), autoregression (the time-lagged self-dependence) and sine/cosine (the cyclical behaviours, including seasonality), which explain the microbial process through its ARIMA components. In summary, we include self-dependent, cyclical and environmental interactions to explain community dynamics. As seen in Fig. ##FIG##1##2b##, all the signals are generally explained more via the ARIMA components rather than the environmental ones. This is partially because some of the environmental variables also have a seasonal trend (for example, temperature) and their impact will be significant in the model if their values explain more than the seasonality (that is, having a fine-tuning effect). Therefore, the cyclical environmental patterns, such as temperature and water inflow, end up being factored into the cyclical part of the model, while only the residual effect is assessed by the properly named variable (for example, temperature). Moreover, it is interesting to note how only few of the environmental variables automatically collected by the BWWTP (variable blocks ‘Inflow’, ‘V1’ and ‘V2’) are significant to the model compared with the ones collected manually (Fig. ##FIG##1##2b##). This may be explained by heterogeneous spatial effects where the surface of the tank is a patchwork of neighbouring habitats with discrepancies in parameter values due to the viscosity of the foam. A similar microenvironment has been observed for flocks in BWWTP where nitrification was shown to happen in the outer 125 μm of the aggregates<sup>##REF##9726900##33##</sup>.</p>", "<p id=\"Par12\">The large importance of a ‘ground state’ in BWWTP is linked to the need for robustness of a system that is operated primarily for public health purposes and that should be hardly perturbed during parameter-controlled operations. Furthermore, it has been shown in an activated sludge population, sampled monthly over 9 yr, that only one out of five microbiome clusters clearly oscillated with the seasons and reached a peak abundance of 22.3% in the community<sup>##REF##34615557##18##</sup>. More possible temporal patterns are depicted in Extended Data Fig. ##FIG##7##4##.</p>", "<title>The ecological events in the microbial community</title>", "<p id=\"Par13\">Even if the signals S1–17 are linearly independent from one another, we hypothesized that there might be some links through time among them. These links might coalesce the system into cliques of temporally concatenated ecological events that follow each other in an ordered sequence (similar to a domino effect). We therefore used the Granger causality test, which assesses the transfer of information across time between two series of observations, to generate a causal network for S1–17 (<italic>P</italic> < 0.05) with a maximum lag of 16 weeks. We also screened the signals for nonlinearity, and in case one of the nonlinear signals had a link, we verified it with a convergent cross-mapping analysis (see <xref rid=\"Sec9\" ref-type=\"sec\">Methods</xref> and Extended Data Fig. ##FIG##8##5##). Incidentally, all signals except for S16 demonstrated a temporal relationship with at least one other signal, resulting in a single network of causality. We decided to focus on two particular cliques of nodes in the network (Fig. ##FIG##1##2c##) to explore the ecological domino effect: C1 (including S1, S10 and S17) and C2 (S9, S4, S7 and S8). To explore the ecological and environmental aspects of the system, we recalled the two-way relationship between the signals and the other eigengenes they clustered with. In doing so, we considered the generative processes and causal links of the signals which were applied to the 17 clusters. In this way, it was possible to use the top/bottom loadings of the EGs to link the high-level depiction of the system (the signals) to microbial community structure and function. The power of this representation is the amalgamation of the temporal signals, the loadings contributing to them (Extended Data Figs. ##FIG##9##6## and ##FIG##10##7##) and the generative model provided by ARIMA (Fig. ##FIG##1##2b##) to generate ecological hypotheses that can be further tested. The analysis of the causal network should be considered as a tool to generate hypotheses on how the ecological events in the community have unfolded, utilizing a data-driven approach facilitated by the multilayered meta-omic angle of the study.</p>", "<p id=\"Par14\">The first clique, C1, is composed of the two ‘crash’ signals, S1 and S10, which predict each other. Indeed, the peak/valley part of the signals, spanning autumn, has a similar shape but opposite sign, while the first part of the signals diverges with S10, showing a sinusoidal shoulder at the beginning. Both signals are strongly dependent on their previous state in time and have clear seasonal components (Fig. ##FIG##1##2b##). While S1 is positively influenced by four variables including oxygen concentration as the sole environmental parameter, S10 is negatively impacted by a range of variables at the sampling site (pH, NH<sub>4</sub>, temperature, dry matter and conductivity). <italic>Podoviridae</italic> and <italic>Mimiviridae</italic>, the two virus families identified in the system, are contributing positively and negatively, respectively, to S1 in the MG (Extended Data Fig. ##FIG##9##6##). Therefore, we infer two opposite viral mechanics involved in the fast valley-to-peak switch in autumn, which also corresponds to a major transient shift in community structure and substrate availability<sup>##REF##33077707##27##</sup>. <italic>Mimiviridae</italic> target amoebas, which are known to prey on bacteria, indicating a possible multistep, interkingdom curbing process. In the case of the <italic>Podoviridae</italic>, it targets Proteobacteria and Firmicutes, which are highly abundant in the LAO (Fig. ##FIG##0##1b##). The other crash signal, S10, is characterized by the inverted reaction of the two most abundant bacterial families in the system: Microthrixaceae and Moraxellaceae (belonging to Phylum Proteobacteria). The family Moraxellaceae contributes positively to S1 in the MG, suggesting a takeover of the community, while the gene expression in members of the Microthrixaceae family is repressed (negative impact on S1, positive on S10) as shown in Extended Data Fig. ##FIG##9##6##. It seems plausible that the rise in Podoviridae would be linked to the rise of its putative host (Moraxellaceae), at the expense of Family Microthrixaceae. However, the decrease in Mimiviridae could have triggered an increase in amoebas, resulting in greater predation on the most abundant bacterial family. These events may subsequently drive S17, a signal solely explained by a cyclic ARIMA component (Fig. ##FIG##1##2b##), suggesting that the temporal behaviours in the systems cannot always be explained by long-term seasonal and environmental factors, but probably by the ecological interactions of the microbes involved. More specifically, S17 sees the rise in abundance or gene expression of three bacterial families: the fermenting Propionibacteriaceae, the polyphosphate-accumulating Intrasporangiaceae and the autotroph Gallionellaceae. These families point to the reaction of the foam community to the observed shift in autumn. Correspondingly, S17 represents the emergence of lipid-independent metabolic strategies. We also generated an ecological hypothesis for clique C2 and specifically addressed the temporal independence regarding presence and expression of pathways for fatty acid and triacylglycerol in the community (Extended Data Fig. ##FIG##11##8##). Both topics are discussed in ##SUPPL##0##Supplementary Information##.</p>", "<title>Forecasting of future timepoints</title>", "<p id=\"Par15\">From the analysis of the signals identified in the training datasets, it is already possible to identify five signal groups: (1) alternative basal states, for example, two alternative stable states of abundance/expression (S5, S14); (2) perturbation, that is, standing wave with varying amplitude and frequency (S4, S8, S15); (3) cyclical, that is, standing wave with constant amplitude and frequency (S6, S11, S12); (4) ‘crashes’, that is, quick shifts in the state and reversion to basal states (S1, S10, S16); and (5) mixed, that is, the other factors (Extended Data Fig. ##FIG##7##4c–f##). Alternative stable states, perturbations and crashes (groups 1, 2 and 4) are hard to model without observing multiple times the shift and the perturbation events, respectively. In addition, these scenarios may include permanent shifts into a new community equilibrium or transitory signals in the community that will be eventually resolved (for example, a viral infection). To forecast such events, experimental information (such as one derived from co-culturing) on microbial interactions would be required, which is beyond the scope of this study.</p>", "<p id=\"Par16\">The 17 signals were used to train three models (with various parameters) from the package fable<sup>##UREF##4##29##</sup>, and the best-performing model on the training set was selected for each of them (see <xref rid=\"Sec9\" ref-type=\"sec\">Methods</xref> and Extended Data Fig. ##FIG##12##9##). In detail, ARIMA, Prophet and neural network models (with up to four Fourier terms for ARIMA and Prophet) were trained for S1–17 using the environmental variables as external regressors. The 51 weeks spanning the 2011–2012 data were used as a training set as well as to select the three best-scoring models to build a combined one (see <xref rid=\"Sec9\" ref-type=\"sec\">Methods</xref>). In the end, the model with the smallest root mean square error (RMSE) was selected for forecasting. A total of 21 new samples were collected in the month of June of the subsequent 5 yr to validate the model for the MG and MT data. The month of June was chosen because it is far from disruptive events (such as rain, snow and very cold temperatures) that occur in autumn and winter. To predict the behaviours of the community in these cases, we would have needed a longer training set spanning multiple yearly cycles. To assess the accuracy of the forecasting, we computed the residues of the model and checked whether they were consistent with a white noise distribution. Therefore, we showed in 16 out of 17 cases that the modelling was sufficient to reproduce the training data (Fig. ##FIG##2##3##). There were six cases in which the modelling was fully successful: S1, S2, S4, S5, S10 and S16. The six correctly forecast signals account for 34.4% of the EV and 37.7% of the EV using the complete S1–17 model. However, the most common outcome of the validation was a good fit to the training set and an insufficient one in the testing (10 out of 17 cases), including signals from all the groups. This could be caused by two phenomena: overfitting of the model to the training set or its insufficient size. Of particular interest is S8, whose signal in the training set remains stable for several months including the end of the training set, probably indicating that the perturbation is over. S4 is strictly tied with S8 (Fig. ##FIG##1##2b##); however, S4 was modelled and predicted correctly, suggesting a new cycle being established rather than a perturbation setting in. It is difficult to put these results in perspective due to the lack of similar studies covering a similar period and sampling frequency. However, a previous study<sup>##REF##34615557##18##</sup> that sampled the same BWWTP monthly for 9 yr showed that while five microbial clusters formed the main community, only one of them presented a clear yearly oscillating pattern. The same cluster was present in the BWWTP even after a bleaching event; therefore, it is reasonable to assume that a fraction of the LAO community had a similar cluster and that the signal(s) underlying it continued in the subsequent years.</p>", "<p id=\"Par17\">Unexpectedly, the correct forecasting of S1, which looked like a crash (Extended Data Fig. ##FIG##7##4f##) and was linked (among other things) to viral increase/decrease, suggests that it is indeed a cycle. We speculate that a recurrent triangular interaction between viruses, amoebas and bacteria might be repeated over time and lead to S1. The integrated meta-omics data should be supported in the future by complementary techniques such as microscopy and co-culturing to confirm this hypothesis. Unfortunately, an analogous trend seen for signal S10 was not equally well represented. Similar to S1, S16 also exhibited a behaviour expected from a system crash. However, the forecasting hinted at a cyclical occurrence; hence, what appeared like a crash is predicted to be a constitutive and repeated behaviour. Another similarity with S1 is that viral families impacted S16, that is, <italic>Mimiviridae</italic> (positively and negatively in the MG) and <italic>Podoviridae</italic> (positively in the MP). Signal S5 showed a sharp upward movement in relation to the general trend before starting to dip towards the end of the time series. Well-known bacteria involved in bulking, such as Moraxellaceae and Gordoniaceae, have loadings contributing towards S5, hinting to a quick jolt in thickening of the foam in summer and an overall cyclical effect that can be forecast over time.</p>", "<title>Forecasting gene abundance and expression</title>", "<p id=\"Par18\">Following the forecasting of the signals, we decided to try to reconstruct the samples taken from the subsequent years. The samples’ information content can be expressed as a linear combination of the signals by creating a linear model using the training set and where the signals are the predictors. Using this approach, we computed how much each of the signals contributed to the samples (that is, finding the betas of the model) and the basal abundance/expression (the intercept) of the samples. We decided to validate the approach using the gene abundance and expression values of the microbial families and reactions (KO term groups). Therefore, we fitted the linear models to those matrices from the training set and combined the results with the previously forecast signals to reconstruct the test matrices. We then compared the reconstructed values with the original ones for each individual sample (Extended Data Fig. ##FIG##13##10##). The comparisons showed a range of results, including samples that were predicted correctly (data points arranged in a narrow diagonal line), samples with poor predictions (unordered distribution of the data points) and samples with an unexpected inverse relationship with the prediction (descending diagonal line). When taking into account the explanatory variables in the ARIMA modelling, we already hypothesized a micro-environmental effect at play in the foam, making it a composition of areas with (slightly) different environmental values. We now extend that hypothesis to the sampling unit itself (the foam ‘islet’, see <xref rid=\"Sec9\" ref-type=\"sec\">Methods</xref>), which might have individual genetic potential and gene expression characteristics imputable to the process of foam formation, permanence and stability. We therefore assume that islet variability, compounded by the temporal evolution of the system, ultimately has an impact on the sample. Intuitively, if the foam islets were composed of the same genetic makeup but subject to (even small) different environmental conditions, one would expect gene abundances to be relatively stable, yet gene expression might change. Instead, observing the coherent response between MG and MT to the reconstructed samples from Extended Data Fig. ##FIG##13##10##, it is apparent that the genetic makeup of the islets changes from week to week and gene expression changes accordingly with this alteration. We assume that our modelling creates a ‘smoother’ representation of the data, necessarily averaging the observed sample to sample variability. This can be imputable to the SVD step of the modelling, which isolates ‘high-level’ patterns that harbour lower noise than any individual ORF- or descriptor-based summarization of the data. Moreover, the scale of the values is often larger in the reconstructed samples than in the test ones (Extended Data Fig. ##FIG##13##10##).</p>", "<p id=\"Par19\">To counter the islet variability, we considered the average of the measured and predicted values over the month of June for each year and computed the coefficient of variation, <italic>R</italic><sup>2</sup>, for each of them (Fig. ##FIG##1##2##). The <italic>R</italic><sup>2</sup> is strikingly high (≥0.87) in all the six matrices for the subsequent 3 yr after the training set, but the predictability starts decreasing from the fourth year after the training samples. This implies that in our system (LAO), the observation through meta-omics data and the environmental parameters for 14 months is sufficient to build a reliable predictive model. Moreover, with this model and the monitoring of the environmental parameters, it is possible to correctly chart the community structure and function at any given point within the subsequent 3 yr after the training set.</p>" ]	[ "<title>Results and Discussion</title>", "<title>Characterization of the microbial community</title>", "<p id=\"Par7\">From the experimental period between 21 March 2011 and 3 May 2012 (ref. <sup>##REF##33077707##27##</sup>), we previously obtained and analysed 51 weekly samples, to which we added 21 samples collected in the month of June during the years 2012–2016. The 72 samples were submitted to the same meta-omic analyses—MG, metatranscriptomics (MT) and metaproteomics (MP)—and processed individually to obtain 72 metagenomic assemblies, collections of metagenome-assembled genomes (MAGs), plasmids, viruses, unbinned prokaryotic chromosomal contigs and the corresponding gene expression at the transcriptional and proteomic levels. The combined datasets of the previous time series alongside the new samples were analysed together with updated bioinformatic workflows to allow a coherent comparison between samples along the time series while addressing the batch effect arising from combining the two sets (see <xref rid=\"Sec9\" ref-type=\"sec\">Methods</xref> and Extended Data Fig. ##FIG##5##2a##). To form coherent sets spanning the whole time series, we individually clustered the bins (prokaryotic and eukaryotic) and the contigs (viral, plasmid and unbinned) according to their sequence (see <xref rid=\"Sec9\" ref-type=\"sec\">Methods</xref>), which led to a total of 144 representative MAGs (rMAGs) and 1,681,736 representative contigs (rContigs), yielding 4,711,952 open reading frames (ORFs) (Supplementary Tables ##SUPPL##3##1## and ##SUPPL##3##2##). A KEGG Orthology group (KO term) was assigned to 55% of the total retrieved ORFs, while taxonomic affiliations were assigned to 38.5%. The number of ORF copies as well as their detected gene expression and protein abundances were determined over the extended dataset (see <xref rid=\"Sec9\" ref-type=\"sec\">Methods</xref>). We found on average 2.2 × 10<sup>6</sup> ± 4.8 × 10<sup>5</sup> (s.d.) ORFs, 9.1 × 10<sup>5</sup> ± 1.7 × 10<sup>5</sup> transcripts and 2.4 × 10<sup>5</sup> ± 2.5 × 10<sup>4</sup> protein groups per sample. However, most of the genes were not found to be expressed over the entire dataset or were only detected in a few samples. This suggests that an important fraction of the gene pool in the LAOs is not specifically required for community function or their expression levels are below the detection limit, hinting that their cumulative functional effort may be compartmentalized. This finding supports previous results<sup>##REF##28721231##30##</sup> showing how a large portion of the community is redundant and only few functions are keystone. Read recruitment (on the ORF level) per sample was on average 59 ± 9% for the MG and 82 ± 3% for the MT, and peptide matching was 27 ± 4%. The recruitment improved from the previous work on the same datasets, which reported that 26 ± 3% and 27 ± 3% of the MG and MT reads mapped against the MAGs, respectively<sup>##REF##33077707##27##</sup>. This is due to an update of the bioinformatic tools used and the inclusion of all the unbinned contigs longer than 1,000 nt in the analysis (see <xref rid=\"Sec9\" ref-type=\"sec\">Methods</xref>).</p>", "<p id=\"Par8\">The rMAGs spanned the expected phyla of the BWWTP community and included members of the Actinobacteria, Bacteroidetes, Chlorobi, Fusobacteria, Nitrospirae, Proteobacteria and Spirochaetes, in addition to <italic>Candidatus</italic> Gracilibacteria (Fig. ##FIG##0##1a##), thereby reproducing previously described results<sup>##REF##33077707##27##</sup>. On a more detailed taxonomic level, we were able to identify three strains of <italic>Microthrix parvicella</italic> and 17 strains of <italic>Moraxella</italic> spp. At no point over the course of the time series did a single rMAG largely dominate the community, but the combined populations of the genera <italic>Microthrix</italic> and <italic>Moraxella</italic> exhibited a percentage abundance with medians of 15.9% and 3.6%, respectively<sup>##REF##33139880##31##,##REF##25424998##32##</sup>. The majority of the contigs were not affiliated with defined MAGs (Fig. ##FIG##0##1b##) and are probably coming from incomplete genomes and alternative regions of the rMAGs, thus encapsulating the within-population diversity of the LAO community.</p>", "<title>The temporal signals underlying the microbial community</title>", "<p id=\"Par9\">Considering that the information necessary to forecast the community dynamics and linked gene expression may be most represented in any biological (for example, taxonomical or functional representation) or environmental data layer, we decided to include multiple layers in our analysis (the whole workflow is depicted in Extended Data Fig. ##FIG##4##1##). Regarding the microbial community, we explored multiple taxonomic and functional levels at once and summarized their temporal characteristics. Thus, the three quantification matrices (MG, MT and MP) were used to compute ‘summary’ matrices according to the ORF descriptors. Hence, we computed one matrix per omic layer for the six formed taxonomic descriptors (phylum, class, order, family, genus and species) and two functional ones (KO terms and pathways) (Extended Data Fig. ##FIG##5##2b##). The resulting 27 matrices (3 original and 24 summary) were used to compute the system’s eigengenes (EGs)<sup>##REF##10963673##22##</sup>. In previous work, the first EG in a time series was shown to represent ‘steady state’ gene expression, encapsulating the largest explained variance (EV). Therefore, the first EG (average EV 50 ± 22% in all the datasets) was removed. We screened the subsequent EGs for time dependency (see <xref rid=\"Sec9\" ref-type=\"sec\">Methods</xref>), selecting a set of 210 EGs, and assessed how much of the data variation they explained (Extended Data Fig. ##FIG##5##2c##).</p>", "<p id=\"Par10\">To reduce potential redundancy associated with the time-resolved EGs identified across multiple data layers and bring together the same temporal behaviours, we clustered the set of 210 EGs into 17 representative EGs (see <xref rid=\"Sec9\" ref-type=\"sec\">Methods</xref>). These are hereafter referred to as signals (S1–17) and shown in Fig. ##FIG##1##2a##. We assumed that the 17 signals were not redundant because they were different enough to not cluster together. Each cluster contained multiple EGs with their associated EV (Extended Data Fig. ##FIG##5##2d##), and we associated the maximum EV of each cluster to its respective signal. In total, signals S1–17 accounted for 91.1% of the ‘temporal’ EV in the system (while the leftover 8.9% represented noise) and covered all temporal information in the training set.</p>", "<p id=\"Par11\">The 17 representative signals (S1–17) were modelled using the environmental parameters as exogenous variables (after collinearity screening, see <xref rid=\"Sec9\" ref-type=\"sec\">Methods</xref> and Extended Data Fig. ##FIG##6##3##) as shown in Fig. ##FIG##1##2b##. Moreover, the model includes predictors derived from the ARIMA, such as the intercept (the basal abundance/expression), autoregression (the time-lagged self-dependence) and sine/cosine (the cyclical behaviours, including seasonality), which explain the microbial process through its ARIMA components. In summary, we include self-dependent, cyclical and environmental interactions to explain community dynamics. As seen in Fig. ##FIG##1##2b##, all the signals are generally explained more via the ARIMA components rather than the environmental ones. This is partially because some of the environmental variables also have a seasonal trend (for example, temperature) and their impact will be significant in the model if their values explain more than the seasonality (that is, having a fine-tuning effect). Therefore, the cyclical environmental patterns, such as temperature and water inflow, end up being factored into the cyclical part of the model, while only the residual effect is assessed by the properly named variable (for example, temperature). Moreover, it is interesting to note how only few of the environmental variables automatically collected by the BWWTP (variable blocks ‘Inflow’, ‘V1’ and ‘V2’) are significant to the model compared with the ones collected manually (Fig. ##FIG##1##2b##). This may be explained by heterogeneous spatial effects where the surface of the tank is a patchwork of neighbouring habitats with discrepancies in parameter values due to the viscosity of the foam. A similar microenvironment has been observed for flocks in BWWTP where nitrification was shown to happen in the outer 125 μm of the aggregates<sup>##REF##9726900##33##</sup>.</p>", "<p id=\"Par12\">The large importance of a ‘ground state’ in BWWTP is linked to the need for robustness of a system that is operated primarily for public health purposes and that should be hardly perturbed during parameter-controlled operations. Furthermore, it has been shown in an activated sludge population, sampled monthly over 9 yr, that only one out of five microbiome clusters clearly oscillated with the seasons and reached a peak abundance of 22.3% in the community<sup>##REF##34615557##18##</sup>. More possible temporal patterns are depicted in Extended Data Fig. ##FIG##7##4##.</p>", "<title>The ecological events in the microbial community</title>", "<p id=\"Par13\">Even if the signals S1–17 are linearly independent from one another, we hypothesized that there might be some links through time among them. These links might coalesce the system into cliques of temporally concatenated ecological events that follow each other in an ordered sequence (similar to a domino effect). We therefore used the Granger causality test, which assesses the transfer of information across time between two series of observations, to generate a causal network for S1–17 (<italic>P</italic> < 0.05) with a maximum lag of 16 weeks. We also screened the signals for nonlinearity, and in case one of the nonlinear signals had a link, we verified it with a convergent cross-mapping analysis (see <xref rid=\"Sec9\" ref-type=\"sec\">Methods</xref> and Extended Data Fig. ##FIG##8##5##). Incidentally, all signals except for S16 demonstrated a temporal relationship with at least one other signal, resulting in a single network of causality. We decided to focus on two particular cliques of nodes in the network (Fig. ##FIG##1##2c##) to explore the ecological domino effect: C1 (including S1, S10 and S17) and C2 (S9, S4, S7 and S8). To explore the ecological and environmental aspects of the system, we recalled the two-way relationship between the signals and the other eigengenes they clustered with. In doing so, we considered the generative processes and causal links of the signals which were applied to the 17 clusters. In this way, it was possible to use the top/bottom loadings of the EGs to link the high-level depiction of the system (the signals) to microbial community structure and function. The power of this representation is the amalgamation of the temporal signals, the loadings contributing to them (Extended Data Figs. ##FIG##9##6## and ##FIG##10##7##) and the generative model provided by ARIMA (Fig. ##FIG##1##2b##) to generate ecological hypotheses that can be further tested. The analysis of the causal network should be considered as a tool to generate hypotheses on how the ecological events in the community have unfolded, utilizing a data-driven approach facilitated by the multilayered meta-omic angle of the study.</p>", "<p id=\"Par14\">The first clique, C1, is composed of the two ‘crash’ signals, S1 and S10, which predict each other. Indeed, the peak/valley part of the signals, spanning autumn, has a similar shape but opposite sign, while the first part of the signals diverges with S10, showing a sinusoidal shoulder at the beginning. Both signals are strongly dependent on their previous state in time and have clear seasonal components (Fig. ##FIG##1##2b##). While S1 is positively influenced by four variables including oxygen concentration as the sole environmental parameter, S10 is negatively impacted by a range of variables at the sampling site (pH, NH<sub>4</sub>, temperature, dry matter and conductivity). <italic>Podoviridae</italic> and <italic>Mimiviridae</italic>, the two virus families identified in the system, are contributing positively and negatively, respectively, to S1 in the MG (Extended Data Fig. ##FIG##9##6##). Therefore, we infer two opposite viral mechanics involved in the fast valley-to-peak switch in autumn, which also corresponds to a major transient shift in community structure and substrate availability<sup>##REF##33077707##27##</sup>. <italic>Mimiviridae</italic> target amoebas, which are known to prey on bacteria, indicating a possible multistep, interkingdom curbing process. In the case of the <italic>Podoviridae</italic>, it targets Proteobacteria and Firmicutes, which are highly abundant in the LAO (Fig. ##FIG##0##1b##). The other crash signal, S10, is characterized by the inverted reaction of the two most abundant bacterial families in the system: Microthrixaceae and Moraxellaceae (belonging to Phylum Proteobacteria). The family Moraxellaceae contributes positively to S1 in the MG, suggesting a takeover of the community, while the gene expression in members of the Microthrixaceae family is repressed (negative impact on S1, positive on S10) as shown in Extended Data Fig. ##FIG##9##6##. It seems plausible that the rise in Podoviridae would be linked to the rise of its putative host (Moraxellaceae), at the expense of Family Microthrixaceae. However, the decrease in Mimiviridae could have triggered an increase in amoebas, resulting in greater predation on the most abundant bacterial family. These events may subsequently drive S17, a signal solely explained by a cyclic ARIMA component (Fig. ##FIG##1##2b##), suggesting that the temporal behaviours in the systems cannot always be explained by long-term seasonal and environmental factors, but probably by the ecological interactions of the microbes involved. More specifically, S17 sees the rise in abundance or gene expression of three bacterial families: the fermenting Propionibacteriaceae, the polyphosphate-accumulating Intrasporangiaceae and the autotroph Gallionellaceae. These families point to the reaction of the foam community to the observed shift in autumn. Correspondingly, S17 represents the emergence of lipid-independent metabolic strategies. We also generated an ecological hypothesis for clique C2 and specifically addressed the temporal independence regarding presence and expression of pathways for fatty acid and triacylglycerol in the community (Extended Data Fig. ##FIG##11##8##). Both topics are discussed in ##SUPPL##0##Supplementary Information##.</p>", "<title>Forecasting of future timepoints</title>", "<p id=\"Par15\">From the analysis of the signals identified in the training datasets, it is already possible to identify five signal groups: (1) alternative basal states, for example, two alternative stable states of abundance/expression (S5, S14); (2) perturbation, that is, standing wave with varying amplitude and frequency (S4, S8, S15); (3) cyclical, that is, standing wave with constant amplitude and frequency (S6, S11, S12); (4) ‘crashes’, that is, quick shifts in the state and reversion to basal states (S1, S10, S16); and (5) mixed, that is, the other factors (Extended Data Fig. ##FIG##7##4c–f##). Alternative stable states, perturbations and crashes (groups 1, 2 and 4) are hard to model without observing multiple times the shift and the perturbation events, respectively. In addition, these scenarios may include permanent shifts into a new community equilibrium or transitory signals in the community that will be eventually resolved (for example, a viral infection). To forecast such events, experimental information (such as one derived from co-culturing) on microbial interactions would be required, which is beyond the scope of this study.</p>", "<p id=\"Par16\">The 17 signals were used to train three models (with various parameters) from the package fable<sup>##UREF##4##29##</sup>, and the best-performing model on the training set was selected for each of them (see <xref rid=\"Sec9\" ref-type=\"sec\">Methods</xref> and Extended Data Fig. ##FIG##12##9##). In detail, ARIMA, Prophet and neural network models (with up to four Fourier terms for ARIMA and Prophet) were trained for S1–17 using the environmental variables as external regressors. The 51 weeks spanning the 2011–2012 data were used as a training set as well as to select the three best-scoring models to build a combined one (see <xref rid=\"Sec9\" ref-type=\"sec\">Methods</xref>). In the end, the model with the smallest root mean square error (RMSE) was selected for forecasting. A total of 21 new samples were collected in the month of June of the subsequent 5 yr to validate the model for the MG and MT data. The month of June was chosen because it is far from disruptive events (such as rain, snow and very cold temperatures) that occur in autumn and winter. To predict the behaviours of the community in these cases, we would have needed a longer training set spanning multiple yearly cycles. To assess the accuracy of the forecasting, we computed the residues of the model and checked whether they were consistent with a white noise distribution. Therefore, we showed in 16 out of 17 cases that the modelling was sufficient to reproduce the training data (Fig. ##FIG##2##3##). There were six cases in which the modelling was fully successful: S1, S2, S4, S5, S10 and S16. The six correctly forecast signals account for 34.4% of the EV and 37.7% of the EV using the complete S1–17 model. However, the most common outcome of the validation was a good fit to the training set and an insufficient one in the testing (10 out of 17 cases), including signals from all the groups. This could be caused by two phenomena: overfitting of the model to the training set or its insufficient size. Of particular interest is S8, whose signal in the training set remains stable for several months including the end of the training set, probably indicating that the perturbation is over. S4 is strictly tied with S8 (Fig. ##FIG##1##2b##); however, S4 was modelled and predicted correctly, suggesting a new cycle being established rather than a perturbation setting in. It is difficult to put these results in perspective due to the lack of similar studies covering a similar period and sampling frequency. However, a previous study<sup>##REF##34615557##18##</sup> that sampled the same BWWTP monthly for 9 yr showed that while five microbial clusters formed the main community, only one of them presented a clear yearly oscillating pattern. The same cluster was present in the BWWTP even after a bleaching event; therefore, it is reasonable to assume that a fraction of the LAO community had a similar cluster and that the signal(s) underlying it continued in the subsequent years.</p>", "<p id=\"Par17\">Unexpectedly, the correct forecasting of S1, which looked like a crash (Extended Data Fig. ##FIG##7##4f##) and was linked (among other things) to viral increase/decrease, suggests that it is indeed a cycle. We speculate that a recurrent triangular interaction between viruses, amoebas and bacteria might be repeated over time and lead to S1. The integrated meta-omics data should be supported in the future by complementary techniques such as microscopy and co-culturing to confirm this hypothesis. Unfortunately, an analogous trend seen for signal S10 was not equally well represented. Similar to S1, S16 also exhibited a behaviour expected from a system crash. However, the forecasting hinted at a cyclical occurrence; hence, what appeared like a crash is predicted to be a constitutive and repeated behaviour. Another similarity with S1 is that viral families impacted S16, that is, <italic>Mimiviridae</italic> (positively and negatively in the MG) and <italic>Podoviridae</italic> (positively in the MP). Signal S5 showed a sharp upward movement in relation to the general trend before starting to dip towards the end of the time series. Well-known bacteria involved in bulking, such as Moraxellaceae and Gordoniaceae, have loadings contributing towards S5, hinting to a quick jolt in thickening of the foam in summer and an overall cyclical effect that can be forecast over time.</p>", "<title>Forecasting gene abundance and expression</title>", "<p id=\"Par18\">Following the forecasting of the signals, we decided to try to reconstruct the samples taken from the subsequent years. The samples’ information content can be expressed as a linear combination of the signals by creating a linear model using the training set and where the signals are the predictors. Using this approach, we computed how much each of the signals contributed to the samples (that is, finding the betas of the model) and the basal abundance/expression (the intercept) of the samples. We decided to validate the approach using the gene abundance and expression values of the microbial families and reactions (KO term groups). Therefore, we fitted the linear models to those matrices from the training set and combined the results with the previously forecast signals to reconstruct the test matrices. We then compared the reconstructed values with the original ones for each individual sample (Extended Data Fig. ##FIG##13##10##). The comparisons showed a range of results, including samples that were predicted correctly (data points arranged in a narrow diagonal line), samples with poor predictions (unordered distribution of the data points) and samples with an unexpected inverse relationship with the prediction (descending diagonal line). When taking into account the explanatory variables in the ARIMA modelling, we already hypothesized a micro-environmental effect at play in the foam, making it a composition of areas with (slightly) different environmental values. We now extend that hypothesis to the sampling unit itself (the foam ‘islet’, see <xref rid=\"Sec9\" ref-type=\"sec\">Methods</xref>), which might have individual genetic potential and gene expression characteristics imputable to the process of foam formation, permanence and stability. We therefore assume that islet variability, compounded by the temporal evolution of the system, ultimately has an impact on the sample. Intuitively, if the foam islets were composed of the same genetic makeup but subject to (even small) different environmental conditions, one would expect gene abundances to be relatively stable, yet gene expression might change. Instead, observing the coherent response between MG and MT to the reconstructed samples from Extended Data Fig. ##FIG##13##10##, it is apparent that the genetic makeup of the islets changes from week to week and gene expression changes accordingly with this alteration. We assume that our modelling creates a ‘smoother’ representation of the data, necessarily averaging the observed sample to sample variability. This can be imputable to the SVD step of the modelling, which isolates ‘high-level’ patterns that harbour lower noise than any individual ORF- or descriptor-based summarization of the data. Moreover, the scale of the values is often larger in the reconstructed samples than in the test ones (Extended Data Fig. ##FIG##13##10##).</p>", "<p id=\"Par19\">To counter the islet variability, we considered the average of the measured and predicted values over the month of June for each year and computed the coefficient of variation, <italic>R</italic><sup>2</sup>, for each of them (Fig. ##FIG##1##2##). The <italic>R</italic><sup>2</sup> is strikingly high (≥0.87) in all the six matrices for the subsequent 3 yr after the training set, but the predictability starts decreasing from the fourth year after the training samples. This implies that in our system (LAO), the observation through meta-omics data and the environmental parameters for 14 months is sufficient to build a reliable predictive model. Moreover, with this model and the monitoring of the environmental parameters, it is possible to correctly chart the community structure and function at any given point within the subsequent 3 yr after the training set.</p>" ]	[ "<title>Conclusions</title>", "<p id=\"Par20\">We present the temporal reconstruction of the surface microbial community of a BWWTP over 1.5 yr of weekly sampling. The gene abundance and expression show 17 distinct and linearly independent signals (S1–17) across time (Fig. ##FIG##1##2a##), many of which were explained by the physicochemical parameters and the mathematical components describing self-dependence and seasonality (Fig. ##FIG##1##2c##). The signals were tied in a ‘temporal domino’ (Fig. ##FIG##1##2b##), from which we selected two cliques to successfully describe the ‘autumn crash’ (C1) and an oscillatory perturbation (C2, see ##SUPPL##0##Supplementary Information##). The models built on the S1–17 signals and paired with the environmental parameters were subsequently used to forecast the next 5 yr of the LAO community. We demonstrate that six of the forecast signals (S1, S4, S7, S9, S14 and S17) are indeed validated by the future samples (Fig. ##FIG##2##3##) and cover some interesting aspects of the BWWTP surface community, such as nitrogen metabolism (S4 and S9) and viral interplay (S1 and possibly S7), as well as changes in foam-related metabolism (S17). Importantly, when rebuilding the gene abundance and expression data at the levels of taxonomic families, reactions and pathways and extrapolating to the future samples (June 2012–2016), the results over the averaged month of June showed a very high degree of predictability for the subsequent 3 yr after the training set (<italic>R</italic><sup>2</sup> ≥ 0.87). However, a clear fading was apparent starting from the third year (Fig. ##FIG##3##4##).</p>", "<p id=\"Par21\">Overall, the present approach covers most of the time-dependent information in the system. It furthermore enables us to describe a complex community with its behaviour in a number of temporal patterns, which is easy for a human to interpret (in our case, 17 signals), and link these to their underlying generative processes, as well as the environmental parameters, taxa and functions supported by them. Furthermore, the method allows reliable forecasting of these fundamental signals that represent a seasonality and temporal span (>1 yr, hence more than one expected full cycle of the system), indicating that the time- and environment-dependent components can explain the community during regular BWWTP operations. We hope that further work, especially sampling the BWWTP at higher time frequencies (for example, hours) and/or for longer periods (multi-annual training sets), could be integrated for a more detailed systemic description and increased forecasting ability to cover those phenomena poorly constrained by the current model. Finally, we infer that there are environmental drivers in the macroscopic composition of the LAO community behaviour and that we can correctly reconstruct the samples from 3 yr into the future when averaged over a 1-month period. However, we also infer that the community exhibits a high degree of variation, making the prediction of a specific sample inaccurate with this method. The current work forecasts a BWWTP during its normal operations, and it could be exploited to predict population and gene expression levels in the temporal medium range when knowing the environmental parameters. However, a potentially interesting development would be to test what happens when introducing ‘critical’ values of the environmental parameters in the model to simulate an environmental disturbance. To use this approach, important details about the experimental design should be considered. The chosen (micro)biological system should be sampled at time intervals that are relevant for the research question (for example, cell doubling times if one wants to study microbial community composition dynamics) and spanning multiple time cycles.</p>" ]	[ "<p id=\"Par1\">Predicting the behaviour of complex microbial communities is challenging. However, this is essential for complex biotechnological processes such as those in biological wastewater treatment plants (BWWTPs), which require sustainable operation. Here we summarize 14 months of longitudinal meta-omics data from a BWWTP anaerobic tank into 17 temporal signals, explaining 91.1% of the temporal variance, and link those signals to ecological events within the community. We forecast the signals over the subsequent five years and use 21 extra samples collected at defined time intervals for testing and validation. Our forecasts are correct for six signals and hint on phenomena such as predation cycles. Using all the 17 forecasts and the environmental variables, we predict gene abundance and expression, with a coefficient of determination ≥0.87 for the subsequent three years. Our study demonstrates the ability to forecast the dynamics of open microbial ecosystems using interactions between community cycles and environmental parameters.</p>", "<p id=\"Par2\">Using high-resolution multi-omic data from biological wastewater treatment plants, the authors develop a method to forecast microbial community composition and function; the forecasting is accurate for 3 yr into the future.</p>", "<title>Subject terms</title>" ]	[ "<title>Main</title>", "<p id=\"Par3\">Microorganisms are ubiquitous on planet Earth<sup>##REF##16415926##1##</sup> and constitute up to 17% of its carbon biomass<sup>##REF##29784790##2##</sup>. Microbial lineages are continuously evolving to fill a diverse set of ecological niches, balancing their complementary metabolic capabilities to form communities<sup>##REF##16415926##1##</sup> which, in turn, affect biogeochemical cycles<sup>##REF##18497287##3##</sup>. Understanding the temporal dynamics of microbial ecosystems and their links to the environment has become a common problem for many research fields spanning biomedicine, agriculture, biotechnology and climate change. While forecasting community composition dynamics has been successfully achieved for some environments (for example, refs. <sup>##REF##22504588##4##,##REF##33289510##5##</sup>) and explored theoretically<sup>##REF##34131131##6##</sup>, the forecasting of gene expression dynamics over time in relation to environmental conditions remains an open challenge<sup>##REF##28540925##7##</sup>. Although previous work<sup>##REF##24449851##8##,##REF##27655888##9##</sup> have approached the problem in marine systems with relatively stable environmental conditions, such as those associated with marine oxygen minimum zones, using meta-omic (DNA, RNA, protein) and environmental parameter information to model biogeochemical cycles, a generalized framework for time-variable integration of meta-omic datasets into models of community ecology remains to be established.</p>", "<p id=\"Par4\">The surface community of biological wastewater treatment plants (BWWTPs) represents an excellent candidate to become a model system to establish such a modelling framework for the following three reasons<sup>##REF##16971007##10##</sup>. Firstly, BWWTPs share the challenges linked to most environments, as it is an open system with a constant influx of new populations<sup>##REF##34187887##11##</sup> and exchange of matter and energy with the environment (that is, access to open air and sun irradiation). However, these challenges can be mitigated by keeping operational parameters (for example, pH, phosphate and nitrate) within a controllable range. In addition, the microbial community biodiversity is of intermediate range, especially for the floating biomass, allowing fairly comprehensive data acquisition. Secondly, BWWTP communities share common metabolic pathways, albeit every local community has its own equilibrium, and its detailed makeup depends on the operational parameters, geographical location and inflow composition<sup>##REF##29328933##12##–##REF##35393411##14##</sup>. Microbial communities in BWWTPs possess dynamics at different temporal scales that are rather well described: the microbial and chemical composition of the inflow is known to change according to the time of day, the day of the week and the inflow volume<sup>##REF##34655869##15##</sup>. In addition, temperature-driven seasonality has been found to influence the community<sup>##REF##29328933##12##,##REF##32305756##16##</sup>, notably the surface community<sup>##REF##16460780##17##</sup>, as well as multi-annual trends. While one-time destructive perturbations show an impact on the community (for example, human interventions (such as bleaching, shutdowns<sup>##REF##34615557##18##,##REF##31266798##19##</sup>) and weather (that is, rain)), they are all monitored or encoded in the standard operational parameters of the plants. Finally, forecasting the behaviour of microbial communities in BWWTPs is highly desirable as stable operation allows reclamation of clean water as well as the harnessing of chemical energy<sup>##REF##24624120##20##</sup>. Moreover, its functioning has to minimize undesired production (and uncontrolled release) of greenhouse gases such as N<sub>2</sub>O<sup>##REF##30708205##21##</sup>. In particular, the surface community is recognized to be a potential source of neutral lipids, a family of molecules of high added value usable for third-generation biodiesel production<sup>##REF##24624120##20##</sup>.</p>", "<p id=\"Par5\">When dealing with complex microbial communities, far from lab-scale experiments, empirical modelling can enable efficient representation and forecasting (see ##SUPPL##0##Supplementary Information## for a short summary of techniques). To achieve this, we explore a combination of strategies to first extract all the temporal information in an agnostic manner, such as through singular value decomposition (SVD)<sup>##REF##10963673##22##</sup>, and then perform forecasting by explicitly computing temporal cycles and link those patterns directly to the explanatory variables. SVD can decompose a matrix into two separate matrices of eigenvectors and a vector of eigenvalues (the technique is further explored in refs. <sup>##UREF##0##23##,##UREF##1##24##</sup>). When applied to gene abundance (or expression) data over time, the first matrix is associated with the set of temporal patterns underlying the data and the second with the ‘loadings’ (that is, how much each individual gene is contributing to each pattern). The seasonal version of the forecasting method, autoregressive integrated moving average (ARIMA), computes cyclical (seasonality), autoregressive (temporal self-dependence), differencing (difference between consecutive timepoints) and moving-average (averaging of consecutive timepoints) components of a time series<sup>##UREF##2##25##</sup>. It thereby offers a very flexible framework for time-series modelling<sup>##UREF##2##25##</sup>.</p>", "<p id=\"Par6\">Here we combine SVD and several time-series algorithms into a generalizable framework for modelling the temporal dynamics of multilayered meta-omics data (Extended Data Fig. ##FIG##4##1##). We demonstrate the power of this framework through analysis of integrated meta-omic and environmental parameter datasets from a microbial community enriched in lipid-accumulating organisms (LAOs) on the surface of the anaerobic tank of the BWWTP in Schifflange (Luxembourg). The sample set comprises 51 time-resolved samples collected between March 2011 and May 2012 for training, with 21 additional samples collected between 2012 and 2016 for testing and validation. For both sets, the biomolecules were co-extracted<sup>##REF##22763648##26##</sup> and the data for the training set were presented in a previous study<sup>##REF##33077707##27##</sup>. We reconstructed the metagenomic (MG) structure of the community, alongside its taxonomy, genetic potential, transcript and protein levels. We employed SVD to extract relevant temporal patterns, which were then clustered into 17 fundamental signals. These were integrated with collected environmental parameters to build an ARIMA model, augmented with seasonal components that could explain the observed signals. Multiple models (ARIMA, Prophet<sup>##UREF##3##28##</sup> and NNETAR neural network model<sup>##UREF##4##29##</sup>) were trained to forecast the signals for the subsequent 5 yr. These models are flexible and customizable, being able to explain complex time series, breaking them down to the individual components (ARIMA and Prophet) or being all-purpose powerful (neural network). However, ARIMA assumes that the parameters behind the process are constant, while Prophet can model a time-dependent evolution of the ARIMA parameters. On the other hand, ARIMA is the only model (in the current R package fable<sup>##UREF##4##29##</sup> implementation) where it is possible to obtain information about the contribution of the individual variables to the forecasting, making it suitable as an explicative model. This allowed us to correctly predict the gene abundance and expression of the populations in the community.</p>", "<title>Supplementary information</title>", "<p>\n\n\n\n\n</p>", "<title>Source data</title>", "<p>\n\n\n\n</p>" ]	[ "<title>Extended data</title>", "<p id=\"Par45\">\n\n</p>", "<p id=\"Par46\">\n\n</p>", "<p id=\"Par47\">\n\n</p>", "<p id=\"Par48\">\n\n</p>", "<p id=\"Par49\">\n\n</p>", "<p id=\"Par50\">\n\n</p>", "<p id=\"Par51\">\n\n</p>", "<p id=\"Par52\">\n\n</p>", "<p id=\"Par53\">\n\n</p>", "<p id=\"Par54\">\n\n</p>", "<title>Extended data</title>", "<p id=\"Par41\">is available for this paper at 10.1038/s41559-023-02241-3.</p>", "<title>Supplementary information</title>", "<p id=\"Par42\">The online version contains supplementary material available at 10.1038/s41559-023-02241-3.</p>", "<title>Acknowledgements</title>", "<p>We thank the Luxembourg National Research Fund (FNR) for supporting this work through various funding instruments. Specifically, a PRIDE doctoral training unit grant (PRIDE/15/10907093), CORE grants (CORE/17/SM/11689322), a European Union ERASysAPP grant (INTER/SYSAPP/14/05) and an ATTRACT grant (A09/03) all awarded to P.W., as well as a CORE Junior (C15/SR/10404839) grant to E.E.L.M. The project received financial support from the Integrated Biobank of Luxembourg with funds from the Luxembourg Ministry of Higher Education and Research. This work was also supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant Agreement No. 863664). The work of P.M. was funded by the ‘Plan Technologies de la Santé du Gouvernement du Grand-Duché de Luxembourg’ through the Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg. S.W. was supported by the Austrian Science Fund (FWF) Elise Richter V585-B31. P.B.P. is grateful for the support from the Research Council of Norway (FRIPRO programme: 250479) and the Novo Nordisk Foundation (Project No. 0054575). The authors acknowledge the ULHPC for providing and maintaining the computing resources. We duly thank Mr G. Bissen and Mr G. Di Pentima of the Syndicat Intercommunal a Vocation Ecologique (SIVEC) for access to the Schifflange wastewater treatment plant.</p>", "<title>Author contributions</title>", "<p>F.D. and P.W. contributed to the planning and designing of the overall study and analyses. F.D. performed the data analyses. P.M.Q. contributed the protein annotation software. B.J.K. performed the MP measurement. E.E.L.M. and L.A.L. collected and performed the biomolecular extractions on the samples. R.H. performed the DNA and RNA sequencing. F.D., P.B.P., P.M., S.W., E.E.L.M. and P.W. participated in discussions related to this work. F.D., P.M., S.W., E.E.L.M. and P.W. wrote and revised the manuscript. All authors read and approved the final manuscript.</p>", "<title>Peer review</title>", "<title>Peer review information</title>", "<p id=\"Par43\"><italic>Nature Ecology & Evolution</italic> thanks Marco Tulio Angulo and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. ##SUPPL##2##Peer reviewer reports## are available.</p>", "<title>Data availability</title>", "<p>The generated MG and MT reads (FASTQ) files, as well as the previously produced data, are available as NCBI BioProject <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA230567\">PRJNA230567</ext-link>. The MP data are available from the PRIDE repository, with accession number <ext-link ext-link-type=\"uri\" xlink:href=\"https://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD013655\">PXD013655</ext-link> (ref. <sup>##REF##33077707##27##</sup>). <xref ref-type=\"sec\" rid=\"Sec27\">Source data</xref> are provided with this paper.</p>", "<title>Code availability</title>", "<p>The meta-omics pipeline IMP (v.3.0)<sup>##REF##27986083##34##</sup> is maintained and developed at the GitLab page: <ext-link ext-link-type=\"uri\" xlink:href=\"https://git-r3lab.uni.lu/IMP/imp3\">https://git-r3lab.uni.lu/IMP/imp3</ext-link>. The code used in the analysis is available at <ext-link ext-link-type=\"uri\" xlink:href=\"https://git-r3lab.uni.lu/ESB/lao/lao_ts\">https://git-r3lab.uni.lu/ESB/lao/lao_ts</ext-link> and <ext-link ext-link-type=\"uri\" xlink:href=\"https://github.com/fdelogu/microforecast\">https://github.com/fdelogu/microforecast</ext-link>, while the data required to start the analysis are available on Zenodo at 10.5281/zenodo.7225349. The full list of software and R package versions are listed in the Git pages.</p>", "<title>Competing interests</title>", "<p id=\"Par44\">The authors declare no competing interests.</p>" ]	[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>Diversity and quality of the rMAGs and their representativeness in the meta-omic dataset.</title><p><bold>a</bold>, Phylogenetic tree of the rMAGs in the LAO community (generated using GTDB-Tk<sup>##UREF##13##79##</sup>) contains the 126 bacterial rMAGs in the system (the 18 archaeal MAGs were not included). The heat map ring contains the CheckM quality measures per rMAG (completeness, contamination and MAG-originally strain heterogeneity), which were filtered to be at least 75% complete and at a maximum 25% contaminated (median: 2%). The violin plots contain the time-averaged (train time series) depth profiles over the contigs forming the rMAG. The two sections of the tree noted as * and ** highlight the strains of <italic>M. parvicella</italic> and <italic>Moraxella</italic> sp., respectively. <bold>b</bold>. The cumulative length of the contigs (longer than 1,000 nt; see <xref rid=\"Sec9\" ref-type=\"sec\">Methods</xref>) for the 25 most abundant phyla displayed for the rMAGs and unbinned contigs.</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>Eigengene modelling using ARIMA augmented with environmental parameters and Fourier terms.</title><p><bold>a</bold>, The signals S1–17 encapsulate the time-dependent dynamics underlying the microbial community. The scale of the <italic>y</italic> axis is dimensionless as the eigenvectors. <bold>b</bold>, The signals S1–17 are explained as ARIMA processes under the influence of the environmental variables. The five blocks of explanatory variables are: Model (ARIMA components), Manual (manually collected environmental variables, directly on the sampling location), Inflow (inflow stream of wastewater in the plant), V1 (first anaerobic tank in the plant) and V2 (second anaerobic tank in the plant). Every circle represents a significant variable according to analysis of variance (Benjamini–Hochberg adjusted <italic>P</italic> < 0.05) for the corresponding signal among S1–17; the size represents the value of the coefficient, the ring colour its sign and the fill colour the log<sub>10</sub>(<italic>P</italic> value). <bold>c</bold>, The signals are connected by a temporal transfer of information, suggesting a succession of ecological events. The signals with a purple edge are putatively nonlinear, and their relationships have been confirmed with convergent cross-mapping analysis. The dashed lines indicate weak transfer of information, while a full arrow and a hollow one represent an imbalance in information transfer (in favour of the solid arrow).</p><p>##SUPPL##4##Source data##</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><title>Forecasting of the signals.</title><p>The 17 signals are predicted for the years 2011–2016 and compared with the data from June for those years. The green and blue dots represent the training and test data, respectively; the solid line depicts the median of the prediction, while the shaded area represents the 95% confidence interval. The green and blue boxplots on the right of every box depict the distribution of the model residuals from the training and the test sets, respectively. Corresponding scales are provided on the right <italic>y</italic> axes. The residue displacement from the null distribution was assessed using a Wilcoxon two-sided test (<italic>n</italic> = 21). The * on top of the boxplot indicates a statistical difference (Benjamini-Yekutieli corrected <italic>P</italic> < 0.01) between the mean of the residual distribution and 0, indicating incorrect/incomplete modelling (exact <italic>P</italic> values in Supplementary Table ##SUPPL##3##7##). In the boxplots, the central line indicates the second quartile, the lower and upper hinges correspond to the first and third quartiles and the whiskers extend from the hinge to the smallest/largest value no further than ±1.5 × the distance between the first and third quartiles. The samples beyond the range are plotted as individual outlier dots.</p><p>##SUPPL##5##Source data##</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><title>Reconstruction of the June months 2012–2016.</title><p>The test samples were reconstructed using the 17 signals and their weights estimated through linear regressions on the training set. The reconstructed matrices are based on MG and MT data summarizing taxonomic families, reactions and pathways. The coefficient of determination <italic>R</italic><sup>2</sup> (computed as the squared Pearson correlation coefficient) is reported for each panel, with a higher coefficient demonstrating a more accurate prediction.</p><p>##SUPPL##6##Source data##</p></caption></fig>", "<fig id=\"Fig5\"><label>Extended Data Fig. 1</label><caption><title>Workflow of the analysis.</title><p>Each box represents a piece of data in the analysis while the arrows show their relationships. When necessary the type of action to move from a box to the next is reported on the side of the arrow. When available the reference figures are indicated in the workflow. The analysis starts with the count matrices for MG, MT and MP and ends with the ecological hypotheses and the validation of the forecasting and the future sample reconstruction.</p></caption></fig>", "<fig id=\"Fig6\"><label>Extended Data Fig. 2</label><caption><title>Technical overview.</title><p>a. Technical effect estimation. The data were regressed with the experimental variables (that is environmental parameters) and the technical ones (that is read length and number of reads). The plot shows the distribution of the betas resulting from the regression for the MG and MT ORF-based matrices. b. The three ORF-based omic quantification matrices are summarised by summing up the lines with the same ORF descriptor. The final result is a collection of 24 matrices + the original three. c. The six panels show the number of time-dependent EGs and the EG weights (equivalent to the Explained Variance) per omic in the nine summarisation matrices. The first EG (that is the basal state of the system) was removed and all the EG weights re-scaled per matrix. In the y axis ‘Fun’ stands for ‘Function’ and ‘Tax’ for ‘Taxonomy’. The number of selected EGs changes depending on the omic and the descriptor, however some trends can be seen in the EG weight. For MG and MT the EG weight is the largest, signifying that it is, if taken alone, the most informative layer of information. Interestingly in MT the second largest, with a decent margin, is the Species level, which can be explained as a level in which most of the individual genes information is conserved (that is genes of the same species will be expressed together over time). d. EG clustering. The columns represent the 17 EG clusters while rows indicate the different types of summarisation matrices. In the top panel the violin plots depict the distribution of the explained variance (EV) from the EGs in the cluster. The red dot indicates the maximal EV in the distribution and the EV of the cluster. On the y-axis there are the 27 matrices.</p></caption></fig>", "<fig id=\"Fig7\"><label>Extended Data Fig. 3</label><caption><title>Correlation of the environmental variables.</title><p>Corr-corr plot of the correlations between the selected starting environmental variables to explain the signals. From here the final variables were selected.</p></caption></fig>", "<fig id=\"Fig8\"><label>Extended Data Fig. 4</label><caption><title>Example of 6 patterns detectable in time series.</title><p>a. Basal level, like the one excluded by removing the first EG in the analysis; b. Random noise; c. level change; d. perturbation; e. cycle; f. Crash. In real time series more patterns are usually combined (at least with noise) to create the main data behaviour over time.</p></caption></fig>", "<fig id=\"Fig9\"><label>Extended Data Fig. 5</label><caption><title>Convergent cross mapping plots for the causality links with putatively nonlinear signals.</title><p>The Cross Map Skill (rho) indicates the goodness of the forecasting across increasing sizes of the number of samples (Library Size). The link S9- > S8 is the only fully confirmed one with a unidirectional information transfer. The edges S10-S17 and S4-S8 have a bi-directional influence which is stronger in the direction already predicted by the Granger causality test. For the edges S7-S8 and S6-S7 the Cross Map Skill shows a faint bi-directional influence; whilst for S1-S17 and S5-S7 a strong bi-directional influence.</p></caption></fig>", "<fig id=\"Fig10\"><label>Extended Data Fig. 6</label><caption><title>Loadings at the family level.</title><p>On the y-axis the taxonomic families intersect the signals they contribute to from the x-axis. If the loading is in the top 5% a green arrow pointing up marks the intersection. Similarly if the loading is in the bottom 5% (strongly negative) a red arrow pointing down marks the intersection. The vertical blocks separate the three omics, whilst the horizontal blocks separate the archaea (A.), bacteria and viruses (V.). No eukaryotic families were found to be in the top/bottom 5% of the loadings. The plot also integrates lower taxonomic labels (that is species and genus) and some of them might have opposite orientations, leading to families with both types of arrows.</p></caption></fig>", "<fig id=\"Fig11\"><label>Extended Data Fig. 7</label><caption><title>Loadings at the pathway level.</title><p>On the y-axis the metabolic pathways intersect the signals they contribute to from the x-axis. If the loading is in the top 5% a green arrow pointing up marks the intersection. Similarly if the loading is in the bottom 5% (strongly negative) a red pointing down marks the intersection. The vertical blocks separate the three omics. The plot integrates also lower metabolic labels (that is KO) and some might disagree in orientation, leading to pathways with both types of arrows.</p></caption></fig>", "<fig id=\"Fig12\"><label>Extended Data Fig. 8</label><caption><title>Triacylglycerol accumulation as a key metabolic community-wide trait.</title><p>a. Enzymatic reactions (with high abundance in at least one of the omics from LAO) leading to triacylglycerol accumulation in the community. GLY: Glycerol, Acyl-ACP: Acyl Carrier Protein, Acyl-P: Acyl phosphate, 3GP: 3-glycerol phosphate, ACAT: Acetyl-CoA, FA: Fatty Acid, DAG: Diacylglycerol, TG: Triacylglycerol, PE: phosphatidylethanolamine, PC: phosphatidylcholine. The enzyme class with KO number K22848 is responsible for the conversion of DAG in TG and, ultimately, the accumulation of TG. b. Gene and gene product abundances for the various enzymatic groups involved in the accumulation of TG varies in amount and taxonomic origin. The families belonging to the same phylum have similar colours to matching phyla in Fig. ##FIG##0##1a##. Therefore, Actinobacteria are in shades of yellow, Proteobacteria in shades of green while Leptospiraceae inherited the bure from the Spirochaetes. c. The gene abundance of K22484 is influenced by S9, indicating a, perhaps indirect effect on NH4 levels.</p></caption></fig>", "<fig id=\"Fig13\"><label>Extended Data Fig. 9</label><caption><title>Selection of the models.</title><p>Absolute error profiles over the training data of the tested models for each of the seventeen signals S1-17; the black dot indicates the RSME. A low RMSE indicates that the predictions and the real data are close; vice-versa a high value shows distant data points. Therefore RMSE is useful when comparing multiple models. Elongated violin plots indicated a spread of values (that is both correctly and incorrectly predicted weeks), a ‘short’ and ‘wide’ distribution with an upper tail indicated a ‘focused’ prediction overall with some outliers, whilst a simple ‘short’ and ‘wide’ distribution is obtained for very coherent predictions (that is constantly correct or incorrect).</p></caption></fig>", "<fig id=\"Fig14\"><label>Extended Data Fig. 10</label><caption><title>Predictions per-week.</title><p>Reconstructed abundance and gene expression of all the microbial families, reactions and pathways in the community versus the real one for each sample in the test set.</p></caption></fig>" ]	[]	[ "<disp-formula id=\"Equ1\"><label>1</label><alternatives><tex-math id=\"M1\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} 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"<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"41559_2023_2241_Fig1_HTML\" id=\"d32e558\"/>", "<graphic xlink:href=\"41559_2023_2241_Fig2_HTML\" id=\"d32e621\"/>", "<graphic xlink:href=\"41559_2023_2241_Fig3_HTML\" id=\"d32e781\"/>", "<graphic xlink:href=\"41559_2023_2241_Fig4_HTML\" id=\"d32e864\"/>", "<graphic xlink:href=\"41559_2023_2241_Article_Equ1.gif\" position=\"anchor\"/>", "<graphic xlink:href=\"41559_2023_2241_Article_Equ2.gif\" position=\"anchor\"/>", "<graphic xlink:href=\"41559_2023_2241_Article_Equ3.gif\" position=\"anchor\"/>", "<graphic xlink:href=\"41559_2023_2241_Article_Equ4.gif\" position=\"anchor\"/>", "<graphic position=\"anchor\" xlink:href=\"41559_2023_2241_Fig5_ESM\" id=\"d32e1600\"/>", "<graphic position=\"anchor\" xlink:href=\"41559_2023_2241_Fig6_ESM\" id=\"d32e1611\"/>", "<graphic position=\"anchor\" xlink:href=\"41559_2023_2241_Fig7_ESM\" id=\"d32e1622\"/>", "<graphic position=\"anchor\" xlink:href=\"41559_2023_2241_Fig8_ESM\" id=\"d32e1633\"/>", "<graphic position=\"anchor\" xlink:href=\"41559_2023_2241_Fig9_ESM\" id=\"d32e1644\"/>", "<graphic position=\"anchor\" xlink:href=\"41559_2023_2241_Fig10_ESM\" id=\"d32e1655\"/>", "<graphic position=\"anchor\" xlink:href=\"41559_2023_2241_Fig11_ESM\" id=\"d32e1666\"/>", "<graphic position=\"anchor\" xlink:href=\"41559_2023_2241_Fig12_ESM\" id=\"d32e1681\"/>", "<graphic position=\"anchor\" xlink:href=\"41559_2023_2241_Fig13_ESM\" id=\"d32e1692\"/>", "<graphic position=\"anchor\" xlink:href=\"41559_2023_2241_Fig14_ESM\" id=\"d32e1703\"/>" ]	[ "<media xlink:href=\"41559_2023_2241_MOESM1_ESM.pdf\"><label>Supplementary Information</label><caption><p>Supplementary discussion and legends for Supplementary Tables 1–7.</p></caption></media>", "<media xlink:href=\"41559_2023_2241_MOESM2_ESM.pdf\"><caption><p>Reporting Summary</p></caption></media>", "<media xlink:href=\"41559_2023_2241_MOESM3_ESM.pdf\"><caption><p>Peer Review File</p></caption></media>", "<media xlink:href=\"41559_2023_2241_MOESM4_ESM.xlsx\"><label>Supplementary Table 1</label><caption><p>Supplementary Tables 1–7.</p></caption></media>", "<media xlink:href=\"41559_2023_2241_MOESM5_ESM.csv\"><label>Source Data Fig. 2</label><caption><p>Source data.</p></caption></media>", "<media xlink:href=\"41559_2023_2241_MOESM6_ESM.csv\"><label>Source Data Fig. 3</label><caption><p>Source data (exact <italic>P</italic> values reported in Supplementary Table 7).</p></caption></media>", "<media xlink:href=\"41559_2023_2241_MOESM7_ESM.csv\"><label>Source Data Fig. 4</label><caption><p>Source data.</p></caption></media>" ]	[{"label": ["23."], "mixed-citation": ["Strang, G. 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PMC10781641	38049481	[]	[ "<title>Methods</title>", "<title>Phylogenetic tree construction</title>", "<p id=\"Par27\">The phylogeny was generated via a ‘metatree’ approach<sup>##REF##28336787##93##</sup>. This approach is similar to formal supertree analysis but differs in that the input is not published trees but the original character matrices or sequence alignments that are themselves reanalysed to generate more complete sets of source topologies. Initially, we input all 1,594 available matrices classified as non-dinosaurian archosauromorphs obtained from an online resource<sup>##REF##26715586##94##</sup> (see Supplementary Information ##SUPPL##0##1## for a full list). Only those matrices containing at least three pseudosuchian taxa were retained for additional analysis. From these matrices, the most parsimonious trees (MPTs) were generated until all unique bipartitions for a data set were sampled. Taxonomy was reconciled via the Paleobiology Database (<ext-link ext-link-type=\"uri\" xlink:href=\"https://paleobiodb.org/\">https://paleobiodb.org/</ext-link>)<sup>##UREF##42##95##</sup> to standardize nomenclature (for example, remove synonyms). These were then encoded into a matrix representation with parsimony matrix, using standard Baum and Ragan coding<sup>##UREF##43##96##</sup>. We also included a molecular tree containing 23 extant taxa<sup>##REF##22023592##97##</sup>. This tree was upweighted in the final matrix to account for the disproportionate influence of morphology on the position of <italic>Gavialis gangeticus</italic><sup>##REF##22023592##97##</sup>. The resulting matrix contained 804 taxa (see Supplementary Data ##SUPPL##2##1## for the final matrix representation with parsimony matrix). We analysed the matrix in TNT v1.5<sup>##REF##34727670##98##</sup> using the ‘xmult=10’ option and ran 1,000 replicates for the analysis. The analysis found 1,320 MPTs of length 1,100,897. The strict and majority rule consensus trees were very poorly resolved, and as the diversification analyses require fully resolved trees, we inferred a Maximum Agreement Subtree (MAST) in PAUP* 4.0a165<sup>##UREF##44##99##</sup> to remove unstable taxa. Due to computational constraints, we computed the MAST from a random sample of 10% of the MPTs. After removal of unstable taxa via MAST construction, the final phylogeny contained 534 taxa and was fully resolved.</p>", "<title>Time calibration</title>", "<p id=\"Par28\">Parsimony methods do not return trees with meaningful branch lengths; therefore, we used external fossil age data to time-calibrate the tree based on the Paleobiology Database and a review of the literature<sup>##REF##26399170##15##,##UREF##8##17##</sup>. Age ranges were standardized to the Geological Society of America Time Scale v5.0<sup>##UREF##45##100##</sup>, with regional ages being converted to their equivalent age in the global timescale (see Supplementary Data ##SUPPL##3##2## for fossil age data). We then used the fossilized birth–death tip dating method, implemented in BEAST2 v2.6.0<sup>##REF##30958812##101##</sup>, to time-scale the phylogeny. Molecular data<sup>##REF##22023592##97##</sup> were used to calibrate the divergence times of extant lineages, whereas the stratigraphically oldest known occurrence of each fossil species, which is clearly attributable to that taxon using an autapomorphy-based approach, was used to calibrate divergence dates for extinct taxa. We set our phylogeny as a topological constraint and set uniform calibration priors based on the fossil occurrence dates. The root of the tree was set to a minimum age of 260 Ma, which is currently the oldest supported age for the origin of Pseudosuchia<sup>##REF##29899066##21##,##UREF##46##102##,##REF##28187191##103##</sup>. The proportion of living species sampled was set to 0.88 on the basis that <italic>Crocodylus suchus</italic>, <italic>G. gangeticus, Mecistops leptorhynchus</italic> and <italic>Paleosuchus trigonatus</italic> were not present in the final topology, whereas all other settings were set to the default. We ran the analysis for 10,000,000 generations, resulting in a posterior distribution of 9,001 phylogenies. We used TreeAnnotator, implemented in BEAST2<sup>##REF##30958812##101##</sup>, to compute the maximum clade credibility tree for use in all downstream analyses. See Supplementary Data ##SUPPL##4##3## for the BEAST2 input file.</p>", "<title>Diversification dynamics</title>", "<p id=\"Par29\">Diversification dynamics were modelled from the phylogeny via Fossil BAMM v.2.6<sup>##REF##29788398##104##</sup>, which is an extension of the BAMM Bayesian framework<sup>##REF##24586858##105##</sup> that uses a Markov Chain Monte Carlo approach to calculate diversification rates. It is explicitly designed to allow the calculation of both speciation and extinction rates, as well as net diversification rates, in phylogenies that contain extinct taxa. The number of fossil occurrences of taxa sampled in the phylogeny was set to 1,639, based on data in the Paleobiology Database and in recent publications<sup>##REF##26399170##15##,##UREF##8##17##</sup>. Synonyms were removed from these data to establish the number of unique fossil operational taxonomic units, with this value then being combined with the number of extant species without fossil data to give the total number of known pseudosuchian taxa. From this, we calculated a global sampling fraction of 0.7. Four chains were executed for the analysis, each with a total of 30 million generations executed, with a minimum clade size of five taxa used to aid convergence. Ten thousand of the results were stored, with 10% discarded as ‘burn-in’, leaving 9,001 samples for subsequent analysis with regards to temperature correlation. For details of the Fossil BAMM set-up, see Supplementary Data ##SUPPL##5##4##.</p>", "<p id=\"Par30\">To evaluate diversification dynamics with respect to environmental change, we first subdivided the tree into marine, freshwater and exclusively terrestrial taxa, based on previous compilations<sup>##REF##26399170##15##,##REF##30679529##24##,##REF##25130564##38##</sup>, coupled with an exhaustive literature search (see Supplementary Data ##SUPPL##3##2## for details). We used the R package BAMMtools<sup>##UREF##47##106##</sup> to extract subtrees for each of these categories from the main supertree. We then used the speciation, extinction and net diversification rate curves obtained from Fossil BAMM<sup>##REF##29788398##104##</sup>, extracted with BAMMtools<sup>##UREF##47##106##</sup>, to test for correlations against two global palaeotemperature<sup>##UREF##48##107##,##REF##11326091##108##</sup> and eustatic sea level<sup>##REF##17818978##109##</sup> time series (Supplementary Data ##SUPPL##3##2##). These data sets were chosen for two reasons: (1) the extent of geological time covered and (2) the relatively smooth nature of the time series. A more recently published sea level data set<sup>##REF##16311326##110##</sup> only extends back as far as 179 Ma, while other temperature data sets<sup>##UREF##49##111##</sup> are not smooth enough to correlate against diversification rate data. Before carrying out the correlations, the temperature time series was smoothed using a Tukey running mean to remove noise. For all environmental time series, the values were linearly interpolated to the same time values available in the diversification rate data, which occurred in 0.1 Myr bins. We used all 9,001 simulations as modelled from the phylogeny, resulting in a distribution for each set of rates and environmental variables (Table ##TAB##0##1##). We used detrended cross-correlation analysis to account for non-stationarity and autocorrelation between time series<sup>##UREF##50##112##</sup>. To test for diversity dependence as an indicator of biotic interactions, we also correlated the number of LTT with speciation, extinction and net diversification rates. We estimated the LTT for each ecology using the R package ‘ape’ v.5.7-1<sup>##REF##30016406##113##</sup>, then correlated the resulting time series against all 9,001 realizations of the speciation, extinction and net diversification curves. We then tested for significance using a Wilcoxon signed-rank test. As for the environmental analyses, we used detrended cross-correlation analysis to account for non-stationarity and autocorrelation between time series<sup>##UREF##50##112##</sup>. All analyses were carried out in R v.3.6.0<sup>##UREF##51##114##</sup>.</p>", "<title>Information transfer</title>", "<p id=\"Par31\">Information theory has previously been used to test for causality in palaeontological and ecological data sets<sup>##UREF##52##115##–##REF##26293753##117##</sup>. Transfer entropy is a directional method for measuring information that quantifies how temporal change in one time series informs that of another<sup>##REF##10991308##118##</sup>. Transfer entropy is based on the mutual information method<sup>##REF##10991308##118##</sup> but takes into account the direction of information transfer by assuming that the processes can be described by a Markov model. It reduces to a linear Granger causality process, whereby a signal in one time series gives a linear response to the second time series. However, it makes fewer assumptions regarding the linearity of the processes involved and is therefore more suitable for analysing causality when the processes involved are unknown<sup>##UREF##53##119##,##UREF##54##120##</sup>, as is the case for information flow in natural systems. All our transfer entropy analyses were implemented in <italic>R</italic><sup>##UREF##51##114##</sup> using the package RTransferEntropy v.0.2.21<sup>##UREF##55##121##</sup>. Our data were placed into bins of equal length, and we allowed the number of bins to vary to minimize the number of bins containing either zero or single counts, as this can lead to bias in the results<sup>##UREF##52##115##</sup>. To determine the number of Markov states that best fit the system, we used a hidden Markov model, implemented in the R package ‘depmixS4’ v.1.5-0<sup>##UREF##56##122##</sup>, varying the number of states between 2 and 20. The number of states in the model with the lowest Akaike Information Criterion value was then used in the transfer entropy calculation. We then calculated transfer entropy 100 times for each pair of time series, that is, speciation and extinction rates for each habitat partition with each of our two abiotic variables and our one biotic variable. A higher positive value of transfer entropy indicates more information transfer. Statistical significance was calculated at the 95% confidence level, and only statistically significant results were retained (Table ##TAB##0##1##).</p>", "<title>Accounting for phylogenetic and temporal uncertainty</title>", "<p id=\"Par32\">Both phylogenetic and temporal uncertainty can impact the results of large-scale macroevolutionary studies, and it has been questioned whether or not large, synthetic phylogenies accurately represent the underlying data and are therefore suitable for conducting macroevolutionary analyses<sup>##REF##27821703##123##</sup>. Therefore, we carried out additional analyses to assess the impact of both phylogenetic and temporal variation on our results.</p>", "<p id=\"Par33\">To assess phylogenetic uncertainty, we ran our analyses on two alternative topologies, one with Thalattosuchia as sister to Crocodyliformes and one with Phytosauria excluded from Pseudosuchia. These alternative topologies represent the most significant source of phylogenetic uncertainty within Pseudosuchia<sup>##REF##25840332##124##,##REF##30581656##125##</sup>. The impact of temporal variation on our results was achieved by taking a sample of 20 topologies from the posterior distribution, each of which were identical topologically but with differing node dates. This generated an additional 22 phylogenies in total (2 with an alternative topology and 20 with the same topology but differing node dates). Our full set of analyses was re-run for each of these 22 trees to assess whether, and to what extent, phylogenetic and temporal uncertainty impact upon our results.</p>", "<title>Reporting summary</title>", "<p id=\"Par34\">Further information on research design is available in the ##SUPPL##1##Nature Portfolio Reporting Summary## linked to this article.</p>" ]	[ "<title>Results</title>", "<title>Pseudosuchian phylogeny</title>", "<p id=\"Par7\">The resultant metatree (Fig. ##FIG##0##1##) contains 534 taxa and, to our knowledge, is the most inclusive pseudosuchian phylogeny published. The overall topology is broadly consistent with recent Crocodylomorpha supertrees<sup>##REF##31390981##22##</sup>. The root of the tree is placed in the Permian, 282 Ma (95% interval, 266–299 Ma), with Crocodylomorpha and Crocodyliformes estimated to have originated in the Middle Triassic (240 Ma; 95% interval, 235–247 Ma) and Late Triassic (214 Ma; 95% interval, 210–219 Ma), respectively. Neosuchia is estimated to have originated in the Early Jurassic (195 Ma; 95% interval, 191–199 Ma), which is broadly consistent with previous estimates<sup>##REF##31390981##22##,##UREF##22##46##</sup>. Crocodylia diverged in the mid-Cretaceous, 100 Ma (95% interval, 90–111 Ma), which is also consistent with recent studies<sup>##REF##35135314##47##,##REF##34567843##48##</sup>. Within Crocodylia, Alligatoroidea is recovered outside of the Crocodyloidea + Gavialoidea clade, reflecting the relationships of molecular, but not most morphological, analyses<sup>##REF##34567843##48##,##REF##30051855##49##</sup>.</p>", "<title>Diversification dynamics through time</title>", "<p id=\"Par8\">Exclusively terrestrial pseudosuchians experienced their highest levels of diversity during the Triassic (Fig. ##FIG##1##2##), followed by a sharp decline over the Triassic/Jurassic boundary, 201 Ma, with only the crocodylomorph lineage surviving<sup>##REF##23536443##50##</sup>. This is followed by low levels of diversity, speciation and extinction throughout the Jurassic and Early Cretaceous. Pseudosuchians regained high levels of diversity in the Late Cretaceous, following a period of heightened speciation rates that marked the notosuchian radiation<sup>##UREF##23##51##</sup>. Towards the end of the Cretaceous, terrestrial pseudosuchians experienced a sharp diversity decline, which continued across the Cretaceous/–Paleogene boundary, with only sebecosuchian notosuchians surviving the mass extinction<sup>##REF##24454686##52##</sup>. Diversity generally declined throughout the Cenozoic, with sebecosuchians disappearing in the Middle Miocene<sup>##UREF##24##53##</sup>. The last exclusively terrestrial group, the mekosuchine crocodyloids that were endemic to Oceania and first appeared in the fossil record in the early Eocene<sup>##UREF##25##54##</sup>, survived until the Holocene, only going extinct sometime in the past 4,000 years<sup>##UREF##26##55##</sup>.</p>", "<p id=\"Par9\">Freshwater pseudosuchians reached an initial diversity peak during the Triassic, followed by a sharp decline at the Triassic/Jurassic boundary (Fig. ##FIG##1##2##). They subsequently radiated, reaching a peak in the Late Jurassic and Early Cretaceous. Although remaining generally high, diversity of freshwater species was volatile throughout the Cretaceous with generally high speciation, extinction and net diversification across the Cretaceous/Paleogene mass extinction event. During the Cenozoic, the highest diversity of freshwater species is observed during the Miocene, comprising members of Crocodylia<sup>##REF##26399170##15##,##UREF##18##40##,##REF##23695701##56##,##REF##25716785##57##</sup>. Subsequently, numbers declined to that of present-day diversity.</p>", "<p id=\"Par10\">The first major marine invasion followed the Triassic/Jurassic mass extinction (Fig. ##FIG##1##2##), with the rapid radiation of thalattosuchian crocodyliforms<sup>##UREF##27##58##,##REF##33083104##59##</sup>, following which speciation rate, extinction rate and diversity all reached their highest levels by the Middle Jurassic. Thalattosuchians then experienced a sharp decrease in diversity and speciation and extinction rate at the end of the Jurassic. Although they do not return to Middle Jurassic levels, both speciation/extinction rates and lineage diversity in the marine realm increased during the Cretaceous (Fig. ##FIG##1##2##), mainly driven by the independent radiation of tethysuchians<sup>##UREF##7##16##</sup>, with an additional diversification of gavialoids in the latest Cretaceous<sup>##UREF##28##60##,##UREF##29##61##</sup>. Speciation rates, extinction rates and overall diversity were unaffected by the Cretaceous/Paleogene mass extinction, with both tethysuchians (primarily dyrosaurids) and gavialoids surviving<sup>##UREF##7##16##,##UREF##29##61##</sup>. Marine diversity reached another peak early in the Paleogene, driven primarily by gavialoids, including taxa traditionally regarded as early diverging tomistomines<sup>##REF##34567843##48##</sup>. Subsequently, diversity and diversification rates were low, but relatively stable, in the marine realm (Fig. ##FIG##1##2##), before the extinction of all remaining fully marine lineages during the Plio-Pleistocene interval.</p>", "<title>Abiotic and biotic correlations</title>", "<p id=\"Par11\">Overall, our results show that all three variables tested, global temperature, global sea level and lineages through time (LTT), influenced pseudosuchian diversification; however, the effects are not homogenous across ecologies (see Fig. ##FIG##2##3## and Table ##TAB##0##1## for full results). Here we only consider results that recovered mean correlation coefficient values of greater than ±0.1 as all were statistically significant (<italic>P</italic> < 2.2 × 10<sup>−16</sup>).</p>", "<p id=\"Par12\">We find that warmer temperatures are associated with increased speciation in both marine and terrestrial lineages, with a strong positive correlation for both partitions, but do not recover any relationship with temperature in freshwater lineages. There is no evidence for a relationship between temperature and extinction rates in any of the ecological partitions. The interaction of speciation and extinction rates results in increased net diversification in both marine and terrestrial lineages with increasing global temperatures.</p>", "<p id=\"Par13\">Lower sea levels are associated with increased speciation in terrestrial and freshwater lineages, whereas higher sea levels are associated with extinction in both these ecologies. There is no relationship recovered between sea level and either speciation or extinction for marine lineages. This results in a decrease in net diversification with higher sea levels for terrestrial and freshwater lineages.</p>", "<p id=\"Par14\">For our LTT analyses, fewer numbers of lineages are strongly associated with increased speciation in terrestrial and freshwater ecologies, whereas increased numbers of lineages have a weak positive effect on marine lineages. Extinction shows a different pattern, with increased numbers of lineages associated with increased extinction in terrestrial and marine lineages, with the reverse observed in freshwater lineages; that is, fewer numbers of lineages is correlated with higher extinction rates in these ecologies. Net diversification decreases with increased numbers of lineages for terrestrial and freshwater lineages, whereas lineage diversity does not show any correlation with net diversification for marine lineages.</p>", "<p id=\"Par15\">Due to the difficulties in confidently assigning some non-marine taxa to either terrestrial or freshwater habitats, we also analysed these categories together in a ‘non-marine’ partition. For this combined partition, we find no evidence for a relationship between temperature and speciation or extinction rates; however, we recover a negative correlation between sea level and speciation rates and a positive correlation between sea level and extinction rates. Net diversification is strongly negatively correlated with sea level. This partition is also strongly and negatively correlated with speciation, extinction and net diversification rates for LTT.</p>", "<title>Information transfer</title>", "<p id=\"Par16\">For all but two of our analyses that indicate that one of our three variables is a driver of diversification, our transfer entropy results reveal the presence of information transfer (we considered our results to show some evidence of information transfer if they returned values >0.1; see Table ##TAB##0##1##). These exceptions are both found in freshwater lineage extinction rates—the first is sea level, which shows a positive correlation, but a low value (0.09) for information transfer. The second is LTT, which shows a negative correlation, but no significant results are returned for information transfer. Otherwise, our results are strongly congruent with the strength of the correlation (as measured by mean rate). All reported information transfer results are significant at <italic>P</italic> < 0.001, and only statistically significant results were retained (see Table ##TAB##0##1## for full results).</p>", "<title>Accounting for phylogenetic and temporal uncertainty</title>", "<p id=\"Par17\">We also tested two alternative phylogenetic hypotheses—Thalattosuchia as the sister clade to Crocodyliformes, rather than Neosuchia, and excluding Phytosauria from Pseudosuchia. Furthermore, we explored the effect of temporal uncertainty on our results. Phylogenetic uncertainty has no notable impact on our results, which are remarkably consistent regardless of the input phylogeny and in some instances yielded even stronger signals than our main results presented above. Temporal uncertainty has a more varied effect on the results, and the key differences are as follows: speciation in marine and terrestrial lineages is now positively correlated with sea level, whereas extinction in marine lineages is now positively correlated with sea level. Net diversification for these pairs of variables remains unchanged. Overall, our results remain unchanged by these sensitivity analyses. See Extended Data Figs. ##FIG##3##1##–##FIG##11##9## for the full results of these analyses.</p>" ]	[ "<title>Discussion</title>", "<p id=\"Par18\">Temperature has long been recognized as a driver of biological turnover<sup>##REF##19197051##6##,##UREF##30##62##–##REF##20875038##64##</sup>. Here we show that pseudosuchian speciation rates are positively correlated with fluctuations in temperature for both marine and terrestrial ecologies, but not in freshwater taxa, whereas there is no clear relationship between temperature and extinction rate for any of our ecological partitions. The one exception to this might be marine lineages, which show a low positive correlation with extinction in our analyses that assess the impact of temporal variation. Previous work has found broadly congruent results, including a positive correlation between warmer temperatures and higher pseudosuchian diversity in general. However, all previous studies either have only evaluated taxonomic diversity or have not separated pseudosuchian taxa into these separate ecological categories<sup>##REF##26399170##15##,##REF##25130564##38##,##UREF##18##40##,##UREF##21##45##,##REF##33414557##65##</sup>, whereas our study quantitatively shows the long-term effect of global warming on speciation rates in fully terrestrial pseudosuchians. Thermophysiology might explain why we see a positive correlation between temperature and speciation, with extant species ectothermic and characterized by a subtropical distribution, with heightened ecological sensitivity to ambient temperature<sup>##UREF##4##11##,##UREF##32##66##,##UREF##33##67##</sup>. Palaeohistological analyses indicate that although the thermophysiology of the earliest pseudosuchians was likely closer to that of endotherms<sup>##REF##27073251##68##</sup>, the transition to ectothermy had occurred in the group at least by the time of the divergence of Metasuchia (the group comprising Neosuchia + Notosuchia)<sup>##UREF##34##69##</sup>, in the Early Jurassic. Although higher temperatures might have directly led to increased speciation rate, this also meant that more of the Earth was habitable for pseudosuchians<sup>##REF##26399170##15##,##UREF##15##34##</sup>, with their fossils known from subpolar latitudes in the Eocene<sup>##UREF##35##70##</sup>. As such, the positive correlation between temperature and speciation rate might partly or primarily reflect a species-area effect, with higher speciation coincident with the latitudinal extension of the warm temperate climatic belt<sup>##UREF##21##45##</sup>. Speciation rate in freshwater pseudosuchians, however, does not appear to be related to fluctuations in global temperature. This is an unexpected finding, given that extant species are primarily freshwater<sup>##UREF##32##66##,##UREF##33##67##</sup>. One potentially confounding factor is the difficulty associated with assigning non-marine fossil taxa to either freshwater or terrestrial habitats. To account for this, we ran analyses with terrestrial and freshwater species combined into a non-marine category, but this also did not show any relationship between speciation rate and temperature. We therefore suggest that this absence of a correlation is a genuine result and not simply the result of difficulty assigning habitat states. One explanation could be that speciation in freshwater taxa is more closely tied to other environmental factors that we did not test, such as aridity<sup>##REF##26399170##15##,##UREF##36##71##,##REF##25311226##72##</sup>. A positive correlation between temperature and speciation rate and either no correlation, or a relatively smaller correlation, with extinction rate in marine pseudosuchians potentially reconciles conflicting results from previous studies that evaluated taxonomic diversity<sup>##REF##26399170##15##,##REF##25130564##38##,##REF##27587285##73##</sup>, which is essentially a product of both rates.</p>", "<p id=\"Par19\">We find that pseudosuchian speciation rates increase with lower sea levels in both terrestrial and freshwater lineages. Conversely, extinction rates for these lineages increase with higher sea levels, suggesting that sea level regressions led to increased speciation, whereas transgressions drove extinction. The net result of this is a negative correlation between net diversification and sea level for terrestrial pseudosuchians, which is in line with previous work<sup>##REF##26399170##15##</sup>, in which either no relationship or a negative correlation was recovered between sea level and taxonomic diversity of non-marine pseudosuchians<sup>##REF##26962137##39##</sup>. It is plausible that both terrestrial and freshwater taxa benefited from sea level regressions via the species area effect, whereby the creation of increased habitat availability allowed for increased diversification<sup>##REF##27587285##73##–##UREF##37##75##</sup>. Conversely, marine transgressions might have led to higher rates of extinction in both terrestrial and freshwater lineages as a result of suitable habitat being lost during periods of continental flooding<sup>##REF##27587285##73##,##UREF##38##76##</sup>. It is important to note that the results for speciation in terrestrial lineages were the most variable of our results when subjected to sensitivity analysis. Therefore, while our results are in line with previous work and our conclusions remain unchanged by our sensitivity analyses, we cannot state with confidence that the impact of sea level on decoupled speciation and extinction rates in terrestrial pseudosuchians can be clearly delineated.</p>", "<p id=\"Par20\">A lack of correlation between diversification rates of marine pseudosuchians and sea level is surprising and also contrasts with previous studies that have tended to recover a positive correlation<sup>##REF##25130564##38##,##REF##26962137##39##</sup>, at least with taxonomic diversity. A positive correlation between sea level and speciation for marine lineages is recovered when temporal variation is taken into account; therefore, our results might still be congruent with previous work. However, these results are from a random sample of just 20 trees, and therefore the maximum clade credibility results may still be representative of the results as a whole. We are therefore cautious in drawing strong conclusions at this stage.</p>", "<p id=\"Par21\">One limitation of our abiotic correlation tests is the use of global proxies. This approach assumes that there is no spatial variation in environmental parameters, but this is patently not the case, especially for palaeotemperature<sup>##UREF##39##77##</sup>. If spatially explicit palaeoenvironmental data were available, a better solution would be to partition both the biotic and abiotic data geographically to obtain a clearer picture of the effect that temperature had on diversification at regional scales. Nevertheless, we believe that our global-scale analyses are still useful in identifying the relative role played by environmental parameters in shaping pseudosuchian diversity over macroevolutionary timescales, even if more precise conclusions cannot currently be drawn.</p>", "<p id=\"Par22\">Diversity dependence, as a proxy for intra-clade competition, drives both speciation and extinction across all three of our ecological partitions. Both terrestrial and freshwater lineages have higher speciation rates when lineage diversity is low. This suggests that low lineage diversity results in opportunities for niche filling, accomplished through increased speciation rates, similar to what we might see in an adaptive radiation<sup>##REF##25628875##78##–##REF##31507039##80##</sup>. The radiation of Neosuchia, following the end-Triassic mass extinction, might be an example of such niche filling<sup>##REF##23536443##50##</sup>. For terrestrial taxa, extinction rates are positively correlated with lineage diversity, which suggests that increased competition with more lineages leads to elevated rates of extinction<sup>##REF##31548392##8##,##REF##31507039##80##</sup>. This might potentially characterize the rise of pseudosuchians during the Triassic<sup>##REF##23536443##50##</sup>, as well as patterns of turnover in notosuchian faunas in the Cretaceous<sup>##UREF##23##51##</sup>. Freshwater lineages show the opposite pattern, with higher extinction rates corresponding to low lineage diversity. However, standard deviation is high and confidence intervals (CIs) broad for freshwater taxa, and we therefore cannot be confident that this result is biologically meaningful. In marine lineages, both speciation and extinction rates are higher with increased lineage diversity. High speciation rates and similarly high extinction rates can result from rapid turnover<sup>##REF##31014234##81##</sup>, as the traits that lead to elevated speciation rates are often the same ones that lead to higher rates of extinction<sup>##REF##30283637##82##</sup>. It is therefore plausible that competition, as a result of higher lineage diversity in marine taxa, stimulated speciation while also driving extinction, as lineages were out-competed. Our analyses do not differentiate between intra-clade and inter-clade competition; therefore, the signal recovered might result from competition between marine clades or between lineages within clades. Qualitatively, such a scenario potentially corresponds to the following macroevolutionary trajectory: thalattosuchians declined as tethysuchians first appeared, with dyrosaurid tethysuchians and gavialoids only appearing towards the end of the Cretaceous, when most non-dyrosaurid tethysuchians disappeared<sup>##UREF##7##16##,##UREF##28##60##</sup>. The surviving marine lineages appear to have thrived after the Cretaceous/Paleogene mass extinction, which has been generally attributed to the vacancy of ecospace<sup>##REF##26399170##15##,##UREF##40##83##</sup>.</p>", "<p id=\"Par23\">It is also possible that competition between pseudosuchians and other clades played a role in shaping their diversity. For example, terrestrial and freshwater pseudosuchians might have been in competition with some dinosaurs; similarly, marine pseudosuchians might have competed with plesiosaurs, ichthyosaurs and mosasaurs in the Mesozoic, cetaceans from the Eocene onwards, and with sharks since pseudosuchians first entered the marine realm. There are also other biotic factors at play that we have not considered here. One such factor is body size, with previous research showing that low body size disparity in crocodylians is associated with increased extinction risk<sup>##UREF##21##45##</sup> and that body size evolution might also be linked to environmental change<sup>##REF##31390981##22##,##REF##33414557##65##</sup>. The full picture of the role played by biotic variables in pseudosuchian macroevolution is therefore undoubtedly far more complex than so far revealed.</p>", "<p id=\"Par24\">Our transfer entropy analyses are strongly supportive of our correlation results, with a high level of congruence indicating the presence of information transfer from the abiotic and biotic variables to diversification rate. The interaction between all our tested variables is most likely complex, and we would not expect to be able to perfectly reconstruct the precise impacts of each driver on diversification, as is reflected in our results. Nevertheless, it is clear that most of our statistically significant correlations show a clear transfer of information from one time series to another, which supports our interpretation that both environmental change and biotic competition played a role in driving speciation and extinction in Pseudosuchia. For marine lineages, speciation is most strongly associated with global temperature, whereas the closest association with extinction is competition. By contrast, speciation in terrestrial and freshwater lineages is most strongly linked to biotic competition, while extinction is most closely associated with sea level. Therefore, while we show that both biotic and abiotic drivers have shaped pseudosuchian macroevolution, their relative contributions differ, which is constrained by ecology.</p>", "<p id=\"Par25\">In summary, we show that the diversification dynamics of Pseudosuchia have been shaped over macroevolutionary timescales by a complex interplay of biotic and abiotic factors, as well as ecology. These intrinsic biotic effects, often referred to as the ‘Red Queen’ hypothesis<sup>##REF##19197051##6##</sup>, have been typically thought to operate at or within the species level and over geologically short timescales. By contrast, the effect of extrinsic changes in the physical environment, known as the ‘Court Jester’ hypothesis, is thought to operate over much longer timescales<sup>##REF##19197051##6##</sup>. Recent research, however, shows an influence of biotic drivers at scales greater than 40 Myr<sup>##REF##35732740##84##</sup>; therefore, the reality is undoubtedly more complex than previously characterized. Similar to the patterns observed in foraminifera<sup>##REF##21493859##9##</sup> and sharks<sup>##REF##31548392##8##,##REF##21493859##9##</sup>, we find that neither the Red Queen nor the Court Jester was the dominant force in shaping pseudosuchian diversity through time; rather, we find evidence for a pluralistic model in which their interaction varies across ecologies. This unexpected complexity is revealed by the decoupling of speciation and extinction rates, which can only be evaluated by taking into account past diversity and the fossil record.</p>" ]	[ "<title>Conclusion</title>", "<p id=\"Par26\">In view of the current focus on using drivers of diversification rates as predictors of clade responses to anthropogenically driven climate change, our findings show that fossils must be included in diversification analyses if we wish to make predictions about the drivers of both speciation and extinction in today’s at-risk clades. This is becoming increasingly important as the number of species threatened with extinction by climate change continues to rise and is particularly consequential for clades that have very low extant diversity such as today’s remaining pseudosuchians. Although many studies have explored drivers of diversification in a phylogenetic framework<sup>##REF##30271903##30##,##REF##30349092##85##–##UREF##41##92##</sup>, our study combines both extant and extinct taxa to explicitly model both speciation and extinction rates in a phylogenetic framework, allowing a more nuanced perspective of the drivers of diversification through time. Furthermore, this type of diversification study has not previously been carried out on such a temporally extensive group: similar studies do not yet exist for other vertebrate groups with comparable evolutionary histories. The fossil record provides a unique window onto the likely drivers that led to lineage success and decline, and that may ultimately lead to their extinction, and the inclusion of extinct taxa in diversification analyses is one way in which the potential of the fossil record can be leveraged.</p>" ]	[ "<p id=\"Par1\">Whereas living representatives of Pseudosuchia, crocodylians, number fewer than 30 species, more than 700 pseudosuchian species are known from their 250-million-year fossil record, displaying far greater ecomorphological diversity than their extant counterparts. With a new time-calibrated tree of >500 species, we use a phylogenetic framework to reveal that pseudosuchian evolutionary history and diversification dynamics were directly shaped by the interplay of abiotic and biotic processes over hundreds of millions of years, supported by information theory analyses. Speciation, but not extinction, is correlated with higher temperatures in terrestrial and marine lineages, with high sea level associated with heightened extinction in non-marine taxa. Low lineage diversity and increased speciation in non-marine species is consistent with opportunities for niche-filling, whereas increased competition may have led to elevated extinction rates. In marine lineages, competition via increased lineage diversity appears to have driven both speciation and extinction. Decoupling speciation and extinction, in combination with ecological partitioning, reveals a more complex picture of pseudosuchian evolution than previously understood. As the number of species threatened with extinction by anthropogenic climate change continues to rise, the fossil record provides a unique window into the drivers that led to clade success and those that may ultimately lead to extinction.</p>", "<p id=\"Par2\">Using a new phylogeny of Pseudosuchia (crocodile-line archosaurs), the authors use diversification analyses and information theory to show that the interplay of abiotic and biotic processes over hundreds of millions of years shaped evolutionary history and diversification dynamics in this clade.</p>", "<title>Subject terms</title>" ]	[ "<title>Main</title>", "<p id=\"Par3\">Global temperature, atmospheric CO<sub>2</sub>, ocean acidification and sea level are all predicted to continue to rise<sup>##UREF##0##1##</sup>, and projected climate scenarios could effectively reverse as much as 50 million years of long-term cooling<sup>##REF##30530685##2##</sup>. These anthropogenically driven environmental changes are already exerting a profound effect on extant biodiversity, with rates of extinction approaching those of the ‘big five’ mass extinctions of the geological past<sup>##REF##26601195##3##</sup>, and new biotic interactions resulting from climatically driven<sup>##UREF##1##4##</sup> and human-mediated<sup>##UREF##2##5##</sup> geographic shifts in species ranges. Yet, through geological time, the diversity of life on Earth has always been shaped by changes in the physical environment<sup>##REF##19197051##6##</sup> and/or by fluctuations in biotic interactions<sup>##REF##29720444##7##</sup>. In reality, it is likely that some combination of these abiotic and biotic factors is responsible for the diversification of many clades<sup>##REF##31548392##8##,##REF##21493859##9##</sup>. The evolutionary history of clade diversification can therefore provide crucial insights into the long-term impact of anthropogenically driven changes to the environment and biosphere on extant biodiversity.</p>", "<p id=\"Par4\">Pseudosuchia is a clade of archosaurian reptiles, defined as all species more closely related to crocodylians than to birds<sup>##UREF##3##10##</sup>. Extant pseudosuchians are all members of Crocodylia, a group of semi-aquatic ambush predators found predominantly in freshwater habitats of the tropics<sup>##UREF##4##11##</sup>. Of the 25–27 extant species of Crocodylia currently recognized, seven are categorized as Critically Endangered, with a further four species identified as vulnerable, with declining populations<sup>##UREF##5##12##</sup>. Many species reside in low-lying areas, meaning that rising sea levels associated with global warming may irreversibly alter the habitats on which they depend<sup>##UREF##6##13##</sup>. Although extant pseudosuchians have low species richness, more than 700 extinct species are currently recognized in the fossil record<sup>##REF##33757349##14##–##UREF##10##19##</sup>. They first appear in the fossil record shortly after the Permian/Triassic mass extinction, 252 million years ago (Ma), and evolved to occupy a variety of habitats and niches, including large terrestrial carnivores, heavily armoured herbivores and fully marine forms<sup>##UREF##3##10##,##UREF##11##20##–##REF##30679529##24##</sup>.</p>", "<p id=\"Par5\">Extant Pseudosuchia is represented by very limited ecomorphological diversity compared to their extinct representatives, yet their closest living relatives, birds (Aves), have diversified to approximately 11,000 extant species<sup>##UREF##12##25##</sup>, showing a vast array of ecomorphological diversity<sup>##REF##31932703##26##</sup>. This asymmetry in the fate of sister clades is a well-documented macroevolutionary phenomenon<sup>##UREF##13##27##–##REF##30271903##30##</sup>, but how it arises is not well understood. In both Pseudosuchia and Aves, climate has been proposed as a major driver of diversity<sup>##REF##26399170##15##,##UREF##14##31##–##UREF##18##40##</sup>. Modern birds radiated rapidly in the wake of the Cretaceous/Paleogene mass extinction, 66 Ma<sup>##UREF##19##41##,##REF##26444237##42##</sup>, and continued to diversify throughout the Cenozoic<sup>##REF##23123857##43##</sup>—an era characterized by a general long-term cooling trend<sup>##UREF##20##44##</sup>. However, pseudosuchians were at their most diverse during periods of global warming<sup>##REF##26399170##15##,##UREF##16##35##</sup>, with evidence for declining diversification correlated with the Cenozoic long-term global cooling trend<sup>##REF##26399170##15##,##UREF##15##34##</sup>. The relative contribution of biotic factors is less evident<sup>##REF##31390981##22##,##REF##26962137##39##</sup>. A recent study provided an attempt to tease apart the relative roles of biotic and abiotic drivers of diversification dynamics of Crocodylia over the past 100 million years<sup>##UREF##21##45##</sup>. These authors found evidence that net diversification of crocodylians over macroevolutionary timescales has likely been shaped by both biotic and abiotic factors.</p>", "<p id=\"Par6\">In this Article, we go further by evaluating the relative roles of abiotic and biotic factors on the entire evolutionary history of pseudosuchian diversification dynamics. We test the effects of environmental change and clade competition, via proxies, throughout the group’s 250 million year evolutionary history with a time-calibrated phylogeny comprising more than 500 species. We demonstrate that pseudosuchian evolutionary history was shaped by the interplay of ecological niche with both biotic and abiotic processes over hundreds of millions of years, supported by a direct transfer of information from our abiotic and biotic time series to speciation and extinction rates.</p>", "<title>Supplementary information</title>", "<p>\n\n\n\n\n\n\n</p>" ]	[ "<title>Extended data</title>", "<p id=\"Par39\">\n\n</p>", "<p id=\"Par40\">\n\n</p>", "<p id=\"Par41\">\n\n</p>", "<p id=\"Par42\">\n\n</p>", "<p id=\"Par43\">\n\n</p>", "<p id=\"Par44\">\n\n</p>", "<p id=\"Par45\">\n\n</p>", "<p id=\"Par46\">\n\n</p>", "<p id=\"Par47\">\n\n</p>", "<title>Extended data</title>", "<p id=\"Par35\">is available for this paper at 10.1038/s41559-023-02244-0.</p>", "<title>Supplementary information</title>", "<p id=\"Par36\">The online version contains supplementary material available at 10.1038/s41559-023-02244-0.</p>", "<title>Acknowledgements</title>", "<p>We thank the Palaeontological Association for an Undergraduate Research Bursary awarded to K.E.D. (Principal Investigator) and A.R.D.P. (grant number PA-UB201903). P.D.M.’s contribution was supported by grants from The Royal Society (UF160216, RGF\\R1\\180020, RGF\\EA\\201037, URF_R_221010) and The Leverhulme Trust (RPG-2021-202). Finally, we thank M. Sadde for finding an error in our initial phylogeny plotting code. This is Paleobiology Database official publication number 460.</p>", "<title>Author contributions</title>", "<p>A.R.D.P. and K.E.D. were awarded funding to support the research and carried out the analyses. G.T.L. collected the phylogenetic data and built the data matrix. A.R.D.P. and K.E.D. built the phylogeny. A.R.D.P., K.E.D. and P.D.M. collated the ecological and stratigraphic data. A.R.D.P. and K.E.D. prepared the figures. K.E.D. conceptualized the project, wrote the <italic>R</italic> code and led the writing of the manuscript. P.D.M. contributed to the writing, and all authors contributed to finalizing the text. All authors contributed to discussion and interpretation of results.</p>", "<title>Peer review</title>", "<title>Peer review information</title>", "<p id=\"Par37\"><italic>Nature Ecology & Evolution</italic> thanks Pedro Godoy, Andres Solorzano and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.</p>", "<title>Data availability</title>", "<p>The authors declare that all data supporting the findings of this study are available within the article and its supplementary information files.</p>", "<title>Code availability</title>", "<p>For equity-related reasons custom code will be available on request to the corresponding author.</p>", "<title>Competing interests</title>", "<p id=\"Par38\">The authors declare no competing interests.</p>" ]	[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>Time-calibrated supertree of Pseudosuchia.</title><p>Maximum agreement subtree of 534 taxa, scaled to geological time. Terminal branches are colour coded according to ecology (blue, marine; green, terrestrial; orange, freshwater), and light grey node bars represent node age ranges (95% highest posterior density). Ecologies were mapped to branches using IToL v.6.8.1<sup>##REF##33885785##126##</sup>. Taxa highlighted by silhouettes from PhyloPic (phylopic.org) to showcase pseudosuchian morphological disparity are (from top to bottom): Phytosauria, Aetosauria, Poposauroidea, Notosuchia, Tethysuchia, Thalattosuchia, Alligatoroidea, Gavialoidea and Crocodyloidea. Silhouettes from S. Hartman, D. Bogdanov, N. Tamura and M. Keesey are licensed under CC BY 3.0; A. Reindl licensed under CC BY 4.0; and F. Sayol, S. Traver and Jagged Fang Designs under CC0 1.0 Universal. The geological timescale was added using the R package ‘strap’ v.1.6-0<sup>##UREF##57##127##</sup>.</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>Abiotic and biotic time series and pseudosuchian diversification dynamics.</title><p><bold>a</bold>, Global temperature; <bold>b</bold>, eustatic sea level; <bold>c</bold>, lineage diversity; <bold>d</bold>, speciation rates; <bold>e</bold>, extinction rates; <bold>f</bold>, net diversification rates. All are scaled to geological time along the <italic>x</italic> axis. Panels <bold>c</bold>–<bold>f</bold> are colour coded according to ecology (blue, marine; green, terrestrial; orange, freshwater). In <bold>d</bold>–<bold>f</bold>, the solid line represents the mean of 9,001 realizations of the diversification rate through time, while lighter shading represents the 95% CI. Diversification dynamics were plotted with the R package ‘BAMMtools’ v.2.1.10<sup>##UREF##47##106##</sup>, LTT were plotted with the R package ‘ape’ v.5.7-1<sup>##REF##30016406##113##</sup>, and the geological timescale was added using the R package ‘strap’ v.1.6-0<sup>##UREF##57##127##</sup>.</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><title>Time series correlations and transfer entropy results.</title><p>Results for all correlations showing the mean, 2.5% and 97.5% CIs, and transfer entropy results for each habitat partition for each time series (blue, marine; green, terrestrial; orange, freshwater). <italic>N</italic> = 9,001 independent samples as derived from the diversification rate analyses. All correlations are significant at <italic>P</italic> < 2.2 × 10<sup>−16</sup> as assessed with a Wilcoxon signed-rank test, while all transfer entropy values are significant at <italic>P</italic> < 0.001 as assessed by a Markov block boot strap<sup>##UREF##55##121##</sup>.</p></caption></fig>", "<fig id=\"Fig4\"><label>Extended Data Fig. 1</label><caption><title>Time series correlations and transfer entropy results for speciation analyses with Thalattosuchia as sister to Neosuchia.</title><p>Results for all correlations showing the mean, 2.5% and 97.5% confidence intervals, and Transfer Entropy (TE) results for each habitat partition for each time series (marine = blue, terrestrial = green, freshwater = orange). N = 9,001 independent samples as derived from the diversification rate analyses. All correlations are significant at p < 2.2e-16 as assessed with a Wilcoxon signed-rank test, while all transfer entropy values are significant at p < 0.001 as assessed by a Markov block boot-strap<sup>##UREF##55##121##</sup>.</p></caption></fig>", "<fig id=\"Fig5\"><label>Extended Data Fig. 2</label><caption><title>Time series correlations and transfer entropy results for extinction analyses with Thalattosuchia as sister to Neosuchia.</title><p>Results for all correlations showing the mean, 2.5% and 97.5% confidence intervals, and Transfer Entropy (TE) results for each habitat partition for each time series (marine = blue, terrestrial = green, freshwater = orange). N = 9,001 independent samples as derived from the diversification rate analyses. Coloured symbols represent results from sensitivity analyses, black symbols represent results from the Maximum Clade Credibility tree. All correlations are significant at p < 2.2e-16 as assessed with a Wilcoxon signed-rank test, while all transfer entropy values are significant at p < 0.001 as assessed by a Markov block boot-strap<sup>##UREF##55##121##</sup>.</p></caption></fig>", "<fig id=\"Fig6\"><label>Extended Data Fig. 3</label><caption><title>Time series correlations and transfer entropy results for net diversification analyses with Thalattosuchia as sister to Neosuchia.</title><p>Results for all correlations showing the mean, 2.5% and 97.5% confidence intervals, and Transfer Entropy (TE) results for each habitat partition for each time series (marine = blue, terrestrial = green, freshwater = orange). N = 9,001 independent samples as derived from the diversification rate analyses. Coloured symbols represent results from sensitivity analyses, black symbols represent results from the Maximum Clade Credibility tree. All correlations are significant at p < 2.2e-16 as assessed with a Wilcoxon signed-rank test, while all transfer entropy values are significant at p < 0.001 as assessed by a Markov block boot-strap<sup>##UREF##55##121##</sup>.</p></caption></fig>", "<fig id=\"Fig7\"><label>Extended Data Fig. 4</label><caption><title>Time series correlations and transfer entropy results for speciation analyses with Phytosauria excluded.</title><p>Results for all correlations showing the mean, 2.5% and 97.5% confidence intervals, and Transfer Entropy (TE) results for each habitat partition for each time series (marine = blue, terrestrial = green, freshwater = orange). N = 9,001 independent samples as derived from the diversification rate analyses. Coloured symbols represent results from sensitivity analyses, black symbols represent results from the Maximum Clade Credibility tree. All correlations are significant at p < 2.2e-16 as assessed with a Wilcoxon signed-rank test, while all transfer entropy values are significant at p < 0.001 as assessed by a Markov block boot-strap<sup>##UREF##55##121##</sup>.</p></caption></fig>", "<fig id=\"Fig8\"><label>Extended Data Fig. 5</label><caption><title>Time series correlations and transfer entropy results for extinction analyses with Phytosauria excluded.</title><p>Results for all correlations showing the mean, 2.5% and 97.5% confidence intervals, and Transfer Entropy (TE) results for each habitat partition for each time series (marine = blue, terrestrial = green, freshwater = orange). N = 9,001 independent samples as derived from the diversification rate analyses. Coloured symbols represent results from sensitivity analyses, black symbols represent results from the Maximum Clade Credibility tree. All correlations are significant at p < 2.2e-16 as assessed with a Wilcoxon signed-rank test, while all transfer entropy values are significant at p < 0.001 as assessed by a Markov block boot-strap<sup>##UREF##55##121##</sup>.</p></caption></fig>", "<fig id=\"Fig9\"><label>Extended Data Fig. 6</label><caption><title>Time series correlations and transfer entropy results for net diversification analyses with Phytosauria excluded.</title><p>Results for all correlations showing the mean, 2.5% and 97.5% confidence intervals, and Transfer Entropy (TE) results for each habitat partition for each time series (marine = blue, terrestrial = green, freshwater = orange). N = 9,001 independent samples as derived from the diversification rate analyses. Coloured symbols represent results from sensitivity analyses, black symbols represent results from the Maximum Clade Credibility tree. All correlations are significant at p < 2.2e-16 as assessed with a Wilcoxon signed-rank test, while all transfer entropy values are significant at p < 0.001 as assessed by a Markov block boot-strap<sup>##UREF##55##121##</sup>.</p></caption></fig>", "<fig id=\"Fig10\"><label>Extended Data Fig. 7</label><caption><title>Time series correlations and transfer entropy results for speciation analyses assessing the impact of temporal uncertainty.</title><p>Results for all correlations showing the mean, 2.5% and 97.5% confidence intervals, and Transfer Entropy (TE) results for each habitat partition for each time series (marine = blue, terrestrial = green, freshwater = orange). N = 9,001 independent samples as derived from the diversification rate analyses. Coloured symbols represent results from sensitivity analyses, black symbols represent results from the Maximum Clade Credibility tree. Violin plots represent the distribution of results from 20 trees from the posterior distribution. All correlations are significant at p < 2.2e-16 as assessed with a Wilcoxon signed-rank test, while all transfer entropy values are significant at p < 0.001 as assessed by a Markov block boot-strap<sup>##UREF##55##121##</sup>.</p></caption></fig>", "<fig id=\"Fig11\"><label>Extended Data Fig. 8</label><caption><title>Time series correlations and transfer entropy results for extinction analyses assessing the impact of temporal uncertainty.</title><p>Results for all correlations showing the mean, 2.5% and 97.5% confidence intervals, and Transfer Entropy (TE) results for each habitat partition for each time series (marine = blue, terrestrial = green, freshwater = orange). N = 9,001 independent samples as derived from the diversification rate analyses. Coloured symbols represent results from sensitivity analyses, black symbols represent results from the Maximum Clade Credibility tree. Violin plots represent the distribution of results from 20 trees from the posterior distribution. All correlations are significant at p < 2.2e-16 as assessed with a Wilcoxon signed-rank test, while all transfer entropy values are significant at p < 0.001 as assessed by a Markov block boot-strap<sup>##UREF##55##121##</sup>.</p></caption></fig>", "<fig id=\"Fig12\"><label>Extended Data Fig. 9</label><caption><title>Time series correlations and transfer entropy results for net diversification analyses assessing the impact of temporal uncertainty.</title><p>Results for all correlations showing the mean, 2.5% and 97.5% confidence intervals, and Transfer Entropy (TE) results for each habitat partition for each time series (marine = blue, terrestrial = green, freshwater = orange). N = 9,001 independent samples as derived from the diversification rate analyses. Coloured symbols represent results from sensitivity analyses, black symbols represent results from the Maximum Clade Credibility tree. Violin plots represent the distribution of results from 20 trees from the posterior distribution. All correlations are significant at p < 2.2e-16 as assessed with a Wilcoxon signed-rank test, while all transfer entropy values are significant at p < 0.001 as assessed by a Markov block boot-strap<sup>##UREF##55##121##</sup>.</p></caption></fig>" ]	[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Time series correlations and transfer entropy results</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th/><th/><th colspan=\"4\">Speciation</th><th colspan=\"4\">Extinction</th><th colspan=\"4\">Net diversification</th></tr><tr><th/><th/><th>Mean</th><th>2.5% CI</th><th>97.5% CI</th><th>Transfer entropy</th><th>Mean</th><th>2.5% CI</th><th>97.5% CI</th><th>Transfer entropy</th><th>Mean</th><th>2.5% CI</th><th>97.5% CI</th><th>Transfer entropy</th></tr></thead><tbody><tr><td><bold>Marine</bold></td><td><bold>Sea level</bold></td><td>−0.003</td><td>−0.184</td><td>0.224</td><td>0.134</td><td>−0.012</td><td>−0.134</td><td>0.237</td><td>NA</td><td>0.011</td><td>−0.327</td><td>0.184</td><td>0.143</td></tr><tr><td/><td><bold>Temperature</bold></td><td><italic>0.204</italic></td><td><italic>0.024</italic></td><td><italic>0.390</italic></td><td><italic>0.124</italic></td><td>−0.016</td><td>−0.101</td><td>0.074</td><td>0.085</td><td><italic>0.425</italic></td><td><italic>0.146</italic></td><td><italic>0.633</italic></td><td><italic>0.145</italic></td></tr><tr><td/><td><bold>LTT</bold></td><td><italic>0.123</italic></td><td><italic>0.067</italic></td><td><italic>0.200</italic></td><td><italic>0.231</italic></td><td><italic>0.123</italic></td><td><italic>0.074</italic></td><td><italic>0.190</italic></td><td><italic>0.157</italic></td><td>0.036</td><td>−0.005</td><td>0.068</td><td>NA</td></tr><tr><td><bold>Terrestrial</bold></td><td><bold>Sea level</bold></td><td>−<italic>0.207</italic></td><td>−<italic>0.579</italic></td><td><italic>0.231</italic></td><td><italic>0.187</italic></td><td><italic>0.239</italic></td><td>−<italic>0.160</italic></td><td><italic>0.511</italic></td><td><italic>0.118</italic></td><td><italic>−0.382</italic></td><td>−<italic>0.642</italic></td><td><italic>0.046</italic></td><td><italic>0.199</italic></td></tr><tr><td/><td><bold>Temperature</bold></td><td><italic>0.239</italic></td><td><italic>0.056</italic></td><td><italic>0.463</italic></td><td><italic>0.156</italic></td><td>0.017</td><td>−0.099</td><td>0.152</td><td>0.108</td><td><italic>0.233</italic></td><td><italic>0.045</italic></td><td><italic>0.438</italic></td><td><italic>0.148</italic></td></tr><tr><td/><td><bold>LTT</bold></td><td>−<italic>0.325</italic></td><td>−<italic>0.587</italic></td><td><italic>0.098</italic></td><td><italic>0.158</italic></td><td><italic>0.162</italic></td><td>−<italic>0.048</italic></td><td><italic>0.416</italic></td><td><italic>0.117</italic></td><td>−<italic>0.438</italic></td><td>−<italic>0.650</italic></td><td>−<italic>0.171</italic></td><td><italic>0.168</italic></td></tr><tr><td><bold>Freshwater</bold></td><td><bold>Sea level</bold></td><td>−<italic>0.300</italic></td><td>−<italic>0.574</italic></td><td><italic>0.105</italic></td><td><italic>0.191</italic></td><td><italic>0.444</italic></td><td>−<italic>0.062</italic></td><td><italic>0.671</italic></td><td><italic>0.085</italic></td><td><italic>−0.549</italic></td><td>−<italic>0.720</italic></td><td>−<italic>0.108</italic></td><td><italic>0.192</italic></td></tr><tr><td/><td><bold>Temperature</bold></td><td>0.087</td><td>−0.107</td><td>0.290</td><td>0.165</td><td>0.039</td><td>−0.131</td><td>0.182</td><td>0.082</td><td>0.058</td><td>−0.111</td><td>0.279</td><td>0.158</td></tr><tr><td/><td><bold>LTT</bold></td><td>−<italic>0.360</italic></td><td>−<italic>0.524</italic></td><td>−<italic>0.126</italic></td><td><italic>0.176</italic></td><td>−<italic>0.154</italic></td><td>−<italic>0.383</italic></td><td><italic>0.262</italic></td><td><italic>NA</italic></td><td>−<italic>0.351</italic></td><td>−<italic>0.524</italic></td><td>−<italic>0.153</italic></td><td><italic>0.237</italic></td></tr><tr><td><bold>Non-marine</bold></td><td><bold>Sea level</bold></td><td>−<italic>0.317</italic></td><td>−<italic>0.604</italic></td><td><italic>0.121</italic></td><td><italic>0.182</italic></td><td><italic>0.329</italic></td><td>−<italic>0.073</italic></td><td><italic>0.584</italic></td><td><italic>0.134</italic></td><td>−<italic>0.517</italic></td><td>−<italic>0.698</italic></td><td>−<italic>0.114</italic></td><td><italic>0.184</italic></td></tr><tr><td/><td><bold>Temperature</bold></td><td>0.090</td><td>−0.106</td><td>0.306</td><td>0.160</td><td>0.055</td><td>−0.104</td><td>0.209</td><td>0.113</td><td>0.043</td><td>−0.131</td><td>0.268</td><td>0.152</td></tr><tr><td/><td><bold>LTT</bold></td><td>−<italic>0.525</italic></td><td>−<italic>0.696</italic></td><td>−<italic>0.224</italic></td><td><italic>0.185</italic></td><td>−<italic>0.253</italic></td><td>−<italic>0.512</italic></td><td><italic>0.125</italic></td><td><italic>0.131</italic></td><td>−<italic>0.470</italic></td><td>−<italic>0.662</italic></td><td>−<italic>0.215</italic></td><td><italic>0.208</italic></td></tr></tbody></table></table-wrap>" ]	[]	[]	[]	[]	[]	[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM3\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM4\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM5\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM6\"></supplementary-material>" ]	[ "<table-wrap-foot><p>Mean, 2.5% and 97.5% CIs and transfer entropy results are given for each habitat partition for each time series. All correlations are significant at <italic>P</italic> < 2.2 × 10<sup>−16</sup>, while all reported transfer entropy values are significant at <italic>P</italic> < 0.001. NA means that no significant transfer entropy values were found at that CI. Correlations with a correlation coefficient of greater than ±0.1 are italicized.</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"41559_2023_2244_Fig1_HTML\" id=\"d32e465\"/>", "<graphic xlink:href=\"41559_2023_2244_Fig2_HTML\" id=\"d32e552\"/>", "<graphic xlink:href=\"41559_2023_2244_Fig3_HTML\" id=\"d32e650\"/>", "<graphic position=\"anchor\" xlink:href=\"41559_2023_2244_Fig4_ESM\" id=\"d32e1941\"/>", "<graphic position=\"anchor\" xlink:href=\"41559_2023_2244_Fig5_ESM\" id=\"d32e1957\"/>", "<graphic position=\"anchor\" xlink:href=\"41559_2023_2244_Fig6_ESM\" id=\"d32e1973\"/>", "<graphic position=\"anchor\" xlink:href=\"41559_2023_2244_Fig7_ESM\" id=\"d32e1989\"/>", "<graphic position=\"anchor\" xlink:href=\"41559_2023_2244_Fig8_ESM\" id=\"d32e2005\"/>", "<graphic position=\"anchor\" xlink:href=\"41559_2023_2244_Fig9_ESM\" id=\"d32e2021\"/>", "<graphic position=\"anchor\" xlink:href=\"41559_2023_2244_Fig10_ESM\" id=\"d32e2037\"/>", "<graphic position=\"anchor\" xlink:href=\"41559_2023_2244_Fig11_ESM\" id=\"d32e2053\"/>", "<graphic position=\"anchor\" xlink:href=\"41559_2023_2244_Fig12_ESM\" id=\"d32e2069\"/>" ]	[ "<media xlink:href=\"41559_2023_2244_MOESM1_ESM.pdf\"><label>Supplementary Information</label><caption><p>Supplementary Information 1—File containing a reference list for all source phylogenies included in the phylogeny.</p></caption></media>", "<media xlink:href=\"41559_2023_2244_MOESM2_ESM.pdf\"><caption><p>Reporting Summary</p></caption></media>", "<media xlink:href=\"41559_2023_2244_MOESM3_ESM.zip\"><label>Supplementary Data 1</label><caption><p>Final MRP data matrix in TNT format.</p></caption></media>", "<media xlink:href=\"41559_2023_2244_MOESM4_ESM.xml\"><label>Supplementary Data 2</label><caption><p>Input file for time calibration.</p></caption></media>", "<media xlink:href=\"41559_2023_2244_MOESM5_ESM.txt\"><label>Supplementary Data 3</label><caption><p>Input file for diversification dynamics analysis.</p></caption></media>", "<media xlink:href=\"41559_2023_2244_MOESM6_ESM.xlsx\"><label>Supplementary Data 4</label><caption><p>Input data for the following: list of all fossil ages used to time-calibrate the phylogeny, habitat classifications used to partition the data set when carrying out correlation analyses, and all time series used in the correlation analyses. 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PMC10781642	38049480	[]	[ "<title>Methods</title>", "<title>Sample and phylogeny</title>", "<p id=\"Par31\">We studied the relationship between neonatal and adult brain and body size in a broad sample of placental mammals whose position along the altricial–precocial spectrum spans all the diversity observed in this clade (Fig. ##FIG##0##1a##). These species differ widely in their absolute adult brain size from less than 1 g in some bats and rodents to more than 7,000 g in the sperm whale, and in their absolute adult body size from 10–20 g in some rodents and bats to 14,000 kg in the sperm whale<sup>##UREF##15##66##</sup>. A sample of 140 species (including 44 primate species, 24 rodent species, 21 carnivoran species, 26 artiodactyl species and 25 species from other mammalian orders) was used. Artiodactyla in our study includes both artiodactyls and cetaceans, a clade that is sometimes termed Cetartiodactyla<sup>##UREF##16##67##</sup>. Data on species’ mean neonatal and adult brain and body size, and well as on gestation length and generation times, were compiled for all the species from different sources. Data on neonatal and adult brain and body size were obtained from refs. <sup>##UREF##17##68##,##REF##28561966##69##</sup>. For those values that were missing or looked too different from those reported in other publications, values were added or double-checked and amended as necessary using refs. <sup>##REF##21444808##30##,##REF##21533219##34##,##REF##27561684##70##</sup> for brain size data and refs. <sup>##REF##30810261##28##,##REF##21444808##30##,##UREF##15##66##,##REF##29121237##71##</sup> for body size data. Data on gestation lengths and generation times were obtained from refs. <sup>##UREF##15##66##,##REF##29121237##71##</sup>, with age at first reproduction used as a proxy for generation time. The species-specific values we obtained from the literature did not represent longitudinal data on neonatal and adult brain and body sizes from the same individuals. Consequently, like previous comparative research, the relationship between neonatal and adult brain and body size in our study is influenced by intraspecific variation. Adult body size values corresponded to maternal values, whereas neonatal brain and body size values, as well as adult brain size values, were normally not assigned to males or females in the source datasets.</p>", "<p id=\"Par32\">Given that some of these datasets were published decades ago, species names were checked and amended as needed to make sure that they matched current taxonomic views as reflected in the employed phylogeny (see below). Species were not included when they had missing data for one or more variables. In a few cases, gestation lengths and generation time values corresponding to a given species were obtained from their closest sister species within the same genus. We used a recently estimated mammalian phylogeny<sup>##REF##34937052##72##</sup>, which was pruned to include only the species included in our dataset.</p>", "<title>Analysis of extant mammals</title>", "<p id=\"Par33\">We first tested whether the best-represented orders within our dataset (Primates, Carnivora, Rodentia and Artiodactyla) differ in their brain and body proportion at birth, defined as the proportion of adult brain (or body) size that is represented by neonatal brain (or body) size. Significance was assessed based on pairwise Wilcoxon rank sum tests with Bonferroni correction. This comparison was carried out for the complete sample and for a selection of those species whose brain sizes are particularly large, which are grouped in five different clades at different taxonomic levels: cetaceans, hominids (great apes and humans), elephants, perissodactyls, and the clade formed by pinnipeds and bears. The mammalian phylogeny used in our study includes pinnipeds and bears as sister clades, with musteloids forming a sister group to both of them. Other mammalian phylogenies, however, consider musteloids as the sister group of pinnipeds, with bears as a sister group to the pinniped–musteloid clade<sup>##REF##31800571##73##</sup>. The sample size of these groups ranged from two (elephants) to eight (pinnipeds–bears).</p>", "<title>Evolutionary rates</title>", "<p id=\"Par34\">To infer the strength of selection over each branch of the mammalian phylogeny, we calculated the amount of change accumulated over each branch relative to the expected amount of change per branch, which depends on branch length (that is, longer branches are expected to accumulate more change than shorter branches). As a first step, we calculated ancestral values at each node of the phylogeny. To do so, we used a variable rates approach implemented in the software BayesTraits V3<sup>##REF##22012260##74##,##UREF##18##75##</sup>. The variable rates model was used with the default priors, and it was run for 10 million iterations with a 20% burn-in period. BayesTraits’ variable rates model detects shifts from an underlying homogenous Brownian motion model of evolution in a phylogeny without prior knowledge of where those shifts have occurred<sup>##REF##22012260##74##,##REF##25848031##76##</sup>. This model finds a set of branch length scalars that optimize the fit of the data to a homogenous Brownian motion model of evolution, which results into a rescaled tree where each branch has been stretched or compressed to conform to a Brownian motion process<sup>##REF##25848031##76##</sup>. Stretched branches represent fast evolutionary change, whereas compressed branches represent slow evolutionary change for a given trait.</p>", "<p id=\"Par35\">The obtained rescaled trees were used to calculate the most likely ancestral value at each node of the mammalian phylogeny using the package ape<sup>##REF##14734327##77##</sup> in R. The amount of change accumulated over each branch was then calculated as the difference between each descendant and ancestral value, and it was compared with the amount of change that each branch would have accumulated had they evolved at the same rate<sup>##REF##28049819##26##</sup>. This expected amount of change was calculated for each branch of the phylogeny as a constant tree-wide per-generation variance parameter (estimated from the values observed in our dataset for each trait) multiplied by the square root of branch lengths after transforming each branch of the phylogeny to generations<sup>##UREF##19##78##</sup>. This transformation was attained using the generation time typical of each species and the reconstructed ancestral generation times<sup>##REF##28049819##26##</sup>. A ratio was then calculated between the observed and expected amount of change per branch. This ratio has an absolute value of 1 when the observed and expected amounts of change are the same for a given branch. An absolute value higher than 1 indicates that branches accumulated more change (and, therefore, evolved faster) than expected, and an absolute ratio between 0 and 1 indicates that branches accumulated less change (and, therefore, evolved slower) than expected<sup>##REF##28049819##26##</sup>, with a positive or negative sign indicating trait increase or decrease with respect to the ancestral value. A value close to 0 indicates that a given branch has not changed from its ancestral to its descendant value, then indicating evolutionary stasis.</p>", "<p id=\"Par36\">Our approach results in distributions of ratios of observed versus expected change that are directly comparable across different traits. These values can be understood as evolutionary rates because they indicate how fast evolutionary change has accumulated, but they are not rates in the strict sense, as they do not measure change per unit of time. Rather, these ratios measure the amount of change accumulated along each branch of the mammalian phylogeny with respect to a neutral expectation, and they indicate both the strength and the directionality of evolutionary change by comparing each descendent value with its ancestral value. This approach is conceptually similar to that we have used in previous publications<sup>##REF##28049819##26##,##REF##28162899##79##</sup>, but it relies on BayesTraits’ variable rates approach to calculate ancestral values. Phylogenetic analyses and visualization of results also relied on the packages phytools<sup>##UREF##20##80##</sup>, geiger<sup>##REF##18006550##81##</sup>, ggplot2<sup>##UREF##21##82##</sup> and smplot2<sup>##REF##34976028##83##</sup>.</p>", "<p id=\"Par37\">Apart from being biologically reductionist and not fully reflective of the complex distinction between altriciality and precociality, brain proportion at birth as a proxy for brain size altriciality is also problematic from a purely quantitative point of view. Percentage values often show distributional issues that may violate the assumptions of subsequent statistical analyses, they increase the measurement error and they may introduce spurious correlations with other variables, among other statistical issues<sup>##UREF##22##84##–##UREF##23##86##</sup>. However, as mentioned in the introduction, we deemed it important to explore the variation of brain and body proportions at birth across mammals because of the historical importance of this value in discussions about human altriciality. We compared the evolutionary rates obtained for brain and body proportion at birth with those obtained when measuring brain and body size altriciality as the residuals obtained from a phylogenetic regression between neonatal and adult size. The evolutionary rates obtained based on brain and body proportions at birth are highly correlated with those based on PGLS regression residuals (Extended Data Fig. ##FIG##7##2##).</p>", "<p id=\"Par38\">Brain and body proportions at birth were arcsine squared root transformed, and absolute adult brain size, absolute neonatal brain size and gestation length (in days) were log-transformed for the measurement of evolutionary rates. Pearson’s correlations between evolutionary rates for brain proportion at birth, body proportion at birth, absolute brain size in adults, absolute brain size in neonates and gestation length were measured across the complete mammalian sample and within each of the four best-represented orders. Because correlations can be influenced by extreme rate values, these correlations were measured twice, first including all the rates and later after removing outliers, corresponding to rates lower than −3 or higher than 3. Before measuring these correlations, we tested whether unsigned rates obtained for the five variables are significantly correlated with branch lengths (BL). Two of these correlations show significant <italic>P</italic> values, but we did not find a consistent negative correlation between BL (measured as millions of years or as generations) and evolutionary rates, which indicates that high rates are not preferentially found in short branches (brain proportion at birth versus BL (Myr): <italic>r</italic> = −0.022, <italic>P</italic> = 0.719; brain proportion at birth versus BL (generations): <italic>r</italic> = −0.096, <italic>P</italic> = 0.111; body proportion at birth versus BL (Myr): <italic>r</italic> = −0.040, <italic>P</italic> = 0.505; body proportion at birth versus BL (generations): <italic>r</italic> = −0.133, <italic>P</italic> = 0.026; absolute adult brain size versus BL (Myr): <italic>r</italic> = 0.093, <italic>P</italic> = 0.120; absolute adult brain size versus BL (generations): <italic>r</italic> = −0.099, <italic>P</italic> = 0.099; neonatal brain size versus BL (Myr): <italic>r</italic> = 0.108, <italic>P</italic> = 0.071; neonatal brain size versus BL (generations): <italic>r</italic> = −0.098, <italic>P</italic> = 0.102; gestation length versus BL (Myr): <italic>r</italic> = 0.240, <italic>P</italic> < 0.001; gestation length versus BL (generations): <italic>r</italic> = −0.056, <italic>P</italic> = 0.349).</p>", "<title>Scaling relationships between neonatal and adult size</title>", "<p id=\"Par39\">To further explore the evolution of developmental patterns across mammals, the scaling relationship between neonatal and adult brain and body size was compared across the four best-represented orders using PGLS regression analysis. This comparison used a generalized pANCOVA approach<sup>##REF##27060983##87##</sup> implemented in the R package evomap<sup>##UREF##24##88##</sup> to test for differences in intercepts and slopes among those orders. This approach allows testing whether individual species, or groups of species, deviate significantly from the expected scaling relationships observed in the other species in their clade. The scaling relationships between these variables were also tested in the species or clades that looked like apparent outliers with respect to the order they belong to, that is, humans with respect to other primates, bears (clade formed by <italic>Ursus arctos</italic>, <italic>U. maritimus</italic> and <italic>U. americanus</italic>) with respect to other carnivorans, boars (<italic>Sus scrofa</italic>) with respect to other artiodactyls and golden hamsters (<italic>Mesocricetus auratus</italic>) with respect to other rodents. For humans, we further assessed whether changes in the scaling relationship between neonatal and adult brain and body size were primarily driven by neonatal or adult values. This was attained by comparing the human neonatal and adult values with the <italic>Homo</italic>–<italic>Pan</italic> estimated ancestral values relative to the scaling coefficient of the non-human primate scaling relationship as described in ref. <sup>##REF##33910907##89##</sup>. Within each order, we also compared the scaling relationship between neonatal and adult brain and body size values between subclades that differ substantially in adult brain size and/or ecological specializations (hominids versus other primates, pinnipeds versus terrestrial carnivorans, cetaceans versus other artiodactyls and murids versus other rodents). Following the temporal sequence of ontogenetic development, our regression analyses consider neonatal values as the independent variable and adult values as the dependent variable.</p>", "<title>Timing of neurodevelopment</title>", "<p id=\"Par40\">Workman et al.’s model was used to infer the pre- or postnatal occurrence of neurodevelopmental events in fossil hominin species<sup>##REF##23616543##23##</sup> (we use the term hominin to refer to fossil species that are more closely related to humans than to chimpanzees, whereas we use the term hominid to refer to the clade formed by humans and the great apes). The model estimates the day after conception at which a particular neurodevelopmental event would happen in a given species as:where <italic>Y</italic> is the log-transformed day after conception at which a given event happens and ‘eventscale’ is the event score calculated by Workman et al. for each of the 271 neurodevelopmental events included in their study (which can be found in Table 1 of ref. <sup>##REF##23616543##23##</sup>). Out of those 271 events, we focused on the 215 events that are specifically related to brain development, which are allocated to the brainstem, cerebellum, limbic system, thalamus, striatum and cortex. We excluded the events that are described as increases in brain size, as variation in the sensory periphery and retina, and those classified as behavioural responses of the whole organism. While the list of neurodevelopmental events studied by Workman et al. is necessarily limited and cannot accurately reflect the whole complexity of neurodevelopment<sup>##REF##23616543##23##</sup>, it is still the most complete compilation that is available for comparison across species. For neurogenetic events happening in the cortex of non-glire mammals (which include primates and, therefore, hominins), an interaction term of 0.263 is added, although this is not relevant to our study because all neurogenetic events are early occurring events that happen prenatally in all primates<sup>##REF##23616543##23##</sup>, so we did not study them in detail. The intercept and slope in equation (##FORMU##0##1##) are species-specific values that are calculated for each species as follows, according to the empirical relationships inferred by Workman et al.<sup>##REF##23616543##23##</sup>:where ‘gestation length’ is the species-specific average gestation length measured in days, and ‘adult brain mass’ is the species-specific average adult brain weight measured in grams. The pre- or postnatal occurrence of each neurodevelopmental event was calculated simply by subtracting the species-specific gestation length from the post-conception day at which each event is estimated to happen:</p>", "<p id=\"Par41\">A positive value indicates a postnatal occurrence, whereas a negative value indicates a prenatal occurrence.</p>", "<p id=\"Par42\">Adult brain weight can be reliably estimated for most hominin species based on endocranial volume. Species-specific endocranial volumes were obtained from refs. <sup>##REF##21199942##18##,##REF##36191229##27##</sup>, and they were transformed to brain masses in grams following ref. <sup>##REF##9144286##90##</sup> (Extended Data Table ##TAB##3##4##). Following ref. <sup>##REF##36191229##27##</sup>, species-specific gestation lengths in hominins were calculated as the neonatal body mass typical of each species (obtained from ref. <sup>##REF##21199942##18##</sup>) divided by the prenatal growth rate estimated in ref. <sup>##REF##36191229##27##</sup>. This approach consistently indicates that earlier hominin species had shorter gestation lengths than later hominin species<sup>##REF##36191229##27##</sup>. However, gestation length values obtained using this approach show unrealistically extreme values that range from a mean gestation length of 168 days (5.6 months) in <italic>Ar. ramidus</italic> to 313 days (10.4 months) in Neanderthals (Extended Data Table ##TAB##3##4##), which are far from the range of variation observed in all the great apes and humans (from 227 days in chimpanzees to 275 days in humans). Therefore, the obtained gestation lengths were rescaled to the interval 245–275 days to obtain an adjusted gestation length for each species. The lower bound of this interval corresponds to the gestation length calculated for the last common ancestor of chimpanzees and humans based on the variable rates approach described above (245 days), and the upper bound corresponds to the value observed in modern humans (275 days)<sup>##UREF##15##66##</sup>. While, based on their larger adult brain size, it is possible that Neanderthals and fossil modern humans had a longer average gestation than present-day modern humans, their gestation lengths are unlikely to have differed radically from that observed in present-day humans.</p>", "<p id=\"Par43\">An error range for the gestation length value was calculated by using a minimum and maximum neonatal body mass estimate based on a human model and an ape model from ref. <sup>##REF##21199942##18##</sup>, respectively. A minimum and maximum gestation length was calculated using the minimum and maximum neonatal body masses. The percentage values with respect to the mean gestation length represented by the minimum and maximum values were subtracted or added to the adjusted mean gestation length to obtain an adjusted minimum and adjusted maximum gestation length. The error associated with other variables involved in the analysis of the timing of neurodevelopment, such as adult brain size and Workman’s event score, was not included in the analysis, as those values are more directly based on empirical data. While this does not imply that these variables are free of error or variation, it does mean that their values are known with more accuracy than that of the gestation length of fossil hominin species.</p>", "<p id=\"Par44\">Workman’s equations were also used to calculate the event score at which each mammalian species is born by making <italic>Y</italic> equal to the day of birth of each species (that is, to their log-transformed gestation length) and solving ‘eventscale’ in equation (##FORMU##0##1##) above. The intercept and slope in equation (##FORMU##0##1##) were calculated from equations (##FORMU##1##2##) and (##FORMU##2##3##) using the average brain size and gestation length corresponding to each species. The gestation length that would be expected in humans if they were born at the same neurodevelopmental stage as the great apes was also calculated using equation (##FORMU##0##1##) by making ‘eventscale’ equal to the event score at birth of each great ape species and solving <italic>Y</italic>.</p>", "<title>Reporting summary</title>", "<p id=\"Par45\">Further information on research design is available in the ##SUPPL##0##Nature Portfolio Reporting Summary## linked to this article.</p>" ]	[ "<title>Results</title>", "<title>Brain and body proportion at birth across mammals</title>", "<p id=\"Par8\">Brain and body proportion at birth (measured as the percentage of adult size that is present at birth) were compared across the four clades of mammals that are the best represented in our sample: artiodactyls, carnivorans, primates and rodents (Fig. ##FIG##0##1a##). All the species included in our study attain less than 21% of their adult body size at the time of birth, with minor differences across the major mammalian orders (Fig. ##FIG##0##1b## and Extended Data Table ##TAB##0##1##). However, other studies have reported higher body proportion values at birth in some groups that are not included in our study, such as some species of bat, which can reach almost 50% of their maternal body size at birth<sup>##REF##30810261##28##</sup>. Among the phylogenetic groups we compared, body proportion at birth is significantly lower in carnivorans relative to artiodactyls (<italic>P</italic> = 0.011) and primates (<italic>P</italic> = 0.003), with no other significant differences observed.</p>", "<p id=\"Par9\">In contrast, ranges of variation for brain proportion at birth are very wide. Within artiodactyls and primates, most species achieve between 25% and 75% of their adult brain size when they are born, with mean values of 40–50% (Extended Data Table ##TAB##0##1##). Humans show the lowest value within primates, with slightly less than 25% of adult brain size attained at birth. Carnivorans and rodents tend to show lower brain proportions at birth, with mean values around 25%, but their ranges of variation are also very wide, with maximum values reaching almost 75% in carnivorans and almost 60% in rodents (Fig. ##FIG##0##1c## and Extended Data Table ##TAB##0##1##). Primates display significantly higher brain proportions at birth than rodents (<italic>P</italic> < 0.001) and carnivorans (<italic>P</italic> < 0.001), but not artiodactyls (<italic>P</italic> = 0.943).</p>", "<p id=\"Par10\">A similar pattern is observed when focusing on mammalian species with particularly large absolute adult brain sizes (Fig. ##FIG##0##1d##). These groups also show narrow ranges of variation for body proportion at birth, with the lowest overall values observed in hominids (great apes and humans) and elephants (Fig. ##FIG##0##1e##). These groups tend to show brain proportions at birth of around 40–50% (Fig. ##FIG##0##1f## and Extended Data Table ##TAB##0##1##), but humans and, especially, bears show particularly low values within their clades.</p>", "<title>Evolutionary rates</title>", "<p id=\"Par11\">To assess the strength of selection over each branch of the mammalian phylogeny, we calculated the amount of change accumulated over each branch with respect to a neutral expectation. We call these values evolutionary rates because they are indicative of how fast individual branches have evolved, although they are not rates in the strict sense, but rather a ratio of the observed amount of change versus the expected amount of change at each branch (see <xref rid=\"Sec8\" ref-type=\"sec\">Methods</xref> for detailed explanations on how these values have been calculated). Humans show the highest evolutionary rate towards a smaller brain proportion at birth (which we refer to as increased brain size altriciality) across all mammals (Fig. ##FIG##1##2a,f##, Extended Data Fig. ##FIG##6##1## and Extended Data Table ##TAB##1##2##), with the same result obtained when brain size altriciality is measured as residuals from a phylogenetic generalized least squares (PGLS) regression line between neonatal and adult brain size (Extended Data Fig. ##FIG##7##2##). Although there are other mammalian species that have lower brain proportions at birth than humans, they have evolved their high levels of brain size altriciality within clades where other closely related species are similarly altricial. Humans, however, show a high level of brain size altriciality within the evolutionary context of primates, which are generally more precocial.</p>", "<p id=\"Par12\">With respect to body proportion at birth, the species that shows the highest rate to increase body size altriciality (that is, to decrease body proportion at birth) is the orca (<italic>Orcinus orca</italic>), followed by the striped dolphin (<italic>Stenella coeruleoalba</italic>; Fig. ##FIG##1##2b## and Extended Data Table ##TAB##1##2##). Notably, the fast increase in brain size altriciality observed in humans is not a secondary result of an increase in body size altriciality, as humans show a moderate increase in body proportion at birth with respect to the last common ancestor of <italic>Homo</italic> and <italic>Pan</italic> (rate = 1.41; Fig. ##FIG##1##2f##). Indeed, humans have a higher body proportion at birth with respect to maternal body size than all the other great apes (6.03% in humans versus 3.92% in chimpanzees, 4.83% in bonobos, 2.32% in gorillas and 4.25% in orangutans, according to our data). When considering our complete mammalian sample, evolutionary rates for brain and body proportions at birth are not significantly correlated (<italic>r</italic> = 0.007, <italic>P</italic> = 0.903; Fig. ##FIG##2##3a##), although they show a moderate correlation when outlier rates are removed (<italic>r</italic> = 0.272, <italic>P</italic> < 0.001; Fig. ##FIG##2##3c##). Within primates, these rates are not significantly correlated when outlier values are included in analyses (<italic>r</italic> = 0.001, <italic>P</italic> = 0.994; Fig. ##FIG##2##3b##), but they are positively correlated when outliers are excluded (<italic>r</italic> = 0.298, <italic>P</italic> = 0.006; Fig. ##FIG##2##3d##).</p>", "<p id=\"Par13\">Although humans show a high rate to increase absolute adult brain size, this rate is not the highest observed across all mammals (Fig. ##FIG##1##2c,f## and Extended Data Table ##TAB##1##2##). The highest rate to increase adult brain size is observed in the branch leading to both species of elephants, and the second highest rate in the branch leading to fereuungulates (carnivorans, artiodactyls and perissodactyls). Evolutionary rates for brain proportion at birth and for absolute adult brain size have a negative borderline significant correlation, such that species that tend to show high rates to increase their absolute adult brain size also tend to show high rates to decrease their brain proportion at birth (<italic>r</italic> = −0.117, <italic>P</italic> = 0.051; Fig. ##FIG##2##3a##), which is not surprising because brain proportion values also reflect changes in adult brain size. Across the complete mammalian sample, this negative correlation is also borderline significant when outlier rates are removed (<italic>r</italic> = −0.119, <italic>P</italic> = 0.053; Fig. ##FIG##2##3c##). This negative correlation is particularly strong within primates (<italic>r</italic> = −0.481, <italic>P</italic> < 0.001; Fig. ##FIG##2##3b##), although the strength of the correlation decreases when outlier rates, such as the human rate, are excluded (<italic>r</italic> = −0.241, <italic>P</italic> = 0.027; Fig. ##FIG##2##3d##). This negative correlation indicates that primate species showing high rates to increase adult brain size also tend to show high rates to decrease brain proportion at birth (in other words, they tend to increase brain size altriciality), a pattern that is not observed outside primates.</p>", "<p id=\"Par14\">Humans have an evolutionary rate for absolute neonatal brain size of 1.59, which is indicative of a quasi-neutral increase with respect to the value that is inferred for the last common ancestor of chimpanzees and humans (Fig. ##FIG##1##2d##). As for gestation length, humans show a rate of 1.66 to increase the length of the gestation period with respect to the last common ancestor of chimpanzees and humans, also indicating a neutrally evolving slight increase in gestation length with respect to the <italic>Homo</italic>–<italic>Pan</italic> ancestral value (Fig. ##FIG##1##2e,f##). Evolutionary rates for brain proportion at birth show a moderate positive correlation with rates for neonatal brain size and gestation length across the complete mammalian sample (Fig. ##FIG##2##3a,c##), which is not observed within primates (Fig. ##FIG##2##3b,d##).</p>", "<title>Scaling relationship between neonatal and adult brain and body size</title>", "<p id=\"Par15\">The relationship between neonatal and adult brain and body size was also explored using PGLS regressions. Neonatal and adult body size scale with a slope similar to 1 in all mammalian orders, with generalized phylogenetic analysis of covariance (pANCOVA) procedures showing no significant differences in slope or intercept between the four orders (Fig. ##FIG##3##4##). Concurring with previous analyses of primate data<sup>##UREF##9##29##</sup>, neonatal and adult brain size scale with a slope that is close to 1 in primates, rodents and artiodactyls, but not in carnivorans (Fig. ##FIG##3##4##). Indeed, primates and carnivorans differ significantly in their intercept and slope (<italic>P</italic> < 0.001), with carnivorans showing a lower slope than all the other orders. Despite their clear differences in absolute brain size and in their ecological specializations, we do not find any differences in the scaling relationship between neonatal and adult brain size between cetaceans and other artiodactyls (<italic>P</italic> = 0.816), between pinnipeds and terrestrial carnivorans (<italic>P</italic> = 0.693), between hominids and other primates (<italic>P</italic> = 0.156), or between murids and other rodents (<italic>P</italic> = 0.338; Extended Data Fig. ##FIG##8##3##).</p>", "<p id=\"Par16\">pANCOVA analyses, however, show that humans differ significantly from all the other primates in their scaling relationship between neonatal and adult brain size (<italic>P</italic> < 0.001), but not in their scaling relationship between neonatal and adult body size (<italic>P</italic> = 0.192; Fig. ##FIG##4##5a,b##). A more detailed analysis of the relationship between neonatal and adult brain size between humans and non-human primates indicates that the change in the scaling relationship in humans is driven by a higher than expected adult brain size, rather than by a lower than expected neonatal brain size (Extended Data Table ##TAB##2##3##).</p>", "<p id=\"Par17\">Regarding other apparent outliers with respect to their orders, bears differ significantly from all the other carnivorans in the slope and intercept of their scaling relationship between neonatal and adult brain size (<italic>P</italic> = 0.014), and of their scaling relationship between neonatal and adult body size (<italic>P</italic> = 0.033; Fig. ##FIG##4##5c,d##). <italic>Sus scrofa</italic> also differs significantly from all the other artiodactyls in its relationship between neonatal and adult brain size (<italic>P</italic> = 0.050), and in its relationship between neonatal and adult body size (<italic>P</italic> = 0.039; Fig. ##FIG##4##5e,f##). Although visually not a clear outlier, the golden hamster (<italic>Mesocricetus auratus</italic>) differs significantly from other rodents in its relationship between neonatal and adult brain size (<italic>P</italic> = 0.005), and it shows the same trend in the difference of its scaling relationship between neonatal and adult body size, although this is not significant (<italic>P</italic> = 0.058; Fig. ##FIG##4##5g,h##). These results indicate that, out of the outliers observed for each order, humans are the only ones that show a scaling relationship between neonatal and adult brain size that is significantly different from the scaling relationship observed for the rest of their order, and that is not associated with a similar difference in the scaling relationship between neonatal and adult body size.</p>", "<title>Timing of neurodevelopment</title>", "<p id=\"Par18\">Next, we aimed to understand how the timing of neurodevelopmental processes relative to birth changed during hominin evolution. To do so, we relied on Workman et al.’s model of neural development<sup>##REF##23616543##23##</sup>, which showed that the progress of neural events across 18 mammalian species (which span the phylogenetic diversity of mammals) is highly conserved. We used this model to calculate the day after conception at which key neurodevelopmental events occurred in fossil hominins, with particular focus on whether they happened before or after birth<sup>##REF##23616543##23##</sup>. This timing was calculated based on the inferred adult brain size and gestation length of each hominin species (Extended Data Table ##TAB##3##4##; see <xref rid=\"Sec8\" ref-type=\"sec\">Methods</xref> for more details).</p>", "<p id=\"Par19\">Workman et al. calculated an ‘event score’ for each neurodevelopmental event that describes the order in which they occur<sup>##REF##23616543##23##</sup>. This score ranges from 0 to 1, with 0 corresponding to the earliest occurring events (peak of neurogenesis of the cranial motor nuclei in the brainstem) and 1 corresponding to the latest occurring event (which corresponds to the end of the myelination of the middle cerebellar peduncle in their dataset)<sup>##REF##23616543##23##</sup>. Using this model to study different hominin species shows that the duration of neurodevelopment is extended in modern humans and Neanderthals with respect to earlier hominins, and that modern humans and Neanderthals are born at an earlier neurodevelopmental stage not because gestation is shorter, but because neurodevelopment takes longer (Fig. ##FIG##5##6a##). This result is further confirmed when comparing the scale of neurodevelopment in humans with that corresponding to the great apes (Extended Data Fig. ##FIG##9##4##). Our results indicate that humans are born with an event score of 0.695, which compares with the event scores in the range of 0.728 to 0.756 shown by chimpanzees, bonobos, gorillas and orangutans at birth (Extended Data Fig. ##FIG##9##4##). If humans (present-day <italic>Homo sapiens</italic>) were born with the same event score as the other great apes (that is, at the same neurodevelopmental stage), this would correspond to an average gestation length of 321 days (10.7 months). Interestingly, the application of Workman et al.’s model<sup>##REF##23616543##23##</sup> to the complete mammalian sample shows that there is a significant correlation between brain proportion at birth (percentage of adult brain size at birth) and neurodevelopmental stage at birth (measured as the event score with which species are born) across mammals (<italic>r</italic> = 0.439, <italic>P</italic> < 0.001), but there is no significant correlation within primates (<italic>r</italic> = 0.289, <italic>P</italic> = 0.057).</p>", "<p id=\"Par20\">Focusing on fossil hominin species and on events related to brain development, there are only 13 out of 215 events (those with event scores ranging from 0.675 to 0.770) that change in their pre- or postnatal occurrence during hominin evolution, depending on species-specific combinations of brain size and gestation lengths (Fig. ##FIG##5##6b## and Extended Data Table ##TAB##4##5##). All events outside this range would have happened prenatally (when the event score is lower than 0.675) or postnatally (when the event score is higher than 0.770) in all hominin species. More specifically, events with lower event scores within the 0.675–0.770 range are the ones that were moved to the postnatal period as later hominins evolved larger brains. A closer examination of these events indicates that they are mostly related to the onset of myelination of different brain structures, including the anterior commissure, hippocampus, striatum and corpus callosum, among others (Fig. ##FIG##5##6b## and Extended Data Table ##TAB##4##5##).</p>" ]	[ "<title>Discussion</title>", "<p id=\"Par21\">Previous work has explored the relationship between neonatal brain size and several other factors, including maternal body size, maternal metabolic rate, overall maternal investment, gestation length, type of placentation and litter size in mammals<sup>##UREF##9##29##–##UREF##10##32##</sup>. These studies have tried to explain human altriciality relative to other primates as the result of certain evolutionary and developmental constraints. In contrast, our study has focused on brain development in an attempt to understand whether human altriciality has evolved as a selectively advantageous trait that may have increased brain plasticity and behavioural complexity<sup>##UREF##0##1##,##UREF##1##2##</sup>.</p>", "<p id=\"Par22\">Our results indicate that brain proportion at birth, which is generally considered a proxy for brain size altriciality, varies extensively within all mammalian orders included in our study. In addition, brain proportion at birth does not show a clear association with the neurodevelopmental stage at which species are born within primates. Therefore, although this percentage value is broadly used in the literature to discuss the evolution of human altriciality, our results indicate that this proportion is not an accurate measure of the level of neurodevelopmental altriciality, particularly within primates. When altriciality is measured as this percentage value, humans appear to have evolved their high level of brain size altriciality at a very fast rate in the context of their more precocial primate relatives, showing the largest accumulated change with respect to a neutral expectation across our complete mammalian sample. Also, although the scaling relationship between neonatal and adult brain size appears to be highly conserved across mammals (with the exception of carnivorans; Fig. ##FIG##3##4##), and particularly within primates, humans are the only species that departs significantly from the scaling relationship observed in their order (primates) in a way that is independent of the scaling relationship between neonatal and adult body size.</p>", "<p id=\"Par23\">Given these results, we evaluated the likely drivers of the fast increase in brain size altriciality observed in humans. Our results indicate that the apparent high level of human brain size altriciality most strongly relates to a larger than expected adult brain size, rather than a smaller than expected neonatal brain size or a shortened gestation length. Indeed, humans show increased neonatal brain size and gestation length with respect to the last common ancestor shared with chimpanzees (Fig. ##FIG##1##2##). In taking body size scaling into account, published studies disagree on whether human gestation length is shorter than expected or not. Comparisons with other primates indicate that humans and the great apes have the gestation length that is expected for their body size<sup>##UREF##11##33##</sup>, whereas other analyses indicate that human gestation length is shorter than expected in comparison with other primates, and that genes involved in parturition may show accelerated evolution in humans<sup>##REF##21533219##34##</sup>.</p>", "<p id=\"Par24\">In our analysis, humans show a moderate rate of 1.66 to increase the length of the gestation period with respect to the last common ancestor shared with chimpanzees and bonobos. If the human gestation length was 7 months longer than it is, as expected if humans were to be born with a chimpanzee value of 40% of adult brain size at birth<sup>##UREF##12##35##,##REF##16824583##36##</sup>, this would require an evolutionary rate of 8.02 to increase the length of the gestation period (Extended Data Fig. ##FIG##10##5##). If the human gestation length was 21 months, which is often invoked as the gestation length that would be required for humans to be born at the same developmental stage of a chimpanzee<sup>##UREF##0##1##</sup>, this would require a rate of 9.88 along the human branch. These rates would be the highest across all mammals and extreme outliers with respect to the rates measured within the primate clade for gestation lengths, which range from −1.18 to 2.05. Even our estimate of a gestation length of 10.7 months, which would be required for humans to be born at the neurodevelopmental stage of the other great apes according to Workman’s model<sup>##REF##23616543##23##</sup>, would require an evolutionary rate of 3.5 along the human branch, which is within the range of variation of mammals, but outside the range of variation observed in primates. These comparisons show that, given the evolutionary context of the variation in gestation length across primates, humans could not have evolved the very long gestation periods that align with what would be expected for the brain size precociality observed in other primate species.</p>", "<p id=\"Par25\">Previous work has shown that pre- and postnatal brain growth rates show a strongly conserved pattern across placental mammals, with brain size at birth simply capturing one particular time point within the dynamic process of neurodevelopment<sup>##REF##28490626##37##</sup>. The timing of birth with respect to neurodevelopment, however, is known to be highly variable across clades, but also between closely related species<sup>##UREF##13##38##</sup>, as also shown by our analyses. Previously published work on a small but diverse sample of mammalian species<sup>##REF##28490626##37##</sup> shows that peak growth velocity happens postnatally in all altricial species and prenatally in all precocial species included in the sample, and also in humans. However, birth happens closer to peak growth velocity in humans than in other precocial species<sup>##REF##28490626##37##</sup>, which results in the accelerated brain growth observed in humans during the first year of life in comparison with chimpanzees<sup>##REF##23256194##39##,##REF##21835623##40##</sup> and other primates<sup>##REF##15027089##41##</sup>. In spite of these differences in postnatal brain growth rates, comparisons between humans and chimpanzees<sup>##REF##33563125##42##</sup> and other great apes<sup>##REF##37554928##43##</sup> based on anatomy, behaviour and transcriptional profiles indicate that humans and great apes are in many ways similar from a developmental point of view during the first year of life, and that differences between species become more marked during later stages of maturation.</p>", "<p id=\"Par26\">Still, humans are often described as developmentally delayed at birth in comparison with other primates. Comparisons of humans and chimpanzees indicate that gross motor control, including the ability to sit up, stand up, walk and climb, develops faster in chimpanzees<sup>##REF##32412141##44##</sup>, although other studies indicate that the timing of walking onset is accurately predicted by brain size in humans and other mammals<sup>##REF##20018704##45##</sup>. Fine motor control and social behaviour seem to emerge at similar paces in chimpanzees and humans, particularly when differences in lifespan and age at first reproduction are taken into account<sup>##REF##32412141##44##</sup>. Focusing on the early postnatal period (that is, the first 30 days after birth), behavioural studies of captive chimpanzees have shown that they differ significantly from humans in only 1 of the 25 traits assessed by the Neonatal Behavioural Assessment Scale, namely in muscle tone<sup>##UREF##14##46##</sup> (assessed traits are related to attention, arousal, motor ability and coping). These comparisons indicate that the assertion that humans are developmentally and behaviourally more altricial than other primates is primarily based on gross motor development, but both newborn and adult humans show substantially less motor strength than their great ape counterparts (also discussed in ref. <sup>##UREF##12##35##</sup>). More comparable data with respect to other behavioural domains are needed to clarify the nature of differences during the early postnatal period between humans and other primates.</p>", "<p id=\"Par27\">Confirming previous studies<sup>##REF##23616543##23##,##UREF##13##38##,##REF##23545184##47##</sup>, our analyses show that the duration of neurodevelopment is longer in modern humans and Neanderthals with respect to earlier hominins with smaller brains (Fig. ##FIG##5##6a##). This effect is amplified towards the later occurring events and, therefore, is particularly marked during the postnatal period. In fact, this extended duration of neurodevelopment drives the apparent altriciality of humans to a much stronger degree than a change in the length of the gestation period, which remains fairly stable among hominids (great apes and humans)<sup>##UREF##5##12##,##UREF##11##33##</sup>. Our analyses indicate that approximately 6% of all the neurodevelopmental events studied by Workman et al.<sup>##REF##23616543##23##</sup> were shifted to the postnatal period during hominin evolution (that is, they would have happened prenatally in early hominins, including <italic>Ardipithecus</italic>\n<italic>ramidus</italic> and australopiths, and postnatally in later hominins, including Neanderthals and modern humans; Fig. ##FIG##5##6b##). Those events are mostly related to the onset of the myelination of some brain regions (including the corpus callosum, hippocampus and striatum, among others), which adds to the existing evidence that there are changes in the development of white matter between chimpanzees and humans that evolved after their divergence<sup>##REF##23256194##39##,##REF##21835623##40##</sup>. The pre- or postnatal occurrence of some other neurodevelopmental events related to brain plasticity, such as those related to synaptogenesis and attainment of peak synaptic density, seem to be shared by both chimpanzees and humans<sup>##REF##33563125##42##,##REF##23754422##48##</sup> and, according to our estimates, by fossil hominins.</p>", "<p id=\"Par28\">Together with observations that myelination is developmentally protracted in humans with respect to chimpanzees<sup>##REF##23012402##49##</sup>, our results indicate that evolved differences in the timing of myelination involve the complete period of postnatal development in humans, from birth to early adulthood. Because differences in brain plasticity between chimpanzees and humans are well described at the anatomical and molecular level<sup>##REF##23615289##50##–##REF##24194709##56##</sup>, our results point to a particularly important role of myelination in driving human brain plasticity. Activity-dependent myelination is a mechanism of brain plasticity that can optimize the timing of information transmission through neural circuits and that can be increasingly important in large brains with complex networks, such as human brains, where conduction delays are substantial and the synchronous arrival of action potentials is critical for optimal network function<sup>##REF##26585800##57##</sup>.</p>", "<p id=\"Par29\">Differences in human brain plasticity further develop later in the postnatal period. The existence of a critical window during the early infancy for the onset of a variety of cognitive functions and species-specific social behaviours is widely documented in humans<sup>##REF##28392082##58##</sup> and other primates<sup>##REF##12460687##59##</sup>, and is also demonstrated by studies showing the effect of early rearing experience on brain structure in humans<sup>##REF##12732221##60##</sup>, chimpanzees<sup>##REF##24206013##61##</sup> and rhesus monkeys<sup>##REF##19487631##62##</sup>. In humans, some key developmental milestones that do not have a clear parallel in other primates are attained during the first year of life, including those that are related to the emergence of language<sup>##REF##20826304##63##</sup> and shared intentionality<sup>##REF##17181709##64##</sup>. By two years of age, humans show significant differences with chimpanzees and bonobos in several aspects related to social cognition, which further develop afterwards<sup>##REF##23765870##65##</sup>. The small proportion of neurodevelopment that has been moved from the prenatal to the postnatal period during human evolution seems to indicate that human specializations for neuroplasticity are more strongly linked to later stages of neurodevelopment, rather than to the early postnatal period. However, this small number of events may have had a large functional significance, and their effects can be increasingly pronounced as neurodevelopment progresses into later postnatal stages.</p>", "<p id=\"Par30\">In summary, our results suggest that humans are not exceptionally altricial in comparison with other species<sup>##UREF##5##12##</sup>, and they help us understand the aspects of early postnatal brain development that have changed during human evolution. While human brains are slightly less developed at birth than expected within their phylogenetic context, this is not because of a shortened gestation, but because of the longer extension of neurodevelopment, which is ultimately linked to increased adult brain size (that is, larger brains need more time to grow). Our results indicate that only a minor proportion of neurodevelopment was shifted from the prenatal to the postnatal period during human evolution, but additional data are required to elucidate whether this apparently minor shift may have had substantial functional effects. More specifically, the slight underdevelopment of human brains at birth is consistently associated with the postnatal occurrence of some developmental events related to myelination that happened prenatally in earlier hominins. Our results point to the interaction between myelination, environmental influences during postnatal development and brain plasticity as a fruitful avenue for future research to shed light on the evolution of human-specific behavioural traits.</p>" ]	[]	[ "<p id=\"Par1\">Human newborns are considered altricial compared with other primates because they are relatively underdeveloped at birth. However, in a broader comparative context, other mammals are more altricial than humans. It has been proposed that altricial development evolved secondarily in humans due to obstetrical or metabolic constraints, and in association with increased brain plasticity. To explore this association, we used comparative data from 140 placental mammals to measure how altriciality evolved in humans and other species. We also estimated how changes in brain size and gestation length influenced the timing of neurodevelopment during hominin evolution. Based on our data, humans show the highest evolutionary rate to become more altricial (measured as the proportion of adult brain size at birth) across all placental mammals, but this results primarily from the pronounced postnatal enlargement of brain size rather than neonatal changes. In addition, we show that only a small number of neurodevelopmental events were shifted to the postnatal period during hominin evolution, and that they were primarily related to the myelination of certain brain pathways. These results indicate that the perception of human altriciality is mostly driven by postnatal changes, and they point to a possible association between the timing of myelination and human neuroplasticity.</p>", "<p id=\"Par2\">Humans have the highest evolutionary rate towards becoming more altricial across all placental mammals, but this results primarily from postnatal enlargement of brain size rather than neonatal changes.</p>", "<title>Subject terms</title>" ]	[ "<title>Main</title>", "<p id=\"Par3\">Mammalian species can be classified according to their developmental patterns as altricial or precocial. Altricial species are characterized by having large litter sizes, short gestations, closed sensory organs and naked skin at birth, and by having limited mobility when they are born. Precocial species have small litters and long gestations, their sensory organs are open and functional at birth, their body hair is present when they are born, and they are able to locomote on their own soon after birth<sup>##UREF##0##1##,##UREF##1##2##</sup>. Human newborns are generally described as altricial because, compared with other primates, infants are underdeveloped at birth and more dependent on parental care for survival. Human altriciality, or helplessness at birth, has been associated with a high level of brain plasticity resulting in increased learning capacities, behavioural flexibility and enhanced capacity for cultural transmission<sup>##UREF##0##1##–##UREF##2##4##</sup>. The logic underlying the link between altricial development, brain plasticity and behavioural complexity is that a higher proportion of brain development happening postnatally allows for neural circuitry to be shaped directly by the environment where the adult individual will have to survive, thus resulting in behaviours that are directly shaped by and adapted to those environments<sup>##UREF##2##4##</sup>.</p>", "<p id=\"Par4\">Although humans appear altricial when compared with other primates, a broader phylogenetic perspective that includes additional mammalian orders shows that many other species are substantially more altricial. However, although a relationship between altricial development, delayed brain maturation and enhanced learning capacities has been hypothesized for birds<sup>##REF##21369349##5##</sup>, the available evidence does not currently indicate a relationship between developmental patterns and adaptations related to social behaviour in mammals<sup>##REF##28115975##6##</sup>. Therefore, our study aims to analyse the evolution of human altriciality relative to other primates and mammals to determine whether the evolution of our species’ development departs from other groups, and whether these differences might be linked to the emergence of human specializations for brain plasticity<sup>##UREF##1##2##,##REF##16022601##7##,##REF##26440111##8##</sup>.</p>", "<p id=\"Par5\">One explanation for human altriciality is that it arose as the result of an obstetrical dilemma: as humans evolved larger brains and the birth canal became constrained due to biomechanical adaptations for bipedality, selection favoured neonates with relatively smaller brains at the time of birth, and a greater proportion of brain growth was offset to the postnatal period<sup>##UREF##3##9##,##UREF##4##10##</sup>. Other authors suggest that metabolic constraints are more likely to have driven the evolution of human altriciality, as foetal energetic demands towards the final part of pregnancy are too high to maintain gestation much beyond 40 weeks<sup>##REF##22932870##11##,##UREF##5##12##</sup>. Whether human altriciality evolved because of obstetrical or metabolic factors, or a combination of both, the resulting opportunity for extrauterine brain growth and maturation may have proved selectively advantageous<sup>##UREF##1##2##,##UREF##2##4##</sup>. Survival of the mother and newborn during labour would be strong selective forces, but additional selective advantages in the form of increased brain plasticity and learning abilities could also explain why giving birth to underdeveloped and vulnerable infants, a seemingly disadvantageous trait, evolved in humans<sup>##UREF##0##1##</sup>.</p>", "<p id=\"Par6\">The altriciality–precociality spectrum is complex and multifaceted. The first part of our study, however, focuses on a simple metric, which is the proportion or percentage of adult brain size that is present at birth. This variable is broadly discussed in the human altriciality literature, as it shows a substantially lower value in humans (different studies report between 20% and 30% of adult brain size at birth<sup>##REF##16226789##13##</sup>) than in all the other primates (35–40% in chimpanzees and even higher values in the other primates<sup>##UREF##1##2##,##UREF##2##4##,##REF##16226789##13##</sup>). It is important to note that this percentage value provides only limited information on the level of altriciality or precociality of each species, it is not exposed to selection per se, and its study poses methodological limitations (<xref rid=\"Sec8\" ref-type=\"sec\">Methods</xref>). However, it is important to understand how this proportion of neonatal to adult brain size has varied across mammalian evolution because of its historical importance in discussions about human altriciality<sup>##UREF##0##1##,##UREF##1##2##,##UREF##5##12##,##REF##14475453##14##–##UREF##6##16##</sup>. After exploring the variation and evolution of brain and body proportion at birth across mammals, we studied the relationship between neonatal and adult brain and body sizes in a regression context.</p>", "<p id=\"Par7\">Given the differences in developmental patterns observed between extant humans and the great apes, increased altriciality must have evolved within the hominin clade, which is the clade that includes fossil species that are more closely related to humans than to chimpanzees. However, there are uncertainties associated with the estimate of neonatal brain and body size in the hominin fossil record<sup>##REF##18789811##17##,##REF##21199942##18##</sup>, which have made it difficult to infer how developmental patterns evolved in hominins. Some studies indicate that altriciality is associated with a large brain size, either through metabolic or obstetrical constraints, and that it evolved in large-brained hominins, perhaps in <italic>Homo erectus</italic><sup>##UREF##2##4##,##REF##25771994##19##</sup> (but see ref. <sup>##REF##15372030##20##</sup>). Recent research, however, suggests that a more altricial pattern of development might have evolved in earlier hominins, perhaps even in some australopiths<sup>##REF##32270044##21##,##UREF##7##22##</sup>, but these claims remain difficult to confirm or refute. Meanwhile, comparative data indicate a strongly conserved nature of neurodevelopment across all placental mammals<sup>##REF##23616543##23##</sup>, as well as an overall increase in adult brain size during hominin evolution<sup>##UREF##8##24##–##REF##28049819##26##</sup> and probably a slight increase in gestation lengths from earlier to later hominins<sup>##REF##36191229##27##</sup>. Based on these comparative data, it is possible to estimate of how the timing of neurodevelopment (that is, the date after conception at which each neurodevelopmental event occurs) with respect to the time of birth has changed during hominin evolution. Our analyses across the mammalian phylogeny and within the hominin clade can help us understand whether humans are unexpectedly altricial given their evolutionary context, and whether a more altricial pattern of development has been selected in humans because of its association with increased neuroplasticity.</p>", "<title>Supplementary information</title>", "<p>\n\n\n</p>" ]	[ "<title>Extended data</title>", "<p id=\"Par50\">\n\n</p>", "<p id=\"Par51\">\n\n</p>", "<p id=\"Par52\">\n\n</p>", "<p id=\"Par53\">\n\n</p>", "<p id=\"Par54\">\n\n</p>", "<p id=\"Par55\">\n\n</p>", "<p id=\"Par56\">\n\n</p>", "<p id=\"Par57\">\n\n</p>", "<p id=\"Par58\">\n\n</p>", "<p id=\"Par59\">\n\n</p>", "<title>Extended data</title>", "<p id=\"Par46\">is available for this paper at 10.1038/s41559-023-02253-z.</p>", "<title>Supplementary information</title>", "<p id=\"Par47\">The online version contains supplementary material available at 10.1038/s41559-023-02253-z.</p>", "<title>Acknowledgements</title>", "<p>We thank C. Charvet, A. Goswami, T. Monson and J. DeSilva for clarifications on different aspects of this study. A.G.-R. is grateful to G. and V. Gómez-Sánchez for their practical lessons on human development. C.C.S. is supported by the National Science Foundation (EF-2021785, DRL-2219759) and the National Institutes of Health (HG011641).</p>", "<title>Author contributions</title>", "<p>A.G.-R. and C.C.S. conceived the study. A.G.-R. designed research. A.G.-R. and C.N. compiled data. A.G.-R., C.N. and J.B.S. analysed data. All the authors interpreted results. A.G.-R. and C.C.S. wrote the manuscript with contributions from the other authors.</p>", "<title>Peer review</title>", "<title>Peer review information</title>", "<p id=\"Par48\"><italic>Nature Ecology & Evolution</italic> thanks Nicole Grunstra and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.</p>", "<title>Data availability</title>", "<p>Datasets used in this study are available at 10.6084/m9.figshare.22242724.</p>", "<title>Code availability</title>", "<p>The scripts used to carry out analyses are available at 10.6084/m9.figshare.22242724.</p>", "<title>Competing interests</title>", "<p id=\"Par49\">The authors declare no competing interests.</p>" ]	[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>Phylogeny and brain and body proportion values.</title><p><bold>a</bold>, Phylogeny highlighting the four best-represented orders of mammals studied in the different analyses (artiodactyls in dark orange, carnivorans in blue, primates in green, rodents in light orange, others in grey). <bold>b</bold>, Comparison of body proportions at birth (percentage of maternal body size at birth) across the four orders of mammals. <bold>c</bold>, Comparison of brain proportions at birth (percentage of adult brain size at birth) across the four orders of mammals. <bold>d</bold>, Mammalian phylogeny highlighting the clades whose species have a particularly large absolute adult brain size (perissodactyls in yellow, cetaceans in maroon, pinnipeds and bears in dark blue, hominids in dark green, elephants in purple). <bold>e</bold>, Comparison of body proportions at birth across the five groups of mammals with large brain sizes. <bold>f</bold>, Comparison of brain proportions at birth across the five groups of mammals with large brain sizes. Artio, artiodactyls (<italic>n</italic> = 26 species); Carn, carnivorans (<italic>n</italic> = 21 species); Prim, primates (<italic>n</italic> = 44 species); Rod, rodents (<italic>n</italic> = 24 species); Periss, perissodactyls (<italic>n</italic> = 5 species); Cet, cetaceans (<italic>n</italic> = 6 species); P&B, pinnipeds and bears (<italic>n</italic> = 8 species); Hom, hominids (<italic>n</italic> = 5 species); Eleph, elephants (<italic>n</italic> = 2 species). Raincloud plots show individual datapoints, probability density distributions and summary statistics in the box plots (median as the thick horizontal line, interquartile range within the box, minimum and maximum as the lower and upper whiskers, and outliers as black circles). Silhouettes are all from <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.phylopic.org\">https://www.phylopic.org</ext-link> and are not to scale.</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>Branch-specific evolutionary rates across the mammalian phylogeny.</title><p><bold>a</bold>, Brain proportion at birth. <bold>b</bold>, Body proportion at birth. <bold>c</bold>, Adult brain size. <bold>d</bold>, Neonatal brain size. <bold>e</bold>, Gestation length. <bold>f</bold>, Comparison of the distribution of evolutionary rates for each trait across the mammalian phylogeny with the human rate (dark green dashed line). The layout of the plots in <bold>f</bold> is the same as in the general figure. For branch colours, yellow indicates fast rates to decrease the value of the trait under study, blue indicates high rates to increase the value of the traits and grey indicates low rates. Nodes are represented in white and tips are represented in black, with tip/node size proportional to the trait value within each phylogeny. Species names are colour-coded according to their order as in Fig. ##FIG##0##1a##, with humans highlighted in dark green. Silhouettes are all from <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.phylopic.org\">https://www.phylopic.org</ext-link>.</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><title>Correlations between evolutionary rates.</title><p>Correlations between rates for brain proportion at birth (Brainprop), body proportion at birth (Bodyprop), absolute adult brain size (Adultbrain), absolute neonatal brain size (Neobrain) and gestation length (Gestation). <bold>a</bold>, All mammalian species are considered together. <bold>b</bold>, Correlations are measured within each of the four best-represented orders. <bold>c</bold>, All mammalian species are considered together, but outlier rate values (rate values greater than 3 or lower than −3) have been excluded from correlation analyses. <bold>d</bold>, Correlations obtained within each of the four best-represented orders after excluding outlier rate values. Asterisks indicate significant correlations at <italic>P</italic> < 0.05 (), <italic>P</italic> < 0.01 () or <italic>P</italic> < 0.001 (**). Silhouettes are all from <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.phylopic.org\">https://www.phylopic.org</ext-link>.</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><title>PGLS regressions between neonatal and adult brain and body size.</title><p>PGLS regressions between neonatal and adult brain size (top) and neonatal and adult body size (bottom) with comparisons between primates and the other three best-represented orders. Intercepts (a) and slopes (b) are indicated for each order, and they are compared based on pANCOVA analyses. <bold>a</bold>, Adult to neonatal brain size in the complete mammalian sample. <bold>b</bold>, Adult to neonatal body size in the complete mammalian sample. <bold>c</bold>, Adult to neonatal brain size in primates versus carnivorans (<italic>F</italic> = 7.765, <italic>P</italic> < 0.001). <bold>d</bold>, Adult to neonatal body size in primates versus carnivorans (<italic>F</italic> = 0.639, <italic>P</italic> = 0.531). <bold>e</bold>, Adult to neonatal brain size in primates versus artiodactyls (<italic>F</italic> = 0. 911, <italic>P</italic> = 0.407). <bold>f</bold>, Adult to neonatal body size in primates versus artiodactyls (<italic>F</italic> = 0.341, <italic>P</italic> = 0.712). <bold>g</bold>, Adult to neonatal brain size in primates versus rodents (<italic>F</italic> = 1.573, <italic>P</italic> = 0.215). <bold>h</bold>, Adult to neonatal body size in primates versus rodents (<italic>F</italic> = 0.612, <italic>P</italic> = 0.545). Mammalian orders are represented using the same colors as in Fig. ##FIG##0##1##, with all species represented in grey in the left-most panels. Silhouettes are all from <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.phylopic.org\">https://www.phylopic.org</ext-link>.</p></caption></fig>", "<fig id=\"Fig5\"><label>Fig. 5</label><caption><title>Scaling relationships between neonatal and adult brain and body size in outlier species.</title><p>Comparison of the scaling relationship between neonatal and adult brain and body size for the four mammalian orders and their apparent outliers in their scaling relationship between neonatal and adult brain size, with significance assessed based on pANCOVA. <bold>a</bold>, Adult versus neonatal brain size in humans with respect to other primates (<italic>F</italic> = 13.799, <italic>P</italic> < 0.001). <bold>b</bold>, Adult versus neonatal body size in humans with respect to other primates (<italic>F</italic> = 1.761, <italic>P</italic> = 0.192). <bold>c</bold>, Adult versus neonatal brain size in bears with respect to other carnivorans (<italic>F</italic> = 5.585, <italic>P</italic> = 0.014). <bold>d</bold>, Adult versus neonatal body size in bears with respect to other carnivorans (<italic>F</italic> = 4.178, <italic>P</italic> = 0.033). <bold>e</bold>, Adult versus neonatal brain size in boars with respect to other artiodactyls (<italic>F</italic> = 4.185, <italic>P</italic> = 0.050). <bold>f</bold>, Adult versus neonatal body size in boars with respect to other artiodactyls (<italic>F</italic> = 4.674, <italic>P</italic> = 0.039). <bold>g</bold>, Adult versus neonatal brain size in golden hamsters with respect to other rodents (<italic>F</italic> = 9.821, <italic>P</italic> = 0.005). <bold>h</bold>, Adult versus neonatal body size in golden hamsters with respect to other rodents (<italic>F</italic> = 4.018, <italic>P</italic> = 0.058). Apparent outliers with respect to each order are represented with a darker shade of their order-specific colour. Confidence intervals (dashed) and prediction intervals (dotted) are plotted. Silhouettes are all from <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.phylopic.org\">https://www.phylopic.org</ext-link>, with the exception of the golden hamster, which is self-generated.</p></caption></fig>", "<fig id=\"Fig6\"><label>Fig. 6</label><caption><title>Change in the timing of neurodevelopment across hominin evolution.</title><p><bold>a</bold>, Variation in the timing of neurodevelopmental events across Workman’s complete scale (event score = 0 to event score = 1). Labels 0–10 in the plot represent Workman’s event scores 0–1 (1 in the plot corresponds to an event score of 0.1 and so on). <bold>b</bold>, Variation in the timing of neurodevelopment of the events whose pre- or postnatal occurrence is inferred to change over hominin evolution, which corresponds to the shaded area in <bold>a</bold>. Only the events whose pre- or postnatal occurrence is inferred to change during hominin evolution are represented in <bold>b</bold>. Labels in the plot correspond to the following events and event scores (from ref. <sup>##REF##23616543##23##</sup>): 1, fornix myelination onset (0.675); 2, anterior commissure myelination onset (0.677); 3, lateral geniculate nucleus myelination onset (0.68); 4, cingulum myelination onset (0.687); 5, mammillothalamic tract myelination onset (0.689); 6, internal capsule myelination onset (0.692); 7, hippocampus myelination onset (0.699); 8, fasciculus retroflexus myelination onset (0.7); 9, stria terminalis myelination onset (0.703); 10, striatum myelination onset (0.715); 11, corpus callosum body myelination onset (0.722); 12, splenium myelination onset (0.732); 13, start of the plasticity/ocular dominance critical period (0.77). In both plots, the <italic>x</italic> axis represents the post-conception day at which events occur, with the plot centred on the day of birth (day 0), and the <italic>y</italic> axis represents hominin species in association with their adult brain size. The <italic>y</italic> axis in the plots is not to scale.</p></caption></fig>", "<fig id=\"Fig7\"><label>Extended Data Fig. 1</label><caption><title>Phylogeny with node numbers.</title><p>Node numbers in this figure match those listed in Extended Data Table ##TAB##1##2##.</p></caption></fig>", "<fig id=\"Fig8\"><label>Extended Data Fig. 2</label><caption><title>Evolutionary rates obtained when quantifying altriciality-precociality as the residuals from a PGLS regression between neonatal and adult brain and body size.</title><p><bold>a</bold> Evolutionary rates for neonatal-adult brain residuals plotted on the phylogeny (top) and distribution of rates compared with the human evolutionary rate (dark green line, bottom). <bold>b</bold> As <bold>a</bold>, but for body residuals. Both sets of rates are strongly correlated with those obtained when calculating rates for brain and body proportions at birth as percentage values of neonatal to adult size, shown Fig. ##FIG##1##2a## and ##FIG##1##b##, respectively (brain: <italic>r</italic> = −0.833, <italic>P</italic> < 0.001; body: <italic>r</italic> = −0.934, <italic>P</italic> < 0.001). Increased altriciality is indicated by a decrease in brain and body proportion values, but by an increase in brain and body residuals, hence the negative correlations.</p></caption></fig>", "<fig id=\"Fig9\"><label>Extended Data Fig. 3</label><caption><title>Scaling relationship between neonatal and adult brain and body size in mammalian subclades.</title><p>pANCOVA-based comparisons of the scaling relationship between neonatal and adult brain and body size in clades that differ substantially in adult brain size and/or ecological specializations within each order. <bold>a</bold> Adult versus neonatal brain size in hominids with respect to other primates (<italic>F</italic> = 1.944, <italic>P</italic> = 0.156). <bold>b</bold> Adult versus neonatal body size in hominids with respect to other primates (<italic>F</italic> = 1.062, <italic>P</italic> = 0.355). <bold>c</bold> Adult versus neonatal brain size in pinnipeds with respect to other carnivorans (<italic>F</italic> = 0.375, <italic>P</italic> = 0.693). <bold>d</bold> Adult versus neonatal body size in pinnipeds with respect to other carnivorans (<italic>F</italic> = 5.580, <italic>P</italic> = 0.030 for differences in slope). <bold>e</bold> Adult versus neonatal brain size in cetaceans with respect to other artiodactyls (<italic>F</italic> = 0.205, <italic>P</italic> = 0.816). <bold>f</bold> Adult versus neonatal body size in cetaceans with respect to other artiodactyls (<italic>F</italic> = 1.304, <italic>P</italic> = 0.288). <bold>g</bold> Adult versus neonatal brain size in murids with respect to other rodents (<italic>F</italic> = 1.145, <italic>P</italic> = 0.338). <bold>h</bold> Adult versus neonatal body size in murids with respect to other rodents (<italic>F</italic> = 0.806, <italic>P</italic> = 0.460). Groups with a larger adult brain size within each order are represented with a darker shade of their order-specific color. Silhouettes are from <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.phylopic.org\">https://www.phylopic.org</ext-link>.</p></caption></fig>", "<fig id=\"Fig10\"><label>Extended Data Fig. 4</label><caption><title>Comparison of the timing of neurodevelopment between humans and the great apes.</title><p>The x-axis shows the day after conception at which events with a given event score happen in each species. The y-axis shows species-specific average brain size and it is not to scale. The human average brain size used in this figure is that of present-day modern humans, which is smaller than the average brain size of fossil modern humans shown in Fig. ##FIG##5##6##. The labels 0 to 10 in the plot represent Workman’s event scores 0 to 1 (1 in the plot corresponds to an event score of 0.1 and so on). The event score at birth for each species is 0.742 (<italic>P. paniscus</italic>), 0.756 (<italic>P. pygmaeus</italic>), 0.728 (<italic>P. troglodytes</italic>), 0.738 (<italic>G. gorilla</italic>), and 0.695 (<italic>H. sapiens</italic>). When transformed to the human scale, those event scores correspond to a mean gestation length of 321 days (10.7 months), which is the gestation length that would correspond to humans if they were born at the same neurodevelopmental stage of the other great apes.</p></caption></fig>", "<fig id=\"Fig11\"><label>Extended Data Fig. 5</label><caption><title>Evolutionary rates for human gestation length.</title><p>Comparison of the human evolutionary rate (dark green dashed line) with the distribution of evolutionary rates across mammals for a typical human gestation length of 275 days (9 months, <bold>a</bold>), and for hypothetical gestation lengths of 321 days (10.7 months, <bold>b</bold>), 480 days (16 months, <bold>c</bold>), and 630 days (21 months, <bold>d</bold>).</p></caption></fig>" ]	[ "<table-wrap id=\"Tab1\"><label>Extended Data Table 1</label><caption><p>Body and brain proportion at birth</p></caption></table-wrap>", "<table-wrap id=\"Tab2\"><label>Extended Data Table 2</label><caption><p>Highest evolutionary rates across the mammalian phylogeny</p></caption></table-wrap>", "<table-wrap id=\"Tab3\"><label>Extended Data Table 3</label><caption><p>Evaluation of the values driving altriciality-precociality in human brain and body size with respect to nonhuman primates</p></caption></table-wrap>", "<table-wrap id=\"Tab4\"><label>Extended Data Table 4</label><caption><p>Variables used in the analysis of the timing of neurodevelopment across hominin evolution</p></caption></table-wrap>", "<table-wrap id=\"Tab5\"><label>Extended Data Table 5</label><caption><p>Pre- or postnatal occurrence of neurodevelopmental events in fossil hominins</p></caption></table-wrap>" ]	[ "<disp-formula id=\"Equ1\"><label>1</label><alternatives><tex-math id=\"M1\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$Y={\\rm{intercept}}+{\\rm{slope}}\\times {\\rm{eventscale}}+({\\rm{{interaction}\\,{term}}})$$\\end{document}</tex-math><mml:math id=\"M2\"><mml:mrow><mml:mi>Y</mml:mi><mml:mo>=</mml:mo><mml:mi mathvariant=\"normal\">intercept</mml:mi><mml:mo>+</mml:mo><mml:mi mathvariant=\"normal\">slope</mml:mi><mml:mo>×</mml:mo><mml:mi mathvariant=\"normal\">eventscale</mml:mi><mml:mo>+</mml:mo><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi mathvariant=\"normal\">interaction</mml:mi><mml:mspace width=\"0.25em\"/><mml:mi>t</mml:mi><mml:mi>e</mml:mi><mml:mi>r</mml:mi><mml:mi>m</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:math></alternatives></disp-formula>", "<disp-formula id=\"Equ2\"><label>2</label><alternatives><tex-math id=\"M3\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\rm{Species}}\\; {\\rm{intercept}}=1.241+0.368 \\times \\log ({\\rm{gestation}}\\; {\\rm{length}})$$\\end{document}</tex-math><mml:math id=\"M4\"><mml:mrow><mml:mi mathvariant=\"normal\">Species</mml:mi><mml:mspace width=\"0.16em\"/><mml:mi mathvariant=\"normal\">intercept</mml:mi><mml:mo>=</mml:mo><mml:mn>1.241</mml:mn><mml:mo>+</mml:mo><mml:mn>0.368</mml:mn><mml:mo>×</mml:mo><mml:mi>log</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi mathvariant=\"normal\">gestation</mml:mi><mml:mspace width=\"0.16em\"/><mml:mi mathvariant=\"normal\">length</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:math></alternatives></disp-formula>", "<disp-formula id=\"Equ3\"><label>3</label><alternatives><tex-math id=\"M5\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\rm{Species}}\\; {\\rm{slope}}=1.474+0.257\\times \\log ({\\rm{adult}}\\; {\\rm{brain}}\\; {\\rm{mass}})$$\\end{document}</tex-math><mml:math id=\"M6\"><mml:mrow><mml:mi mathvariant=\"normal\">Species</mml:mi><mml:mspace width=\"0.16em\"/><mml:mi mathvariant=\"normal\">slope</mml:mi><mml:mo>=</mml:mo><mml:mn>1.474</mml:mn><mml:mo>+</mml:mo><mml:mn>0.257</mml:mn><mml:mo>×</mml:mo><mml:mi>log</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi mathvariant=\"normal\">adult</mml:mi><mml:mspace width=\"0.16em\"/><mml:mi mathvariant=\"normal\">brain</mml:mi><mml:mspace width=\"0.16em\"/><mml:mi mathvariant=\"normal\">mass</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:math></alternatives></disp-formula>", "<disp-formula id=\"Equ4\"><label>4</label><alternatives><tex-math id=\"M7\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\rm{Pre-}}\\; {\\rm{or}}\\; {\\rm{postnatal}}\\; {\\rm{day}}=\\exp (Y){\\rm{-}}{\\rm{gestation}}\\; {\\rm{length}}$$\\end{document}</tex-math><mml:math id=\"M8\"><mml:mrow><mml:mi mathvariant=\"normal\">Pre-</mml:mi><mml:mspace width=\"0.16em\"/><mml:mi mathvariant=\"normal\">or</mml:mi><mml:mspace width=\"0.16em\"/><mml:mi mathvariant=\"normal\">postnatal</mml:mi><mml:mspace width=\"0.16em\"/><mml:mi mathvariant=\"normal\">day</mml:mi><mml:mo>=</mml:mo><mml:mi>exp</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>Y</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mi mathvariant=\"normal\">-</mml:mi><mml:mi mathvariant=\"normal\">gestation</mml:mi><mml:mspace width=\"0.16em\"/><mml:mi mathvariant=\"normal\">length</mml:mi></mml:mrow></mml:math></alternatives></disp-formula>" ]	[]	[]	[]	[]	[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>" ]	[ "<table-wrap-foot><p>Mean values and ranges of variation of body and brain proportions at birth observed in the four best represented orders of mammals and in the five clades including big-brained species.</p></table-wrap-foot>", "<table-wrap-foot><p>Top 10 rates to increase and decrease the values of brain proportion at birth, body proportion at birth, absolute adult brain size, absolute neonatal brain size, and gestation length as shown in Fig. ##FIG##1##2##. Nodes can be identified in Extended Data Fig. ##FIG##6##1##. When nodes are listed, the relevant evolutionary rate is the rate of the branch leading to that node.</p></table-wrap-foot>", "<table-wrap-foot><p>Descendant values observed in <italic>H. sapiens</italic> are compared with ancestral values inferred in the <italic>Pan-Homo</italic> last common ancestral species (LCA) to calculate neonatal and adult differences (Adult diff. and Neo diff.) and a scaling ratio between them (Adult/Neo). This ratio is then compared with the upper and lower bound of the confidence interval of the nonhuman primate scaling relationship (CI lower bound and CI upper bound). A value higher than 0 for ‘Diff. max. expectation’, as observed for brain size, indicates more change in adult brain size relative to neonatal brain size than the upper bound expectation of the ancestral grade. In other words, this means that the human adult brain size is larger than expected with respect to nonhuman primates and that this drives the relationship between neonatal and adult brain size. A value lower than 0 for ‘Min. diff. expectation’, as observed for body size, indicates less change in adult body size relative to neonatal body size than the lower bound expectation of the ancestral grade. In other words, this means that neonatal body size is larger than expected in humans with respect to nonhuman primates and that this drives the relationship between neonatal and adult body size.</p></table-wrap-foot>", "<table-wrap-foot><p>Variable abbreviations: ECV: Endocranial volume; Brain mass: brain weight in grams; PGR brain: Prenatal growth rate based on endocranial volume (from ref. <sup>##REF##36191229##27##</sup>); Neo body mean: estimated neonatal body mass according to the intermediate model in ref. <sup>##REF##21199942##18##</sup>; Neo body min: estimated minimum neonatal body mass according to the human model in ref. <sup>##REF##21199942##18##</sup>; Neo body max: estimated maximum neonatal body mass according to the ape model in ref. <sup>##REF##21199942##18##</sup>; Gest mean: estimated mean gestation length based on Monson and colleagues’ model and on the mean neonatal body mass; Gest min: estimated minimum gestation length based on the minimum neonatal body mass; Gest max: estimated maximum gestation length based on the maximum neonatal body mass; Adj gest mean: adjusted mean gestation length; Adj gest min: adjusted minimum gestation length; Adj gest max: adjusted maximum gestation length.</p></table-wrap-foot>", "<table-wrap-foot><p>Only the events that differ in their pre- or postnatal occurrence across the hominin fossil record are represented, excluding those related to increases in brain size, to variation in sensory organs, and those categorized as behavioural responses of the whole organism<sup>##REF##23616543##23##</sup>. For the range-based estimates, perinatal events are the ones that may have happened pre- or postnatally depending on the exact gestation length typical of each species. Species are listed in the pre- or postnatal columns only if a pre- or postnatal occurrence is inferred for that event for the whole range of possible gestation lengths. For the mean-based estimates, only the pre- or postnatal occurrence of each event at the mean estimate of species-specific gestation length is considered. Species abbreviations: RAM: <italic>Ar. ramidus</italic>; AFA: <italic>Au. afarensis</italic>; AFR: <italic>Au. africanus</italic>; BOI: <italic>P. boisei</italic>; ROB: <italic>P. robustus</italic>; HAB: <italic>H. habilis</italic>; ERG: <italic>H. ergaster</italic>; ERE: <italic>H. erectus</italic>; HEI: <italic>H. heidelbergensis</italic>; NEA: <italic>H. neanderthalensis</italic>; SAP: <italic>H. sapiens</italic>.</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"41559_2023_2253_Fig1_HTML\" id=\"d32e450\"/>", "<graphic xlink:href=\"41559_2023_2253_Fig2_HTML\" id=\"d32e542\"/>", "<graphic xlink:href=\"41559_2023_2253_Fig3_HTML\" id=\"d32e639\"/>", "<graphic xlink:href=\"41559_2023_2253_Fig4_HTML\" id=\"d32e818\"/>", "<graphic xlink:href=\"41559_2023_2253_Fig5_HTML\" id=\"d32e921\"/>", "<graphic xlink:href=\"41559_2023_2253_Fig6_HTML\" id=\"d32e1049\"/>", "<graphic xlink:href=\"41559_2023_2253_Article_Equ1.gif\" position=\"anchor\"/>", "<graphic xlink:href=\"41559_2023_2253_Article_Equ2.gif\" position=\"anchor\"/>", "<graphic xlink:href=\"41559_2023_2253_Article_Equ3.gif\" position=\"anchor\"/>", "<graphic xlink:href=\"41559_2023_2253_Article_Equ4.gif\" position=\"anchor\"/>", "<graphic position=\"anchor\" xlink:href=\"41559_2023_2253_Fig7_ESM\" id=\"d32e1861\"/>", "<graphic position=\"anchor\" xlink:href=\"41559_2023_2253_Fig8_ESM\" id=\"d32e1900\"/>", "<graphic position=\"anchor\" xlink:href=\"41559_2023_2253_Fig9_ESM\" id=\"d32e1992\"/>", "<graphic position=\"anchor\" xlink:href=\"41559_2023_2253_Fig10_ESM\" id=\"d32e2023\"/>", "<graphic position=\"anchor\" xlink:href=\"41559_2023_2253_Fig11_ESM\" id=\"d32e2047\"/>", "<graphic position=\"anchor\" xlink:href=\"41559_2023_2253_Tab1_ESM\" id=\"d32e2057\"><caption><p>Body and brain proportion at birth</p></caption></graphic>", "<graphic position=\"anchor\" xlink:href=\"41559_2023_2253_Tab2_ESM\" id=\"d32e2072\"><caption><p>Highest evolutionary rates across the mammalian phylogeny</p></caption></graphic>", "<graphic position=\"anchor\" xlink:href=\"41559_2023_2253_Tab3_ESM\" id=\"d32e2094\"><caption><p>Evaluation of the values driving altriciality-precociality in human brain and body size with respect to nonhuman primates</p></caption></graphic>", "<graphic position=\"anchor\" xlink:href=\"41559_2023_2253_Tab4_ESM\" id=\"d32e2116\"><caption><p>Variables used in the analysis of the timing of neurodevelopment across hominin evolution</p></caption></graphic>", "<graphic position=\"anchor\" xlink:href=\"41559_2023_2253_Tab5_ESM\" id=\"d32e2148\"><caption><p>Pre- or postnatal occurrence of neurodevelopmental events in fossil hominins</p></caption></graphic>" ]	[ "<media xlink:href=\"41559_2023_2253_MOESM1_ESM.pdf\"><caption><p>Reporting Summary</p></caption></media>", "<media xlink:href=\"41559_2023_2253_MOESM2_ESM.pdf\"><caption><p>Peer Review File</p></caption></media>" ]	[{"label": ["1."], "mixed-citation": ["Portmann, A. 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PMC10781643	38110578	[]	[]	[]	[]	[]	[ "<title>Subject terms</title>" ]	[ "<p id=\"Par1\">Correction to: <italic>Nature</italic> 10.1038/s41586-023-06193-3 Published online 26 July 2023</p>", "<p id=\"Par2\">This article was originally published under a standard Springer Nature licence (© The Author(s), under exclusive licence to Springer Nature Limited). It is now available as an open-access paper under a Creative Commons Attribution 4.0 International licence, © The Author(s). The status has been updated in the HTML and PDF versions of the article.</p>" ]	[]	[]	[]	[]	[]	[]	[]	[]	[]	[]	[]	[]	[]	{ "acronym": [], "definition": [] }	0	CC BY	no	2024-01-13 00:02:19	Nature. 2024 Dec 18; 625(7994):E7	oa_package/73/05/PMC10781643.tar.gz
PMC10781644	37932383	[]	[ "<title>Methods</title>", "<title><italic>I. elegans</italic> samples</title>", "<p id=\"Par33\">Samples for morph-specific genome assemblies of <italic>I. elegans</italic> were obtained from F<sub>1</sub> individuals with genotypes <italic>Ao</italic>, <italic>Io</italic> and <italic>oo</italic> (one adult female of each genotype). In June 2019, recently mated O females were captured in field populations in southern Sweden. These females oviposited in the lab within 48 h, and their eggs were then released into outdoor cattle tanks seeded with <italic>Daphnia</italic> and covered with synthetic mesh. Larvae thus developed under normal field conditions and emerged as adults during the summer of 2020. Emerging females were kept in outdoor enclosures until completion of adult colour development<sup>##REF##32402112##25##,##UREF##11##65##</sup>. Fully mature females were phenotyped, collected in liquid nitrogen and kept at −80 °C. Because all of these females carry a copy of the most recessive allele <italic>o</italic>, individuals of the A and I morph are heterozygous, with genotypes <italic>Ao</italic> and <italic>Io</italic>, respectively.</p>", "<p id=\"Par34\">A total of 19 resequencing samples of each female morph of <italic>I. elegans</italic> were also collected from local populations in southern Sweden, within a 40 × 40 km area (Supplementary Table ##SUPPL##0##1##). Samples were submerged in 95% ethanol and stored in a −20 °C freezer until extraction. Additionally, 24 individuals (six adult females of each morph and six males) were collected for RNA-seq analysis in a natural field population (Bunkeflostrand) in southern Sweden, in early July 2019. These samples were transported on carbonated ice and stored in −80 °C until extraction.</p>", "<title><italic>I. senegalensis</italic> samples</title>", "<p id=\"Par35\">Adults of <italic>I. senegalensis</italic> (30 adult females of each morph) were collected for pool sequencing from a population on Okinawa Island in Japan (26.148° N, 127.795° E) in May 2016. Samples were visually determined to sex and morph and stored in 99% ethanol until extraction. Samples for morph-specific genome assemblies of <italic>I. senegalensis</italic> were obtained from a population in Clementi Forest, Singapore (1.33° N, 103.78° E). Because the <italic>A</italic> allele is recessive in <italic>I. senegalensis</italic>, all females with the A phentoype are homozygous. To obtain a homozygous O-like sample, we developed primers (forward: CGCGGTATGATATGGTCCGA, reverse: GGCTGCTTACACCAATGCAA) for an A-specific sequence that is shared by A females of the two species (318,131–318,213 bp on the A haplotype of <italic>I. elegans</italic>). We used the mapped pool-seq data to identify fixed SNPs between species and tailor the primer sequences accordingly. We then tested the primers in 20 A females of <italic>I. senegalensis</italic> using a 328 bp fragment of the histone H3 gene (forward: ATGGCTCGTACCAAGCAGACGGC, reverse: ATATCCTTGGGCATGATGGTGAC)<sup>##UREF##12##66##</sup> as a positive control for the polymerase chain reaction. Once validated, we utilized these primers to identify O-like females lacking the <italic>A</italic> allele and selected one of these samples for whole genome sequencing.</p>", "<title>DNA extraction, library preparation and sequencing</title>", "<p id=\"Par36\">High molecular weight (HMW) DNA was extracted from one <italic>I. elegans</italic> female of each genotype (<italic>Ao</italic>, <italic>Io</italic>, <italic>oo</italic>), using the Nanobind Tissue Big Extraction Kit (NB-900-701-01, Circulomics Inc. (PacBio)). HMW DNA was isolated from homozygous females of each morph of <italic>I. senegalensis</italic>, using the Monarch HMW DNA Extraction Kit for Tissue (T3060S, New England BioLabs Inc.). DNA from resequencing samples was isolated using either a modified protocol for the DNeasy Blood and Tissue Kit (19053, Qiagen) or the KingFisher Cell and Tissue DNA Kit (Cat no. N11997, ThermoFisher Scientific). <italic>I. senegalensis</italic> DNA was extracted from muscle tissues in thoraxes using Maxwell 16 LEV Plant DNA Kit (AS1420, Promega). Details on extraction and library preparation protocols are provided in Supplementary Text ##SUPPL##0##1##.</p>", "<p id=\"Par37\">Sequencing libraries were constructed from each HMW DNA sample for the Nanopore LSK-110 ligation kit (Oxford Nanopore Technologies). Adapter ligation and sequencing of <italic>I. elegans</italic> samples were carried out at the Uppsala Genome Centre, hosted by SciLife Lab. Each sample was sequenced on a PromethION R10.4 with one nuclease wash and two library loadings. Library preparation and sequencing of <italic>I. senegalensis</italic> samples were carried out by the Integrated Genomics Platform, Genome Institute of Singapore, A-STAR, Singapore. Each sample was sequenced on a PromethION R9.4 flow cell, with two nuclease washes and three library loadings.</p>", "<title>RNA extraction and sequencing</title>", "<p id=\"Par38\">Whole-thorax samples were ground into a fine powder using a TissueLyser and used as input for the Spectrum Plant Total RNA Kit (STRN50, Sigma Aldrich), including DNase I treatment (DNASE10, Sigma Aldrich). Library preparation and sequencing were performed by SciLife Lab at the Uppsala Genome Centre. Sequencing libraries were prepared from 300 ng of RNA, using the TrueSeq stranded mRNA library preparation kit (20020595, Illumina Inc.) including polyA selection and unique dual indexing (20022371, Illumina Inc.), according to the manufacturer’s protocol. Sequencing was performed on the Illumina NovaSeq 6000 SP flowcell with paired-end reads of 150 bp.</p>", "<title>De novo genome assembly</title>", "<p id=\"Par39\">Bases in raw Oxford Nanopore Technologies reads from <italic>I. elegans</italic> were called using Guppy v.4.0.11 (<italic>Ao</italic> and <italic>Io</italic> data) and Guppy v.5.0.11 (<italic>oo</italic> data) (<ext-link ext-link-type=\"uri\" xlink:href=\"https://nanoporetech.com/\">https://nanoporetech.com/</ext-link>). Low-quality reads (<italic>q</italic>-score <7 for v.4.0.11 and <10 for v.5.0.11) were subsequently discarded. High quality reads were assembled using the Shasta long-read assembler v.0.7.0<sup>##REF##32686750##67##</sup>. Each assembly was conducted under four different configuration schemes, which modified the June 2020 nanopore configuration file (<ext-link ext-link-type=\"uri\" xlink:href=\"https://github.com/chanzuckerberg/shasta/blob/master/conf/Nanopore-Jun2020.conf\">https://github.com/chanzuckerberg/shasta/blob/master/conf/Nanopore-Jun2020.conf</ext-link>) in alternative ways (Supplementary Table ##SUPPL##0##3##). Assembly metrics were compared among Shasta configurations for each morph using AsmQC<sup>##REF##18341692##68##</sup> (<ext-link ext-link-type=\"uri\" xlink:href=\"https://sourceforge.net/projects/amos/\">https://sourceforge.net/projects/amos/</ext-link>) and the stats.sh script in the BBTools suite (<ext-link ext-link-type=\"uri\" xlink:href=\"https://sourceforge.net/projects/bbmap\">https://sourceforge.net/projects/bbmap</ext-link>). The assembly with greater contiguity (that is, highest contig N50, highest average contig length and highest percentage of the main genome in scaffolds >50 kb) was selected for polishing and downstream analyses.</p>", "<p id=\"Par40\">Bases in raw Oxford Nanopore Technologies reads from <italic>I. senegalensis</italic> samples were called using Guppy v.6.1.5. Reads with quality score <7 were subsequently discarded. High quality reads were assembled using the Shasta long-read assembler v.0.7.0<sup>##REF##32686750##67##</sup> and the configuration file T2 (Supplementary Table ##SUPPL##0##3##), which was also selected for the <italic>Io</italic> and <italic>oo</italic> assemblies of <italic>I. elegans</italic>.</p>", "<p id=\"Par41\">Morph-specific assemblies of <italic>I. elegans</italic> were first polished using the Oxford Nanopore Technologies reads mapped back to their respective assembly with minimap2 v.2.22-r1110<sup>##REF##29750242##69##</sup>, and the PEPPER-Margin-DeepVariant pipeline r0.4<sup>##REF##34725481##70##</sup>. Alternative haplotypes were subsequently filtered using purge_dups v.0.0.3<sup>##REF##31971576##71##</sup>, to produce a single haploid genome assembly for each sample. The <italic>I. elegans</italic> draft assemblies were then polished with short read data from one resequencing sample (TE-2564-SwD172_S37; Supplementary Table ##SUPPL##0##1##), using the POLCA tool in MaSuRCA v.4.0.4<sup>##REF##28130360##72##</sup>. For every draft and final assembly of <italic>I. elegans</italic>, we computed quality metrics as mentioned above and assessed the completeness of conserved insect genes using BUSCO v.5.0.0<sup>##REF##26059717##73##</sup> and the ‘insecta_odb10’ database (Supplementary Fig. ##SUPPL##0##1##). For <italic>I. senegalensis</italic>, we report quality metrics of the final assemblies (Supplementary Fig. ##SUPPL##0##2##).</p>", "<title>Scaffolding with the DToL super assembly</title>", "<p id=\"Par42\">During the course of this study, a chromosome-level genome of <italic>I. elegans</italic> was assembled by the DToL Project<sup>##UREF##3##34##</sup>, based on long-read (PacBio) and short-read (Illumina) data, as well as Hi-C (Illumina) chromatin interaction data. Of the total length of this assembly, 99.5% is distributed across 14 chromosomes, one of which (no. 13) is fragmented and divided into a main assembly and five unlocalized scaffolds.</p>", "<p id=\"Par43\">We used RagTag v.2.10<sup>##UREF##13##74##</sup> to scaffold each of our morph-specific assemblies based on the DToL reference (Supplementary Text ##SUPPL##0##2##). Scaffolding was conducted using the nucmer v.4.0.0<sup>##REF##12034836##75##</sup> aligner and default RagTag options. Morph-specific scaffolded genomes were also aligned to each other using nucmer and a minimum cluster length of 100 bp. Alignments were then filtered to preserve only the longest alignments in both reference and query sequences, and alignments of at least 5 kb. These assembly alignments were then used to visualize synteny patterns across morphs, in the region uncovered in our association analyses (Extended Data Fig. ##FIG##6##1##), using the package RIdeogram v.0.2.2<sup>##UREF##14##76##</sup> in <italic>R</italic> v.4.2.2<sup>##UREF##15##77##</sup>.</p>", "<title>Reference-based (SNP) GWAS</title>", "<p id=\"Par44\">We first investigated genomic divergence between morphs using a standard GWAS approach based on SNPs (Extended Data Fig. ##FIG##6##1##). Initially, we conducted preliminary analyses using different morph assemblies as mapping reference. Once the A-specific genomic region was confirmed, we designated the A assembly as the mapping reference for the main analyses. Short-read data were mapped using bwa-mem v.0.7.17<sup>##UREF##16##78##</sup>. Optical and polymerase chain reaction duplicates were then flagged in the unfiltered bam files using GATK v.4.2.0.0<sup>##UREF##17##79##</sup>. Variant calling, filtering and sorting were conducted using bcftools v.1.12<sup>##REF##21903627##80##</sup>, excluding the flagged reads. We retained only variant sites with mapping quality >20, genotype quality >30 and minor allele frequency >0.02 (that is, the variant is present in more than one sample). To avoid highly repetitive content, we filtered variants that had a combined depth across samples >1,360 (equivalent to all samples having ~50% higher than average coverage), and variants located in sites annotated as repetitive in either RepeatMasker v.1.0.93<sup>##UREF##18##81##</sup> or Red v.0.0.1<sup>##UREF##19##82##</sup>. The final variant calling file was analysed in pairwise comparisons (A versus O, A versus I, I versus O) using PLINK v.1.9<sup>##REF##17701901##83##</sup> (<ext-link ext-link-type=\"uri\" xlink:href=\"https://zzz.bwh.harvard.edu/plink/\">https://zzz.bwh.harvard.edu/plink/</ext-link>). We report the −log<sub>10</sub> of <italic>P</italic> values for SNP associations in these pairwise comparisons.</p>", "<title>Reference-free (<italic>k</italic>-mer) GWAS</title>", "<p id=\"Par45\">We created a list of all <italic>k</italic>-mers of length 31 in the short-read data (19 females per morph; Extended Data Fig. ##FIG##6##1##) following ref. <sup>##REF##32284578##35##</sup>, and counted <italic>k</italic>-mers in each sample using KMC v.3.1.0<sup>##REF##28472236##84##</sup>. The <italic>k</italic>-mer list was filtered by the minor allele count; <italic>k</italic>-mers that appeared in less than five individuals were excluded. <italic>k</italic>-mers were also filtered by per cent canonized (that is, the per cent of samples for which the reverse complement of the <italic>k</italic>-mer was also present). If at least 20% of the samples including a given <italic>k</italic>-mer contained its canonized form, the <italic>k</italic>-mer was kept in the list. The <italic>k</italic>-mer list was then used to create a table recording the presence or absence of each <italic>k</italic>-mer in each sample. A kinship matrix for all samples was calculated from this <italic>k</italic>-mer table, and was converted to a PLINK<sup>##REF##17701901##83##</sup> binary file, where the presence or absence of each <italic>k</italic>-mer is coded as two homozygous variants. In this step, we further filtered the <italic>k</italic>-mers with a minor allele frequency below 5%.</p>", "<p id=\"Par46\">Because a single variant, be it an SNP or SV, will probably be captured by multiple <italic>k</italic>-mers, significance testing of <italic>k</italic>-mer associations requires a method to control for the non-independence of overlapping <italic>k</italic>-mers. We followed the approach developed by ref. <sup>##REF##32284578##35##</sup>, which uses a linear mixed model genome-wide association analysis implemented in GEMMA v.0.98.5<sup>##REF##22706312##85##</sup>, and computes <italic>P</italic> value thresholds for associated <italic>k</italic>-mers based on phenotype permutations. We thus report <italic>k</italic>-mers below the 5% false-positive threshold as <italic>k</italic>-mers significantly associated with the female polymorphism in <italic>I. elegans</italic>. We conducted three <italic>k</italic>-mers based GWAS: (1) comparing male mimics to the putatively ancestral female morph (A versus O); (2) comparing male mimics to the most derived female morph (A versus I); and (3) comparing both derived female morphs (A and I) to the ancestral O females. For every analysis, we then mapped the significant <italic>k</italic>-mers to all reference genomes using Blast v.2.22.28<sup>##UREF##20##86##</sup> for short sequences, and removed alignments that were below 100% identity and below full length. The mapped <italic>k</italic>-mers thus indicate the proportion of relevant genomic content present in each morph and how this content is distributed across each genome (Extended Data Table ##TAB##0##1##).</p>", "<title>Read-depth analysis</title>", "<p id=\"Par47\">To validate the <italic>k</italic>-mer GWAS results of unique genomic content in A females relative to both I and O females, we plotted read depth across our region of interest (the unlocalized scaffold 2 of chromosome 13; see ‘Results’) in the A assembly (Extended Data Fig. ##FIG##6##1##). Short-read data (19 samples per morph) were mapped to the assembly with bwa-mem v.0.7.17<sup>##UREF##16##78##</sup> and reads with mapping score <20 were filtered, using Samtools v.1.14<sup>##REF##19505943##87##</sup>. Long-read data (one sample per morph) were also mapped to the assembly using minimap2 v.2.22-r1110<sup>##REF##29750242##69##</sup>, and quality filtering was conducted as above. Read depth was then averaged for each sample across 500 bp, non-overlapping windows using mosdepth v.0.2.8<sup>##REF##29096012##88##</sup>. We also annotated repetitive content in the reference genome using RepeatMasker v.1.0.93<sup>##UREF##18##81##</sup> and Red v.0.0.1<sup>##UREF##19##82##</sup>, and filtered windows with more than 10% repetitive content under either method.</p>", "<p id=\"Par48\">To account for differences in overall coverage between samples, we conducted the same procedure on a large (~15 mb) non-candidate region in chromosome 11 and calculated a ‘background read depth’ as the mean read depth across the non-repetitive windows of this region. We then expressed read depth in the candidate region as a proportion of the background read depth. Values around 1 thus indicate that a sample is homozygous for the presence of the sequence in a window. Values around 0.5 suggest that the sample only has one copy of this sequence in its diploid genome (that is, it is heterozygous). Finally, values of 0 imply that the 500 bp reference sequence is not present in the sample (that is, the window is part of an insertion or deletion).</p>", "<p id=\"Par49\">We also investigated read-depth coverage on the I assembly, specifically across the region that was identified in the <italic>k</italic>-mer based GWAS as capturing content that differentiated both A and I females from O females (Fig. ##FIG##2##3b## and Extended Data Fig. ##FIG##6##1##). To do so, we followed the same strategy as above, except here we used a 15 mb region from chromosome 1 to estimate background read depth.</p>", "<title>Population genetics</title>", "<p id=\"Par50\">We investigated the evolutionary consequences of morph divergence by estimating between-morph <italic>F</italic><sub>ST</sub> and population-wide Tajima’s <italic>D</italic> and <italic>π</italic>. For these analyses, we used the A assembly as mapping reference and the same variant-calling approach as described for the SNP-based GWAS, but applied different filtering criteria (Extended Data Fig. ##FIG##6##1##). Specifically, invariant sites were retained and we only filtered sites with mapping quality score <20 and combined depth across samples >1,360 (equivalent to ~50% excess coverage in all samples). <italic>F</italic><sub>ST</sub> and <italic>π</italic> were estimated in pixy v.1.2.5<sup>##REF##33453139##89##</sup> across 30 kb windows. <italic>F</italic><sub>ST</sub> was computed using the Hudson estimator<sup>##REF##1427045##90##</sup>. Negative <italic>F</italic><sub>ST</sub> values were converted to zero for plotting. Tajima’s <italic>D</italic> was estimated across 30 kb using vcftools v.0.1.17<sup>##REF##21653522##91##</sup>. In all analyses, windows with >10% repetitive content according to either RepeatMasker v.1.0.93<sup>##UREF##18##81##</sup> or Red v.0.0.1<sup>##UREF##19##82##</sup> annotation were excluded.</p>", "<title>SVs</title>", "<p id=\"Par51\">We used two complimentary approaches to identify SVs overlapping the genomic region uncovered by both <italic>k</italic>-mer-based and SNP-based GWAS. First, we mapped the raw data from each long-read sample to the assemblies of alternative morphs (for example <italic>Ao</italic> data mapped to <italic>Io</italic> and <italic>oo</italic> assemblies), and called SVs using Sniffles v.1.0.10<sup>##REF##29713083##92##</sup> (Extended Data Fig. ##FIG##6##1##). These SV calls may represent fixed differences between morphs, within-morph polymorphisms or products of assembly error. We therefore used SamPlot v.1.3.0<sup>##REF##34034781##93##</sup> and our short-read samples (<italic>n</italic> = 19 per morph) to validate morph-specific SV calls (Extended Data Fig. ##FIG##6##1##). Samplot identifies and plots reads with discordant alignments, which can result from specific types of SVs. For example, if Sniffles called a 10 kb deletion in the <italic>Ao</italic> and <italic>Io</italic> long-read samples relative to the <italic>oo</italic> assembly, we then constructed a Samplot for this region using short-read data, and expected to find support for such deletion in I and A samples, but not in O samples. We complemented this validation approach with a scan of the region of interest in each assembly, in windows of 250 and 500 kb, again using Samplot and the short-read data. If a SV appeared to be supported by the majority of short-read samples from an alternative morph, we zoomed in this SV and recorded the number of samples supporting the call in each morph.</p>", "<title>Linkage disequilibrium and TEs</title>", "<p id=\"Par52\">To estimate linkage disequilibrium (LD), we used the same variant-calling file as for the SNP-based GWAS, which included only variant sites and was filtered by mapping quality, genotyping quality, minimum allele frequency and read depth, as described above (Extended Data Fig. ##FIG##6##1##). The file was downsampled to 1 variant every 100th using vcftools v.0.1.17<sup>##REF##21653522##91##</sup>, prior to LD estimation. We estimated LD using PLINK v.1.9<sup>##REF##17701901##83##</sup>, and recorded <italic>R</italic><sup>2</sup> values >0.05 for pairs up to 15 mb apart or with 10,000 or fewer variants between them. We estimated LD for the unlocalized scaffold 2 of chromosome 13, which contains the morph loci and is ~15 mb in the A assembly. For comparison, we also estimated LD across the first 15 mb of the fully assembled chromosomes (1–12 and X), the main scaffold of chromosome 13, and the unlocalized scaffolds 1, 3 and 4 of chromosome 13.</p>", "<p id=\"Par53\">We used the TE annotations from RepeatModeler v.2.0.1, RepeatMasker v.1.0.93<sup>##UREF##18##81##</sup> and ‘One code to find them all’<sup>##UREF##21##94##</sup> to quantify TE coverage in chromosome 13 in comparison to the rest of the genome. We divided each chromosome into 1.5 mb windows, and computed the proportion of each window covered by each TE family.</p>", "<title>Evidence of a trans-species polymorphism</title>", "<p id=\"Par54\">We used pool-seq data from the closely related tropical bluetail damselfly (<italic>I. senegalensis</italic>) to determine whether male mimicry has a shared genetic basis in the two species (Extended Data Fig. ##FIG##6##1##). First, we aligned the short-read data from the the two <italic>I. senegalensis</italic> pools (A and O-like) to the A-morph assembly of <italic>I. elegans</italic> using bwa-mem v.0.7.17<sup>##UREF##16##78##</sup>, and filtered reads with mapping score <20, using Samtools v.1.14<sup>##REF##19505943##87##</sup>. We then quantified read depth as for the <italic>I. elegans</italic> resequencing data (see ‘Read-depth analysis’). To confirm that the higher read-depth coverage of the A pool is specific to the putative morph locus, we also plotted the distribution of read-depth differences between O-like and A pools across the rest of the genome and compared it to the morph locus (Supplementary Text ##SUPPL##0##5##). Next, we determined if the ~20 kb SV that characterizes A and I females of <italic>I. elegans</italic> is also present in A females of <italic>I. senegalensis</italic>. To do this, we mapped the pool-seq data to the O assembly of <italic>I. elegans</italic> as above, and scanned the region at the start of the scaffold 2 of chromosome 13 for SVs using Samplot v.1.3.0<sup>##REF##34034781##93##</sup>. Finally, we aligned the morph-specific assemblies of <italic>I. senegalensis</italic> to the A assembly of <italic>I. elegans</italic> using nucmer v.4.0.0<sup>##REF##12034836##75##</sup>, and preserving alignments >500 bp with identity >70% (Extended Data Fig. ##FIG##6##1##). We visualized synteny patterns across the morph locus using the package RIdeogram v.0.2.2<sup>##UREF##14##76##</sup> in R v.4.2.2<sup>##UREF##15##77##</sup>.</p>", "<title>Gene content and expression in the morph locus</title>", "<p id=\"Par55\">We assembled transcripts in the A morph genome (Extended Data Fig. ##FIG##6##1##) to identify potential gene models unique to the A or A and I morphs, which would therefore be absent from the <italic>I. elegans</italic> reference annotation (based on the O haplotype). First, all raw RNA-seq data from <italic>I. elegans</italic> samples were mapped to the A assembly using HISAT2 v.2.2.1<sup>##REF##31375807##95##</sup> and reads with mapping quality <60 were filtered using Samtools v.1.19<sup>##REF##19505943##87##</sup>. Transcripts were then assembled in StringTie v.2.1.4<sup>##REF##25690850##96##</sup> under default options, and merged into a single gtf file. Transcript abundances were quantified using this global set of transcripts as targets, and a transcript count matrix was produced using the prepDE.py3 script provided with StringTie. Mapped RNA-seq data from <italic>I. senegalensis</italic> were also used to assemble transcripts (Extended Data Fig. ##FIG##6##1##), but this time the HISAT2 assembly was guided by the annotation based on <italic>I. elegans</italic> data, while allowing the identification of novel transcripts. Transcript abundances were quantified as for <italic>I. elegans</italic>.</p>", "<p id=\"Par56\">We analysed differential gene expression using the package edgeR v.3.36<sup>##REF##19910308##97##</sup> in R v.4.2.2<sup>##UREF##15##77##</sup>. Transcripts with fewer than one count per million in more than three samples were filtered. Library sizes were normalized across samples using the trimmed mean of M-values method<sup>##REF##20196867##98##</sup>, and empirical Bayes tagwise dispersion<sup>##REF##17881408##99##</sup> was estimated prior to pairwise expression analyses. Differential expression of genes in the morph loci was tested using two-tailed exact tests<sup>##REF##17728317##100##</sup>, assuming negative binomially distributed transcript counts and applying the Benjamini and Hochberg’s algorithm to control the false discovery rate<sup>##UREF##22##101##</sup>.</p>", "<p id=\"Par57\">Nucleotide sequences of all transcripts mapped to the 1.5 mb morph locus in the A assembly were selected. Coding sequences in these transcripts were predicted using Transdecoder v.5.5.0 (<ext-link ext-link-type=\"uri\" xlink:href=\"https://github.com/TransDecoder/TransDecoder\">https://github.com/TransDecoder/TransDecoder</ext-link>). Predicted coding sequences and peptide sequences were read from the assemblies using the genome-based coding region annotation produced with Transdecoder and gffread v.0.12.7<sup>##UREF##23##102##</sup>. We investigated whether any inferred peptides or transcripts were unique to A or A and I by comparing these sequences to the DToL reference transcriptome and proteome (based on the O haplotype). We then searched for homologous and annotated proteins in other taxa in the Swissprot database using Blast v.2.9.0<sup>##UREF##20##86##</sup>. We found three gene models that are protein-coding and present in both A and O females (see ‘Results’ and Fig. ##FIG##5##6##). We scanned these protein sequences for functional domains using InterProScan<sup>##REF##24451626##103##</sup> and searched for orthologous groups and functional annotations in eggNOG v.5.0<sup>##REF##30418610##104##</sup>.</p>", "<title>Reporting summary</title>", "<p id=\"Par58\">Further information on research design is available in the ##SUPPL##1##Nature Portfolio Reporting Summary## linked to this article.</p>" ]	[ "<title>Results</title>", "<title>Male mimicry is encoded by a locus with a signature of balancing selection</title>", "<p id=\"Par8\">We started by conducting three reference-based GWAS, comparing all morphs against each other in a pairwise fashion (Extended Data Fig. ##FIG##6##1##). We used an A morph genome assembly (Supplementary Text ##SUPPL##0##1##) as mapping reference because SV analyses revealed that A females harbour genomic content that is absent in the other two morphs (see ‘Female morphs differ in genomic content’). The draft assembly was scaffolded against the Darwin Tree of Life (DToL) reference genome to place the contigs in a chromosome-level framework<sup>##UREF##3##34##</sup>. The DToL reference genome contains the <italic>O</italic> allele (see Supplementary Text ##SUPPL##0##2##) and is assembled with chromosome resolution, except for chromosome 13, which is fragmented and consists of one main and several unlocalized scaffolds.</p>", "<p id=\"Par9\">All pairwise GWAS between morphs pointed to one and the same unlocalized scaffold of chromosome 13 as the causal morph locus (Fig. ##FIG##1##2a##). Closer examination of this scaffold revealed two windows of elevated divergence between morphs (Fig. ##FIG##1##2b##). First, a narrow region near the start of the scaffold (~50 kb–0.2 mb) captures highly significant single nucleotide polymorphisms (SNPs) in both A versus O and I versus O comparisons (Fig. ##FIG##1##2b##). Thereafter, and up to ~1.5 mb, an abundance of SNPs differentiates A females from both O and I females, especially between ~0.6 and ~1.0 mb (Fig. ##FIG##1##2b##). These results are mirrored by genetic differentiation (<italic>F</italic><sub>ST</sub>) values across both regions (Fig. ##FIG##1##2c##).</p>", "<p id=\"Par10\">Next, we investigated whether the morph locus carries a signature of balancing selection, as suggested by previous field studies of morph-frequency dynamics<sup>##REF##25996869##31##</sup>. The larger genomic window that uniquely distinguishes A females from both I and O females displays a signature of balancing selection, indicated by highly positive values of Tajima’s <italic>D</italic>, exceeding the 95th percentile of genome-wide estimates (Fig. ##FIG##1##2d##). Conversely, values of both Tajimas’s <italic>D</italic> and nucleotide diversity (<italic>π</italic>) in the narrower window that differentiates O females from both A and I females (~50 kb−0.2 mb) fall within the 95th percentile of genome-wide estimates (Fig. ##FIG##1##2d–e##).</p>", "<title>Female morphs differ in genomic content</title>", "<p id=\"Par11\">Previous studies have found that complex phenotypic polymorphisms are often underpinned by SVs, arising from genomic rearrangements such as insertions, deletions and inversions<sup>##REF##26569123##10##,##REF##31848330##13##,##REF##26804558##15##,##REF##33958755##20##</sup>. As these variants can be difficult to detect in a reference-based analysis, we employed a <italic>k</italic>-mer based GWAS approach<sup>##REF##32284578##35##</sup> (Extended Data Fig. ##FIG##6##1##), which enables reference-free identification of genomic divergence between morphs. Significant <italic>k</italic>-mers in these analyses could represent regions that are present in one morph and absent in the other (that is, insertions or deletions), or regions that are highly divergent in their sequence (as in a traditional GWAS).</p>", "<p id=\"Par12\">First, we investigated the divergence associated with the male-mimicking A morph. Pairwise analyses revealed 568,039 and 508,031 <italic>k</italic>-mers (length = 31 bp) significantly associated with the A versus O and A versus I comparisons, respectively. To determine whether the associated <italic>k</italic>-mers represent differences in genomic content or sequence between the morphs, we mapped these <italic>k</italic>-mers to morph-specific reference genomes. If the associated <italic>k</italic>-mers are owing to novel sequences found in one morph but not the other, we would expect a vast majority of the significant <italic>k</italic>-mers to be found in only one of the two morphs in a pairwise comparison. If the significant <italic>k</italic>-mers are instead owing to point mutations in high-identity sequences, there should be morph-specific <italic>k</italic>-mers in both morphs.</p>", "<p id=\"Par13\">Most (>98%) of the mapped <italic>k</italic>-mers in the A versus O and A versus I comparisons aligned perfectly to a single ~1.5 mb region of the unlocalized scaffold 2 of chromosome 13, in the A-morph assembly (Fig. ##FIG##2##3a## and Extended Data Table ##TAB##0##1##). This is the same region of the A-morph assembly that was previously identified in the standard GWAS (Fig. ##FIG##1##2##). In contrast, only ~0.3% of the associated <italic>k</italic>-mers in the A versus O comparison were found anywhere in the O assembly and, similarly, only ~0.2% of the significant <italic>k</italic>-mers in the A versus I analysis mapped to the I assembly (Extended Data Table ##TAB##0##1##). These results thus suggested that a large region of genomic content is unique to the A haplotype.</p>", "<p id=\"Par14\">Given that A and I females share their immature colour pattern<sup>##REF##30991898##29##,##UREF##4##36##</sup>, we then tested for <italic>k</italic>-mer associations that would distinguish both A and I females from O females and found 85,134 such <italic>k</italic>-mers (Extended Data Table ##TAB##0##1##). When mapped to the A assembly, a majority of these <italic>k</italic>-mers were found near the start of the unlocalized scaffold 2 of chromosome 13 (Fig. ##FIG##2##3a##), where we previously reported pronounced divergence of O females (Fig. ##FIG##1##2b,c##). However, when mapped to the I assembly, most of the significant <italic>k</italic>-mers were found in a different region of the same scaffold, separated by approximately 3.5 mb (Fig. ##FIG##2##3b##). These results thus suggested that A and I females share genomic content that is absent in O. However, in the I haplotype this content occupies a different chromosomal location.</p>", "<p id=\"Par15\">To further investigate the distribution of genomic content among morphs, we plotted the standardized number of mapped reads (read depths) along the ~1.5 mb region of the A assembly that included most of the significant <italic>k</italic>-mers (Extended Data Fig. ##FIG##6##1##). Here, we expected read depth values around 0.5 (heterozygous) or 1.0 (homozygous) for all A samples, whereas I and O samples should have read depths of 0, if genomic content is uniquely present in the <italic>A</italic> allele (because I and O individuals lack the <italic>A</italic> allele; Fig. ##FIG##0##1##). Read depths confirmed that male-mimicking A females are differentiated by genomic content. Specifically, there are two windows of the A assembly (of ~400 kb and ~500 kb) where no I or O data maps to the assembly after filtering repetitive sequences (Fig. ##FIG##2##3c##), and that are therefore uniquely present in A females. These two windows of A-specific content are separated by a region between ~0.6 and ~1.0 mb that is shared among all morphs (Fig. ##FIG##2##3c##), and highly divergent in SNP-based comparisons involving the A morph (Fig. ##FIG##1##2b##). Finally, the region including most significant <italic>k</italic>-mers in the A and I versus O comparison is present in all A and I samples but absent in all O samples, except for one individual (Fig. ##FIG##2##3c## and Supplementary Text ##SUPPL##0##3##). As noted in the <italic>k</italic>-mer GWAS, this region of genomic content shared by A and I individuals is located in different regions, separated by ~3.5 mb, in the two assemblies (Fig. ##FIG##2##3d##).</p>", "<p id=\"Par16\">By combining reference-based GWAS, reference-free GWAS and read-depth approaches, we have identified three haplotypes controlling morph development in the common bluetail. The A and I haplotypes share ~150 kb that are absent in O. The A haplotype has two additional windows of unique genomic content, adding up to ~900 kb. In the A haplotype, a single ~1.5 mb window (hereafter the morph locus) thus contains the regions of unique genomic content, the region exclusively shared between A and I, and the SNP-rich region present in all morphs. In the I haplotype the region exclusively shared with A occupies a single and different locus separated by about 3.5 mb (Fig. ##FIG##3##4a##). These large and compounded differences in genomic content between haplotypes suggest that multiple structural changes on a multi-million base-pair region were responsible for the evolution of novel female morphs in <italic>Ischnura</italic> damselflies.</p>", "<title>TE propagation and recombination probably explain the origins of novel female morphs</title>", "<p id=\"Par17\">Based on previous inferences of the historical order in which female morphs evolved (Fig. ##FIG##0##1##), we hypothesized that genomic divergence first occurred between O and A females, with some genomic content being then translocated from A into an O background, leading to the evolutionary origin of I females. We analysed SVs between morphs to test this hypothesis (Extended Data Fig. ##FIG##6##1## and Supplementary Text ##SUPPL##0##4##) and uncovered evidence of a ~20 kb sequence in the O haplotype that is duplicated and inverted in tandem in derived morphs (A and I; Fig. ##FIG##3##4b## and Extended Data Fig. ##FIG##7##2##). An investigation of the reads mapping to the inversion breakpoints suggested that additional duplications in the A genome, presumably via TE proliferation, may be related to the evolution of inter-sexual mimicry (Fig. ##FIG##3##4b## and Extended Data Fig. ##FIG##8##3##). Interestingly, TE content is enriched and recombination is reduced not just in the vicinity of the morph locus, but across the entire chromosome 13 (Extended Data Figs. ##FIG##9##4## and ##FIG##10##5##, and Supplementary Text ##SUPPL##0##4##). Finally, evidence of a translocation of an A-derived genomic region back into an O background (Extended Data Fig. ##FIG##11##6## and Supplementary Text ##SUPPL##0##4##) implied that the I morph evolved from an ectopic recombination event between A and O morphs (Fig. ##FIG##3##4b##). This scenario is also consistent with our previous <italic>k</italic>-mer GWAS and read-depth results, where we found that the only region differentiating both A and I females from O females is located ~3.5 mb in the I haplotype.</p>", "<title>Male mimicry is a trans-species polymorphism</title>", "<p id=\"Par18\">Ancestral state reconstruction of female colour states had previously pointed to an ancient origin of male mimicry in the clade that includes <italic>I. elegans</italic> and several other widely distributed <italic>Ischnura</italic> damselflies<sup>##REF##33677008##28##</sup> (Fig. ##FIG##0##1##). We investigated whether male mimicry is in fact a trans-species polymorphism using de novo genome assemblies from the closely related tropical bluetail (<italic>I. senegalensis</italic>; Extended Data Fig. ##FIG##6##1##). <italic>I. senegalensis</italic> shares a common ancestor with <italic>I. elegans</italic> about 5 Myr ago<sup>##REF##33677008##28##</sup>, and has both a male-mimicking A morph and a non-mimicking morph, which resembles the O females of <italic>I. elegans</italic><sup>##REF##33677008##28##,##UREF##5##37##</sup> (Fig. ##FIG##4##5a##).</p>", "<p id=\"Par19\">We reasoned that if morph divergence is ancestral, the genomic content that is uniquely present in A females or shared by A and I females in <italic>I. elegans</italic> should be at least partly present in A females of <italic>I. senegalensis</italic>, but absent in the alternative O-like female morph (Supplementary Text ##SUPPL##0##5##). This prediction was supported by differences in standardized read depths between the A and O-like pool of <italic>I. senegalensis</italic>, specifically at the morph locus of <italic>I. elegans</italic> (Fig. ##FIG##4##5b## and Supplementary Text ##SUPPL##0##5##). A shared genomic basis of inter-sexual mimicry for the two species was also supported by the same ~20 kb inversion signature in the A pool against an O assembly, as detected in A and I females of <italic>I. elegans</italic> (Extended Data Fig. ##FIG##12##7##). Finally, assembly alignments between O-like and A haplotypes of <italic>I. senegalensis</italic> showed that the A-specific genomic region of <italic>I. elegans</italic> is partly present in the A but not the O-like assembly of <italic>I. senegalensis</italic> (Fig. ##FIG##4##5c##).</p>", "<title>Shared and morph-specific genes reside in the morph locus</title>", "<p id=\"Par20\">Finally, we examined gene content and expression patterns in the morph locus. As female morphs differ in genomic content as well as sequence, the phenotypic effects of the morph locus could come about in at least three non-exclusive ways. First, entire gene models may be present in some morphs and absent in others. Second, genes present in all morphs may differ in expression patterns. Third, genes may encode different amino acid sequences in different female morphs. We used newly generated and previously published<sup>##REF##35217609##38##</sup> RNA sequencing (RNA-seq) data to investigate these questions (Extended Data Fig. ##FIG##6##1##), and capitalized on the annotations of the reference genome of <italic>I. elegans</italic><sup>##UREF##3##34##</sup>, as well as transcripts assembled de novo in our A-morph genome assembly. Because the genetic basis of inter-sexual mimicry is shared between <italic>I. elegans</italic> and <italic>I. senegalensis</italic> (Fig. ##FIG##4##5##), we focus on genes that are expressed in both species in at least one individual (Fig. ##FIG##5##6a##).</p>", "<p id=\"Par21\">Three transcripts (from two predicted genes) in the morph locus are expressed in A females of <italic>I. senegalensis</italic>, and in A and I females of <italic>I. elegans</italic>, but never in O or O-like females (Fig. ##FIG##5##6b##). Only one of these gene models (Afem.4094) could be functionally annotated, and appears to encode a long interspaced nuclear element (LINE) retrotransposon in the clade Jockey (Supplementary Text ##SUPPL##0##6##). This gene also exhibited expression changes in I females that reflect their colour development trajectory of initial resemblance to A females, followed by an overall appearance similar to O females upon sexual maturation (Supplementary Text ##SUPPL##0##6##). Notably, RepeatModeler and RepeatMasker detected signatures of the Jockey family at the same locus as the mapping locations of the A reads that had suggested a propagation of TEs in our SV analyses (Fig. ##FIG##5##6a## and Extended Data Fig. ##FIG##8##3##). Thus, these results further support that TEs are responsible for the evolution and expansion of the male-mimicry allele.</p>", "<p id=\"Par22\">We also identified three gene models that are shared by all haplotypes and expressed in both species. The three predicted genes encode zinc-finger domain proteins (Fig. ##FIG##5##6b## and Supplementary Text ##SUPPL##0##6##), which are known to participate in transcriptional regulation<sup>##REF##11179890##39##</sup>. However, we found no conclusive evidence of differential expression, nor evidence of non-synonymous substitutions between morphs shared by both <italic>I. elegans</italic> and <italic>I. senegalensis</italic> (Supplementary Text ##SUPPL##0##6##). While we see genes of a potentially regulatory function reside in the morph locus, understanding their role in morph development will probably require higher temporal and spatial resolution of gene expression data.</p>" ]	[ "<title>Discussion</title>", "<p id=\"Par23\">Sexual dimorphism, where males and females have markedly distinct colour patterns, has led to multiple evolutionary origins of female-limited polymorphisms and potential male mimicry in <italic>Ischnura</italic> damselflies<sup>##REF##33677008##28##</sup>. Here, we present a genomic glance into how these morphs evolve, setting the stage for future functional work to unravel the reversal of sexual phenotypes in damselfly sexual mimicry. Male mimicry in the common bluetail is controlled by a single genomic region in chromosome 13 (Figs. ##FIG##1##2## and ##FIG##2##3##). Our data suggest that this morph locus probably evolved with the accumulation of novel and potentially TE-derived sequences in the male mimicry haplotype (Fig. ##FIG##3##4##), which is shared by male-mimicking females of species diverging more than 5 Myr ago (Fig. ##FIG##4##5##). More recently, a rare recombination event involving part of the novel A genomic content has triggered the origin of a third female morph (Fig. ##FIG##3##4##), which shares its sexually immature colouration and patterning with A females, and shares its sexually mature overall appearance with O females<sup>##REF##9480685##27##</sup>. The morph locus contains a handful of genes, some of which may have evolved with TE propagation in the A haplotype, and are therefore absent from O individuals (Fig. ##FIG##5##6##). However, existing annotations provide only a hint on how these genes may influence morph development. Our results thus echo recent calls for a broader application of functional validation tools, in order to understand how lineage-specific genes contribute to phenotypic variation in natural populations<sup>##REF##35914975##40##</sup>.</p>", "<p id=\"Par24\">This study underscores two increasingly recognized insights in evolutionary genomics. First, there is mounting evidence that SVs abound in natural populations and often underpin complex and ecologically relevant phenotypic variation<sup>##REF##31653862##41##</sup>, such as discrete phenotypic polymorphisms<sup>##REF##26569123##10##,##REF##31848330##13##,##REF##26804558##15##,##REF##33958755##20##</sup>. Nonetheless, traditional GWAS approaches based on SNPs can easily miss SVs, as these approaches are contingent on the genomic content of the reference assembly<sup>##UREF##6##42##</sup>. Among other novel approaches to tackle this problem<sup>##UREF##6##42##</sup>, a reference-free <italic>k</italic>-mer-based GWAS, as implemented here, is a powerful method to identify variation in genomic content and sequence, especially when the genomic architecture of the trait of interest is initially unknown<sup>##REF##32284578##35##</sup>. In this study, we did not know a priori which of the three morphs, if any, would harbour unique genomic content. Had we ignored differences in genomic content between morphs and based our GWAS analysis solely on the DToL (O) reference assembly, we would have failed to identify SNPs between I and O morphs (Extended Data Fig. ##FIG##13##8##), and the origin of I females via a translocation of A content would have been obscured.</p>", "<p id=\"Par25\">Second, a role for TEs in creating novel and even adaptive phenotypic variation is increasingly being recognized<sup>##REF##30003608##43##,##REF##34883307##44##</sup>. Here, we found that a ~400 kb region of unique genomic content, possibly driven by LINE transposition, is associated with the male-mimicry phenotype in at least two species of <italic>Ischnura</italic> damselflies. TE activity can contribute to phenotypic evolution by multiple mechanisms. For instance, TEs may modify the regulatory environment of genes in their vicinity, by altering methylation<sup>##REF##26621743##45##</sup> and chromatin conformation patterns<sup>##REF##32286261##46##</sup>, or by providing novel <italic>cis</italic>-regulatory elements<sup>##REF##32075564##47##</sup>. The male-mimicry region in <italic>I. elegans</italic> is located between two coding genes with putative DNA-binding domains, and that may thus act as transcription factors. However, our expression data do not provide unequivocal support for differential regulation of either of these genes between female morphs. Importantly, currently available expression data come from adult specimens, as female morphs are not visually discernible in aquatic nymphs. Yet, the key developmental differences that produce the adult morphs are probably directed by regulatory variation during earlier developmental stages. Now that the morph locus has been identified, future work can address differential gene expression at more relevant developmental stages, before colour differences between morphs become apparent.</p>", "<p id=\"Par26\">TEs can also contribute to phenotypic evolution if they become domesticated, for example, when TE-encoded proteins are remodelled through evolutionary change to perform adaptive host functions<sup>##REF##28844698##48##</sup>. We found two transcripts located in A-specific or A/I-specific regions that are probably derived from LINE retrotransposons and are actively expressed in the genomes that harbour them (Fig. ##FIG##5##6b##). It is therefore possible that these transcripts participate in the development of adult colour patterns, which are initially more similar between A and I females than between either of these morphs and O females<sup>##REF##9480685##27##,##REF##30991898##29##</sup>. Yet, functional work on these transcripts is required to ascertain their role in morph determination. Finally, TEs can become sources of novel small RNAs that play important regulatory roles<sup>##REF##23040327##49##</sup>, including in insect sex determination<sup>##REF##24828047##50##</sup>. Thus, future work should also address non-coding RNA expression and function in the morph locus.</p>", "<p id=\"Par27\">Our results also provide molecular evidence for previous insights, gained by alternative research approaches, on the micro- and macroevolution of female-limited colour polymorphisms. A wealth of population data in southern Sweden has shown that female-morph frequencies are maintained by balancing selection, as they fluctuate less than expected due to genetic drift<sup>##REF##25996869##31##</sup>. Behavioural and field experimental studies indicate that such balancing selection on female morphs is mediated by negative frequency-dependent male harassment<sup>##UREF##7##51##,##REF##25034518##52##</sup>. We add to these earlier results by showing a molecular signature consistent with balancing selection in the genomic region where A females differ from both of the non-mimicking morphs. Sexual conflict is expected to have profound effects on genome evolution, but there are few examples of sexually antagonistic traits with a known genetic basis, in which predictions about these genomic effects can be tested<sup>##REF##30016158##53##,##REF##31740847##54##</sup>. Here, the signature of balancing selection on the morph locus matches the expectation of inter-sexual conflict resulting in negative frequency-dependent selection and maintaining alternative morph alleles over long periods.</p>", "<p id=\"Par28\">Similarly, a recent comparative study based on phenotypic and phylogenetic data inferred a single evolutionary origin of the male-mimicking morph shared by <italic>I. elegans</italic> and <italic>I. senegalensis</italic><sup>##REF##33677008##28##</sup>. Our present results strongly support this common origin. This is because A females in both species share unique genomic content, including associated transcripts, and an inversion signature against the ancestral O morph (Fig. ##FIG##4##5## and Extended Data Fig. ##FIG##12##7##). Nonetheless, these data are consistent with both an ancestral polymorphism and ancestral introgression being responsible for the spread of male mimicry across the clade. A potential role for introgression in the evolution of male mimicry is also suggested by rampant hybridization between <italic>I. elegans</italic> and its closest relatives<sup>##REF##29876058##55##</sup>, and by the fact that <italic>I. elegans</italic> and <italic>I. senegalensis</italic> can hybridize millions of years after their divergence, at least in laboratory settings<sup>##UREF##8##56##</sup>. The identification of the morph locus in <italic>I. elegans</italic> enables future comparative genomics studies to disentangle the relative roles of long-term balancing selection and introgression in shaping the widespread phylogenetic distribution of female-limited polymorphisms in <italic>Ischnura</italic> damselflies.</p>", "<p id=\"Par29\">Finally, our results open up new lines of enquiry on how the genomic architecture and chromosomal context of the female polymorphism may influence its evolutionary dynamics. Our data are consistent with the evolution of a third morph due to an ectopic recombination event that translocated genomic content from the A haplotype back into an O background. Ectopic recombination can occur when TE propagation generates homologous regions in different genomic locations<sup>##REF##1783293##57##,##REF##19936241##58##</sup>, and may be facilitated by the excess of TE content in chromosome 13 (Exteded Data Fig. ##FIG##9##4##). The male reproductive morphs in the ruff (<italic>Calidris pugnax</italic>) are one of few previous examples of a novel phenotypic morph arising via recombination between two pre-existing morph haplotypes<sup>##REF##26569123##10##</sup>. In pond damselflies, female polymorphisms with three or more female morphs are not uncommon, and in some cases female morphs exhibit graded resemblance to males<sup>##REF##10336708##59##</sup>. It is therefore possible that recombination, even if rare, has repeatedly generated diversity in damselfly female morphs.</p>", "<p id=\"Par30\">While recombination might have had a role in generating the the novel I morph, we observe reduced recombination over the morph locus in comparison to the rest of the genome of <italic>I. elegans</italic> (Extended Data Fig. ##FIG##10##5##). However, this reduction in recombination is not limited to the morph locus and its vicinity, but rather pervasive across chromosome 13 (Extended Data Fig. ##FIG##10##5##). This unexpected finding suggests two alternative causal scenarios. First, selection for reduced recombination at the morph locus, following the origin of sexual mimicry, could have spilled over chromosome 13, facilitating a subsequent accumulation of TEs and further reducing recombination<sup>##REF##29109221##60##</sup>. Second, TE enrichment and reduced recombination may have preceded the evolution of female morphs, and facilitated the establishment and maintenance of the female polymorphism by balancing selection.</p>", "<p id=\"Par31\">Both historical scenarios are compatible with a morph locus reminiscent of a supergene, which is defined by tight genetic linkage of multiple functional loci<sup>##REF##24642887##61##</sup>. However, an alternative and parsimonious explanation is that the novel sequences in A and I females and their flanking genes may not code for anything important for the male-mimicking phenotype as such, but simply disrupt a region of chromosome 13 that is required for the development of ancestral sexual differentiation. The observation that I females carry part of the sequence that originated in A in a different location of the scaffold (Fig. ##FIG##3##4b##), and still develop some male-like characters (for example, black thoracic stripes), could come about if insertions anywhere on a larger chromosomal region disrupt female suppression of the male phenotype, although with variable efficacy depending on the exact location or insertion size.</p>", "<title>Concluding remarks</title>", "<p id=\"Par32\">Recent years have witnessed an explosion of studies uncovering the loci behind complex phenotypic polymorphisms in various species. An emerging outlook is that not all polymorphisms are created equal, with some governed by massive chromosomal rearrangements<sup>##REF##26804558##15##–##REF##33786937##17##</sup>, and others by a handful of regulatory sites<sup>##REF##30819892##11##,##REF##31015412##12##,##REF##36947619##18##</sup>. Our results contribute to this growing field by uncovering a single causal locus that features structural variation and morph-specific transcripts in the female-limited morphs of <italic>Ischnura</italic> damselflies. These morphs not only differ in numerous morphological and life-history traits<sup>##REF##31692246##32##,##REF##28123090##62##,##UREF##9##63##</sup> and gene expression profiles<sup>##REF##29713090##24##,##REF##32402112##25##</sup>, but they include a male mimic that is maintained by balancing frequency-dependent selection. Our findings enable future studies on the developmental basis of such male mimicry, with consequences for a broader understanding of the evolutionary dynamics of sexual dimorphism and the cross-sexual transfer of trait expression<sup>##UREF##10##64##</sup>.</p>" ]	[]	[ "<p id=\"Par1\">Sex-limited morphs can provide profound insights into the evolution and genomic architecture of complex phenotypes. Inter-sexual mimicry is one particular type of sex-limited polymorphism in which a novel morph resembles the opposite sex. While inter-sexual mimics are known in both sexes and a diverse range of animals, their evolutionary origin is poorly understood. Here, we investigated the genomic basis of female-limited morphs and male mimicry in the common bluetail damselfly. Differential gene expression between morphs has been documented in damselflies, but no causal locus has been previously identified. We found that male mimicry originated in an ancestrally sexually dimorphic lineage in association with multiple structural changes, probably driven by transposable element activity. These changes resulted in ~900 kb of novel genomic content that is partly shared by male mimics in a close relative, indicating that male mimicry is a trans-species polymorphism. More recently, a third morph originated following the translocation of part of the male-mimicry sequence into a genomic position ~3.5 mb apart. We provide evidence of balancing selection maintaining male mimicry, in line with previous field population studies. Our results underscore how structural variants affecting a handful of potentially regulatory genes and morph-specific genes can give rise to novel and complex phenotypic polymorphisms.</p>", "<p id=\"Par2\">The authors investigate the genetic basis of inter-sexual mimicry in <italic>Ischnura elegans</italic> damselflies, where females are polymorphic and one female morph mimics males. By combining genomic, transcriptomic and phylogenetic evidence, they identify a causal locus and structural variants associated with the evolution of female polymorphism and male mimicry in this species.</p>", "<title>Subject terms</title>" ]	[ "<title>Main</title>", "<p id=\"Par3\">Sexual dimorphism is one of the most fascinating forms of intra-specific phenotypic variation in animals. Sexes often differ in size and colour, as well as the presence of elaborated ornaments and weaponry. Theoretical and empirical studies over many decades have developed a detailed framework of sexual selection and sexual conflict, explaining why these differences arise and how they become encoded in sex differentiation systems<sup>##REF##20374139##1##–##REF##33848958##3##</sup>. However, a growing number of examples of inter-sexual mimicry<sup>##REF##17148353##4##–##REF##36069013##7##</sup> suggest that sexual dimorphism can be evolutionarily fragile and quite dynamic. Inter-sexual mimicry has evolved in several lineages, when individuals of one sex gain a fitness advantage, usually frequency- or density-dependent, due to their resemblance to the opposite sex. For example, males who mimic females, as seen in the ruff (<italic>Calidris pugnax</italic>) and the Melanzona guppy (<italic>Poecilia parae</italic>), forgo courtship and ‘sneak’ copulations from dominant males<sup>##REF##17148353##4##,##UREF##1##5##</sup>, while females who mimic males, in damselflies and hummingbirds, avoid excessive male-mating harassment<sup>##REF##19382852##6##,##REF##34450085##8##</sup>. Inter-sexual mimicry thus requires the evolution of a novel sex-mimicking morph in a sexually dimorphic ancestor. The occurrence of inter-sexual mimicry may be a intermediate step in the evolution of sexual monomorphism, it may be an ephemeral state or it may be maintained as a stable polymorphism. In any case, sexual mimics harbour genetic changes that attenuate or prevent the development of sex-specific phenotypes, and can therefore provide insights into the essential building blocks of sexual dimorphism<sup>##UREF##2##9##</sup>.</p>", "<p id=\"Par4\">Considerable research effort has been devoted to uncover the genetic basis of discrete phenotypic polymorphisms, such as those associated with alternative reproductive or life-history strategies<sup>##REF##26569123##10##–##REF##34949821##14##</sup>. Together, these studies highlight a vast diversity of mechanisms used by evolution to package complex phenotypic differences into a single locus that is protected from the eroding effects of recombination. At one extreme, phenotypic morphs may evolve via massive insertions, deletions or inversions that lock together dozens to hundreds of genes into supergenes<sup>##REF##26804558##15##–##REF##33786937##17##</sup>. At the other end, much smaller structural variants (SVs), confined to a few thousand base pairs, can modulate the expression of one or a few regulators of pleiotropic networks, resulting in markedly different morphs<sup>##REF##30819892##11##,##REF##31015412##12##,##REF##36947619##18##</sup>. We are clearly only starting to get a glimpse of the major themes among these genetic mechanisms. For example, it is not known whether genomic architecture determines the type and breadth of co-varying traits or the likelihood of polymorphisms evolving in specific lineages<sup>##REF##33739390##19##</sup>.</p>", "<p id=\"Par5\">A few of these studies have focused on sex-limited polymorphisms, where one of the morphs shares the overall appearance, such as the colour pattern, of the opposite sex<sup>##REF##26569123##10##,##REF##34949821##14##,##REF##33958755##20##</sup>. Such sex-limited morphs may illustrate novel origins of sexual dimorphism, driven by either sexual selection in males<sup>##REF##34949821##14##</sup> or natural selection in females<sup>##REF##36947619##18##,##REF##24598547##21##</sup>. Alternatively, sex-limited polymorphisms may arise with the evolution of inter-sexual mimicry. Crucially, empirical support for the evolution of inter-sexual mimicry demands both a macroevolutionary context for the polymorphism, showing that sexually dimorphism is ancestral, and a documented advantage of sexual mimics in at least some social contexts. There is therefore a need to integrate genomic, microevolutionary and phylogenetic evidence into our understanding of the evolutionary dynamics of sexual dimorphism and inter-sexual mimicry. This integrative approach has been overall rare, and applied mostly to the study of alternative male reproductive strategies<sup>##REF##36947619##18##,##REF##35263124##22##</sup>. Yet, female mimicry of males may be more common than historically appreciated<sup>##REF##23339236##23##</sup>, and the genetic basis of such mimicry remains largely unexplored<sup>##REF##29713090##24##–##REF##34102071##26##</sup>.</p>", "<p id=\"Par6\">The common bluetail damselfly <italic>Ischnura elegans</italic> (Odonata) has three female-limited morphs (namely O, A and I) that differ in colouration, whereas males are always monomorphic<sup>##REF##9480685##27##</sup>. O females display the colour pattern and developmental colour changes inferred as ancestral in a comparative analysis of the genus <italic>Ischnura</italic><sup>##REF##33677008##28##</sup> (Fig. ##FIG##0##1##). Male-like (A) females are considered male mimics, who experience a frequency-dependent advantage of reduced male mating and premating harassment due to their resemblance to males<sup>##REF##19382852##6##</sup>. Finally, the I morph shares its stripe pattern and immature colouration with the A morph<sup>##REF##9480685##27##</sup> (Fig. ##FIG##0##1##), but develops a yellow-brown background colouration with age, eventually resembling the O morph upon sexual maturation<sup>##REF##30991898##29##</sup>. I females are only known in <italic>I. elegans</italic> and a few close relatives<sup>##REF##33677008##28##</sup> (Fig. ##FIG##0##1##), and their evolutionary relationship to A and O females remains unresolved. The behaviour, ecology and population biology of <italic>I. elegans</italic> have been intensely investigated for over two decades, making it one of the best understood female-limited polymorphisms, in terms of how morphs differ in fitness-related traits and how alternative morphs are maintained sympatrically over long periods<sup>##REF##15795853##30##–##REF##31613669##33##</sup>. Nonetheless, the molecular basis of this polymorphism remains unknown.</p>", "<p id=\"Par7\">To advance our understanding of the evolution of complex phenotypes, such as sexual dimorphism and sex-specific morphs, we identify the genomic region responsible for the female-limited colour polymorphism in <italic>I. elegans</italic>. Using a combination of reference-based and reference-free genome-wide association studies (GWAS), upon morph-specific genome assemblies, we revealed two novel regions adding up to ~900 kb that are associated with the evolutionary origin of the male-mimicking A morph. These SVs, probably generated and expanded by transposable element (TE) activity, are partly shared by male-mimicking females of the tropical bluetail damselfly (<italic>Ischnura senegalensis</italic>), indicating that male mimicry is a trans-species polymorphism. We also show that the novel I morph evolved via an ectopic recombination event, where part of the A-unique genomic content was translocated into an O genomic background. Finally, we examined the evolutionary dynamics of the colour morph locus and explored expression patterns of genes located in this region. Together, our results indicate that structural variation affecting a handful of genes and maintained by balancing selection provides the raw material for the evolution of a male-mimicking phenotype in pond damselflies.</p>", "<title>Supplementary information</title>", "<p>\n\n\n</p>", "<title>Source data</title>", "<p>\n\n\n\n\n\n\n\n\n\n\n</p>" ]	[ "<title>Extended data</title>", "<p id=\"Par63\">\n\n</p>", "<p id=\"Par64\">\n\n</p>", "<p id=\"Par65\">\n\n</p>", "<p id=\"Par66\">\n\n</p>", "<p id=\"Par67\">\n\n</p>", "<p id=\"Par68\">\n\n</p>", "<p id=\"Par69\">\n\n</p>", "<p id=\"Par70\">\n\n</p>", "<p id=\"Par71\">\n\n</p>", "<title>Extended data</title>", "<p id=\"Par59\">is available for this paper at 10.1038/s41559-023-02243-1.</p>", "<title>Supplementary information</title>", "<p id=\"Par60\">The online version contains supplementary material available at 10.1038/s41559-023-02243-1.</p>", "<title>Acknowledgements</title>", "<p>We thank H. Dort, R. A. Steward, J. Haushofer, P. de Sessions and the Monteiro Lab for suggestions and helpful discussions. We are also grateful to M. P. Celorio-Mancera and H. M. Low for invaluable technical support. A. Monteiro hosted B.W. at the National University of Singapore during part of the duration of this study. Computation and data handling were enabled by resources provided by the Swedish National Infrastructure for Computing (SNIC) through the Uppsala Multidisciplinary Center for Advanced Computational Science (UPPMAX), under projects 2022/6-230 and 2022/5-419 awarded to E.I.S. Specimens of <italic>I. senegalensis</italic> were collected in Singapore, under a research permit (NP/RP22-015b) from the National Parks Board, Singapore. This work was supported by an International Postdoc Grant from the Swedish Research Council (VR) (grant no. 2019-06444 to B.W.). Funding was also provided by the Swedish Research Council (VR) (grant no. 2017-04386 to C.W.W. and grant no. 2016-03356 to E.I.S.), by the Stina Werners Foundation (grant no. 2018-017 to E.I.S.) and Erik Philip Sörensens Stiftelse (grant no. 2019-033 to E.I.S.). S.N. was funded by a scholarship grant for Master’s students from Sven and Lily Lawski’s Foundation.</p>", "<title>Author contributions</title>", "<p>B.W. conceived the study with input from C.W.W. E.I.S. organized field work in the long-term population study of <italic>I. elegans</italic> during 2019 and 2020, and planned and prepared the outdoor rearing experiments. E.I.S. and S.N. collected DNA and RNA samples of <italic>I. elegans</italic>. M.T. and Y.T. collected samples for pool sequencing of <italic>I. senegalensis</italic>, and B.W. collected samples of <italic>I. senegalensis</italic> in Singapore. S.N., B.W. and K.T. conducted laboratory work on <italic>I. elegans</italic>. M.T., Y.T. and B.W. conducted laboratory work on <italic>I. senegalensis</italic>. B.W. analysed the data with input from C.W.W., K.T., R.C. and T.L. B.W. wrote the paper with contributions from all authors.</p>", "<title>Peer review</title>", "<title>Peer review information</title>", "<p id=\"Par61\"><italic>Nature Ecology & Evolution</italic> thanks Riddhi Deshmukh, Elina Immonen and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.</p>", "<title>Funding</title>", "<p>Open access funding provided by Stockholm University.</p>", "<title>Data availability</title>", "<p>Sequencing data from this study have been submitted to the NCBI Sequence Read Archive (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra/\">https://www.ncbi.nlm.nih.gov/sra/</ext-link>) under BioProject <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA940276\">PRJNA940276</ext-link>. For details, please see Supplementary Tables ##SUPPL##0##1## and ##SUPPL##0##2##. Morph-specific genome assemblies and intermediate output files required to reproduce the figures in the main text and supporting material are available on Zenodo<sup>##UREF##24##105##</sup>. <xref ref-type=\"sec\" rid=\"Sec28\">Source data</xref> are provided with this paper.</p>", "<title>Code availability</title>", "<p>All code necessary to reproduce the results of this study can be found on Zenodo at 10.5281/zenodo.8304055 and Github at <ext-link ext-link-type=\"uri\" xlink:href=\"https://github.com/bwillink/Morph-locus\">https://github.com/bwillink/Morph-locus</ext-link>.</p>", "<title>Competing interests</title>", "<p id=\"Par62\">The authors declare no competing interests.</p>" ]	[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>The evolution of female-limited colour polymorphisms in <italic>Ischnura</italic> damselflies.</title><p><bold>a</bold>, A previous phylogenetic study and ancestral state reconstruction<sup>##REF##33677008##28##</sup> proposed that the genus <italic>Ischnura</italic> had a sexually dimorphic ancestor, with O-like females (red circle). The O morph is markedly different from males, having a bronze-brown thorax and faint stripes, instead of the black thoracic stripes on a bright blue background of males. <bold>b</bold>, Male mimicry (A females, blue circle) has evolved more than once, for instance, in an ancestor of the (expanded) clade that includes the common bluetail (<italic>I. elegans</italic>, outlined with solid line) and the tropical bluetail (<italic>I. senegalensis</italic>, outlined with dashed line). <bold>c</bold>, <italic>I. elegans</italic> is a trimorphic species, due to the recent evolution of a third female morph, I (yellow circle). In <italic>I. elegans</italic>, morph inheritance follows a dominance hierarchy, where the most dominant allele produces the A morph and two copies of the most recessive allele are required for the development of O females. In contrast, the <italic>O</italic> allele is dominant in <italic>I. senegalensis</italic><sup>##UREF##25##106##</sup>. Terminal nodes in the phylogeny represent different species. Grey triangles represent other clades of <italic>Ischnura</italic>, which are collapsed for clarity. Damselfly images adapted from ref. <sup>##REF##32402112##25##</sup> under a Creative Commons licence <ext-link ext-link-type=\"uri\" xlink:href=\"https://creativecommons.org/licenses/by/4.0/\">CC BY 4.0</ext-link>.</p><p>##SUPPL##2##Source data##</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>Morph determination in <italic>I. elegans</italic> is controlled in a ~1.5 mb region of chromosome 13.</title><p><bold>a</bold>, SNP-based GWAS in all pairwise analyses between morphs. Genomic DNA from 19 wild-caught females of each colour morph and of unknown genotype was extracted and sequenced for these analyses. Illumina short reads were aligned against an A morph genome assembly, generated from nanopore long-read data (Extended Data Fig. ##FIG##6##1##). <bold>b</bold>, A closer look at the SNP associations on the unlocalized scaffold 2 of chromosome 13, which contained all highly significant SNPs. Transcripts expressed in at least one adult of both <italic>I. elegans</italic> and <italic>I. senegalensis</italic> are shown at the bottom (see also Fig. ##FIG##5##6##). Grey transcripts are shared by all morphs, whereas blue transcripts are uniquely present in A or A and I samples (see ‘Shared and morph-specific genes reside in the morph locus’). The <italic>y</italic> axis in <bold>a</bold> and <bold>b</bold> indicates unadjusted −log<sub>10</sub>\n<italic>P</italic> values calculated from chi-squared tests. <bold>c</bold>, <italic>F</italic><sub>ST</sub> values averaged across 30 kb windows for the same pairwise comparisons as in the SNP-based GWAS. The dashed line marks the 95th percentile of all non-zero <italic>F</italic><sub>ST</sub> values across the entire genome. The red double arrow shows the region of elevated divergence between O and both A and I samples (∼50 kb–0.2 mb). The blue double arrow shows the region of elevated divergence between A and both O and I samples (∼0.2 mb–1.5 mb). <bold>d</bold>,<bold>e</bold>, Population-level estimates of Tajima’s <italic>D</italic> (<bold>d</bold>) and <italic>π</italic> (<bold>e</bold>) averaged across 30 kb windows. The shaded area contains the 5th–95th percentile of all genome-wide estimates.</p><p>##SUPPL##3##Source data##</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><title>Female morphs of <italic>I. elegans</italic> differ in genomic content.</title><p><bold>a</bold>,<bold>b</bold>, Number of significant <italic>k</italic>-mers (below the 5% false-positive threshold; <xref rid=\"Sec10\" ref-type=\"sec\">Methods</xref>) associated with pairwise genome-wide analyses and mapped to the unlocalized scaffold 2 of chromosome 13, in the A-morph assembly (<bold>a</bold>) and the I-morph assembly (<bold>b</bold>). <bold>c</bold>,<bold>d</bold>, Standardized read depths along the unlocalized scaffold 2 of chromosome 13, relative to background coverage of the A-morph assembly (<bold>c</bold>) and the I-morph assembly (<bold>d</bold>). Solid lines (yellow, blue and red) show short-read data (19 samples per morph) and black dashed lines represent long-read data (1 sample per morph).Grey areas show regions of genomic content present in A and I individuals, but absent in all but one O sample. Note that different regions of the scaffold are plotted for the two assemblies (see main text).</p><p>##SUPPL##4##Source data##</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><title>SVs differentiate morph haplotypes in the common bluetail damselfly (<italic>I. elegans</italic>).</title><p><bold>a</bold>, Alignment between morph-specific genomes assembled from long-read nanopore samples with genotypes <italic>Ao</italic>, <italic>Io</italic> and <italic>oo</italic>. Grey lines represent alignments of at least 5 kb and >70% identity. The black line connects regions of genomic content shared by the three morphs within the morph locus. The red to blue gradient represents a ~20 kb region that carries an inversion signature in A and I females relative to the O haplotype (Extended Data Fig. ##FIG##7##2##). The blue to yellow gradient represents a ~150 kb alignment between the start of the unlocalized scaffold 2 of chromosome 13 in A and a region ~3.5 mb apart in the I haplotype. Coordinates at the bottom are based on the DToL reference assembly. <bold>b</bold>, Schematic illustration of the hypothetical sequence of events responsible for the evolution of novel female morphs. First, a sequence originally present in O was duplicated and inverted in tandem, potentially causing the initial divergence of the <italic>A</italic> allele. Second, part of this inversion was subsequently duplicated in A, in association with a putative TE, leading to multiple inversion signatures in the A haplotype relative to an O reference (Extended Data Fig. ##FIG##8##3##). Finally, part of the A duplications were translocated into a position ~3.5 mb downstream into an O background, giving rise to the <italic>I</italic> allele. Currently, A females are also characterized by another region of unique content and unknown origin (question mark). A females show elevated sequence divergence in the internal region of the morph locus that is shared by all haplotypes (dark grey bars; see also black line in <bold>a</bold>). Coordinates on the O haplotype are based on the (DToL) reference assembly. Grey numbers in IV give the approximate size of genomic sequences in A and I that are absent in O. Damselfly images adapted from ref. <sup>##REF##32402112##25##</sup> under a Creative Commons licence <ext-link ext-link-type=\"uri\" xlink:href=\"https://creativecommons.org/licenses/by/4.0/\">CC BY 4.0</ext-link>.</p><p>##SUPPL##5##Source data##</p></caption></fig>", "<fig id=\"Fig5\"><label>Fig. 5</label><caption><title>A shared genomic basis of A females in <italic>I. elegans</italic> and <italic>I. senegalensis</italic>.</title><p><bold>a</bold>, <italic>I.</italic>\n<italic>senegalensis</italic> is a female-dimorphic species, where one female morph (O-like) is distinctly different from males and resembles O females in <italic>I. elegans</italic>, and the other female morph (A) is a male mimic. Photo credit: Mike Hooper. <bold>b</bold>, Standardized read depth of pool-seq samples (<italic>n</italic> = 30 females of each morph per pool) of <italic>I. senegalensis</italic>, against the A-morph assembly of <italic>I. elegans</italic>, calculated in 500 bp windows. The <italic>x</italic>-axis shows the first 1.5 mb of the unlocalized scaffold 2 of chromosome 13. <bold>c</bold>, Alignments between morph-specific genomes from a homozygous O-like female of <italic>I. senegalensis</italic> (top), an <italic>Ao</italic> female of <italic>I. elegans</italic> (middle) and a homozygous A female of <italic>I. senegalensis</italic> (bottom). Lines connecting the assemblies represent alignments of at least 500 bp and >70% identity. The black line connects genomic content in the morph locus, which is shared by the three morphs of <italic>I. elegans</italic>. In <italic>I. elegans</italic>, this region is rich in SNPs differentiating A females from the other two morphs (see Fig. ##FIG##1##2b##). The blue–turquoise gradient connects sequences uniquely present in the A morphs of <italic>I. elegans</italic> and <italic>I. senegalensis</italic>.</p><p>##SUPPL##6##Source data##</p></caption></fig>", "<fig id=\"Fig6\"><label>Fig. 6</label><caption><title>The morph locus of <italic>I. elegans</italic> is situated in the unlocalized scaffold 2 of chromosome 13.</title><p><bold>a</bold>, Diagram of the ~1.5 mb morph locus on the A-morph assembly, showing from top to bottom: morph-specific read depth coverage; the location of LINE retrotransposons in the the Jockey family; the mapping locations of A-derived reads with a previously detected inversion signature against O females; and transcripts expressed in at least one adult individual of both <italic>I. elegans</italic> and <italic>I. senegalensis</italic>. Transcripts plotted in black are present in both the A and O assemblies, while transcripts in blue are located in genomic regions that are unique to the A haplotype or are shared between A and I but not the <italic>O</italic> allele. <bold>b</bold>, Functional annotations and sex- and morph-specific expression of transcripts. Square fill indicates whether transcript expression was detected in each group. RNA-seq data for <italic>I. elegans</italic> comes from whole-thorax samples from sexually immature and sexually mature wild-caught adults (<italic>n</italic> = 3 females of each morph and 3 males). RNA-seq data for <italic>I. senegalensis</italic> comes from a recent study in which the abdomen, head, thorax and wings were sampled in two females of each morph and two males (one individual of each group sampled upon emergence and one sampled after two days).</p><p>##SUPPL##7##Source data##</p></caption></fig>", "<fig id=\"Fig7\"><label>Extended Data Fig. 1</label><caption><title>Outline of data and analyses used in this study.</title><p>For our main study species <italic>Ischnura elegans</italic>, we obtained short-read genomic data from 19 field-caught females per morph, and long-read genomic data from three females with genotypes <italic>Ao</italic>, <italic>Io</italic>, and <italic>oo</italic>. The long-read samples were used to assemble morph-specific genomes, scaffolded against the Darwin Tree of Life reference assembly. We obtained whole-thorax RNAseq data from females of each morph in both sexually immature and sexually mature colour phases (n = 3 of each morph and colour phase). Immature and mature males (n = 3 of each) were also sampled for whole-thorax RNAseq data. We used short-read pool-seq data (n = 30 individuals of each morph per pool) of the close relative <italic>Ischnura senegalensis</italic> to investigate whether the female polymorphisms in both species share a genomic basis. We also analysed expression levels of candidate genes in this species, using samples from a previously published study<sup>##REF##35217609##38##</sup>, which produced transcriptomic data from four body parts (head, thorax, wing and abdomen) of each A females, O females and males (n = 2), sampled at adult emergence and two days thereafter. The <italic>k</italic>-mer based GWAS is reference-free, but significant <italic>k</italic>-mers were mapped to the morph-specific assemblies to determine their chromosomal context. Damselfly images adapted from ref. <sup>##REF##32402112##25##</sup> under a Creative Commons licence <ext-link ext-link-type=\"uri\" xlink:href=\"https://creativecommons.org/licenses/by/4.0/\">CC BY 4.0</ext-link>.</p></caption></fig>", "<fig id=\"Fig8\"><label>Extended Data Fig. 2</label><caption><title>An inversion signature differentiates A and I individuals from the O morph.</title><p>Read mapping and sample coverage at the start of the scaffold 2 of chromosome 13 in <bold>a</bold> our O assembly and <bold>b</bold> the DToL reference assembly, showing a signature of a ~ 20 kb inversion in <italic>A</italic> and I samples. A single O sample also exhibited this signature but was excluded here for clarity (see Supporting Text ##SUPPL##0##3##). Note that the first 415 kb of the reference DToL assembly are absent in our scaffolded O assembly, and therefore the x-axis is shifted by 415 kb in <bold>b</bold>.</p></caption></fig>", "<fig id=\"Fig9\"><label>Extended Data Fig. 3</label><caption><title>The A and I reads mapped to inversion break points on the O assembly (see Extended Data Fig. ##FIG##7##2##) map to multiple locations on the A assembly.</title><p><bold>a</bold> Reads from the first inversion breakpoint. <bold>b</bold> Reads from the second inversion breakpoint. Each row represents a sample and each circle an individual read. The x-axis corresponds to coordinates on the A assembly.</p><p>\n##SUPPL##8##Source data##\n</p></caption></fig>", "<fig id=\"Fig10\"><label>Extended Data Fig. 4</label><caption><title>Proportion of TE content in non-overlapping 1.5 mb regions.</title><p>The gray dots correspond to genomic windows outside chromosome 13. The main assembly and the unlocalized scaffolds of chromosome 13 are depicted with different colours. The dashed line marks the 95 percentile of TE coverage across all windows.</p><p>\n##SUPPL##9##Source data##\n</p></caption></fig>", "<fig id=\"Fig11\"><label>Extended Data Fig. 5</label><caption><title>Linkage disequilibrium (LD) in the genome of <italic>Ischnura elegans</italic>.</title><p>LD estimates are shown for the first 15 mb of each chromosome and all unlocalized scaffolds of chromosome 13. The morph locus is found in the first ~ 1.5 mb of the unlocalized scaffold 2 of chromosome 13, which has a total size of ~ 15 mb. Each dot represent the square correlation coefficient (R<sup>2</sup>) between two variant sites on the x axis, separated by the number of sites indicated in the y axis.</p></caption></fig>", "<fig id=\"Fig12\"><label>Extended Data Fig. 6</label><caption><title>Evidence of a translocation between A and I haplotypes.</title><p>Mapping and coverage of long reads from an <italic>Io</italic> sample across the first 5.6 mb of the unlocalized scaffold 2 of chromosome 13 in the A assembly, showing a signature consistent with either a 5.54 mb inversion or a translocation of inverted A content. Absence of morph divergence beyond ~1.5 mb on the A assembly supports the translocation scenario.</p></caption></fig>", "<fig id=\"Fig13\"><label>Extended Data Fig. 7</label><caption><title>Structural variants between <italic>A</italic> and O-like females of <italic>I. senegalensis</italic> along the morph locus identified in <italic>I. elegans</italic>.</title><p><bold>a</bold> Read mapping and sample coverage of <italic>I. senengalensis</italic> pool-seq data at the start of the unlocalized scaffold 2 of chromosome 13 in the O assembly of <italic>I. elegans</italic>. The same ~ 20 kb inversion signature is found in A and I samples of <italic>I. elegans</italic> (see Extended Data Fig. ##FIG##7##2##). <bold>b-c</bold> The A-pool reads mapped to break points on the O assembly map to multiple locations on the A assembly. <bold>b</bold> Reads from the first breakpoint. <bold>c</bold> Reads from the second breakpoint. Each row represents a pool of <italic>I. senegalensis</italic> and each circle an individual read. The x-axis corresponds to the A assembly of <italic>I. elegans</italic>.</p><p>\n##SUPPL##10##Source data##\n</p></caption></fig>", "<fig id=\"Fig14\"><label>Extended Data Fig. 8</label><caption><title>Morph divergence using the DToL assembly (O haplotype) as mapping reference.</title><p><bold>a</bold> SNP-based genome-wide associations in all pairwise analyses between morphs. Genomic DNA from 19 wild-caught females of each colour morph and of unknown genotype was extracted and sequenced for these analyses. Illumina short reads were aligned against the DToL reference assembly. <bold>b</bold> A closer look of the SNP associations on the unlocalized scaffold 2 of chromosome 13, which contained all highly significant SNPs. The <italic>y</italic> axis in <bold>a</bold> and <bold>b</bold> indicates unadjusted -Log<sub>10</sub> P-values calculated from chi-squared tests. <bold>c</bold> Fst values averaged across 30 kb windows for the same pairwise comparisons as in the SNP based GWAS. The dashed line marks the 95 percentile of all non-zero Fst values across the entire genome. The red double arrow shows the region of elevated divergence between A and both O and I samples. Population-level estimates of <bold>d</bold> Tajima′s D, and <bold>e</bold> nucleotide diversity (π) averaged across 30 kb windows. The shaded area contains the 5–95 percentile of all genome-wide estimates.</p><p>\n##SUPPL##11##Source data##\n</p></caption></fig>" ]	[ "<table-wrap id=\"Tab1\"><label>Extended Data Table 1</label><caption><p>Significant <italic>k</italic>-mers associated with morph comparisons in <italic>I. elegans</italic></p></caption></table-wrap>" ]	[]	[]	[]	[]	[]	[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM3\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM4\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM5\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM6\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM7\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM8\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM9\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM10\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM11\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM12\"></supplementary-material>" ]	[ "<table-wrap-foot><p>For each comparison (A vs O, A vs I and A and I vs O), we show the total number of significant <italic>k</italic>-mers, and the total number of significant <italic>k</italic>-mers that map without any mismatching position to morph-specific reference assemblies. Of the mapping <italic>k</italic>-mers, we then show the number located in the unlocalized scaffold 2 of chromosome 13, which includes the putative morph locus. For the DToL assembly, we show the number of significant <italic>k</italic>-mers mapping to both the primary assembly (capturing the <italic>O</italic> allele) and the haplotigs, where the haplotig RAPID_106 comprises the <italic>A</italic> allele (see Supporting Text ##SUPPL##0##2##).</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"41559_2023_2243_Fig1_HTML\" id=\"d32e545\"/>", "<graphic xlink:href=\"41559_2023_2243_Fig2_HTML\" id=\"d32e676\"/>", "<graphic xlink:href=\"41559_2023_2243_Fig3_HTML\" id=\"d32e822\"/>", "<graphic xlink:href=\"41559_2023_2243_Fig4_HTML\" id=\"d32e961\"/>", "<graphic xlink:href=\"41559_2023_2243_Fig5_HTML\" id=\"d32e1134\"/>", "<graphic xlink:href=\"41559_2023_2243_Fig6_HTML\" id=\"d32e1247\"/>", "<graphic position=\"anchor\" xlink:href=\"41559_2023_2243_Fig7_ESM\" id=\"d32e2511\"/>", "<graphic position=\"anchor\" xlink:href=\"41559_2023_2243_Fig8_ESM\" id=\"d32e2538\"/>", "<graphic position=\"anchor\" xlink:href=\"41559_2023_2243_Fig9_ESM\" id=\"d32e2564\"/>", "<graphic position=\"anchor\" xlink:href=\"41559_2023_2243_Fig10_ESM\" id=\"d32e2580\"/>", "<graphic position=\"anchor\" xlink:href=\"41559_2023_2243_Fig11_ESM\" id=\"d32e2598\"/>", "<graphic position=\"anchor\" xlink:href=\"41559_2023_2243_Fig12_ESM\" id=\"d32e2613\"/>", "<graphic position=\"anchor\" xlink:href=\"41559_2023_2243_Fig13_ESM\" id=\"d32e2670\"/>", "<graphic position=\"anchor\" xlink:href=\"41559_2023_2243_Fig14_ESM\" id=\"d32e2713\"/>", "<graphic position=\"anchor\" xlink:href=\"41559_2023_2243_Tab1_ESM\" id=\"d32e2729\"><caption><p>Significant <italic>k</italic>-mers associated with morph comparisons in <italic>I. elegans</italic></p></caption></graphic>" ]	[ "<media xlink:href=\"41559_2023_2243_MOESM1_ESM.pdf\"><label>Supplementary Information</label><caption><p>Supplementary Texts 1–6, Supplementary Tables 1–6 and Supplementary Figs. 1–14.</p></caption></media>", "<media xlink:href=\"41559_2023_2243_MOESM2_ESM.pdf\"><caption><p>Reporting Summary</p></caption></media>", "<media xlink:href=\"41559_2023_2243_MOESM3_ESM.zip\"><label>Source Data Fig. 1</label><caption><p>Tree file.</p></caption></media>", "<media xlink:href=\"41559_2023_2243_MOESM4_ESM.zip\"><label>Source Data Fig. 2</label><caption><p>Statistical Source Data.</p></caption></media>", "<media xlink:href=\"41559_2023_2243_MOESM5_ESM.zip\"><label>Source Data Fig. 3</label><caption><p>Statistical Source Data.</p></caption></media>", "<media xlink:href=\"41559_2023_2243_MOESM6_ESM.zip\"><label>Source Data Fig. 4</label><caption><p>Statistical Source Data.</p></caption></media>", "<media xlink:href=\"41559_2023_2243_MOESM7_ESM.zip\"><label>Source Data Fig. 5</label><caption><p>Statistical Source Data.</p></caption></media>", "<media xlink:href=\"41559_2023_2243_MOESM8_ESM.zip\"><label>Source Data Fig. 6</label><caption><p>Statistical Source Data.</p></caption></media>", "<media xlink:href=\"41559_2023_2243_MOESM9_ESM.zip\"><label>Source Data Extended Data Fig. 3</label><caption><p>Statistical Source Data.</p></caption></media>", "<media xlink:href=\"41559_2023_2243_MOESM10_ESM.csv\"><label>Source Data Extended Data Fig. 4</label><caption><p>Statistical Source Data.</p></caption></media>", "<media xlink:href=\"41559_2023_2243_MOESM11_ESM.zip\"><label>Source Data Extended Data Fig. 7</label><caption><p>Statistical Source Data.</p></caption></media>", "<media xlink:href=\"41559_2023_2243_MOESM12_ESM.zip\"><label>Source Data Extended Data Fig. 8</label><caption><p>Statistical Source Data.</p></caption></media>" ]	[{"label": ["2."], "surname": ["De Lisle"], "given-names": ["SP"], "article-title": ["Understanding the evolution of ecological sex differences: integrating character displacement and the Darwin\u2013Bateman paradigm"], "source": ["Evol. 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PMC10781645	38123682	[]	[ "<title>Methods</title>", "<p id=\"Par22\">All experiments were performed in accordance with the Norwegian Animal Welfare Act and the European Convention for the Protection of Vertebrate Animals used for Experimental and Other Scientific Purposes, Permit numbers 18011 and 29893.</p>", "<title>Subjects</title>", "<p id=\"Par23\">Male C57/Bl6 mice were housed in social groups of 2–6 individuals per cage (calcium imaging experiments) or individually (electrophysiology experiments, after implantation). The mice had access to nesting material and a planar running wheel and were kept on a 12 h light/12 h darkness schedule in a temperature and humidity-controlled vivarium. Food and water were provided ad libitum. Two-photon calcium imaging data were collected from a cohort of 12 mice (5 implanted in MEC, 4 in PaS, and 3 in VIS). Electrophysiological data from the MEC were collected from 2 mice.</p>", "<title>Surgeries</title>", "<p id=\"Par24\">For all surgeries, anaesthesia was induced by placing the subjects in a plexiglass chamber filled with isoflurane vapour (5% isoflurane in medical air, flow of 1 l min<sup>−1</sup>). Surgery was performed on a heated surgery table (38 °C). Air flow was kept at 1 l min<sup>−1</sup> with 1–3% isoflurane as determined from physiological monitoring of breathing and heartbeat. The mice were allowed to recover from surgery in a heated chamber (33 °C) until they regained complete mobility and alertness. Postoperative analgesia was given in the form of subcutaneous injections of Metacam (5 mg kg<sup>−1</sup>) 24 and 48 h after the first Metacam injection as long as was deemed necessary. Additionally, the mice were given subcutaneous injections or oral administration of Temgesic (0.05–0.1 mg kg<sup>−1</sup>) with 6- to 8-h (injections) or 12-h (oral) intervals for the first 36 h after the first Temgesic injection.</p>", "<title>Surgeries for calcium imaging</title>", "<p id=\"Par25\">Surgeries were performed according to a two-step protocol. During the first procedure, newborn pups or adult mice were injected in MEC or PaS, or adult mice were injected in VIS with a virus carrying a construct for the expression of the calcium indicator GCaMP6m. The virus (for all injections: AAV1-Syn-GcaMP6m; titre 3.43 × 10<sup>13</sup> genome copies per ml, AV-1-PV2823, UPenn Vector Core, University of Pennsylvania, USA) was diluted 1:1 in sterile DPBS (1× Dulbecco’s Phosphate Buffered Saline, Gibco, ThermoFisher). During the second procedure, two weeks later, a microprism was implanted to gain optical access to infected neurons located in MEC and PaS, or a glass window was inserted to obtain similar access in VIS.</p>", "<title>Virus injection and microprism implantation in MEC and PaS</title>", "<p id=\"Par26\">In the first surgical procedure, newborn pups received injections of AAV1-Syn-GCaMP6m one day after birth<sup>##REF##28154241##51##</sup>. An analgesic was provided immediately before the surgery (Rymadil, Pfizer, 5 mg kg<sup>−1</sup>). Pre-heated ultrasound gel (39 °C, Aquasonic 100, Parker) was generously applied on the pup’s head in order to create a large medium for the transmission of ultrasound waves. Real-time ultrasound imaging (Vevo 1100 System, Fujifilm Visualsonics) allowed for targeted delivery of the viral mixture to specific areas of the brain. During ultrasound imaging, the pup was immobilized through a custom-made mouth adapter. The ultrasound probe (MS-550S) was lowered to be in close contact with the gel and thus the pup’s head to allow visualization of the targeted structures. The probe was kept in place for the whole duration of the procedure via the VEVO injection mount (VEVO Imaging Station. Imaging in B-Mode, frequency: 40 MHz; power: 100%; gain: 29 dB; dynamic range: 60 dB). Target regions were identified by structural landmarks: the MEC or PaS were identified in the antero–posterior and medio–lateral axis by the appearance of the aqueduct of Sylvius and the lateral sinus. The target area for injection was comparable to a coronal section at ∼−4.7 mm from bregma in the adult mouse. The solution containing the virus (250 ± 50 nl per injection) was injected in the target regions via beveled glass micropipettes (Origio, custom made; outer tip opening: 200 μm; inner tip opening: 50 μm) using a pressure-pulse system (Visualsonics, 5 pulses, 50 nl per pulse). The pipette tip was pushed through the brain without any incision on the skin, or a craniotomy, and, to reduce the duration of the procedure, retracted immediately after depositing the virus in the target area. The anatomical specificity of the infection was verified by imaging serial sections of the infected hemispheres after experiment completion (see ‘Histology of calcium imaging mice and reconstruction of field-of-view location’).</p>", "<p id=\"Par27\">Two weeks after the viral injection, we performed a second procedure, in which a microprism was implanted in the left hemisphere to gain optical access to the superficial layers of MEC and PaS<sup>##REF##25503366##52##</sup>. The implanted microprism was a right-angle prism with 2 mm side length and reflective enhanced aluminium coating on the hypotenuse (Tower Optical). The prism was glued to a 4-mm-diameter (CS-4R, thickness no. 1) round coverslip with UV-curable adhesive (Norland). On the day of surgery, mice were anaesthetized with isoflurane (IsoFlo, Zoetis, 5% isoflurane vapourised in medical air delivered at 0.8–1 l min<sup>−1</sup>) after which two analgesics were provided through intraperitoneal injection (Metacam, Boehringer Ingelheim, 5 mg kg<sup>−1</sup> or Rimadyl, Pfizer, 5 mg kg<sup>−1</sup>, and Temgesic, Indivior, 0.05–0.1 mg kg<sup>−1</sup>) and one local analgesic was applied underneath the skin covering the skull (Marcain, Aspen, 1–3 mg kg<sup>−1</sup>). Their scalp was removed with surgical scissors and the surface of the bone was dried before being generously covered with optibond (Kerr). To increase the thickness and stability of the skull and overall preparation, a thin layer of dental cement (Charisma, Kulzer) was applied on the exposed skull, except in the location above the implant, where a 4-mm-wide circular craniotomy was made. The craniotomy was positioned over the dorsal surface of the cortex and cerebellum, with the centre positioned ∼ 4 mm lateral from the centre of the medial sinus, and above the transverse sinus just above the MEC and PaS. After the dura was removed above the cerebellum, the lower edge of the prism was slowly pushed in the empty space between the forebrain and the cerebellum, just posterior to the transverse sinus. The edges of the coverslip were secured to the surrounding skull with UV-curable dental cement (Venus Diamond Flow, Kulzer). A custom-designed steel headbar was attached to the dorsal surface of the skull, centred upon and positioned parallel to the top face of the microprism. All exposed areas of the skull, including the headbar, were finally covered with dental cement (Paladur, Kulzer) and made opaque by adding carbon powder (Sigma Aldrich) until the dental cement powder became dark grey.</p>", "<title>Virus injection and glass window implantation in VIS</title>", "<p id=\"Par28\">In a different cohort of mice than those used for MEC/PaS imaging, we induced the expression of GCaMP6m in neurons of the adult VIS for subsequent imaging. We targeted the injection of the same AAV1-Syn-GCaMP6m viral solution used in the developing MEC and PaS to the primary visual cortex. On the day of surgery, 3- to 5-month-old mice were anaesthetized with isoflurane (IsoFlo, Zoetis, 5 % isoflurane vapourized in medical air delivered at 0.8–1 l min<sup>−1</sup>) after which two analgesics were provided through intraperitoneal injection (Metacam, Boehringer Ingelheim, 5 mg kg<sup>−1</sup> or Rimadyl, Pfizer, 5 mg kg<sup>−1</sup>, and Temgesic, Indivior, 0.05–0.1 mg kg<sup>−1</sup>) and one local anaesthetic was applied underneath the skin covering the skull (Marcain, Aspen, 1–3 mg kg<sup>−1</sup>). The virus was injected at three locations in VIS, all of which were within the following anatomical ranges in the right hemisphere: 2.3–2.5 mm lateral from the midline, 0.9–1.3 mm anterior from lambda<sup>##UREF##3##53##</sup>. At each injection site, 50 nl of the virus was injected 0.5 mm below the dura and the pipette was left in place for 3–4 min to enable the virus to diffuse. The pipette was then brought to 0.3 mm below the dura and another 50 nl was injected. The pipette was then left in place for 5–10 min before retracting it completely. The speed of the injections was 5 nl s<sup>−1</sup>.</p>", "<p id=\"Par29\">Two weeks after the viral injection, a surgery to chronically implant a glass window over VIS was performed. The mice were handled as previously described for the prism surgery in MEC/PaS, including anaesthesia, delivery of analgesics, and scalp removal. Optibond was applied to the exposed skull except in the location of the craniotomy. A 4-mm-wide craniotomy was made, centred on the virus injection coordinates, and a 4-mm glass window was placed underneath the skull edges of the craniotomy. The glass was slightly larger than the craniotomy, so after it was manoeuvred in place, the upward pressure exerted by the brain secured it in place against the skull, thereby minimizing the presence of empty gaps that might favour tissue and bone regrowth. The edges of the window were secured with UV-curable dental cement and superglue before the positioning of the headbar as described for the MEC–PaS implantation. All exposed areas of the skull, including the headbar, were finally covered with dental cement (Paladur, Kulzer) that was made opaque by adding carbon powder (Sigma Aldrich) until the dental cement powder became dark grey.</p>", "<title>Neuropixels probe implants</title>", "<p id=\"Par30\">Two adult mice (4 to 5 months old) were implanted with four-shank Neuropixels 2.0 silicon probes<sup>##REF##33859006##54##</sup> targeting the superficial layers of MEC in the left hemisphere. Prior to the surgery, the mice were given general analgesics (Metacam, Boehringer Ingelheim, 5 mg kg<sup>−1</sup> and Temgesic, Indivior, 0.05–0.1 mg kg<sup>−1</sup>) subcutaneously and one local anaesthetic was applied underneath the skin covering the skull (Marcain, Aspen, 1–3 mg kg<sup>−1</sup>). After incision, a hole was drilled over the cerebellum for an anchor screw connected to a ground wire. Craniotomies were then drilled. Probes targeting the MEC were lowered from the surface to depths between 2.5 mm and 2.7 mm relative to the dura mater. They were implanted with the most medial shank placed on the brain surface 3.2 mm lateral to the midline and 0.4 mm anterior to the transverse sinus edge. The four shanks were oriented with the electrode sites on the posterior side. In one of the two mice (no. 104638), the probe was first rotated 7° in the horizontal plane (angle with reference to the coronal plane), with the most lateral shank in the most posterior position such that the shanks were parallel to the transverse sinus. The four shanks were then lowered vertically from this position.</p>", "<p id=\"Par31\">The Neuropixels probe of the second mouse (no. 102335) was not rotated in the horizontal plane—that is, all shanks had the same anterior–posterior coordinates. The electrode shanks of this mouse were lowered from the surface with a 2° angle relative to the coronal plane, such that the shank tips were the most posterior. The shanks remained within the same sagittal plane as they were lowered. This second mouse was also implanted with a probe targeting the CA1 region in the right hemisphere, 1.225–1.975 mm relative to the midline, at a depth of 3 mm relative to dura mater, with all shanks 2.1 mm posterior to bregma. The hippocampal data were not used in the present study. The probes were secured to the skull using an adhesive (OptiBond, Kerr), UV-curable dental cement (Venus Diamond Flow, Kulzer), and dental cement (Meliodent, Kulzer). A headbar was attached as described above for the calcium imaging studies.</p>", "<title>Self-paced running behaviour under sensory-minimized conditions</title>", "<p id=\"Par32\">Training of mice began 2 days after the prism implantation in MEC and PaS, 12 days after the implantation of a cranial window in VIS, and 5–7 days after Neuropixels probe implantation. All mice used for calcium imaging recordings and one Neuropixels-implanted mouse (no. 104638) were head-restrained by a headbar with their limbs resting on a freely rotating styrofoam wheel with a metal shaft fixed through the centre. The radius of the wheel was ∼85 mm and the width 70 mm. Low friction ball bearings (HK 0608, Kulelager) were affixed to the ends of the metal shaft and held in place on the optical table using a custom mount. This arrangement allowed the mice to self-regulate their movement. The position of the mouse on the rotating wheel was measured using a rotary encoder (E6B2-CWZ3E, YUMO) attached to its centre axis. Step values of the encoder (4,096 per full revolution, ∼130 µm resolution) were digitized by a microcontroller (Teensy 3.5, PJRC) and recorded using custom Python scripts at 40–50 Hz. Wheel tracking was triggered at the start of imaging and synchronized to the ongoing image acquisition through a digital input from the 2-photon microscope. In a subset of mice recorded with calcium imaging (3 out of 12; 2 implanted in MEC, 1 implanted in PaS), the precise synchronization was not available to us and these data were hence not used for comparison of movement and imaging data. A T-slot photo interrupter (EE-SX672, Omron) served as a lap (full revolution) counter. Design and code of the wheel are publicly available under <ext-link ext-link-type=\"uri\" xlink:href=\"https://github.com/kavli-ntnu/wheel_tracker\">https://github.com/kavli-ntnu/wheel_tracker</ext-link>.</p>", "<p id=\"Par33\">The other Neuropixels probe-implanted mouse (no. 102335) was head-restrained by a headbar while resting on a circular disc coated with rubber spray. The radius of this wheel was ∼85 mm. The mouse was allowed self-paced movement on the wheel. Three-dimensional motion capture (OptiTrack Flex 6 cameras and Motive recording software) was used to track the rotation of the wheel by tracking retroreflective markers placed on the wheel edge. Digital pulses were generated using an Arduino microcontroller which were used to align the Neuropixels acquisition system and the OptiTrack system via direct TTL input and infra-red LEDs.</p>", "<p id=\"Par34\">In all mice, the self-paced task was performed under conditions of minimal sensory stimulation, in darkness, and with no rewards to signal elapsed time or distance run<sup>##REF##26494280##16##,##REF##26063915##17##</sup>. Prior to the imaging sessions, the calcium imaging mice were accustomed to the setup through daily exposures over the course of between 5 and 15 sessions, one session per day. Neuropixels-implanted mice were habituated to the setup by gradually increasing the time spent on the wheel over four days. In each session, after the mice were positioned on the wheel, they were gently head-restrained and free to run or rest<sup>##REF##17920014##55##,##REF##25467986##56##</sup> for 30, 45 or 60 min.</p>", "<p id=\"Par35\">Recording sessions of Neuropixels-implanted mice also consisted of trials where the mice were freely foraging in a 80 cm × 80 cm open field arena for 30 min. These open field trials preceded the self-paced wheel trials and were not used in the present study.</p>", "<title>Two-photon imaging in head-fixed mice</title>", "<p id=\"Par36\">A custom-built 2-photon benchtop microscope (Femtonics, Hungary) was used for 2-photon imaging of the target areas (that is, superficial layers of MEC, PaS and VIS). A Ti:Sapphire laser (MaiTai Deepsee eHP DS, Spectra-Physics) tuned to a wavelength of 920 nm was used as the excitation source. Average laser power at the sample (after the objective) was 50–120 mW. Emitted GCaMP6m fluorescence was routed to a GaAsP detector through a 600 nm dichroic beamsplitter plate and 490–550 nm band-pass filter. Light was transmitted through a 16×/0.8 NA water-immersion objective (MRP07220, Nikon) carefully lowered in close contact to the coverslip glued to the microprism (for MEC–PaS imaging) or above the coverslip in contact with the brain surface (for VIS imaging). For the microprism-implanted mice, the objective lens was aligned to the ventro–lateral corner of the prism, to consistently identify the position of MEC and PaS across mice. Ultrasound gel (Aquasonic 100, Parker) or water was used to fill the gap between the objective lens and the glass coverslips. The software MESc (v 3.3 and 3.5, Femtonics, Hungary) was used for microscope control and data acquisition. Imaging time series of either ∼30 min or ∼60 min were acquired at 512 × 512 pixels (sampling frequency: 30.95 Hz, frame duration: ∼32 ms; pixel size: either 1.78 × 1.78 µm<sup>2</sup> or 1.18 × 1.18 µm<sup>2</sup>). Time series acquisition was initiated arbitrarily after the mouse was head-restrained on the setup.</p>", "<title>Neuropixels recordings in head-fixed mice</title>", "<p id=\"Par37\">Signals were recorded using a Neuropixels acquisition system as described previously<sup>##REF##35022611##25##,##REF##37148872##57##</sup>. In short, the electrophysiological signal was amplified with a gain of 80, low-pass-filtered at 0.5 Hz, high-pass-filtered at 10 kHz, and digitized at 30 kHz on the probe circuit board. The digitized signal was then multiplexed by the ‘headstage’ circuit board and transmitted along a 5 m tether cable using twisted pair wiring to a Neuropixels PXIe acquisition module. The data was visualized and recorded using SpikeGLX version 20201103 software (<ext-link ext-link-type=\"uri\" xlink:href=\"https://billkarsh.github.io/SpikeGLX\">https://billkarsh.github.io/SpikeGLX</ext-link>).</p>", "<title>Histology</title>", "<title>Histology of calcium imaging mice and reconstruction of field-of-view location</title>", "<p id=\"Par38\">On the last day of imaging, after the imaging session, the mice were anaesthetized with isoflurane (IsoFlo, Zoetis) and then received an overdose of sodium pentobarbital before transcardial perfusion with freshly prepared PFA (4% in PBS). After perfusion, the brain was extracted from the skull and kept in 4% PFA overnight for post-fixation. The PFA was then exchanged with 30% sucrose to cryoprotect the tissue.</p>", "<p id=\"Par39\">To verify the anatomical location of the imaged FOVs in the microprism-implanted mice, we used small, custom-made pins, derived from a thin piano wire coated with a solution of 1,1′-dioctadecyl-3,3,3′,3′-tetramethylindocarbocyanine perchlorate (DiI; DiIC18(3)) (ThermoFischer), to mark the location of the imaged tissue in relation to the prism footprint. A DiI-coated pin was inserted into the brain tissue at the location left empty by the prism footprint, and specifically targeted to the ventro–lateral corner of the footprint (see ‘Surgeries’). The pin was left in place to favour transfer of DiI from the metal pin to the brain tissue, and to leave a fluorescent mark on the location of the imaged FOV. After 30 to 60 s, the pin was removed and the brain was sliced on a cryostat in 30–50 µm thick sagittal sections. All slices were collected sequentially in a 24-well plate filled with PBS, before being mounted in their appropriate anatomical order on a glass slide in custom-made mounting medium. For confocal imaging, a Zeiss LSM 880 microscope (Carl Zeiss) was used to scan through the whole series of slices and locate the position of the DiI fluorescent mark. Images were then acquired using an EC Plan-Neofluar 20×/0.8 NA air immersion, 40×/1.3 oil immersion, or 63×/1.4 oil immersion objective (Zeiss, laser power: 2–15%; optical slice: 1.28–1.35 airy units, step size: 2 µm). Before acquisition, gain and digital offset were established to optimize the dynamic range of acquisition to the dynamic range of the GCaMP6m and DiI signals. Settings were kept constant during acquisition across brains. Based on the location of the red fluorescent mark, we could infer where, on the medio–lateral and dorso–ventral extent of the brain, the ventro–lateral corner of the microprism (and hence the 2-photon FOV aligned to it) was located.</p>", "<p id=\"Par40\">We used the Paxinos mouse brain atlas<sup>##UREF##3##53##</sup> to produce a reference flat map representing the medio–lateral and dorso–ventral extent of the MEC and PaS. Flat maps helped delineate the extent of the FOV that fell within the anatomical boundaries of either the MEC and adjacent PaS, and allowed for a standardized comparison across mice. For each imaged mouse, we mapped the dorso–ventral and medio–lateral location of the DiI mark on the refence flat map (Extended Data Fig. ##FIG##5##1c##). Mice were assigned to ‘MEC imaging’ or ‘PaS imaging’ groups depending on the location of the FOV: a mouse would be further analysed as being part of the MEC imaging group if more than 50% of the area of the FOV occupied by GCaMP6m-expressing cells could be located in the MEC.</p>", "<p id=\"Par41\">To verify the anatomical location of the FOVs in VIS in the glass window implanted mice, we sliced the brain until we reached the anatomical coordinates at which the virus was infused (see ‘Surgeries’). Coronally cut slices of 50 µm thickness were collected sequentially in a 24-well plate, and immediately mounted in their appropriate anatomical order on a glass slide in custom-made mounting medium. For confocal imaging, a Zeiss LSM 880 microscope (Carl Zeiss) was used according to the same specification as described above for MEC/PaS.</p>", "<title>Histology and reconstruction of Neuropixels probe placement</title>", "<p id=\"Par42\">After the end of experiments, the mice were anaesthetized and received an overdose of isoflurane (IsoFlo, Zoetis) before transcardial perfusion with saline followed by 4% formaldehyde. The brain was either extracted after perfusion or kept overnight in 4% formaldehyde for post-fixation before extraction. The brains were then stored in 4% formaldehyde. Frozen 30 µm thick sagittal sections were cut on a cryostat, mounted on glass, and stained with Cresyl violet (Nissl). To estimate the shank locations, we used an Axio Scan.Z1 (Carl Zeiss) slide scanner microscope for brightfield detection at 20x magnification. We used Paxinos mouse brain atlas<sup>##UREF##3##53##</sup> and the Allen Mouse Brain Common Coordinate Framework<sup>##REF##32386544##58##</sup> version 3 through the siibra-explorer (Forschungszentrum Juelich, <ext-link ext-link-type=\"uri\" xlink:href=\"https://atlases.ebrains.eu/viewer/\">https://atlases.ebrains.eu/viewer/</ext-link>) to estimate anatomical location of recording sites. A map of the probe shank was aligned to the histology assuming that the cutting plane was near-parallel to the sagittal plane. When possible, the anatomical locations were calculated using the tip of the probe shanks and the intersection of the shank with the brain surface as reference frames. When this was not possible, the profile of a nearby brain region (for example, the hippocampus) was used to estimate the MEC implant site. We observed theta-rhythmicity of neural activity on all recorded shanks, as expected for recording locations in the MEC.</p>", "<title>Analysis of imaging time series</title>", "<p id=\"Par43\">Imaging time series data were analysed using the Suite2p<sup>##UREF##4##59##</sup> Python library (<ext-link ext-link-type=\"uri\" xlink:href=\"https://github.com/MouseLand/suite2p\">https://github.com/MouseLand/suite2p</ext-link>). We used its built-in routines for motion correction, region of interests (ROI) extraction, neuropil signal estimation, and spike deconvolution. Non-rigid motion correction was chosen to align each frame iteratively to a template. Quality was assessed by visual inspection of the corrected stacks and built-in motion correction metrics. The Suite2p GUI was used to manually sub-select putative neurons based on anatomical and signal characteristics and to discard obvious artefacts that accumulated during the analysis—for example, ROIs with footprints spanning large areas of the FOV, ROIs that did not have clearly delineated circumferences in the generated maximum intensity projection, or ROIs that were extracted automatically but showed no visible calcium transients.</p>", "<p id=\"Par44\">Raw fluorescence calcium traces of each ROI were neuropil-corrected to create a fluorescence calcium signal <italic>F</italic><sub>corr</sub> by subtracting 0.7 times the neuropil signal from the raw fluorescence traces. We used the Suite2p integrated version of non-negative deconvolution<sup>##REF##28291787##60##</sup> with tau = 1 s to deconvolve <italic>F</italic><sub>corr</sub>, yielding the basis for the binarized sequences that we refer to as the calcium activity (see ‘Binary deconvolved calcium activity and matrix of calcium activity’). Due to the absence of ground truth data for our combination of indicator, region, and imaging conditions, we used a decay tau that was at the lower end of biologically plausible values (tau = 1 s), which allowed even short and low amplitude spiking responses to be picked up by the analysis and therefore did not bias our analysis towards large-amplitude calcium transients (presumed bursting responses). To estimate the signal-to-noise ratio (SNR) of each cell individually, we further thresholded the calcium activity (without binarization) at 1 s.d. over the mean, yielding filtered calcium activity, and classified the remaining activity as noise. We additionally ensured that noise was temporally well segregated from filtered calcium activity by requiring data points classified as noise to be separated by at least one second before and ten seconds after filtered calcium activity. The SNR of the cell was then estimated as the ratio of the mean amplitude of <italic>F</italic><sub>corr</sub> during episodes of filtered calcium activity over the s.d. of <italic>F</italic><sub>corr</sub> during episodes of noise. If no data points remained after the filtering of calcium activity, the cell was assigned a SNR of zero.</p>", "<title>Binary deconvolved calcium activity and matrix of calcium activity</title>", "<p id=\"Par45\">In order to denoise the recorded fluorescence calcium signals and have good temporal resolution, all analyses in the study were performed using the deconvolved calcium activity of the recorded cells. For each cell whose SNR was larger than 4, the deconvolved calcium activity (see ‘Analysis of imaging time series’) was downsampled by a factor of 4 by calculating the mean over time windows of ∼129 ms (original sampling frequency = 30.95 Hz, sampling frequency used in the analyses = 7.73 Hz). Because the ultraslow oscillations and periodic sequences unfolded at the time scales of seconds to minutes, this downsampling step gave a good temporal resolution for all quantifications while allowing us to work with smaller arrays (ultraslow oscillations and the oscillatory sequences were also detectable when using the original sampling frequency), which in some of the analyses reduced the computing time. Next, the downsampled deconvolved calcium activity was averaged over time and its s.d. was calculated. A threshold equal to this average plus 1.5 times the s.d. was used to convert the deconvolved calcium activity into a binary deconvolved calcium activity, such that all values above the threshold were set to 1 (calcium events), and all values below or equal to that threshold were set to 0. Unless stated otherwise, for all analyses throughout the study we used the deconvolved and binary calcium activity, to which for simplicity we refer to as ‘deconvolved calcium activity’ or simply ‘calcium activity’. The calcium activity of all cells in a session with SNR > 4 was stacked to construct a binary matrix of calcium activity which had as many rows as neurons, and as many columns as time bins sampled at 7.73 Hz. The population vectors are the columns of the matrix of calcium activity.</p>", "<p id=\"Par46\">Note that the recorded calcium signals likely reflect a combination of groups of single spikes and higher-frequency bursts, although it was not possible to distinguish between the two types of firing. The sensitivity of the calcium indicator was likely not high enough to detect subthreshold potentials.</p>", "<title>Spike Sorting and single-unit selection</title>", "<p id=\"Par47\">Spike sorting of Neuropixels data was performed using a version of KiloSort 2.5 (ref. <sup>##REF##33859006##54##</sup>) with some customizations to improve performance on recordings from the MEC region as described previously<sup>##REF##35022611##25##</sup>. All trials in a session were clustered together. Single units were discarded from analysis based on a < 20% estimated contamination rate with spikes from other neurons. These units were automatically labelled by the KiloSort 2.5 algorithm as ‘good’ units. In the example session from mouse no. 104638 only good units were considered. In the example session of mouse no. 102335, because the number of good units was lower (<250), we also used multi-unit activity (MUA).</p>", "<title>Autocorrelations and spectral analysis of single-cell calcium activity</title>", "<p id=\"Par48\">To determine if the calcium activity of single cells displays ultraslow oscillations, for each neuron the PSD was calculated on the autocorrelation of its calcium activity. The PSD was computed using Welch’s method (pwelch, built-in Matlab function), with Hamming windows of 17.6 min (8,192 bins of 129 ms in each window) and 50% of overlap between consecutive windows. Note that when calculating the PSD a large window was needed to identify oscillation frequencies ≪0.1 Hz.</p>", "<p id=\"Par49\">To visualize whether specific oscillatory patterns at fixed frequencies were present in the neural population, all autocorrelations from one session were sorted and stacked into a matrix, where rows are cells and columns are time lags. The sorting of autocorrelations was performed according to the maximum power of each PSD in a descending manner. The frequency at which the PSD peaked was used as an estimate of the oscillatory frequency of the cell’s calcium activity.</p>", "<p id=\"Par50\">In order to determine significance for the peak of the PSD, we considered two extreme and opposite shuffling procedures: On the one hand, given that circularly shuffling the data preserves all inter calcium events (Extended Data Fig. ##FIG##7##3c,d##), taking this approach would preserve the shape and the position of the peak in the PSD calculated on experimental data. On the other hand, destroying the inter calcium event intervals by assigning a random position to each calcium event in the time series would lead to a flat PSD (Extended Data Fig. ##FIG##7##3c,d##). In the latter approach, all cells would be classified as oscillatory. To bridge these two approaches we developed a new shuffling procedure. For each cell we divided its calcium activity vector into <italic>n</italic> epochs of length <italic>W</italic>, with , where <italic>T</italic> is the total number of time bins sampled at a frequency SF = 7.73 Hz (that is, bin size = 129 ms). We next shuffled those epochs (and preserved the ordering of the time bins within each epoch). This method preserved the inter calcium event interval, but at the same time disrupted the periodicity. In the limit where <italic>W</italic> = 129 ms, this method coincides with shuffling all calcium events without preserving the inter calcium event intervals; in the limit where <italic>W</italic> = <italic>T/SF</italic>, this method is equivalent to circularly shuffling the data. For each of the 200 shuffled realizations we calculated the PSD and the fraction of cells for which the peak of the PSD in experimental data was above the 95th percentile of a shuffled distribution built with the values of the PSDs calculated on shuffled data (and at the frequency at which the PSD computed on experimental data peaked). Here we present the results for 5 different epoch lengths:</p>", "<p id=\"Par51\"><italic>W</italic> = 1 s: 6226 oscillatory cells out of 6231 (99%)</p>", "<p id=\"Par52\"><italic>W</italic> = 10 s: 6153 oscillatory cells out of 6231 (99%)</p>", "<p id=\"Par53\"><italic>W</italic> = 20 s: 5695 oscillatory cells out of 6231 (91%)</p>", "<p id=\"Par54\"><italic>W</italic> = 50 s: 4642 oscillatory cells out of 6231 (74%)</p>", "<p id=\"Par55\"><italic>W</italic> = 100 s: 3521 oscillatory cells out of 6231 (56%)</p>", "<p id=\"Par56\">When <italic>W</italic> is below the typical duration of the sequences (<italic>W</italic> < 50 s), the great majority of cells are classified as having a peak in the PSD. As expected, when <italic>W</italic> is similar to the duration of the sequences (<italic>W</italic> <italic>≥</italic> 50 s), the fraction of oscillatory cells quickly drops. This fraction is no longer significantly above a chance level of 5%.</p>", "<p id=\"Par57\">This approach was used for determining the fraction of oscillatory cells both in calcium imaging and in Neuropixels data. In the main text we present the results corresponding to <italic>W</italic> <italic>=</italic> 20 s.</p>", "<p id=\"Par58\">Finally, we note that there was some variability in the frequency at which the PSD peaked across cells within a session. For example, in the example session shown in Fig. ##FIG##0##1b–d## and Fig. ##FIG##1##2a##, some single-cell PSDs peaked at a frequency of 0.0066 Hz, while others did so at a frequency of 0.0075 Hz. However, in many cases the PSDs were wide enough to exhibit high power in neighbouring frequencies too, providing support to the frequencies being rather clustered among a subset of values, with some slight variability around those values. When all cells were analysed (<italic>n</italic> = 6,231 cells pooled across 15 oscillatory sessions, 5 mice), in approximately half of the MEC data the oscillatory frequency at the single-cell level was very similar to the frequency at the population level (Extended Data Fig. ##FIG##11##7e##). This finding points to a small variability in the frequency of single-cell activity in MEC, as expected in the presence of recurring sequences.</p>", "<title>Correlation and PCA sorting methods</title>", "<p id=\"Par59\">To determine whether neural population activity exhibits temporal structure we visualized the population activity by means of raster plots in which we sorted all cells according to different methods.</p>", "<title>Correlation method</title>", "<p id=\"Par60\">This method sorts cells such that those that are nearby in the sorting are more synchronized than those that are further away. First, each calcium activity was downsampled by a factor 4 by calculating the mean over counts of calcium events in bins of 0.52 s. The obtained calcium activity was then smoothed by convolving it with a gaussian kernel of width equal to four times the oscillation bin size, a bin size that was representative of the temporal scale of the population dynamics (see ‘Oscillation bin size’). The cross correlations between all pairs of cells were calculated using time bins as data points, and a maximum time lag of 10 time points, equivalent to ∼ 5 s. This small time lag allowed us to identify near instantaneous correlation while keeping information about the temporal order of activity between cell pairs. The maximum value of the cross-correlation between cell <italic>i</italic> and cell <italic>j</italic> was stored in the entry (<italic>i</italic>,<italic>j</italic>) of the correlation matrix <italic>C</italic>, which was a square matrix of N rows and N columns, where <italic>N</italic> was the total number of recorded neurons in the session with SNR > 4. If the cross-correlation peaked at a negative time lag the value in the entry (<italic>i</italic>,<italic>j</italic>) was multiplied by −1. The entry with the highest cross-correlation value was identified and its row, denoted by <italic>i</italic><sub>max,</sub> was used as the ‘seed’ cell for the sorting procedure and chosen to be the first cell in the sorting. Cells were then sorted according to the values in the entries , , <italic>j</italic> <italic>≠</italic> <italic>i</italic><sub>max</sub>, that is, their correlations with the seed cell, in a descending manner.</p>", "<title>PCA method</title>", "<p id=\"Par61\">Computing correlations from the calcium activity or the calcium signals can be noisy due to fine tuning of hyperparameters (for example, the size of the kernel used to smooth the calcium activity of all cells). To avoid this, we leveraged the fact that the periodic sequences of neural activity constitute low-dimensional dynamics with intrinsic dimensionality equal to 1, and sorted the cells based on an unsupervised dimensionality reduction<sup>##REF##25151264##61##</sup> approach (a similar approach was used in ref. <sup>##UREF##5##62##</sup>). For each recording session, PCA was applied to the matrix of calcium activity (bin size = 129 ms; using Matlab’s built-in pca function), including all epochs of movement and immobility and using the rows (neurons) as variables and the columns (time bins) as observations. The first two principal components (PCs) were kept, since 2 is the minimum number of components needed to embed non-linear 1-dimensional dynamics. Cells were sorted according to their loadings in PC1 and PC2, expecting that the relationship between these loadings would express the ordering in cell activation during the sequences.</p>", "<p id=\"Par62\">The plane spanned by PC1 and PC2 was named the PC1–PC2 plane. In the PC1–PC2 plane, the loadings of each neuron (the components of the eigenvectors without being multiplied by the eigenvalues) defined a vector, for which we computed its angle , 1 ≤ <italic>i</italic> <italic>≤</italic> <italic>N</italic>, with respect to the axis of PC1, where is the loading of cell <italic>i</italic> on <italic>PCj</italic>. Cells were sorted according to their angle <italic>θ</italic> in a descending manner.</p>", "<p id=\"Par63\">Note that while we keep the first 2 principal components to sort the neurons, all principal components and the full matrices of calcium activity were used in the analyses (except for visualization purposes—for example, see ‘Manifold visualization for MEC sessions’). Finally, note that because in PCA a principal component is equivalent to −1 times the principal component, the sorting and an inversion of the sorting are equivalent. The sorting was chosen so that sequences would progress from the bottom to the top in the raster plot.</p>", "<p id=\"Par64\">The PCA method was used throughout the paper for sorting the recorded cells unless otherwise stated.</p>", "<title>Random sorting of cell identities</title>", "<p id=\"Par65\">A random ordinal integer , where <italic>N</italic> is the total number of recorded cells with SNR > 4, was assigned to each neuron without repetition across cells. Neurons were sorted according to those assigned numbers (see example session in Extended Data Fig. ##FIG##8##4d##, top row).</p>", "<title>Sorting of circularly shuffled data</title>", "<p id=\"Par66\">A shuffled matrix of calcium activity was built by circularly shuffling the calcium activity of each cell separately. For each cell a random ordinal integer , where <italic>T</italic> is the total number of time bins (bin size = 129 ms), was chosen and the calcium activity was rigidly shifted by this integer using periodic boundary conditions. The assignment of random ordinal integers was made separately for each cell. The PCA method was then applied to the shuffled matrix of calcium activity (see example session in Extended Data Fig. ##FIG##8##4d##, second row).</p>", "<title>Sorting of temporally shuffled data</title>", "<p id=\"Par67\">Because circularly shuffling the data preserves the oscillations in the single-cell calcium activity, a second shuffling approach was considered (for single-cell data shuffling procedures see ‘Autocorrelations and spectral analysis of single-cell calcium activity’). A shuffled matrix of calcium activity was built by temporally shuffling the calcium activity of each cell separately. For each cell, each time bin of the calcium activity was assigned a random ordinal integer without repetition across time bins, where <italic>T</italic> is the total number of time bins (bin size = 129 ms), and time bins were ordered according to their assigned number. The assignment of random ordinal integers was made separately for each cell, so that the obtained random orderings were not shared across cells. The PCA method was then applied to the shuffled matrix of calcium activity.</p>", "<title>Sortings are preserved when different portions of data are used for obtaining the sortings</title>", "<p id=\"Par68\">To determine whether using different portions of the session for sorting the neurons lead to different sortings, the PCA method was applied to: (i) all data within a session; (ii) the first half of the session; and (iii) the second half of the session. This procedure gave three sortings per session. Next, for each cell pair in a session the distance between the two cells in each of the three sortings was calculated. We illustrate this calculation with a toy example: if 5 neurons were recorded, and sorting (i) was: (1,4,5,2,3), the distance between cells 1 and 5 was 2, because those two cells were 2 positions apart in the sorting. The distance between cells 1 and 3 was 1 and not 4, however, because in the calculation of distances we took into account that the sorting mirrors the position of the cells in the ring, which has periodic boundary conditions.</p>", "<p id=\"Par69\">We next calculated the correlation between the distances in: sorting (i) versus sorting (ii), sorting (i) versus sorting (iii) and sorting (ii) versus sorting (iii). If sortings obtained with different portions of data preserve the ordering of the neurons, we would expect high correlation values. We compared the obtained correlation values with the 95th percentile of a shuffled distribution obtained by assigning, to each cell, a random position in each of the sortings.<list list-type=\"bullet\"><list-item><p id=\"Par70\">Sorting (i) versus sorting (ii): 15 of 15 oscillatory sessions (see ‘Oscillation score’) were above the cutoff of significance. Correlation values in experimental data ranged from 0.38 to 0.85. The 95th percentile of shuffled data ranged from 0.004 to 0.015 (<italic>n</italic> = 15 in both experimental and shuffled data).</p></list-item><list-item><p id=\"Par71\">Sorting (i) versus sorting (iii): 15 of 15 oscillatory sessions were above the cutoff of significance. Correlation values in experimental data ranged from 0.52 to 0.86. The 95th percentile of shuffled data ranged from 0.005 to 0.013 (<italic>n</italic> = 15 in both experimental and shuffled data).</p></list-item><list-item><p id=\"Par72\">Sorting (ii) versus sorting (iii): 15 of 15 oscillatory sessions were above the cutoff of significance. Correlation values in experimental data ranged from 0.17 to 0.53. The 95th percentile of shuffled data ranged from 0.005 to 0.013 (<italic>n</italic> = 15 in both experimental and shuffled data).</p></list-item></list></p>", "<p id=\"Par73\">The high correlation values obtained provide support for what is illustrated in Extended Data Fig. ##FIG##8##4e##: using different portions of data for sorting the cells unveils the same dynamics.</p>", "<title>Sorting methods based on non-linear dimensionality reduction techniques</title>", "<p id=\"Par74\">The PCA method for sorting cells relies on a two-dimensional linear embedding. This linear embedding might not be optimal if the population vectors describe temporal trajectories that, despite being low-dimensional, lie on a curved surface. To take into account potential non-linearities, four additional sorting methods were implemented, based on the following non-linear dimensionality reduction techniques<sup>##UREF##6##63##</sup>: <italic>t</italic>-distributed stochastic neighbour embedding (<italic>t</italic>-SNE), LEM, Isomap and uniform manifold approximation and projection (UMAP)<sup>##UREF##7##64##</sup> (see parameters below). First, to express in the sortings the ordering of the cells during the slow temporal progression of the sequences, the four methods used a resampled matrix of calcium activity as input. To compute this matrix, for each session, we downsampled each calcium activity by a factor 4 by calculating its mean in bins of 0.52 s. The calcium activity of all cells was then smoothed by convolving them with a gaussian kernel whose width was given by the oscillation bin size (see ‘Oscillation bin size’). After applying <italic>t</italic>-SNE, LEM, Isomap or UMAP to the resampled matrix of calcium activity, we kept the first two dimensions obtained with each method, for the same reasons as presented for the PCA sorting method. To obtain the sorting, the following procedure was applied: We let Dim1 and Dim2 be the first two dimensions obtained with the chosen dimensionality reduction technique that we had applied to the resampled matrix. In analogy with the PCA method, the Dim1–Dim2 plane was spanned by Dim1 and Dim2 and for each cell the components on those dimensions defined a vector in this plane for which the angle with respect to the axis of Dim1 was computed. Cells were then sorted according to their angles in a descending manner.</p>", "<p id=\"Par75\">To apply <italic>t</italic>-SNE to the population activity we used a perplexity value of 50. First, we applied PCA to the resampled matrix of calcium activity, and then we used the projection of the neural activity onto the first 50 principal components as input to <italic>t</italic>-SNE. To apply LEM to the population activity, we used as hyperparameters <italic>k</italic> = 15 and <italic>σ</italic> = 2. Similarly, we used <italic>k</italic> = 15 for running isomap. Finally, we used n_neighbors=30, min_dist=0.3 and correlation as metric for running UMAP.</p>", "<p id=\"Par76\">We used the MATLAB implementation of UMAP<sup>##UREF##8##65##</sup> and the Matlab Toolbox for Dimensionality Reduction (<ext-link ext-link-type=\"uri\" xlink:href=\"https://lvdmaaten.github.io/drtoolbox/\">https://lvdmaaten.github.io/drtoolbox/</ext-link>). Finally, when displaying the raster plots that resulted from the different sortings, the first cell (located at the bottom of the raster plot) was always the same. This was accomplished by circularly shifting the cells in the different sortings such that the initial cell in all sortings coincided with the initial cell of the sorting obtained with the PCA method.</p>", "<title>Manifold visualization for MEC sessions</title>", "<p id=\"Par77\">Sorting the cells and visualizing their combined neural activity through raster plots revealed the presence of oscillatory sequences of neural activity in the recorded data. To visualize the topology of the manifold underlying the oscillatory sequences of activity, both PCA and LEM were used.</p>", "<p id=\"Par78\">PCA was applied to the matrix of calcium activity, which first had each row convolved with a gaussian kernel of width equal to four times the oscillation bin size (see ‘Oscillation bin size’). The manifold was visualized by plotting the neural activity projected onto the embedding defined by PC1 and PC2. In Fig. ##FIG##1##2c## (left) the neural activity of the entire session was projected onto the low-dimensional embedding. In Extended Data Fig. ##FIG##8##4c##, the neural activity corresponding to the concatenated epochs of uninterrupted oscillatory sequences was projected onto the embedding.</p>", "<p id=\"Par79\">For the LEM approach, first PCA was applied to the matrix of calcium activity, which was previously resampled to bins of 0.52 s as in ‘Sorting methods based on non-linear dimensionality reduction techniques’, and the first five principal components were kept. Next LEM was applied to the matrix composed of the 5 principal components, using as parameters <italic>k</italic> = 15 and <italic>σ</italic> = 2. We decided to keep 5 principal components prior to applying LEM to denoise the data, for which we leveraged the fact that sequences of activity constitute low-dimensional dynamics with intrinsic dimensionality equal to 1, and therefore truncating the data to the first 5 principal components should preserve the sequential activity. The manifold was visualized by plotting the neural activity projected onto the embedding defined by the first two LEM dimensions. In Fig. ##FIG##1##2c## (right) the neural activity of the entire session was projected onto the embedding.</p>", "<p id=\"Par80\">Both approaches revealed a ring-shaped manifold along which the population activity propagated repeatedly with periodic boundary conditions. One sequence was equivalent to one full turn of the population activity along the ring-shaped manifold. Finally, we note that when using PCA for visualizing the manifold, in some sessions the ring was less evident (Extended Data Fig. ##FIG##8##4c##). This is because the population activity had more variations from sequence to sequence, which resulted on the rings that corresponded to each sequence not completely overlapping in the PC1 versus PC2 plane. While recovering rings with PCA is challenging due to PCA being a linear method, using a non-linear method would have helped in visualizing the ring (as in Fig. ##FIG##1##2c##, right), but we decided not to do this for all quantifications because non-linear methods require more fine tuning and are usually harder to interpret.</p>", "<title>Phase of the oscillation</title>", "<p id=\"Par81\">To track the progression of the population activity over time, we leveraged the low dimensionality of the ring-shaped manifold and the circular nature of the population activity, and parametrized the population activity with a single time-dependent parameter, which we called the phase of the oscillation. Hence, the phase of the oscillation varied as a function of time (bin size = 129 ms) and tracked the progression of the neural population activity during the oscillatory sequences. The neural activity was projected onto a two-dimensional plane using PCA. The use of PCA avoided the selection of hyperparameters, which is required in all non-linear dimensionality reduction techniques including LEM. Let be the projection of the neural population activity onto principal component <italic>i</italic> (PC<italic>i</italic>). The neural population activity at time point <italic>t</italic> projected onto the plane defined by PC1 and PC2 is then given by (), which defines a vector in this plane. The phase of the oscillation is defined as the angle of this vector with respect to the PC1 axis and is given by</p>", "<p id=\"Par82\">During one sequence, the phase of the oscillation continuously traversed the range rad, which was consistent with the population activity propagating through the network and describing one turn along the ring-shaped manifold. The repetitive and almost linear dependence between the phase of the oscillation and time illustrates how stereotyped the sequences were (Fig. ##FIG##1##2d##).</p>", "<p id=\"Par83\">We note that the quantity is always defined, regardless of whether the session is or is not classified as oscillatory. In the case of the oscillatory sessions, tracks the progression of the oscillatory sequences.</p>", "<title>Joint distribution of cross-correlation time lag and angular distance in the PCA sorting</title>", "<p id=\"Par84\">To further characterize the sequential activation in the MEC neural population and to introduce a score that would determine the extent to which a session exhibited oscillatory sequences (see ‘Oscillation score’), we determined the relationship between the time lags that maximized the cross-correlation between the calcium activity of two cells (<italic>τ</italic>) and their angular distances in the PCA sorting (<italic>d</italic>). In the plane generated by PC1 and PC2, the loadings of each neuron defined a vector, for which we computed the angle , 1 ≤ <italic>i</italic> <italic>≤</italic> <italic>N</italic>, with respect to the axis of PC1, where is the loading of cell <italic>i</italic> on PC<italic>j</italic> and <italic>N</italic> is the total number of recorded neurons (see ‘Correlation and PCA sorting methods’). The angular distance <italic>d</italic> between any two cells in the PCA sorting was calculated as the difference between their angles wrapped in the interval (see Extended Data Fig. ##FIG##9##5b##, left),where . The Matlab function angdiff was used for computing this distance. Note that the angular distance maps how far apart two cells are in the raster plot when cells are sorted according to the PCA method.</p>", "<p id=\"Par85\">To estimate the joint distribution of cross-correlation time lags and angular distances in the PCA sorting, the cross correlations between all pairs of cells were calculated using a maximum time lag of 248 s. For each cell pair the time lag at which the cross-correlation peaked (<italic>τ</italic>) and the angular distance in the PCA sorting (<italic>d</italic>) were calculated. A discrete representation was used for these two variables: in all analyses, and unless stated otherwise, the range of possible <italic>τ</italic> values—that is, [−248,248] s—was discretized into 96 bins of size and the range of possible <italic>d</italic> values—that is, [−π, π) rad—was discretized into 11 bins of size . Using those bins, the joint distribution of <italic>τ</italic> and <italic>d</italic> was expressed as a two-dimensional histogram that counted the number of cell pairs observed for every combination of <italic>τ</italic> bins and <italic>d</italic> bins, normalized by the total number of cell pairs.</p>", "<p id=\"Par86\">An example of joint distribution of cross-correlation time lags and angular distances in the PCA sorting is presented in Extended Data Fig. ##FIG##9##5b##, right, built on the example session shown in Fig. ##FIG##1##2a##. In sessions with clear periodic sequences, the time lag <italic>τ</italic> increased with the distance <italic>d</italic>. This dependence was observed a discrete number of times in each session, which indicated that cells were active periodically and at a fixed frequency or at an integer multiple of it (see Extended Data Fig. ##FIG##9##5c##, top for another example with a different time scale). In sessions without detectable periodic sequences such structure was not observed (Extended Data Fig. ##FIG##9##5c##, bottom).</p>", "<title>Oscillation score</title>", "<p id=\"Par87\">While striking oscillatory sequences were observed in multiple sessions and mice, the population activity exhibited considerable variability, ranging from non-patterned activity to highly stereotypic and periodic sequences (Extended Data Fig. ##FIG##9##5a##). This variability prompted us to quantify, for each session, the extent to which the population activity was oscillatory, which we did by computing an oscillation score. For each session, we first calculated the phase of the oscillation (bin size = 129 ms, equation (##FORMU##11##1##)), which tracks the progression of the population activity in the presence of oscillatory sequences (see ‘Phase of the oscillation’ and Fig. ##FIG##1##2d##). Next the PSD of was calculated using Welch’s method with Hamming windows of 17.6 min (8,192 bins of 129 ms in each window) and 50% of overlap between consecutive windows (pwelch Matlab function, see ‘Autocorrelations and spectral analysis of single-cell calcium activity’). If the PSD peaked at 0 Hz and the PSD was strictly decreasing, the phase of the oscillation was not oscillatory and hence the population activity was not periodic in the analysed session. In this case the oscillation score was set to zero. Otherwise, prominent peaks in the PSD at a frequency larger than 0 Hz were identified. In order to disentangle large-amplitude peaks from small fluctuations in the PSD, a peak at frequency <italic>f</italic><sub>max</sub> was considered prominent and indicative of periodic activity if its amplitude was larger than (1) 9 times the mean of the tail of the PSD (that is, <PSD(<italic>f</italic> > <italic>f</italic><sub>max</sub>)>, where <<italic>x</italic>> indicates the average over frequencies <italic>x</italic>) and (2) 9 times the minimum of the PSD between 0 Hz and <italic>f</italic><sub>max</sub> (that is, min(PSD(<italic>f</italic> < <italic>f</italic><sub>max</sub>))). If no peak in the PSD met these criteria the oscillation score was set to zero. Otherwise, the presence of a prominent peak in the PSD calculated on was considered indicative of periodic activity at the population level. Yet a crucial component for observing oscillatory sequences is that cells fire periodically and that the time lag that maximizes the cross correlations between the calcium activity of pairs of cells that are located at a fixed distance in the sequence comes in integer multiples of a minimum time lag, which ensures that cells oscillate at a fixed frequency and that the calcium activity of one cell is temporally shifted with respect to the other. To quantify the extent to which these features were present in the data, we computed the joint distribution of time lags and angular distance in the PCA sorting (<italic>τ</italic> was discretized into 240 bins and <italic>d</italic> was discretized into 11 bins, see ‘Joint distribution of cross-correlation time lag and angular distance in the PCA sorting’). Next for each bin <italic>i</italic> of <italic>d</italic>, , we calculated the PSD of the distribution of <italic>τ</italic> conditioned on the distance bin <italic>i</italic> (Welch’s methods, Hamming windows of 128 <italic>τ</italic> bins with 50% overlap between consecutive windows, pwelch Matlab function). The presence of a peak in this signal indicated that for bin <italic>i</italic> of <italic>d</italic>, the time lag that maximizes the cross correlations between cells was oscillatory (that is, it peaked at multiples of one specific time lag), as expected when cells are active periodically with an approximately fixed frequency and also with harmonics of the primary frequency (see example joint distribution in Extended Data Fig. ##FIG##9##5b##, right). The presence (or absence) of a peak that satisfied the condition of being larger than (1) 10 times the mean of the tail of the PSD (same definition as above), and (2) 4.5 times larger than the minimum between 0 Hz and the frequency at which the PSD peaked, was identified (same definition as above, the parameters are different from the ones used above because the signals are very different). The oscillation score was then calculated as the fraction of angular distance bins for which a peak was identified.</p>", "<p id=\"Par88\">Based on the bimodal distribution of oscillation scores obtained in the calcium imaging data from MEC (Extended Data Fig. ##FIG##9##5d##), a session was considered to express oscillatory sequences if the oscillation score was ≥0.72. This cutoff (0.72) corresponded to the smallest oscillation score within the group with high scores (shown in green in Extended Data Fig. ##FIG##9##5d##). Note that because the distribution of oscillation scores was bimodal any other choice of threshold between 0.27 and 0.72 would have led to the same results. Using as cutoff 0.72 was also equivalent to asking that at least 8 out of the 11 distributions of <italic>τ</italic> conditioned on bin <italic>i</italic> of <italic>d</italic>, , had a significant peak in their PSD, which accounted for the fact that for distances in the PCA sorting that are close to zero, cells exhibit instantaneous co-activity rather than co-activity shifted by some specific time lag, which makes the conditional probability not oscillatory. After applying the cutoff, 15 of 27 calcium imaging sessions in MEC in 5 mice were classified as oscillatory (Extended Data Fig. ##FIG##9##5d##, shown in green), and among those 15 sessions, 10 were recorded with synchronized behavioural tracking (see ‘Self-paced running behaviour under sensory-minimized conditions’). The number of recorded cells in the calcium imaging oscillatory sessions ranged from 207 to 520. In the rest of the calcium imaging data, 0 of 25 PaS sessions in 4 mice were classified as oscillatory, and 0 of 19 VIS sessions in 3 mice were classified as oscillatory.</p>", "<title>Oscillation bin size</title>", "<p id=\"Par89\">The oscillatory sequences progressed at frequencies <0.1 Hz that varied from session to session. The oscillation bin size was a temporal bin size representative of the time scale of the oscillatory sequences in each session. It was used to quantify single-cell and neural population dynamics, for which describing the neural activity at the right time scale was fundamental (for example, see ‘Transition probabilities’). For each oscillatory session the period of the oscillatory sequences, denoted by <italic>P</italic><sub>osc</sub>, was calculated as the inverse of the frequency <italic>f</italic><sub>max</sub> at which the PSD of the signal peaked (see equation (##FORMU##11##1##) and ‘Oscillation score’), that is, . Note that this estimate of the period was reliable when during most of the session the network engaged in the oscillatory sequences, in which case the estimate was equivalent to the length of the session divided by the total number of sequences. However, it became less reliable the more interrupted the oscillatory sequences were.</p>", "<p id=\"Par90\">The oscillation bin size was computed as the period of the oscillatory sequences divided by 10,</p>", "<p id=\"Par91\">This choice of bin size was made so that each sequence would progress across ∼10 time points. Across 15 oscillatory sessions, the oscillation bin size ranged from 3 to 17 s (see Extended Data Fig. ##FIG##13##9d##).</p>", "<p id=\"Par92\">In sessions without oscillatory sequences, there was not a well-defined peak in the PSD of , and therefore the oscillation bin size was not possible or meaningful to calculate. Yet, to perform the quantifications of network dynamics at temporal scales similar to the ones investigated in oscillatory sessions, the mean oscillation bin size computed across all oscillatory sessions was used (mean oscillation bin size = 8.5 s).</p>", "<p id=\"Par93\">Unless otherwise indicated, the utilized bin size was 129 ms.</p>", "<title>Identification of individual sequences</title>", "<p id=\"Par94\">The characterization of the oscillatory sequences required multiple analyses that relied on identifying individual sequences, for example to quantify the duration of the sequences and their variability. The procedure for identifying individual sequences was based on finding the time points at which each sequence began (visualized typically at the bottom of the raster plot) and ended (visualized typically at the top of the raster plot, see Extended Data Fig. ##FIG##10##6a##). Note that the beginning and the end of the sequence are arbitrary because of the periodic boundary conditions in the sequence progression, and therefore a different pair of phases that are 2π apart could have been used for defining the beginning and the end of the sequence.</p>", "<p id=\"Par95\">One sequence was equivalent to one full turn of the population activity around the ring-shaped manifold—that is, during one sequence the phase of the oscillation traversed 2π (see ‘Phase of the oscillation’). To calculate the phase of the oscillation and determine the time epochs during which it traversed 2π, we smoothed the calcium activity of all cells (bin size = 129 ms) using a gaussian kernel of width equal to the oscillation bin size. Next, the phase of the oscillation was calculated and discretized into 10 bins (that is, the range was discretized into 10 bins). Time points at which the phase of the oscillation belonged to a bin that was 3 or more bins away from the bin in the previous time point were considered as discontinuity points and were used to define the beginning and the end of putative sequences. Putative sequences were classified as sequences if the phase of the oscillation smoothly traversed the range rad in an ascending manner. To account for variability, decrements of up to 1 bin of the phase of the oscillation were allowed. This means that there could be fluctuations of up to 0.6 rad in the phase within one individual sequence, and still be considered a sequence. Points of sustained activity were disregarded. Segments of sequences in which the phase of the oscillation covered at least 5 bins (that is, 50% or more of the range rad) were also identified.</p>", "<title>Sequence duration, sequence frequency and ISI</title>", "<p id=\"Par96\">The duration of individual sequences was defined as the amount of time that it takes the phase of the oscillation to cover the range in a smooth and increasing manner, which is consistent with the population activity completing one full turn along the ring-shaped manifold. To calculate the sequence duration, the time interval between the beginning and the end of the sequence was determined (see ‘Identification of individual sequences’).</p>", "<p id=\"Par97\">To quantify the variability in sequence duration within and between sessions, two approaches were adopted. In approach 1 (Extended Data Fig. ##FIG##10##6f## left), the s.d. of sequence durations was computed for each oscillatory session. To estimate significance, in each of 500 iterations all sequences across 15 oscillatory sessions were pooled (421 sequences in total) and randomly assigned to each session while keeping the original number of sequences per session unchanged. For each iteration the s.d. of the sequence durations randomly assigned to each session was calculated. In approach 2 (Extended Data Fig. ##FIG##10##6f##, right), for each session <italic>i</italic>, 1 ≤ <italic>i</italic> <italic>≤</italic> 15, where 15 is the total number of oscillatory sessions, we considered all pairs of sequences within session <italic>i</italic> (within session group) or alternatively all pairs of sequences such that one sequence belongs to session <italic>i</italic> and the other sequence to session (between session group). For each sequence pair in each group, the ratio between the shortest sequence duration and the longest sequence duration was calculated. The mean was computed over pairs of sequences in each group for each session separately. Notice that the larger this ratio the more similar the sequence durations are.</p>", "<p id=\"Par98\">The sequence frequency was calculated as the total number of identified individual sequences in a session, divided by the total amount of time the network engaged in the oscillatory sequences during the session, which was computed as the length of the temporal window of concatenated sequences.</p>", "<p id=\"Par99\">The ISI was defined as the length of the epoch from the termination of one sequence and the beginning of the next one. In other words, the ISI was calculated as the amount of time that elapsed between the time point at which the phase of the oscillation reached π (after completing one turn along the ring-shaped manifold), and the time point at which it is equal to −π (prior to initiating the next turn along the ring).</p>", "<title>Mean event rate during segments of the sequences</title>", "<p id=\"Par100\">To determine how population activity varied during individual sequences (Extended Data Fig. ##FIG##10##6c##), the following approach was adopted. For each oscillatory session (see ‘Oscillation score’) all individual sequences were identified (see ‘Identification of individual sequences’). Each sequence was divided into ten segments of equal length. For each sequence segment, the mean event rate was calculated as the total number of calcium events across cells divided by sequence segment duration and number of cells. For each session the mean event rate per segment was calculated over sequences. Across sessions we found that the percentage rate change from the segment with the minimum event rate to the segment with the maximum rate was no more than 18% (Extended Data Fig. ##FIG##10##6c##).</p>", "<title>Analysis of Neuropixels data</title>", "<p id=\"Par101\">Neuropixels data was different from the calcium imaging data in that it consisted of spike times and not calcium traces. Despite this fundamental difference, for most of the analyses we applied the same methods to both datasets. When this was not possible (see below), we tried to minimize the differences between the two analyses pipelines.</p>", "<title>Spike matrices</title>", "<p id=\"Par102\">In order to create arrays that were similar to the matrices of calcium activity, for each recorded unit a spike train was built using a bin size of 120 ms (similar to the bin size used in calcium imaging data, 129 ms). Each time bin contained the number of spikes produced by the recorded unit in that bin. Spike matrices were built by stacking the spike trains of all recorded units (469 units in the example session presented in Fig. ##FIG##1##2f##, 410 units in the example session shown in Extended Data Fig. ##FIG##8##4g##).</p>", "<p id=\"Par103\">Calcium traces are temporally correlated due to the slow dynamics of the calcium indicator. In addition, the observed periodic sequences unfolded over a time scale of minutes. To take these two factors into account, we smoothed the spike train of each recorded unit with a Gaussian kernel of width equal to 5 s.</p>", "<p id=\"Par104\">Both the original spike matrix and the smoothed spike matrix were then binarized using, for each spike train, a threshold equal to the mean plus either 1 or 1.5 times the s.d. (1 for smoothed matrices; 1.5 for non-smoothed matrices; as a reference, the threshold for binarization used in calcium data was the mean plus 1.5 times the s.d.; see ‘Binary deconvolved calcium activity and matrix of calcium activity’).</p>", "<p id=\"Par105\">In the calcium imaging experiment, it took approximately 5 min to initiate the recording after the mouse was positioned on the wheel (mainly due to the time that was needed to find the imaging planes). In the Neuropixels data there was no such delay between positioning the mice on the wheel and starting the data acquisition. In order to make both datasets as comparable as possible, and in order to remove any effects due to arousal, the first 5 min of the Neuropixels sessions were discarded.</p>", "<title>Autocorrelation and spectral analysis</title>", "<p id=\"Par106\">The autocorrelations were calculated on the spike trains (without smoothing), and the PSD was calculated on the autocorrelations. Methods and parameters used for calculating the autocorrelation and PSDs were the same as in calcium imaging data (‘Autocorrelations and spectral analysis of single-cell calcium activity’).</p>", "<title>Calculation of oscillation score</title>", "<p id=\"Par107\">As in the calcium imaging data, in order to quantify the amount of oscillatory activity in the Neuropixels sessions, an oscillation score was computed. Because in the Neuropixels recordings (unlike in the calcium imaging data) there were some long periods of non-sequence activity between bouts of periodic sequences, possibly due to small differences in training protocol, we computed the oscillation score not on the full spike matrix but on the matrix of concatenated sequences (built by identifying all individual sequences in the smoothed spike matrix and concatenating them as described for the calcium imaging data in ‘Identification of individual sequences’ and ‘Sequence duration, sequence frequency and ISI’ above).</p>", "<title>Sorting calculation and raster plot visualization</title>", "<p id=\"Par108\">Neural population activity was visualized by means of raster plots, for which units were sorted using the PCA method (‘Correlation and PCA sorting methods’). The sorting was calculated on the smoothed spike matrix (Fig. ##FIG##1##2f## and Extended Data Fig. ##FIG##8##4g##, top), and the obtained sorting was applied also to the non-smoothed spike matrices (Extended Data Fig. ##FIG##8##4f,g##, bottom).</p>", "<p id=\"Par109\">While the sorting and visualization of neural population activity were performed as we did in calcium imaging data, there was one difference in how the two datasets were analysed. Because in the Neuropixels data the periodic sequences were more salient in some subsets of the sessions than others, for visualization purposes we calculated the sorting on a subset of the smoothed transition matrices. Those subsets are given by [1,200, 1,700] s for the example session of mouse no. 104368 (Fig. ##FIG##1##2f##) and [1,100, 1,400] s for the example session of mouse no. 102335 (Extended Data Fig. ##FIG##8##4g##). Note, however, that sequences were identified outside these session subsets too, indicating that the sorting unveils stereotyped sequences also outside the used subsets of data (see ‘Sortings are preserved when different portions of data are used for obtaining the sortings’).</p>", "<title>Locking to the phase of the oscillation</title>", "<p id=\"Par110\">To calculate the extent to which individual cells in the calcium imaging experiments were tuned to the oscillatory sequences, two quantities were used: the locking degree and the mutual information between the calcium event counts and the phase of the oscillation. For each oscillatory session, the phase of the oscillation was computed (see equation (##FORMU##11##1##)) and individual sequences were identified (see ‘Identification of individual sequences’). Next, the time points that corresponded to all individual sequences in one session were concatenated, which generated a new signal with the phase of the oscillation for all consecutive sequences, and a new matrix of calcium activity in which the network engaged in the oscillatory sequences uninterruptedly.</p>", "<p id=\"Par111\">The locking degree was computed for each cell as the mean resultant vector length over the phases of the oscillatory sequences at which the calcium events occurred (bin size = 129 ms, function circ_r from the Circular Statistics Toolbox for Matlab<sup>##UREF##9##66##</sup>). The locking degree has a lower bound of 0 and upper bound of 1. It is equal to 1 if all oscillation phases at which the calcium events occurred are the same (that is, perfect locking), and equal to zero if all phases at which the calcium events occurred are evenly distributed (total absence of locking). To estimate significance, for each cell a null distribution of locking degrees was built by temporally shuffling the calcium activity of that cell 1,000 times while the phase of the oscillation remained unchanged, and by computing, for each shuffle realization, the locking degree (shuffling was performed as in ‘Sorting of temporally shuffled data’). The 99th percentile of the estimated null distribution was used as a threshold for significance.</p>", "<p id=\"Par112\">In order to assess the robustness of the locking degree, the obtained results were compared with a second measure based on information theory<sup>##UREF##10##67##</sup>: the mutual information between the counts of calcium events (event counts) and the phase of the oscillation (bin size = 0.52 s). To estimate the reduction in uncertainty about the phase of the oscillation (<italic>P</italic>) given the event counts of the calcium activity (<italic>S</italic>), Shannon’s mutual information was computed as follows<sup>##UREF##11##68##</sup>:where is the joint probability of observing a phase of the oscillation <italic>p</italic> and an event count <italic>s</italic>, is the marginal probability of event counts and is the marginal probability of the phase of the oscillation. All probability distributions were estimated from the data using discrete representations of the phase of the oscillation and the event counts. The event counts were partitioned into <italic>s</italic><sub>max</sub> + 1 bins to account for the absence of event counts as well as all possible event counts, where <italic>s</italic><sub>max</sub> is the maximum number of event counts per cell in a 0.52 s bin, and the phase of the oscillation was discretized into 10 bins of size .</p>", "<p id=\"Par113\">The mutual information is a non-negative quantity that is equal to zero only when the two variables are independent—that is, when the joint probability is equal to the product of the marginals . However, limited sampling can lead to an overestimation in the mutual information in the form of a bias<sup>##REF##17615128##69##</sup>. In order to correct for this bias, the calcium activity was temporally shuffled (as in ‘Sorting of temporally shuffled data’) and the mutual information between the event counts of the shuffled calcium activity and the phase of the oscillation, which remained unchanged, was calculated. This procedure, which destroyed the pairing between event counts and phase of the oscillation, was repeated 1,000 times and the average mutual information across the 1,000 iterations was computed and used as an estimation of the bias in the mutual information calculation. In the right panel of Fig. ##FIG##2##3a##, we report both the mutual information and the bias. In Extended Data Fig. ##FIG##11##7a##, the corrected mutual information was reported (MI<sub>c</sub>), where the bias (⟨MI<sub>sh</sub>⟩<sub>iterations</sub>) was subtracted out from the Shannon’s mutual information (MI): MI<sub>c</sub> <italic>=</italic> MI − ⟨MI<sub>sh</sub>⟩<sub>iterations</sub>.</p>", "<p id=\"Par114\">Note that the locking degree and the mutual information between the event counts and the phase of the oscillation yielded consistent results (see Fig. ##FIG##2##3a## and Extended Data Fig. ##FIG##11##7a##).</p>", "<title>Tuning of single cells to the phase of the oscillation</title>", "<p id=\"Par115\">The selectivity of each cell to the phase of the oscillation in the calcium imaging data was visualized through tuning curves and quantified through their preferred phase. As in the analysis of ‘Locking to the phase of the oscillation’, the phase of the oscillation was computed, individual sequences were identified, and the time points of the phase of the oscillation and the matrix of calcium activity that corresponded to all individual sequences in one session were concatenated.</p>", "<title>Tuning curves</title>", "<p id=\"Par116\">The range of phases rad was partitioned into 40 bins of size rad. For each cell the tuning curve in the phase bin <italic>j</italic>, <italic>j</italic> = 0<italic>,…</italic>,39, was calculated as the total number of event counts that occurred at phases within the range divided by the total number of event counts during the concatenated oscillatory sequences.</p>", "<title>Preferred phases</title>", "<p id=\"Par117\">The preferred phase of each cell was calculated as the circular mean over the oscillation phases at which the calcium events occurred (function circ_mean from the Circular Statistics Toolbox for Matlab<sup>##UREF##9##66##</sup>). In most of the analysis the preferred phase was calculated, for each cell, after concatenating all sequences. However, in a subset of analyses (see ‘Anatomical distribution of preferred phases’), the preferred phase was also calculated for individual sequences, as the circular mean over the oscillation phases at which the calcium events occurred in each sequence.</p>", "<p id=\"Par118\">Unless otherwise stated, the preferred phase refers to the calculation performed on concatenated sequences (and not on individual sequences).</p>", "<title>Distribution of preferred phases</title>", "<p id=\"Par119\">To determine the extent to which the preferred phases across locked cells were uniformly distributed in one recorded session, the distribution of the cells’ preferred phases, that we shall denote <italic>Q</italic>, was estimated by discretizing the preferred phases into 10 bins of size rad. The entropy of this distribution was calculated and used to compute the entropy ratio <italic>H</italic><sub>ratio</sub> which quantifies how much <italic>Q</italic> departs from a flat distribution:where is the entropy of a flat distribution using 10 bins—that is, bits. The closer is to 1 the flatter <italic>Q</italic> is, and therefore all preferred phases tend to be equally represented. The smaller is, the more uneven <italic>Q</italic> is and some preferred phases tend to be more represented than others.</p>", "<p id=\"Par120\">To estimate significance, for each session the procedure for calculating was repeated for 1,000 iterations of a shuffling procedure where the preferred phase of the cells was calculated after the values of the phase of the oscillation were temporally shuffled. In Extended Data Fig. ##FIG##11##7c##, both panels, for each session the 1,000 shuffle realizations were averaged.</p>", "<title>Participation index</title>", "<p id=\"Par121\">The Participation Index (PI) quantifies the extent to which a cell’s calcium events were distributed across all sequences, or rather concentrated in a few sequences. For neurons that were active only in a few sequences the participation index was small (participation index ∼ 0), and for neurons that were reliably active during most of the sequences the participation index was high (participation index ∼ 1; Extended Data Fig. ##FIG##11##7g## shows three example neurons of the session in Fig. ##FIG##1##2a##).</p>", "<p id=\"Par122\">The participation index was calculated for each cell separately as the fraction of sequences needed to account for 90% of the total number of calcium events. To compute the participation, individual sequences were identified (see ‘Identification of individual sequences’), and for each cell the number of calcium events per sequence was calculated and normalized by the total number of calcium events across all concatenated sequences, which yields the fraction of calcium events per sequence. This quantity was sorted in an ascending manner and its cumulative sum was calculated. The participation index is the minimum fraction of the total number of sequences for which the cumulative sum of the fraction of calcium events per sequence ≥0.9 (results remain unchanged when the cumulative sum is required to be ≥0.95).</p>", "<title>Relationship between tuning to the phase of the oscillation and single-cell oscillatory frequency</title>", "<p id=\"Par123\">To determine whether the frequency of oscillation of single-cell calcium activity was correlated with the extent to which the cell was locked and participated in the oscillatory sequences, for each cell the ratio between its oscillatory frequency (see ‘Autocorrelations and spectral analysis of single-cell calcium activity’) and the sequence frequency (see ‘Sequence duration, sequence frequency and ISI’) was calculated and denoted relative frequency. Next, for each session cells were divided into two groups: one group had cells with relative frequency ~1 (cells whose oscillatory frequencies were most similar to the sequence frequency), and the other group had cells with relative frequency ≠1 (cells whose oscillatory frequencies were most different from the sequence frequency). The size of each group was the same and was given by a percentage <italic>α</italic> of the total number of recorded cells in a session. For each group the locking degree (see ‘Locking to the phase of the oscillation’) and the participation index (see ‘Participation index’) were compared. For the quantification across all 15 oscillatory sessions, the mean locking degree and participation index were calculated for each group separately and for each session separately, and all 15 sessions were pooled. <italic>α</italic> varied from 5% to 50%.</p>", "<title>Anatomical distribution of preferred phases</title>", "<p id=\"Par124\">To determine whether the entorhinal oscillatory sequences resembled travelling waves, during which neural population activity moves progressively across anatomical space<sup>##REF##2035024##20##,##REF##19489117##21##,##REF##8240814##70##–##REF##29563572##74##</sup>, we took three complimentary approaches.</p>", "<title>Correlation between differences in preferred phase and anatomical distance</title>", "<title>Preferred phases calculated using data from the entire session (after concatenating individual sequences)</title>", "<p id=\"Par125\">For each of the 15 oscillatory sessions (across 5 mice) the Pearson correlation between the anatomical distance between cells in the FOV and the difference in their preferred phases (see ‘Tuning of single cells to the phase of the oscillation’) was calculated. In order not to count the same data twice, each correlation value was calculated using <italic>N</italic> × (N − 1)/2 samples (each sample was a cell pair), where <italic>N</italic> was the total number of cells recorded in the session. In the presence of travelling waves, a significant correlation between differences in preferred phase and anatomical distance between cells within the FOV is to be expected. To determine statistical significance the cells’ preferred phase were shuffled within the FOV 100 times, and for each shuffled realization the correlation values were calculated. Because we were interested in significant correlations, regardless of whether they were positive or negative, both in experimental and shuffled data we took the absolute value of the correlations. Next, the 95th percentile of the shuffled distribution (100 shuffled realizations per session) was used as cutoff for significance and compared with the correlation value in experimental data.</p>", "<p id=\"Par126\">In order to rule out that the small correlation values observed in experimental data could be masking a dependency such that for larger distances the differences in preferred phase increased in absolute value, the same calculations were repeated but now taking the absolute value of the difference in preferred phase. Statistical significance was determined as in the previous paragraph.</p>", "<title>Preferred phases calculated using data from individual sequences</title>", "<p id=\"Par127\">Travelling waves could still be present if they move in different directions from sequence to sequence. To test for the presence of travelling waves without assuming similar wave directions across successive sequences, the quantification of correlation between the difference in preferred phase as a function of pairwise anatomical distance was repeated for each sequence separately. To calculate the preferred phase of each cell in each sequence (see ‘Tuning of single cells to the phase of the oscillation’), the mean phase at which the calcium events occurred in that individual sequence was computed. In each sequence, only cells that had at least 5 calcium events were included in the analysis. This analysis was performed separately on 421 sequences across 15 oscillatory sessions. Similarly to the analysis described above, when sequences were concatenated within a session, the calculations were repeated after taking the absolute value of differences in preferred phase.</p>", "<p id=\"Par128\">Results are presented in Fig. ##FIG##2##3f,g##. In Fig. ##FIG##2##3f##, the correlation value was also non-significant when calculated using the absolute value of the differences in preferred phase (correlation = 0.0028, cutoff for significance of the correlation = 0.0146). In Fig. ##FIG##2##3g##, in the experimental data the absolute value of the correlations ranged from 6.4 × 10<sup>−6</sup> to 0.147 (<italic>n</italic> = 421). Out of 421 sequences, 27 were classified as significant when compared to the 95th percentile of a shuffled distribution (cutoffs ranged from 0.007 to 0.237, <italic>n</italic> = 421). The fraction 27/421 was slightly above a chance level of 0.05 (0.05 × 421 = 21 sequences), yet for those 27 sequences the correlation values were very low, ranging from 0.008 to 0.137.</p>", "<title>Calculation of local gradients of preferred phase</title>", "<p id=\"Par129\">Previous studies have investigated the presence of travelling waves by computing local anatomical gradients of the phase of the oscillation, when the phase is calculated through the Hilbert transform applied to the activity of each electrode (for example, ref. <sup>##REF##29887341##75##</sup>, Ecog data). In order to perform a similar analysis but applied to each sequence separately, two different approaches were taken.</p>", "<title>Similarity of preferred phases in spatial bins of the FOV</title>", "<p id=\"Par130\">First, the similarity in preferred phases of all cells within spatial bins of the FOV was used as a proxy for local gradients. The similarity in preferred phases was calculated as the mean vector length (MVL) of the distribution of preferred phases within each bin of the FOV. The analysis was performed for individual sequences separately.</p>", "<p id=\"Par131\">For each of the 15 oscillatory sessions (over 5 mice), the FOV was divided into spatial bins of 100 μm x 100 μm (6 × 6 bins in 10 sessions, 10 × 10 bins in 5 sessions), or 200 μm x 200 μm (3 × 3 bins in 10 sessions, 5 × 5 bins in 5 sessions) (note that for 10 of the 15 oscillatory sessions the FOV was 600 μm x 600 μm, mice no. 60355, no. 60584, no. 60585; while for 5 of the 15 oscillatory sessions the FOV was 1,000 μm × 1,000 μm, mouse no. 59914; mouse no. 59911 did not show the oscillatory sequences). Next, the preferred phase of each cell per sequence was calculated (as we did in ‘Correlation between differences in preferred phase and anatomical distance’) and for each sequence and every spatial bin of the FOV the MVL was computed (only spatial bins with 10 or more cells were considered). If the MVL was 0, then all preferred phases in that bin were different and homogeneously distributed between −π and π, whereas if the MVL was 1 then all preferred phases were the same. In the presence of a travelling wave, each bin should have a high MVL value compared to chance levels. Statistical significance was determined by repeating the same MVL calculation after shuffling the cells’ preferred phases within the FOV 200 times, and using, for each spatial bin, a cutoff for significant of 95th percentile of the shuffled distribution. A non-significant fraction of spatial bins had a MVL value above the cutoff for significance.</p>", "<title>Differences in preferred phase among pairs of cells in small neighbourhoods of the spatial domain</title>", "<p id=\"Par132\">The analysis presented above is focused on the degree of similarity between preferred phases in spatial bins. In order to avoid small cell sample effects, and effects of adding a threshold number of cells for bins to be included when calculating similarity with the MVL measure above, we decided to also calculate the difference in preferred phases for all pairs of cells that were located within small neighbourhoods in the FOV, expecting that in the presence of travelling waves the differences in preferred phases of cell pairs within small neighbourhoods would be smaller than expected by chance. For each cell in the FOV, all other cells that were located within a circular neighbourhood of radius 50, 100 or 200 μm were identified and the differences in preferred phase between cell pairs within those areas were calculated. Next, for each sequence and each radius separately all phase differences were pooled, and the mean and the median of the obtained distributions were calculated. To determine significance, the preferred phases across all cells were shuffled 200 times and for each shuffled realization a distribution of differences in preferred phase was obtained and used to calculate the mean and median. Because in the presence of travelling waves smaller differences in preferred phases than in the shuffled data were expected, the mean and median calculated on experimental data were compared with the 5th percentile of the distribution of means and medians obtained from shuffled data. This comparison was performed for each sequence and each radius separately.</p>", "<title>Centre-of-mass calculation of the population activity</title>", "<p id=\"Par133\">To determine whether the population calcium activity was anatomically localized, as expected in the presence of travelling waves, we calculated its centre of mass (COM). First, all individual sequences were identified and the neural data was averaged in time bins of 5 s. We chose bins of 5 s because the sequences are very slow, however, results remain unchanged if bins of 1 s or 2 s are used instead. For each time point (bin size = 5 s) and for each sequence separately the COM of the population activity was calculated as:where <italic>N</italic> is the total number of recorded cells in the session, <bold>r</bold><sub><italic>i</italic></sub> is the position of neuron <italic>i</italic> in the FOV, <italic>m</italic><sub><italic>i</italic></sub> is the total number of calcium events of neuron <italic>i</italic> within the 5 s time bin, and . The COM was visualized for one example sequence both in experimental data, and after randomly shuffling the position of the cells within the FOV (Extended Data Fig. ##FIG##12##8d##). To quantify the temporal trajectory of the COM across individual sequences, we calculated the cumulative distance travelled by the COM as the sum of the distances travelled by the COM between consecutive time points (bin size = 5 s). The cumulative distance travelled calculated on experimental data was compared with the 5th and 95th percentile of a distribution built by shuffling the positions of the cells in the FOV 500 times.</p>", "<title>Procedure for merging steps</title>", "<p id=\"Par134\">In order to average out the variability observed in single cells at the level of locking degree and participation index while preserving the temporal properties of the oscillatory sequences, an iterative process that defines new variables from combining the calcium activity of cells was implemented for each session separately (Extended Data Fig. ##FIG##13##9a##). This process is similar to a coarse-graining approach<sup>##REF##31702278##76##</sup>.</p>", "<p id=\"Par135\">First, the <italic>N</italic> recorded cells in one session were sorted according to the PCA method. In the first iteration of the procedure, named merging step one, the calcium activity (see ‘Binary deconvolved calcium activity and matrix of calcium activity’) of pairs of cells that were positioned next to each other in the PCA sorting were added up (merging step 1 in Extended Data Fig. ##FIG##13##9a##). This resulted in new variables, which in merging step 2 were grouped together in pairs of adjacent variables by adding up their activity, which yielded new variables. Note that because in the PCA sorting cells whose activity is synchronous are positioned adjacent to each other, the new variables consist of groups of co-active cells.</p>", "<p id=\"Par136\">In general, merging step <italic>j</italic> generates variables by adding up the activity of pairs of variables from merging step <italic>j</italic> − 1,<italic> j</italic> <italic>></italic> 1, with each new variable defined as:where is the <italic>i</italic>th new variable that results from adding and , which were computed in the previous merging step, . In merging step 1, and are the calcium activity of cells in the position and , , in the sorting obtained with the PCA method.</p>", "<p id=\"Par137\">This procedure was repeated 6 times until ~10 variables were obtained in each session (the exact number of variables depended on the number of recorded cells, <italic>N</italic>, in each session). If <italic>N</italic> was an odd number, the last cell in the sorting obtained with the PCA method was discarded and the procedure was applied to the first <italic>N</italic> − 1 cells in the sorting. In every merging step the participation index (see ‘Participation index’) of each new variable was calculated (see Extended Data Fig. ##FIG##13##9b##).</p>", "<title>Division of cells into ensembles</title>", "<p id=\"Par138\">After 5 merging steps (and for approximately 10 variables), the participation index reached a plateau (Extended Data Fig. ##FIG##13##9b##). This motivated the decision to split the recorded cells into 10 variables, which we later used to quantify the population dynamics (see ‘Analysis of population dynamics using ensembles of co-active cells’). From now on we will refer to those variables as ensembles, to highlight the fact that cells in each ensemble are co-active. The same number of ensembles was used in sessions that did not exhibit oscillatory sequences.</p>", "<p id=\"Par139\">To distribute cells into 10 ensembles, cells were sorted according to the PCA method. If is an integer, where <italic>N</italic> is the total number of cells in one session, then each ensemble contains cells and the set of cells that belong to ensemble <italic>i</italic>, 1 ≤ <italic>i</italic> <italic>≤</italic> 10, is . If is not an integer then ensembles 1 to 9 contain cells and ensemble 10 contains cells, where and is the set of natural numbers. In this case the set of cells that belongs to each ensemble is:</p>", "<p id=\"Par140\">Note that each cell was assigned to only one ensemble.</p>", "<p id=\"Par141\">After each cell was assigned to one of the ten ensembles, the activity of each ensemble as a function of time was calculated as the mean calcium activity across cells in that ensemble.</p>", "<p id=\"Par142\">Finally, to calculate the oscillation frequency of ensemble activity, the PSD was calculated (Welch’s methods, 8.8 min Hamming window with 50% overlap between consecutive windows, pwelch Matlab function). The oscillation frequency was estimated as the frequency at which the PSD peaked. For each session, the oscillation frequency of the activity of the ensembles was compared to the sequence frequency, which was computed as the total number of sequences in the session divided by the amount of time the network engaged in the oscillatory sequences. The latter was calculated as the length of the temporal window of concatenated sequences (see ‘Identification of individual sequences’).</p>", "<title>Analysis of population dynamics using ensembles of co-active cells</title>", "<p id=\"Par143\">We adopted an ensemble approach to quantify the population dynamics (see ‘Procedure for merging steps’ and ‘Division of cells into ensembles’). With a total of 10 ensembles this approach averaged out the variability observed in single-cell locking degree and participation index while keeping the temporal progression of the oscillatory sequences (Extended Data Fig. ##FIG##13##9f##). In sessions with oscillatory sequences, all individual sequences were identified (see ‘Identification of individual sequences’) and the corresponding time bins were concatenated, which yielded a new matrix of calcium activity in which the oscillatory sequences were uninterrupted. Next, cells were divided into ensembles (see ‘Division of cells into ensembles’) and ensemble activity was downsampled using as bin size the oscillation bin size of the session (see ‘Oscillation bin size’). This procedure yielded a matrix, the ensemble matrix, with the activity of each ensemble corresponding to a single row (10 rows in total), and as many columns as time points sampled at the oscillation bin size. In non-oscillatory sessions, the full matrix of calcium activity was used and the temporal downsampling was conducted at the mean oscillation bin size computed across all 15 oscillatory sessions; that is, bin size = 8.5 s (see ‘Oscillation bin size’ for a description of the bin size used in non-oscillatory sessions). For both types of sessions (with and without oscillations), the activity of the 10 ensembles was described through a vector expressing, at each time point, the ensemble number with the highest activity at that time point (see Extended Data Fig. ##FIG##13##9e,f##). This vector was used to perform the following analyses: transition probabilities, probability of sequential activation of ensembles, and sequence score.</p>", "<title>Transition probabilities</title>", "<p id=\"Par144\">The transition probability from ensemble <italic>i</italic> to ensemble <italic>j</italic> was quantified as the number of times the transition was observed in the data of one session, normalized by the total number of transitions in one session. Transitions were identified from the vector that contained the ensemble number with maximum activity at each time point (transitions to the same ensemble between consecutive time points were disregarded). Transitions were allocated in a matrix of transition probabilities <italic>T</italic> of size 10 × 10, since 10 ensembles were used. In this matrix, the component expressed the transition probability from ensemble <italic>i</italic> to ensemble <italic>j</italic>.</p>", "<p id=\"Par145\">To establish statistical significance of the transition probabilities, the data was shuffled 500 times. In each shuffle realization, each row of the matrix of calcium activity (with concatenated sequences in the case of oscillatory sessions) was temporally shuffled (as in ‘Sorting of temporally shuffled data’), and the procedure for calculating the ensemble matrix and transition probabilities was applied to the shuffled data. For each transition, the 95th percentile of the shuffled distribution was used to define a cutoff.</p>", "<title>Probability of sequential activation of ensembles</title>", "<p id=\"Par146\">We calculated the probability of sequential ensemble activation according to the following procedure. From the vector expressing the ensemble number with the highest activity at each time point (sampled at the oscillation bin size), strictly increasing sequences of all possible lengths (from 2 to 10 ensembles) were identified. The number of ensembles in each sequence was the number of ensembles that were active in consecutive time points (epochs of sustained activity were disregarded). While the sequences had to be strictly increasing, they did not have to be continuous. Sequences could skip ensembles, in which case the maximum number of ensembles in one sequence was less than 10. The probability of the sequential activation of <italic>k</italic> ensembles, <italic>k</italic> = 2,…,10, was next estimated as the number of times a sequence of <italic>k</italic> ensembles was found, normalized by the total number of identified sequences. Note that all subsequences were also included in this estimation. For example, if the ensembles 1, 2 and 3 were active in consecutive time points, a sequence of three ensembles was identified, as well as three subsequences of two ensembles each: 1, 2, as well as 2, 3 and 1, 3.</p>", "<p id=\"Par147\">In order to test for significance, the shuffled data from ‘Transition probabilities’ was used. The procedure to compute the probability of sequential activation of ensembles was applied to each of the 500 shuffle realizations performed per session. Shuffled data was compared with recorded data.</p>", "<title>Sequence score</title>", "<p id=\"Par148\">The sequence score measures how sequential the ensemble activity is. It is calculated from the probability of sequential activation of ensembles as the probability of observing sequences of three or more ensembles. The sequence score was calculated for each session of the dataset separately. To determine if the obtained scores were significant, for each session the 500 shuffle realizations used in ‘Probability of sequential activation of ensembles’ for assessing significance of the probability of sequential activation of ensembles were used to calculate the sequence score on shuffled data. Those values were used to build a shuffled distribution, and the 99<sup>th</sup> percentile of this distribution was chosen as the threshold for significance.</p>", "<title>Estimation of number of completed laps on the wheel, speed and acceleration</title>", "<p id=\"Par149\">Features of the mouse’s behaviour were used to determine whether the MEC oscillatory sequences were modulated by running.</p>", "<p id=\"Par150\">The wheel had a radius of 8.54 cm (see ‘Self-paced running behaviour under sensory-minimized conditions’) and a perimeter of 53.66 cm. Therefore mice had to run for ∼53.7 cm to complete one lap on the wheel. For each session, we estimated the number of completed laps on the wheel from the position on the wheel recorded as a function of time. The number of completed laps during one sequence (see ‘Identification of individual sequences’) was calculated as the total distance run during the sequence divided by 53.7 cm.</p>", "<p id=\"Par151\">The speed of the mouse was numerically calculated as the first derivative of the position on the wheel as a function of time (the sampling frequency of the position was 40 Hz for mice 60355 (MEC), 60353, 60354 and 60356 (PaS). The sampling frequency was 50 Hz for mice 60584 and 60585 (MEC), 60961, 92227 and 92229 (VIS). For mice 59911, 59914 (MEC) and 59912 (PaS), the wheel tracking was not synchronized to the ongoing image acquisition; see ‘Self-paced running behaviour under sensory-minimized conditions’. The obtained speed signal from the former two groups of mice was interpolated so that the speed values matched the downsampled imaging time points (sampling frequency = 7.73 Hz), and smoothed using a square kernel of 2 s width. A threshold was applied such that all speed values that were smaller than 2 cm s<sup>−1</sup> were set to zero and all speed values larger than 2 cm s<sup>−1</sup> remained unchanged. We decided to threshold for immobility at a non-zero speed value (2 cm s<sup>−1</sup>) in order to avoid classifying as running behaviour frames that only had minor movements of the wheel (‘twitches’), which were detected when mice slightly moved on the wheel but did not fully engage in locomotion. The threshold that we used is consistent with the one used in other studies, as in ref. <sup>##REF##26494280##16##</sup>.</p>", "<p id=\"Par152\">The speed signal obtained after applying the threshold was used to define immobility (running) bouts as the set of consecutive time points (bin size = 129 ms) for which the speed was equal to (larger than) zero (a similar approach was used in ref. <sup>##REF##26494280##16##</sup>). We found that the median of velocities was 0 cm s<sup>−1</sup> when all velocity values across the 10 MEC oscillatory sessions (over 3 mice) for which we had imaging data synchronized with behavioural data were pooled. This is because for some of the sessions the mice were immobile for most of the session.</p>", "<p id=\"Par153\">When the threshold for immobility (2 cm s<sup>−1</sup>, see above) was discarded (that is, set to 0 cm s<sup>−1</sup>), the median was 1.3 cm s<sup>−1</sup>—that is, still very low. In the absence of a threshold, our main result, which is that the oscillatory sequences traverse epochs of running and immobility, remained the same (median of probability of sequences during running = 0.85; median of probability of sequences during immobility = 0.65; two sample Wilcoxon signed-rank test on the probability of sequences for running versus immobility, <italic>n</italic> = 10 oscillatory sessions over the 3 mice that had the tracking synchronized to imaging, <italic>P</italic> = 0.002, <italic>W</italic> = 55).</p>", "<p id=\"Par154\">The acceleration was numerically calculated as the first derivative of the speed signal. Notice that in this case no interpolation was needed.</p>", "<p id=\"Par155\">Because the available data did not have enough statistical power, it was not possible to compare the behaviour of the mice, for example in terms of its running speed and acceleration, between periods with and without ongoing oscillatory sequences.</p>", "<p id=\"Par156\">Finally, mice that were imaged from the PaS or VIS performed the same minimalistic self-paced running task as the mice that were imaged from the MEC recordings. The range of speed values in PaS or VIS mice across sessions = 0–58.6 cm s<sup>−1</sup> (PaS) or 0–60.3 cm s<sup>−1</sup> (VIS); median number of completed laps on rotating wheel in PaS or VIS mice across sessions = 145 (PaS) or 104 (VIS); maximum number of completed laps on rotating wheel in PaS or VIS mice across sessions = 502 (PaS) or 1,743 (VIS). These values are reported for MEC mice in the legend of Extended Data Fig. ##FIG##6##2a##.</p>", "<title>Estimation of the probability of observing oscillatory sequences</title>", "<p id=\"Par157\">To determine whether the MEC oscillatory sequences were observed during different behavioural states, the probability of observing the oscillatory sequences was calculated conditioned on whether the mouse was running or immobile. For each oscillatory session with behavioural tracking synchronized to the imaging data (10 sessions over 3 mice, see ‘Self-paced running behaviour under sensory-minimized conditions’ and ‘Oscillation score’), all individual sequences were identified (see ‘Identification of individual sequences’). The subset of time bins that belonged to individual sequences were extracted and labelled as oscillation (bin size = 129 ms). The fraction of bins labelled as oscillation bins was 0.73 ± 0.07 (mean ± s.e.m., n = 10 sessions). Next, a second label was assigned to the time bins depending on whether they occurred during running or immobility bouts (bins labelled ‘running’ or ‘immobility’, respectively, see ‘Estimation of number of completed laps on the wheel, speed and acceleration’). The fraction of bins labelled as running = 0.43 ± 0.09, mean ± s.e.m., <italic>n</italic> = 10 sessions. After applying this procedure, each time bin had two labels, one indicating the running behaviour, and one indicating the presence (or absence) of oscillatory sequences. To estimate the probability of observing the oscillatory sequences conditioned on the mouse’s running behaviour, all bins labelled as running or immobility were identified and from each subset, the fraction of bins labelled as oscillation was calculated. These probabilities were computed for each session separately.</p>", "<title>Sequences during immobility bouts of different lengths</title>", "<p id=\"Par158\">The oscillatory sequences occurred both during running and immobility bouts. To quantify the extent to which individual sequences progressed during different lengths of immobility bouts, the following procedure was adopted. First, for each session, all immobility bouts were identified and assigned to bins of different lengths (see ‘Estimation of number of completed laps on the wheel, speed and acceleration’; length bins = 0–3 s, 3–5 s, 5–10 s, 10–15 s, 15–20 s, >25 s). Second, all individual sequences were identified (see ‘Identification of individual sequences’). Third, for each session and each length bin, the fraction of immobility bouts that were fully occupied by uninterrupted sequences was calculated. To estimate significance, for each session the time bins that belonged to all individual sequences were temporally shuffled. The third step of the procedure described above was performed for 500 shuffle iterations per session. In Fig. ##FIG##3##4c##, the recorded data has 10 data points per length bin, and the shuffled data has 5,000 data points per length bin, since 500 shuffled realizations per session were pooled.</p>", "<title>Analysis of speed and sequence onset</title>", "<p id=\"Par159\">To determine whether the onset of the MEC oscillatory sequences was modulated by the mouse’s running speed, changes in speed before and after sequence onset were investigated. For each session all individual sequences were identified (see ‘Identification of individual sequences’) and for each sequence the mean speed over windows of 10 s before and after sequence onset was calculated. Because no differences in the mean speed were observed before and after onset (Extended Data Fig. ##FIG##6##2f## left panel), we next determined whether changes in speed were correlated with the onset of sequence epochs, which were defined as epochs with uninterrupted sequences—that is, epochs with recurring sequences. The same analysis described above was repeated but only for the subset of sequences that were 10 s or more apart—that is, for sequences that belonged to different epochs.</p>", "<p id=\"Par160\">The obtained results remained unchanged when the analysis was performed for 2 s windows before and after sequence onset.</p>", "<p id=\"Par161\">We complemented this analysis by investigating whether new epochs of sequences were more likely to be initiated during running bouts. In each of the 10 oscillatory sessions we first identified all running and immobility bouts that were 20 s long, or longer. We then counted the number of times that a sequence onset occurred in each behavioural state. For this analysis we only considered sequences that were not preceded by other sequences (sequences that were 10 s apart or more). Results were upheld with running and immobility bouts of 40 s or longer, in which case sequence onset was 2.8 times more frequent during running.</p>", "<title>Manifold visualization for example session in VIS and PaS</title>", "<p id=\"Par162\">To visualize whether the topology of the manifold underlying the population activity in example sessions recorded in VIS and PaS was also a ring, PCA was used and a similar procedure to the one described in ‘Manifold visualization for MEC sessions’ was adopted.</p>", "<p id=\"Par163\">For each example session, one corresponding to VIS and one corresponding to PaS (Fig. ##FIG##4##5e,f##), PCA was applied to the matrix of calcium activity, which first had each row convolved with a gaussian kernel of width equal to four times 8.5 s, which is the mean oscillation bin size computed across oscillatory sessions (see ‘Oscillation bin size’). Neural activity was projected onto the embedding generated by PC1 and PC2. Extended Data Fig. ##FIG##15##11d,e## shows the absence of a ring-shaped manifold in VIS and PaS example sessions.</p>", "<title>Co-activity and synchronization in PaS and VIS sessions</title>", "<p id=\"Par164\">Sessions recorded in PaS and VIS did not exhibit oscillatory sequences. To further characterize their population activity, synchronization and neural co-activity were calculated.</p>", "<title>Synchronization</title>", "<p id=\"Par165\">Neural synchronization was calculated as the absolute value of the Pearson correlation between the calcium activity of pairs of cells (bin size = 129 ms). For each session, the Pearson correlation was calculated for all pairs of calcium activity (correlations with the same calcium activity were not considered) and used to build a distribution of synchronization values. In Extended Data Fig. ##FIG##15##11j##, these distributions were averaged across sessions for each brain area separately.</p>", "<title>Co-activity</title>", "<p id=\"Par166\">For each time bin in a session (bin size = 129 ms) the co-activity was calculated as the number of cells that had simultaneous calcium events divided by the total number of recorded cells in the session. This number represented the fraction of cells that was active in individual time bins. Using all time bins of the session, a distribution of co-activity values was calculated. In Extended Data Fig. ##FIG##15##11k##, the distributions were averaged across sessions for each brain area separately.</p>", "<title>Model</title>", "<p id=\"Par167\">To determine whether long sequences act as a template for the formation of given activity patterns in a neural population, we built a simple perceptron model in which 500 units were connected to an output unit (Extended Data Fig. ##FIG##16##12a##). There was a total of 500 weights in the network, one per input unit. The total simulation time was 120 s, with 3,588 simulation steps and a time step of 33.44 ms (original time step was 129 ms, to mimic the bin size used in calcium data, rescaled so that the length of one of the input sequences was 120 s, similar to the length of the sequences in Fig. ##FIG##1##2b##). The response of the output unit was given by <italic>R</italic> = <italic>WX</italic>, where <italic>W</italic> was the vector of weights, and <italic>X</italic> the matrix of input activity (each column is a time step, each row is the activity of one input unit). The weights were trained such that the output unit performed one of two target responses (see below). For each target, we trained the model using as input periodic sequences with 5 different lengths (one length per training), covering the range from very slow to very fast as compared to the characteristic time scale of the targets (100 s).</p>", "<title>Inputs</title>", "<p id=\"Par168\">The activity of input unit <italic>i</italic> was represented by a Gaussian: , , , . Across input units, the means of the Gaussians were temporally displaced such that, all together: (1) units fired in a sequence, and (2) the distance between the means of two consecutive cells in the sequence was the same for all pairs of consecutive cells.</p>", "<p id=\"Par169\">This sequence was the slowest of the 5 sequence lengths we considered. Using this sequence as template, in order to build slower and periodic sequences we compressed the template and repeated it periodically by a factor of 2, 3, 4 and 8, to generate faster and periodic sequences of lengths 120, 60, 40 and 30 s respectively.</p>", "<title>Targets</title>", "<p id=\"Par170\">Two target responses were considered: ramp and Ornstein–Uhlenbeck process.</p>", "<title>Ramp</title>", "<p id=\"Par171\">The output neuron linearly increased its activity such that it was equal to 0 at time step = 0 (0 s), and to 1 at time step = 2,990 (100 s).</p>", "<title>Ornstein–Uhlenbeck process</title>", "<p id=\"Par172\">Unlike the first target, which was deterministic, the second target was stochastic and generated by an Ornstein–Uhlenbeck process.where <italic>μ</italic><sub>OU</sub> = 1 denotes the long-term mean, <italic>ξ</italic> is a white noise of zero mean and variance <italic>σ</italic><sub>OU</sub> <italic>=</italic> 0.005, and <italic>τ</italic> <italic>=</italic> 25.6 s denotes the correlation time.</p>", "<title>Training of weights</title>", "<p id=\"Par173\">The weights between the inputs and the output unit were trained such that the output unit performed one of the two target responses explained above. At the end each of the 1,000 learning iterations, the weights were updated through the perceptron learning rule , where <italic>x</italic><sub><italic>i</italic></sub> was the input from neuron <italic>i</italic>, , and <italic>η</italic> = 1 was the learning rate. In each learning iteration, the error <italic>e</italic> was calculated as the sum over time steps <italic>t</italic> of the difference between the target response and the output response—that is, where <italic>T(t)</italic> is the target response (either the ramp or the Ornstein–Uhlenbeck process) at time point <italic>t</italic>, and <italic>X(t)</italic> is the vector of input activity at time point <italic>t</italic>. The mean total error plotted in Extended Data Fig. ##FIG##16##12d## was calculated as the mean error over the last 100 learning iterations.</p>", "<title>Data analysis and statistical analysis</title>", "<p id=\"Par174\">Data analyses were performed with custom-written scripts in Python and Matlab (R2021b). Results were expressed as the mean ± s.e.m. unless indicated otherwise. Statistical analysis was performed using MATLAB and <italic>P</italic> values are indicated in the figure legends and figures (NS: <italic>P</italic> > 0.05; <italic>P</italic> < 0.05, <italic>P</italic> < 0.01, **<italic>P</italic> < 0.001). For data that displayed no Gaussian distribution and that was unpaired, the Wilcoxon rank-sum test was used. For paired data or one-sampled data, the Wilcoxon signed-rank test was used. Two-tailed tests were used unless otherwise indicated. Correlations were determined using Pearson or Spearman correlations. Friedman tests were used for analyses between groups. The Bonferroni correction was used when multiple comparisons were performed.</p>", "<p id=\"Par175\">Power analysis was not used to determine sample sizes. The study did not involve any experimental subject groups; therefore, random allocation and experimenter blinding did not apply and were not performed.</p>", "<title>Reporting summary</title>", "<p id=\"Par176\">Further information on research design is available in the ##SUPPL##0##Nature Portfolio Reporting Summary## linked to this article.</p>" ]	[]	[ "<title>Discussion</title>", "<p id=\"Par18\">Our experiments identify sequences of neural activity in MEC that repeat periodically during running as well as during intermittent periods of rest. Across recording sessions, the duration of individual sequences can range from tens of seconds to minutes, but the time scale is generally fixed within an individual recording session. In Neuropixels data, the sequences were somewhat noisier than in the calcium imaging data, as expected when sampling from multiple layers, across a wider dorso–ventral range, and with better capture of the fast dynamics of interneurons. The ultraslow periodic sequences observed in our data stand out from instances of slow sequential neural activity that have not been described in terms of oscillations. In the hippocampus, neural activity in CA1 cells that is organized into stereotypic sequences<sup>##REF##18772431##29##,##REF##27634534##30##</sup> is more coupled to ongoing behavioural activity and running distance than in our data<sup>##REF##26494280##16##</sup>. Moreover, whereas nearly 94% of MEC neurons in the present study were significantly locked to the oscillatory sequences, reported hippocampal sequences involve only a small fraction of the network (5% in ref. <sup>##REF##26494280##16##</sup>). This difference in participation would be in agreement with the view that the MEC supports a low-dimensional population code where the cells’ responses covary across environments<sup>##REF##17322902##31##</sup>, whereas the hippocampus supports a more high-dimensional population code that may orthogonalize distinct experiences<sup>##REF##15272123##32##,##REF##25489089##33##</sup>. The MEC oscillatory sequences also differ from travelling waves<sup>##REF##2035024##20##,##REF##19489117##21##</sup>, which move progressively through anatomical space.</p>", "<p id=\"Par19\">The widespread nature of the ultraslow oscillatory activity in individual neurons would be consistent with a role for ascending neuromodulatory arousal-associated brain-stem circuits in controlling these oscillations<sup>##REF##28246641##14##,##REF##23638082##34##,##REF##32791040##35##</sup>. In contrast to the oscillations, sequential organization of neural population activity was only present in MEC, pointing to MEC as having unique network mechanisms for sequence formation. The oscillatory sequences of the MEC are consistent with dynamics expected in a ring-shaped continuous attractor network<sup>##REF##7731993##36##,##REF##11539168##37##</sup>. However, sequential activity could also be generated in recurrently connected networks<sup>##REF##26971945##38##</sup> or in feedforward networks through synfire chains or rate propagation<sup>##UREF##2##39##,##REF##20725095##40##</sup>, or by plasticity rules operating on slow time scales<sup>##REF##28883072##41##</sup>.</p>", "<p id=\"Par20\">The oscillatory sequences might have a role in large-scale coordination of entorhinal circuit elements<sup>##UREF##0##5##</sup>, either by synchronizing faster oscillatory activity, such as theta and gamma<sup>##REF##23354386##1##,##REF##8466179##4##,##REF##18599763##6##,##REF##26961163##8##</sup>, or by organizing neural activity across functionally dissociable cell classes, such as grid and head-direction cells<sup>##REF##15965463##2##,##REF##16675704##3##</sup>. Coordination may help functional cell classes, for example different grid cell modules, keeping the same phase relationships over time, enabling a consistent readout of position or other variables represented in MEC activity<sup>##REF##31469365##42##,##REF##35385698##43##</sup>. As illustrated by our model, the oscillatory sequences may also act as a template to enable the formation of new firing patterns over long and behaviourally relevant time scales. By doing so, they may facilitate storage of memories associated with one-time experiences in downstream networks<sup>##REF##26063915##17##,##REF##21179088##44##,##REF##31235906##45##</sup>. Downstream sequences may be generated via plasticity in connections from MEC, in reminiscence of sequence formation during zebra finch song learning<sup>##REF##12214232##46##</sup>. The MEC sequences may also serve a role in temporal coding during extended behavioural experiences, by enabling the circuit to keep track of time<sup>##REF##32946745##47##,##REF##26539893##48##</sup> or by facilitating the slowly drifting neural population activity in lateral entorhinal cortex<sup>##REF##30158699##28##</sup>.</p>", "<p id=\"Par21\">It remains an open question whether the ultraslow oscillatory sequences are present across a broader spectrum of behaviours, including sleep and free exploration, and in the presence of salient visual feedback. If so, it is possible that the sequences reset in the presence of strong landmarks or sensory stimulation and that only subpopulations of the neurons demonstrate it. The potentially richer dynamics of the periodic sequences during more natural behaviours must interface with the dynamics of MEC cells on a number of manifolds, such as in ensembles of head-direction cells and grid cells<sup>##REF##35022611##25##,##REF##30462582##49##,##REF##31406365##50##</sup>.</p>" ]	[]	[ "<p id=\"Par1\">The medial entorhinal cortex (MEC) hosts many of the brain’s circuit elements for spatial navigation and episodic memory, operations that require neural activity to be organized across long durations of experience<sup>##REF##23354386##1##</sup>. Whereas location is known to be encoded by spatially tuned cell types in this brain region<sup>##REF##15965463##2##,##REF##16675704##3##</sup>, little is known about how the activity of entorhinal cells is tied together over time at behaviourally relevant time scales, in the second-to-minute regime. Here we show that MEC neuronal activity has the capacity to be organized into ultraslow oscillations, with periods ranging from tens of seconds to minutes. During these oscillations, the activity is further organized into periodic sequences. Oscillatory sequences manifested while mice ran at free pace on a rotating wheel in darkness, with no change in location or running direction and no scheduled rewards. The sequences involved nearly the entire cell population, and transcended epochs of immobility. Similar sequences were not observed in neighbouring parasubiculum or in visual cortex. Ultraslow oscillatory sequences in MEC may have the potential to couple neurons and circuits across extended time scales and serve as a template for new sequence formation during navigation and episodic memory formation.</p>", "<p id=\"Par2\">Neural population activity in the medial entorhinal cortex of mice can be organized into ultraslow oscillatory sequences, with periods extending up to the minute range.</p>", "<title>Subject terms</title>" ]	[ "<title>Main</title>", "<p id=\"Par3\">Brain function emerges from the dynamic coordination of interconnected neurons<sup>##REF##8466179##4##–##REF##32640928##7##</sup>. At sub-second time scales, cells are coordinated within and across brain regions by way of neuronal oscillations<sup>##REF##26961163##8##</sup>. Studies have also reported oscillations at slower time scales, with frequencies lower than 0.1 Hz and periods lasting from tens of seconds to minutes (ultraslow oscillations), in individual neurons<sup>##REF##10579564##9##–##UREF##1##11##</sup> and in local field potentials<sup>##REF##13430746##12##–##REF##28246641##14##</sup>. However, it remains unknown how pervasive these ultraslow oscillations are. Moreover, it remains to be determined whether and how they organize the activity of participating neurons in space and time across the neural circuit.</p>", "<p id=\"Par4\">We directed our search for ultraslow oscillations to the MEC, a brain circuit that by containing many of the elements involved in navigational behaviour<sup>##REF##23354386##1##–##REF##16675704##3##</sup> and episodic memory formation<sup>##REF##23354386##1##,##REF##15217334##15##</sup>, may possess mechanisms to organize neural activity at behavioural time scales, from seconds to minutes. Activity was recorded from hundreds of MEC cells at the same time using either two-photon calcium imaging or Neuropixels probes (Extended Data Fig. ##FIG##5##1##). To rule out variations in external stimuli as sources of modulation, we allowed head-fixed mice to run on a rotating wheel for 30 or 60 min, in darkness and with no scheduled rewards<sup>##REF##26494280##16##,##REF##26063915##17##</sup> (Fig. ##FIG##0##1a## and Extended Data Fig. ##FIG##6##2a##).</p>", "<title>Ultraslow oscillations in MEC neurons</title>", "<p id=\"Par5\">To determine whether neural activity in MEC exhibits ultraslow oscillations, for each recorded cell we deconvolved the calcium signal and binarized the obtained signal (‘calcium activity’, bin size = 129 ms). For each cell, we then calculated the autocorrelation of the calcium activity and the corresponding power spectral density (PSD). Autocorrelation diagrams for stacks of cells from the same session showed vertical bands (Fig. ##FIG##0##1b##), suggesting that the calcium activity of many cells was oscillatory and oscillated at similar frequencies. Some cells had only one prominent peak in their PSD (Fig. ##FIG##0##1c##), suggesting that they were active at a fixed frequency. Other cells had several peaks, often with the higher frequencies appearing as harmonics of a fundamental frequency (Fig. ##FIG##0##1d##). In the example session in Fig. ##FIG##0##1b##, for most of the cells (72%, 348 out of 484) the frequency at which the PSD peaked (the ‘primary frequency’) was lower than 0.01 Hz (44% of the cells had a primary frequency within the range 0.006–0.008 Hz), and there were no cells whose PSD peaked at frequencies higher than 0.1 Hz. In the complete dataset (15 sessions over 5 mice), the oscillations were detectable in the majority of the recorded neurons (91%, 5,691 out of 6,231) but not in shuffled versions of the same data (Extended Data Fig. ##FIG##7##3## and Methods). Although there was some variation in frequencies across sessions and mice, the primary frequency was always below 0.1 Hz (all oscillatory 5,691 cells; range of maximum frequencies across 15 sessions: 0.036–0.057 Hz).</p>", "<p id=\"Par6\">To verify that the ultraslow oscillations manifest in spiking activity, we implanted two mice with Neuropixels 2.0 probes in the MEC (Extended Data Fig. ##FIG##5##1d##). Similar to the calcium imaging data, we observed oscillations at frequencies lower than 0.1 Hz in the majority of the units (78%, 683 out of 879 units, bin size = 120 ms; Fig. ##FIG##0##1e,f##).</p>", "<title>Oscillatory sequences in MEC activity</title>", "<p id=\"Par7\">To determine whether the ultraslow oscillations of different cells are coordinated at the neural population level, we first calculated, for the calcium imaging data, instantaneous correlations between the calcium activity of all pairs of cells. The cell pair with the highest correlation value was identified and one of the two cells was defined as the ‘seed’ cell. The remaining cells were sorted based on their correlation value with the seed cell, in a descending manner. Using this sorting procedure, we observed periodic sequences of neuronal activation (Fig. ##FIG##1##2a## and Extended Data Fig. ##FIG##8##4a##). The sequences unfolded successively with no interruption for tens of minutes (Fig. ##FIG##1##2a##). Because sequences of activity constitute low-dimensional dynamics, we also sorted the cells using dimensionality reduction methods, which do not depend on hyperparameters. For each recording session, we applied principal component analysis (PCA) to the matrix of calcium activity and measured, for each cell, the angle of the vector defined by the pair of loadings on principal components 1 and 2, and sorted the neurons based on these angles in a descending manner (Extended Data Fig. ##FIG##8##4b##). This sorting (‘PCA method’) revealed the same stereotyped periodic sequences of neuronal activation, which we hereafter refer to as oscillatory sequences; however, the sequential organization was now more salient (Fig. ##FIG##1##2b## and Extended Data Fig. ##FIG##9##5a##). When projecting the population activity onto a two-dimensional embedding, the manifold resembled a ring (Fig. ##FIG##1##2c## and Extended Data Fig. ##FIG##8##4c##). The instantaneous population activity was estimated from the position on the ring (‘phase of the oscillation’, Fig. ##FIG##1##2d##). The oscillatory sequences were not evident if cells were not sorted, nor if the PCA method was applied to shuffled data (Extended Data Fig. ##FIG##8##4d##). The sequences were similarly apparent when neurons were sorted according to non-linear dimensionality reduction techniques (Extended Data Fig. ##FIG##8##4d##), as well as when the neurons were sorted using subsets of data (Extended Data Fig. ##FIG##8##4e## and Methods), and when the neurons’ calcium activity was visualized using the unprocessed calcium signals (Fig. ##FIG##1##2e##).</p>", "<p id=\"Par8\">We also observed ultraslow oscillatory sequences in the data from two mice with Neuropixels probes (469 and 410 units, respectively), indicating that our findings do not reflect factors unique to calcium imaging (Fig. ##FIG##1##2f## and Extended Data Fig. ##FIG##8##4f,g##). Some of the Neuropixels sequences were noisier than those of the calcium imaging data, possibly reflecting a broader mix of cell types located more ventrally and across several cell layers (Extended Data Fig. ##FIG##5##1d##). To maximize the number of cells recorded in layer II, and to minimize variability, we focused on calcium imaging data for the rest of the study.</p>", "<p id=\"Par9\">Although striking oscillatory sequences were observed across multiple sessions and mice, the population activity exhibited considerable variability (Extended Data Figs. ##FIG##8##4f,g## and ##FIG##9##5a–c##). To capture this variability, we calculated an oscillation score that ranged from 0 (no oscillations) to 1 (oscillations throughout the session). The distribution of scores in the calcium imaging data was bimodal (Extended Data Fig. ##FIG##9##5d##), with oscillatory sequences showing up in 15 sessions (Extended Data Fig. ##FIG##9##5a##). All Neuropixels sessions were classified as oscillatory (Fig. ##FIG##1##2f## and Extended Data Fig. ##FIG##8##4f,g##). For each oscillatory session, we identified all sequences (Extended Data Fig. ##FIG##10##6a–c##) and found that sequence durations ranged from tens of seconds to minutes (Fig. ##FIG##1##2g##), with high variability across sessions and mice but little variability within individual sessions (Extended Data Fig. ##FIG##10##6d–g##). Inter-sequence intervals (ISI) were similarly present at different lengths, ranging from 0 s when sequences were consecutive (279 out of 406 ISIs (69%)) to a maximum of 452 s (Fig. ##FIG##1##2h## and Extended Data Fig. ##FIG##10##6h,i##).</p>", "<title>MEC neurons are locked to the sequences</title>", "<p id=\"Par10\">To determine the extent to which calcium activity was tuned to the oscillatory sequences, we computed for each neuron its degree of locking to the phase of the oscillation, which ranged from 0 (no locking) to 1 (perfect locking). Significant locking degrees were observed for the vast majority of the recorded cells (Fig. ##FIG##2##3a##, left; 458 out of 484 significantly locked neurons (95%)). Results were upheld with the mutual information between calcium events and phase of the oscillation (Fig. ##FIG##2##3a##, right and Extended Data Fig. ##FIG##11##7a##). The predominance of phase-locked neurons was observed in all 15 oscillatory sessions (Fig. ##FIG##2##3b##, 5,841 out of 6,231 locked neurons (93.7%)). Each locked neuron exhibited a preference for activity within a narrow range of phases of the oscillation (‘preferred phase’, Fig. ##FIG##2##3c## and Extended Data Fig. ##FIG##11##7b–e##). Although sequences were still observed if high phase locking neurons were excluded, suggesting that sequences recruit widespread networks, the more cells that were excluded the more difficult it was to observe the sequences, indicating that the dynamics manifests more clearly at the neural population level (Extended Data Fig. ##FIG##11##7f##). Because the oscillatory sequences involve the vast majority of neurons recorded in MEC, and multiple cell types can be recorded within fields of view (FOV) of comparable size<sup>##REF##30215597##18##,##REF##35135885##19##</sup>, the sequences most probably include a mixture of functional cell types such as grid and head-direction cells, with grid cells spanning more than one module.</p>", "<p id=\"Par11\">Not all neurons participated in each individual sequence. We quantified the degree to which cells skipped sequences through a participation index (Extended Data Fig. ##FIG##11##7g##). Participation index variability was observed both within and across oscillatory sessions (Fig. ##FIG##2##3d## and Extended Data Fig. ##FIG##11##7h##).</p>", "<title>MEC sequences are not travelling waves</title>", "<p id=\"Par12\">We next explored whether the oscillatory sequences in MEC could have features of travelling waves, in which the population activity moves progressively across anatomical space<sup>##REF##2035024##20##,##REF##19489117##21##</sup>. First, we found that cells with similar and dissimilar preferred phases were anatomically intermingled (Fig. ##FIG##2##3e##, Extended Data Fig. ##FIG##12##8a## and Supplementary Video ##SUPPL##2##1##), suggesting the absence of travelling waves with a constant direction in the propagation of activity across sequences. We next investigated the presence of travelling waves in individual sequences by calculating the preferred phase of each cell in the sequence and correlating, for all cell pairs, their difference in preferred phases with their anatomical distance (Fig. ##FIG##2##3f##). Across sequences, the correlation values were very small, ranging from −0.068 to 0.147, and below the level of statistical significance (Fig. ##FIG##2##3g##, 421 sequences across 15 oscillatory sessions over 5 mice), suggesting a lack of topographical organization (see complementary analyses in Extended Data Fig. ##FIG##12##8b,c## and Methods). In agreement with the proposed absence of travelling waves, we observed that during a single sequence, the neural activity spread across the entire FOV, and that the distance traversed by the centre of mass was similar in experimental and shuffled data (Extended Data Fig. ##FIG##12##8d–f##).</p>", "<title>Sequential activation of ensembles</title>", "<p id=\"Par13\">To quantify the sequential activation of neural activity in the population, and to average out single-cell variability, we next studied ensembles of co-active cells (Extended Data Fig. ##FIG##13##9a,b##). We assigned neurons to a total of 10 ensembles, based on their proximity in the sorting obtained through the PCA method (Extended Data Fig. ##FIG##13##9c##) and then calculated the probability by which activity transitioned between ensembles across adjacent time bins (Extended Data Fig. ##FIG##13##9d–f##), with probabilities displayed in a transition matrix (Extended Data Fig. ##FIG##13##9g##). Transitions occurred mostly between adjacent ensembles and with a preferred directionality (Extended Data Fig. ##FIG##13##9g,h##). In the oscillatory sessions the sequential activation of three or more ensembles was 2.3 times more likely in the recorded data than in shuffled data (Extended Data Fig. ##FIG##13##9i##). The probability of observing sequential activation of three or more ensembles (‘sequence score’) was significant in 100% of the oscillatory sessions (15 out of 15). Significant sequential activity was demonstrated also in 41% of the non-oscillatory sessions (5 out of 12, Extended Data Fig. ##FIG##13##9j##).</p>", "<title>Sequences do not map position</title>", "<p id=\"Par14\">Fast oscillations and single-cell firing in the entorhinal-hippocampal system can be modulated by a number of movement-associated parameters, such as position and running state<sup>##REF##15965463##2##,##REF##16675704##3##,##REF##22623683##22##,##REF##33567253##23##</sup>. We next investigated whether similar dependencies are present for the minute-scale oscillatory sequences (Fig. ##FIG##3##4a##). We first calculated the probability of observing the oscillatory sequences given that the mouse was either running (mouse moves along the wheel) or immobile (position on the wheel remains unchanged) (Extended Data Fig. ##FIG##6##2a##). The oscillatory sequences were predominant during running bouts, but they were also observed during immobility (Fig. ##FIG##3##4b##). During immobility, oscillatory sequences were continuous for durations spanning from 1 s to 258 s (Fig. ##FIG##3##4c## and Extended Data Fig. ##FIG##6##2b##). The continued presence of the oscillatory sequences during long epochs of immobility suggests that behavioural state and running distance have a limited role in driving the progression of the sequences in MEC, in contrast to previous observations in CA1 of the hippocampus<sup>##REF##26494280##16##</sup>. In line with this result, the number of laps the mice completed on the wheel during one sequence was highly heterogeneous, ranging from 0 to 86 laps per sequence across all mice (lap length = 53.7 cm, Fig. ##FIG##3##4d## and Extended Data Fig. ##FIG##6##2c##).</p>", "<p id=\"Par15\">Sequences took place during a wide range of speed and acceleration values (Extended Data Fig. ##FIG##6##2d,e##). Although we found no difference in speed 10 s before and after sequence onset (Extended Data Fig. ##FIG##6##2f–j##), new epochs of sequences were more likely to be initiated during running bouts (onset of sequences was 3.1 times more frequent in running bouts than in immobility bouts).</p>", "<title>Sequences are specific to MEC</title>", "<p id=\"Par16\">Since ultraslow oscillations have been reported in widely different brain areas<sup>##REF##10579564##9##–##REF##28246641##14##</sup>, we investigated whether the oscillatory sequences were observed in other regions too. We recorded the activity of hundreds of cells in two regions: (1) the parasubiculum (PaS), a parahippocampal region abundant with grid and head-direction cells but with a different circuit structure than MEC<sup>##REF##20657591##24##</sup> (25 sessions over 4 mice, Extended Data Fig. ##FIG##14##10a,b##), and (2) the visual cortex (VIS), which differs from MEC<sup>##REF##35022611##25##</sup> in its network architecture and in the high dimensionality of its neural population activity<sup>##REF##31243367##26##</sup> (19 sessions over 3 mice, Extended Data Fig. ##FIG##14##10c##). The mice performed the same minimalistic self-paced running task as in the MEC recordings. We found that while the calcium activity of a fraction of cells in both brain areas was ultraslow and periodic (Fig. ##FIG##4##5a–d##), in neither brain region were these oscillations organized into oscillatory sequences (Fig. ##FIG##4##5e,f## and Extended Data Fig. ##FIG##15##11a–h##), and for all sessions the oscillation scores were lower than the threshold defined from the MEC data to classify sessions as oscillatory (Extended Data Fig. ##FIG##15##11i##, threshold = 0.72) (Fig. ##FIG##4##5g##). Moreover, data from VIS were more synchronous than PaS data (Extended Data Fig. ##FIG##15##11j,k##), consistent with previous observations<sup>##REF##26063915##17##</sup>. Finally, calcium activity was more correlated with the speed of the mouse in VIS than in MEC and PaS (Extended Data Fig. ##FIG##15##11l##), suggesting that ultraslow oscillations in VIS might reflect slow changes in the running speed of the mouse. Altogether, these results suggest that MEC has network mechanisms for sequential coordination of single-cell oscillations that are not present in PaS or VIS.</p>", "<title>Sequences may enable specific patterns</title>", "<p id=\"Par17\">The ultraslow time scale of the oscillatory sequences raises questions as to their possible function. To determine whether they could serve as a scaffold—or ‘template’—for the formation of new activity patterns, we developed a simple model. In this model, 500 units that fired in a sequential manner, the template, were connected to an output neuron (Extended Data Fig. ##FIG##16##12a##; the results can be generalized to more output neurons). We trained the weights of the connections to enable a specific ‘target’ activity pattern in the output neuron. As example targets we considered first a ramp of activity (Extended Data Fig. ##FIG##16##12b##, left), mirroring activity observed in many neurons in decision making tasks<sup>##REF##12417672##27##</sup> or during free foraging<sup>##REF##30158699##28##</sup>, and second a less stereotyped target generated with a stochastic process (Extended Data Fig. ##FIG##16##12b##, right). The output unit could reproduce the target activity when the input sequence was slower or as slow as the target pattern, but not when the input sequences were faster (Extended Data Fig. ##FIG##16##12c,d##). These results suggest that neural activity patterns that unfold at behavioural time scales may only be supported by sequences that unfold at similarly slow or slower time scales—that is, over durations of many seconds or more.</p>", "<title>Online content</title>", "<p id=\"Par177\">Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41586-023-06864-1.</p>", "<title>Supplementary information</title>", "<p>\n\n\n\n</p>", "<title>Source data</title>", "<p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n</p>" ]	[ "<title>Extended data figures and tables</title>", "<p id=\"Par180\">\n\n</p>", "<p id=\"Par181\">\n\n</p>", "<p id=\"Par182\">\n\n</p>", "<p id=\"Par183\">\n\n</p>", "<p id=\"Par184\">\n\n</p>", "<p id=\"Par185\">\n\n</p>", "<p id=\"Par186\">\n\n</p>", "<p id=\"Par187\">\n\n</p>", "<p id=\"Par188\">\n\n</p>", "<p id=\"Par189\">\n\n</p>", "<p id=\"Par190\">\n\n</p>", "<p id=\"Par191\">\n\n</p>", "<title>Extended data</title>", "<p>is available for this paper at 10.1038/s41586-023-06864-1.</p>", "<title>Supplementary information</title>", "<p>The online version contains supplementary material available at 10.1038/s41586-023-06864-1.</p>", "<title>Acknowledgements</title>", "<p>The authors thank D. W. Tank for sharing hardware, software and advice when 2-photon imaging was introduced to the lab in 2013–14; A. Tsao, G. B. Keller and T. Bonhoeffer for subsequent help in setting up 2-photon imaging procedures; Ø. Høydal and E. Ranheim Skytøen for recent help with setting up Neuropixels recordings in mice; W. Zong, I. Davidovich, Y. Roudi and E. Kropff for discussion; Y. Burak for discussion and comments on the manuscript; and S. Ball, K. Haugen, E. Holmberg, K. Jenssen, E. Kråkvik, I. Ulsaker-Janke and H. Waade for technical assistance. The work was supported by a Synergy Grant to E.I.M. from the European Research Council ERC (‘KILONEURONS’, grant agreement no. 951319), FRIPRO grants to E.I.M. (grant no. 286225) and M.-B.M. (grant no. 300394/H10), a Centre of Excellence grant to M.-B.M. and E.I.M. (Centre of Neural Computation, grant no. 223262), and a National Infrastructure grant to E.I.M. and M.-B.M. from the Research Council of Norway (NORBRAIN, grant no. 295721), as well as the Kavli Foundation (M.-B.M. and E.I.M.), a direct contribution to M.-B.M. and E.I.M. from the Ministry of Education and Research of Norway, and an ERC Starting Grant (ERC-ST2019 850769) and an Eccellenza Grant from the Swiss National Science Foundation (PCEGP3_194220) to F.D.</p>", "<title>Author contributions</title>", "<p>F.D., H.A.O., M.-B.M. and E.I.M. planned and designed the initial experiments, with later input from S.G.C. Experiments were performed by F.D., R.I.J., S.O.A. and A.L. H.A.O. developed hardware and imaging software, preprocessed the data and performed initial analysis. H.A.O.’s contribution to data preprocessing and A.L.’s contribution to data collection were equal. S.G.C., M.-B.M. and E.I.M. conceptualized and designed analyses, with initial input from F.D. S.G.C. performed analyses of neural activity. S.G.C and C.C. developed the model. F.D. and A.L. performed histological analyses. S.G.C., M.-B.M. and E.I.M. interpreted data, with initial input from F.D. S.G.C. and F.D. visualized data. S.G.C. and E.I.M. wrote the paper, with initial contributions from F.D. and with periodic input from all authors. M.-B.M. and E.I.M. supervised and funded the project.</p>", "<title>Peer review</title>", "<title>Peer review information</title>", "<p id=\"Par178\"><italic>Nature</italic> thanks Jozsef Csicsvari and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. ##SUPPL##1##Peer review reports## are available.</p>", "<title>Data availability</title>", "<p>The datasets generated during the current study will be available after publication, on EBRAINS (10.25493/SKKX-4W3). <xref ref-type=\"sec\" rid=\"Sec83\">Source data</xref> are provided with this paper.</p>", "<title>Code availability</title>", "<p>Code for reproducing the analyses in this article are available through this link: <ext-link ext-link-type=\"uri\" xlink:href=\"https://github.com/soledadgcogno/Ultraslow-oscillatory-sequences.git\">https://github.com/soledadgcogno/Ultraslow-oscillatory-sequences.git</ext-link>.</p>", "<title>Competing interests</title>", "<p id=\"Par179\">The authors declare no competing interests.</p>" ]	[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>Ultraslow oscillations in MEC neurons.</title><p><bold>a</bold>, Neural activity was recorded through a prism from GCaMP6m-expressing neurons of the MEC in head-fixed mice running in darkness on a non-motorized wheel. Cartoon of a running mouse on the right created with BioRender.com. <bold>b</bold>, Stacked <italic>z</italic>-scored autocorrelations of single-cell calcium activity for one example session (484 neurons), plotted as a function of time lag. Neurons are sorted according to the maximum power of the PSD calculated on each autocorrelation separately, in descending order. <bold>c</bold>, PSD (left) calculated on the autocorrelation (right) of one example cell’s calcium activity. The dashed red line indicates the frequency at which the PSD peaks (0.0066 Hz). <bold>d</bold>, As in <bold>c</bold> but for another example cell. The PSD peaks at 0.0066 Hz and has harmonics at 0.0132, 0.0207 and 0.0273 Hz. <bold>e</bold>,<bold>f</bold>, As in <bold>c</bold>,<bold>d</bold> but for two example cells recorded using Neuropixels probes. The PSDs peak at 0.016 Hz (<bold>e</bold>) and 0.015 Hz (<bold>f</bold>).</p><p>##SUPPL##3##Source Data##</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>Ultraslow oscillations are organized into oscillatory sequences.</title><p><bold>a</bold>, Raster plot of calcium activity of all cells recorded in the example session shown in Fig. ##FIG##0##1b## (bin size = 129 ms, <italic>n</italic> = 484 cells). Time bins with calcium events are indicated with black dots; those without calcium events are indicated with white dots. Cells were sorted according to their correlation values with one arbitrary cell, in a descending manner. The example sequence indicated in red is 121 s long. <bold>b</bold>, As in <bold>a</bold> but now with neurons sorted according to the PCA method. <bold>c</bold>, Projection of neural activity of the session in <bold>a</bold>,<bold>b</bold> onto the first two principal components of PCA (left), and the first two dimensions of a Laplacian eigenmaps (LEM) analysis (right). Time is colour coded. One sequence is equivalent to one rotation along the ring-shaped manifold. <bold>d</bold>, Raster plot as in <bold>b</bold>. The phase of the oscillation, overlaid in red, was used to track the position of the population activity on the sequence. <bold>e</bold>, As in <bold>b</bold>, but showing the <italic>z</italic>-scored fluorescence calcium signals. <bold>f</bold>, Raster plot of binarized spiking activity of all units recorded in one example session using Neuropixels probes (bin size = 120 ms, <italic>n</italic> = 469 units). Neurons are sorted according to the PCA method. <bold>g</bold>, Distribution of sequence durations across 15 oscillatory sessions over 5 mice (imaging data only; one mouse did not have detectable sequences; 421 sequences in total). Each count is one sequence. <bold>h</bold>, Distribution of ISI (406 ISIs in total across 15 oscillatory sessions). Each count is an ISI. During periodic sequences the ISI is 0. Note that the <italic>y</italic> axis has a log scale.</p><p>##SUPPL##4##Source Data##</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><title>Nearly all MEC neurons are locked to the oscillatory sequences.</title><p><bold>a</bold>, Left, locking degrees of neurons from the session shown in Fig. ##FIG##1##2a##. Black dots indicate locked neurons; red dots indicate non-locked neurons; and grey dots show the 99th percentiles of the corresponding shuffle distributions, one per cell (458 out of 484 cells were significantly locked to the phase of the oscillation). Right, similar to left, but for mutual information (MI) between phase of the oscillation and count of calcium events. Black dots indicate MI and grey dots show the estimated bias in the MI. For all cells, the MI is larger than the bias. Neurons are sorted according to ascending locking degree (left) or MI (right). <bold>b</bold>, Box plot showing percentage of locked neurons over all oscillatory sessions (median = 94%; two-sided Wilcoxon signed-rank test, <italic>n</italic> = 15 sessions, <italic>P</italic> <italic>=</italic> 6.1 × 10<sup>5</sup>, <italic>W</italic> <italic>=</italic> 120). Red line shows median across sessions; blue bottom and top lines delineate bottom and top quartiles, respectively; whiskers extend to 1.5 times the interquartile range; and red crosses show outliers exceeding 1.5 times the interquartile range. <bold>c</bold>, Each row shows the tuning curve (colour coded) to the phase of the oscillation of one locked neuron in Fig. ##FIG##1##2a## (<italic>n</italic> = 458) calculated on experimental (left) and shuffled (right) data. <bold>d</bold>, Distribution of participation indexes across neurons in the session in Fig. ##FIG##1##2a## (<italic>n</italic> = 484 cells, left) and across all 15 oscillatory sessions (<italic>n</italic> = 6,231 cells, right). <bold>e</bold>, Anatomical distribution of neurons in the FOV of the session in Fig. ##FIG##1##2a##. Neuronal preferred phase is colour coded. Neurons in red are not significantly locked. Dorsal MEC on top, medial on the right. <bold>f</bold>, A two-dimensional histogram of differences in preferred phase between pairs of neurons for sequence no. 19 of the session in Fig. ##FIG##1##2a##, and their distance in the FOV. In the presence of travelling waves, high values along the diagonal would be expected. Normalized frequency is colour coded. Each count is a cell pair (<italic>n</italic> = 116,886 cell pairs for 484 recorded cells). Correlation = 0.0026, cutoff for significance = 0.0099. <bold>g</bold>, Distribution of correlation values between differences in preferred phase and anatomical distance in experimental data (blue bars, <italic>n</italic> = 421 sequences across 15 oscillatory sessions) and shuffled data (orange dotted line, <italic>n</italic> = 42,100, 100 shuffled iterations per sequence) (Methods). *<italic>P</italic> < 0.001, <italic>P</italic> < 0.01, <italic>P</italic> < 0.05; NS, not significant (<italic>P</italic> > 0.05).</p><p>##SUPPL##5##Source Data##</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><title>The oscillatory sequences transcend periods of running and immobility.</title><p><bold>a</bold>, Top, raster plot of one recorded session (520 neurons). Time bins in aquamarine indicate that the mouse ran faster than 2 cm s<sup>−1</sup>. Second from top, expanded view showing 160 s of neural activity. Third from top, instantaneous speed of the mouse. Bottom, position of the mouse on the wheel. <bold>b</bold>, Probability of observing the oscillatory sequences given that the mouse was either running or immobile (median probability during running and immobility was 0.93 and 0.69, respectively; two-sided two sample Wilcoxon signed-rank test, <italic>n</italic> = 10 sessions over 3 mice, <italic>P</italic> = 0.002, <italic>W</italic> = 55). <bold>c</bold>, Fraction of immobility epochs with oscillatory sequences as a function of length of the immobility epoch (data are mean ± s.d.). For each length bin, the fraction of epochs was averaged across sessions. Orange, recorded data (<italic>n</italic> = 10 per length bin); blue: shuffled data (<italic>n</italic> = 5,000 per length bin, 500 shuffled realizations per session). Recorded versus shuffled data: <italic>P</italic> ≤ 2.62 × 10<sup>−6</sup>, 4.7 ≤ <italic>Z</italic> <italic>≤</italic> 47.5, two-sided Wilcoxon rank-sum test. <bold>d</bold>, Number of completed laps as a function of sequence number for one mouse. Each dot indicates one sequence. Dashed lines indicate separation between recorded sessions.</p><p>##SUPPL##6##Source Data##</p></caption></fig>", "<fig id=\"Fig5\"><label>Fig. 5</label><caption><title>The oscillatory sequences are not observed in PaS or VIS.</title><p><bold>a</bold>,<bold>b</bold>, PSD (left) calculated on the autocorrelation (right) of calcium activity in one example cell recorded in PaS (<bold>a</bold>) or VIS (<bold>b</bold>). The PSDs peaked at 0.015 Hz (<bold>a</bold>) and 0.011 Hz (<bold>b</bold>). <bold>c</bold>,<bold>d</bold>, Stacked autocorrelations (as in Fig. ##FIG##0##1b##) for two example sessions recorded in PaS (<bold>c</bold>; 402 neurons) and VIS (<bold>d</bold>; 289 neurons). <bold>e</bold>,<bold>f</bold>, PCA-sorted raster plots (as in Fig. ##FIG##1##2b##) for two example sessions recorded in PaS (<bold>a</bold>,<bold>c</bold>) and VIS (<bold>b</bold>,<bold>d</bold>). Oscillation score and sequence score are indicated at the top. <bold>g</bold>, Number of sessions with and without oscillatory sequences in MEC (blue, 27 sessions), VIS (green, 19 sessions) and PaS (yellow, 25 sessions) based on oscillation scores and threshold defined from the MEC dataset (Extended Data Fig. ##FIG##9##5d##).</p><p>##SUPPL##7##Source Data##</p></caption></fig>", "<fig id=\"Fig6\"><label>Extended Data Fig. 1</label><caption><title>Histology showing imaging locations for each animal in the MEC group.</title><p><bold>a</bold>. Left: Representative image indicating GCaMP6m expression in the superficial layers of the MEC upon local viral injection at postnatal day P1 (sagittal section). Images were acquired with a 20× objective mounted on a confocal laser scanning microscope LSM 880 (Zeiss), operated by ZEN 3 software (blue edition). Red inset, top right: 60× magnification of the most dorsal portion of the MEC. Bottom right: Fraction of MEC neurons (Nissl +) expressing GCaMP6m; data are shown for all 5 animals with MEC imaging. Data are presented as mean values, error bar indicates the S.D. calculated across multiple (n = 8) adjacent slices. Each dot represents one slice. <bold>b</bold>. Location of the ventro-lateral edge of the prism in stereotactic coordinates, and area of the FoV occupied by cells expressing GCaMP6m. Data are shown for each MEC-imaged animal. Mouse #59911 had no oscillatory sequences. <bold>c</bold>. Prism location in mice that underwent calcium imaging in MEC in the left hemisphere. Top: Maximum of 50 μm thick sagittal brain sections. For each of the 5 mice in (b), 3 sections, shown from lateral (left) to medial (right), were acquired with an LSM 880, 20×. A DiI-coated piano wire pin was inserted at the ventrolateral corner of FoV to enable identification of the FoV on histology sections. Green is GCaMP6m signal, red is DiI signal. Scale bar is 400 μm. The white stippled line encapsulates the superficial layers of MEC. The blue dot adjacent to the leftmost image of the series marks the location of the ventro-lateral corner of the prism. Bottom: estimated location of the FoV for two-photon imaging, projected onto a flat map encompassing MEC (brown outline) and parasubiculum (PaS, orange outline). The blue dot marks the location of the pin used to demarcate the most lateral-ventral border of the prism, while the green square inset is the microscope’s FoV. Inset image shows the maximum intensity projections of the FoV. Anteroposterior (AP), Mediolateral (ML), and dorso–ventral (DV) axes are indicated in panels (a) and (c). <bold>d.</bold> Micrographs of Cresylviolet stained sagittal brain sections from all 2 mice implanted with four-shank Neuropixels 2.0 silicon probes in the left hemisphere. Sections are organised from the most laterally placed shank(s) (left) to the most medially placed shank(s) (right). Mouse ID, shank number, and scale bar (1000 μm) are indicated next to each section. The brain of one mouse (#104638) was damaged during extraction, and parts of the MEC and cortex are missing from the section. Coloured arrows indicate MEC borders (dorsal, ventral) and the identified or estimated probe tip in the section. Black arrows indicate estimated dorsoventral range of the probe’s active recording sites (as indicated by the insert). For each section, inserts show the number of units recorded at each depth of the probe shank (histogram bin size = 60 μm). Note that the anatomical location of probe shanks can only be approximately estimated, and indicated unit locations are subject to measurement error, e.g., due to the shank tips exiting the cortex, the brain shrinking during perfusion and error in estimating the position of the tip of the probe. Stippled lines indicate borders between brain regions (MEC, medial entorhinal cortex; LEC, lateral entorhinal cortex; PaS, parasubiculum, HF, hippocampal formation; PoR, postrhinal cortex; VISpl, posterolateral visual area; TR, postpiriform transition area; CoA, cortical amygdalar area; PA, posterior amygdalar nucleus). D = dorsal; V = ventral; A = anterior; P = posterior.</p><p>\n##SUPPL##8##Source Data##\n</p></caption></fig>", "<fig id=\"Fig7\"><label>Extended Data Fig. 2</label><caption><title>Relationship between the oscillatory sequences and behavior.</title><p><bold>a</bold>. Quantification of the animals’ behavior during head-fixation on the wheel. Duration of epochs of running (speed ≥ 2 cm/s, left) and immobility (speed <2 cm/s, right) for 10 oscillatory sessions over the 3 animals in which behavioral tracking was synchronized with imaging (1289 running bouts and 1286 immobility bouts in total). Each count is an epoch, and one epoch is obtained by concatenating consecutive time bins with the same behaviour (running or immobility, bin size = 129 ms). For each of the 10 sessions the smallest speed value was always 0 cm/s. The largest speed value ranged from 16.4 to 75.3 cm/s. The median calculated over the entire session ranged from 0 cm/s (in 4 out of 10 sessions) to 18.8 cm/s. Across the 10 sessions, the median of speed values was 0 cm/s (indicating that some of the animals spent much of the session time being immobile, yet those animals exhibited oscillatory sequences too, e.g. animal #60355, Extended Data Fig. ##FIG##9##5a##; see also Fig. ##FIG##3##4a## and ##FIG##3##c##). The median speed during running epochs was 7.8 cm/s. The acceleration values ranged from −86.3 to 108.9 cm/s<sup>2</sup>, with a median of 0 cm/s<sup>2</sup> for all the data as well as the running epochs specifically. <bold>b</bold>. Left: Schematic of the change in phase of the oscillation during immobility epochs that were longer than 25 s and that occurred during the oscillatory sequences. Right: 44 of these epochs from the same 3 mice as in (a). As in the schematic on the left, each line represents the progression of the phase of the oscillation (from –π to π rad) as a function of time. The start of each immobility epoch is aligned at t = 0, and the epoch lasts for as long as the line continues. Different epochs have different lengths, covering a range from 25 s to 258 s. For visualization purposes only the first 120 s are displayed (3 of the epochs were truncated; these had durations of 127.9 (first column, second row), 258.2 (third column, bottom row), 136.1 s (fourth column, second row)). Sudden transitions from π to –π rad reflect the periodic nature of the sequences. <bold>c</bold>. Number of completed laps on the wheel per sequence as a function of the sequence number after pooling sessions (range of completed laps on rotating wheel across 10 sessions = 10 to 1164 laps, median = 624 laps). Sessions are pooled for each animal separately (mouse #60584, 4 sessions; mouse #60585, 3 sessions; the third animal is shown in Fig. ##FIG##3##4d##). Each dot indicates one individual sequence. The dashed line indicates separation between sessions. A number of laps equal to 1 would indicate an approximate one-to-one mapping between the position on the wheel and the progression of one full sequence. <bold>d</bold>. To determine if sequences are associated with specific running speeds, we extracted all time bins participating in oscillatory sequences and calculated the distribution of observed speed values during those bins (blue bars; n = 167389 time bins concatenated across 314 sequences pooled over 10 oscillatory sessions, over 3 animals, bin size = 129 ms). This distribution was almost identical to the distribution of speed values observed during the full length of the sessions, which also included epochs without the oscillatory sequences (blue solid line, with and without oscillatory sequences; n = 238505 time bins across 10 oscillatory sessions, over 3 animals, bin size = 129 ms). <bold>e</bold>. As in (d) but for the distribution of acceleration values. There is no difference in the range of acceleration values during parts of the session with oscillatory sequences. <bold>f</bold>. Left: To determine whether the oscillatory sequences are modulated by onset of running we calculated the mean running speed during time intervals of 10 s right before and right after the sequence onset (one sample Wilcoxon signed-rank test on the difference between speed before and after sequence onset, <italic>n</italic> = 310 equence onsets over 10 sessions from 3 animals, <italic>p</italic> = 0.82, <italic>W</italic> = 25). Right: Same as left but only for sequences that were 10 s or more apart, i.e. for sequences belonging to different oscillatory epochs (one sample Wilcoxon signed-rank test on the difference between speed before and after sequence onset, <italic>n</italic> = 70 sequence onsets over 10 sessions from 3 animals, <italic>p</italic> = 0.12, <italic>W</italic> = 857). Note that there is no systematic change in speed after onset of sequences. Results remain unchanged if the analysis is repeated for 2 s windows before and after sequence onset (Analysis for all sequences: one sample Wilcoxon signed-rank test on the difference between speed before and after sequence onset, <italic>n</italic> = 310 equence onsets over 10 sessions from 3 animals, <italic>p</italic> = 0.82, <italic>W</italic> = 25; Analysis for all sequences that were 10 s or more apart, one sample Wilcoxon signed-rank test, <italic>n</italic> = 70 sequence onsets over 10 sessions from 3 animals, <italic>p</italic> = 1.0, <italic>W</italic> = 0). <bold>g</bold>–<bold>j</bold>. Examples of sections of sessions with increased speed after sequence onset (exceptions from the general pattern shown in (f)). Top of each panel: Raster plots, symbols as in Fig. ##FIG##1##2a## (bin size = 129 ms). Bottom of each panel: Instantaneous speed of the animal during the recording in the top panel. Length of the displayed section was 400, 1000, 400 and 500 s, respectively, for (g–j). Notice that while speed is higher after onset of the sequence in these examples, the increase of speed does not always occur right after sequence onset, but sometimes before (g,h), and sometimes tens of seconds after (i,j). Analyses were restricted to 10 oscillatory sessions in 3 animals, for which the behavioural tracking was synchronized to the imaging (Methods).</p><p>\n##SUPPL##9##Source Data##\n</p></caption></fig>", "<fig id=\"Fig8\"><label>Extended Data Fig. 3</label><caption><title>Examples of ultraslow oscillations in single cell calcium activity.</title><p><bold>a</bold>. Autocorrelation of 15 example cells’ calcium activity (one per oscillatory session). <bold>b</bold>. PSD calculated on the autocorrelation of the example cell shown in (a). The dashed line indicates the frequency at which the PSD peaks. Note that the peak is at a frequency <0.1 Hz. <bold>c</bold>. As in (a) but for the signal obtained after the calcium activity was circularly shuffled (blue) or shuffled by destroying the inter calcium event intervals (red). Note that circularly shuffling the calcium activity preserves its periodicity. <bold>d</bold>. PSD calculated on the autocorrelations in (c). Blue indicates circularly shuffled data. Red indicates data that was shuffled by destroying the inter calcium event intervals. <bold>e</bold>. Mean z-scored autocorrelation calculated over all recorded cells in the session. Error bars indicate S.E.M. Black: Experimental data. Red: Shuffled data (obtained by destroying the inter calcium event intervals). <bold>f</bold>. Mean z-scored PSD calculated over all recorded cells in the session. For each cell the PSD was calculated on the autocorrelation of the cell’s calcium activity. Error bars indicate S.E.M. Color convention as in (e). Each row shows data from one oscillatory session (15 rows in total, each row corresponds to one oscillatory session). Animal number and session number are indicated at the top.</p><p>\n##SUPPL##10##Source Data##\n</p></caption></fig>", "<fig id=\"Fig9\"><label>Extended Data Fig. 4</label><caption><title>Oscillatory sequences shown by cell sorting based on correlation or dimensionality reduction.</title><p><bold>a</bold>. Left: Because neural activity progresses sequentially, the time lag that maximizes the correlation between the calcium activity of pairs of cells increases with their distance in the correlation sorting. Sorting is performed as in Fig. ##FIG##1##2a##. Time lag is expressed in seconds, distance is expressed as the number of cells between the two cells in the sorting. Notice that for large distances (e.g. > 300 cells), the time lag to peak correlation is either larger than 60 s or close to zero. This bimodality is due to the periodicity of the MEC sequences. The dashed line indicates a linear regression (<italic>n</italic> = 301 cell pairs, , , two-sided t-test. The line was fitted to the intermediate samples to avoid the effect of the periodic boundary conditions). Right: The cross correlation between the calcium activity of pairs of cells is oscillatory and temporally shifted. Examples are shown for 3 cell pairs with different distances in the sorting based on correlation values. Orange: cells are 5 cells apart; purple: cells are 199 cells apart; green: cells are 401 cells apart. The dotted line indicates the time lag at which the cross correlation peaks within the first peak. Note that the larger the distance between the cells in the sorting, the larger the time lag that maximizes the cross correlation. <bold>b</bold>. Schematic representation of the “PCA method”. Principal component analysis (PCA) was applied to the binarized matrix of deconvolved calcium activity (“matrix of calcium activity”) of individual sessions by considering every neuron as a variable, and every time point as an observation. The first two principal components (PC1, PC2) were identified. In the plane defined by PC1 and PC2 (left), the loadings of each neuron defines a vector, which has an associated angle θ rad with respect to the axis of PC1 (in the schematic, neuron N<sub>i</sub> (orange) is characterized by an angle θ<sub>i</sub>). Neurons were sorted according to their angles θ in a descending order (right). Cyan: neuron sorting before application of the PCA method. Orange: neuron sorting after the application of the PCA method. <bold>c</bold>. Projection of neural activity during the oscillatory sequences onto a low-dimensional embedding generated by the first two principal components obtained by applying PCA to the matrix of calcium activity of each session. Each plot shows one session; all 15 oscillatory sessions from the calcium imaging data set are presented. Time is color-coded and shown in minutes, and the temporal range corresponds to all concatenated epochs with oscillatory sequences in the session. Neural trajectories are often circular, with population activity propagating along a ring-shaped manifold. The ring-shaped manifold became even more salient when we applied a non-linear dimensionality reduction method (Laplacian Eigenmaps, LEM) instead of PCA to the data (Fig. ##FIG##1##2c##, right), suggesting that at least some of the data might lie on a curved surface. <bold>d</bold>. Oscillatory sequences are not revealed with a random sorting of the cells (first row) or when the PCA sorting method is applied to circularly shuffled data (second row). Oscillatory sequences similar to those of Fig. ##FIG##1##2a,b## (with correlation sorting or PCA method) are recovered when neurons are sorted according to non-linear dimensionality reduction techniques (UMAP, Isomap, LEM, t-SNE, third to sixth row). Each row of each raster plot is a neuron, whose calcium activity is plotted as a function of time (as in Fig. ##FIG##1##2a##). Every black dot represents a time bin where a neuron was active (bin size = 129 ms). <bold>e</bold>. Raster plot of calcium activity of the session presented in Fig. ##FIG##1##2a##. Neurons are sorted according to the PCA method. For calculating the sorting, only the first (top), second (middle) and third (bottom) third of the data was used. The portion of the data used for calculating the sorting is indicated in red. Otherwise, conventions are as in Fig. ##FIG##1##2a##. This visualization was extended to a quantification for all sessions. For each session we calculated the sortings using (i) all data, (ii) the first half of the data, (iii) the second half of the data. Next we calculated the correlation between the distances in the different sortings. If sortings obtained with different chunks of data preserve the ordering of the neurons, we would expect high correlation values. We compared the obtained correlation values with the 95<sup>th</sup> percentile of a shuffled distribution obtained by shuffling the position of the cells in the sortings. When comparing sorting (i) vs. sorting (ii), (i) vs. (iii), and (ii) vs. (iii), all oscillatory sessions (15 of 15) were above the cutoff of significance (see <xref rid=\"Sec11\" ref-type=\"sec\">Methods</xref>). The high correlation values obtained in these distance estimates provide support to the fact that using different chunks of data for sorting the cells unveils the same dynamics. <bold>f</bold>. Neuropixels recording showing ultraslow sequences without prior smoothing of the data. Same data as in Fig. ##FIG##1##2f##. While in Fig. ##FIG##1##2f## spike trains were first convolved with a Gaussian kernel of width equal to 5 s and next binarized according to the mean plus one standard deviation (Methods), here the spike trains are not convolved with a Gaussian kernel. The bin size is 120 ms. The threshold for binarization of the spike trains is equal to the mean + 1.5 standard deviations. Sorting and conventions as in Fig. ##FIG##1##2f##. Example session from animal #104638. Sequences are still visible. This session had an oscillation score of 1.0. In this session we identified 12 sequences of durations spanning 18–43 s. <bold>g</bold>. Oscillatory sequences from a Neuropixels recording in a different mouse than in (f) (and Fig. ##FIG##1##2f##). Top: Similar to Fig. ##FIG##1##2f##, but from mouse #102335 (n = 410 units). Bottom: Similar to (f), but for the same session as presented in the top panel, without prior smoothing of the data. This session had an oscillation score of 0.91 (see <xref rid=\"Sec11\" ref-type=\"sec\">Methods</xref>). See comparable example sessions for calcium data in Extended Data Fig. ##FIG##9##5a##. In this session 9 sequences were identified, with durations ranging from 14 to 69 s.</p><p>\n##SUPPL##11##Source Data##\n</p></caption></fig>", "<fig id=\"Fig10\"><label>Extended Data Fig. 5</label><caption><title>Sorted raster plots for the complete MEC calcium imaging dataset.</title><p><bold>a</bold>. PCA-sorted raster plots (as in Fig. ##FIG##1##2b##) for all analysed sessions across the 5 animals in which MEC population activity was recorded, sorted by animals and day of recording. Session numbering starts the first day of habituation on the wheel, with either 5 or 15 habituation sessions. One session was recorded per day, and recordings were conducted on consecutive days. Note that sessions had lengths of approximately 1800 s or 3600 s. Oscillation score and sequence score were calculated for each session separately and are indicated at the top right corner of every plot. Scores colored in green correspond to sessions with oscillatory sequences (see panel d), scores colored in red to sessions without oscillatory sequences. <bold>b</bold>. Left: Distance <italic>d</italic> between two neurons in the PCA sorting is calculated as the difference between the angles of the vectors defined by the loadings of each neuron on PC1 and PC2 with respect to PC1. The schematic shows the distance between two neurons, one in orange and the other in green. The length of the vectors is disregarded in this quantification. Right: Joint distribution of the time lag τ that maximizes the cross-correlation between the calcium activity of any given pair of neurons and their distance <italic>d</italic> in the PCA sorting. Color code: normalized frequency, each count is a cell pair. The increasing relationship between τ and <italic>d</italic> indicates sequential organization of neural activity. <bold>c</bold>. Example sessions with (top) and without (bottom) oscillatory sequences. These sessions were recorded in the same area of the MEC in the same animal, but on different days (Mouse #60355 in panel a). Left: Raster plots of the matrices of calcium activity. Right: Joint distributions of the time lag <italic>τ</italic> that maximizes the correlation between the calcium activity of any given pair of neurons and their distance <italic>d</italic> in the PCA sorting (as in panel b). Color code: normalized frequency, each count is a cell pair. Notice the lack of linear pattern in the session without oscillatory sequences. <bold>d</bold>. Left: Distribution of oscillation scores for calcium-imaging sessions recorded in MEC (27 sessions in total over 5 animals). Each count is a session. The oscillation score quantifies the extent to which single cell calcium activity is periodic, and ranges from 0 (no oscillations) to 1 (oscillations). Dashed line: Threshold used for classifying sessions as oscillatory (oscillation score ≥ 0.72) or non-oscillatory sessions (oscillation score <0.72). The threshold was chosen based on the bimodal nature of the distribution (no values between 0.27 and 0.72). 12/27 sessions exhibited scores between 0 and 0.27 (no oscillatory sequences), and 15/27 sessions exhibited scores between 0.72 and 1 (‘oscillatory sessions’). Right: List of sessions sorted by animal and number of sessions the animals experienced on the wheel. Session numbering as in (a). Red, sessions classified as not oscillatory; green, session classified as oscillatory.</p><p>\n##SUPPL##12##Source Data##\n</p></caption></fig>", "<fig id=\"Fig11\"><label>Extended Data Fig. 6</label><caption><title>Identification of individual sequences and characterization of the oscillatory sequences.</title><p><bold>a</bold>. Top: Raster plot of the PCA-sorted matrix of calcium activity of the example session in Fig. ##FIG##1##2a##. Bottom: Phase of the oscillation calculated on the session presented in the top panel is shown in black, and phase of individual sequences is colored in cyan (bin size = 129 ms). During one sequence the phase of the oscillation traversed smoothly rad. We identified individual sequences by extracting the subset of adjacent time bins where the phase of the oscillation increased smoothly within the range rad. First the phase of the oscillation was calculated across the entire session, second discontinuities in the succession of such phases were identified and used to extract putative sequences and third, putative sequences were classified as sequences if the phase of the oscillation progressed smoothly and in an ascending manner, allowing for the exception of small fluctuations (lower than 10% of 2π, e.g. as in the sequence at 500 s). Points of sustained activity were ignored. Fractions of sequences in which the phase of the oscillation traversed 50% or more of the range rad were also analysed (for example at the beginning of this session). <bold>b</bold>. Total number of individual sequences per session, across 15 oscillatory sessions. Animal number is color-coded. Note that 4 of 5 MEC calcium imaging animals had identifiable oscillatory sequences. <bold>c</bold>. Box plot showing mean event rate as a function of sequence segment for all 15 oscillatory sessions. Each sequence was divided into 10 segments of equal length, and for each sequence segment the mean event rate was calculated as the total number of calcium events across cells divided by the length of the segment and the number of recorded cells. Red lines indicate median across sessions, the bottom and top lines in blue (bounds of box) indicate lower and upper quartiles, respectively. The length of the whiskers indicates 1.5 times the interquartile range. Red crosses show outliers that lie more than 1.5 times outside the interquartile range. The mean event rate remained approximately constant across the length of the sequence. While a non-parametric analysis revealed an overall difference (<italic>n</italic> = 15 oscillatory sessions per segment, <italic>p</italic> = 0.0052, , Friedman test), the rate change from the segment with minimum to maximum event rate was no more than 18% and there were no significant differences in the event rate between pairs of segments (Wilcoxon rank-sum test with Bonferroni correction, p > 0.05 for all pairs). <italic>p</italic> < 0.001, <italic>p</italic> < 0.01, * <italic>p</italic> < 0.05, n.s. <italic>p</italic> > 0.05. <bold>d</bold>. Box plot of sequence duration, for the 15 oscillatory sessions. Note the relatively fixed duration of sequences in individual sessions. Box plot symbols as in (c). <bold>e</bold>. Sequence durations shown separately for each animal with oscillatory sequences (421 sequences in total over 5 animals, only 4 presented sequences). For each animal all oscillatory sessions were pooled. Sequence duration was heterogenous across sessions and animals. <bold>f</bold>. Left: Box plot of the standard deviation of sequence duration within a session, in experimental and shuffled data. The standard deviation of sequence duration is smaller in the experimental data (<italic>n</italic> = 15 oscillatory sessions, 7500 shuffle realizations where sequences were randomly reassigned to the 15 sessions, preserving the original number of sequences per session, , <italic>Z</italic> = 5.08, one-tailed Wilcoxon rank-sum test). Right: Box plot of the ratio between the shortest sequence duration and the longest sequence duration for all pairs of sequences within and between sessions. This fraction is larger for sequence pairs in the within-session group (<italic>n</italic> = 15 oscillatory sessions, the mean fraction per session and group was calculated separately, , <italic>Z</italic> = 4.64, one-tailed Wilcoxon rank-sum test). Notice that for each sequence pair, the larger this ratio, the more similar the length of the sequences are. Symbols as in (c). <bold>g</bold>. Sequence duration is not correlated with the number of recorded cells in the session (<italic>n</italic> = 421 sequences across 15 oscillatory sessions, <italic>ρ</italic> = 0.02, <italic>p</italic> = 0.64, Spearman correlation, two-sided t-test). Each dot is a sequence. Animal number is color-coded as in (b). <bold>h</bold>. Fraction of the session in which the MEC population engaged in the oscillatory sequences. Session length was 30 min for mice 59914 and 60355, and 60 min for mice 60584 and 60585. The fraction of session time with oscillatory sequences varied within and across animals. <bold>i</bold>. Duration of the longest epoch with uninterrupted oscillatory sequences. Only epochs that met the strict criterion of no separation between sequences were considered. Sequences could progress uninterruptedly for minutes in each of the animals and span up to 23 consecutive sequences.</p><p>\n##SUPPL##13##Source Data##\n</p></caption></fig>", "<fig id=\"Fig12\"><label>Extended Data Fig. 7</label><caption><title>Characterization of locking degree and participation index.</title><p><bold>a</bold>. Consistency between two measures of phase locking for individual neurons. The locking degree was calculated for each cell as the length of the mean vector over the distribution of oscillation phases ([−π,π) rad) at which the calcium events occurred (bin size = 129 ms). The locking degree was consistent with the mutual information between the calcium event counts and the phase of the oscillation (bin size = 0.52 s). Scatter plots show the relation between the two measures, with each dot representing one neuron. Left: Data from the example session in Fig. ##FIG##1##2a## (n = 484 cells). Right: All neurons from all 15 oscillatory sessions are pooled (n = 6231 cells over 5 animals). Red dots indicate neurons that did not meet criteria for locking. The consistency between the two measures strengthens the conclusion that the vast majority of the neurons in MEC are locked to the oscillatory sequences. <bold>b</bold>. Distribution of preferred phases (the mean phase at which the calcium events occurred) in the population of locked neurons for all 15 oscillatory sessions. Black line indicates the preferred phases; red intervals indicate one standard deviation (calculated over the oscillation phases at which the calcium events of an individual cell occurred). Neurons are sorted according to their preferred phase in an ascending manner. Across the 15 oscillatory sessions, the smallest preferred phases ranged from −3.14 to −3.11 rad, and the largest preferred phase ranged from 3.08 to 3.14 rad, suggesting that the entire range of phases was covered. <bold>c</bold>. Phase preferences are distributed evenly across the MEC cell population. Left: The nearly-flat nature of the phase distribution is illustrated by comparing the entropy of the distribution of preferred phases in recorded (y axis) and shuffled data (x axis). H<sub>ratio</sub> is the entropy of the distribution of preferred phases (calculated as in (b)) estimated from the data and divided by the entropy of a flat distribution (H<sub>ratio</sub> = 1 if the distribution of preferred phases is perfectly flat, H<sub>ratio</sub> = 0 if all neurons have the same preferred phase). Each point in the scatterplot indicates one session (15 sessions). Horizontal error bars indicate one S.D. across shuffled realizations, and are centered around the mean across shuffled realizations. The black dashed line indicates identical values for recorded and shuffled data. Animal number if color-coded. Notice the discontinuity in the y axis between 0 and 0.85. H<sub>ratio</sub> is substantially larger for recorded data than for shuffled data. Right: Box plot of H<sub>ratio</sub> for recorded and shuffled data. For each session the 1000 shuffled realizations were averaged (<italic>n</italic> = 15 oscillatory sessions, , <italic>Z</italic> = 4.52, two-sided Wilcoxon rank-sum test). Red lines indicate median across sessions, the bottom and top lines in blue (bounds of box) indicate lower and upper quartiles, respectively. The length of the whiskers indicates 1.5 times the interquartile range. Red crosses show outliers that lie more than 1.5 times outside the interquartile range. <bold>d</bold>. Left: Box plot comparing locking degree for cells with an oscillatory frequency that was similar (relative frequency ~ 1) or different (relative frequency ≠ 1) from the sequence frequency in the example session in Fig. ##FIG##1##2a## (<italic>n</italic> = 48 cells in each group from a total of 484 cells in the recorded session, , <italic>Z</italic> = 6.63, two-sided Wilcoxon rank-sum test). Right: As left panel but for the locking degree across all 15 oscillatory sessions, including the example in the left panel (<italic>n</italic> = 15 sessions over 5 animals, , <italic>Z</italic> = 4.19, two-sided Wilcoxon rank-sum test). Ten per cent of the total number of cells was used to define each of the groups with similar (relative frequency ~ 1) and different (relative frequency ≠ 1) oscillatory frequency as compared to the sequence frequency. Relative frequency was calculated for each cell as the oscillatory frequency of the cell’s calcium activity divided by the sequence frequency in the session. Box plot symbols as in (c). Note that cells with relative frequency similar to 1 are more locked to the phase of the oscillation. For all percentages considered to define similar and different groups (5, 10, 20, 30, 40, and 50%) the p-values were significant. <bold>e</bold>. Histogram showing the distribution of single-cell oscillatory frequency divided by the sequence frequency of the session (n = 6231 cells pooled across 15 oscillatory sessions). A value of 1.0 indicates that single-cell and sequence frequency coincide. The left and right dashed lines indicate 25<sup>th</sup> (0.52) and 75<sup>th</sup> (1.08) percentiles respectively. Note that for approximately half of the data the oscillatory frequency is very similar at single-cell and population level. <bold>f</bold>. The oscillatory sequences remain visible after excluding increasing fractions of neurons and keeping only those with the lowest locking degree. Each row shows a PCA-sorted raster plot (left, rasterplot conventions as in Fig. ##FIG##1##2b##) and the corresponding joint distributions of the time lag <italic>τ</italic> that maximizes the correlation between the calcium activity of neuron pairs and their distance <italic>d</italic> in the PCA sorting (right, symbols as in Extended Data Fig. ##FIG##9##5b##). The fraction of included neurons is indicated on top of the raster plot. For building the raster plots, neurons were sorted according to their locking degree value and neurons with the highest locking degrees were removed. <bold>g</bold>. Examples of different participation degrees in 3 example neurons from the session in Fig. ##FIG##1##2a##. Top: PCA sorted raster plot of the calcium matrix shown in Fig. ##FIG##1##2a##. Calcium events from the neuron with high participation index (PI, 0.72) are highlighted in light blue, from the neuron with intermediate PI (0.56) in purple, and from the neuron with low PI (0.36) in orange. Bottom three panels: Z-scored fluorescence calcium signals as a function of time from the above neurons with high (top), intermediate (middle), and low (bottom) PIs. Colored arrows represent the time points at which the oscillatory sequences are at the neuron’s preferred phase. Notice how the neuron with high PI tends to exhibit a peak in the calcium signal for most of the sequences. Neurons with intermediate and low PIs demonstrate the same but to a lesser extent, with the calcium signal not peaking in each sequence. <bold>h</bold>. Similar to (d), but for the participation index. Box plot symbols as in (c). Left: Data from the example session shown in Fig. ##FIG##1##2a## (<italic>n</italic> = 48 cells in each group, <italic>p</italic> = 0.51, <italic>Z</italic> = 0.66, two-sided Wilcoxon rank-sum test). Right: As left panel but for data pooled across 15 oscillatory sessions. The mean participation index was calculated for each group (“relative frequency ~ 1” and “relative frequency ≠ 1”) and each session separately and the data was then pooled across sessions (<italic>n</italic> = 15 sessions, <italic>p</italic> = 0.56, <italic>Z</italic> = 0.58, two-sided Wilcoxon rank-sum test). For all percentages considered to define the similar and different groups (5, 10, 20, 30, 40, and 50%) the p-values were non-significant. *** <italic>p</italic> < 0.001, n.s. <italic>p</italic> > 0.05.</p><p>\n##SUPPL##14##Source Data##\n</p></caption></fig>", "<fig id=\"Fig13\"><label>Extended Data Fig. 8</label><caption><title>The oscillatory sequences are not topographically organized.</title><p><bold>a</bold>. 2D histograms of differences in preferred phase between pairs of neurons and their anatomical distance in the FoV for all 15 oscillatory sessions (5 animals, of which 4 had oscillatory sequences). Preferred phases were calculated as the mean oscillation phase at which the calcium events occurred (after pooling all sequences in a session and not on each sequence separately; see Fig. ##FIG##2##3f, g## for one individual sequence). Each histogram was built using N(N-1)/2 samples, where N is the total number of recorded cells in the session. One count is a cell pair, the color bar indicates normalized frequency. The absolute Pearson correlation values were calculated for each session, and ranged from 8.5 x 10<sup>−5</sup> to 0.015. Only session 6 from animal #60585 (first row, fourth column) had a correlation value above the 95<sup>th</sup> percentile of a shuffled distribution built by shuffling the preferred phases in the FoV (1/15, probability = 0.37, binomial probability distribution; not statistically significant at a chance level of 5%). For the participation index (not shown) the correlation values were also very small and ranged from 9.3 x 10<sup>−4</sup> to 0.040. Out of 15 oscillatory sessions, 2 sessions (sessions 6 and 8 from animal #60584, correlation = 0.033 and 0.040 respectively) were classified as significant (2/15 sessions, probability = 0.13, binomial distribution, not statistically significant at a chance level of 5%). <bold>b</bold>. Analysis of similarity of preferred phases within spatial bins for one single example sequence (number 19) of the session presented in Fig. ##FIG##1##2a##. Similarity was calculated as the mean vector length (MVL) of the distribution of preferred phases in the spatial bin. In the presence of travelling waves, large MVL values in every bin are expected. Top: The FoV is binned into 6×6 bins, each of size 100 um x 100 um. The heat map shows the number of cells located within each spatial bin. Counts are color coded. Bottom: Each panel indicates a spatial bin in the FoV, and shows the shuffled distribution of MVL values obtained after shuffling the preferred phases in the FoV (histogram), the 95<sup>th</sup> percentile of the shuffled distribution (dotted blue line), and the MVL calculated on experimental data (dotted red line). To have good statistics only spatial bins that had more than 10 neurons were included in the quantifications. The plots that are missing are for bins with 10 or fewer cells, as indicated in the heat map. When using 100 μm x 100 μm bins, only 17 bins had more than 10 cells. From the 17 bins, one was classified as having similar phases (1/17, probability = 0.37, binomial distribution, not statistically significant at a chance level of 5%); when using 200 μm x 200 μm, only one bin out of eight with more than 10 cells was classified as having cells with similar phases (1/8, probability = 0.28, binomial distribution, not statistically significant at a chance level of 5%). When all sequences across all calcium imaging sessions are considered (n = 421, 15 oscillatory sessions over 5 animals), the MVL values calculated on experimental data ranged from 0.0082 to 0.98 (the 95<sup>th</sup> percentile MVL value was 0.3399, i.e. small), and were larger than the cutoff for significance in 121 out of 2448 spatial bins (121/2448, smaller than expected at a chance level of 0.05: 122/2448). This analysis was focused on the degree of similarity between preferred phases in spatial bins. In order to avoid small cell sample effects, we performed a second analysis based on the difference in preferred phases for all pairs of cells that were located within small neighborhoods in the FoV (Methods). We expected that in the presence of travelling waves the mean and median of the distributions of differences in preferred phases of cell pairs within small neighborhoods would be smaller than expected by chance. For neighborhoods of 50 μm, only 16 out of 421 sequences had a mean below the cutoff for significance (16/421, smaller than expected at a chance level of 0.05: 21/421), and 16 out of 421 sequences a median below the cutoff for significance (16/421, smaller than expected at a chance level of 0.05: 21/421). For neighborhoods of 100 μm, 16 and 19 sequences (out of 421) were below the cutoff for the mean and median, respectively (16/421 and 19/451, both below a chance level of 0.05: 21/421). For neighborhoods of 200 μm, 25 sequences were slightly above the cutoff for the mean and 18 were below the cutoff for the median (chance level of 0.05: 21/421). <bold>c</bold>. Similar to (b), but with spatial bins of 200 μm x 200 μm. For all sequences, the MVL values calculated on experimental data ranged from 0.0037 to 0.975 (the median of MVL values was 0.3105, i.e. small), and were larger than the cutoff for significance in 115 spatial bins out of 2392 (115/2392, smaller than expected at a chance level of 0.05: 120/2392). The lack of similarity in preferred phases within spatial bins is inconsistent with a coherent oscillation in that spatial bin, and therefore inconsistent with the presence of travelling waves. <bold>d</bold>. Top: Rasterplots showing one example sequence from the session in Fig. ##FIG##1##2a## (sequence #19). Y axis: Neuron #. X axis: Time (s). Each panel shows the same sequence, and a total of 150 s (the length of the illustrated sequence). Neurons that were active in one particular time bin are indicated in red. The visualized time bin is indicated at the top of each panel (bin size = 1 s). Middle: Anatomical distribution of the population activity in each of the time bins in the top panel (bin size is now 5 s). The FoV (600 μm x 600 μm) was divided into 50×50 square spatial bins. The total number of calcium events across cells in one spatial bin is color coded (yellow indicates high activity, purple no activity). The big red dots indicate the position of the center-of-mass (COM) of the population activity in that time bin. Bottom: Similar to the middle panel, but for one shuffle realization in which the position of the cells was randomly shuffled within the FoV. <bold>e</bold>. Quantification of the flow of the COM for the example sequence shown in (d). Cumulative distance travelled, quantified as the sum of the distances travelled by the COM between consecutive time points (bin size = 5 s), in experimental data (dotted red line), in shuffled data (blue histogram, built by shuffling the positions of the cells in the FoV 500 times), and the 5<sup>th</sup> and 95<sup>th</sup> percentile of the shuffled distribution (dotted blue and green lines, respectively). The data shows no significant difference from cumulative distances expected by chance. <bold>f.</bold> Quantification of the flow of the COM for all sequences. Cumulative normalized frequency of the cumulative distance travelled in experimental data (n = 421 sequences, orange) and the median of the shuffled distributions (n = 421 sequences, blue). Out of 421 sequences, 21 were below the cutoff for significance (21/421, at the chance level of 0.05: 21/421, bin size = 5 s). The results are similar when changing the temporal bin size used for the quantifications (23/421 for bin size = 1 s, 23/421 for bin size = 2 s, chance level of 0.05: 21/421).</p><p>\n##SUPPL##15##Source Data##\n</p></caption></fig>", "<fig id=\"Fig14\"><label>Extended Data Fig. 9</label><caption><title>Analysis of ensemble activation during the oscillatory sequences.</title><p><bold>a</bold>. Schematic of calcium activity merging steps (data are not included in this panel). We began by sorting the neurons according to the PCA method. Next, in successive iterations, or merging steps, we added up the calcium activity of pairs of consecutive neurons (merging step = 1) or consecutive ensembles (merging step > 1). <bold>b</bold>. Participation index (PI) as a function of merging step (mean ± S.D.). Black trace, example session in Fig. ##FIG##1##2a##; red trace, all 15 oscillatory sessions. The more neurons per ensemble, the higher the participation index of the ensemble. Note that the participation index plateaus after 5 merging steps, which corresponds to approximately 10 ensembles in most of the sessions (two-sided Wilcoxon rank-sum test to compare the participation indexes in merging steps 5 and 6; Black trace: <italic>n</italic> = 30 PIs in merging step 5, <italic>n</italic> = 15 PIs in merging step 6, <italic>p</italic> = 0.23, <italic>Z</italic> = 1.20; Red trace: <italic>n</italic> = 15 PIs in merging step 5 and 6, PIs of each merging step were averaged for each session separately, <italic>p</italic> = 0.14, <italic>Z</italic> = 1.49). <bold>c</bold>. Schematic of the process for splitting neurons into ensembles of co-active cells. Neurons sorted according to the PCA method are allocated to 10 equally sized ensembles (color-coded). Note that the participation index plateaued after 5 merging iterations, consisting of approximately 10 ensembles depending on the session (panel b). <bold>d</bold>. To quantify the temporal progression of the population activity at the time scale at which the oscillatory sequences evolved, we calculated, for each session, an oscillation bin size. This bin size is proportional to the inverse of the peak frequency of the PSD calculated on the phase of the oscillation, and hence captures the time scale at which the sequences progress. The oscillation bin size is shown for each of the 15 oscillatory sessions (4 out of 5 animals, those that had oscillatory sequences). <bold>e</bold>. Schematic of the method used for quantifying temporal dynamics of ensemble activity. For each session and each ensemble we calculated the mean ensemble activity at each time bin (oscillation bin size). Only the ensemble with the highest activity within each time bin (red rectangle) was considered. The number of transitions between ensembles in adjacent time bins divided by the total number of transitions was used to calculate the transition matrices in (g). <bold>f</bold>. The ensemble with the highest activity in each time bin, indicated in yellow and calculated as in (e), plotted as a function of time for the example session in Fig. ##FIG##1##2a##. All other ensembles are indicated in purple. Notice that the transformation in (e) preserves the oscillatory sequences. <bold>g</bold>. Left: Matrix of transition probabilities between pairs of ensembles at consecutive time points. Rows indicate the ensemble at time point <italic>t</italic>, columns indicate the ensemble at time point <italic>t</italic> + 1. Data are from the example session in Fig. ##FIG##1##2a## (bin size = 15.12 s). Right: Same as left panel but for one shuffle realization. Transition probabilities are color coded. In the left diagram, note the higher probability of transitions between consecutive ensembles (increased probabilities near the diagonal), the directionality of transitions (increased probabilities above diagonal) and the periodic boundary conditions in ensemble activation (presence of transitions from ensemble 10 to ensemble 1). <bold>h</bold>. Box plot showing transition probabilities between consecutive ensembles for all 15 oscillatory sessions. The probabilities remain approximately constant across transitions between ensemble pairs (<italic>n</italic> = 15 oscillatory sessions per transition, <italic>p</italic> = 0.56, , Friedman test), and there were no significant differences between pairs of transitions (two-sided Wilcoxon rank-sum test with Bonferroni correction, <italic>p</italic> > 0.05 for all transitions). Transitions from ensemble 10 to ensemble 1 were equally frequent as transitions between consecutive ensembles, as expected from the periodic nature of the sequences. Red lines indicate median across sessions, the bottom and top lines in blue (bounds of box) indicate lower and upper quartiles, respectively. The length of the whiskers indicates 1.5 times the interquartile range. Red crosses show outliers that lie more than 1.5 times outside the interquartile range. <bold>i</bold>. Probability of sequential ensemble activation as a function of the number of ensembles that are sequentially activated (mean ± S.D.; For 3–9 ensembles: <italic>n</italic> = 15 oscillatory sessions over 5 animals, 7500 shuffle realizations; , , , , respectively, range of <italic>Z</italic> values: 6.45 to 59.18, one-tailed Wilcoxon rank-sum test). Orange, recorded data; blue, shuffled data. For each session, the probability of sequential ensemble activation was calculated over 500 shuffled realizations, and shuffled realizations were pooled across sessions. The recorded data contained significantly longer sequences than the shuffled control. Probability of sequential activation of ≥ 3 ensembles in recorded data = 0.62; probability of sequential activation of ≥ 3 ensembles in shuffled data = 0.27. <bold>j</bold>. Percentage of sessions with significant sequence score in sessions classified as oscillatory vs non-oscillatory. In MEC sessions with oscillatory sequences, 100% (15 of 15) of the sessions showed significant sequence scores, while in MEC sessions without oscillations, 41% (5 of 12) of the sessions demonstrated significant sequence scores. For corresponding raster plots, see Extended Data Fig. ##FIG##9##5a##. **<italic>p</italic> < 0.01, ns <italic>p</italic> > 0.05.</p><p>\n##SUPPL##16##Source Data##\n</p></caption></fig>", "<fig id=\"Fig15\"><label>Extended Data Fig. 10</label><caption><title>Histology showing imaging location in animals with FoVs in parasubiculum and visual cortex.</title><p><bold>a</bold>. Histological determination of prism location in mice that were implanted more medially, touching parasubiculum more than MEC. Top: Maximum intensity projection of 50 μm thick sagittal brain sections (sections acquired with an LSM 880, 20x). Three consecutive sections from the same mouse are shown, from the most lateral (left) to the most medial (right). Green is GCaMP6m signal, while red is DiI signal (used to demarcate ventrolateral corner of the prism, as in Extended Data Fig. ##FIG##5##1##). Scale bar is 400 μm. The white stippled line encapsulates the superficial layers of the parasubiculum (PaS). Dorsal PaS on top, layer 1 on the left. Bottom: Estimated location of the field of view (FoV) on a flat map encompassing MEC (brown outline) and PaS (yellow outline). The blue dot marks the location of the pin used to demarcate the most lateral-ventral border of the prism, while the green square inset shows the microscope FoV. Inset images show maximum intensity projections of the FoV. Dorsoventral (DV), and mediolateral (ML) axes are indicated. <bold>b</bold>. Location of the ventro-lateral edge of the prism in stereotactic coordinates, and area of the FoV occupied by cells expressing GCaMP6m for each PaS-imaged animal. <bold>c</bold>. Histological determination of imaging location in the visual cortex (VIS) of three mice that underwent calcium imaging. Green is GCaMP6m signal. Images are taken from coronal slices, and zoomed in on visual cortex (Scale bar is 100 μm; L1 at the top, L6 at the bottom). Dorsal pole of the brain is on top. Maximum intensity projection, LSM 880, 20x.</p></caption></fig>", "<fig id=\"Fig16\"><label>Extended Data Fig. 11</label><caption><title>Lack of oscillatory sequences in parasubiculum and visual cortex.</title><p><bold>a</bold>. Alternative sorting methods, as in Extended Data Fig. ##FIG##8##4d##, but applied to sessions recorded in the PaS (left) or VIS (right). The PCA sorting method applied to temporally shuffled data did not unveil oscillatory sequences (first row). No oscillatory sequences were recovered when neurons were sorted according to their correlation values (second row), or according to different dimensionality reduction techniques (UMAP, Isomap, LEM, t-SNE). Each row of each raster plot shows the calcium activity of a single neuron, with activity plotted as a function of time, as in Fig ##FIG##1##2a##. Every dot indicates that one neuron was active at one specific time bin (bin size = 129 ms). Sequence scores and oscillation scores are presented in Fig. ##FIG##4##5e,f##. <bold>b</bold>,<bold>c</bold>. Joint distributions of time lag τ that maximizes the cross-correlation between any given pair of neurons and their distance <italic>d</italic> in the PCA sorting (as in Extended Data Fig. ##FIG##9##5b##), applied to the recordings in Fig. ##FIG##4##5e## (PaS) and Fig. ##FIG##4##5f## (VIS). Normalized frequency is color-coded. Notice lack of linear relationship between <italic>d</italic> and τ, in contrast to Extended Data Fig. ##FIG##9##5b##. <bold>d</bold>,<bold>e</bold>. Projection of the neural activity onto the low-dimensional embedding defined by the first two principal components obtained from applying PCA to the matrix of calcium activity of the PaS session (d) and the VIS session (e) shown in Fig. ##FIG##4##5e, f##. Bin size = 8.5 s. Note lack of obvious ring topology. Time is color-coded. <bold>f</bold>. Transition probabilities between ensembles across consecutive time bins (bin size <italic>~</italic> 8.5 s, Methods) for the PaS example session in Fig. ##FIG##4##5e## (left) and the VIS example session in Fig. ##FIG##4##5f## (right). <bold>g</bold>. Probability of sequential ensemble activation as a function of the number of ensembles that are sequentially activated in PaS (left) and VIS (right) (mean ± S.D.). Orange, recorded data (25 PaS sessions; 19 VIS sessions); blue, shuffled data. For each session, the probability of sequential ensemble activation was calculated over 500 shuffled realizations, and shuffled realizations were pooled across sessions for each brain area separately. Probability is shown on a log-scale. In PaS the probability of long sequences was significantly larger in experimental data than in shuffled data (<italic>n</italic> = 25 PaS sessions, 12500 shuffled realizations; For 2 ensembles: <italic>p</italic> = 0.998, <italic>Z</italic> = −2.90; For 3–7 ensembles: range of <italic>p</italic> values: to 0.036, range of <italic>Z</italic> values: 1.80 to 3.25, one-tailed Wilcoxon rank-sum test). This was not the case in VIS (<italic>n</italic> = 19 VIS sessions, 9500 shuffled realizations; For 2 ensembles: <italic>p</italic> = 0.106, <italic>Z</italic> = 1.25; For 3–6 ensembles: range of <italic>p</italic> values: 0.087 to 0.999, range of <italic>Z</italic> values: −3.34 to 1.36, one-tailed Wilcoxon rank-sum test). <bold>h</bold>. Percentage of sessions with significant sequence score (MEC oscillatory sessions: 15 of 15, PaS: 7 of 25; VIS: 1 of 19). The sequence score quantifies the probability of observing sequential activation of 3 or more ensembles. <bold>i</bold>. Distribution of oscillation scores for the entire calcium imaging data set, as in Extended Data Fig. ##FIG##9##5d## (19 VIS sessions over 3 animals, 25 PaS sessions over 4 animals, 27 MEC sessions of which 15 were classified as oscillatory, over 5 animals). Dashed line indicates threshold for classifying sessions as oscillatory with reference to the MEC data. Note that the bars for different brain regions sometimes overlap, and that bars are colored with transparency for visualization purposes (e.g. for sessions in PaS with oscillation score 0, the count is 24). <bold>j</bold>. Normalized distribution of the Pearson correlation values (absolute value) between the activity of cell pairs in VIS (green) and in PaS (yellow). Each dot indicates the mean across sessions (25 PaS sessions, 19 VIS sessions; all sessions in the data set were used, not only those with behavioural tracking synchronized to imaging), error bars indicate S.E.M. Probability is shown on a log-scale. <bold>k</bold>. Same as (j) but for the distribution of values of coactivity for all sessions recorded in PaS (yellow) and VIS (green). Coactivity was estimated for each session separately as the fraction of the recorded cells that was simultaneously active in 129 ms bins. Probability is shown on a log-scale. <bold>l</bold>. Cumulative probability of correlation values calculated between the calcium activity of one cell and the speed of the animal in that session for MEC (n = 4595 cells from 10 sessions, 3 animals), PaS (n = 6851 cells from 18 sessions, 3 animals), VIS (n = 6037 cells from 19 sessions, 3 animals). Only sessions for which the imaging data was synchronized to behavioural data were used (VIS-PaS: , <italic>Z</italic> = 27.7; VIS-MEC: , <italic>Z</italic> = 19.6, MEC-PaS: , <italic>Z</italic> = 6.80, one tailed Wilcoxon rank-sum test). Calcium activity was more correlated with the speed of the animal in visual cortex than in MEC and PaS.</p><p>\n##SUPPL##17##Source Data##\n</p></caption></fig>", "<fig id=\"Fig17\"><label>Extended Data Fig. 12</label><caption><title>Ultraslow oscillatory sequences might serve as template for generating new activity patterns.</title><p><bold>a</bold>. Schematic of the model. 500 input units (depicted in purple, on the left) are connected to an output unit (blue dot, on the right). Each arrow represents a connection, and there are 500 connections in total. The activity of each input unit is represented by a periodic Gaussian bump. The means of the Gaussians are temporally displaced such that, all together, input units fire in a sequence (the ‘template’). <bold>b</bold>. The weights between the input units and the output unit were trained such that the output unit reproduced a target activity. Two targets were considered: a ramp of activity, which is deterministic (left), and an Ornstein-Uhlenbeck process, which is stochastic (right). Both targets had a characteristic time scale of 100 s. <bold>c</bold>. Input (left) and output (right) activity for three different sequence lengths: sequences are very slow (top row), slow (middle) or fast (bottom) as compared to the targets. Left: Heat map of the activity of the input units as a function of time, in seconds. Blue indicates no activity, yellow indicates maximal activity. Top: Sequences are 400 s long. Middle: Sequences are 120 s long. Bottom: Sequences are 30 s long. Right: Output response corresponding to the three sequences regimes: very slow, slow and fast sequences. Target response is shown in blue, obtained response after training the networks using the sequences as input is shown in orange. Note that when the sequences have a time scale that is similar (middle) or slower (top) than the targets, the output unit can reproduce the desired target. <bold>d</bold>. Mean total error, calculated as the difference between the target and the obtained response after training, as a function of the input sequence length. Top: Target is the ramp of activity. Bottom: Target is the Ornstein-Uhlenbeck process.</p><p>\n##SUPPL##18##Source Data##\n</p></caption></fig>" ]	[]	[ "<inline-formula id=\"IEq1\"><alternatives><tex-math id=\"M1\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$n=T/(W\\,\\bullet \\,{\\rm{SF}})$$\\end{document}</tex-math><mml:math id=\"M2\"><mml:mrow><mml:mi>n</mml:mi><mml:mo>=</mml:mo><mml:mi>T</mml:mi><mml:mo>/</mml:mo><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>W</mml:mi><mml:mspace width=\"0.15em\"/><mml:mo>∙</mml:mo><mml:mspace width=\"0.15em\"/><mml:mi mathvariant=\"normal\">SF</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq2\"><alternatives><tex-math id=\"M3\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${(i}_{\\max },j)$$\\end{document}</tex-math><mml:math id=\"M4\"><mml:mrow><mml:msub><mml:mrow><mml:mo>(</mml:mo><mml:mi>i</mml:mi></mml:mrow><mml:mrow><mml:mi>max</mml:mi></mml:mrow></mml:msub><mml:mo>,</mml:mo><mml:mi>j</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq3\"><alternatives><tex-math id=\"M5\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$j=\\mathrm{1,2},\\ldots ,N$$\\end{document}</tex-math><mml:math id=\"M6\"><mml:mrow><mml:mi>j</mml:mi><mml:mo>=</mml:mo><mml:mn>1,2</mml:mn><mml:mo>,</mml:mo><mml:mo>…</mml:mo><mml:mo>,</mml:mo><mml:mi>N</mml:mi></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq4\"><alternatives><tex-math id=\"M7\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\theta }_{i}={\\rm{arctg}}\\,\\left(\\frac{{l}_{{\\rm{PC}}2}^{i}}{{l}_{{\\rm{PC}}1}^{i}}\\right)\\in \\left[-{\\rm{\\pi }},{\\rm{\\pi 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mathvariant=\"normal\">π</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq5\"><alternatives><tex-math id=\"M9\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${l}_{{\\rm{PC}}j}^{i}$$\\end{document}</tex-math><mml:math id=\"M10\"><mml:msubsup><mml:mrow><mml:mi>l</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">PC</mml:mi><mml:mi>j</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msubsup></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq6\"><alternatives><tex-math id=\"M11\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} 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\n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\rm{PC}}{i}_{t}\\left(t\\right)$$\\end{document}</tex-math><mml:math id=\"M20\"><mml:mrow><mml:mi mathvariant=\"normal\">PC</mml:mi><mml:msub><mml:mrow><mml:mi>i</mml:mi></mml:mrow><mml:mrow><mml:mi>t</mml:mi></mml:mrow></mml:msub><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:mi>t</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq11\"><alternatives><tex-math id=\"M21\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\rm{PC}}{1}_{t}\\left(t\\right),{\\rm{PC}}{2}_{t}\\left(t\\right)$$\\end{document}</tex-math><mml:math id=\"M22\"><mml:mrow><mml:mi mathvariant=\"normal\">PC</mml:mi><mml:msub><mml:mrow><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mi>t</mml:mi></mml:mrow></mml:msub><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:mi>t</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced><mml:mo>,</mml:mo><mml:mi mathvariant=\"normal\">PC</mml:mi><mml:msub><mml:mrow><mml:mn>2</mml:mn></mml:mrow><mml:mrow><mml:mi>t</mml:mi></mml:mrow></mml:msub><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:mi>t</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow></mml:math></alternatives></inline-formula>", "<disp-formula id=\"Equ1\"><label>1</label><alternatives><tex-math id=\"M23\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\varphi \\left(t\\right)={\\rm{arctg}}\\,\\left(\\frac{{\\rm{PC}}{2}_{t}\\left(t\\right)}{{\\rm{PC}}{1}_{t}\\left(t\\right)}\\right).$$\\end{document}</tex-math><mml:math id=\"M24\" display=\"block\"><mml:mrow><mml:mi>φ</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:mi>t</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced><mml:mo>=</mml:mo><mml:mi mathvariant=\"normal\">arctg</mml:mi><mml:mrow><mml:mrow><mml:mspace width=\"0.15em\"/><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mfrac><mml:mrow><mml:mi mathvariant=\"normal\">PC</mml:mi><mml:msub><mml:mrow><mml:mn>2</mml:mn></mml:mrow><mml:mrow><mml:mi>t</mml:mi></mml:mrow></mml:msub><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:mi>t</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">PC</mml:mi><mml:msub><mml:mrow><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mi>t</mml:mi></mml:mrow></mml:msub><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:mi>t</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow></mml:mfrac></mml:mrow></mml:mfenced></mml:mrow></mml:mrow><mml:mo>.</mml:mo></mml:mrow></mml:math></alternatives></disp-formula>", "<inline-formula id=\"IEq12\"><alternatives><tex-math id=\"M25\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$[-{\\rm{\\pi }},{\\rm{\\pi }})$$\\end{document}</tex-math><mml:math id=\"M26\"><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mo>−</mml:mo><mml:mi mathvariant=\"normal\">π</mml:mi><mml:mo>,</mml:mo><mml:mi mathvariant=\"normal\">π</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq13\"><alternatives><tex-math id=\"M27\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\varphi \\left(t\\right)$$\\end{document}</tex-math><mml:math id=\"M28\"><mml:mrow><mml:mi>φ</mml:mi><mml:mrow><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:mi>t</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq14\"><alternatives><tex-math id=\"M29\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\varphi \\left(t\\right)$$\\end{document}</tex-math><mml:math id=\"M30\"><mml:mrow><mml:mi>φ</mml:mi><mml:mrow><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:mi>t</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq15\"><alternatives><tex-math id=\"M31\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\theta }_{i}={\\rm{arctg}}\\,\\left(\\frac{{l}_{{\\rm{PC}}2}^{i}}{{l}_{{\\rm{PC}}1}^{i}}\\right)\\in \\left[-{\\rm{\\pi }},{\\rm{\\pi }}\\right)$$\\end{document}</tex-math><mml:math id=\"M32\"><mml:mrow><mml:msub><mml:mrow><mml:mi>θ</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mi mathvariant=\"normal\">arctg</mml:mi><mml:mrow><mml:mrow><mml:mspace width=\"0.15em\"/><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mfrac><mml:mrow><mml:msubsup><mml:mrow><mml:mi>l</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">PC</mml:mi><mml:mn>2</mml:mn></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msubsup></mml:mrow><mml:mrow><mml:msubsup><mml:mrow><mml:mi>l</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">PC</mml:mi><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msubsup></mml:mrow></mml:mfrac></mml:mrow></mml:mfenced></mml:mrow></mml:mrow><mml:mo>∈</mml:mo><mml:mfenced close=\")\" open=\"[\"><mml:mrow><mml:mrow><mml:mrow><mml:mo>−</mml:mo><mml:mi mathvariant=\"normal\">π</mml:mi><mml:mo>,</mml:mo><mml:mi mathvariant=\"normal\">π</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq16\"><alternatives><tex-math id=\"M33\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${l}_{{\\rm{PC}}j}^{i}$$\\end{document}</tex-math><mml:math id=\"M34\"><mml:msubsup><mml:mrow><mml:mi>l</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">PC</mml:mi><mml:mi>j</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msubsup></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq17\"><alternatives><tex-math id=\"M35\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\left[-{\\rm{\\pi }},{\\rm{\\pi }}\\right)$$\\end{document}</tex-math><mml:math id=\"M36\"><mml:mfenced close=\")\" open=\"[\"><mml:mrow><mml:mrow><mml:mrow><mml:mo>−</mml:mo><mml:mi mathvariant=\"normal\">π</mml:mi><mml:mo>,</mml:mo><mml:mi mathvariant=\"normal\">π</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:math></alternatives></inline-formula>", "<disp-formula id=\"Equ2\"><label>2</label><alternatives><tex-math id=\"M37\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${d}_{i,j}={(\\theta }_{i}-{\\theta }_{j}),$$\\end{document}</tex-math><mml:math id=\"M38\" display=\"block\"><mml:mrow><mml:msub><mml:mrow><mml:mi>d</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mrow><mml:mo>(</mml:mo><mml:mi>θ</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>−</mml:mo><mml:msub><mml:mrow><mml:mi>θ</mml:mi></mml:mrow><mml:mrow><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mo>)</mml:mo><mml:mo>,</mml:mo></mml:mrow></mml:math></alternatives></disp-formula>", "<inline-formula id=\"IEq18\"><alternatives><tex-math id=\"M39\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$1\\le i\\le N,1\\le j\\le N$$\\end{document}</tex-math><mml:math id=\"M40\"><mml:mrow><mml:mn>1</mml:mn><mml:mo>≤</mml:mo><mml:mi>i</mml:mi><mml:mo>≤</mml:mo><mml:mi>N</mml:mi><mml:mo>,</mml:mo><mml:mn>1</mml:mn><mml:mo>≤</mml:mo><mml:mi>j</mml:mi><mml:mo>≤</mml:mo><mml:mi>N</mml:mi></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq19\"><alternatives><tex-math id=\"M41\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\varDelta \\tau =\\frac{496\\,{\\rm{s}}}{96} \\sim 5\\,{\\rm{s}}$$\\end{document}</tex-math><mml:math id=\"M42\"><mml:mrow><mml:mi>Δ</mml:mi><mml:mi>τ</mml:mi><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mn>496</mml:mn><mml:mspace width=\"0.25em\"/><mml:mi mathvariant=\"normal\">s</mml:mi></mml:mrow><mml:mrow><mml:mn>96</mml:mn></mml:mrow></mml:mfrac><mml:mo>~</mml:mo><mml:mn>5</mml:mn><mml:mspace width=\"0.10em\"/><mml:mi mathvariant=\"normal\">s</mml:mi></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq20\"><alternatives><tex-math id=\"M43\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\varDelta d=\\frac{2{\\rm{\\pi }}}{11} \\sim 0.57\\,{\\rm{rad}}$$\\end{document}</tex-math><mml:math id=\"M44\"><mml:mrow><mml:mi>Δ</mml:mi><mml:mi>d</mml:mi><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mn>2</mml:mn><mml:mi mathvariant=\"normal\">π</mml:mi></mml:mrow><mml:mrow><mml:mn>11</mml:mn></mml:mrow></mml:mfrac><mml:mo>~</mml:mo><mml:mn>0.57</mml:mn><mml:mspace width=\"0.25em\"/><mml:mi mathvariant=\"normal\">rad</mml:mi></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq21\"><alternatives><tex-math id=\"M45\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\varphi \\left(t\\right)$$\\end{document}</tex-math><mml:math id=\"M46\"><mml:mrow><mml:mi>φ</mml:mi><mml:mrow><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:mi>t</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq22\"><alternatives><tex-math id=\"M47\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\rm{si}}{\\rm{n}}\\left(\\varphi \\left(t\\right)\\right)$$\\end{document}</tex-math><mml:math id=\"M48\"><mml:mrow><mml:mi mathvariant=\"normal\">si</mml:mi><mml:mi mathvariant=\"normal\">n</mml:mi><mml:mrow><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:mi>φ</mml:mi><mml:mrow><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:mi>t</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq23\"><alternatives><tex-math id=\"M49\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\rm{si}}{\\rm{n}}\\left(\\varphi \\left(t\\right)\\right)$$\\end{document}</tex-math><mml:math id=\"M50\"><mml:mrow><mml:mi mathvariant=\"normal\">si</mml:mi><mml:mi mathvariant=\"normal\">n</mml:mi><mml:mrow><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:mi>φ</mml:mi><mml:mrow><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:mi>t</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq24\"><alternatives><tex-math id=\"M51\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$1\\le i\\le 11$$\\end{document}</tex-math><mml:math id=\"M52\"><mml:mrow><mml:mn>1</mml:mn><mml:mo>≤</mml:mo><mml:mi>i</mml:mi><mml:mo>≤</mml:mo><mml:mn>11</mml:mn></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq25\"><alternatives><tex-math id=\"M53\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$1\\le i\\le 11$$\\end{document}</tex-math><mml:math id=\"M54\"><mml:mrow><mml:mn>1</mml:mn><mml:mo>≤</mml:mo><mml:mi>i</mml:mi><mml:mo>≤</mml:mo><mml:mn>11</mml:mn></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq26\"><alternatives><tex-math id=\"M55\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\sin \\left(\\varphi \\left(t\\right)\\right)$$\\end{document}</tex-math><mml:math id=\"M56\"><mml:mrow><mml:mi>sin</mml:mi><mml:mrow><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:mi>φ</mml:mi><mml:mrow><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:mi>t</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq27\"><alternatives><tex-math id=\"M57\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${P}_{{\\rm{osc}}}={f}_{\\max }^{-1}$$\\end{document}</tex-math><mml:math id=\"M58\"><mml:mrow><mml:msub><mml:mrow><mml:mi>P</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">osc</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:msubsup><mml:mrow><mml:mi>f</mml:mi></mml:mrow><mml:mrow><mml:mi>max</mml:mi></mml:mrow><mml:mrow><mml:mo>−</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msubsup></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq28\"><alternatives><tex-math id=\"M59\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${T}_{{\\rm{osc}}}$$\\end{document}</tex-math><mml:math id=\"M60\"><mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">osc</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<disp-formula id=\"Equ3\"><label>3</label><alternatives><tex-math id=\"M61\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${T}_{{\\rm{osc}}}=\\frac{{P}_{{\\rm{osc}}}}{10}=\\frac{1}{10\\times {f}_{\\max }}.$$\\end{document}</tex-math><mml:math id=\"M62\" display=\"block\"><mml:mrow><mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">osc</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mrow><mml:mi>P</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">osc</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mn>10</mml:mn></mml:mrow></mml:mfrac><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mn>10</mml:mn><mml:mo>×</mml:mo><mml:msub><mml:mrow><mml:mi>f</mml:mi></mml:mrow><mml:mrow><mml:mi>max</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfrac><mml:mo>.</mml:mo></mml:mrow></mml:math></alternatives></disp-formula>", "<inline-formula id=\"IEq29\"><alternatives><tex-math id=\"M63\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\sin \\left(\\varphi \\left(t\\right)\\right)$$\\end{document}</tex-math><mml:math id=\"M64\"><mml:mrow><mml:mi>sin</mml:mi><mml:mrow><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:mi>φ</mml:mi><mml:mrow><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:mi>t</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq30\"><alternatives><tex-math id=\"M65\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$[-{\\rm{\\pi }},{\\rm{\\pi }})$$\\end{document}</tex-math><mml:math id=\"M66\"><mml:mrow><mml:mo>[</mml:mo><mml:mrow><mml:mo>−</mml:mo><mml:mi mathvariant=\"normal\">π</mml:mi><mml:mo>,</mml:mo><mml:mi mathvariant=\"normal\">π</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq31\"><alternatives><tex-math id=\"M67\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\left[-{\\rm{\\pi }},{\\rm{\\pi }}\\right)$$\\end{document}</tex-math><mml:math id=\"M68\"><mml:mfenced close=\")\" open=\"[\"><mml:mrow><mml:mrow><mml:mrow><mml:mo>−</mml:mo><mml:mi mathvariant=\"normal\">π</mml:mi><mml:mo>,</mml:mo><mml:mi mathvariant=\"normal\">π</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq32\"><alternatives><tex-math id=\"M69\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\left[-{\\rm{\\pi }},{\\rm{\\pi }}\\right)$$\\end{document}</tex-math><mml:math id=\"M70\"><mml:mrow><mml:mfenced close=\")\" open=\"[\"><mml:mrow><mml:mrow><mml:mrow><mml:mo>−</mml:mo><mml:mi mathvariant=\"normal\">π</mml:mi><mml:mo>,</mml:mo><mml:mi mathvariant=\"normal\">π</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq33\"><alternatives><tex-math id=\"M71\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\left[-{\\rm{\\pi }},{\\rm{\\pi }}\\right)$$\\end{document}</tex-math><mml:math id=\"M72\"><mml:mfenced close=\")\" open=\"[\"><mml:mrow><mml:mrow><mml:mrow><mml:mo>−</mml:mo><mml:mi mathvariant=\"normal\">π</mml:mi><mml:mo>,</mml:mo><mml:mi mathvariant=\"normal\">π</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq34\"><alternatives><tex-math id=\"M73\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$j,j\\ne i$$\\end{document}</tex-math><mml:math id=\"M74\"><mml:mrow><mml:mi>j</mml:mi><mml:mo>,</mml:mo><mml:mi>j</mml:mi><mml:mo>≠</mml:mo><mml:mi>i</mml:mi></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq35\"><alternatives><tex-math id=\"M75\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\varphi \\left(t\\right)$$\\end{document}</tex-math><mml:math id=\"M76\"><mml:mrow><mml:mi>φ</mml:mi><mml:mrow><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:mi>t</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow></mml:mrow></mml:math></alternatives></inline-formula>", "<disp-formula id=\"Equa\"><alternatives><tex-math id=\"M77\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\rm{MI}}(S,P)=\\sum _{p,s}{\\rm{Prob}}(p,s){\\log }_{2}\\frac{{\\rm{Prob}}(p,s)}{{\\rm{Prob}}(p){\\rm{Prob}}(s)},$$\\end{document}</tex-math><mml:math id=\"M78\" display=\"block\"><mml:mrow><mml:mi mathvariant=\"normal\">MI</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>S</mml:mi><mml:mo>,</mml:mo><mml:mi>P</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:munder class=\"MJX-TeXAtom-OP MJX-fixedlimits\"><mml:mrow><mml:mo mathsize=\"big\">∑</mml:mo></mml:mrow><mml:mrow><mml:mi>p</mml:mi><mml:mo>,</mml:mo><mml:mi>s</mml:mi></mml:mrow></mml:munder><mml:mi mathvariant=\"normal\">Prob</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>p</mml:mi><mml:mo>,</mml:mo><mml:mi>s</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:msub><mml:mrow><mml:mi>log</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub><mml:mfrac><mml:mrow><mml:mi mathvariant=\"normal\">Prob</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>p</mml:mi><mml:mo>,</mml:mo><mml:mi>s</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">Prob</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>p</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mi mathvariant=\"normal\">Prob</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>s</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:mfrac><mml:mo>,</mml:mo></mml:mrow></mml:math></alternatives></disp-formula>", "<inline-formula id=\"IEq36\"><alternatives><tex-math id=\"M79\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\rm{Prob}}\\left(p,s\\right)$$\\end{document}</tex-math><mml:math id=\"M80\"><mml:mrow><mml:mi mathvariant=\"normal\">Prob</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:mi>p</mml:mi><mml:mo>,</mml:mo><mml:mi>s</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq37\"><alternatives><tex-math id=\"M81\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\rm{Prob}}\\left(s\\right)$$\\end{document}</tex-math><mml:math id=\"M82\"><mml:mrow><mml:mi mathvariant=\"normal\">Prob</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:mi>s</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq38\"><alternatives><tex-math id=\"M83\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\rm{Prob}}\\left(p\\right)$$\\end{document}</tex-math><mml:math id=\"M84\"><mml:mrow><mml:mi mathvariant=\"normal\">Prob</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:mi>p</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq39\"><alternatives><tex-math id=\"M85\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\frac{2{\\rm{\\pi }}}{10}$$\\end{document}</tex-math><mml:math id=\"M86\"><mml:mfrac><mml:mrow><mml:mn>2</mml:mn><mml:mi mathvariant=\"normal\">π</mml:mi></mml:mrow><mml:mrow><mml:mn>10</mml:mn></mml:mrow></mml:mfrac></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq40\"><alternatives><tex-math id=\"M87\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\rm{Prob}}\\left(p,s\\right)={\\rm{Prob}}\\left(p\\right){\\rm{Prob}}\\left(s\\right)$$\\end{document}</tex-math><mml:math id=\"M88\"><mml:mrow><mml:mi mathvariant=\"normal\">Prob</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:mi>p</mml:mi><mml:mo>,</mml:mo><mml:mi>s</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced><mml:mo>=</mml:mo><mml:mi mathvariant=\"normal\">Prob</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:mi>p</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced><mml:mi mathvariant=\"normal\">Prob</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:mi>s</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq41\"><alternatives><tex-math id=\"M89\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\varphi \\left(t\\right)$$\\end{document}</tex-math><mml:math id=\"M90\"><mml:mrow><mml:mi>φ</mml:mi><mml:mrow><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:mi>t</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq42\"><alternatives><tex-math id=\"M91\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\left[-{\\rm{\\pi }},{\\rm{\\pi }}\\right)$$\\end{document}</tex-math><mml:math id=\"M92\"><mml:mfenced close=\")\" open=\"[\"><mml:mrow><mml:mrow><mml:mrow><mml:mo>−</mml:mo><mml:mi mathvariant=\"normal\">π</mml:mi><mml:mo>,</mml:mo><mml:mi mathvariant=\"normal\">π</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq43\"><alternatives><tex-math id=\"M93\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\frac{2{\\rm{\\pi }}}{40}$$\\end{document}</tex-math><mml:math id=\"M94\"><mml:mfrac><mml:mrow><mml:mn>2</mml:mn><mml:mi mathvariant=\"normal\">π</mml:mi></mml:mrow><mml:mrow><mml:mn>40</mml:mn></mml:mrow></mml:mfrac></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq44\"><alternatives><tex-math id=\"M95\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\left[-{\\rm{\\pi }}+j\\frac{2{\\rm{\\pi }}}{40},-{\\rm{\\pi }}+(\\,j+1)\\frac{2{\\rm{\\pi }}}{40}\\right)$$\\end{document}</tex-math><mml:math id=\"M96\"><mml:mrow><mml:mrow><mml:mfenced close=\")\" open=\"[\"><mml:mspace width=\"-0.25em\"/><mml:mrow><mml:mo>−</mml:mo><mml:mi mathvariant=\"normal\">π</mml:mi><mml:mo>+</mml:mo><mml:mi>j</mml:mi><mml:mfrac><mml:mrow><mml:mn>2</mml:mn><mml:mi mathvariant=\"normal\">π</mml:mi></mml:mrow><mml:mrow><mml:mn>40</mml:mn></mml:mrow></mml:mfrac><mml:mo>,</mml:mo><mml:mo>−</mml:mo><mml:mi mathvariant=\"normal\">π</mml:mi><mml:mo>+</mml:mo><mml:mrow><mml:mo>(</mml:mo><mml:mspace width=\"0.20em\"/><mml:mrow><mml:mi>j</mml:mi><mml:mo>+</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mfrac><mml:mrow><mml:mn>2</mml:mn><mml:mi mathvariant=\"normal\">π</mml:mi></mml:mrow><mml:mrow><mml:mn>40</mml:mn></mml:mrow></mml:mfrac></mml:mrow></mml:mfenced></mml:mrow></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq45\"><alternatives><tex-math id=\"M97\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\frac{2{\\rm{\\pi }}}{10}$$\\end{document}</tex-math><mml:math id=\"M98\"><mml:mfrac><mml:mrow><mml:mn>2</mml:mn><mml:mi mathvariant=\"normal\">π</mml:mi></mml:mrow><mml:mrow><mml:mn>10</mml:mn></mml:mrow></mml:mfrac></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq46\"><alternatives><tex-math id=\"M99\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${H}_{Q}=-{\\sum }_{x=1}^{10}Q\\left(x\\right){\\log }_{2}\\left(Q\\left(x\\right)\\right)$$\\end{document}</tex-math><mml:math id=\"M100\"><mml:mrow><mml:msub><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mrow><mml:mi>Q</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mo>−</mml:mo><mml:msubsup><mml:mrow><mml:mo mathsize=\"big\">∑</mml:mo></mml:mrow><mml:mrow><mml:mi>x</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mn>10</mml:mn></mml:mrow></mml:msubsup><mml:mi>Q</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:mi>x</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced><mml:msub><mml:mrow><mml:mi>log</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:mi>Q</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:mi>x</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow></mml:math></alternatives></inline-formula>", "<disp-formula id=\"Equ4\"><label>5</label><alternatives><tex-math id=\"M101\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${H}_{{\\rm{ratio}}}=\\frac{{H}_{Q}}{{H}_{{\\rm{flat}}}}$$\\end{document}</tex-math><mml:math id=\"M102\" display=\"block\"><mml:mrow><mml:msub><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">ratio</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mrow><mml:mi>Q</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">flat</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfrac></mml:mrow></mml:math></alternatives></disp-formula>", "<inline-formula id=\"IEq47\"><alternatives><tex-math id=\"M103\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${H}_{{\\rm{flat}}}$$\\end{document}</tex-math><mml:math id=\"M104\"><mml:msub><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">flat</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq48\"><alternatives><tex-math id=\"M105\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${H}_{{\\rm{flat}}}=3.32$$\\end{document}</tex-math><mml:math id=\"M106\"><mml:mrow><mml:msub><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">flat</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mn>3.32</mml:mn></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq49\"><alternatives><tex-math id=\"M107\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${H}_{{\\rm{ratio}}}$$\\end{document}</tex-math><mml:math id=\"M108\"><mml:msub><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">ratio</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq50\"><alternatives><tex-math id=\"M109\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${H}_{{\\rm{ratio}}}$$\\end{document}</tex-math><mml:math id=\"M110\"><mml:msub><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">ratio</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq51\"><alternatives><tex-math id=\"M111\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${H}_{{\\rm{ratio}}}$$\\end{document}</tex-math><mml:math id=\"M112\"><mml:msub><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">ratio</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<disp-formula id=\"Equb\"><alternatives><tex-math id=\"M113\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\rm{COM}}=\\frac{1}{M}\\mathop{\\sum }\\limits_{i=1}^{N}{m}_{i}{{\\bf{r}}}_{i},$$\\end{document}</tex-math><mml:math id=\"M114\" display=\"block\"><mml:mrow><mml:mi mathvariant=\"normal\">COM</mml:mi><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mi>M</mml:mi></mml:mrow></mml:mfrac><mml:munderover accent=\"false\" accentunder=\"false\"><mml:mrow><mml:mo mathsize=\"big\">∑</mml:mo></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mi>N</mml:mi></mml:mrow></mml:munderover><mml:msub><mml:mrow><mml:mi>m</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mi mathvariant=\"bold\">r</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>,</mml:mo></mml:mrow></mml:math></alternatives></disp-formula>", "<inline-formula id=\"IEq52\"><alternatives><tex-math id=\"M115\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$M={\\sum }_{i=1}^{N}{m}_{i}$$\\end{document}</tex-math><mml:math id=\"M116\"><mml:mrow><mml:mi>M</mml:mi><mml:mo>=</mml:mo><mml:msubsup><mml:mrow><mml:mo mathsize=\"big\"> ∑</mml:mo></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mi>N</mml:mi></mml:mrow></mml:msubsup><mml:msub><mml:mrow><mml:mi>m</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq53\"><alternatives><tex-math id=\"M117\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\frac{N}{2}$$\\end{document}</tex-math><mml:math id=\"M118\"><mml:mfrac><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:mfrac></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq54\"><alternatives><tex-math id=\"M119\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\frac{N}{4}$$\\end{document}</tex-math><mml:math id=\"M120\"><mml:mfrac><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mn>4</mml:mn></mml:mrow></mml:mfrac></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq55\"><alternatives><tex-math id=\"M121\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\frac{N}{{2}^{j}}$$\\end{document}</tex-math><mml:math id=\"M122\"><mml:mfrac><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:msup><mml:mrow><mml:mn>2</mml:mn></mml:mrow><mml:mrow><mml:mi>j</mml:mi></mml:mrow></mml:msup></mml:mrow></mml:mfrac></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq56\"><alternatives><tex-math id=\"M123\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\frac{N}{{2}^{j-1}}$$\\end{document}</tex-math><mml:math id=\"M124\"><mml:mfrac><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:msup><mml:mrow><mml:mn>2</mml:mn></mml:mrow><mml:mrow><mml:mi>j</mml:mi><mml:mo>−</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:mfrac></mml:math></alternatives></inline-formula>", "<disp-formula id=\"Equc\"><alternatives><tex-math id=\"M125\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\mathop{\\sigma }\\limits^{ \\sim }}_{i}=\\frac{{\\sigma }_{2i-1}+{\\sigma }_{2i}}{2}\\,\\,\\,\\,\\,i=1,\\ldots ,\\frac{N}{{2}^{j}}$$\\end{document}</tex-math><mml:math id=\"M126\" display=\"block\"><mml:msub><mml:mrow><mml:mrow><mml:mover><mml:mrow><mml:mi>σ</mml:mi></mml:mrow><mml:mo>~</mml:mo></mml:mover></mml:mrow></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mrow><mml:mrow><mml:mi>σ</mml:mi></mml:mrow></mml:mrow><mml:mrow><mml:mn>2</mml:mn><mml:mi>i</mml:mi><mml:mo>−</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mrow><mml:mrow><mml:mi>σ</mml:mi></mml:mrow></mml:mrow><mml:mrow><mml:mn>2</mml:mn><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mn>2</mml:mn></mml:mfrac><mml:mspace width=\"thickmathspace\"/><mml:mspace width=\"thickmathspace\"/><mml:mspace width=\"thickmathspace\"/><mml:mspace width=\"thickmathspace\"/><mml:mspace width=\"thickmathspace\"/><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mo>…</mml:mo><mml:mo>,</mml:mo><mml:mfrac><mml:mi>N</mml:mi><mml:msup><mml:mrow><mml:mn>2</mml:mn></mml:mrow><mml:mrow><mml:mi>j</mml:mi></mml:mrow></mml:msup></mml:mfrac></mml:math></alternatives></disp-formula>", "<inline-formula id=\"IEq57\"><alternatives><tex-math id=\"M127\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\widetilde{\\sigma }}_{i}$$\\end{document}</tex-math><mml:math id=\"M128\"><mml:msub><mml:mrow><mml:mover accent=\"true\"><mml:mrow><mml:mi>σ</mml:mi></mml:mrow><mml:mo>~</mml:mo></mml:mover></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq59\"><alternatives><tex-math id=\"M129\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\sigma }_{2i-1}$$\\end{document}</tex-math><mml:math id=\"M130\"><mml:msub><mml:mrow><mml:mi>σ</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn><mml:mi>i</mml:mi><mml:mo>−</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq60\"><alternatives><tex-math id=\"M131\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\sigma }_{2i}$$\\end{document}</tex-math><mml:math id=\"M132\"><mml:msub><mml:mrow><mml:mi>σ</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq61\"><alternatives><tex-math id=\"M133\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$j-1$$\\end{document}</tex-math><mml:math id=\"M134\"><mml:mrow><mml:mi>j</mml:mi><mml:mo>−</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq62\"><alternatives><tex-math id=\"M135\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\sigma }_{2i-1}$$\\end{document}</tex-math><mml:math id=\"M136\"><mml:msub><mml:mrow><mml:mi>σ</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn><mml:mi>i</mml:mi><mml:mo>−</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq63\"><alternatives><tex-math id=\"M137\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\sigma }_{2i}$$\\end{document}</tex-math><mml:math id=\"M138\"><mml:msub><mml:mrow><mml:mi>σ</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq64\"><alternatives><tex-math id=\"M139\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$2i-1$$\\end{document}</tex-math><mml:math id=\"M140\"><mml:mrow><mml:mn>2</mml:mn><mml:mi>i</mml:mi><mml:mo>−</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq65\"><alternatives><tex-math id=\"M141\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$2i$$\\end{document}</tex-math><mml:math id=\"M142\"><mml:mrow><mml:mn>2</mml:mn><mml:mi>i</mml:mi></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq66\"><alternatives><tex-math id=\"M143\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$1\\le i\\le N$$\\end{document}</tex-math><mml:math id=\"M144\"><mml:mrow><mml:mn>1</mml:mn><mml:mo>≤</mml:mo><mml:mi>i</mml:mi><mml:mo>≤</mml:mo><mml:mi>N</mml:mi></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq67\"><alternatives><tex-math id=\"M145\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\frac{N}{10}$$\\end{document}</tex-math><mml:math id=\"M146\"><mml:mfrac><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mn>10</mml:mn></mml:mrow></mml:mfrac></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq68\"><alternatives><tex-math id=\"M147\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\frac{N}{10}$$\\end{document}</tex-math><mml:math id=\"M148\"><mml:mfrac><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mn>10</mml:mn></mml:mrow></mml:mfrac></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq69\"><alternatives><tex-math id=\"M149\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\left\\{\\left(i-1\\right)\\times \\frac{N}{10}+1,\\left(i-1\\right)\\times \\frac{N}{10}+2,\\ldots ,i\\times \\frac{N}{10}\\right\\}$$\\end{document}</tex-math><mml:math id=\"M150\"><mml:mrow><mml:mrow><mml:mfenced close=\"}\" open=\"{\"><mml:mrow><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mo>−</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:mrow></mml:mrow></mml:mfenced><mml:mo>×</mml:mo><mml:mfrac><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mn>10</mml:mn></mml:mrow></mml:mfrac><mml:mo>+</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mo>−</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:mrow></mml:mrow></mml:mfenced><mml:mo>×</mml:mo><mml:mfrac><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mn>10</mml:mn></mml:mrow></mml:mfrac><mml:mo>+</mml:mo><mml:mn>2</mml:mn><mml:mo>,</mml:mo><mml:mo>…</mml:mo><mml:mo>,</mml:mo><mml:mi>i</mml:mi><mml:mo>×</mml:mo><mml:mfrac><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mn>10</mml:mn></mml:mrow></mml:mfrac></mml:mrow></mml:mfenced></mml:mrow></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq70\"><alternatives><tex-math id=\"M151\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\frac{N}{10}$$\\end{document}</tex-math><mml:math id=\"M152\"><mml:mfrac><mml:mrow><mml:mi>N</mml:mi></mml:mrow><mml:mrow><mml:mn>10</mml:mn></mml:mrow></mml:mfrac></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq71\"><alternatives><tex-math id=\"M153\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\lfloor \\frac{N}{10}\\rfloor $$\\end{document}</tex-math><mml:math id=\"M154\"><mml:mrow><mml:mo>⌊</mml:mo><mml:mfrac><mml:mi>N</mml:mi><mml:mn>10</mml:mn></mml:mfrac><mml:mo>⌋</mml:mo></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq72\"><alternatives><tex-math id=\"M155\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$N-9\\times \\lfloor \\frac{N}{10}\\rfloor $$\\end{document}</tex-math><mml:math id=\"M156\"><mml:mi>N</mml:mi><mml:mo>−</mml:mo><mml:mn>9</mml:mn><mml:mo>×</mml:mo><mml:mrow><mml:mo>⌊</mml:mo><mml:mfrac><mml:mi>N</mml:mi><mml:mn>10</mml:mn></mml:mfrac><mml:mo>⌋</mml:mo></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq73\"><alternatives><tex-math id=\"M157\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\lfloor x\\rfloor =max\\{m\\in {\\mathbb{N}}/m\\le x\\}$$\\end{document}</tex-math><mml:math id=\"M158\"><mml:mo fence=\"false\" stretchy=\"false\">⌊</mml:mo><mml:mi>x</mml:mi><mml:mo fence=\"false\" stretchy=\"false\">⌋</mml:mo><mml:mo>=</mml:mo><mml:mo form=\"prefix\" movablelimits=\"true\">max</mml:mo><mml:mo fence=\"false\" stretchy=\"false\">{</mml:mo><mml:mi>m</mml:mi><mml:mo>∈</mml:mo><mml:mrow><mml:mrow><mml:mi mathvariant=\"double-struck\">N</mml:mi></mml:mrow></mml:mrow><mml:mrow><mml:mo>/</mml:mo></mml:mrow><mml:mi>m</mml:mi><mml:mo>≤</mml:mo><mml:mi>x</mml:mi><mml:mo fence=\"false\" stretchy=\"false\">}</mml:mo></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq74\"><alternatives><tex-math id=\"M159\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\mathbb{N}}$$\\end{document}</tex-math><mml:math id=\"M160\"><mml:mi mathvariant=\"double-struck\">N</mml:mi></mml:math></alternatives></inline-formula>", "<disp-formula id=\"Equd\"><alternatives><tex-math id=\"M161\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\left\\{\\begin{array}{l}\\left\\{(i-1)\\times \\lfloor \\frac{N}{10}\\rfloor +1,(i-1)\\times \\lfloor \\frac{N}{10}\\rfloor +2,\\ldots ,i\\times \\lfloor \\frac{N}{10}\\rfloor \\right\\},\\,1\\le {\\rm{e}}{\\rm{n}}{\\rm{s}}{\\rm{e}}{\\rm{m}}{\\rm{b}}{\\rm{l}}{\\rm{e}}\\le 9\\\\ \\left\\{9\\times \\lfloor \\frac{N}{10}\\rfloor +1,9\\times \\lfloor \\frac{N}{10}\\rfloor +2,\\ldots ,N\\right\\},\\,{\\rm{e}}{\\rm{n}}{\\rm{s}}{\\rm{e}}{\\rm{m}}{\\rm{b}}{\\rm{l}}{\\rm{e}}=10\\end{array}\\right.$$\\end{document}</tex-math><mml:math id=\"M162\" display=\"block\"><mml:mrow><mml:mo stretchy=\"true\">{</mml:mo><mml:mtable columnalign=\"left\" columnspacing=\"1em\" rowspacing=\"4pt\"><mml:mtr><mml:mtd><mml:mfenced close=\"}\" open=\"{\"><mml:mrow><mml:mo>(</mml:mo><mml:mi>i</mml:mi><mml:mo>−</mml:mo><mml:mn>1</mml:mn><mml:mo>)</mml:mo><mml:mo>×</mml:mo><mml:mo fence=\"false\" stretchy=\"true\">⌊</mml:mo><mml:mfrac><mml:mi>N</mml:mi><mml:mn>10</mml:mn></mml:mfrac><mml:mo fence=\"false\" stretchy=\"true\">⌋</mml:mo><mml:mo>+</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mo>(</mml:mo><mml:mi>i</mml:mi><mml:mo>−</mml:mo><mml:mn>1</mml:mn><mml:mo>)</mml:mo><mml:mo>×</mml:mo><mml:mo fence=\"false\" stretchy=\"true\">⌊</mml:mo><mml:mfrac><mml:mi>N</mml:mi><mml:mn>10</mml:mn></mml:mfrac><mml:mo fence=\"false\" stretchy=\"true\">⌋</mml:mo><mml:mo>+</mml:mo><mml:mn>2</mml:mn><mml:mo>,</mml:mo><mml:mo>…</mml:mo><mml:mo>,</mml:mo><mml:mi>i</mml:mi><mml:mo>×</mml:mo><mml:mo fence=\"false\" stretchy=\"true\">⌊</mml:mo><mml:mfrac><mml:mi>N</mml:mi><mml:mn>10</mml:mn></mml:mfrac><mml:mo fence=\"false\" stretchy=\"true\">⌋</mml:mo></mml:mrow></mml:mfenced><mml:mo>,</mml:mo><mml:mspace width=\"thickmathspace\"/><mml:mn>1</mml:mn><mml:mo>≤</mml:mo><mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">e</mml:mi><mml:mi mathvariant=\"normal\">n</mml:mi><mml:mi mathvariant=\"normal\">s</mml:mi><mml:mi mathvariant=\"normal\">e</mml:mi><mml:mi mathvariant=\"normal\">m</mml:mi><mml:mi mathvariant=\"normal\">b</mml:mi><mml:mi mathvariant=\"normal\">l</mml:mi><mml:mi mathvariant=\"normal\">e</mml:mi></mml:mrow></mml:mrow><mml:mo>≤</mml:mo><mml:mn>9</mml:mn></mml:mtd></mml:mtr><mml:mtr><mml:mtd><mml:mfenced close=\"}\" open=\"{\"><mml:mrow><mml:mn>9</mml:mn><mml:mo>×</mml:mo><mml:mo fence=\"false\" stretchy=\"true\">⌊</mml:mo><mml:mfrac><mml:mi>N</mml:mi><mml:mn>10</mml:mn></mml:mfrac><mml:mo fence=\"false\" stretchy=\"true\">⌋</mml:mo><mml:mo>+</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mn>9</mml:mn><mml:mo>×</mml:mo><mml:mo fence=\"false\" stretchy=\"true\">⌊</mml:mo><mml:mfrac><mml:mi>N</mml:mi><mml:mn>10</mml:mn></mml:mfrac><mml:mo fence=\"false\" stretchy=\"true\">⌋</mml:mo><mml:mo>+</mml:mo><mml:mn>2</mml:mn><mml:mo>,</mml:mo><mml:mo>…</mml:mo><mml:mo>,</mml:mo><mml:mi>N</mml:mi></mml:mrow></mml:mfenced><mml:mo>,</mml:mo><mml:mspace width=\"thickmathspace\"/><mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">e</mml:mi><mml:mi mathvariant=\"normal\">n</mml:mi><mml:mi mathvariant=\"normal\">s</mml:mi><mml:mi mathvariant=\"normal\">e</mml:mi><mml:mi mathvariant=\"normal\">m</mml:mi><mml:mi mathvariant=\"normal\">b</mml:mi><mml:mi mathvariant=\"normal\">l</mml:mi><mml:mi mathvariant=\"normal\">e</mml:mi></mml:mrow></mml:mrow><mml:mo>=</mml:mo><mml:mn>10</mml:mn></mml:mtd></mml:mtr></mml:mtable><mml:mo fence=\"true\" stretchy=\"true\" symmetric=\"true\"/></mml:mrow></mml:math></alternatives></disp-formula>", "<inline-formula id=\"IEq75\"><alternatives><tex-math id=\"M163\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$i\\to j$$\\end{document}</tex-math><mml:math id=\"M164\"><mml:mrow><mml:mi>i</mml:mi><mml:mo>→</mml:mo><mml:mi>j</mml:mi></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq76\"><alternatives><tex-math id=\"M165\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\left(i,j\\right)$$\\end{document}</tex-math><mml:math id=\"M166\"><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mo>,</mml:mo><mml:mi>j</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq77\"><alternatives><tex-math id=\"M167\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$i\\to j$$\\end{document}</tex-math><mml:math id=\"M168\"><mml:mrow><mml:mi>i</mml:mi><mml:mo>→</mml:mo><mml:mi>j</mml:mi></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq78\"><alternatives><tex-math id=\"M169\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${x}_{i}(t)={{\\rm{e}}}^{-\\frac{{(t-{\\mu }_{i})}^{2}}{2{{\\sigma }_{i}}^{2}}}$$\\end{document}</tex-math><mml:math id=\"M170\"><mml:mrow><mml:msub><mml:mrow><mml:mi>x</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>t</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:msup><mml:mrow><mml:mi mathvariant=\"normal\">e</mml:mi></mml:mrow><mml:mrow><mml:mo>−</mml:mo><mml:mfrac><mml:mrow><mml:msup><mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>t</mml:mi><mml:mo>−</mml:mo><mml:msub><mml:mrow><mml:mi>μ</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:mrow><mml:mrow><mml:mn>2</mml:mn><mml:msup><mml:mrow><mml:msub><mml:mrow><mml:mi>σ</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:mfrac></mml:mrow></mml:msup></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq79\"><alternatives><tex-math id=\"M171\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$1\\le i\\le 500,\\,0\\le t\\le 240\\,{\\rm{s}}$$\\end{document}</tex-math><mml:math id=\"M172\"><mml:mrow><mml:mn>1</mml:mn><mml:mo>≤</mml:mo><mml:mi>i</mml:mi><mml:mo>≤</mml:mo><mml:mn>500</mml:mn><mml:mo>,</mml:mo><mml:mspace width=\"0.25em\"/><mml:mn>0</mml:mn><mml:mo>≤</mml:mo><mml:mi>t</mml:mi><mml:mo>≤</mml:mo><mml:mn>240</mml:mn><mml:mspace width=\"0.25em\"/><mml:mi mathvariant=\"normal\">s</mml:mi></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq80\"><alternatives><tex-math id=\"M173\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\sigma }_{i}=\\sigma =7.6\\,{\\rm{s}}$$\\end{document}</tex-math><mml:math id=\"M174\"><mml:mrow><mml:msub><mml:mrow><mml:mi>σ</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mi>σ</mml:mi><mml:mo>=</mml:mo><mml:mn>7.6</mml:mn><mml:mspace width=\"0.25em\"/><mml:mi mathvariant=\"normal\">s</mml:mi></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq81\"><alternatives><tex-math id=\"M175\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\forall i$$\\end{document}</tex-math><mml:math id=\"M176\"><mml:mrow><mml:mo>∀</mml:mo><mml:mi>i</mml:mi></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq82\"><alternatives><tex-math id=\"M177\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\mu }_{i}$$\\end{document}</tex-math><mml:math id=\"M178\"><mml:msub><mml:mrow><mml:mi>μ</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<disp-formula id=\"Eque\"><alternatives><tex-math id=\"M179\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${F}_{{\\rm{R}}}(t)=\\frac{t}{100}$$\\end{document}</tex-math><mml:math id=\"M180\" display=\"block\"><mml:mrow><mml:msub><mml:mrow><mml:mi>F</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">R</mml:mi></mml:mrow></mml:msub><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>t</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mi>t</mml:mi></mml:mrow><mml:mrow><mml:mn>100</mml:mn></mml:mrow></mml:mfrac></mml:mrow></mml:math></alternatives></disp-formula>", "<disp-formula id=\"Equf\"><alternatives><tex-math id=\"M181\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\frac{{\\rm{d}}{F}_{{\\rm{OU}}}}{{\\rm{d}}t}=\\frac{{\\mu }_{{\\rm{OU}}}-{F}_{{\\rm{OU}}}(t)}{\\tau }+{\\sigma }_{{\\rm{OU}}}\\xi (t)$$\\end{document}</tex-math><mml:math id=\"M182\" display=\"block\"><mml:mrow><mml:mfrac><mml:mrow><mml:mi mathvariant=\"normal\">d</mml:mi><mml:msub><mml:mrow><mml:mi>F</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">OU</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">d</mml:mi><mml:mi>t</mml:mi></mml:mrow></mml:mfrac><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mrow><mml:mi>μ</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">OU</mml:mi></mml:mrow></mml:msub><mml:mo>−</mml:mo><mml:msub><mml:mrow><mml:mi>F</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">OU</mml:mi></mml:mrow></mml:msub><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>t</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:mi>τ</mml:mi></mml:mrow></mml:mfrac><mml:mo>+</mml:mo><mml:msub><mml:mrow><mml:mi>σ</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">OU</mml:mi></mml:mrow></mml:msub><mml:mi>ξ</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>t</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:math></alternatives></disp-formula>", "<inline-formula id=\"IEq83\"><alternatives><tex-math id=\"M183\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\varDelta {w}_{i}=\\eta e{x}_{i}$$\\end{document}</tex-math><mml:math id=\"M184\"><mml:mrow><mml:mi>Δ</mml:mi><mml:msub><mml:mrow><mml:mi>w</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mi>η</mml:mi><mml:mi>e</mml:mi><mml:msub><mml:mrow><mml:mi>x</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq84\"><alternatives><tex-math id=\"M185\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$1\\le i\\le 500$$\\end{document}</tex-math><mml:math id=\"M186\"><mml:mrow><mml:mn>1</mml:mn><mml:mo>≤</mml:mo><mml:mi>i</mml:mi><mml:mo>≤</mml:mo><mml:mn>500</mml:mn></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq85\"><alternatives><tex-math id=\"M187\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$e=\\sum _{t}T(t)-WX(t),$$\\end{document}</tex-math><mml:math id=\"M188\"><mml:mi>e</mml:mi><mml:mo>=</mml:mo><mml:munder><mml:mo>∑</mml:mo><mml:mrow><mml:mi>t</mml:mi></mml:mrow></mml:munder><mml:mi>T</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo></mml:mrow><mml:mo>−</mml:mo><mml:mi>W</mml:mi><mml:mi>X</mml:mi><mml:mo>(</mml:mo><mml:mi>t</mml:mi><mml:mo>)</mml:mo><mml:mo>,</mml:mo></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq86\"><alternatives><tex-math id=\"M189\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${R}^{2}=0.17$$\\end{document}</tex-math><mml:math id=\"M190\"><mml:mrow><mml:msup><mml:mrow><mml:mi>R</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup><mml:mo>=</mml:mo><mml:mn>0.17</mml:mn></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq87\"><alternatives><tex-math id=\"M191\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$p=2\\times {10}^{-14}$$\\end{document}</tex-math><mml:math id=\"M192\"><mml:mrow><mml:mi>p</mml:mi><mml:mo>=</mml:mo><mml:mn>2</mml:mn><mml:mo>×</mml:mo><mml:msup><mml:mrow><mml:mn>10</mml:mn></mml:mrow><mml:mrow><mml:mo>−</mml:mo><mml:mn>14</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq88\"><alternatives><tex-math id=\"M193\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\in [-{\\rm{\\pi }},{\\rm{\\pi }})$$\\end{document}</tex-math><mml:math id=\"M194\"><mml:mrow><mml:mo>∈</mml:mo><mml:mo>[</mml:mo><mml:mo>−</mml:mo><mml:mi mathvariant=\"normal\">π</mml:mi><mml:mo>,</mml:mo><mml:mi mathvariant=\"normal\">π</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq89\"><alternatives><tex-math id=\"M195\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\left[-{\\rm{\\pi }},{\\rm{\\pi }}\\right)$$\\end{document}</tex-math><mml:math id=\"M196\"><mml:mrow><mml:mfenced close=\")\" open=\"[\"><mml:mrow><mml:mrow><mml:mrow><mml:mo>−</mml:mo><mml:mi mathvariant=\"normal\">π</mml:mi><mml:mo>,</mml:mo><mml:mi mathvariant=\"normal\">π</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq90\"><alternatives><tex-math id=\"M197\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$[\\,-\\,{\\rm{\\pi }},{\\rm{\\pi }})$$\\end{document}</tex-math><mml:math id=\"M198\"><mml:mrow><mml:mo>[</mml:mo><mml:mspace width=\"-0.20em\"/><mml:mo>−</mml:mo><mml:mspace width=\"-0.10em\"/><mml:mi mathvariant=\"normal\">π</mml:mi><mml:mo>,</mml:mo><mml:mi mathvariant=\"normal\">π</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq91\"><alternatives><tex-math id=\"M199\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\left[-{\\rm{\\pi }},{\\rm{\\pi }}\\right)$$\\end{document}</tex-math><mml:math id=\"M200\"><mml:mrow><mml:mfenced close=\")\" open=\"[\"><mml:mrow><mml:mrow><mml:mrow><mml:mo>−</mml:mo><mml:mi mathvariant=\"normal\">π</mml:mi><mml:mo>,</mml:mo><mml:mi mathvariant=\"normal\">π</mml:mi></mml:mrow></mml:mrow></mml:mrow></mml:mfenced></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq92\"><alternatives><tex-math id=\"M201\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{\\rm{\\chi }}}^{2}=23.5$$\\end{document}</tex-math><mml:math id=\"M202\"><mml:mrow><mml:msup><mml:mi mathvariant=\"normal\">χ</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mo>=</mml:mo><mml:mn>23.5</mml:mn></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq93\"><alternatives><tex-math id=\"M203\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$p=1.8\\times {10}^{-7}$$\\end{document}</tex-math><mml:math id=\"M204\"><mml:mrow><mml:mi>p</mml:mi><mml:mo>=</mml:mo><mml:mn>1.8</mml:mn><mml:mo>×</mml:mo><mml:msup><mml:mrow><mml:mn>10</mml:mn></mml:mrow><mml:mrow><mml:mo>−</mml:mo><mml:mn>7</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq94\"><alternatives><tex-math id=\"M205\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$p=1.7\\times {10}^{-6}$$\\end{document}</tex-math><mml:math id=\"M206\"><mml:mrow><mml:mi>p</mml:mi><mml:mo>=</mml:mo><mml:mn>1.7</mml:mn><mml:mo>×</mml:mo><mml:msup><mml:mrow><mml:mn>10</mml:mn></mml:mrow><mml:mrow><mml:mo>−</mml:mo><mml:mn>6</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq95\"><alternatives><tex-math id=\"M207\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$p=6\\times {10}^{-6}$$\\end{document}</tex-math><mml:math id=\"M208\"><mml:mrow><mml:mi>p</mml:mi><mml:mo>=</mml:mo><mml:mn>6</mml:mn><mml:mo>×</mml:mo><mml:msup><mml:mrow><mml:mn>10</mml:mn></mml:mrow><mml:mrow><mml:mo>−</mml:mo><mml:mn>6</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq96\"><alternatives><tex-math id=\"M209\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$p=3.4\\times {10}^{-11}$$\\end{document}</tex-math><mml:math id=\"M210\"><mml:mrow><mml:mi>p</mml:mi><mml:mo>=</mml:mo><mml:mn>3.4</mml:mn><mml:mo>×</mml:mo><mml:msup><mml:mrow><mml:mn>10</mml:mn></mml:mrow><mml:mrow><mml:mo>−</mml:mo><mml:mn>11</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq97\"><alternatives><tex-math id=\"M211\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$p=2.8\\times {10}^{-5}$$\\end{document}</tex-math><mml:math id=\"M212\"><mml:mrow><mml:mi>p</mml:mi><mml:mo>=</mml:mo><mml:mn>2.8</mml:mn><mml:mo>×</mml:mo><mml:msup><mml:mrow><mml:mn>10</mml:mn></mml:mrow><mml:mrow><mml:mo>−</mml:mo><mml:mn>5</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq98\"><alternatives><tex-math id=\"M213\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{\\rm{\\chi }}}^{2}=7.77$$\\end{document}</tex-math><mml:math id=\"M214\"><mml:mrow><mml:msup><mml:mi mathvariant=\"normal\">χ</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mo>=</mml:mo><mml:mn>7.77</mml:mn></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq99\"><alternatives><tex-math id=\"M215\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$p=5.4\\times {10}^{-11}$$\\end{document}</tex-math><mml:math id=\"M216\"><mml:mrow><mml:mi>p</mml:mi><mml:mo>=</mml:mo><mml:mn>5.4</mml:mn><mml:mo>×</mml:mo><mml:msup><mml:mrow><mml:mn>10</mml:mn></mml:mrow><mml:mrow><mml:mo>−</mml:mo><mml:mn>11</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq100\"><alternatives><tex-math id=\"M217\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$1.0\\times {10}^{-11}$$\\end{document}</tex-math><mml:math id=\"M218\"><mml:mrow><mml:mn>1.0</mml:mn><mml:mo>×</mml:mo><mml:msup><mml:mrow><mml:mn>10</mml:mn></mml:mrow><mml:mrow><mml:mo>−</mml:mo><mml:mn>11</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq101\"><alternatives><tex-math id=\"M219\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$5.9\\times {10}^{-13}$$\\end{document}</tex-math><mml:math id=\"M220\"><mml:mrow><mml:mn>5.9</mml:mn><mml:mo>×</mml:mo><mml:msup><mml:mrow><mml:mn>10</mml:mn></mml:mrow><mml:mrow><mml:mo>−</mml:mo><mml:mn>13</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq102\"><alternatives><tex-math id=\"M221\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$4.5\\times {10}^{-49}$$\\end{document}</tex-math><mml:math id=\"M222\"><mml:mrow><mml:mn>4.5</mml:mn><mml:mo>×</mml:mo><mml:msup><mml:mrow><mml:mn>10</mml:mn></mml:mrow><mml:mrow><mml:mo>−</mml:mo><mml:mn>49</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq103\"><alternatives><tex-math id=\"M223\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$0,0,9.0\\times {10}^{-220}$$\\end{document}</tex-math><mml:math id=\"M224\"><mml:mrow><mml:mn>0</mml:mn><mml:mo>,</mml:mo><mml:mn>0</mml:mn><mml:mo>,</mml:mo><mml:mn>9.0</mml:mn><mml:mo>×</mml:mo><mml:msup><mml:mrow><mml:mn>10</mml:mn></mml:mrow><mml:mrow><mml:mo>−</mml:mo><mml:mn>220</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq104\"><alternatives><tex-math id=\"M225\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${5.7\\times 10}^{-4}$$\\end{document}</tex-math><mml:math id=\"M226\"><mml:msup><mml:mrow><mml:mn>5.7</mml:mn><mml:mo>×</mml:mo><mml:mn>10</mml:mn></mml:mrow><mml:mrow><mml:mo>−</mml:mo><mml:mn>4</mml:mn></mml:mrow></mml:msup></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq105\"><alternatives><tex-math id=\"M227\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$p=3.15\\times {10}^{-169}$$\\end{document}</tex-math><mml:math id=\"M228\"><mml:mrow><mml:mi>p</mml:mi><mml:mo>=</mml:mo><mml:mn>3.15</mml:mn><mml:mo>×</mml:mo><mml:msup><mml:mrow><mml:mn>10</mml:mn></mml:mrow><mml:mrow><mml:mo>−</mml:mo><mml:mn>169</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq106\"><alternatives><tex-math id=\"M229\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$p=1.05\\times {10}^{-85}$$\\end{document}</tex-math><mml:math id=\"M230\"><mml:mrow><mml:mi>p</mml:mi><mml:mo>=</mml:mo><mml:mn>1.05</mml:mn><mml:mo>×</mml:mo><mml:msup><mml:mrow><mml:mn>10</mml:mn></mml:mrow><mml:mrow><mml:mo>−</mml:mo><mml:mn>85</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq107\"><alternatives><tex-math id=\"M231\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$p=5.16\\times {10}^{-12}$$\\end{document}</tex-math><mml:math id=\"M232\"><mml:mrow><mml:mi>p</mml:mi><mml:mo>=</mml:mo><mml:mn>5.16</mml:mn><mml:mo>×</mml:mo><mml:msup><mml:mrow><mml:mn>10</mml:mn></mml:mrow><mml:mrow><mml:mo>−</mml:mo><mml:mn>12</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:math></alternatives></inline-formula>" ]	[]	[]	[]	[]	[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM3\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM4\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM5\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM6\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM7\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM8\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM9\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM10\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM11\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM12\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM13\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM14\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM15\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM16\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM17\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM18\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM19\"></supplementary-material>" ]	[ "<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>These authors contributed equally: Flavio Donato, May-Britt Moser, Edvard I. 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xlink:href=\"41586_2023_6864_Article_IEq100.gif\"/>", "<inline-graphic xlink:href=\"41586_2023_6864_Article_IEq101.gif\"/>", "<inline-graphic xlink:href=\"41586_2023_6864_Article_IEq102.gif\"/>", "<inline-graphic xlink:href=\"41586_2023_6864_Article_IEq103.gif\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6864_Fig14_ESM\" id=\"d32e5971\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6864_Fig15_ESM\" id=\"d32e5994\"/>", "<inline-graphic xlink:href=\"41586_2023_6864_Article_IEq104.gif\"/>", "<inline-graphic xlink:href=\"41586_2023_6864_Article_IEq105.gif\"/>", "<inline-graphic xlink:href=\"41586_2023_6864_Article_IEq106.gif\"/>", "<inline-graphic xlink:href=\"41586_2023_6864_Article_IEq107.gif\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6864_Fig16_ESM\" id=\"d32e6208\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6864_Fig17_ESM\" id=\"d32e6236\"/>" ]	[ "<media xlink:href=\"41586_2023_6864_MOESM1_ESM.pdf\"><caption><p>Reporting Summary</p></caption></media>", "<media xlink:href=\"41586_2023_6864_MOESM2_ESM.pdf\"><caption><p>Peer Review File</p></caption></media>", "<media xlink:href=\"41586_2023_6864_MOESM3_ESM.mp4\"><label>Supplementary Video 1</label><caption><p>The oscillatory sequences are not travelling waves. Motion corrected video showing the progression of 6 consecutive sequences in session 7 from mouse no. 60584. Cells were sorted as in Fig. 2b. Top left: imaging data, 5-frame moving average. Scale bar, 100 µm. Bottom: raster plot of the matrix of calcium activity, as in Fig. 2b. Cells are colored according to their position in the sorting. Top right: deconvolved calcium activity shown as opacity changes in single cells (normalized by maximum of each cell’s activity). Colour code as in bottom plot. Between 2,186 s and 2,666 s, the video was sped up by a factor of 4.</p></caption></media>", "<media xlink:href=\"41586_2023_6864_MOESM4_ESM.xlsx\"><caption><p>Source Data Fig. 1</p></caption></media>", "<media xlink:href=\"41586_2023_6864_MOESM5_ESM.xlsx\"><caption><p>Source Data Fig. 2</p></caption></media>", "<media xlink:href=\"41586_2023_6864_MOESM6_ESM.xlsx\"><caption><p>Source Data Fig. 3</p></caption></media>", "<media xlink:href=\"41586_2023_6864_MOESM7_ESM.xlsx\"><caption><p>Source Data Fig. 4</p></caption></media>", "<media xlink:href=\"41586_2023_6864_MOESM8_ESM.xlsx\"><caption><p>Source Data Fig. 5</p></caption></media>", "<media xlink:href=\"41586_2023_6864_MOESM9_ESM.xlsx\"><caption><p>Source Data Extended Data Fig. 1</p></caption></media>", "<media xlink:href=\"41586_2023_6864_MOESM10_ESM.xlsx\"><caption><p>Source Data Extended Data Fig. 2</p></caption></media>", "<media xlink:href=\"41586_2023_6864_MOESM11_ESM.xlsx\"><caption><p>Source Data Extended Data Fig. 3</p></caption></media>", "<media xlink:href=\"41586_2023_6864_MOESM12_ESM.xlsx\"><caption><p>Source Data Extended Data Fig. 4</p></caption></media>", "<media xlink:href=\"41586_2023_6864_MOESM13_ESM.xlsx\"><caption><p>Source Data Extended Data Fig. 5</p></caption></media>", "<media xlink:href=\"41586_2023_6864_MOESM14_ESM.xlsx\"><caption><p>Source Data Extended Data Fig. 6</p></caption></media>", "<media xlink:href=\"41586_2023_6864_MOESM15_ESM.xlsx\"><caption><p>Source Data Extended Data Fig. 7</p></caption></media>", "<media xlink:href=\"41586_2023_6864_MOESM16_ESM.xlsx\"><caption><p>Source Data Extended Data Fig. 8</p></caption></media>", "<media xlink:href=\"41586_2023_6864_MOESM17_ESM.xlsx\"><caption><p>Source Data Extended Data Fig. 9</p></caption></media>", "<media xlink:href=\"41586_2023_6864_MOESM18_ESM.xlsx\"><caption><p>Source Data Extended Data Fig. 11</p></caption></media>", "<media xlink:href=\"41586_2023_6864_MOESM19_ESM.xlsx\"><caption><p>Source Data Extended Data Fig. 12</p></caption></media>" ]	[{"label": ["5."], "mixed-citation": ["von der Malsburg, C. 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"], "italic": ["MATLAB Central File Exchange"], "ext-link": ["https://www.mathworks.com/matlabcentral/fileexchange/71902"]}, {"label": ["66."], "surname": ["Berens"], "given-names": ["P"], "article-title": ["CircStat: a MATLAB toolbox for circular statistics"], "source": ["J. Stat. Softw."], "year": ["2009"], "volume": ["31"], "fpage": ["1"], "lpage": ["21"], "pub-id": ["10.18637/jss.v031.i10"]}, {"label": ["67."], "surname": ["Shannon"], "given-names": ["CE"], "article-title": ["A mathematical theory of communication"], "source": ["Bell Syst. Tech. J."], "year": ["1948"], "volume": ["27"], "fpage": ["379"], "lpage": ["423"], "pub-id": ["10.1002/j.1538-7305.1948.tb01338.x"]}, {"label": ["68."], "mixed-citation": ["Cover, T. M. & Thomas, J. A. "], "italic": ["Elements of Information Theory"]}]	{ "acronym": [], "definition": [] }	76	CC BY	no	2024-01-13 00:11:06	Nature. 2024 Dec 20; 625(7994):338-344	oa_package/be/42/PMC10781645.tar.gz
PMC10781646	38057668	[]	[ "<title>Methods</title>", "<title>Cytokine injection</title>", "<p id=\"Par24\">A list of the cytokines studied and their alternative names are shown in Fig. ##FIG##0##1d## and Supplementary Table ##SUPPL##2##1##. We selected 86 cytokines representing most members of major cytokine families, including the following families: IL-1 (IL-1α, IL-1β, IL-1Ra, IL-18, IL-33, IL-36α and IL-36Ra); common γ-chain/IL-13/TSLP (IL-2, IL-4, IL-13, IL-7, TSLP, IL-9, IL-15 and IL-21); common β-chain (GM-CSF, IL-3 and IL-5); IL-6/IL-12 (IL-6, IL-11, IL-27, IL-30, IL-31, LIF, OSM, CT-1, NP, IL-12, IL-23 and IL-Y); IL-10 (IL-10, IL-19, IL-20, IL-22 and IL-24); IL-17 (IL-17A, IL-17B, IL-17C, IL-17D, IL-17E, IL-17F); interferon (type I: IFNα1, IFNβ, IFNε and IFNκ; type II: IFNγ; and type III: IFNλ2); TNF (LTα1/β2, LTα2/β1, TNF, OX40L, CD40L, FasL, CD27L, CD30L, 4-1BBL, TRAIL, RANKL, TWEAK, APRIL, BAFF, LIGHT, TL1A and GITRL); complement (C3a and C5a); growth factor (FLT3L, IL-34, M-CSF, G-CSF, SCF, EGF, VEGF, FGFβ, HGF and IGF-1). Representative cytokines (TGFβ1, GDNF, persephin (PSPN), prolactin (PRL), leptin, adiponectin (AdipoQ), resistin (ADSF), noggin, decorin and thrombopoietin (TPO)) from other protein families were also included.</p>", "<p id=\"Par25\">Every recombinant mouse cytokine was obtained from at least two separate orders. The endotoxin level was <0.1 ng µg<sup>–1</sup> of protein for every cytokine per the information from the vendors (Peprotech and R&D). To preserve cytokine activities, carrier-free cytokines were freshly reconstituted according to the manufacturer’s instructions, stored at 4 °C in sterile conditions and used within 28 h after reconstitution. For each cytokine, 5 μg in 100 μl sterile PBS was injected into each animal. Wild-type female C57BL/6 mice were purchased from the Jackson Laboratory and used in studies as 11–15-week-old young adults after resting for at least 1 week in the facility. Mice were maintained on a 12-h light–dark cycle at room temperature (21 ± 2 °C) and 40 ± 10% humidity. Cytokines were injected under the skin (50% subcutaneous, 50% intradermal) bilaterally in the abdominal flank of each mouse. Bilateral skin-draining inguinal lymph nodes were collected 4 h after injection at 6:00–8:00 and pooled for downstream processing. For each of the 86 cytokines, replicate experiments were performed in three independent C57BL/6 mice to ensure reproducibility. As a control, PBS alone was injected into mice for each experimental batch, totalling 14 PBS-injected mice. All experiments were reviewed and approved by the Broad Institute’s Institutional Animal Care and Use Committee.</p>", "<title>Data generation quality assurance</title>", "<p id=\"Par26\">All samples were processed using an optimized experimental pipeline to ensure quality. In particular, batch effects that arise from experiments performed on different days are known to be a major source of artefact in transcriptomic studies. Therefore, batch-to-batch consistency was strictly experimentally ensured and then computationally verified. Specifically, the mice were ordered from the same batch and housed in the same environment. Animals were randomly allocated to the experimental groups. Lymph nodes were collected at 6:00–8:00 in all experiments to exclude the impact of circadian clocks on transcriptomic profiles. Samples were processed fresh in every experiment and were kept on ice during processing whenever possible. The same researchers performed the same steps of the sample processing and sequencing pipeline following the same, highly optimized procedures. The investigators performing animal experiments and RNA sequencing were blinded from each other during data collection. The number of batches was minimized whenever possible. The three replicated mice for each cytokine were processed in different batches to ensure that batch effects, if any, would not influence biological interpretations. All samples were sequenced on two sequencing runs, with the first sequencing run containing the first set of replicates and the second containing the second and third set of replicates. PBS controls were included in every batch to ensure comparability, and transcriptomic profiles of PBS samples from different batches were computationally compared to verify batch-to-batch consistency (Extended Data Fig. ##FIG##6##2a##). In brief, Euclidean distances were calculated for each pair of PBS-treated cells of the same cell type based on the entire transcriptome to ensure that the within-batch distances and between-batch distances were comparable.</p>", "<title>Lymph node dissociation and cell sorting for scRNA-seq experiments</title>", "<p id=\"Par27\">An optimized pipeline for viable cell recovery and more balanced cell-type representation was used to process lymph nodes for scRNA-seq. Lymph nodes were enzymatically digested using a protocol that maximizes the recovery of myeloid and stromal cells while maintaining high viability<sup>##UREF##1##40##</sup>. In brief, lymph nodes were placed in RPMI with collagenase IV, dispase and Dnase I at 37 °C, and cells were collected once they were detached. The cells were then immediately placed on ice and washed with PBS supplemented with 2 mM EDTA and 0.5% biotin-free BSA, then filtered through a 70 µm cell strainer. Cells were incubated with Fc blocking antibodies 4 °C, then with a biotinylated anti-CD3 and anti-CD19 antibody cocktail. Antibodies were used at a dilution of 1:100. Streptavidin microbeads were then added and the cells were magnetically sorted using MACS MS columns according to the manufacturer’s protocol (Miltenyi Biotec). After cell sorting, a small fraction of the CD3<sup>+</sup> or CD19<sup>+</sup> cells was pooled with CD3<sup>–</sup>CD19<sup>–</sup> cells for more balanced representation of all cell types and proceeded immediately to scRNA-seq.</p>", "<title>scRNA-seq</title>", "<p id=\"Par28\">Cell hashing was used to combine multiple samples into the same single-cell emulsion channel<sup>##REF##30567574##41##</sup>. The mouse cells obtained from different stimulation conditions were stained with TotalSeq antibodies (BioLegend anti-mouse hashtags 1–8; used at 1:100 dilution), washed 5 times at 4 °C and pooled in PBS with 0.04% BSA according to the manufacturer’s protocol. Next, 55,000 cells were loaded onto a 10x Genomics Chromium instrument (10x Genomics) according to the manufacturer’s instructions. The scRNA-seq libraries were processed using a Chromium Single Cell 3′ Library & Gel Bead v3 kit (10x Genomics) with modifications for generating hashtag libraries<sup>##REF##30567574##41##</sup>. Quality control for amplified cDNA libraries and final sequencing libraries was performed using a Bioanalyzer High Sensitivity DNA kit (Agilent). scRNA-seq and hashing libraries were normalized to 4 nM concentration and pooled. The pooled libraries were paired-end sequenced on a NovaSeq S4 platform targeting an average sequencing depth of 20,000 reads per cell for gene expression libraries, and on a NovaSeq S4 or SP platform targeting 5,000 reads per cell for hashtag libraries.</p>", "<title>scRNA-seq data pre-processing</title>", "<p id=\"Par29\">The raw bcl sequencing data were processed using the CellRanger (v.3.0) Gene Expression pipeline (10x Genomics), including demultiplexing and alignment. Sequencing reads were aligned to the mm10 mouse reference genome, and transcriptomic count matrices were assembled. Hashtag library FASTQ files were processed using the CITE-seq-Count tool (v.1.4.3; <ext-link ext-link-type=\"uri\" xlink:href=\"https://github.com/Hoohm/CITE-seq-Count\">github.com/Hoohm/CITE-seq-Count</ext-link>). Gene expression and hashtag were matched using the MULTIseqDemux function of the Seurat R package (v.4.1)<sup>##REF##34062119##42##</sup>. Cells with multiple hashtags were considered multiplets (for example, doublets or triplets) and were excluded from further analysis. The Seurat R pipeline was used to perform quality control to include only cells with >500 genes, >1,000 unique molecular identifiers and <10% mitochondrial gene content. The expression matrix was globally scaled by normalizing the gene expression measurements by the total expression per cell. The resulting values were multiplied by a scale factor of 10,000 and natural log-transformed.</p>", "<p id=\"Par30\">For the initial global analysis of all cells, the top 3,000 variable genes were selected for dimensionality reduction analysis. Principal component analysis (PCA) was then used to denoise and to find a lower-dimensional representation of the data. The top 75 principal components (PCs) were used for global clustering and for visualization using a <italic>t</italic>-SNE map<sup>##UREF##2##43##</sup>. Clusters were identified using the Louvain clustering algorithm. This step resulted in a total of 61 non-singleton global (level 1) clusters (Extended Data Fig. ##FIG##5##1d##). We removed potential multiplets by removing the cells with the top 2% gene counts in each cluster. As different cell types have variations in the numbers of genes detected on average, this step was done at the cluster level rather than for all data. For each level 1 cluster of cells, we then performed another round of clustering (level 2) to further verify the identity of each cluster and to remove potential doublets. This step resulted in a total of 183 global level 2 clusters. The cell-type identity of each level 2 cluster was assigned on the basis of the expression of 115 known marker genes (Supplementary Table ##SUPPL##3##2##). Clusters enriched for marker genes of multiple cell types were considered multiplets and removed. The top DEGs between each cell type and others are listed in Supplementary Table ##SUPPL##3##2##.</p>", "<title>Quantitative measures of reproducibility across animal replicates</title>", "<p id=\"Par31\">A gene expression vector for each biological replicate was created for each cytokine stimulation condition in a given cell type by taking the difference between the average expression vector of cytokine-treated cells and the average expression vector of PBS-treated cells. Genes were included if they were significantly differentially expressed (FDR < 0.05 and absolute log<sub>2</sub>(FC) > 0.25) compared with PBS controls and were expressed in >10% of cytokine-treated cells for upregulated genes or >10% of PBS-treated cells for downregulated genes. <italic>Rps</italic>, <italic>Rpl</italic>, mitochondrial genes and unlabelled genes were excluded. Pairwise Pearson correlation coefficients were then calculated for these vectors (Extended Data Fig. ##FIG##6##2b##).</p>", "<title>Assessment of access to injected cytokines by each lymph node cell type</title>", "<p id=\"Par32\">To determine whether the injected cytokines can be accessed by each cell type in the draining lymph nodes, we examined ISG expression levels in cells treated with IFNα1 or IFNβ (IFNα1/IFNβ) or PBS for each cell type. Type I interferon was chosen for this analysis given its strong induction of antiviral programmes in a wide range of cell types. A maximum of 100 cells were sampled from each condition. ISG scores were obtained by summing the normalized expressions of ISGs in each cell. These scores were then used to predict whether each cell was treated with IFNα1/IFNβ or PBS, and the accuracy of the prediction was represented as receiver operating characteristics curves (Extended Data Fig. ##FIG##6##2c##). The ISGs were obtained from the MSigDB hallmark gene set.</p>", "<title>Differential gene expression analysis</title>", "<p id=\"Par33\">Analyses of DEGs were performed to identify marker genes for cell clusters or cytokine-responsive genes. Analyses of DEGs were performed between two groups of cells using the two-sided Wilcoxon rank-sum test on normalized gene expression values. The <italic>P</italic> values obtained from the tests were then adjusted (Bonferroni or FDR) to address multiple testing. Genes were considered DEGs in the cytokine signature if they had FDR < 0.05 and absolute log<sub>2</sub>(FC) > 0.25 between cytokine-treated and PBS-treated cells of the same cell type, were expressed in >10% of cytokine-treated cells for upregulated genes or >10% of PBS-treated cells for downregulated genes, and satisfied the FC threshold for at least two out of the three mice (to mitigate the influence of potential single-mouse outliers). <italic>Rps</italic>, <italic>Rpl</italic>, mitochondrial genes and unlabelled genes were excluded from the signatures. Cell-type-specific cytokine signatures are listed for all major cell types in Supplementary Table ##SUPPL##4##3##.</p>", "<title>Quantification of overall magnitude of transcriptomic responses to each cytokine in each cell type</title>", "<p id=\"Par34\">We constructed a global reference map that quantified the overall gene expression changes induced by each cytokine in each cell type (Fig. ##FIG##0##1d##). This maps takes into account two metrics: the number of DEGs and the magnitude of change across the entire transcriptome. The number of DEGs is the total number of genes in each cytokine signature. The overall magnitude of cytokine-induced differential expression was computed as the Euclidean distance between the centroid vectors of cytokine-treated cells and PBS-treated cells. This value was normalized to a scale ranging from 0 (low) to 100 (high). To reduce the impact of outliers in the normalization process, winsorization was applied such that values above the 95th percentile were replaced with values at the 95th percentile before normalization. A maximum of 100 cells from each cytokine treatment condition were sampled for each cell type for the magnitude calculation. A distinct colour ramp was used for each cell type to emphasize that cell types have different properties (for example, different number of genes expressed on average) and were independently analysed. Cytokine–cell type combinations with five or more cells sampled were included in this analysis.</p>", "<title>Identification of GPs per cytokine treatment and per cell type</title>", "<p id=\"Par35\">GP analysis was used to identify co-regulated genes for each cytokine treatment across cell types (Fig. ##FIG##1##2c## and Supplementary Fig. ##SUPPL##0##1##) or for each cell type across all treatment conditions (Extended Data Figs. ##FIG##9##5i,j##, ##FIG##10##6i,j##, ##FIG##11##7i,j## and ##FIG##12##8i,j## and Supplementary Figs. ##SUPPL##0##2i,j##, ##SUPPL##0##3i,j##, ##SUPPL##0##4i,j##, ##SUPPL##0##5i,j##, ##SUPPL##0##6i,j##, ##SUPPL##0##7i,j##, ##SUPPL##0##8i,j##, ##SUPPL##0##9i,j##, ##SUPPL##0##10i,j## and ##SUPPL##0##11i,j##). GPs were constructed using the non-negative matrix factorization (NMF) algorithm<sup>##UREF##3##44##</sup> using the R package NMFN (v.2.0). We removed genes associated with tissue dissociation<sup>##REF##28960196##45##</sup> and the cell cycle, as well as mitochondrial genes, <italic>Rps</italic> and <italic>Rpl</italic>, unlabelled genes, globally overabundant genes and those expressed in fewer than ten cells.</p>", "<p id=\"Par36\">In the cytokine-centric analysis, NMF was used to identify cell-type-specific GPs in response to each cytokine. Cells treated with the specified cytokines or PBS were used for NMF, with a maximum of 100 cells per cell type per condition. NMF was run separately for each cytokine in this analysis, except that IFNα1 and IFNβ were processed together and IL-1α and IL-1β were processed together in Fig. ##FIG##1##2c## and Supplementary Table ##SUPPL##6##5##. We identified 40 GPs per cytokine treatment, with some predominantly corresponding to cell-type identity and others predominantly to cellular responses to cytokine stimulation. To quantitatively identify GPs predominantly corresponding to cellular responses to cytokine stimulation, a two-sided Wilcoxon rank-sum test was used to identify GPs with weights that were significantly different between the cytokine-treated cells and PBS-treated cells. GPs showing significant upregulation in any cell types were displayed. The top 30 genes with the highest weights for each GP were used to identify enriched biological processes using clusterProfiler (v.4.2.1)<sup>##REF##34557778##46##</sup> on the Hallmark gene sets from the MSigDB database<sup>##REF##16199517##47##</sup>. Genes highlighted in the text for biological significance satisfied two criteria: (1) they were among the top 30 highest-weighted genes for a significantly upregulated GP in response to cytokines; and (2) the genes individually also showed significant upregulation in response to cytokines in the DEG analysis of cytokine signatures.</p>", "<p id=\"Par37\">In the cell-type-centric analysis, NMF was used to analyse GPs of each cell type across all cytokine treatment conditions to identify cytokines that induce similar cellular processes. We identified ten GPs for each cell type and visualized the relationship between GPs and subclusters for each cell type using heatmaps in Extended Data Fig. ##FIG##9##5##–##FIG##12##8## and Supplementary Figs. ##SUPPL##0##2##–##SUPPL##0##11##. The top genes with the highest weights for each GP are shown in Extended Data Figs. ##FIG##9##5j##, ##FIG##10##6j##, ##FIG##11##7j## and ##FIG##12##8j##, Supplementary Figs. ##SUPPL##0##2j##, ##SUPPL##0##3j##, ##SUPPL##0##4j##, ##SUPPL##0##5j##, ##SUPPL##0##6j##, ##SUPPL##0##7j##, ##SUPPL##0##8j##, ##SUPPL##0##9j##, ##SUPPL##0##10j## and ##SUPPL##0##11j## and Supplementary Table ##SUPPL##9##8##.</p>", "<title>Analysis of secondary responses to induced IFNγ</title>", "<p id=\"Par38\">The IFNγ-induced gene expression signature was used to infer the level of cellular responses to cytokine-induced IFNγ. The IFNγ signature score for each cell type was constructed by summing the expressions of significantly overexpressed genes in IFNγ-treated cells relative to PBS-treated cells (FDR-adjusted <italic>P</italic> < 0.001 and log<sub>2</sub>(FC) > 1) in the corresponding cell type. The log<sub>2</sub>(FC) of the signature scores and FDR-adjusted <italic>P</italic> value relative to PBS-treated cells were calculated for every cytokine treatment and shown in Extended Data Fig. ##FIG##8##4e##.</p>", "<title>Identification of cellular polarization states</title>", "<p id=\"Par39\">To identify cellular polarization states induced by cytokines, subclustering was performed for cells of each cell type. For heterogeneous cell types (for example, macrophage, MigDC and γδ T cell), the most abundant homogeneous subset was analysed to identify cytokine-induced states instead of re-deriving cell subsets in the polarization state analysis. We used PCs to subcluster on the basis of discriminating genes, defined as genes with a large absolute log<sub>2</sub>(FC) (between 0.75 and 1.5 depending on cell type) in any cytokine-treated cells compared with PBS-treated cells. We removed genes associated with tissue dissociation<sup>##REF##28960196##45##</sup> and the cell cycle, as well as mitochondrial genes and <italic>Rps</italic> and <italic>Rpl</italic>. We then performed PCA and visualized the cells using UMAP<sup>##UREF##4##48##</sup>. The proportion of cells falling into each cluster was calculated for each cytokine or PBS control. Major polarization states were identified on basis of two criteria: (1) cell clusters with significantly (FDR-adjusted <italic>P</italic> < 0.01) more than the expected number of cytokine-stimulated cells using a hypergeometric test; and (2) manual verification of biological relevance of the highly expressed genes or GPs in the subcluster and cytokines inducing the changes. To find discriminating markers and biological functions of each state, we analysed DEGs and co-regulated GPs per state relative to all other cells for the cell type. DEGs were identified using the two-sided Wilcoxon rank-sum test between each polarization state and other cells of the same cell type. The significantly overexpressed genes with the largest log<sub>2</sub>(FC) are shown. The most strongly polarized states are summarized in Fig. ##FIG##2##3##. The complete landscape, including less-strongly polarized states, in each cell type can be found in Extended Data Figs. ##FIG##9##5##–##FIG##12##8## and Supplementary Figs. ##SUPPL##0##2##–##SUPPL##0##11##. We compared the polarization states by calculating the pairwise Pearson correlation coefficients between the gene expression profiles of each polarization state after subtracting the profiles of PBS-treated cells of the same cell type to remove cell-type-specific gene expression. These results are displayed in Extended Data Figs. ##FIG##9##5b##, ##FIG##10##6b##, ##FIG##11##7b## and ##FIG##12##8b## and Supplementary Figs. ##SUPPL##0##2b##, ##SUPPL##0##3b##, ##SUPPL##0##4b##, ##SUPPL##0##5b##, ##SUPPL##0##6b##, ##SUPPL##0##7b##, ##SUPPL##0##8b##, ##SUPPL##0##9b##, ##SUPPL##0##10b## and ##SUPPL##0##11b##.</p>", "<p id=\"Par40\">We assigned a unique identifier to each polarization state using the following convention: ‘<cell type abbreviation>-<lower case letters>’. When applicable, the letters a–d were reserved for type I interferon, type II interferon, IL-1α and IL-1β, and TNF, respectively, which are cytokines that induce polarization states across a large number of cell types.</p>", "<title>Comparative global view of polarization states across immune cell types</title>", "<p id=\"Par41\">To gain a global view of the 66 polarization states across immune cell types defined in Fig. ##FIG##2##3##, we used Jaccard similarity index to evaluate similarity between each pair of cell states (Extended Data Fig. ##FIG##13##9##). The gene expression profile of each polarization state was compared with PBS-treated cells of the same cell type to remove cell-type-specific gene expression. The genes with an absolute log<sub>2</sub>(FC) > 0.5 compared with PBS-treated cells were used to compute the Jaccard similarity score. Upregulated and downregulated genes were separately calculated. The rows and columns were hierarchically clustered using the average-linkage method on the Euclidean distances to identify groups of similar polarization states. To visualize unique polarization states with low similarity to other states, the same results were illustrated using a force-directed network, with a higher circle size indicating a more unique state, which was calculated on the basis of the inverse of mean Jaccard similarity value with other states.</p>", "<title>Pathway enrichment analysis for NK cells</title>", "<p id=\"Par42\">To identify biological processes enriched for the IL-18-treated NK cells, a pre-ranked gene list was computed by subtracting average gene expression values of PBS-treated NK cells from those in IL-18-treated NK cells. Gene set enrichment analysis was performed using clusterProfiler (v.4.2.1) on the gene ontology biological processes gene sets. Gene sets with a FDR-adjusted <italic>P</italic> < 0.1 are shown. As a comparison, representative cytokines from other NK cell polarization states were analysed using the same method.</p>", "<title>Cytokine and cytokine–receptor gene expression maps</title>", "<p id=\"Par43\">A map of cell-type-specific production of cytokines was derived from our dataset. Cytokine genes expressed in at least 50 cells were included in the cytokine expression heatmap. The cells were obtained from all conditions (PBS or cytokine treated) to provide a map of cytokine expression under all unstimulated or cytokine stimulation conditions (that is, to account for induced expression). The gene expression level was then normalized relative to the cell type with the maximum expression level (whereby the maximum level is capped at 1 expression unit before normalization) to account for the variation in the number of transcripts produced or detected for each cytokine. A cytokine was considered expressed in a cell type if more than 0.1 normalized expression units were detected.</p>", "<p id=\"Par44\">The cytokine–receptor expression map was constructed using the same approach. This included signalling receptors, decoy receptors and receptors that form complexes with cytokines. A list of genes encoding known functional receptors for the 86 studied cytokines are listed in Supplementary Table ##SUPPL##2##1##. The cytokine expression map and the cytokine–receptor expression map are shown in Fig. ##FIG##3##4a##, Extended Data Fig. ##FIG##14##10c## and Supplementary Table ##SUPPL##10##9##.</p>", "<title>Cell–cell interactome network construction</title>", "<p id=\"Par45\">A cell–cell interactome network was constructed to chart available cytokine-mediated cell–cell communication channels. The network was constructed such that the source and sink nodes are cell types and intermediate nodes are cytokines. The paths between source cell-type nodes and sink cell-type nodes through cytokine nodes were established on the basis of the detectability of the cytokine mRNA in the cell population (normalized expression > 0.1) and the responsiveness of the cell type to the cytokine (more than ten DEGs in the corresponding cytokine signature). For heteromeric cytokines or cytokine complexes composed of two subunits (IL-12, IL-23, IL-27, LTα1/β2 and LTα2/β1), the cytokine is shown as expressed and is annotated with an asterisk if the genes encoding at least one subunit are expressed as there is evidence of extracellular assembly of some components into functional cytokines under healthy or pathological conditions<sup>##REF##24821971##49##</sup>. The network was plotted separately for each source node for ease of interpretability.</p>", "<p id=\"Par46\">To construct the ligand–receptor interactome, we identified functional cognate receptors for each cytokine from the literature, which is listed in Supplementary Table ##SUPPL##2##1##. For a receptor to be considered expressed in a cell type, the normalized expression value of the receptor gene needed to be greater than a cut-off threshold (default of 0.1 expression unit). For heteromeric receptors, all components needed to be expressed for the receptor to be considered expressed. For cytokines with more than one functional receptor, the receptor was considered expressed if any functional receptors are expressed. We then connected the cytokines with the cell types expressing the cognate receptors. The cytokine production portion of the interactome is the same as the one in the ligand–response interactome. The ligand–response and ligand–receptor networks were then compared to generate the cell–cell communication paths that are common or different between these two approaches.</p>", "<title>IREA for cytokine response analysis</title>", "<p id=\"Par47\">We offer two types of IREA analysis options to assess cytokine responses in a user’s data depending on the input, which can be a gene set or a gene expression matrix. The cell type in the user data is specified by the user. User data are then compared with the transcriptional cytokine responses of the same cell type from the Immune Dictionary using the following methods:<list list-type=\"order\"><list-item><p id=\"Par48\">For the gene set input, we first find gene set scores by summing the normalized expression value of all genes in the gene set in each of the cytokine-treated cells or PBS-treated cells. Statistical significance is assessed using a two-sided Wilcoxon rank-sum test between gene set scores on cytokine-treated cells and gene set scores on PBS-treated cells, and an FDR correction is applied to all cytokine calculations. Enrichment can also be calculated using the hypergeometric test on significant DEGs (FDR < 0.01 between cytokine-treated cells and PBS-treated cells), a method commonly used in pathway analyses.</p></list-item><list-item><p id=\"Par49\">For the gene expression matrix input, the expression matrices are first normalized such that the total expression per cell sums to 10,000 units; the expression is then log-transformed. Genes giving significant contribution to the enrichment score, with the default being those having an average of more than 0.25 expression values, were included. Next, the projection score is calculated by finding the cosine similarity score between user input and cytokine-treated or PBS-treated cells. Statistical significance is assessed using a two-sided Wilcoxon rank-sum test between projection scores on cytokine-treated cells and projection scores on PBS-treated cells, and an FDR correction is applied to all cytokine calculations. The effect size is the mean difference between projection scores on cytokine-treated cells and on PBS-treated cells. The effect size and FDR-adjusted <italic>P</italic> value for each of the 86 cytokines can then be visualized using a compass plot shown in Fig. ##FIG##4##5d##. Conceptually, this method takes into consideration the direction and magnitude of expression of each gene. That is, a strongly upregulated gene in both the user dataset and Immune Dictionary reference dataset is given a high weight that increases the overall likelihood of enrichment; a strongly upregulated gene in one dataset but not the other is given lower weight; and a gene that is upregulated in one dataset but downregulated in the other is given negative weight that decreases the overall likelihood of enrichment. The genes contributing the highest weights to the enrichment can be visualized using a diverging bar plot shown in Extended Data Fig. ##FIG##16##12b##.</p></list-item></list></p>", "<title>IREA for cellular polarization analysis</title>", "<p id=\"Par50\">IREA polarization analysis implements the same statistical test as the IREA cytokine response analysis. In IREA polarization, user data are compared with the polarization state gene expression profiles. A polarized radar plot is shown if at least one cellular polarization state is significantly enriched (FDR-adjusted <italic>P</italic> < 0.05). If no state is significantly enriched, the radar plot shows an enrichment score of 0 for every state, which signifies that the input cells are unpolarized. The enrichment score is normalized to be between 0 and 1 on the radar plot.</p>", "<title>IREA for cell–cell communication network construction</title>", "<p id=\"Par51\">We constructed models of cell–cell communication networks by taking into account cytokine production and cytokine response. Cytokine production was obtained by examining the transcripts mapped to each of the 86 cytokines. The cytokine response was assessed using IREA, and cytokines with IREA output of FDR < 0.01 were included. For heteromeric cytokines or cytokine complexes composed of two subunits, the cytokine was displayed as expressed if at least one subunit is expressed as there is evidence of extracellular assembly of some components into functional cytokines<sup>##REF##24821971##49##</sup> (same method as for the cell–cell interactome). Cytokine networks can be visualized as shown in Fig. ##FIG##4##5e## and Extended Data Fig. ##FIG##16##12c##.</p>", "<title>IREA analysis of mouse tumour scRNA-seq data</title>", "<p id=\"Par52\">The scRNA-seq data were downloaded as 10x Genomics data files<sup>##REF##30343900##36##</sup>. Data were processed using the same approach as described above but with a minor modification, whereby 40 PCs were used for downstream analysis. Cell types were annotated as shown in the publication<sup>##REF##30343900##36##</sup>. The IREA analysis was done between anti-PD-1 treatment and controls for each cell type using the transcriptome-wide approach with default parameters. A receptor expression threshold of 0.05 was applied to produce the data in the receptor ring in the cytokine enrichment plot.</p>", "<title>IREA analysis of human COVID-19 blood scRNA-seq data</title>", "<p id=\"Par53\">The scRNA-seq data were downloaded as a Seurat object from the human COVID-19 blood study<sup>##REF##32514174##39##</sup>. Cluster annotations were used as defined in the Seurat object. IREA analysis was performed using data from ventilated patients with COVID-19 and compared with healthy individuals for each cell type using the transcriptome-wide approach with default parameters and species specified as human. IREA implements mouse and human homologue gene conversion using the most recent release of the National Center for Biotechnology Information HomoloGene database (release 68). A receptor expression threshold of 0.05 was applied to produce the data in the receptor ring in the compass plot.</p>", "<title>Statistical analysis</title>", "<p id=\"Par54\">The statistical tests used are described for each analysis in the corresponding text. Two-sided statistical tests were used unless otherwise specified. FDR or Bonferroni adjustments were made for the analyses for which multiple hypothesis testing applies.</p>", "<title>Reporting summary</title>", "<p id=\"Par55\">Further information on research design is available in the ##SUPPL##1##Nature Portfolio Reporting Summary## linked to this article.</p>" ]	[]	[ "<title>Discussion</title>", "<p id=\"Par23\">Our dictionary of in vivo immune responses to cytokines enabled a high-resolution view of the cytokine network, which showed that the complexity of cytokine responses and plasticity of immune cells are much greater than previously appreciated. Even a single cytokine, such as IL-1β, can trigger distinct responses in each cell type to create a coordinated multicellular immune response. Extending early discoveries of macrophage polarization, we systematically identified cytokine-induced polarization states in each immune cell type, thereby highlighting the general property of immune cells in their plastic responses to environmental cues. We created cytokine response and cytokine–receptor expression maps and used them to derive a cell–cell interactome that illustrated diverse ways that immune cells can interact with one another and the role of rare cell types in immune cell–cell communication. Finally, we introduced IREA, a method for inferring cytokine activities, immune cell polarization and cell–cell communication networks in any immune process for which gene expression data have been collected. Note that because the dictionary was collected at single-cell resolution, one can easily re-analyse the responses in any cell subpopulation of interest. Future directions include studying different cytokine doses, times, biological contexts and combinations of stimuli. In summary, our study created a systematic cell-type-specific dictionary of cytokine responses, providing new insights into cytokine functions and a basis for inferring cell–cell communication networks in any immune response.</p>" ]	[]	[ "<p id=\"Par1\">Cytokines mediate cell–cell communication in the immune system and represent important therapeutic targets<sup>##REF##1695833##1##–##UREF##0##3##</sup>. A myriad of studies have highlighted their central role in immune function<sup>##REF##20081871##4##–##REF##24530056##13##</sup>, yet we lack a global view of the cellular responses of each immune cell type to each cytokine. To address this gap, we created the Immune Dictionary, a compendium of single-cell transcriptomic profiles of more than 17 immune cell types in response to each of 86 cytokines (>1,400 cytokine–cell type combinations) in mouse lymph nodes in vivo. A cytokine-centric view of the dictionary revealed that most cytokines induce highly cell-type-specific responses. For example, the inflammatory cytokine interleukin-1β induces distinct gene programmes in almost every cell type. A cell-type-centric view of the dictionary identified more than 66 cytokine-driven cellular polarization states across immune cell types, including previously uncharacterized states such as an interleukin-18-induced polyfunctional natural killer cell state. Based on this dictionary, we developed companion software, Immune Response Enrichment Analysis, for assessing cytokine activities and immune cell polarization from gene expression data, and applied it to reveal cytokine networks in tumours following immune checkpoint blockade therapy. Our dictionary generates new hypotheses for cytokine functions, illuminates pleiotropic effects of cytokines, expands our knowledge of activation states of each immune cell type, and provides a framework to deduce the roles of specific cytokines and cell–cell communication networks in any immune response.</p>", "<p id=\"Par2\">An extensive global transcriptomics analysis of in vivo responses to 86 cytokines across more than 17 immune cell types reveals enormous complexity of cellular responses to cytokines, providing the basis of the Immune Dictionary and its companion software Immune Response Enrichment Analysis.</p>", "<title>Subject terms</title>" ]	[ "<title>Main</title>", "<p id=\"Par3\">Cytokines are a broad class of small, secreted proteins that act locally or systemically by binding to cognate receptors on target cells, which in turn trigger downstream signalling and orchestrate activities among cell types of the immune system. Cytokine-based therapies and cytokine antagonists are used to treat a wide range of disorders, including cancer and autoimmunity<sup>##REF##36131080##14##</sup>. However, the large number of immune cell types and cytokines and complex cellular responses have made it challenging to elucidate in vivo immune responses to cytokines. </p>", "<title>The Immune Dictionary</title>", "<p id=\"Par4\">To obtain a comprehensive view of cellular responses to cytokines, we systematically profiled single-cell transcriptomic (single-cell RNA sequencing (scRNA-seq)) responses to 86 cytokines across more than 17 immune cell types in mouse lymph nodes in vivo to generate a large-scale perturbational scRNA-seq dataset of the immune system (Fig. ##FIG##0##1a##). The 86 cytokines represent most members of major cytokine families, including interleukin-1 (IL-1), common γ-chain/IL-13/thymic stromal lymphopoietin (TSLP), common β-chain, IL-6/IL-12, IL-10, IL-17, interferon (types I, II and III), tumour necrosis factor (TNF), complement and growth factor families, as well as representative molecules from other families with cytokine functions (for example, certain hormones) (<xref rid=\"Sec8\" ref-type=\"sec\">Methods</xref> and Supplementary Table ##SUPPL##2##1##).</p>", "<p id=\"Par5\">We injected each freshly reconstituted carrier-free cytokine or phosphate buffered saline (PBS; as vehicle control) under the skin of the abdominal flank of wild-type C57BL/6 mice (three independent, replicate mice per cytokine) in the upper range of previously reported bioactive doses (<xref rid=\"Sec8\" ref-type=\"sec\">Methods</xref> and Supplementary Table ##SUPPL##2##1##). We collected skin-draining lymph nodes (specialized immune organs that integrate signals from surrounding tissues) 4 h after injection, one of the earliest time points at which the majority of the transcriptome responds to immune stimuli<sup>##REF##26824662##15##,##REF##19729616##16##</sup>. We then processed the lymph nodes using an optimized protocol for viable cell recovery, balanced cell-type representation and high-throughput sample multiplexing (<xref rid=\"Sec8\" ref-type=\"sec\">Methods</xref>). Data quality, including batch-to-batch consistency, was strictly experimentally controlled and computationally verified (<xref rid=\"Sec8\" ref-type=\"sec\">Methods</xref>). Cells were profiled using a droplet-based system (10x Genomics) to generate high-quality single-cell transcriptomes for 386,703 cells (Fig. ##FIG##0##1b## and Extended Data Figs. ##FIG##5##1## and ##FIG##6##2##).</p>", "<p id=\"Par6\">After partitioning cells into global clusters, we observed that most cells were segregated by cell-type identity rather than stimulation conditions (Fig. ##FIG##0##1b##, Extended Data Fig. ##FIG##5##1b## and Supplementary Table ##SUPPL##3##2##). Although cytokine-treated cells did not typically form distinct clusters, they were often separated from PBS controls within each cell-type cluster (Extended Data Fig. ##FIG##5##1b,c##). After manual inspection to ensure the accuracy of cell-type identification, more than 20 cell types were identified. These corresponded to B cell, CD4<sup>+</sup> T cell, CD8<sup>+</sup> T cell, γδ T cell, regulatory T (T<sub>reg</sub>) cell, natural killer (NK) cell, innate lymphoid cell (ILC), plasmacytoid dendritic cell (pDC), conventional dendritic cell type 1 (cDC1), cDC2, migratory DC (MigDC), Langerhans cell<sup>##REF##19029989##17##</sup>, extrathymic Aire-expressing cell (eTAC)<sup>##REF##18687966##18##</sup>, macrophage (<italic>Marco</italic><sup>+</sup> or <italic>Lyz1</italic><sup>+</sup>), monocyte, neutrophil, mast cell, and a small number of less abundant cell types, including basophil, blood endothelial cell (BEC), lymphatic endothelial cell (LEC) and fibroblastic reticular cell (FRC) (Fig. ##FIG##0##1b## and Extended Data Fig. ##FIG##5##1d,e##). The frequencies of most cell types remained stable after stimulation, with the notable exception being a significant increase in monocyte fractions in lymph nodes after certain cytokine treatments (Extended Data Fig. ##FIG##5##1f,g##). To identify cytokine signatures, we computed the significantly differentially expressed genes (DEGs) in response to cytokine treatment in each cell type (<xref rid=\"Sec8\" ref-type=\"sec\">Methods</xref> and Supplementary Table ##SUPPL##4##3##). We identified an average of 51 DEGs (span of 0–1,510) per cytokine–cell type combination, and the majority (72%) of the DEGs responding to cytokines were upregulated rather than downregulated. We verified that the transcriptomic signatures were consistent across replicate animals (Extended Data Fig. ##FIG##6##2b##) and that every lymph node cell type was able to access the injected cytokines (Extended Data Fig. ##FIG##6##2c##). We also confirmed robust upregulation of well-established cytokine-responsive genes, such as <italic>Tnfaip3</italic> in response to TNF, <italic>Il4i1</italic> in response to IL-4, and <italic>Isg15</italic> and other interferon-stimulated genes (ISGs) in response to IFNβ (Fig. ##FIG##0##1c## and Supplementary Table ##SUPPL##4##3##).</p>", "<p id=\"Par7\">To chart the immune cell responses to each cytokine, we created a map that quantified the global transcriptomic changes between cytokine-treated and PBS-treated cells for each cell type (Fig. ##FIG##0##1d## and <xref rid=\"Sec8\" ref-type=\"sec\">Methods</xref>). The map captured well-known cellular targets of cytokines, such as NK cells responding to IL-2, IL-12, IL-15 and IL-18, and many less characterized responses. Certain cytokines, such as IFNα1, IFNβ, IL-1α, IL-1β, IL-18, IL-36α, IL-15 and TNF, induced strong changes in gene expression in nearly all cell types. Some cytokines preferentially targeted one lineage (for example, IL-21 affected the lymphoid lineage, whereas IL-3 affected the myeloid lineage) or a subset of cell types. The transcriptomic responses of each major immune cell type to each major cytokine constituted an in vivo dictionary of immune responses, which we term the Immune Dictionary.</p>", "<title>Cell-type-specific cytokine responses</title>", "<p id=\"Par8\">We performed a cytokine-centric analysis of this dictionary to explore how different cell types respond to the same cytokine. Gene expression heatmaps of top upregulated genes in response to IFNβ, IL-1β and TNF revealed that cytokines induced cell-type-specific gene expression changes (Fig. ##FIG##1##2a##). This observation was consistent across 15 cytokines that induced strong transcriptomic changes in a large number of cell types shown in Fig. ##FIG##0##1d##, including IL-1α, IL-36α and IL-7 (Extended Data Fig. ##FIG##7##3a##). We computed the number of upregulated genes that were either specific to a cell type or shared among multiple cell types (Fig. ##FIG##1##2b## and Supplementary Table ##SUPPL##5##4##). Most of the upregulated genes in response to a particular cytokine were specific to one cell type regardless of thresholds for defining DEGs (Extended Data Fig. ##FIG##7##3b##). Some cytokines induced substantial changes on one or a small number of cell types, such as IL-18 on NK cells, IL-3 on pDCs and GM-CSF on MigDCs. The most shared DEGs were for IFNα1 and IFNβ (across all cell types), IL-4 (across several combinations of cell types), and IL-2, IL-15 and IL-18 (all three of which induced cytotoxic genes in CD8<sup>+</sup> T cells and NK cells).</p>", "<p id=\"Par9\">To represent complex cytokine responses across cell types in a compact manner, we identified gene programmes (GPs) that consisted of co-expressed genes that became upregulated as a group in response to cytokines (Fig. ##FIG##1##2c##, Supplementary Fig. ##SUPPL##0##1## and Supplementary Table ##SUPPL##6##5##). IFNα1 and IFNβ, as related cytokines, induced highly similar responses to each other, as did IL-1α and IL-1β. IFNα1 and IFNβ, as expected, induced common antiviral GPs across almost all cell types (GP numbers GP27, GP33 and GP34) but also some lineage-specific and cell-type-specific programmes. By contrast, IL-1α and IL-1β, pro-inflammatory cytokines with many known functions in the activation of both innate and adaptive immune cells<sup>##REF##29247995##19##</sup>, induced highly cell-type-specific GPs with a diverse set of enriched biological processes (Fig. ##FIG##1##2c##). These GPs seemed to enhance the known functions of several of these cell types, including: (1) neutrophils upregulating chemokine and inflammatory genes such as <italic>Cd14</italic> (GP27), consistent with their role as first responders; (2) MigDCs and Langerhans cells upregulating migration programmes including <italic>Ccr7</italic> (GP12); and (3) T<sub>reg</sub> cells inducing <italic>Hif1a</italic> and <italic>Ctla4</italic> that can mediate immune suppression (GP22). Our results illustrate how the type I interferon response includes a common and autonomous viral defence programme, while IL-1α and IL-1β trigger a coordinated multicellular response composed of highly cell-type-specific functions.</p>", "<p id=\"Par10\">Many cell-type-specific responses to a single cytokine were not explained by secondary effects to the other cytokines studied (Extended Data Fig. ##FIG##8##4a,b##). However, some responses could be attributed to secondary effects to induced cytokines, such as the induction of <italic>Ifng</italic> (which encodes IFNγ) in NK cells by IL-2, IL-12, IL-15 and IL-18, which probably in turn stimulate B cells, DCs and macrophages to strongly express IFNγ signatures (Extended Data Fig. ##FIG##8##4c–e##). As IL-2, IL-12, IL-15 and IL-18 have been applied as therapies with the intention of activating T cells, our results highlight the importance of considering rapidly induced secondary effects on non-intended cell types due to complex in vivo immune responses to a single cytokine. In summary, our systematic analysis of how different cell types respond to each cytokine provides a molecular map for observed pleiotropic effects of cytokines<sup>##REF##12072446##20##</sup>.</p>", "<title>Cytokine-driven cell polarization states</title>", "<p id=\"Par11\">We next performed a cell-type-centric analysis of the dictionary to identify cell states induced by cytokines. Cytokines are major drivers of immune cell polarization, with a classic example being distinct cytokines driving macrophages into pro-inflammatory M1-like or reparative M2-like states<sup>##REF##27813830##21##</sup>. However, the polarization states of many immune cell types have not been comprehensively characterized, and even macrophage polarization is more complex than the M1/M2 dichotomy<sup>##REF##24669294##22##</sup>. Gene expression profiling can simultaneously measure the entire transcriptome and has been particularly useful for defining cell states driven by environmental cues<sup>##REF##24530056##13##,##REF##30413361##23##,##REF##25480296##24##</sup>. Here we leveraged the dictionary to systematically identify single cytokine-induced cell polarization states. We subclustered each immune cell type and defined 66 major polarization states as subclusters significantly enriched for cytokine-treated relative to PBS-treated cells and expressing meaningful biological programmes (<xref rid=\"Sec8\" ref-type=\"sec\">Methods</xref> and Fig. ##FIG##2##3##, with the complete landscape in Extended Data Figs. ##FIG##9##5##–##FIG##12##8##, Supplementary Figs. ##SUPPL##0##2##–##SUPPL##0##11## and Supplementary Tables ##SUPPL##7##6##–##SUPPL##9##8##). Each polarization state was induced by one or a handful of dominant cytokine drivers (Fig. ##FIG##2##3a–n##, Extended Data Figs. ##FIG##9##5f,g##, ##FIG##10##6f,g##, ##FIG##11##7,f,g## and ##FIG##12##8f,g## and Supplementary Figs. ##SUPPL##0##2f,g##, ##SUPPL##0##3f,g##, ##SUPPL##0##4f,g##, ##SUPPL##0##5f,g##, ##SUPPL##0##6f,g##, ##SUPPL##0##7f,g##, ##SUPPL##0##8f,g##, ##SUPPL##0##9f,g##, ##SUPPL##0##10f,g## and ##SUPPL##0##11f,g##).</p>", "<p id=\"Par12\">We examined macrophages and monocytes to see whether this approach uncovers previously established polarization states (Fig. ##FIG##2##3l,m##, Extended Data Fig. ##FIG##12##8## and Supplementary Fig. ##SUPPL##0##10##). As expected from previous studies<sup>##REF##24530056##13##</sup>, IFNγ induced a ‘Mac-b’ state that overexpresses M1-associated pro-inflammatory genes (for example, <italic>Cxcl9</italic> and <italic>Cxcl10</italic>). IL-4 and IL-13 induced a distinct ‘Mac-e’ state that was not marked by these pro-inflammatory genes but by <italic>Chchd10</italic>, <italic>Glrx</italic> and <italic>Retnla</italic>. We also confirmed previous findings that monocytes had higher <italic>Il1b</italic> expression when treated with IL-1α, IL-1β or IL-36α (Supplementary Fig. ##SUPPL##0##10h##), thereby potentially creating an inflammatory feed-forward loop<sup>##REF##29247995##19##</sup>. In addition, IFNα1, IFNβ and TNF triggered other polarization states.</p>", "<p id=\"Par13\">NK cells are known for their cytotoxic functions, but other functions are still being discovered<sup>##REF##29166586##25##</sup>. As expected from previous knowledge, IL-2, IL-12 and IL-15 induced a state with increased expression of cytotoxic genes, and type I interferons induced the expression of both cytotoxic genes and ISGs (Fig. ##FIG##2##3f,o,p## and Extended Data Fig. ##FIG##10##6##). IL-1α and IL-1β induced a distinct ‘NK-c’ state with low correlation with other NK cell states and lacking overexpression of cytotoxic molecules (Extended Data Fig. ##FIG##10##6b##). However, in this state, <italic>Ifngr1</italic> was upregulated, which potentially enhances NK cell activation by IFNγ (Fig. ##FIG##2##3p##, Extended Data Fig. ##FIG##10##6## and Supplementary Fig. ##SUPPL##0##12##). Although IL-18 is known to activate NK cells, we found that IL-18-activated NK cells, predominantly in the ‘NK-f’ state, displayed markedly different properties than NK cells activated by IL-2, IL-12 or IL-15. IL-18 triggered the upregulation of more than 1,000 genes (Fig. ##FIG##1##2b## and Supplementary Table ##SUPPL##4##3##), an order of magnitude more than cells stimulated with other cytokines. This had a partial overlap with the IL-2, IL-12, IL-15 and interferon states, including <italic>Gzmb</italic> and <italic>Xcl1</italic>. Compared with PBS treatment, the IL-18-induced state was strongly enriched in biosynthetic processes, including the induction of <italic>Myc</italic> (which controls growth and proliferation), immune processes such as maturation of myeloid cells (<italic>Csf2</italic> (which encodes GM-CSF)), recruitment of DCs (<italic>Xcl1</italic>), cytotoxicity (<italic>Gzmb</italic>) and regulators of differentiation (<italic>Kit</italic> and <italic>Batf</italic>) (Fig. ##FIG##2##3p##, Extended Data Fig. ##FIG##10##6## and Supplementary Fig. ##SUPPL##0##12##). IL-18 has shown promise in preclinical studies of cancer immunotherapy<sup>##REF##32581358##26##</sup> and can activate T cells, NK cells and other cell types<sup>##REF##30717382##27##</sup>. This unique and strong NK cell response to IL-18 suggests a polyfunctional role for the IL-18–NK cell axis in the immune system.</p>", "<p id=\"Par14\">B cells were polarized by IL-4 to a distinct <italic>Il4i1</italic><sup>+</sup> state and by CD40L or IL-21 to a proliferating phenotype <bold>(</bold>Fig. ##FIG##2##3a## and Extended Data Fig. ##FIG##9##5##). Similar to their effect on NK cells, IL-1α and IL-1β induced <italic>Ifngr1</italic> upregulation in T cell subsets (Supplementary Figs. ##SUPPL##0##2h## and ##SUPPL##0##3h##). γδ T cells exhibited diverse polarization states, including a distinct ‘Tgd-f’ state induced by IL-23 that overexpresses <italic>Il22</italic>, and a ‘Tgd-d’ state induced by TNF, TL1A or IL-17E that overexpresses <italic>Odc1</italic> (Fig. ##FIG##2##3d## and Supplementary Fig. ##SUPPL##0##4##). While prior studies of lymphocyte differentiation states have been in the context of TCR or BCR stimulation, our findings demonstrate that resting lymph node B cells and T cells can also be polarized by cytokines to express diverse GPs.</p>", "<p id=\"Par15\">We observed shared polarization states and cytokine drivers in MigDCs and Langerhans cells (Fig. ##FIG##2##3j,k## and Supplementary Figs. ##SUPPL##0##8## and ##SUPPL##0##9##). TNF uniquely increased the proportion of cells in a state marked by <italic>Cd40</italic> and <italic>Ccl22</italic> expression, thereby potentially enhancing antigen presentation and interaction with T cells through the CCR4–CCL22 axis. IL-1α and IL-1β increased the expression of <italic>Ccr7</italic>, and GM-CSF and IL-1 family cytokines induced the upregulation of <italic>Nr4a3</italic>, which has a key role in DC migration<sup>##REF##27820700##28##</sup>. These results suggest that TNF can trigger local inflammation and DC–T cell interactions, and that the IL-1 family cytokines can boost DC migration to prime T cell responses in lymph nodes.</p>", "<p id=\"Par16\">Overall, our reference map of cytokine-driven cellular polarization states reveals the plasticity of all immune cell types. These polarization states may be shared across cell types, such as type I interferons inducing ISG I states and type II interferons inducing ISG II states in each cell type (Extended Data Fig. ##FIG##13##9a##), or specific for one cell type, such as ‘cDC1-f’ induced by IL-10 to cDC1 (Extended Data Fig. ##FIG##11##7##), ‘pDC-e’ induced by IL-36α to pDCs (Supplementary Fig. ##SUPPL##0##6##) and ‘NK-c’ induced by IL-1α or IL-1β to NK cells (Extended Data Fig. ##FIG##13##9b##).</p>", "<title>Cytokine production–response map</title>", "<p id=\"Par17\">To better understand the cell–cell communication carried out by each cell type, we identified the production sources of each cytokine by quantifying the corresponding transcript levels averaged across all baseline and cytokine-stimulated conditions to account for stimulation-induced expression (Fig. ##FIG##3##4a## and Supplementary Table ##SUPPL##10##9##). FRCs expressed the highest number of distinct cytokines, consistent with previous findings regarding their heterogeneity and maintenance functions in various immune and non-immune compartments<sup>##REF##32205888##29##</sup>. Other rare cell types in lymph nodes, such as basophils and ILCs, also expressed a large number of cytokines. There was an inverse correlation between the abundance of an immune cell type and the number of cytokines that the cell type produced (Fig. ##FIG##3##4b## and Extended Data Fig. ##FIG##14##10a##), with cytokine production calculated using the same number of cells per cell type to ensure comparability. These findings, which were robust against different values of analysis parameters (Extended Data Fig. ##FIG##14##10b##), suggest that rarer cell types are crucial players in immune cell–cell communication networks despite their low numbers.</p>", "<p id=\"Par18\">On the basis of cytokine production levels inferred from the abundance of transcripts encoding each cytokine and of cytokine responses obtained from the global analysis (Fig. ##FIG##0##1d##), we built a cell–cell interactome charting available cell–cell communication channels in the immune system (Fig. ##FIG##3##4c##, Extended Data Fig. ##FIG##15##11## and Supplementary Table ##SUPPL##11##10##). Our data indicated that FRCs can influence nearly every cell type through a multitude of produced cytokines (Fig. ##FIG##3##4c##, left). cDC1 cells can also affect almost all cell types, but through a smaller number of cytokines, most prominently through IL-1β, which affects many cells (Fig. ##FIG##3##4c##, right). A global view of the network showed that most cell types can affect almost every other cell type through at least one cytokine (with the exception of B cells and T cells, which are not stimulated with antigens used in our study), demonstrating a high level of interconnectivity in the immune system (Supplementary Fig. ##SUPPL##0##13##).</p>", "<p id=\"Par19\">In addition, we created a cytokine–receptor expression map per cell type (Extended Data Fig. ##FIG##14##10c## and Supplementary Table ##SUPPL##10##9##). Cytokine treatment induced changes in cytokine or receptor expression, such as the upregulation of <italic>Cd40</italic> in cDC1 cells in response to TNF and IFNβ (Extended Data Fig. ##FIG##14##10d##), which potentially sensitizes cells to subsequent response to other cytokines. Some cytokines induced responses even in the absence of highly expressed receptors, such as IL-1α and IL-1β affecting T cells, NK cells and DCs (Supplementary Fig. ##SUPPL##0##12j## and Supplementary Table ##SUPPL##11##10##). This effect could be due to insensitive detection of receptor transcripts, rapid secondary effects to molecules induced by the cytokine or unknown receptors. Thus, our cell–cell interactome reveals diverse ways by which cells can interact with one another in vivo through the cytokine network.</p>", "<title><bold>Immune Response Enrichment Analysis</bold></title>", "<p id=\"Par20\">The use of transcriptomics to study immune processes and diseases has become standard and has led to the generation of large public datasets<sup>##REF##28983043##30##,##REF##28787399##31##</sup>. However, transcriptomic data do not reveal the factors that trigger the observed cell states and their functions, calling for an approach for inferring cytokine responses and revealing cell–cell communication networks based on cytokine-induced gene expression programmes<sup>##REF##34594031##32##,##REF##31819264##33##</sup>. Most methods to infer cell–cell interactions from transcriptomic data use ligand and receptor expression associations<sup>##REF##30429548##34##,##REF##26198319##35##</sup>. However, receptor expression alone is not an accurate predictor of cytokine responses, as the ligands may not reach the cell or downstream pathways may not be functional. A more precise approach should also consider whether cells express the response signature of a cytokine as defined in our dictionary. Furthermore, there is a need for a computational approach to automatically assess immune cell polarization from transcriptomic data. </p>", "<p id=\"Par21\">To infer immune cell polarization and cytokine responses from any transcriptomic data, we created Immune Response Enrichment Analysis (IREA), which is the companion software for the Immune Dictionary. IREA implements statistical tests to assess the enrichment of either cell polarization or cytokine signatures in transcriptomes, which can then be used to derive cell–cell communication networks that explain observed immune responses (<xref rid=\"Sec8\" ref-type=\"sec\">Methods</xref> and Fig. ##FIG##4##5a,b##).</p>", "<p id=\"Par22\">We applied IREA to a published single-cell transcriptomic dataset from immune cells in tumours of mice treated with an anti-PD-1 checkpoint blockade therapy<sup>##REF##30343900##36##</sup> (Fig. ##FIG##4##5c–e## and Extended Data Fig. ##FIG##16##12a–c##). IREA automatically inferred that monocytes and macrophages after treatment polarized into the IFNγ-induced ‘Mac-b’ (M1-like) state and away from the IL-4-induced ‘Mac-e’ state, which is in accordance with the known antitumour properties of IFNγ-polarized M1-like macrophages (Fig. ##FIG##4##5c##). This method enabled the characterization of immune cells across multiple polarization states on a continuous scale, consistent with recent views that immune cells are not dichotomously polarized<sup>##REF##24669294##22##</sup>. Polarization was also identified in other cell types, including the polarization of NK cells into a cytotoxic ‘NK-e’ state, which can be induced by IL-2, IL-12 and IL-15 (Fig. ##FIG##4##5c## and Extended Data Fig. ##FIG##16##12a##). NK cells were enriched for signatures of these cytokines, and IL-12 subunit genes were expressed in DCs and other myeloid cells (Fig. ##FIG##4##5d##, red bars, Fig. ##FIG##4##5e##), in agreement with the known role of IL-12 in anti-PD-1 therapy<sup>##REF##30552023##37##</sup>. Among the 86 cytokines, IREA found that the immunosuppressive cytokine TGFβ1 showed the most negative response in anti-PD-1-treated cells compared with untreated cells, consistent with its known role in attenuating the immune enhancement from PD-1 and PD-L1 blockade<sup>##REF##29443960##38##</sup>. These and various other cytokines were produced by and acted on specific cell types in the tumour, which created a cytokine network in the effective response to immunotherapy (Fig. ##FIG##4##5d,e##). Receptors were expressed for cytokines with inferred responses, but also for some cytokines without responses, highlighting that the presence of receptors is not sufficient for response (Fig. ##FIG##4##5d##). Applying IREA to severe COVID-19 infection<sup>##REF##32514174##39##</sup> revealed responses to cytokines in B cells and T cells in severe disease, reflecting known increases in plasma cytokines that regulate lymphocytes (Extended Data Fig. ##FIG##16##12d## and Supplementary Fig. ##SUPPL##0##14##). Our framework therefore enabled us to infer the key secreted factors that trigger observed cellular responses (for example, Fig. ##FIG##4##5c,d##) and to generate a molecular model of cell–cell interactions (for example, Fig. ##FIG##4##5e##) that underlie a complex immune response.</p>", "<title>Online content</title>", "<p id=\"Par56\">Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41586-023-06816-9.</p>", "<title>Supplementary information</title>", "<p>\n\n\n\n\n\n\n\n\n\n\n\n\n</p>", "<title>Source data</title>", "<p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n</p>" ]	[ "<title>Extended data figures and tables</title>", "<p id=\"Par59\">\n\n</p>", "<p id=\"Par60\">\n\n</p>", "<p id=\"Par61\">\n\n</p>", "<p id=\"Par62\">\n\n</p>", "<p id=\"Par63\">\n\n</p>", "<p id=\"Par64\">\n\n</p>", "<p id=\"Par65\">\n\n</p>", "<p id=\"Par66\">\n\n</p>", "<p id=\"Par67\">\n\n</p>", "<p id=\"Par68\">\n\n</p>", "<p id=\"Par69\">\n\n</p>", "<p id=\"Par70\">\n\n</p>", "<title>Extended data</title>", "<p>is available for this paper at 10.1038/s41586-023-06816-9.</p>", "<title>Supplementary information</title>", "<p>The online version contains supplementary material available at 10.1038/s41586-023-06816-9.</p>", "<title>Acknowledgements</title>", "<p>We would like to thank D. Pe’er, D. H. Raulet, M. Merad, J. Schoggins, D. J. Cua, M. Artyomov, M. Gubin, W. J. Leonard, J. Ding, A. Subramanian, M. Hofree, S. Simmons, M. S. Cuoco, staff at the Broad Institute vivarium, members of the Hacohen Laboratory, the Fraenkel Laboratory and the Regev Laboratory for helpful discussions and technical assistance; B. S. Yao, V. Vanchinathan, P. Bishnu, L. Wang, C. Yoon and other members of the Cui Laboratory for assistance with software development and helpful discussions; and S. Turley and J. Astarita for sharing technical protocols. This work was supported by the NIH Grant RM1HG006193 and an Adelson Medical Research Foundation Grant to N.H.; a Natural Sciences and Engineering Research Council of Canada (NSERC) Doctoral Fellowship, a Whitaker Health Sciences Fund Fellowship, and a Wellington and Irene Loh Fund Fellowship to A.C.; an NCI Research Specialist Award (R50CA251956) to S.L.; and MIT Undergraduate Research Opportunities Program awards to A.M and J.L.P. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.</p>", "<title>Author contributions</title>", "<p>A.C. and N.H. conceived and designed the study, acquired funding, performed investigations, designed software and wrote the manuscript. A.C. performed in vivo experiments, sample processing, computational analyses and software development. T.H. and S.L. performed scRNA-seq library preparation and sequencing. A.M. and J.L.P. assisted with sample processing and investigations. C.S. and E.F. provided input on computational analysis, software development and investigations. D.B.K. and C.J.W. provided input on animal experiments and investigations. All authors edited and approved the final manuscript.</p>", "<title>Peer review</title>", "<title>Peer review information</title>", "<p id=\"Par57\"><italic>Nature</italic> thanks the anonymous reviewers for their contribution to the peer review of this work.</p>", "<title>Data availability</title>", "<p>The Immune Dictionary interactive web portal can be accessed at <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.immune-dictionary.org\">www.immune-dictionary.org</ext-link>, where the single-cell transcriptomic data generated in this study are made publicly available through a user-friendly interface. Raw fastq files of the data are available from the Gene Expression Omnibus (GEO) database under accession number <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE202186\">GSE202186</ext-link>. Source tables are included for every figure and extended data figure at appropriate locations in the article. In addition, the following publicly available datasets were used: the MSigDB database was used for annotating biological processes and can be accessed at <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.gsea-msigdb.org/gsea/msigdb\">www.gsea-msigdb.org/gsea/msigdb</ext-link>; mouse and human homologue gene conversion was based on the National Center for Biotechnology Information HomoloGene database (release 68); the mouse tumour dataset is available from the GEO under accession number <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE119352\">GSE119352</ext-link>; and the human COVID-19 dataset is available at <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.covid19cellatlas.org\">www.covid19cellatlas.org</ext-link>. <xref ref-type=\"sec\" rid=\"Sec35\">Source data</xref> are provided with this paper.</p>", "<title>Code availability</title>", "<p>IREA is available at <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.immune-dictionary.org\">www.immune-dictionary.org</ext-link>.</p>", "<title>Competing interests</title>", "<p id=\"Par58\">N.H. and C.J.W. hold equity in BioNTech. N.H. is an advisor for Related Sciences/Danger Bio, Repertoire Immune Medicines and CytoReason. A.C. was a consultant for Foresite Capital and Altimmune for unrelated work. D.B.K is a scientific advisor for Immunitrack and Breakbio. DBK owns equity in Affimed N.V., Agenus, Armata Pharmaceuticals, Breakbio, BioMarin Pharmaceutical, Celldex Therapeutics, Editas Medicine, Gilead Sciences, Immunitybio and Lexicon Pharmaceuticals. BeiGene, a Chinese biotechnology company, supports unrelated research at the Translational Immunogenomics Lab. The remaining authors declare no competing interests. N.H. and A.C. have filed patent applications related to this work.</p>" ]	[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>Generation of a scRNA-seq dictionary of gene expression signatures in more than 17 immune cell types in response to each of 86 cytokines in vivo.</title><p><bold>a</bold>, Schematic of the experimental and computational workflow. First row, data generation procedures; second row, illustration of the Immune Dictionary and its companion software IREA; third row, analyses of the Immune Dictionary. <bold>b</bold>, <italic>t</italic>-distributed stochastic neighbour embedding (<italic>t</italic>-SNE) map of all cells collected from lymph nodes after cytokine stimulation or without stimulation (PBS controls) coloured by cell-type identity. Cells were sorted to rebalance frequencies of major cell types. <bold>c</bold>, Violin plots of expression levels of well-established cytokine-responsive genes following PBS or cytokine treatment. **False discovery rate (FDR)-adjusted <italic>P</italic> < 0.001, two-sided Wilcoxon rank-sum test. <bold>d</bold>, Quantitative representation of overall transcriptomic response levels in each cell type 4 h after cytokine stimulation compared with PBS controls. Each cell type is analysed independently and is represented by a distinct colour, following the colour codes in <bold>b</bold> and <bold>c</bold>. Colour saturation indicates the magnitude of the response. Size indicates the number of genes with significant differential expression (absolute log<sub>2</sub>(fold change (FC)) > 0.25 and FDR-adjusted <italic>P</italic> < 0.05, two-sided Wilcoxon rank-sum test) in each cytokine signature.</p><p>##SUPPL##12##Source data##</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>Cytokines induce cell-type-specific transcriptomic responses.</title><p><bold>a</bold>, Heatmaps of the top DEGs per cell type in response to IFNβ, IL-1β and TNF relative to PBS controls. Colour gradient represents log<sub>2</sub>(FC) (capped at twofold) in comparison with PBS treatment for the respective cell type. <bold>b</bold>, Number of DEGs (log<sub>2</sub>(FC) > 0.3 and FDR-adjusted <italic>P</italic> < 0.05, two-sided Wilcoxon rank-sum test) following each cytokine treatment, grouped by sharing pattern, either specifically overexpressed by one cell type (top) or shared by two or more cell types (bottom). Cell-type combinations with the most shared genes (>16 DEGs in any given treatment) are shown. A maximum of 100 cells per cytokine treatment for each of the 7 representative cell types were sampled to ensure comparability across cell types for this analysis. The <italic>x</italic> axes span from 0 to the highest DEG counts. <bold>c</bold>, Upregulated GPs following IFNα1 and IFNβ (top) or IL-1α and IL-1β (bottom) treatment with respect to PBS control. GPs that are significantly upregulated between cytokine and PBS treatment (effect size > 1 and FDR-adjusted <italic>P</italic> < 0.01, two-sided Wilcoxon rank-sum test) in any cell type are shown. Significant GPs (FDR < 0.05) for each cell type are represented as circles, with the circle size indicating significance and the colour representing the effect size (capped at 10). Representative enriched biological processes (FDR-adjusted <italic>P</italic> < 0.05; black tiles) for the top-weighted genes in each GP are shown.</p><p>##SUPPL##13##Source data##</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><title>Cytokines drive diverse polarization states in each cell type.</title><p><bold>a</bold>–<bold>n</bold>, Uniform manifold approximation and projection (UMAP) plots of cells shown for each cell type. <bold>a</bold>, B cell. <bold>b</bold>, CD4<sup>+</sup> T cell. <bold>c</bold>, CD8<sup>+</sup> T cell. <bold>d</bold>, γδ T cell. <bold>e</bold>, T<sub>reg</sub> cell. <bold>f</bold>, NK cell. <bold>g</bold>, pDC. <bold>h</bold>, cDC1. <bold>i</bold>, cDC2. <bold>j</bold>, MigDC. <bold>k</bold>, Langerhans cell. <bold>l</bold>, <italic>Marco</italic><sup>+</sup> macrophage. <bold>m</bold>, Monocyte, <bold>n</bold>, Neutrophil. Coloured circles in the UMAP plots and next to state names correspond to polarization states. Cells coloured grey do not map to polarization states described. Cell polarization state name, single cytokine drivers and top marker genes are shown in the table for each cell type. Cytokine drivers coloured blue are probably indirect inducers. Top marker genes are defined as highly upregulated genes in the polarization state relative to all other cells of the same cell type. Colours in different panels are unrelated. <bold>o</bold>,<bold>p</bold>, Additional views of <bold>f</bold> using NK cells as an example to illustrate polarization-state analyses. <bold>o</bold>, UMAP plots coloured by cytokine or PBS treatment for major cytokine drivers of polarization states shown in <bold>f</bold>. <bold>p</bold>, Violin plots of expression levels of selected marker genes after cytokine or PBS treatment. Colours correspond to the polarization states that the cytokines are most strongly associated. This figure is a summary of the complete landscape for each cell type in Extended Data Figs. ##FIG##9##5##–##FIG##12##8## and Supplementary Figs. ##SUPPL##0##2##–##SUPPL##0##11##.</p><p>##SUPPL##14##Source data##</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><title>Cytokine production map by cell type.</title><p><bold>a</bold>, Heatmap of row-normalized gene expression of the 86 cytokines studied. Protein names encoded by the genes are in parentheses. <bold>b</bold>, Scatter plot showing the abundance of each cell type (log<sub>10</sub> scaled) in lymph nodes in PBS-treated controls compared with the number of cytokine genes expressed (calculated from an equal number of cells per cell type; threshold of detection = 0.1). Smoothed conditional means and 95% confidence intervals from a fitted linear model are shown. Pearson correlation coefficient and its associated <italic>P</italic> value obtained from two-sided <italic>t</italic>-test are shown. <bold>a</bold> included all cells in the study, whereas <bold>b</bold> sampled an equal number of cells per cell type to ensure comparability across cell types. <bold>c</bold>, Cytokine-mediated cell–cell interactome shown for FRC and cDC1 cells (complete interactome presented in Extended Data Fig. ##FIG##15##11##). Asterisks indicate multimeric cytokines.</p><p>##SUPPL##15##Source data##</p></caption></fig>", "<fig id=\"Fig5\"><label>Fig. 5</label><caption><title>IREA enables the inference of cytokine activities, immune cell polarization and cell–cell communications based on transcriptomic data.</title><p><bold>a</bold>, Problem statement for cytokine response inference, <bold>b</bold>, Illustration of the IREA software input and output. Colours in the heatmaps represent different expression values. User input can be gene sets or transcriptome matrices. Blue backgrounds represent cell polarization analysis; orange backgrounds represent cytokine response analysis. <bold>c</bold>–<bold>e</bold>, Examples of IREA analysis output on scRNA-seq data collected from cells in the tumour microenvironment following anti-PD-1 treatment relative to control antibody treatment. <bold>c</bold>, IREA radar plots showing enrichment of macrophage, NK cell and CD8<sup>+</sup> T cell polarization states described in Fig. ##FIG##2##3##. Cytokine drivers of selected polarization states are indicated by arrows. <bold>d</bold>, IREA cytokine enrichment plot showing the enrichment score (ES) for each of the 86 cytokine responses in NK cells following anti-PD-1 treatment. Bar length represents the ES, shading represents the FDR-adjusted <italic>P</italic> value (two-sided Wilcoxon rank-sum test), with darker colours representing more significant enrichment (red, enriched in anti-PD-1 treatment, blue, enriched in untreated control). Cytokines with receptors expressed are indicated by black filled boxes. <bold>e</bold>, Inferred cell–cell communication network mediated by cytokines. For ease of identification, cytokines are plotted from left to right in each segment in the same order as the legend. For clarity, individual cytokine plots following the same visualization scheme are shown on the right and in Extended Data Fig. ##FIG##16##12c##.</p><p>##SUPPL##16##Source data##</p></caption></fig>", "<fig id=\"Fig6\"><label>Extended Data Fig. 1</label><caption><title>scRNA-seq data summary and quality metrics.</title><p><bold>a</bold>, Violin plots showing the distributions of percentage of mitochondrial gene content (top), number of genes detected (middle), and number of unique molecular identifiers (UMIs) detected (bottom) per cell post-quality control across cytokine or PBS treatment conditions. The interquartile range is shown as a white box inside each violin plot. n = 386,703 independent cells over 272 independent mice (3 mice per cytokine and 14 mice for PBS control). <bold>b</bold>, Two-dimensional t-SNE visualization of all cells (following the coordinates in Fig. ##FIG##0##1b##), colored by any cytokine treatment (pink) or PBS control (blue). <bold>c</bold>, Contour plot of the t-SNE map in <bold>b</bold>. <bold>d</bold>, t-SNE visualization of all cells, colored by level-1 Louvain clusters identified from global clustering. The dominant cell type associated with each cluster is indicated in the accompanying table. Each cluster is further divided into level-2 clusters to refine cell type identification (Supplementary Table ##SUPPL##3##2##). <bold>e</bold>, Dot plot showing the scaled average expression of cell type marker genes and percentage of cells expressing the genes in each annotated cell type. <bold>f</bold>, Cell type composition in each treatment. <bold>g</bold>, Changes in the fraction of non-B, non-T immune cell types after cytokine treatment relative to PBS controls. denotes <italic>P</italic>-value < 0.05, one-sided Wilcoxon rank-sum test with FDR adjustment. Only CD3– and CD19– immune cells are shown as these cell types are not influenced by the cell sorting strategy used.</p><p>\n##SUPPL##17##Source data##\n</p></caption></fig>", "<fig id=\"Fig7\"><label>Extended Data Fig. 2</label><caption><title>Additional scRNA-seq data quality metrics.</title><p><bold>a</bold>, Distributions of Euclidean distances between individual PBS-treated cells based on their transcriptomic profiles, colored by whether cells compared are from the same sample processing batch or across different sample processing batches. <bold>b</bold>, Pearson correlation coefficients between cytokine-induced gene expression signatures obtained from different animal replicates, using cDC1 as a representative example. <bold>c</bold>, As a positive control for the ability of each lymph node cell type to access the injected cytokines, IFN-α/β responses in each cell type are shown. At the single-cell level, ISG scores can accurately classify IFN-α/β-treated cells vs. PBS-treated cells based on areas under receiver operating characteristics (AUROC) curves in each cell type, confirming that the vast majority of the cells can access the injected cytokines. ISG expression in each cell is shown as individual dots and violin plots on the left side, and the classification accuracy is shown as ROC curves on the right side.</p><p>\n##SUPPL##18##Source data##\n</p></caption></fig>", "<fig id=\"Fig8\"><label>Extended Data Fig. 3</label><caption><title>Cell type-specific gene expression changes induced by IFN-α1, IFN-β, IFN-κ, IFN-γ, IL-1α, IL-1β, IL-18, IL-36α, IL-2, IL-4, IL-7, IL-15, IL-3, GM-CSF, and TNF-α.</title><p><bold>a</bold>, Gene expression heatmaps illustrating top genes upregulated by cytokine treatment compared to PBS treatment in any of the cell types shown. All genes shown have at least log<sub>2</sub>FC > 0.3 and FDR < 0.05 in one or more cell types. Color gradient indicates average log<sub>2</sub>FC relative to PBS treatment of the same cell type. Genes expressed in fewer than 10% of cells in both cytokine and PBS treatment conditions are denoted as no change. <bold>b</bold>, Percentage of upregulated DEGs exclusive to a single cell type (green) or shared among two or more cell types (other colors) after cytokine treatment at various DEG cutoff thresholds. Each box shows a different log<sub>2</sub>FC and FDR threshold for defining DEGs. This is an extension of the analysis in Fig. ##FIG##1##2b##, showing consistency of the cell type-specific effects irrespective of DEG cutoffs.</p><p>\n##SUPPL##19##Source data##\n</p></caption></fig>", "<fig id=\"Fig9\"><label>Extended Data Fig. 4</label><caption><title>Cell type-specific responses to a cytokine can be exclusive to the cytokine or can be attributed to secondary effects from induced cytokines.</title><p><bold>a-b</bold>, Examples of cell type-specific responses to a cytokine that are not observed in the responses to the other cytokines studied. <bold>a</bold>, Examples of cell type-specific gene regulation in response to IL-1α/β and exclusively to IL-1α/β. Left, Heatmaps showing differential expression of IL-1α/β-regulated genes relative to PBS treatment per cell type, highlighting cell type-specific responses to the same cytokines. Three independent mice for each cytokine treatment are shown in adjacent columns. Right, for the IL-1α/β-induced cell type-specific DEGs in the heatmaps, the plots show whether they can be induced by any other of the 86 cytokine stimulations. Color of square, log<sub>2</sub> fold change in each cytokine treatment relative to PBS; size of square, –log<sub>10</sub> transformed FDR-adjusted <italic>P</italic>-value obtained from two-sided Wilcoxon rank-sum test. Results with FDR < 0.05 and log<sub>2</sub>FC > 0.5 are shown. IL-1α/β treatments are highlighted in gray. Cell types in columns on the left and rows on the right follow the same color code. Note that both IL-36α and IL-1α/β are proinflammatory cytokines in the IL-1 cytokine family. <bold>b</bold>, Examples of cell type-specific gene regulation in response to IFN-α1/β and exclusively to IFN-α1/β; following the same visualization as in <bold>a</bold>. Note that both IFN-κ and IFN-α1/β are type I interferons and share receptors. <bold>c-e</bold>, Examples of cell type-specific responses to a cytokine that can be attributed to secondary effects from induced cytokines. <bold>c</bold>, Heatmaps showing DEGs in response to IL-2, IL-12, IL−15, IL−18 relative to PBS treatment, highlighting different responses to the same cytokine in NK cells vs. other cell types. Heatmap for IFN-γ treatment is included as a comparison. <italic>Stat1</italic> is known to be regulated by IFN-γ signaling. <bold>d</bold>, Violin plot showing <italic>Ifng</italic> gene expression in NK cells after each of the 86 cytokine treatments or PBS treatment. Samples with a high expression (>0.8 normalized expression units) are colored dark blue. <bold>e</bold>, Induction of IFN-γ signatures across cell types after each cytokine treatment. The IFN-γ signature is obtained for each cell type from the IFN-γ treatment. Expression of the signature is obtained from summing normalized expression units of all genes in the signature for each cell type. Significant (FDR < 0.01) responses are shown as squares, with color gradient representing average log<sub>2</sub>FC in each cytokine treatment relative to PBS (capped at 50), and size representing –log<sub>10</sub> transformed FDR-adjusted <italic>P</italic>-value obtained from two-sided Wilcoxon rank-sum test. Cytokines inducing a high expression of <italic>Ifng</italic> are highlighted in gray in <bold>d</bold> and <bold>e</bold>. <bold>d</bold> and <bold>e</bold> are vertically aligned to illustrate that the cytokine treatments inducing an upregulation of <italic>Ifng</italic> in NK cells display a strong IFN-γ signature in other cell types.</p><p>\n##SUPPL##20##Source data##\n</p></caption></fig>", "<fig id=\"Fig10\"><label>Extended Data Fig. 5</label><caption><title>B cell responses to cytokines: polarization states, subclusters, marker gene expression, and gene programs.</title><p><bold>a</bold>, Top, UMAP visualization of B cells for all cytokines, colored by polarization states; bottom, table with cell type polarization states (left column), single cytokine drivers (middle column), and top marker genes (right column); reproduced from Fig. ##FIG##2##3## for ease of reference. <bold>b</bold>, Pairwise Pearson correlation coefficients between polarization states. <bold>c</bold>, UMAP visualization of B cells shown independently for each cytokine treatment, colored by cytokine treatment (blue) or PBS treatment control (gray). <bold>d</bold>, UMAP visualization of B cells for all cytokine or PBS treatment; cells colored for B cell Louvain subclusters. <bold>e</bold>, Top overexpressed genes in each Louvain subcluster in <bold>d</bold>; color, column-scaled average expression; size of circle, percentage of cells in the subcluster expressing each gene. <bold>f-i</bold>, B cell responses to each cytokine stimulation. <bold>f</bold>, Fraction of cells per subcluster in each cytokine treatment. Colors represent subclusters defined in <bold>d</bold>. <bold>g</bold>, Enrichment of each subcluster in each cytokine treatment; size of circle, Bonferroni-adjusted <italic>P</italic>-value of hypergeometric test relative to PBS; black fills, <italic>P</italic> < 0.01. <bold>h</bold>, Row-normalized relative expression of representative marker genes of each polarization state in cytokine-treated vs. PBS-treated cells. <bold>i</bold>, Enrichment of B cell gene programs obtained from NMF analysis of all B cells in cytokine-treated cells relative to PBS-treated cells; size of circle, FDR-adjusted <italic>P</italic>-value from two-sided Wilcoxon rank-sum test; shade, effect size representing the mean difference in gene program weight. <bold>j</bold>, Top weighted genes in each gene program in <bold>i</bold>. <bold>k</bold>, Average gene program weight in each subcluster. Rows and columns were hierarchically clustered using the complete-linkage method on Euclidean distances.</p><p>\n##SUPPL##21##Source data##\n</p></caption></fig>", "<fig id=\"Fig11\"><label>Extended Data Fig. 6</label><caption><title>NK cell responses to cytokines: polarization states, subclusters, marker gene expression, and gene programs.</title><p><bold>a</bold>, Top, UMAP visualization of NK cells for all cytokines, colored by polarization states; bottom, table with cell type polarization states (left column), single cytokine drivers (middle column), and top marker genes (right column); reproduced from Fig. ##FIG##2##3## for ease of reference. <bold>b</bold>, Pairwise Pearson correlation coefficients between polarization states. <bold>c</bold>, UMAP visualization of NK cells shown independently for each cytokine treatment, colored by cytokine treatment (blue) or PBS treatment control (gray). <bold>d</bold>, UMAP visualization of NK cells for all cytokine or PBS treatment; cells colored for NK cell Louvain subclusters. <bold>e</bold>, Top overexpressed genes in each Louvain subcluster in <bold>d</bold>; color, column-scaled average expression; size of circle, percentage of cells in the subcluster expressing each gene. <bold>f-i</bold>, NK cell responses to each cytokine stimulation. <bold>f</bold>, Fraction of cells per subcluster in each cytokine treatment. Colors represent subclusters defined in <bold>d</bold>. <bold>g</bold>, Enrichment of each subcluster in each cytokine treatment; size of circle, Bonferroni-adjusted <italic>P</italic>-value of hypergeometric test relative to PBS; black fills, <italic>P</italic> < 0.01. <bold>h</bold>, Row-normalized relative expression of representative marker genes of each polarization state in cytokine-treated vs. PBS-treated cells. <bold>i</bold>, Enrichment of NK cell gene programs obtained from NMF analysis of all NK cells in cytokine-treated cells relative to PBS-treated cells; size of circle, FDR-adjusted <italic>P</italic>-value from two-sided Wilcoxon rank-sum test; shade, effect size representing the mean difference in gene program weight. <bold>j</bold>, Top weighted genes in each gene program in <bold>i</bold>. <bold>k</bold>, Average gene program weight in each subcluster. Rows and columns were hierarchically clustered using the complete-linkage method on Euclidean distances.</p><p>\n##SUPPL##22##Source data##\n</p></caption></fig>", "<fig id=\"Fig12\"><label>Extended Data Fig. 7</label><caption><title>cDC1 responses to cytokines: polarization states, subclusters, marker gene expression, and gene programs.</title><p><bold>a</bold>, Top, UMAP visualization of cDC1 cells for all cytokines, colored by polarization states; bottom, table with cell type polarization states (left column), single cytokine drivers (middle column), and top marker genes (right column); reproduced from Fig. ##FIG##2##3## for ease of reference. <bold>b</bold>, Pairwise Pearson correlation coefficients between polarization states. <bold>c</bold>, UMAP visualization of cDC1 cells shown independently for each cytokine treatment, colored by cytokine treatment (blue) or PBS treatment control (gray). <bold>d</bold>, UMAP visualization of cDC1 cells for all cytokine or PBS treatment; cells colored for cDC1 Louvain subclusters. <bold>e</bold>, Top overexpressed genes in each Louvain subcluster in <bold>d</bold>; color, column-scaled average expression; size of circle, percentage of cells in the subcluster expressing each gene. <bold>f-i</bold>, cDC1 responses to each cytokine stimulation. <bold>f</bold>, Fraction of cells per subcluster in each cytokine treatment. Colors represent subclusters defined in <bold>d</bold>. <bold>g</bold>, Enrichment of each subcluster in each cytokine treatment; size of circle, Bonferroni-adjusted <italic>P</italic>-value of hypergeometric test relative to PBS; black fills, <italic>P</italic> < 0.01. <bold>h</bold>, Row-normalized relative expression of representative marker genes of each polarization state in cytokine-treated vs. PBS-treated cells. <bold>i</bold>, Enrichment of cDC1 gene programs obtained from NMF analysis of all cDC1 cells in cytokine-treated cells relative to PBS-treated cells; size of circle, FDR-adjusted <italic>P</italic>-value from two-sided Wilcoxon rank-sum test; shade, effect size representing the mean difference in gene program weight. <bold>j</bold>, Top weighted genes in each gene program in <bold>i</bold>. <bold>k</bold>, Average gene program weight in each subcluster. Rows and columns were hierarchically clustered using the complete-linkage method on Euclidean distances.</p><p>\n##SUPPL##23##Source data##\n</p></caption></fig>", "<fig id=\"Fig13\"><label>Extended Data Fig. 8</label><caption><title>Macrophage (<italic>Marco</italic>+) responses to cytokines: polarization states, subclusters, marker gene expression, and gene programs.</title><p><bold>a</bold>, Top, UMAP visualization of <italic>Macro</italic>+ macrophages for all cytokines, colored by polarization states; bottom, table with cell type polarization states (left column), single cytokine drivers (middle column), and top marker genes (right column); reproduced from Fig. ##FIG##2##3## for ease of reference. <bold>b</bold>, Pairwise Pearson correlation coefficients between polarization states. <bold>c</bold>, UMAP visualization of macrophages shown independently for each cytokine treatment, colored by cytokine treatment (blue) or PBS treatment control (gray). <bold>d</bold>, UMAP visualization of macrophages for all cytokine or PBS treatment; cells colored for macrophage Louvain subclusters. <bold>e</bold>, Top overexpressed genes in each Louvain subcluster in <bold>d</bold>; color, column-scaled average expression; size of circle, percentage of cells in the subcluster expressing each gene. <bold>f-i</bold>, Macrophage responses to each cytokine stimulation. <bold>f</bold>, Fraction of cells per subcluster in each cytokine treatment. Colors represent subclusters defined in <bold>d</bold>. <bold>g</bold>, Enrichment of each subcluster in each cytokine treatment; size of circle, Bonferroni-adjusted <italic>P</italic>-value of hypergeometric test relative to PBS; black fills, <italic>P</italic> < 0.01. <bold>h</bold>, Row-normalized relative expression of representative marker genes of each polarization state in cytokine-treated vs. PBS-treated cells. <bold>i</bold>, Enrichment of macrophage gene programs obtained from NMF analysis of all macrophages in cytokine-treated cells relative to PBS-treated cells; size of circle, FDR-adjusted <italic>P</italic>-value from two-sided Wilcoxon rank-sum test; shade, effect size representing the mean difference in gene program weight. <bold>j</bold>, Top weighted genes in each gene program in <bold>i</bold>. <bold>k</bold>, Average gene program weight in each subcluster. Rows and columns were hierarchically clustered using the complete-linkage method on Euclidean distances.</p><p>\n##SUPPL##24##Source data##\n</p></caption></fig>", "<fig id=\"Fig14\"><label>Extended Data Fig. 9</label><caption><title>A comparative global view of the 66 major polarization states across immune cell types.</title><p><bold>a</bold>, Heatmap showing pairwise Jaccard similarity index between immune cell polarization states defined in Fig. ##FIG##2##3##, Extended Data Figs. ##FIG##9##5##–##FIG##12##8##, and Supplementary Figs. ##SUPPL##0##2##–##SUPPL##0##11##. Jaccard similarity index is defined based on genes with >0.5 log<sub>2</sub> fold difference in each polarization state compared to PBS-treatment of the same cell type. Cell types are marked by colors on the edges of the heatmap. Cytokine drivers are indicated in square brackets. Groups of similar polarization states are annotated above the heatmap. <bold>b</bold>, Force-directed graph visualization of Jaccard similarity index between immune cell polarization states to highlight unique polarization states. Vertices represent polarization states. A line connects a pair of vertices that have Jaccard similarity index > 0.15. Unconnected states are randomly positioned in the force-directed graph. A larger circle represents a more unique polarization state, based on a lower median Jaccard similarity index with other polarization states. Cell types are marked by colors indicated in <bold>a</bold>.</p><p>\n##SUPPL##25##Source data##\n</p></caption></fig>", "<fig id=\"Fig15\"><label>Extended Data Fig. 10</label><caption><title>A map of cytokine receptor expression by cell type and additional information on the cytokine production map.</title><p><bold>a</bold>, Correlation between cell type abundance and number of distinct cytokine genes expressed; added B cells and T cells (hollow circles) to Fig. ##FIG##3##4b##. B cell and T cell abundances are estimates integrated from literature, all other cell types are obtained from PBS-treated conditions in lymph nodes from our dictionary. Smoothed conditional means and 95% confidence intervals from a fitted linear model are shown. <bold>b</bold>, Robustness analysis for Fig. ##FIG##3##4b##; showing correlations between the abundance of each cell type and the numbers of distinct cytokine genes expressed in the cell type under various cutoff thresholds for a cytokine gene to be considered expressed. Both Pearson and Spearman correlation coefficients are shown. <bold>c</bold>, A map of cytokine receptor expression by cell type. The map includes signaling receptors, decoy receptors, as well as receptors that form complexes with cytokines. The values are normalized to the maximum expression in each row. <bold>d</bold>, Expression of cytokine (left) or receptor (right) genes in cDC1s following PBS or cytokine treatment. * represents FDR-adjusted <italic>P</italic> < 0.05 for significant change in expression relative to PBS control.</p><p>\n##SUPPL##26##Source data##\n</p></caption></fig>", "<fig id=\"Fig16\"><label>Extended Data Fig. 11</label><caption><title>A draft network of cytokine-mediated cell-cell interactome.</title><p><bold>a</bold>, An interactome network showing cell-cell communication potential based on cytokine expression and the impact of cytokine on each cell type. Lime box, source nodes or cell types secreting cytokines; red box, cytokines mediating the communication; blue box, sink nodes or cell types responding to cytokines. A path is established between source and sink cell types through a cytokine if the source cell type produces the cytokine (normalized expression > 0.1) and the sink cell type shows a significant response to the cytokine (>10 DEGs in the cytokine signatures). Asterisks indicate heteromeric cytokines or cytokine complexes. Rare cell types, including basophils, BECs, LECs, and FRCs, were not analyzed for the response, but were aggregated across treatment conditions to generate the production map. <bold>b-v</bold>, The interactome using same conventions as in <bold>a</bold> plotted separately by source node for ease of visualization, shown for <bold>b</bold>, B cell; <bold>c</bold>, CD4+ T cell; <bold>d</bold>, CD8+ T cell; <bold>e</bold>, γδ T cell; <bold>f</bold>, Treg; <bold>g</bold>, NK cell; <bold>h</bold>, ILC; <bold>i</bold>, pDC; <bold>j</bold>, cDC1; <bold>k</bold>, cDC2; <bold>l</bold>, MigDC; <bold>m</bold>, Langerhans cell; <bold>n</bold>, eTAC; <bold>o</bold>, macrophage; <bold>p</bold>, monocyte; <bold>q</bold>, neutrophil; <bold>r</bold>, mast cell; <bold>s</bold>, basophil; <bold>t</bold>, BEC; <bold>u</bold>, LEC; <bold>v</bold>, FRC. <bold>j</bold> and <bold>v</bold> are reproduced from Fig. ##FIG##3##4c##.</p><p>\n##SUPPL##27##Source data##\n</p></caption></fig>", "<fig id=\"Fig17\"><label>Extended Data Fig. 12</label><caption><title>IREA software output on mouse tumor samples following anti-PD-1 treatment and on human blood samples in severe COVID-19.</title><p><bold>a-c</bold>, Additional IREA analyses on mouse tumor samples following anti-PD-1 treatment relative to the control. <bold>a</bold>, IREA radar plots showing enrichments of immune cell polarization states described in Fig. ##FIG##2##3##. <bold>b</bold>, To improve the interpretability of the enrichment scores in Fig. ##FIG##4##5d##, top genes contributing to the IL-12 enrichment in NK cells are shown. Bar length represents gene expression fold change; left: changes in gene expression in NK cells in response to IL-12 in the Immune Dictionary; right: changes in gene expression in NK cells after anti-PD-1 treatment in the tumor dataset (red: upregulated relative to control; blue: downregulated relative to control). <bold>c</bold>, Inferred cell-cell communication network mediated by cytokines. The plot on the top is reproduced from Fig. ##FIG##4##5e## for ease of reference. Individual cytokine plots following the same visualization scheme are shown below. <bold>d</bold>, IREA analysis on peripheral blood cells collected from severe COVID-19 patients relative to healthy volunteers. Top, IREA compass plot showing enrichment scores for each of the 86 cytokines in B cells. Bar length represents enrichment score, shade represents FDR adjusted <italic>P</italic>-value (two-sided Wilcoxon rank-sum test), with darker colors representing more significant enrichment (red: enriched in ventilated COVID-19 patients, blue: enriched in healthy control). Cytokines with receptors expressed are indicated by black filled boxes. Bottom, IREA radar plots showing enrichments of immune cell polarization states in monocytes, B cells, and CD4+ T cells. The reference polarization states are described in Fig. ##FIG##2##3##.</p><p>\n##SUPPL##28##Source data##\n</p></caption></fig>" ]	[]	[]	[]	[]	[]	[]	[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM3\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM4\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM5\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM6\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM7\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM8\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM9\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM10\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM11\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM12\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM13\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM14\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM15\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM16\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM17\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM18\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM19\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM20\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM21\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM22\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM23\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM24\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM25\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM26\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM27\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM28\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM29\"></supplementary-material>" ]	[ "<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"41586_2023_6816_Fig1_HTML\" id=\"d32e417\"/>", "<graphic xlink:href=\"41586_2023_6816_Fig2_HTML\" id=\"d32e582\"/>", "<graphic xlink:href=\"41586_2023_6816_Fig3_HTML\" id=\"d32e854\"/>", "<graphic xlink:href=\"41586_2023_6816_Fig4_HTML\" id=\"d32e1114\"/>", "<graphic xlink:href=\"41586_2023_6816_Fig5_HTML\" id=\"d32e1238\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6816_Fig6_ESM\" id=\"d32e1952\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6816_Fig7_ESM\" id=\"d32e1977\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6816_Fig8_ESM\" id=\"d32e2008\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6816_Fig9_ESM\" id=\"d32e2091\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6816_Fig10_ESM\" id=\"d32e2167\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6816_Fig11_ESM\" id=\"d32e2243\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6816_Fig12_ESM\" id=\"d32e2319\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6816_Fig13_ESM\" id=\"d32e2402\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6816_Fig14_ESM\" id=\"d32e2445\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6816_Fig15_ESM\" id=\"d32e2483\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6816_Fig16_ESM\" id=\"d32e2585\"/>", "<graphic position=\"anchor\" xlink:href=\"41586_2023_6816_Fig17_ESM\" id=\"d32e2632\"/>" ]	[ "<media xlink:href=\"41586_2023_6816_MOESM1_ESM.docx\"><caption><p>Supplementary Information</p></caption></media>", "<media xlink:href=\"41586_2023_6816_MOESM2_ESM.pdf\"><caption><p>Reporting Summary</p></caption></media>", "<media xlink:href=\"41586_2023_6816_MOESM3_ESM.xlsx\"><caption><p>Supplementary Table 1</p></caption></media>", "<media xlink:href=\"41586_2023_6816_MOESM4_ESM.xlsx\"><caption><p>Supplementary Table 2</p></caption></media>", "<media xlink:href=\"41586_2023_6816_MOESM5_ESM.xlsx\"><caption><p>Supplementary Table 3</p></caption></media>", "<media xlink:href=\"41586_2023_6816_MOESM6_ESM.xlsx\"><caption><p>Supplementary Table 4</p></caption></media>", "<media xlink:href=\"41586_2023_6816_MOESM7_ESM.xlsx\"><caption><p>Supplementary Table 5</p></caption></media>", "<media xlink:href=\"41586_2023_6816_MOESM8_ESM.xlsx\"><caption><p>Supplementary Table 6</p></caption></media>", "<media xlink:href=\"41586_2023_6816_MOESM9_ESM.xlsx\"><caption><p>Supplementary Table 7</p></caption></media>", "<media xlink:href=\"41586_2023_6816_MOESM10_ESM.xlsx\"><caption><p>Supplementary Table 8</p></caption></media>", "<media xlink:href=\"41586_2023_6816_MOESM11_ESM.xlsx\"><caption><p>Supplementary Table 9</p></caption></media>", "<media xlink:href=\"41586_2023_6816_MOESM12_ESM.xlsx\"><caption><p>Supplementary Table 10</p></caption></media>", "<media xlink:href=\"41586_2023_6816_MOESM13_ESM.xlsx\"><caption><p>Source Data Fig. 1</p></caption></media>", "<media xlink:href=\"41586_2023_6816_MOESM14_ESM.xlsx\"><caption><p>Source Data Fig. 2</p></caption></media>", "<media xlink:href=\"41586_2023_6816_MOESM15_ESM.xlsx\"><caption><p>Source Data Fig. 3</p></caption></media>", "<media xlink:href=\"41586_2023_6816_MOESM16_ESM.xlsx\"><caption><p>Source Data Fig. 4</p></caption></media>", "<media xlink:href=\"41586_2023_6816_MOESM17_ESM.xlsx\"><caption><p>Source Data Fig. 5</p></caption></media>", "<media xlink:href=\"41586_2023_6816_MOESM18_ESM.xlsx\"><caption><p>Source Data Extended Data Fig. 1</p></caption></media>", "<media xlink:href=\"41586_2023_6816_MOESM19_ESM.xlsx\"><caption><p>Source Data Extended Data Fig. 2</p></caption></media>", "<media xlink:href=\"41586_2023_6816_MOESM20_ESM.xlsx\"><caption><p>Source Data Extended Data Fig. 3</p></caption></media>", "<media xlink:href=\"41586_2023_6816_MOESM21_ESM.xlsx\"><caption><p>Source Data Extended Data Fig. 4</p></caption></media>", "<media xlink:href=\"41586_2023_6816_MOESM22_ESM.xlsx\"><caption><p>Source Data Extended Data Fig. 5</p></caption></media>", "<media xlink:href=\"41586_2023_6816_MOESM23_ESM.xlsx\"><caption><p>Source Data Extended Data Fig. 6</p></caption></media>", "<media xlink:href=\"41586_2023_6816_MOESM24_ESM.xlsx\"><caption><p>Source Data Extended Data Fig. 7</p></caption></media>", "<media xlink:href=\"41586_2023_6816_MOESM25_ESM.xlsx\"><caption><p>Source Data Extended Data Fig. 8</p></caption></media>", "<media xlink:href=\"41586_2023_6816_MOESM26_ESM.xlsx\"><caption><p>Source Data Extended Data Fig. 9</p></caption></media>", "<media xlink:href=\"41586_2023_6816_MOESM27_ESM.xlsx\"><caption><p>Source Data Extended Data Fig. 10</p></caption></media>", "<media xlink:href=\"41586_2023_6816_MOESM28_ESM.xlsx\"><caption><p>Source Data Extended Data Fig. 11</p></caption></media>", "<media xlink:href=\"41586_2023_6816_MOESM29_ESM.xlsx\"><caption><p>Source Data Extended Data Fig. 12</p></caption></media>" ]	[{"label": ["3."], "mixed-citation": ["Ye, Q., Wang, B. & Mao, J. The pathogenesis and treatment of the \u2018cytokine storm\u2019 in COVID-19. "], "italic": ["J. Infect."]}, {"label": ["40."], "mixed-citation": ["Fletcher, A. L. et al. Reproducible isolation of lymph node stromal cells reveals site-dependent differences in fibroblastic reticular cells. "], "italic": ["Front. Immunol."]}, {"label": ["43."], "surname": ["van der Maaten", "Hinton"], "given-names": ["L", "G"], "article-title": ["Visualizing data using t-SNE"], "source": ["J. Mach. Learn. Res."], "year": ["2008"], "volume": ["9"], "fpage": ["2579"], "lpage": ["2605"]}, {"label": ["44."], "mixed-citation": ["Lee, D. D. & Sebastian Seung, H. Learning the parts of objects by non-negative matrix factorization. "], "italic": ["Nature"]}, {"label": ["48."], "mixed-citation": ["Becht, E. et al. Dimensionality reduction for visualizing single-cell data using UMAP. "], "italic": ["Nat. Biotechnol."]}]	{ "acronym": [], "definition": [] }	49	CC BY	no	2024-01-13 00:02:19	Nature. 2024 Dec 6; 625(7994):377-384	oa_package/91/cb/PMC10781646.tar.gz
PMC10781647	38197177	[]	[]	[]	[]	[]	[ "<title>Abstract</title>", "<p>Bile acids are signaling mediators, enabling intricate communication between tissues and the gut microbiota, and are involved in the pathophysiology of several immune and metabolic disorders. In this commentary, we discuss the importance of the gut microbiota in the <italic toggle=\"yes\">Cyp2c70</italic> knock-out mice, which are considered as a promising ‘humanized’ experimental resource for studying bile acids and their role in pathological conditions. We also discuss how <italic toggle=\"yes\">Cyp2c70</italic>-deficient mice contribute to enhancing the translatability of preclinical studies in murine models to humans.</p>" ]	[ "<p>Mice are commonly used as experimental models to mimic and study human pathologies. However, when investigating conditions related to bile acids, conventional mice may not be the optimal choice, due to species-specific differences (recently reviewed in [##REF##36601783##1##]). Bile acid metabolism, including interactions between host and gut microbiota, significantly differs between humans and mice. In humans, the bile acid pool predominantly contains cholic (CA), chenodeoxycholic (CDCA), and deoxycholic (DCA) acids, whereas muricholic acids (MCAs) account for a large proportion of the bile acid pool in mice (##FIG##0##Figure 1##). The production of 6β-hydroxylated MCAs by murine livers has been documented for decades; however, the identification of CYP2C70, the enzyme responsible for the hydroxylation of bile acids at the 6β position (as schematically depicted in ##FIG##0##Figure 1##), was only achieved in 2016 by Takahashi and colleagues [##REF##27638959##2##]. Expanding on this significant advancement, Folkert Kuipers’ group successfully generated hepatic <italic toggle=\"yes\">Cyp2c70</italic> knock-out mice by employing the CRISPR/Cas9 technology [##REF##31506275##3##]. They documented a drastic reduction of MCAs and an accumulation of CDCA in the bile acid pool of mice lacking <italic toggle=\"yes\">Cyp2c70</italic> (##FIG##0##Figure 1##) [##REF##31506275##3##]. Subsequently, several independent research groups swiftly confirmed these initial observations, illustrating that <italic toggle=\"yes\">Cyp2c70</italic>-deficient mice exhibited a bile acid pool free of MCAs, resembling that of humans [##REF##31645370##4##,##REF##32086245##5##]. Therefore, <italic toggle=\"yes\">Cyp2c70</italic> knock-out mice were considered as a promising ‘humanized’ experimental resource for investigating bile acids and their role in pathological contexts, for developing and testing potential treatments, and for significantly enhancing the translatability of findings from mice to humans.</p>", "<p>The hydroxylation at the 6β position significantly impacts on the physicochemical properties of bile acids. Having an additional hydroxyl group, MCAs are inherently more hydrophilic compared with CDCA. Consequently, the substantial increase of CDCA within the bile acid pool of <italic toggle=\"yes\">Cyp2c70</italic>-deficient mice results in a more hydrophobic and therefore potentially more toxic bile acid pool (##FIG##0##Figure 1##). Indeed, mice lacking <italic toggle=\"yes\">Cyp2c70</italic> have liver damage (##FIG##0##Figure 1##). Neonatal cholestasis has been documented, accompanied by elevated transaminases, cholangiocyte proliferation, and a pro-inflammatory and pro-fibrotic gene signature not seen in controls [##REF##33309945##6##]. As <italic toggle=\"yes\">Cyp2c70</italic>-deficient mice age, liver damage progresses with ductular reaction, hepatocyte necrosis, lymphocytes and neutrophils infiltration ultimately evolving to fibrosis in the livers of adult <italic toggle=\"yes\">Cyp2c70</italic>-deficient mice [##REF##31645370##4##,##REF##33309945##6##]. However, the mechanisms by which bile acids cause damage to liver cells and initiate the inflammatory response in <italic toggle=\"yes\">Cyp2c70</italic>-deficient mice remain undetermined. Additionally, it is noteworthy that the <italic toggle=\"yes\">Cyp2c70</italic> deletion induces a more pronounced liver phenotype in females compared with males [##REF##33309945##6##]. The spontaneous hepatobiliary injuries observed in <italic toggle=\"yes\">Cyp2c70</italic>-deficient mice thus raise questions about their suitability to study pathophysiological processes involved in liver diseases.</p>", "<p>In addition to liver enzymes, bacterial enzymes also shape the composition and the physiochemical properties of the bile acid pool [##REF##33388088##7##]. Bile salt hydrolases remove glycine and taurine conjugates from bile acids. Enzymes for 7α-dehydroxylation pathway convert CA and CDCA to DCA and lithocholic acid (LCA), respectively. Additionally, CDCA can be epimerized by hydroxysteroid dehydrogenases to form ursodeoxycholic acid (UDCA). Deconjugation and dehydroxylation carried by gut bacteria increase the hydrophobicity index of bile acids, while epimerization of CDCA to UDCA reduces it. In turn, bile acids shape the gut microbial composition by exerting bacteriostatic and bactericidal effects. To illustrate, <italic toggle=\"yes\">Cyp2c70</italic>-deficient mice exhibit a different microbial composition in their caeca compared with wild-type mice, with over 40 genera showing differential abundance [##REF##33309945##6##].</p>", "<p>In two recent research papers published in <italic toggle=\"yes\">Clinical Science</italic>, the interaction between bile acids and the gut microbiota was investigated to gain a deeper understanding of the mechanisms of hepatobiliary injuries observed in <italic toggle=\"yes\">Cyp2c70</italic>-deficient mice. The approach consisted of depleting the gut microbiota. In a first study by Sjöland and colleagues, <italic toggle=\"yes\">Cyp2c70</italic>-deficient mice were rederived as germ-free, and were then colonized (or not) with human or mouse gut microbial communities [##REF##37384590##8##]. In a second study by Verkade et al., <italic toggle=\"yes\">Cyp2c70</italic>-deficient mice received broad-spectrum antibiotics to eliminate their gut microbiota [##REF##37910096##9##]. The bile acid pool of germ-free <italic toggle=\"yes\">Cyp2c70</italic>-deficient mice was composed of TCDCA and TCA (##FIG##0##Figure 1##), in line with the absence of <italic toggle=\"yes\">Cyp2c70</italic> to convert CDCA to MCAs and with the absence of bacterial enzymes to deconjugate bile acids and convert them to UDCA, DCA or LCA [##REF##37384590##8##]. Colonization of germ-free <italic toggle=\"yes\">Cyp2c70</italic>-deficient mice with a human gut microbiota did not significantly change the bile acid pool. By contrast, colonization with a mouse gut microbiota massively increased the proportion of UDCA, as a result of the conversion of CDCA to UDCA by bacterial enzymes [##REF##37384590##8##]. The increase in UDCA (and conjugated forms) at the expense of CDCA resulted in a less hydrophobic bile acid pool [##REF##37384590##8##]. In the study by Verkade et al., gut microbiota depletion resulted in a bile acid pool composed mainly of TCDCA in <italic toggle=\"yes\">Cyp2c70</italic>-deficient mice (##FIG##0##Figure 1##) [##REF##37910096##9##]. The cumulated absence of hepatic <italic toggle=\"yes\">Cyp2c70</italic> and bacterial 7α/β-hydroxysteroid dehydrogenase prevent the conversion of CDCA to MCAs and to UDCA [##REF##37910096##9##]. Moreover, the repression of hepatic CYP8B1 compromised the production of 12α-hydroxylated CA, tilting the balance towards the production of non 12α-hydroxylated CDCA in livers of antibiotic-treated <italic toggle=\"yes\">Cyp2c70</italic>-deficient mice [##REF##37910096##9##]. In the two studies, the combination of <italic toggle=\"yes\">Cyp2c70</italic> deletion and gut microbiota depletion massively increased the proportion of TCDCA - up to 90% - and thus, the hydrophobicity of the bile acid pool (##FIG##0##Figure 1##).</p>", "<p>The changes in bile acid pool hydrophobicity had a noticeable effect on the mouse phenotype. Germ-free <italic toggle=\"yes\">Cyp2c70</italic>-deficient mice had enlarged livers, with significant liver fibrosis and cholangiocyte proliferation compared with germ-free WT mice [##REF##37384590##8##]. High mortality in young germ-free <italic toggle=\"yes\">Cyp2c70</italic>-deficient mice was reduced when mice were colonized with human or mouse gut microbial communities [##REF##37384590##8##]. In line with decreased hydrophobicity of the bile acid pool, the colonization of <italic toggle=\"yes\">Cyp2c70</italic>-deficient mice with a mouse, but not with a human, gut microbiota completely resolved fibrosis back to normal in adult mice [##REF##37384590##8##]. In the study by Verkade et al., treatment of mice lacking <italic toggle=\"yes\">Cyp2c70</italic> with antibiotics, resulting in a highly hydrophobic bile acid pool, exacerbated inflammation, fibrogenesis and ductular reaction [##REF##37910096##9##]. In the specific context of <italic toggle=\"yes\">Cyp2c70</italic> deletion, the depletion of gut bacteria even compromised survival of the animals, highlighting the severity of the hepatobiliary damage.</p>", "<p>The notion that a hydrophobic bile acid pool contributes to liver injury received further validation through two independent experiments conducted by Verkade and colleagues. In one experiment, they reduced the production of 12α-hydroxylated bile acids by knocking down <italic toggle=\"yes\">Cyp8b1</italic> in <italic toggle=\"yes\">Cyp2c70</italic>-deficient mice, promoting the production of CDCA [##REF##37910096##9##]. This manipulation increased the hydrophobicity of the bile acid pool and promoted hepatobiliary damage [##REF##37910096##9##]. Conversely, in a distinct experiment, antibiotic-treated <italic toggle=\"yes\">Cyp2c70</italic>-deficient mice were fed a diet containing UDCA, which lowered the hydrophobicity of the bile acid pool, lessening liver injury [##REF##37910096##9##]. These findings are in line with two other studies conducted by the group of Folkert Kuipers and Jan Freark de Boer, in which UDCA was shown to reverse cholangiopathy and improve liver function in young <italic toggle=\"yes\">Cyp2c70</italic>-deficient mice [##REF##33309945##6##,##REF##36151295##10##]. Collectively, these studies highlight (i) the importance of regulating the hydrophobicity of the bile acid pool to maintain liver homeostasis and prevent toxicity, and (ii) the role played by the gut microbiota in adjusting the composition and physicochemical properties of the bile acid pool.</p>", "<p>These two studies undeniably enhance our understanding of the pathological changes that take place in <italic toggle=\"yes\">Cyp2c70</italic>-deficient mice with a so-called ‘humanized’ bile acid liver metabolism. They also emphasize the importance of the gut microbiota and its capacity to produce (T)UDCA, constraining the hydrophobicity of the bile acid pool. Nevertheless, despite this significant progress, numerous substantial questions still await answers. It may be necessary to ‘humanize’ the gut microbiota of <italic toggle=\"yes\">Cyp2c70</italic>-deficient mice to enhance the translational relevance of this model. However, colonization of mice lacking <italic toggle=\"yes\">Cyp2c70</italic> with a human microbiota was less efficient in alleviating liver pathology than their colonization with a mouse microbiota. The reasons why human and mouse microbiota result in divergent responses deserve further investigations. Notably, it would be of great interest to pinpoint the specific bacteria, bacterial enzymes or bacterial metabolites responsible for the divergences. Besides, host factors such as immune system for defense against microbes, bile acid transport system or response to bile acid receptor activation may also be at play. Identifying these factors would establish a causality, advancing the research beyond mere associations. As a future application, targeting bile acid metabolism in the gut could be considered for therapy in patients with cholestatic liver diseases.</p>" ]	[ "<title>Data Availability</title>", "<p>N/A</p>", "<title>Competing Interests</title>", "<p>The authors declare that they have no conflict of interest.</p>", "<title>CRediT Author Contribution</title>", "<p><bold>Justine Gillard:</bold> Writing—original draft, Writing—review & editing. <bold>Isabelle A. Leclercq:</bold> Writing—review & editing.</p>" ]	[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><title>Effects of <italic toggle=\"yes\">Cyp2c70</italic> deletion and gut microbiota depletion on the composition and hydrophobicity of the bile acid pool and liver damage.</title><p>The upper panel illustrates the simplified conversion of CDCA to αMCA and βMCA by CYP2C70 in mouse livers, and the conversion of CDCA to UDCA by gut bacterial enzymes. In the lower panels, the composition and hydrophobicity of gallbladder bile, as well as liver damage, are depicted in humans, WT mice, and <italic toggle=\"yes\">Cyp2c70</italic>-deficient mice with and without a gut microbiota. Bile acids are conjugated to taurine in mice, whereas they are conjugated to both taurine and glycine in humans. Human data is sourced from [##REF##7768394##11##], and mouse data from [##REF##37384590##8##,##REF##37910096##9##].</p></caption></fig>" ]	[]	[]	[]	[]	[]	[]	[]	[]	[ "<graphic xlink:href=\"cs-138-cs20231465-g1\" position=\"float\"/>" ]	[]	[]	{ "acronym": [ "CA", "CDCA", "DCA", "LCA", "MCA", "UDCA" ], "definition": [ "cholic acid", "chenodeoxycholic", "deoxycholic", "lithocholic acid", "muricholic acid", "ursodeoxycholic acid" ] }	11	CC BY	no	2024-01-13 00:02:19	Clin Sci (Lond). 2024 Jan 10; 138(1):61-64	oa_package/b2/70/PMC10781647.tar.gz
PMC10781648	38197178	[]	[]	[]	[]	[ "<title>Conclusions</title>", "<p>Dysregulation of the adaptive immune system is a defining feature of sepsis, but the exact manifestation is widely variable between individuals. For this reason, developing novel therapeutics for sepsis has proved to be a challenge for over 30 years and, indeed, progress has been failing to meet the increasing demand as the burden of sepsis on hospitals worsens across the globe. A marked lymphopenia is a common feature across the literature; however, the phenotype of remaining cells is less well-defined. It is vital to develop a better understanding of the mechanisms underpinning the observed immune dysregulation to be able to suggest new targets for treatment or diagnostic biomarkers. Based on the diverse findings of several groups, it seems that considering sepsis as multiple separate conditions by grouping individuals displaying similar characteristics could show more promise for translating results to clinical practice. Patients frequently experience immunosuppression in some form during the course of sepsis, which can result in high susceptibility to secondary infections whilst hospitalised, and a decline in the long-term function of their immune system post-recovery. This may present as an impaired ability to produce high-affinity antibodies against pathogens, and as such may also have a negative impact on how individuals respond to vaccination post-sepsis. The relationship between CD4<sup>+</sup> T<sub>FH</sub> cells and B cells in sepsis remains to be thoroughly addressed, and also how the regulation of CD4<sup>+</sup> T<sub>FH</sub> cells by CD4<sup>+</sup> T<sub>FR</sub> cells is affected in this setting. Further work in this area could provide important insight into the decline in antibody production observed in many cases, and uncover new targets for treatment or modulation of the adaptive immune system long-term post-discharge from ICU.</p>" ]	[ "<title>Abstract</title>", "<p>Sepsis is a heterogeneous condition defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. For some, sepsis presents as a predominantly suppressive disorder, whilst others experience a pro-inflammatory condition which can culminate in a ‘cytokine storm’. Frequently, patients experience signs of concurrent hyper-inflammation and immunosuppression, underpinning the difficulty in directing effective treatment. Although intensive care unit mortality rates have improved in recent years, one-third of discharged patients die within the following year. Half of post-sepsis deaths are due to exacerbation of pre-existing conditions, whilst half are due to complications arising from a deteriorated immune system. It has been suggested that the intense and dysregulated response to infection may induce irreversible metabolic reprogramming in immune cells. As a critical arm of immune protection in vertebrates, alterations to the adaptive immune system can have devastating repercussions. Indeed, a marked depletion of lymphocytes is observed in sepsis, correlating with increased rates of mortality. Such sepsis-induced lymphopenia has profound consequences on how T cells respond to infection but equally on the humoral immune response that is both elicited by B cells and supported by distinct CD4<sup>+</sup> T follicular helper (T<sub>FH</sub>) cell subsets. The immunosuppressive state is further exacerbated by functional impairments to the remaining lymphocyte population, including the presence of cells expressing dysfunctional or exhausted phenotypes. This review will specifically focus on how sepsis destabilises the adaptive immune system, with a closer examination on how B cells and CD4<sup>+</sup> T<sub>FH</sub> cells are affected by sepsis and the corresponding impact on humoral immunity.</p>" ]	[ "<title>Sepsis</title>", "<p>The inflammatory response to infection is a fundamental aspect of immune protection, aiming to rapidly combat the invading pathogen whilst causing minimal damage to the host [##REF##29467962##1##]. Under homeostasis, this is a tightly controlled network, and inflammation wanes following resolution of infection. However, the response is not always proportionate to the threat, and an exaggerated reaction can lead to tissue damage, organ failure, and death [##REF##28117397##2##].</p>", "<p>Indeed, sepsis is defined as life-threatening organ dysfunction caused by a dysregulated host response to infection [##REF##26903338##3##]. Sepsis is a heterogeneous condition in which the clinical presentation can vary substantially between patients, in part because it can be triggered by different pathogen types, even though the majority of cases are bacterial [##REF##36735197##4##]. However, in a large proportion of cases, the infectious organism cannot be identified, with many clinical manifestations of sepsis deemed ‘culture-negative’ in routine tests [##REF##27615024##5–8##]. The health and functional state of the immune system plays an important role in dictating susceptibility to sepsis and the subsequent prognosis. Sepsis in vulnerable populations tends to present as a predominantly suppressive disorder due to an already dampened immune system [##REF##26687340##9##]. Patients show reduced capacity to clear the primary infection and indeed any opportunistic pathogens secondary to the initial insult. Such protracted immunosuppression renders patients highly susceptible to nosocomial infections, proving a dominant cause of death. A retrospective trial investigating an association between survival and microbial burden found a significant correlation between late death and positive blood-culture results, particularly regarding opportunistic pathogens [##REF##21798063##10##]. At the other end of the spectrum, some individuals experience a predominantly pro-inflammatory condition which culminates in a ‘cytokine storm’. Commonly regarded as the hallmark of sepsis, such a response triggers a multitude of innate pathways including the complement and coagulation cascades, which in turn release additional pro-inflammatory mediators [##REF##19725914##11##,##REF##30471557##12##]. The resulting endothelial leakage and intravascular coagulation contribute to systemic damage which itself can be life-threatening. This type of response is typical of sepsis in otherwise young and healthy individuals [##REF##23034322##13##]. If the infection is not brought under control, patients frequently experience signs of concurrent hyper-inflammation and immunosuppression [##REF##28117397##2##,##REF##22187279##14##]. This paradoxical phenomenon underpins the difficulty in directing effective immunomodulatory treatment in sepsis.</p>", "<p>Sepsis is estimated to be the cause of 1 in 5 deaths worldwide [##REF##31954465##15##], identifying it as a bigger threat to life than cancer. Now recognised as a global health priority by the World Health Organization [##REF##28658587##16##], sepsis can affect anyone with the highest-risk groups including the elderly, the immunocompromised, pregnant women, and also the very young. Indeed, statistics from 2017 have demonstrated that almost half of global sepsis cases occurred in children [##REF##31954465##15##]. In addition, socioeconomic class is one of the greatest risk-factors, with 85% of cases and sepsis-related deaths occurring in low- and middle-income countries [##REF##31954465##15##]. Although intensive care unit (ICU) mortality rates have improved in recent years, 40% of survivors are re-hospitalised within 90 days of discharge, and a striking one-third of discharged patients die within the following year [##REF##29297082##17##]. Half of post-sepsis deaths are due to exacerbation of pre-existing conditions [##REF##27189000##18##], whilst half are explained by a deterioration of health status as a complication of sepsis, recently coined ‘post-sepsis syndrome’. One-sixth of survivors experience post-sepsis syndrome with at least one cognitive, psychological, or physical impairment, and indeed are more prone to recurrent infection, renal failure, and cardiovascular episodes than matched patients hospitalised for other diagnoses [##REF##29297082##17##]. As such, sepsis poses a significant medical and financial burden on healthcare services worldwide, with the National Health Service in the United Kingdom alone estimated to face annual costs of >£1 billion [##UREF##0##19##]. Although late-mortality and long-term symptoms following sepsis are well-studied, the causes of sequelae are poorly understood [##REF##33658992##20##]. It has been suggested that the intense and dysregulated response to infection may induce irreversible metabolic reprogramming, manifesting in multiple organs. Such alterations may divert metabolism in immune cells, changing how they interact with their microenvironment and respond to subsequent stimuli [##REF##25746953##21–23##].</p>", "<p>Prompt intervention is crucial to increase chances of survival. Aside from initial infection control, modulation of the immune system is a key aspect of treatment in sepsis [##REF##36470828##24##]. There have been no major therapeutic breakthroughs in the last 30 years, with current strategies targeting general aspects of the immune system rather than specifically targeting individual elements [##REF##32176432##25##,##REF##33936071##26##]. Although promise has been shown in multiple pre-clinical trials, treatments often fail to advance past the stage of large-scale randomised clinical trials. This failure is due in part to the vast range of disorders with diverse characteristics that are encompassed by the term ‘sepsis’. The resulting inappropriate selection of patients results in treatments that have shown potential in early studies being disregarded. The overall effect poses a huge challenge in translating research to clinical practice. As a dysfunctional response to infection by definition, there is an essential requirement to uncover the mechanisms underpinning the destabilisation of the immune response to infection in sepsis, to explore new targets for drug development and produce effective ways of modulating the immune system long-term post-recovery. Surprisingly, clinical trials blocking excessive inflammation have proved unsuccessful in reducing mortality rates [##REF##24815605##27##]. Instead, recent work has suggested more promise in exploring therapies aiming to restore the activity of ‘exhausted’ or suppressed immune cells [##REF##36209190##28##].</p>", "<title>The adaptive immune system</title>", "<p>The immune response to infection by harmful pathogens in vertebrates utilises two main components, the innate and adaptive immune systems, which cooperate to help eliminate the infection and restore homeostasis. The innate immune system provides a rapid defence strategy that responds to infectious insult in a non-specific manner to quickly address the threat [##REF##30263032##29##]. Although a vital first line of defence, the use of pattern- and damage-recognition receptors restricts cells of the innate immune system to recognition of highly conserved microbial structures. Instead, the adaptive immune system supports the initial innate response through the incorporation of cellular (T cells) and humoral (antibodies produced by B cells) components that generate a highly specific response to invading pathogens [##REF##30263032##29##]. In addition, the adaptive immune system is able to establish immunological memory and distinguish foreign antigens from self. Autoimmune conditions with devastating effects may arise through impaired ability to separate self from non-self, demonstrating the power of the adaptive immune system [##REF##28386263##30##,##REF##22466659##31##].</p>", "<p>Adaptive immunity is governed by classes of highly specialised T cells and B cells, which develop via a common lymphoid progenitor [##REF##18725575##32##,##REF##23715539##33##]. Both T cells and B cells possess a diverse repertoire of antigen-sensing receptors that are generated through the rearrangement of receptor gene segments during somatic recombination. The process, which occurs in the bone marrow for B cells and the thymus for T cells, gives rise to naïve cells which enter the circulation and peripheral lymphoid tissues to patrol for foreign antigens. Two main types of conventional T cells exist: CD8<sup>+</sup> T cells which kill infected cells following antigen recognition, and CD4<sup>+</sup> T cells which support CD8<sup>+</sup> T cell responses and antibody-generating B cells, amongst other functions [##REF##25677493##34–36##].</p>", "<p>In sepsis, a marked depletion of T cells and B cells is observed, correlating with increased rates of mortality [##REF##22187279##14##,##REF##11359857##37–39##]. Such lymphopenia occurs during the onset of sepsis and has been found to persist up to 28 days post-admission to intensive care [##REF##20220566##40–42##]. The majority of sepsis-related deaths occur when lymphopenia is evident, which can persist for years, exposing survivors to opportunistic bacterial infections and reactivating herpesviruses [##REF##18647984##43##,##REF##28158551##44##]. T cells appear to be disproportionately affected by sepsis with CD4<sup>+</sup> T cells known to decline to levels seen in patients with AIDS [##REF##20220566##40##]. Consequently, B cells tend to constitute a greater percentage of remaining lymphocytes, although this does not necessarily translate to enhanced B cell activity as a combination of sustained inflammation by high antigen-load and cytokine activity results in functional changes to remaining cells [##REF##20220566##40##]. As such, it has been shown that B cells from patients with septic shock lose their proliferative capacity and display a CD21<sup>low</sup>CD95<sup>high</sup> phenotype associated with B cell exhaustion [##REF##29459404##45##].</p>", "<p>The main causes of lymphopenia in sepsis are not fully understood, nor why this can recover in some patients and not in others. Sepsis-associated apoptosis is thought to be a leading cause of T cell and B cell depletion during sepsis [##REF##22187279##14##,##REF##11359857##37##,##REF##10446814##46–48##]. Indeed, post-mortem analyses of spleens from septic patients showed significantly higher levels of caspase-3 activity compared to non-septic patients [##REF##10446814##46##]. Other potential mechanisms underpinning the observed depletion of lymphocytes are relatively understudied but include reduced production of precursor cells. One study reported a significant depletion of haematopoietic stem cells in a mouse model of group A <italic toggle=\"yes\">Streptococcus</italic>-induced sepsis, which was associated with severe immunological stress and early mortality [##REF##35166205##49##]. Additionally, a separate study in humans showed that persistent lymphopenia following cease of initial pro-apoptotic activity correlated with a reduction in common lymphoid progenitor cells caused by osteocyte ablation in septic patients [##REF##27317262##50##]. Alternatively, a reduced pool of peripheral lymphocytes could in part be due to increased recruitment to infected tissues, as has been observed in acute lung injury and chronic inflammatory disorders [##REF##20331475##51–53##]. Such sepsis-induced lymphopenia has profound consequences on how T cells respond to infection but equally on the humoral immune response that is both elicited by B cells and supported by CD4<sup>+</sup> T follicular helper (T<sub>FH</sub>) cells. The immunosuppressive state is further exacerbated by functional impairments to the remaining lymphocyte population, including the presence of cells expressing dysfunctional or exhausted phenotypes [##REF##22187279##14##,##REF##29459404##45##,##REF##19332785##54–56##] (##FIG##0##Figure 1##). The majority of studies examining the state of immune dysfunction during sepsis in humans involve analysis of peripheral blood samples, with findings summarised in ##TAB##0##Table 1##. This review will specifically focus on how sepsis destabilises the adaptive immune system, with a closer examination on how B cells and CD4<sup>+</sup> T<sub>FH</sub> cells are affected by sepsis and the corresponding impact on humoral immunity.</p>", "<title>B cells</title>", "<p>The emergence of adaptive immunity dates back 500 million years, with the added protective value of a specific combinatorial receptor system increasing survivability in vertebrates [##REF##16497590##57##]. Within this time, B cells have evolved several strategies for increasing the diversity of their receptors, enabling identification of almost any antigen [##REF##25340015##58##]. In addition to the initial rearrangement of receptor segments during somatic recombination, B cells increase their receptor variability through processes such as somatic hypermutation, gene conversion, and class-switch recombination [##REF##26802217##59##]. These processes vastly amplify the immunoglobulin repertoire and contribute to a fine-tuned adaptive response. During development in the bone marrow, Pax5 is known to be the master transcription factor behind B cell lineage commitment, acting alongside E2A, EBF1 and IKZF1 [##REF##17582344##60##,##REF##17440452##61##]. Pax5 is a key regulator of many genes important for B cell adhesion and migration (CD55, CD157, CD97, Sdc4, CD44), and signalling (PTEN) [##REF##17658281##62##,##UREF##1##63##]. This has been demonstrated in Pax5 deficient mice which have a complete absence of mature B cells in the periphery, with a separate study showing ‘dedifferentiation’ of B cells to a common haemopoietic progenitor under conditional Pax5 deletion [##REF##17851532##64##,##REF##8001127##65##]. Immature, ‘transitional’ B cells exit the bone marrow to reach full maturity at peripheral lymphoid sites, completing their development [##REF##26243514##66##].</p>", "<p>B cells can be divided into sub-types distinguished by their phenotype and individualised functions [##REF##18434123##67##]. Naïve B cells have traditionally been described either as B-1 B cells, or conventional B-2 B cells, and together they fulfil a range of critical roles in both the innate and adaptive immune system to assist with antimicrobial defence [##REF##26700440##68##]. While the majority of the literature describing B-1 B cells is based on data from mice, a population of CD20<sup>+</sup> CD27<sup>+</sup> CD43<sup>+</sup> CD70<sup>−</sup> cells has been identified in humans which fulfil key functions characteristic of murine B-1 B cells [##REF##21220451##69##], including the secretion of natural immunoglobulin in the absence of antigenic stimulation [##REF##16039575##70##]. These antibodies have a low affinity for pathogens, but nonetheless confer initial protection in an innate-like response. The role of B-1 B cells in humans remains to be clearly defined. However, they may play an important role in bacterial clearance since a subpopulation of CD5<sup>−</sup> B-1 B cells can generate antibodies against capsular antigens of <italic toggle=\"yes\">Streptococcus pneumoniae</italic> [##REF##22664161##71##]. To this end, their reported decline with age may play a part in increased susceptibility to infection [##REF##21220451##69##,##REF##30941130##72##].</p>", "<p>Conventional B-2 B cells constitute the majority of mature B cells, and are further categorised dependent on their localisation and role [##REF##36159874##73##]. A subset described as marginal zone (MZ) B cells are considered to be innate-like cells, expressing polyreactive B cell receptors (BCRs) capable of binding multiple microbial ‘patterns’ [##REF##11861602##74##]. As such, these cells are strategically positioned in regions prone to frequent microbial exposure such as mucosa and the skin, although circulating MZ B cells have also been reported [##REF##11905826##75##]. Their name describes their predominant localisation to a specialised area of the spleen positioned between the circulation and lymphoid compartment. This region, known as the marginal zone, allows rapid activation of MZ B cells upon interaction with pathogens in the blood [##REF##16861066##76##]. Their importance in bacterial infections is depicted in individuals following splenectomy, with studies reporting increased risk of infection by encapsulated bacteria [##REF##22621150##77##,##REF##19841456##78##]. Their function has been linked to regulation of neutrophil recruitment to the spleen in the early stages of infection, with a study demonstrating MZ B cell-deficient mice to be more susceptible to <italic toggle=\"yes\">Staphylococcus aureus (S. aureus)</italic> infection than wildtype (WT) mice [##REF##34040603##79##].</p>", "<p>Although B cells possess the ability to modulate multiple aspects of immune protection through cytokine secretion and their action as antigen presenting cells, they are most commonly associated with their role in antibody production [##REF##26700440##68##]. Follicular (FO) B cells constitute another type of conventional B-2 B cell, occupying the greatest percentage of all B cell lineages. FO B cells differ from MZ B cells through their expression of a highly specific, monoreactive BCR [##REF##30356862##80##]. The fate of precursor cells into FO or MZ B cell subtypes is dictated, in part, by the strength of BCR signalling [##REF##19855403##81##], with stronger signalling favouring precursors to follow the FO B cell differentiation pathway. FO B cells are freely circulating cells that home to secondary lymphoid organs, such as lymph nodes and the spleen, where they may differentiate into plasmablasts or short-lived plasma cells upon activation by antigen [##REF##25698678##82##]. Antibodies secreted by these cells only display moderate affinity for antigen, but nonetheless are important for facilitating early protection [##REF##12846803##83##]. Alternatively, activation may trigger vigorous B cell proliferation, resulting in the formation of specialised microstructures within the B cell follicles known as germinal centres (GCs) [##REF##30410492##84##]. GCs provide the primary site for the interaction of B cells with specialised T cells (i.e. CD4<sup>+</sup> T<sub>FH</sub> cells) that support the generation of high-affinity, long-lasting antibodies and memory cells [##REF##25698678##82##]. This system is critical to establish sustained humoral protection against pathogens and underpins the mechanism of protection of most successful vaccines [##REF##33353987##85##]. Under typical conditions, B cells form the foundation of the immune system, modulating the action of other cells through both direct interactions and chemical signals [##REF##36425582##86##]. In sepsis, these relationships come under threat. As the centre of homeostasis, functional changes to B cells offset the entire landscape of the immune system.</p>", "<title>B cells and sepsis</title>", "<p>The observed lymphopenia in sepsis appears to be non-homogeneous amongst B cell subsets. Indeed, one study observed a marked plasmacytosis in patients with septic shock compared with healthy controls, which seemingly contradicts the literature reporting decreased concentrations of circulating immunoglobulin [##REF##29459404##45##]. Specifically, the levels of IgM in the sera of sepsis patients have been found to negatively correlate with assessments of disease severity, notably Sequential Organ Failure Assessment (SOFA) and Acute Physiology and Chronic Health Evaluation (APACHE) II scores [##REF##27172158##87##]. Additionally, <italic toggle=\"yes\">ex vivo</italic> stimulated B cells from the same patients displayed reduced capacity to produce IgM [##REF##27172158##87##]. In line with these findings, higher plasma concentrations of IgM within the first 24 h of sepsis have been found to differentiate survivors from non-survivors, highlighting a key protective role of IgM, particularly in fighting Gram-negative infections [##REF##29413726##39##]. Low IgM levels have also been associated with a reduction in the frequency of resting memory B cells, the effect of which was more pronounced in non-survivors [##REF##32304414##88##]. A meta-analysis of studies investigating hypogammaglobulinaemia in sepsis found that as many as 70% of cases experienced low levels of circulating IgG on the day of diagnosis, although an association with clinical outcome remains to be clearly defined [##REF##26597932##89##]. A reduction in general immunoglobulin levels early in infection may, in part, be due to a decline in B-1 B cells. As innate-like producers of natural antibodies, B-1 B cells are suggested to play an important role in compensating for the delay in an FO B cell-mediated adaptive immune response [##REF##31154567##90##]. Early release of low-affinity immunoglobulin by B-1 B cells may infer critical protection in situations where the infectious pathogen has spread to the bloodstream early in infection [##REF##9858525##91##]. The frequency of B-1 B cells has been shown to significantly decline in a murine model of sepsis [##REF##28630091##92##]. The same group found that adoptive transfer of B-1 cells restored IgM levels and significantly reduced lung injury compared with WT mice [##REF##30134811##93##]. In addition to the local and systemic increase in IgM, this result was achieved through attenuation of pro-inflammatory cytokine release and apoptosis, suggesting additional protective roles of B-1 B cells in the response to infection [##REF##30134811##93##]. Sepsis-induced changes to B-1 B cells in humans remain to be characterised but could have therapeutic value if data are consistent with observations in mice.</p>", "<p>Despite these findings, the relationship between circulating immunoglobulin levels and mortality in sepsis has proved controversial. Indeed, initial serum IgG levels have been reported to be both positively and negatively associated with clinical outcome [##REF##27677760##94##,##REF##32859530##95##]. A multicentre study measuring IgG<sub>1</sub>, IgM and IgA levels on the first day of severe sepsis or septic shock found that low concentrations of all three antibody types had the highest odds ratio for death [##REF##24815605##27##]. Conversely, the ALBIOS trial found that high IgA and IgG levels at sepsis onset were significantly predictive of both 28- and 90-day mortality [##REF##34825972##96##]. In this trial, low levels of IgG on day 1 were associated with higher risk of secondary infections. These findings again reflect the heterogeneous nature of sepsis, and such variation is likely attributed to subjects experiencing different degrees of inflammation or immunosuppression at the point of testing. Low concentrations of circulating antibodies are indicative of a dampened adaptive response, and so may underpin mortality through a reduced capacity to clear infection. An association between high immunoglobulin levels and mortality in some patients could be explained by the ability of IgG and IgM to activate innate pathways such as the complement cascade, exacerbating an existing state of hyperinflammation through complement-dependent cytotoxicity [##REF##29063907##97##]. Additionally, immune cells such as macrophages, neutrophils and natural killer cells express receptors that bind the Fc portion of antibodies, and so may facilitate the exaggerated host-response through antibody-dependent cellular cytotoxicity and antibody-dependent cellular phagocytosis in the presence of high levels of circulating immunoglobulin [##REF##29063907##97##]. Clearly, gaps remain in defining the association between circulating immunoglobulin and clinical outcome in sepsis. It is likely that there is no clear consensus, and perhaps categorising patients based on a range of clinical observations including plasma immunoglobulin levels amongst other parameters may provide better prognostic value and guidance for treatment.</p>", "<p>Beyond antibody production, B cells can also modulate the immune response to infection through their ability to act as a professional antigen presenting cells (APCs) [##REF##36159874##73##]. As professional APCs, B cells are armed with the necessary tools to capture and present processed antigen to T cells. As such, B cells prime and expand antigen-specific T cells, a crucial step for generation of a specific immune response. B cells express both major histocompatibility complex (MHC) I and II molecules, thus enabling them to interact with antigen-specific CD4<sup>+</sup> and CD8<sup>+</sup> T cells [##REF##36159874##73##]. In this way, B cells can trigger both T<sub>H</sub>1 and T<sub>H</sub>2 responses to suit the context. One mode of action is through the direct presentation of antigenic peptides to T cells following capture and internalisation of pathogens [##REF##23797063##98##]. Direct presentation is dependent on the antigenic specificity of B cells, defined by their clonotypically expressed BCR. Alternatively, B cells may cross-present free-floating antigen from the extracellular matrix to CD8<sup>+</sup> T cells [##REF##30061897##99##]. This dual ability is critical for cellular responses against viruses and tumours, where the antigen-presenting B cells are not directly infected.</p>", "<p>Following T cell receptor (TCR)-mediated recognition of MHC-restricted antigens on the B cell surface, an immunological synapse is established that promotes T cell activation and drives signals for proliferation, differentiation, and survival. This synaptic connection is strengthened by interactions between co-stimulatory molecules on both cell types, notably CD80/CD86 on B cells with CD28 on T cells [##REF##28116319##100##]. These interactions induce expression of additional costimulatory molecules including CD40 on B cells, as well as adhesion molecules such as LFA-1 and its ligand ICAM-1, that support the process of antigen presentation [##REF##21904389##101##]. Finally, the appropriate effector phenotype is achieved through differential cytokine secretion, polarising the immune response [##REF##14747532##102##]. For example, secretion of interferon-γ (IFN-γ) and interleukin-12 (IL-12) induce signalling cascades which result in T-bet transcription and differentiation towards a T<sub>H</sub>1 phenotype, important for clearance of intracellular pathogens such as viruses and certain bacteria [##REF##17983439##103##]. Secretion of IL-4 induces transcription of GATA-3 and subsequent commitment to a T<sub>H</sub>2 phenotype, important in the response to extracellular infections by parasites and helminths [##REF##17983439##103##]. Other cytokines such as transforming growth factor-β (TGF-β), IL-6, IL-21 and IL-23 support differentiation of alternative helper subsets including T<sub>H</sub>17 cells, and lesser-defined phenotypes including T<sub>H</sub>9 and T<sub>H</sub>22 cells [##REF##33126494##104##]. During sepsis, the expression of MHC II molecules, including human leukocyte antigen-DR (HLA-DR) has been shown to decrease on B cells, altering their ability to present peptides to T cells [##REF##31738315##105##]. This effect has been observed in sepsis patients at the time of admission to ICU and persists in samples taken at a follow-up time of 8 days [##REF##31738315##105##]. A reduction in HLA-DR expression acts to impair the ability for B cells to function as professional APCs, lessening their ability to trigger antigen-specific responses in T cells. In addition, expression of CD40 was significantly reduced on B cells in septic patients at ICU admission compared to healthy donors [##REF##23721745##41##]. No difference in CD40 expression was observed between surviving and non-surviving patients; however, the expression of co-stimulatory molecule CD80 was found to be significantly higher in non-survivors of septic shock at ICU admission [##REF##23721745##41##]. The expression normalised after 3 days, suggesting an enhanced ability to stimulate T cells very early in infection, which perhaps contributes to the hyper-inflammatory state associated with early mortality.</p>", "<p>In addition to antigen presentation for stimulation of T cells, B cells themselves can act as cellular effectors [##REF##19124721##106##]. During infection, B cells mediate changes in the inflammatory response through an acquired ability to secrete effector cytokines such as IFN-γ, tumour necrosis factor-α (TNF-α) and IL-17 [##REF##15004141##107##]. Transcriptome analyses in murine models of sepsis show B cells with distinct gene expression profiles, with notable alterations in the expression of genes for several cytokines [##REF##31536782##108##]. In particular, increased expression of pro-inflammatory cytokines such as IL-3, IFN-γ, TNF-α and IL-6, and reduced expression of anti-inflammatory cytokines such as IL-10 and TGF-β1 [##REF##31536782##108##]. In addition to driving systemic inflammation, secretion of cytokines can polarise T cells towards specific helper phenotypes as detailed above [##REF##17983439##103##]. In a murine caecal ligation and puncture (CLP) model of sepsis, B cell deficient (µMT) mice showed reduced concentrations of inflammatory cytokines in sera compared with WT mice, which was not replicated in T cell deficient (TCR αβ<sup>−/−</sup>) mice [##REF##21746813##109##]. These data indicate a role of B cells in triggering an early inflammatory response in sepsis, with further experiments showing the importance of such cytokine production on successful bacterial clearance. Splenic MZ B cells have been shown to produce large quantities of IL-6 and the chemokine CXCL10 after lipopolysaccharide (LPS) challenge <italic toggle=\"yes\">in vivo</italic> in mice [##REF##27146354##110##]. The significance of such a pro-inflammatory response was investigated in mice lacking IL-6-producing MZ B cells (MZ B-IL-6-KO). These mice produced significantly lower amounts of serum IL-6 and CXCL10 and demonstrated improved survival compared with WT mice [##REF##27146354##110##]. Furthermore, administration of an anti-IL-6 receptor (IL-6R) antibody shortly following intravenous injection of <italic toggle=\"yes\">Escherichia coli</italic> (<italic toggle=\"yes\">E. coli</italic>) or the induction of CLP resulted in prolonged survival compared with mice treated with a control antibody [##REF##27146354##110##]. These results indicate a pathogenic role of IL-6 in exacerbating endotoxic shock in sepsis. This finding does not contradict earlier findings that IL-6 plays an anti-inflammatory role very early in sepsis [##REF##21746813##109##], as injection of anti-IL-6R at time-points concurrent with LPS or <italic toggle=\"yes\">E. coli</italic> injection did not affect the survival of mice. At the very early stages of sepsis, IL-6 production by B cells may not augment the inflammatory response to toxin, with delayed onset of its pathogenic role. In addition to IL-6, IL-3 production by B cells in a mouse model of abdominal sepsis has been reported to potentiate inflammation through enhanced production of monocytes and neutrophils, with IL-3 deficiency inferring protection [##REF##25766237##111##]. These findings correlated with observations in humans showing an association between high plasma IL-3 levels and mortality [##REF##25766237##111##]. Despite the reported pro-inflammatory signatures of B cells in sepsis, strategies aiming to modulate cytokine levels have failed to prove beneficial [##REF##29225343##112##]. Patterns of cytokine release change throughout the course of disease, and so timing of administration is likely an important consideration for these types of therapies [##REF##21746813##109##]. Investigations into IL-6 blocking early in infection still show promise [##REF##36716318##113##].</p>", "<title>Regulatory B (B<sub>REG</sub>) cells</title>", "<p>B<sub>REG</sub> cells represent a specialised subtype of B cells that can suppress T cells and the action of other pro-inflammatory cells through the production of IL-10, IL-35 and TGF-β [##REF##33995344##114##]. B<sub>REG</sub> cells, constituting less than 1% of PBMCs in humans, show heterogeneity in the expression of surface proteins and indeed may differentiate into distinct subsets dependent on the inflammatory stimuli to which they are exposed [##REF##33193391##115##]. For example, studies have reported CD19<sup>+</sup>CD25<sup>hi</sup> B<sub>REG</sub> cells that support T regulatory (T<sub>REG</sub>) cell function <italic toggle=\"yes\">in vitro</italic> in co-culture experiments, but also several populations of B<sub>REG</sub> cells which suppress an anti-tumour response in cancer such as those expressing granzyme B in solid tumour infiltrates, and CD19<sup>+</sup>CD24<sup>+</sup>CD38<sup>+</sup> cells in breast cancer [##REF##23384943##116–118##]. It is generally accepted that their suppressive ability is enhanced under highly inflammatory conditions to limit further damage, for example, in the case of autoimmune conditions [##REF##22315945##119–121##]. Although sepsis is generally characterised by a protracted lymphopenia, the balance of subsets within the total population of B cells is disturbed. In a CLP model of sepsis in mice, an increase in the frequency of B<sub>REG</sub> cells was one of the first observable changes, exacerbating an immunosuppressive state [##REF##31843193##122##]. Conversely, B<sub>REG</sub> cells can play a protective role, with reduced number and function correlating with the development of severe septic shock in mice exposed to endotoxin [##REF##31536782##108##]. Human patients with sepsis have decreased numbers of B<sub>REG</sub> cells compared with controls, with frequency negatively correlating with likelihood of septic shock [##REF##36357845##123##]. In fact, the levels of B<sub>REG</sub> cells over the first week post-admission to ICU appear to have particular prognostic value in elderly patients with sepsis [##REF##28795662##124##]. The same was observed in neonates, with an increase in B<sub>REG</sub> cells positively correlating with survival [##REF##29239829##125##]. Following the onset of septic shock, there is an increase in cells expressing a B<sub>REG</sub>-like cell phenotype, and an associated increase in IL-10 production mirroring the observed immunosuppressive state [##REF##29459404##45##]. Together, these findings suggest a protective role of the immunosuppression elicited by B<sub>REG</sub> cells early in sepsis, perhaps aiding against deaths caused by overwhelming inflammation and consequent septic shock. In surviving patients, however, B<sub>REG</sub> cells may tip towards a pathogenic function through continued promotion of an immunosuppressive state in the midst of other cells becoming anergic and unable to respond to subsequent stimuli.</p>", "<title>The potential of B cells in clinical practice</title>", "<p>Given the numerical and functional changes exhibited by B cells during sepsis, and the association of certain alterations with morbidity and mortality, it is unsurprising that B cells have been the focus of several studies investigating prognostic biomarkers and therapeutic targets. For example, one group suggested that a low percentage of CD23<sup>+</sup> B cells at ICU admission enables discrimination between survivors and non-survivors with a sensitivity of 90.9% [##REF##23721745##41##], whilst another demonstrated poor prognostic survival outcome in patients with low IgM levels within the initial 24 h of sepsis onset [##REF##31934274##126##]. In terms of treatment, supplementation of specific B cell subsets that are depleted or dysfunctional during sepsis may restore immune function. For example, adoptively transferring B-1 cells could replenish natural immunoglobulin and suppress excessive inflammation [##REF##28630091##92##,##REF##30134811##93##]. Although levels of circulating immunoglobulin have proved controversial in dictating disease course, considerable attention has been given to the use of intravenous immunoglobulin (IVIG) as an approach to modulate inflammation in sepsis, particularly in neonatal cases [##REF##28041678##127##]. Although IVIG therapy is an approved treatment for multiple conditions of immune dysregulation, including Kawasaki disease which is often difficult to differentiate from sepsis during the early stage of onset [##REF##21785430##128##], IVIG has proved unsuccessful in reducing mortality in several large randomised controlled trials of patients with sepsis [##REF##18074471##129–132##]. Potential limitations to trials include choice of subjects and timing of treatment; with discrepancy in the literature reporting circulating immunoglobulin levels and prognosis in patients with sepsis, treatment needs to be more specific and tailored to the individual. A method of first identifying the state of immunosuppression in patients may enable guided selection for trials, and generate more promising results [##REF##28742550##133##]. The failure of clinical trials has resulted in guidance against the use of IVIG in sepsis and septic shock. Despite this, several studies have reported benefits of IgM- and IgA-enriched immunoglobulin administration [##REF##37510760##134##] and indeed, such preparations are widely used in addition to other treatments in septic shock to enhance immune function [##REF##33026597##135##]. The potential benefit of their combined administration has been suggested to stem from their dual action in both the bloodstream and mucosal surfaces. The overarching consensus for best clinical practice remains a personalised approach, with guidelines for dosage and timing of administration highly dependent on the clinical phenotype.</p>", "<title>CD4<sup>+</sup> T<sub>FH</sub> cells</title>", "<p>The process of pathogen-specific antibody production is reliant on help signals provided by specialised CD4<sup>+</sup> T<sub>FH</sub> cells, which interact with B cells in the GCs of secondary lymphoid organs [##REF##32572238##136##]. GCs provide the primary site for high affinity antibody production via somatic hypermutation and class switching of B cells [##REF##30410492##84##]. CD4<sup>+</sup> T<sub>FH</sub> cells govern the movement of B cells throughout the GC, and determine which cells are selected for differentiation into long-lived plasma cells and memory B cells. Not only are CD4<sup>+</sup> T<sub>FH</sub> cells crucial for supporting B cells, they play a critical role in GC formation and maintenance [##REF##30410492##84##]. CD4<sup>+</sup> T<sub>FH</sub> cells were first described in the early 2000s, following work observing a unique CXCR5<sup>+</sup> subset of CD4<sup>+</sup> T cells in tonsillar tissue [##REF##11104798##137##,##REF##11104797##138##]. These cells were shown to express several markers important for B cell activation, indicating their involvement in tonsillar immune responses. Co-culture with naïve B cells demonstrated their capacity to induce class-switched antibody production, which was replicated and built-upon in subsequent studies [##REF##11413192##139##]. However, at this time, CD4<sup>+</sup> T<sub>FH</sub> cells were not widely accepted as being distinct from T<sub>H</sub>1 or T<sub>H</sub>2 cells as the transcription factor driving their differentiation was unknown. Years later, CD4<sup>+</sup> T<sub>REG</sub> and CD4<sup>+</sup> T<sub>H</sub>17 cell types were characterised, based on the identification of lineage-determining transcription factors for these populations (FOXP3 for T<sub>REG</sub> cells and RORγt for T<sub>H</sub>17 cells). It was not until 2009, when the discovery of BCL-6 as a transcription factor essential for GC generation and high affinity antibody production allowed recognition of these cells as an individual CD4<sup>+</sup> T cell type, acknowledging their distinct role as follicular B cell helpers [##REF##19608860##140–142##].</p>", "<p>The GC is divided into two compartments described as the light zone and dark zone, so called due to their histological appearance [##REF##30410492##84##]. These zones form distinct sites for separation of the steps involved in the GC reaction. Within the light zone, B cells present antigen-MHC class II complexes to CD4<sup>+</sup> T<sub>FH</sub> cells. In return, select B cells receive co-stimulation and survival signals from CD4<sup>+</sup> T<sub>FH</sub> cells to encourage migration to the dark zone. Such signals include IL-21, IL-4, and IL-10 secreted by CD4<sup>+</sup> T<sub>FH</sub> cells [##REF##19252490##143##,##REF##12446913##144##]. IL-21 induces transcription of activation-induced cytidine deaminase in B cells, an essential factor for somatic hypermutation [##REF##18354204##145##]. This process involves the introduction of BCR point mutations to generate cells with a range of affinities for antigen. The somatically hypermutated B cells then return to the light zone, where those with highest affinity for antigen are positively selected for proliferation and survival. Further signalling via co-stimulatory molecules, IL-21, and IL-4, initiates their return to the dark zone for isotype class-switching [##REF##30410492##84##]. Class-switched B cells may then either differentiate into plasma cells to secrete high-affinity antigen-specific antibodies or instead become long-lived memory B cells. After fulfilling their role, CD4<sup>+</sup> T<sub>FH</sub> cells leave the GC and may either enter a GC in a neighbouring follicle, or re-enter the same GC. Alternatively, CD4<sup>+</sup> T<sub>FH</sub> cells may downregulate BCL-6 and enter the blood stream as memory CD4<sup>+</sup> T<sub>FH</sub> cells.</p>", "<p>Expression of inducible co-stimulator (ICOS) on CD4<sup>+</sup> T<sub>FH</sub> cells is important for all stages of differentiation and maintenance. Initially, ICOS on pre-CD4<sup>+</sup> T<sub>FH</sub> cells binds to ICOS ligand (ICOSL) on dendritic cells to initiate priming and migration towards the B cell zone of the GC. Later, ICOS/ICOSL signalling between CD4<sup>+</sup> GC-T<sub>FH</sub> cells and B cells ensures maintenance of CD4<sup>+</sup> T<sub>FH</sub> cells for supporting antibody production. Other markers essential for CD4<sup>+</sup> T<sub>FH</sub> cell function include OX40 and CD40 ligand (CD40L). Expression of both proteins is up-regulated following activation of CD4<sup>+</sup> T<sub>FH</sub> cells, promoting their accumulation at the T-B border where they bind their ligands on cognate B cells [##REF##27895177##146##,##REF##19426221##147##]. Bidirectional signalling results in IL-21 secretion to assist with B cell activation and proliferation, and GC maintenance [##REF##18602282##148##].</p>", "<p>Tight regulation of the GC reaction is necessary to prevent generation of autoantibodies [##REF##19935804##149##,##REF##20727041##150##]. A fine balance is required to enable effective humoral immunity, whilst maintaining self-tolerance. One arm of control is achieved by a specialised subset of CD4<sup>+</sup> T<sub>REG</sub> cells known as T follicular regulatory (T<sub>FR</sub>) cells [##REF##21785433##151##]. CD4<sup>+</sup> T<sub>FR</sub> cells are similar to CD4<sup>+</sup> T<sub>FH</sub> cells in that they express BCL-6 and CXCR5 but are distinguished by their expression of FOXP3. CD4<sup>+</sup> T<sub>FR</sub> cells suppress both CD4<sup>+</sup> T<sub>FH</sub> and B cells to regulate the GC reaction [##REF##21785430##128##,##REF##26091728##152##]. The mechanisms underpinning suppression remain to be completely elucidated, but one known method involves expression of the co-inhibitory receptor cytotoxic T lymphocyte-associated antigen 4 (CTLA-4), which functions to dampen co-stimulatory interactions between cognate CD4<sup>+</sup> T<sub>FH</sub> cells and B cells [##REF##18845758##153##]. In addition, CD4<sup>+</sup> T<sub>FR</sub> cells suppress IL-21 and IL-4 transcripts in CD4<sup>+</sup> T<sub>FH</sub> cells, two cytokines vital for the selection of high-affinity antibodies in the GC [##REF##27695002##154##].</p>", "<title>CD4<sup>+</sup> T<sub>FH</sub> cells and sepsis</title>", "<p>Although multiple studies have reported defects in humoral immunity in cases of severe infection and sepsis, these have largely focussed on B cells and alterations in immunoglobulin release [##REF##11359857##37##,##REF##23721745##41##,##REF##24312349##155##]. For patients showing reduced levels of circulating immunoglobulin, proposed mechanisms include an impaired activation-capacity of plasmacytes, with increased expression of markers indicative of an exhausted phenotype [##REF##25698678##82##]. Secondary lymphoid organs from septic patients have been demonstrated to have a lower cellular density than those from healthy controls, encompassing the total follicular B cell population, but also follicular dendritic cells and CD4<sup>+</sup> T<sub>FH</sub> cells [##REF##11359857##37##,##REF##31764616##156##]. These findings are consistent with a decline in circulating CD4<sup>+</sup> T<sub>FH</sub> cells, and correlate with reduced B cell numbers and increased mortality [##REF##31764616##156##]. Despite these findings, a mechanism whereby impaired B cell maturation could be attributed to changes in the CD4<sup>+</sup> T<sub>FH</sub> cell population has yet to be determined. Considering the close relationship between B cells and CD4<sup>+</sup> T<sub>FH</sub> cells in the GC, and the dependency of follicular B cells on signals from CD4<sup>+</sup> T<sub>FH</sub> cells for proliferation and survival, it seems plausible that a lacking humoral response could stem from insufficient support. Data from a murine model of sepsis showed blunted differentiation and class-switching of B cells in septic mice compared to controls, with reduced expansion and differentiation of CD4<sup>+</sup> T<sub>FH</sub> cells following immunisation [##REF##30429857##157##]. Additionally, the importance of CD4<sup>+</sup> T<sub>FH</sub> cells in supporting an antigen-specific B cell response has been demonstrated in ‘immune educated’ mice which, compared to standard laboratory mice, present a diverse repertoire of memory T cells [##REF##32903485##158##]. Following induction of CLP-induced sepsis, increased IL-21 production was indicative of increased functionality in CD4<sup>+</sup> T<sub>FH</sub> cells, which in turn were able to reverse the sepsis-induced decline in splenic B cells seen in controls. Such an effect was accompanied by enhanced follicular B cell and GC development [##REF##32903485##158##]. These results demonstrate the critical role of CD4<sup>+</sup> T<sub>FH</sub> cells in supporting antigen-specific B cell responses in conditions of inflammation. The commonly observed alterations in B cell development and functionality reported in humans suggest a potential defect in this relationship in sepsis. A lack of functional CD4<sup>+</sup> T<sub>FH</sub> cells could induce apoptosis of B cells, through a loss of BCR signalling.</p>", "<p>The underlying mechanisms driving changes in CD4<sup>+</sup> T<sub>FH</sub> cells that could explain defects in immunoglobulin secretion are poorly characterised. Conditions of persistent stimulation during severe bacterial and viral infections have been well-reported to drive ‘immunoparalysis’ in remaining T cells, describing an inability to mount or support an effective immune response [##REF##30429857##157##]. In a study of the response to SARS-CoV-2 infection and vaccination, the neutralising antibody response robustly correlated with the frequency and phenotypic polarisation of circulating CD4<sup>+</sup> T<sub>FH</sub> cells [##REF##34730247##159##]. Specific subsets of circulating CD4<sup>+</sup> T<sub>FH</sub> cells have been described, distinguished by their differential expression of the chemokine receptors CXCR3 and CCR6. Such subsets exhibit the behaviour of T<sub>H</sub>1, T<sub>H</sub>2 or T<sub>H</sub>17 cells, coined T<sub>FH</sub>1 (CXCR3<sup>+</sup>CCR6<sup>−</sup>), T<sub>FH</sub>2 (CXCR3<sup>−</sup>CCR6<sup>−</sup>), and T<sub>FH</sub>17 (CXCR3<sup>−</sup>CCR6<sup>+</sup>) cells respectively [##REF##21215658##160##]. High titres of SARS-CoV-2 spike-specific or neutralising antibodies have consistently been associated with the frequency of T<sub>FH</sub>1 cells, with variability in reported relationships between antibody responses and T<sub>FH</sub>2 or T<sub>FH</sub>17 cells across studies [##REF##33564749##161–163##]. The phenotype of circulating CD4<sup>+</sup> T<sub>FH</sub> cells has been reported for several other viral infections or vaccinations, with no clear consensus on an overarching subgroup best equipped for supporting antibody production. For example, T<sub>FH</sub>1 and T<sub>FH</sub>17 cells were found to predominate in non-responders to influenza virus vaccination, with a skewed IL-2/IL-21 axis incapable of supporting B cells [##REF##31100059##164##]. In contrast, an increase in the frequency of T<sub>FH</sub>17 cells was demonstrated to correlate with enhanced antigen-specific antibody production following vaccination against Ebola virus [##REF##27323685##165##]. Data in patients with human immunodeficiency virus (HIV) show a positive correlation between the frequency of T<sub>FH</sub>2 cells and the development of broadly neutralising antibodies, whilst T<sub>FH</sub>2 cells have been reported to impede an antiviral humoral response in chronic hepatitis B virus infection [##REF##24035365##166##,##REF##37421985##167##]. These varied findings potentially suggest a pathogen-specific aspect to the usefulness of different CD4<sup>+</sup> T<sub>FH</sub> cell subgroups in supporting B cells. Although many groups have reported skewing of CD4<sup>+</sup> T<sub>FH</sub> subsets in a virus-specific context, there are substantial gaps in the literature in the case of bacterial infections and sepsis. Based on the data, it seems clear that measurement of CD4<sup>+</sup> T<sub>FH</sub> cell frequencies in sepsis alone may be insufficient to explain a dampened ’helper’ response, and that phenotypic differences in CD4<sup>+</sup> T<sub>FH</sub> cells could alter their overall functional capacity. A separate study demonstrated impaired function of CD4<sup>+</sup> T<sub>FH</sub> cells in HIV-infected individuals, displaying downregulation of genes from immune- and GC-resident CD4<sup>+</sup> T<sub>FH</sub> cell-associated pathways including c-MAF and its upstream mediators [##REF##34280251##168##]. These changes were associated with the resulting inefficient antigen-specific antibody response and death of memory B cells. Expression of c-MAF has been demonstrated as important in supporting BCL-6 expression in CD4<sup>+</sup> T<sub>FH</sub> cells following immunisation [##REF##28496444##169##]. c-MAF and BCL-6 are crucial for upregulation of CD40L and ICOS expression on CD4<sup>+</sup> T<sub>FH</sub> cells as well as IL-21 signalling. Therefore, these transcriptional changes in HIV-infected individuals likely render CD4<sup>+</sup> T<sub>FH</sub> cells incapable of positioning themselves correctly within the GC to interact with and support their cognate B cells [##REF##28496444##169##]. As HIV is a condition of chronic stimulation, it is plausible that sustained activation by high antigen load in sepsis could drive similar transcriptional changes in CD4<sup>+</sup> T<sub>FH</sub> cells, rendering them incapable of supporting B cell development. The inadequate help provided by CD4<sup>+</sup> T<sub>FH</sub> cells in HIV-infected individuals has sparked interest into the role of CD4<sup>+</sup> T<sub>FR</sub> cells in this context. In a study using an <italic toggle=\"yes\">ex vivo</italic> model of tonsillar HIV infection and <italic toggle=\"yes\">in vivo</italic> model of simian immunodeficiency virus infection in rhesus macaques, virus infection was associated with an expansion of suppressive CD4<sup>+</sup> T<sub>FR</sub> cells, expressing increased levels of co-inhibitory receptors CTLA-4 and lymphocyte-activation gene 3 (LAG-3), and increased production of anti-inflammatory cytokines IL-10 and TGF-β [##REF##26482032##170##]. These cells were subsequently shown to impair CD4<sup>+</sup> T<sub>FH</sub> function through inhibition of cell proliferation and production of IL-4 and IL-21. The literature describing the role of CD4<sup>+</sup> T<sub>FR</sub> cells in sepsis is sparse, however, could provide important insight into functional changes to CD4<sup>+</sup> T<sub>FH</sub> cells if severe bacterial infections drive a similar expansion of CD4<sup>+</sup> T<sub>FR</sub> cells as seen in HIV infection. Further studies are required to determine if this is the case for sepsis, but also to expand our knowledge of CD4<sup>+</sup> T<sub>FH</sub> cell-mediated humoral immunity in the context of bacterial infections and sepsis (##FIG##1##Figure 2##).</p>", "<title>Alterations in other conventional and unconventional T cell types during sepsis</title>", "<p>Sepsis-induced changes to T cells have been widely studied and implicated as important factors in determining the overall response and likelihood of survival. The sepsis-driven lymphopenia disproportionately targets the pool of antigen-inexperienced T cells in both mouse models and human studies [##REF##23355736##171##,##REF##25595784##172##]. This has been attributed to both a thymic defect affecting the output of newly generated T cells, and the acquisition of memory-like characteristics in otherwise naïve cells [##REF##28708784##173##]. Such changes to the composition of the overall T cell repertoire contributes to increased susceptibility to secondary infections and may impair memory T cell generation [##REF##23355736##171##,##REF##25595784##172##]. In elderly patients, whose naive T cell pool is substantially reduced, destruction of this pool could cause long-term defects in mounting an effective immune response to new antigens [##REF##19124721##106##,##REF##18332179##174##]. Although naïve cells are particularly susceptible to sepsis-induced apoptosis and phenotypic changes, a numerical loss of existing memory CD4<sup>+</sup> and CD8<sup>+</sup> T cells has also been demonstrated [##REF##27286793##175##,##REF##26362089##176##]. Within the pool of memory CD4<sup>+</sup> T cells, a preferential loss of ‘helper’ subpopulations including T<sub>H</sub>1, T<sub>H</sub>2 and T<sub>H</sub>17 cells shifts the balance towards a greater proportion of FOXP3<sup>+</sup> T<sub>REG</sub> cells [##REF##26362089##176–178##]. T<sub>REG</sub> cells represent a subset of CD4<sup>+</sup> T cells implicated in negative immunomodulation, and the effects of their representative increase has been debated. Mouse models have demonstrated that the relative increase in T<sub>REG</sub> cells is accompanied by an increased suppressive capacity. Indeed, T<sub>REG</sub> cells were shown to suppress T cell proliferation to a greater degree in septic mice than those in sham-injured mice, with particular suppression of T<sub>H</sub>1-type cytokine production [##REF##17304105##179##]. Additionally, T<sub>REG</sub> cells induced apoptosis of monocytes and neutrophils in a CLP mouse model of sepsis through either Fas/FasL signalling or IL-10 secretion [##REF##17056586##180##]. This enhanced suppression by T<sub>REG</sub> cells has been correlated with worsened severity, however, other studies have correlated increased T<sub>REG</sub> cell representation with an improved outcome and pathogen control [##REF##20156359##181##,##REF##23724126##182##]. Discrepancies may be due to timing of sample collection and infection course, with T<sub>REG</sub> cells perhaps proving beneficial in patients experiencing overwhelming inflammation, whilst damaging in cases of immune exhaustion. T<sub>REG</sub> cells have been suggested as a potential target for therapeutic intervention, however further analysis is necessary to determine approach [##REF##20156359##181##,##REF##33915108##183##].</p>", "<p>The overall numerical reduction of CD4<sup>+</sup> T cells is accompanied by functional defects, evidenced by increased rates of latent viral reactivation in septic patients [##REF##18647984##43##,##REF##28158551##44##,##REF##22132884##184##,##REF##17234903##185##]. A global, post-sepsis state of anergy has been proposed in CD4<sup>+</sup> T cells, through evidence of little or no pro- or anti-inflammatory cytokine production evident following anti-CD3/CD28 stimulation in post-mortem spleen and lung samples [##REF##22187279##14##]. Additionally, studies have shown a reduction in proliferative capacity and lineage-specific transcription factor expression, affecting the regulation of CD4<sup>+</sup> T cell subset differentiation [##REF##25595784##172##,##REF##15596410##186##]. These observations are in line with increased co-inhibitory receptor expression such as PD-1 CTLA-4, LAG-3 and T cell immunoglobulin and mucin domain-containing protein 3 (TIM-3), altering how CD4<sup>+</sup> T cells communicate with and modulate the responses of other immune cells [##REF##21418617##55##,##REF##21349174##187##]. In a normal immune response, T<sub>H</sub>1, T<sub>H</sub>2 and T<sub>H</sub>17 cells provide help to naïve CD8<sup>+</sup> T cells to ensure a highly controlled and functionally specific response [##REF##12690201##36##]. In addition, such signals promote clonal expansion upon re-encounter with antigen [##REF##15744305##188##,##REF##23071253##189##]. ‘Helpless’ T cells are instead destined for apoptosis. Decline of helper T cell populations during sepsis creates an environment in which CD8<sup>+</sup> T cells could proceed to respond to antigen without CD4<sup>+</sup> T cell help. This effect has been suggested to impair the early T cell effector response and contribute to a suppressive environment, through apoptosis of CD8<sup>+</sup> T cells [##REF##15744305##188##,##REF##23071253##189##]. In addition, lack of CD4<sup>+</sup> T cell help during primary infection results in memory CD8<sup>+</sup> T cells which lack the capacity to respond during re-infection [##REF##12690201##36##]. Memory CD8<sup>+</sup> T cells from survivors are prone to exhaustion during chronic infection, with reduced capacity to secrete pro-inflammatory cytokines and increased expression of co-inhibitory receptors [##REF##23355736##171##,##REF##25980007##190##].</p>", "<p>Research exploring sepsis-induced changes to T cells is largely focussed on conventional αβ T cells, with substantial gaps in the literature describing changes in unconventional T cell populations with antimicrobial functions, such as γδ T cells and mucosal-associated invariant T (MAIT) cells. As the first T cell population formed during embryonic development, γδ T cells constitute 0.5–5% of circulating CD3<sup>+</sup> T cells in adult humans [##REF##26482978##191##,##REF##32858901##192##]. γδ T cells rapidly produce effector cytokines in response to bacterial infections and mediate protective immune responses against pathogenic microorganisms such as <italic toggle=\"yes\">Mycobacterium tuberculosis</italic> (reviewed in [##REF##26482978##191##]). Additionally, certain γδ T cells appear to possess potent antigen-presenting abilities during infections [##REF##28584241##193##,##REF##27652377##194##]. These unconventional T cells exist as two main populations in humans based on their encoded TCR δ-chain: Vδ1<sup>+</sup> or Vδ2<sup>+</sup> T cells. Vδ2<sup>+</sup> T cells constitute the majority of peripheral blood γδ T cells whilst Vδ1<sup>+</sup> T cells are less frequent in the blood and are more abundant in epithelial and mucosal tissues such as the skin, intestine and uterus [##REF##26482978##191##,##REF##20870939##195–198##]. In humans, the number of circulating γδ T cells decline in patients with sepsis compared to healthy controls, with an imbalance of pro- or anti-inflammatory functional changes depending on the subtype [##REF##32346434##199–201##]. One study found an association between the degree of γδ T cell reduction and severity, whilst a separate study showed that impaired IFN-γ expression following <italic toggle=\"yes\">in vitro</italic> antigen stimulation correlated with mortality [##REF##23515014##200##,##REF##28362715##202##]. Furthermore, the ability for γδ T cells to act as APCs is impaired during sepsis [##REF##35020851##203##]. These sepsis-induced effects on γδ T cells appear to be specific to Vδ2<sup>+</sup> T cells as it has been reported that peripheral Vδ1<sup>+</sup> T cells increase in frequency during sepsis and correlate with increasing SOFA score and mortality [##REF##32346434##199##]. Additionally, the expression of the co-inhibitory receptors CTLA-4 and TIM-3 were increased on these peripheral Vδ1<sup>+</sup> T cells which are thought to possess an immunosuppressive function [##REF##32346434##199##].</p>", "<p>MAIT cells are ‘innate-like’ αβ T cell populations that make up 1-10% of all T cells in blood and mediate rapid, protective immune responses against bacterial species with intact riboflavin biosynthesis pathways, including <italic toggle=\"yes\">E. coli</italic> and <italic toggle=\"yes\">S. aureus</italic> [##REF##32858901##192##,##REF##31406380##204–206##]. MAIT cells use semi-invariant αβ TCRs to recognise ribityllumazine- and pyrimidine-based metabolite antigens from the riboflavin biosynthesis pathway, such as 5-OP-RU, that are presented by the non-classical MHC-like molecule, MR1 [##REF##24695216##207##,##REF##23051753##208##]. Such TCRs typically contain conserved usage of TCR α-chain variable gene 1-2 (TRAV1-2) paired with a biased pattern of TCR β-chain variable (TRBV) genes, such as TRBV20-1, TRBV6-4 or TRBV6-2/6-3 [##REF##31406380##204##,##REF##24101382##209##,##REF##10377186##210##]. MAIT cell-deficient (<italic toggle=\"yes\">Mr1</italic><sup>−/−</sup>) mice demonstrate an enhanced susceptibility to bacterial infection [##REF##31406380##204##] and increased mortality upon experimentally-induced sepsis [##UREF##3##211##]. Furthermore, this and other studies found reduced frequencies of MAIT cells in human patients with sepsis [##UREF##3##211–214##]. Whilst MAIT cells from these patients expressed more activation makers (e.g. CD69, CD38 and HLA-DR), they also exhibited higher levels of co-inhibitory receptors (e.g. LAG-3 and TIM-3) and were functionally deficient [##UREF##3##211##,##REF##36604951##212##,##REF##37163215##214##]. Indeed, in one study, such functional impairment of MAIT cells worsened over time during patient recovery from sepsis [##REF##36604951##212##]. Furthermore, the phenotypic status of MAIT cells in sepsis patients may serve as a possible prognostic marker as the percentage of HLA-DR<sup>+</sup> MAIT cells has been shown to be effective in predicting mortality and patient APACHE II scores [##REF##37163215##214##]. Despite this knowledge, the impact of sepsis on MAIT cells and γδ T cells is poorly understood and also particularly understudied compared to more conventional αβ T cell populations. Data in mouse models of sepsis further illustrate the importance of MAIT cells and γδ T cells in modulating the host response to sepsis and their positive influence on survival [##UREF##3##211##,##REF##18063696##215##]. Thus, further studies are required to expand our knowledge of sepsis-induced alterations in MAIT and γδ T cell immunity and to determine their utility as a prognostic biomarker or as a target for therapeutic intervention.</p>" ]	[ "<title>Acknowledgements</title>", "<p>We thank Professor Matthias Eberl for critical analysis of the manuscript. All figures in this manuscript were created using BioRender.com</p>", "<title>Data Availability</title>", "<p>Data sharing is not applicable for this manuscript</p>", "<title>Competing Interests</title>", "<p>The authors declare that there are no competing interests associated with the manuscript.</p>", "<title>Funding</title>", "<p>This work was supported by grant funding from the Royal Society [grant number RGS\\R2\\222186]. K.D. is supported by a Medical Research Council GW4 BioMed2 Doctoral Training Partnership studentship.</p>", "<title>Open Access</title>", "<p>Open access for this article was enabled by the participation of Cardiff University in an all-inclusive <italic toggle=\"yes\">Read & Publish</italic> agreement with Portland Press and the Biochemical Society under a transformative agreement with JISC.</p>", "<title>CRediT Author Contribution</title>", "<p><bold>Kate Davies:</bold> Conceptualization, Writing—original draft, Writing—review & editing. <bold>James E. McLaren:</bold> Conceptualization, Supervision, Writing—review & editing.</p>" ]	[ "<fig position=\"float\" id=\"F1\"><label>Figure 1</label><caption><title>Destabilisation of the adaptive immune system in sepsis</title><p>A marked lymphopenia is a common feature of patients with sepsis, predominantly attributed to apoptosis of lymphocytes. Other suggested causes include reduced production of precursor cells, and increased migration of lymphocytes to infected tissues, thus reducing the frequency of circulating cells. Remaining cells are reported to exhibit phenotypic and functional alterations, including skewed cytokine production, reduced HLA-DR expression on B cells and increased expression of co-inhibitory receptors on CD4<sup>+</sup> T cells, which decline in number and provide inadequate help to CD8<sup>+</sup> T cells. Equally, CD4<sup>+</sup> T<sub>REG</sub> cells increase in proportion, but whether this is positively or negatively associated with prognosis has been debated. Furthermore, the benefit of immunosuppression elicited by B<sub>R</sub><sub>EG</sub> cells is not clearly defined. Immunoglobulin levels decline, but this has been reported to correlate with both improved and worsened outcomes across different studies; HSC, haematopoietic stem cell</p></caption></fig>", "<fig position=\"float\" id=\"F2\"><label>Figure 2</label><caption><title>Suggested mechanisms of impaired CD4<sup>+</sup> T<sub>FH</sub> cell activity during sepsis</title><p>During a normal response to infection (left panel), CD4<sup>+</sup> T cells are initially primed by dendritic cells, inducing transcription of BCL-6 and subsequent expression of CXCR5 and other proteins important for migration to the B cell follicle, and generation of the germinal centre (GC). Within the GC, CD4<sup>+</sup> T<sub>FH</sub> cells provide signals (IL-21, IL-4, IL-10) to B cells for somatic hypermutation (SHM) and class-switch recombination (CSR), selecting those with highest affinity for antigen to differentiate into plasma cells or long-lived memory B cells. This process is regulated by CD4<sup>+</sup> T<sub>FR</sub> cells. GC-CD4<sup>+</sup> T<sub>FH</sub> cells may then down-regulate BCL-6 and enter the periphery as circulating memory cells, displaying different phenotypes through differential expression of CXCR3 and CCR6. During sepsis (right), multiple aspects of this process may be altered to result in inadequate B cell support. Suggested mechanisms include impaired transcription of c-MAF and BCL-6, resulting in reduced migration to the follicle to interact with cognate B cells. This could result in downstream effects of reduced numbers of GC-CD4<sup>+</sup> T<sub>FH</sub> cells with the correct protein expression profile needed to provide support. Alternatively, proliferation of CD4<sup>+</sup> T<sub>FR</sub> cells may result in enhanced suppression of GC-CD4<sup>+</sup> T<sub>FH</sub> cells. Both of these effects could result in a reduction in plasma cell differentiation and thus reduced antibody secretion. Alternatively, skewed expression of CXCR3 and CCR6 on circulating CD4<sup>+</sup> T<sub>FH</sub> cells could alter their cytokine signatures and subsequent ‘helper’ ability in the periphery. DZ: dark zone; LZ: light zone.</p></caption></fig>" ]	[ "<table-wrap position=\"float\" id=\"T1\"><label>Table 1</label><caption><title>Numerical or phenotypic changes to B and T cells in human patients with sepsis or septic shock</title></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"1\" colspan=\"1\">Cell</th><th align=\"left\" rowspan=\"1\" colspan=\"1\">Timepoint</th><th align=\"left\" rowspan=\"1\" colspan=\"1\">Observations</th><th align=\"center\" rowspan=\"1\" colspan=\"1\">Reference</th></tr></thead><tbody><tr><td rowspan=\"1\" colspan=\"1\">\n<bold>B cells</bold>\n</td><td rowspan=\"1\" colspan=\"1\">\n<bold>ICU admission</bold>\n</td><td rowspan=\"1\" colspan=\"1\">↓ Combined low serum levels of IgG1, IgM and IgA distinguished patients with highest odds ratio for death</td><td rowspan=\"1\" colspan=\"1\">[##REF##24815605##27##]</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">↓ Plasma IgG associated with 28-day mortality</td><td rowspan=\"1\" colspan=\"1\">[##REF##32859530##95##]</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">↓ Frequency of B<sub>REG</sub> cells associated with increased susceptibility to septic shock and death</td><td rowspan=\"1\" colspan=\"1\">[##REF##36357845##123##]</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">+ 28-days post-admission</italic>\n</td><td rowspan=\"1\" colspan=\"1\">↓ Circulating B cells<break/> CD40 expression</td><td rowspan=\"1\" colspan=\"1\">[##REF##23721745##41##]</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">↑ Expression of CD80 and the apoptotic marker CD95 in non-survivors</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">+ 4- and 8-days post-admission</italic>\n</td><td rowspan=\"1\" colspan=\"1\">↓ HLA-DR expression<break/> Circulating B cells</td><td rowspan=\"1\" colspan=\"1\">[##REF##31738315##105##]</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">↑ Proportional increase in plasmablasts</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">↑ Plasma levels of IgG on day 1, which dropped with time</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">+ 3- and 7-days post-admission</italic>\n</td><td rowspan=\"1\" colspan=\"1\">↓ Frequency of B<sub>REG</sub> cells associated with poor outcome, serving as a powerful prognostic marker in elderly patients</td><td rowspan=\"1\" colspan=\"1\">[##REF##28795662##124##]</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">\n<bold>Sepsis onset</bold>\n</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">+ 2- and 7-days post-onset</italic>\n</td><td rowspan=\"1\" colspan=\"1\">↑ Plasma IgG and IgA on day 1 associated with reduced 90-day survival</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">↑ Proportion of exhausted (CD21<sup>−</sup>/low) B cells</td><td rowspan=\"1\" colspan=\"1\">[##REF##34825972##96##]</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">Within 72 h</italic>\n</td><td rowspan=\"1\" colspan=\"1\">↓ Plasma IgM levels, which negatively correlated with severity in elderly patients</td><td rowspan=\"1\" colspan=\"1\">[##REF##27172158##87##]</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">↓ Capacity for immunoglobulin production when stimulated <italic toggle=\"yes\">ex vivo</italic></td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">Within 24 h + 24 h post-onset</italic>\n</td><td rowspan=\"1\" colspan=\"1\">↓ Plasma levels of IgA and IgG in non-survivors</td><td rowspan=\"1\" colspan=\"1\">[##REF##31764616##156##]</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">\n<bold>Septic shock onset</bold>\n</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">+ 3- and 7-days post-onset</italic>\n</td><td rowspan=\"1\" colspan=\"1\">↓ Serum IgM levels, more pronounced in non-survivors</td><td rowspan=\"1\" colspan=\"1\">[##REF##32304414##88##]</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">↓ Capacity for IgM production when stimulated <italic toggle=\"yes\">ex vivo</italic></td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\">\n<bold>T cells</bold>\n</td><td rowspan=\"1\" colspan=\"1\">\n<bold>ICU admission</bold>\n</td><td rowspan=\"1\" colspan=\"1\">↑ Proportion of Vδ1 T cells, with up-regulation of immunosuppressive co-IRs upon stimulation</td><td rowspan=\"1\" colspan=\"1\">[##REF##32346434##199##,##REF##23515014##200##,##REF##28362715##202##]</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">↓ Proportion of Vδ2 T cells, with reduced capacity for pro-inflammatory cytokine production<break/> Both observations correlated with increased severity and reduced survival</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">↓ Antigen-presenting function of γδ T cells</td><td rowspan=\"1\" colspan=\"1\">[##REF##35020851##203##]</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">↓ Frequency of MAIT cells</td><td rowspan=\"1\" colspan=\"1\">[##UREF##3##211##,##REF##24322275##213##]</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">↑ Markers of activation on remaining MAIT cells along with a reduced cytokine-secreting capacity</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">+ 4-days post-admission</italic>\n</td><td rowspan=\"1\" colspan=\"1\">↓ γδ T cells, associated with mortality</td><td rowspan=\"1\" colspan=\"1\">[##REF##23515014##200##]</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">+ 6-days post-admission</italic>\n</td><td rowspan=\"1\" colspan=\"1\">↓ Percentage of HLA-DR<sup>+</sup> MAIT cells predicted poor prognosis in patients</td><td rowspan=\"1\" colspan=\"1\">[##REF##37163215##214##]</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">+ 5 timepoints up until discharge</italic>\n</td><td rowspan=\"1\" colspan=\"1\">↓ Functional capacity of MAIT cells, which continued to decline with time</td><td rowspan=\"1\" colspan=\"1\">[##REF##36604951##212##]</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">+ 3-, 5-, and 7-days post-admission</italic>\n</td><td rowspan=\"1\" colspan=\"1\">↑ Percentage of T<sub>REG</sub> cells was associated with reduced severity</td><td rowspan=\"1\" colspan=\"1\">[##REF##20156359##181##]</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">\n<bold>Sepsis onset</bold>\n</td><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">Within 24 h + 24 h post-onset</italic>\n</td><td rowspan=\"1\" colspan=\"1\">↓ Circulating T<sub>FH</sub> cells which correlated with increased mortality and low IgA, IgM, and IgG levels</td><td rowspan=\"1\" colspan=\"1\">[##REF##31764616##156##]</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">\n<bold>Septic shock onset</bold>\n</td><td rowspan=\"1\" colspan=\"1\">↑ Expression of pro-apoptotic markers, annexin-V binding, active caspase-3 on CD4<sup>+</sup> and CD8<sup>+</sup> T cells</td><td rowspan=\"1\" colspan=\"1\">[##REF##21349174##187##]</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">↑ Expression of PD-1 on CD4<sup>+</sup> and CD8<sup>+</sup> T cells, correlated with increased rates of nosocomial infection and death</td><td rowspan=\"1\" colspan=\"1\"/></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">+ 1-2- and 3–6-days post-onset</italic>\n</td><td rowspan=\"1\" colspan=\"1\">↑ Proportion of T<sub>REG</sub> cells as a result of a selective depletion of CD25<sup>−</sup> populations</td><td rowspan=\"1\" colspan=\"1\">[##REF##15640650##177##]</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">\n<bold>Post-mortem</bold>\n</td><td rowspan=\"1\" colspan=\"1\">↓ Number and area of lymphoid follicles in patients with sepsis</td><td rowspan=\"1\" colspan=\"1\">[##REF##11359857##37##]</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">↓ Capacity of splenic and lung T cells to secrete cytokines when stimulated <italic toggle=\"yes\">in vitro</italic></td><td rowspan=\"1\" colspan=\"1\">[##REF##22187279##14##]</td></tr><tr><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\"/><td rowspan=\"1\" colspan=\"1\">↑ Expression of co-inhibitory receptors</td><td rowspan=\"1\" colspan=\"1\"/></tr></tbody></table></table-wrap>" ]	[]	[]	[]	[]	[]	[]	[]	[ "<graphic xlink:href=\"cs-138-cs20230517-g1\" position=\"float\"/>", "<graphic xlink:href=\"cs-138-cs20230517-g2\" position=\"float\"/>" ]	[]	[{"label": ["19."], "surname": ["Bray", "Kampouraki", "Winter", "Jesuthasan", "Messer", "Graziadio"], "given-names": ["A.", "E.", "A.", "A.", "B.", "S."], "year": ["2020"], "article-title": ["High variability in sepsis guidelines in UK: why does it matter?"], "source": ["Int. J. Environ. Res. Public Health"], "volume": ["17"], "pub-id": ["10.3390/ijerph17062026"]}, {"label": ["63."], "surname": ["Calderon", "Schindler", "Malin", "Schebesta", "Sun", "Schwickert"], "given-names": ["L.", "K.", "S.G.", "A.", "Q.", "T."], "etal": ["et al"], "year": ["2021"], "article-title": ["Pax5 regulates B cell immunity by promoting PI3K signaling via PTEN down-regulation"], "source": ["Sci. Immunol."], "volume": ["6"], "pub-id": ["10.1126/sciimmunol.abg5003"]}, {"label": ["178."], "surname": ["Neumann", "Prezzemolo", "Vanderbeke", "Roca", "Gerbaux", "Janssens"], "given-names": ["J.", "T.", "L.", "C.P.", "M.", "S."], "etal": ["et al"], "year": ["2020"], "article-title": ["Increased IL-10-producing regulatory T cells are characteristic of severe cases of COVID-19"], "source": ["Clin. Transl. Immunol."], "volume": ["9"], "fpage": ["e1204"], "pub-id": ["10.1002/cti2.1204"]}, {"label": ["211."], "surname": ["Trivedi", "Labuz", "Anderson", "Araujo", "Blair", "Middleton"], "given-names": ["S.", "D.", "C.P.", "C.V.", "A.", "E.A."], "etal": ["et al"], "year": ["2020"], "article-title": ["Mucosal-associated invariant T (MAIT) cells mediate protective host responses in sepsis"], "source": ["Elife"], "volume": ["9"], "pub-id": ["10.7554/eLife.55615"]}]	{ "acronym": [ "APACHE", "APC", "BCR", "BREG", "CD40L", "CLP", "CSR", "CTLA-4", "\nE. coli\n", "FO", "GC", "HIV", "HLA-DR", "HSC", "ICOS", "ICU", "IFN-γ", "IL", "IVIG", "LAG-3", "LPS", "MAIT", "MHC", "MZ", "pAPCs", "\nS. aureus\n", "SHM", "SOFA", "TCR", "TFH", "TFR", "TGF-β", "TH", "TIM-3", "TNF-α", "TRBV", "TREG" ], "definition": [ "Acute Physiology and Chronic Health Evaluation", "Antigen presenting cell", "B cell receptor", "B regulatory", "CD40 ligand", "Caecum ligation puncture", "Class Switch Recombination", "Cytotoxic T Lymphocyte-associated Antigen 4", "\nEscherichia coli\n", "Follicular", "Germinal Centre", "Human Immunodeficiency Virus", "Human Leukocyte Antigen-DR", "Haematopoeitic Stem Cell", "Inducible co-stimulator", "Intensive Care Unit", "Interferon-γ", "Interleukin", "Intravenous immunoglobulin", "Lymphocyte-activation gene 3", "Lipopolysaccharide", "Mucosal-associated invariant T", "Major Histocompatibility Complex", "MarginalZ one", "Professional antigen presenting cells", "\nStaphylococcus aureus\n", "Somatic hypermutation", "Sequential Organ Failure Assessment", "T cell receptor", "T follicular helper", "T follicular regulatory", "Transforming Growth Factor-β", "T helper", "T cell immunoglobulin and mucin domain-containing protein 3", "Tumour Necrosis Factor-α", "TCR β-chain variable", "T regulatory" ] }	215	CC BY	no	2024-01-13 00:02:19	Clin Sci (Lond). 2024 Jan 10; 138(1):65-85	oa_package/1a/a7/PMC10781648.tar.gz
PMC10781652	0	[]	[]	[]	[]	[]	[ "<p>Open Access funding enabled and organized by Projekt DEAL.</p>" ]	[ "<p id=\"Par1\">Prostate cancer remains one of the most prevalent malignancies in men globally (Preisser et al. ##REF##32303478##2020##). Accurate diagnosis and differentiation of the disease are paramount for effective treatment planning and improved patient outcomes (Hsieh et al. ##REF##35488246##2022##). Traditionally, the diagnosis of prostate cancer heavily relied on invasive biopsy procedures, which, although effective, are associated with potential complications and discomfort for patients. With the advancements in medical imaging, positron emission tomography/computed tomography (PET/CT) using prostate-specific membrane antigen (PSMA) tracers has emerged as a revolutionary diagnostic tool (Obek et al. ##REF##28624849##2017##). This technique, often termed as 'virtual biopsy', provides a non-invasive alternative to traditional biopsy. Baseline PSMA PET/CT offers detailed and precise imaging of prostate lesions, allowing clinicians to pinpoint not only the location but also the potential aggressiveness of the tumor. The capability of PSMA PET/CT to bind specifically to PSMA-expressing cells gives it a unique edge in differentiating between benign and malignant prostate lesions. This specificity also aids in identifying metastatic or recurrent diseases, often before they become evident on conventional imaging modalities (Papp et al. ##REF##33341915##2021##).</p>", "<p id=\"Par2\">In this comprehensive investigation, a patient cohort, consisting of 138 individuals exhibiting clinical indicators suggestive of prostate carcinoma, underwent detailed imaging studies using piflufolastat F-18 (<sup>18</sup>F-DCFPyL) PET/CT. This imaging modality captured a wide spectrum of prostate pathologies, encompassing benign hyperplasia, benign inflammatory processes, and a range of malignant neoplasms, each varying in their gleason score (GS) and D'Amico scores. Beyond just providing the conventional PET metrics, which include but are not limited to standard uptake value max (SUVmax), standard uptake value mean (SUVmean), standard uptake value ratio (SUVR), total lesion PSMA (TL-PSMA), and total volume PSMA (PSMA-TV), this advanced imaging modality yielded granular tumor texture attributes, contextualized against the background data. Li et al. leveraged an advanced machine learning algorithm, meticulously rooted in the radiomics of <sup>18</sup>F-DCFPyL positron emission tomography/computed tomography (PET/CT) imaging. This algorithm was meticulously developed with the aim of providing an innovative, non-invasive strategy for the efficacious stratification of prostate lesions (Lambin et al. ##REF##28975929##2017##; Perandini et al. ##REF##27648166##2016##). Its capabilities extend from differentiating benign from malignant prostate lesions, pinpointing high-grade pathological prostate neoplasms (with a Gleason score exceeding seven), to identifying prostate malignancies associated with an elevated D'Amico risk score.</p>", "<p id=\"Par3\">This approach was pivotal in discerning the intricate interplay between in vivo attributes, patient demographics, and their respective significance in prognosticating malignancies, especially those of elevated risk. Through a systematic amalgamation and deployment of these weighted features, they succeeded in formulating robust prognostic models. These models catered to a spectrum of diagnostic challenges, from general malignancy diagnosis (Mm) to high-grade prostate carcinoma identification (Mgs) and risk stratification of high clinical-risk prostate pathologies (Mamico).</p>", "<p id=\"Par4\">Ensuring the validity and applicability of the findings, these formulated models underwent rigorous validation via a Monte Carlo cross-validation paradigm. The results were promising. For the entire subset of primary prostate carcinoma subjects, the Monte Carlo metrics reflected compelling efficacy of the models with AUC values of 0.97 for Mm, 0.73 for Mgs, and 0.82 for Mamico. Beyond mere numbers, these findings underscore the transformative potential of <sup>18</sup>F-DCFPyL PET/CT radiomics. It signifies a paradigm shift, enabling clinicians to discern between benign and malignant prostate tumors, as well as to identify high-risk neoplasms, all while negating the invasiveness of traditional biopsy approaches.</p>", "<p id=\"Par5\">This innovation's implications are vast in the clinical landscape of prostate patient management. Particularly, when it comes to initial assessments and baseline evaluations, the diagnostic accuracy this method offers is unparalleled. It acts as a beacon, illuminating clinical therapeutic strategy formulations, and more importantly, mitigating potential physiological and financial adversities induced by redundant or unnecessary biopsies.</p>", "<p id=\"Par6\">This study, born from a collaboration of clinical radiologists and academia spanning from China to Europe, may contribute significantly to a better management of patients with (suspected) prostate cancer. The integration of machine learning has paved the way for a unified, global benchmark for prostate patient diagnosis and risk stratification, ensuring that patients are spared from physical harm during their diagnostic journey. The methodology, being avant-garde, methodologically sound, systematized, and embedded with clinical pragmatism, is positioned for global acceptance and adoption. Its merits, beyond doubt, warrant recognition and integration into clinical guidelines. However, a transparent acknowledgment of this study's limitations includes its sample size constraints and the limited number of associated research institutions. These aspects warrant attention and amplification in forthcoming research endeavors.</p>", "<p id=\"Par7\">The clinical significance of this 'virtual biopsy' technique is profound. Early PSMA PET/CT facilitates the formulation of more personalized treatment plans based on the precise location and extent of the disease. Furthermore, by reducing the need for invasive biopsy procedures, patients are spared potential complications such as infection, bleeding, and discomfort. The non-invasive nature of the PSMA PET/CT not only enhances patient comfort but also expedites the diagnostic process, ensuring that therapeutic interventions can commence promptly. All in all, the advent of an initial PSMA PET/CT as a 'virtual biopsy' tool in the realm of prostate cancer diagnosis and differentiation has transformed the clinical landscape. By providing precise, detailed, and non-invasive insight into prostate pathology, it plays a pivotal role in improving patient outcomes and revolutionizing prostate cancer management.</p>" ]	[ "<title>Acknowledgements</title>", "<p>Not applicable.</p>", "<title>Authors' Contribution</title>", "<p>MCK performed all content of the comment.</p>", "<title>Funding</title>", "<p>Open Access funding enabled and organized by Projekt DEAL.</p>", "<title>Data Availability</title>", "<p>Not applicable.</p>", "<title>Declarations</title>", "<title>Conflict of Interests</title>", "<p id=\"Par8\">The author has no competing interests to declare that are relevant to the content of this article.</p>", "<title>Ethical Approval</title>", "<p id=\"Par9\">Not applicable.</p>", "<title>Consent to Participate</title>", "<p id=\"Par10\">Not applicable.</p>", "<title>Consent for Publication</title>", "<p id=\"Par11\">The author has consented to the submission of the commentary to the journal and publication.</p>" ]	[]	[]	[]	[]	[]	[]	[]	[]	[]	[]	[]	[]	{ "acronym": [], "definition": [] }	6	CC BY	no	2024-01-13 00:02:19	Phenomics. 2023 Dec 4; 3(6):639-641	oa_package/b0/ad/PMC10781652.tar.gz
PMC10781653	38198071	[ "<title>Introduction</title>", "<p id=\"Par8\">Liver transplantation (LT) is universally performed as a treatment for end-stage liver disease, acute hepatic failure, hepatocellular carcinoma, and several metabolic disorders [##REF##32827564##1##–##UREF##1##6##]. In Japan, the proportion of deceased-donor liver transplantation (DDLT) in LT is lower than in other countries. As a result, many patients are unable to undergo LT [##REF##25891711##7##].</p>", "<p id=\"Par9\">Marfan syndrome (MS) is an autosomal dominant inheritance of connective tissue disease with an estimated incidence of 1 in 5000; and in 90% of cases, it is caused by mutations in the gene for fibrillin-1, the main component of extracellular microfibrils. The cardiovascular, ocular, and skeletal systems are the main targets of the disease. Early detection and appropriate management are important because patients with MS are prone to life-threatening cardiovascular complications, such as aortic aneurysms and aortic dissection [##REF##32439107##8##]. Therefore, clinical surgeons may often hesitate to perform LT using MS donors in practical terms. However, it was reported that MS cases are likely to have fewer vascular complications in the hepatic artery and other visceral arteries during abdominal surgery [##UREF##2##9##].</p>", "<p id=\"Par10\">To date there is only one reported case of LT with MS as a donor, and there is little information on whether it is eligible as a donor for LT. This report was first successful liver transplantation donor in a patient with MS with a history of abdominal aortic surgery.</p>" ]	[]	[]	[ "<title>Discussion</title>", "<p id=\"Par12\">We experienced DDLT with a donor with MS. DDLT with a donor with MS is rare, with only one case reported in the past (Table ##TAB##0##1##) [##REF##17519798##10##]. Vascular changes due to MS occur mostly in the main vessels (30–50% incidence). This is associated with the high amount of connective tissue within the vessel wall and the high pressure and blood flow to which the artery is exposed [##REF##16325700##11##–##REF##15968491##13##]. The wall of the aorta is composed of three layers, with a large number of elastin fibers in the tunica media in the middle. The tunica media provides elasticity to the arteries and binds the outer and inner membranes together. In MS, the fibrillin-1 gene is abnormal, which prevents the formation of elastin fibers in the tunica media, making the aortic wall brittle. As a result, aortic aneurysms are more likely to form, and aortic dissection is more likely to occur [##UREF##3##14##]. When aortic cannulation for perfusion, we need careful cannulation for possible dissection of abdominal aorta because of possible abnormality in the structure of aorta. However, in this case, the common iliac artery had normal elasticity on intraoperative palpation, and the lumen did not feel any change from normal. We think that good perfusion was achieved by careful and careful debridement to avoid divergence and intimal detachment. The incidence of visceral artery changes is approximately 0.4–2.0% and usually manifests with cystic medionecrosis in MS [##REF##16325700##11##–##REF##15968491##13##]. If the biopsy result was strong degeneration with cystic medionecrosis, it is not suitable for anastomosis and liver transplantation may not be performed as planned. The frequency of aneurysms of abdominal viscera is around 0.01–0.2% at autopsy. The patient will continue to undergo computed tomography scan and other imaging evaluations.</p>", "<p id=\"Par13\">In the literature, only two hepatic artery complications are described in patients with MS [##REF##4540102##15##, ##REF##6524136##16##]. Santiago-Delpin described a case of incomplete MS with multiple aneurysms of the aorta and its branches, in which a hepatic artery aneurysm perforated into the common bile duct. Ruschen published a case in which a spontaneous hepatic artery rupture occurred in a patient with MS.</p>", "<p id=\"Par14\">In the present case, our rationale to accept a donor with MS was based on the low incidence of hepatic artery complications and total absence of hepatic dysfunction in patients with MS. The normal hepatic artery biopsy was also important in the acceptance process. Fibrillin-1 gene mutations persist in the transplanted graft and, theoretically, may increase the risk of vascular complications such as dilation, aneurysmal changes, dissection, and intimal tears. Farese et al. [##REF##16889552##17##] suggested another potential complication occurring in this setting: the possibility of antibodies developed against mutated fibrillin-1 donor liver cells. To date, we have not detected any posttransplant liver dysfunction and the recipient has not presented graft rejection. Three cases of kidney transplantation from MS have been reported. These three patients showed good progress with no postoperative complications [##REF##16889552##17##]. In a case of heart transplantation, Marfanoid aneurysm in donor aorta after transplantation was reported [##REF##9205184##18##]. This report was that the donor has the possibility to be MS due to his physique and other factors, and organ transplantation with main vessels from MS patients may have a certain risks but is feasible.</p>", "<p id=\"Par15\">In summary, a patient with MS and a history of abdominal aortic surgery can still be a donor for DDLT with appropriate judgment. Considering reports of LT using MS donors are infrequent, a biopsy should be performed when transplanting a liver from an MS patient. In addition, a detailed follow-up of the transplanted vessels by Doppler ultrasound is advocated for patients undergoing such transplants [##REF##17519798##10##]. In addition, before organ procurement, recipients should receive adequate informed consent regarding MS. There is no information on the number of DDLT using MS as a donor or their long-term outcome, and this information needs to be gathered in the future.</p>" ]	[ "<title>Conclusion</title>", "<p id=\"Par16\">We experienced a successful case of DDLT in which a patient with a history of abdominal aortic surgery due to MS was used as a donor. Patients with MS and a history of abdominal aortic surgery can still be donors for DDLT with appropriate judgment based on an intraoperative hepatic artery biopsy.</p>" ]	[ "<title>Background</title>", "<p id=\"Par1\">Liver transplantation is the definitive therapy for patients with decompensated cirrhosis. Marfan syndrome is a systemic inheritable connective tissue disease associated with fibrillin-1 gene mutations, which cause abnormalities in connective tissue. Vascular changes due to Marfan syndrome occur mostly in the main vessels due to the high amount of connective tissue within the vessel wall and the high pressure and blood flow to which they are exposed. The incidence of changes in visceral arteries is about 0.42% and usually presents with cystic medial necrosis. This report is the first deceased-donor liver transplantation with a donor with Marfan syndrome with a history of abdominal surgery.</p>", "<title>Case presentation</title>", "<p id=\"Par2\">A patient in his 50s underwent liver transplantation for decompensated alcoholic cirrhosis. The donor, a 50s male with Marfan syndrome, was diagnosed with brain-death due to a cerebral hemorrhage caused by a cerebral aneurysm. The donor’s clinical presentation as Marfan syndrome was aortic dissection, with multiple surgical procedures performed from the aortic root to the abdominal aorta. An intraoperative biopsy of the hepatic artery showed no abnormality, so this organ was considered appropriate. The surgery was completed without any problems of the arterial anastomosis. The patient’s postoperative course was uneventful, and he was transferred to a hospital for recuperation on the 18th postoperative day. One year after the surgery, the patient is still alive without any complications from the transplantation or arterial problems.</p>", "<title>Conclusions</title>", "<p id=\"Par3\">Even if the patient had a history of surgery for vascular anomalies extending to the abdominal aorta due to Marfan syndrome, the patient can be a donor for liver transplantation under appropriate judgment, including intraoperative biopsy.</p>", "<title>Keywords</title>" ]	[ "<title>Case presentation</title>", "<p id=\"Par11\">A male patient in his 50s was on a waiting list for liver transplantation with decompensated alcoholic cirrhosis. The model for end-stage liver disease score was 30, and the Child–Pugh score was 13 (Grade: C). At that time, a brain-dead donor was available. The donor was a man in his 50s and had MS. He had a history of aortic dissection related to MS and multiple surgeries. He had previously undergone total aortic replacement of the ascending arch, aortic root replacement, descending thoracic aortic replacement, abdominal aortic replacement, and aortic aneurysmectomy plus artificial vessel replacement (reconstruction of celiac artery, superior mesenteric artery, bilateral renal arteries). Therefore, during hepatectomy, the liver was removed up to the normal artery that is not artificial blood vessel, and the common hepatic artery was used for reconstruction. Only the liver was procured from this brain-dead donor. Because a biopsy of the hepatic artery was performed during surgery and was normal, this organ was considered suitable. The DDLT procedure proceeded without problems and the arterial anastomosis was completed. The operation duration was 621 min, blood loss was 804 ml, and the type of implantation was conventional, recipient's proper hepatic artery and the donor’s proper hepatic artery were anastomosed with interrupted by using 8-0 Prolene (totally 12 sutures). The postoperative course was uneventful and a daily ultrasound Doppler examination for 2 weeks confirmed that blood flow in the anastomosed vessels was normal. A computed tomography scan 2 weeks after DDLT also confirmed that there were no problems (Fig. ##FIG##0##1##). The patient was discharged on the 18th postoperative day without major post-implantation complications. One year after surgery, the patient’s systemic condition is still intact.</p>" ]	[ "<title>Acknowledgements</title>", "<p>We thank J. Ludovic Croxford, PhD, from Edanz (<ext-link ext-link-type=\"uri\" xlink:href=\"https://jp.edanz.com/ac\">https://jp.edanz.com/ac</ext-link>) for editing a draft of this manuscript.</p>", "<title>Author contributions</title>", "<p>Study conception: SI, TT, and TY. Writing: TI. Final approval of the article: all authors. Accountability for all aspects of the work: all authors.</p>", "<title>Funding</title>", "<p>This study was not supported by any funding.</p>", "<title>Availability of data and materials</title>", "<p>The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par17\">Not applicable.</p>", "<title>Consent for publication</title>", "<p id=\"Par18\">Written informed consent was obtained from the patient for publication of this case report and accompanying images.</p>", "<title>Competing interests</title>", "<p id=\"Par19\">The authors declare no conflicts of interest in association with the present study.</p>" ]	[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p><bold>a</bold> Anastomosis of the recipient’s common hepatic artery with the graft’s common hepatic artery. <bold>b</bold> Evaluation of blood flow in anastomosed arteries by Doppler ultrasound</p></caption></fig>" ]	[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Liver transplantation using a deceased donor with Marfan syndrome</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\">Case</th><th align=\"left\" colspan=\"4\">Donor</th><th align=\"left\" colspan=\"3\">Recipient</th></tr><tr><th align=\"left\">Age/sex</th><th align=\"left\">MS comorbidities</th><th align=\"left\">Cause of brain death</th><th align=\"left\">Biopsy of hepatic artery</th><th align=\"left\">Age/sex</th><th align=\"left\">Etiology of liver disease</th><th align=\"left\">MELD</th></tr></thead><tbody><tr><td align=\"left\">No. 1 (Ref. 10)</td><td align=\"left\">10s female</td><td align=\"left\">Mitral valve deviation, and osteoarticular abnormalities</td><td align=\"left\">Hypoxic encephalopathy</td><td align=\"left\">No abnormality</td><td align=\"left\">40s male</td><td align=\"left\">HCV, HCC</td><td align=\"left\">N/A</td></tr><tr><td align=\"left\">No. 2 (the present case)</td><td align=\"left\">50s male</td><td align=\"left\">Aortic dissection</td><td align=\"left\">Cerebral hemorrhage</td><td align=\"left\">No abnormality</td><td align=\"left\">55 male</td><td align=\"left\">Decompensated alcoholic cirrhosis</td><td align=\"left\">30</td></tr></tbody></table></table-wrap>" ]	[]	[]	[]	[]	[]	[]	[ "<table-wrap-foot><p>HCC: hepatocellular carcinoma, HCV: hepatitis C virus, MELD: model for end-stage liver disease, MS: Marfan syndrome,</p><p>N/A: not available</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher's Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"40792_2024_1807_Fig1_HTML\" id=\"MO1\"/>" ]	[]	[{"label": ["2."], "surname": ["Yoshizumi", "Ikegami", "Kimura", "Uchiyama", "Ikeda", "Shirabe"], "given-names": ["T", "T", "K", "H", "T", "K"], "article-title": ["Selection of a right posterior sector graft for living donor liver transplantation"], "source": ["Liver Transplant"], "year": ["2014"], "volume": ["20"], "fpage": ["1089"], "lpage": ["1096"], "pub-id": ["10.1002/lt.23924"]}, {"label": ["6."], "surname": ["Rawal", "Yazigi"], "given-names": ["N", "N"], "article-title": ["Pediatric liver transplantation"], "source": ["Pediatr Clin N Am"], "year": ["2017"], "volume": ["64"], "fpage": ["677"], "lpage": ["684"], "pub-id": ["10.1016/j.pcl.2017.02.003"]}, {"label": ["9."], "surname": ["Grotemeyer", "Duran", "Park", "Hoffmann", "Blondin", "Iskandar"], "given-names": ["D", "M", "EJ", "N", "D", "F"], "article-title": ["Visceral artery aneurysms\u2014follow-up of 23 patients with 31 aneurysms after surgical or interventional therapy"], "source": ["Langenbeck\u2019s Archives Surg"], "year": ["2009"], "volume": ["394"], "fpage": ["1093"], "pub-id": ["10.1007/s00423-009-0482-z"]}, {"label": ["14."], "surname": ["Halper", "Kjaer"], "given-names": ["J", "M"], "article-title": ["Progress in heritable soft connective tissue diseases"], "source": ["Adv Exp Med Biol"], "year": ["2013"], "volume": ["802"], "fpage": ["31"], "lpage": ["47"], "pub-id": ["10.1007/978-94-007-7893-1_3"]}]	{ "acronym": [ "DDLT", "LT", "MELD", "MS" ], "definition": [ "Deceased-donor liver transplantation", "Liver transplantation", "Model for end-stage liver disease", "Marfan syndrome" ] }	18	CC BY	no	2024-01-13 00:02:19	Surg Case Rep. 2024 Jan 10; 10:14	oa_package/d1/69/PMC10781653.tar.gz
PMC10781657	38200276	[ "<title>Background</title>", "<p id=\"Par9\">At the beginning of the twentieth century, lung abscess (LA) was severely life-threatening disease, with a mortality rate of 75% [##REF##22115254##1##]. However, treatment results for LA have improved with the development of antibiotics. Although antibiotic therapy has decreased the mortality rate associated with LA, it is still high (approximately 8.7%) [##REF##26366400##2##]. Surgery is indicated for approximately 10% of LA cases, owing to complications such as bronchopleural fistula, empyema, and hemorrhage [##UREF##0##3##]. However, pyothorax and fistula occur upon rupturing of the LA, and are difficult to treat. We report the successful outcome of surgical treatment comprising free pericardial fat (FPF) implantation in the abscess cavity of an LA that ruptured intraoperatively.</p>" ]	[]	[]	[ "<title>Discussion and conclusions</title>", "<p id=\"Par11\">Despite the development of antibiotics and medical therapy, LA is a life-threatening disease with a high mortality rate [##REF##26366400##2##]. Surgical interventions are indicated for cases refractory to medical therapy, life-threatening hemoptysis, cavitary lesions with a diameter larger than 6 cm, bronchopleural fistulas, prolonged sepsis and febricity, abscess rupture with pyopneumothorax, and empyema [##REF##26366400##2##–##REF##35868131##4##]. If surgical intervention is required, the risk of a bronchopleural fistula is increased [##REF##35868131##4##].</p>", "<p id=\"Par12\">The treatment of a ruptured or perforated LA is difficult. Including our case, 18 cases involving empyema associated with LA rupture or perforation have been reported (Table ##TAB##0##1##) [##REF##35868131##4##–##REF##36548445##20##]. The coexistence of diabetes mellitus occurred in eight cases [##UREF##1##6##–##UREF##2##8##, ##REF##21674316##10##–##UREF##3##12##, ##UREF##4##14##, ##REF##20214353##19##]. <italic>Streptococcus</italic> was identified in five cases [##REF##33620526##7##, ##REF##37153891##9##, ##UREF##3##12##, ##UREF##4##14##, ##REF##37187631##18##]. Four patients who experienced respiratory failure or acute respiratory distress syndrome [##REF##37153891##9##, ##UREF##3##12##, ##REF##16281864##13##, ##REF##20715468##17##], and two patients who experienced preoperative septic shock [##REF##35868131##4##, ##REF##28293000##11##] required mechanical ventilation and extracorporeal membrane oxygenation. Fenestration was performed during the first surgery for two cases [##UREF##4##14##, ##REF##20214353##19##]. However, bronchial embolization using an endobronchial Watanabe spigot resulted in the successful in three cases [##UREF##2##8##, ##UREF##3##12##, ##REF##36548445##20##]. Lobectomy was performed for three cases [##REF##35868131##4##, ##REF##9766366##5##, ##REF##16281864##13##]. An omental or muscle flap was applied to the fistula in four cases [##UREF##1##6##, ##REF##33620526##7##, ##REF##21674316##10##, ##REF##20214353##19##]. The fistula was directly sutured in three cases [##REF##33620526##7##, ##REF##18323196##16##, ##REF##20715468##17##]. The abscess wall of our patient was severely damaged, and air leakage was difficult to control. Therefore, we implanted FPF in the ruptured abscess cavity because the abscess wall tissue was too fragile to close with direct suture. Subsequently, the abscess wall was sutured to the FPF.</p>", "<p id=\"Par13\">Coverage can be performed by suturing the use of FPF pad without artificial materials, resulting in the effective control of air leakage [##REF##27127147##21##, ##REF##25704860##22##]. In our department, FPF is actively used during surgery to cover bronchial stump, thus preventing bronchopleural fistulas [##REF##27127147##21##]. Depending on the patient's body type, it is easy to handle and collect sufficient amounts of FPF. Although the abscess cavity was relatively large in our patient, we were able to collect a sufficient amount of FPF; therefore, the cavity was filled using FPF and fibrin glue. Furthermore, the fistula was controlled by suturing the FPF and fragile abscess wall. This is the first case report of the use of FPF for a ruptured LA.</p>", "<p id=\"Par14\">In conclusion, we successfully treated an LA that ruptured intraoperatively. Therefore, FPF implantation in the ruptured abscess cavity can effectively treat this condition.</p>" ]	[ "<title>Discussion and conclusions</title>", "<p id=\"Par11\">Despite the development of antibiotics and medical therapy, LA is a life-threatening disease with a high mortality rate [##REF##26366400##2##]. Surgical interventions are indicated for cases refractory to medical therapy, life-threatening hemoptysis, cavitary lesions with a diameter larger than 6 cm, bronchopleural fistulas, prolonged sepsis and febricity, abscess rupture with pyopneumothorax, and empyema [##REF##26366400##2##–##REF##35868131##4##]. If surgical intervention is required, the risk of a bronchopleural fistula is increased [##REF##35868131##4##].</p>", "<p id=\"Par12\">The treatment of a ruptured or perforated LA is difficult. Including our case, 18 cases involving empyema associated with LA rupture or perforation have been reported (Table ##TAB##0##1##) [##REF##35868131##4##–##REF##36548445##20##]. The coexistence of diabetes mellitus occurred in eight cases [##UREF##1##6##–##UREF##2##8##, ##REF##21674316##10##–##UREF##3##12##, ##UREF##4##14##, ##REF##20214353##19##]. <italic>Streptococcus</italic> was identified in five cases [##REF##33620526##7##, ##REF##37153891##9##, ##UREF##3##12##, ##UREF##4##14##, ##REF##37187631##18##]. Four patients who experienced respiratory failure or acute respiratory distress syndrome [##REF##37153891##9##, ##UREF##3##12##, ##REF##16281864##13##, ##REF##20715468##17##], and two patients who experienced preoperative septic shock [##REF##35868131##4##, ##REF##28293000##11##] required mechanical ventilation and extracorporeal membrane oxygenation. Fenestration was performed during the first surgery for two cases [##UREF##4##14##, ##REF##20214353##19##]. However, bronchial embolization using an endobronchial Watanabe spigot resulted in the successful in three cases [##UREF##2##8##, ##UREF##3##12##, ##REF##36548445##20##]. Lobectomy was performed for three cases [##REF##35868131##4##, ##REF##9766366##5##, ##REF##16281864##13##]. An omental or muscle flap was applied to the fistula in four cases [##UREF##1##6##, ##REF##33620526##7##, ##REF##21674316##10##, ##REF##20214353##19##]. The fistula was directly sutured in three cases [##REF##33620526##7##, ##REF##18323196##16##, ##REF##20715468##17##]. The abscess wall of our patient was severely damaged, and air leakage was difficult to control. Therefore, we implanted FPF in the ruptured abscess cavity because the abscess wall tissue was too fragile to close with direct suture. Subsequently, the abscess wall was sutured to the FPF.</p>", "<p id=\"Par13\">Coverage can be performed by suturing the use of FPF pad without artificial materials, resulting in the effective control of air leakage [##REF##27127147##21##, ##REF##25704860##22##]. In our department, FPF is actively used during surgery to cover bronchial stump, thus preventing bronchopleural fistulas [##REF##27127147##21##]. Depending on the patient's body type, it is easy to handle and collect sufficient amounts of FPF. Although the abscess cavity was relatively large in our patient, we were able to collect a sufficient amount of FPF; therefore, the cavity was filled using FPF and fibrin glue. Furthermore, the fistula was controlled by suturing the FPF and fragile abscess wall. This is the first case report of the use of FPF for a ruptured LA.</p>", "<p id=\"Par14\">In conclusion, we successfully treated an LA that ruptured intraoperatively. Therefore, FPF implantation in the ruptured abscess cavity can effectively treat this condition.</p>" ]	[ "<title>Background</title>", "<p id=\"Par1\">Lung abscess treatment results the treatment results improved with the development of antibiotics; however, surgical treatment is indicated when pyothorax is present, surgical treatment is indicated. When a lung abscess ruptures, pyothorax and fistula occur, which are difficult to treat.</p>", "<title>Case presentation</title>", "<p id=\"Par2\">A 74-year-old woman who experienced exacerbated dyspnea and left back pain for 10 days was diagnosed with a lung abscess caused by an odontogenic infection. The patient’s medical history included hypertension, angina pectoris, untreated dental caries, and periodontitis. Despite administration of meropenem for 5 days, inflammatory markers increased. Chest radiography revealed pleural effusion exacerbation; therefore, the patient immediately underwent chest drainage and surgery was planned. Thoracic debridement and parietal and visceral decortication were performed. However, the lung abscess in the lateral basal segment ruptured during visceral decortication. As the tissue was fragile and difficult to close with sutures, free pericardial fat was implanted in the ruptured abscess cavity and fixed with fibrin glue, and sutured to the abscess wall. No signs of postoperative air leakage or infection of the implanted pericardial fat were observed. All drainage tubes were removed by postoperative day 9. The patient was discharged on postoperative day 12 and underwent careful observation during follow-up as an outpatient. At 1 year and 2 months after surgery, empyema recurrence was not observed.</p>", "<title>Conclusions</title>", "<p id=\"Par3\">A lung abscess that ruptured intraoperatively was successfully and effectively treated by implantation of free pericardial fat in the abscess cavity.</p>", "<title>Keywords</title>" ]	[ "<title>Case presentation</title>", "<p id=\"Par10\">A 74-year-old woman who experienced dyspnea exacerbation and left back pain for 10 days was diagnosed with an LA caused by odontogenic infection. Her medical history revealed comorbidities, such as hypertension, angina pectoris, untreated dental caries, and periodontitis. Although the patient was administered meropenem (MPMN) for 5 days, her body temperature reached 37.8 °C and inflammatory marker levels increased. Chest radiography revealed pleural effusion exacerbation (Fig. ##FIG##0##1##a). The patient’s white blood cell count and C-reactive protein level markedly increased to 21,130/µL and 33.74 mg/dL, respectively, and her procalcitonin level was elevated (0.28 ng/mL). Chest computed tomography (CT) revealed a large, multilocular pleural effusion and an LA in the left lower lobe (Fig. ##FIG##0##1##b, c). Although the patient immediately underwent chest drainage, poor drainage was observed in the apex. The pH of the pleural effusion markedly decreased to 6.9, and surgery was planned accordingly. First, thoracoscopic debridement was attempted; however, both the parietal and visceral pleura were markedly thickened. Therefore, we converted to thoracotomy, and performed parietal and visceral decortication. An LA was also observed in the lateral basal segment. The cause of empyema was perforation of the LA, and the abscess wall was fragile and torn during decortication (Fig. ##FIG##1##2##a). Large amounts of pus and air leakage were observed in the ruptured abscess cavity. Because the tissue was fragile, it was difficult to close with sutures. Therefore, FPF was implanted in the ruptured abscess cavity (Fig. ##FIG##1##2##b, c), fixed with fibrin glue, and sutured to the abscess wall (Fig. ##FIG##1##2##d). The thoracic cavity was irrigated with 10,000 mL of saline using a pulse lavage irrigation system. Air leakage was not observed with the 20cmH<sub>2</sub>O during the intraoperative leak test. Therefore, we considered that the abscess was sufficiently reduced, and that filling of the omental flap and reinforcement of the muscle flap were not necessary. Three drainage tubes were placed over the ventral lung apex, dorsal lung apex, and diaphragm. The operative time was 151 min, and the blood loss volume was 300 mL. Immediately after surgery, air leakage was not observed. On postoperative day (POD) 1, type 1 respiratory failure did not improve; therefore, bronchial sputum toileting was performed. Chest CT showed poor drainage on the mediastinal side; therefore, the patient underwent fibrinolytic therapy with intrathoracic urokinase to promote lung expansion on PODs 2 and 5. The culture detected <italic>Streptococcus intermedius</italic> was detected in the intraoperative pleural fluid and pus in the LA. Therefore, <italic>Streptococcus intermedius</italic> was the causative bacterium, and antibiotics treatment was de-escalated from MPEM to sulbactam/ampicillin on POD 6. Signs of postoperative air leakage and infection of the implanted FPF were not observed (Fig. ##FIG##2##3##a, b). All drainage tubes were removed by POD 9. The patient was discharged on POD 12 and underwent careful observation as an outpatient. At 1 year and 2 months after surgery, empyema recurrence was not observed.</p>" ]	[ "<title>Acknowledgements</title>", "<p>We would like to thank Editage (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.editage.jp\">www.editage.jp</ext-link>) for English language editing.</p>", "<title>Author contributions</title>", "<p>YI participated in the surgery, conceived and conducted the study, and performed the literature search. MI and HU participated in the surgery. TM, SI, NM, and HU supervised the manuscript preparation and revision of the manuscript. All the authors have read and approved the final version of the manuscript.</p>", "<title>Funding</title>", "<p>None.</p>", "<title>Availability of data and materials</title>", "<p>All data generated or analyzed during this study are included in this published article.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par15\">Not applicable.</p>", "<title>Consent for publication</title>", "<p id=\"Par16\">Written informed consent was obtained from the patient for the publication of this report and its accompanying images.</p>", "<title>Competing interests</title>", "<p id=\"Par17\">All authors declare that they have no competing interests.</p>" ]	[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Chest radiography and computed tomography (CT) during the first visit. <bold>a</bold> Chest radiograph revealing collapse of the left lung and pleural effusion. Chest CT image showing, <bold>b</bold> multiple cavitated left pleural effusions and (c) a lung abscess in the left lower lobe</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Intraoperative findings. <bold>a</bold> The abscess wall was fragile and torn during decortication. <bold>b</bold>, <bold>c</bold> Free pericardial fat (FPF) was implanted in the ruptured abscess cavity. (d) Fibrin glue was injected in the abscess cavity and FPF was sutured to the abscess wall</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><p>Chest radiography and computed tomography (CT) after discharge. <bold>a</bold> Chest radiograph showing decreased transparency in the middle lung field because of the implanted free pericardial fat (FPF); however, other parts of the lungs were well expanded. <bold>b</bold> Chest CT image showing the implanted FPF within the abscess cavity as a low-density area (white arrow)</p></caption></fig>" ]	[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Reported cases of empyema associated with lung abscess rupture or perfolation</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Case</th><th align=\"left\">Age</th><th align=\"left\">Sex</th><th align=\"left\">Site of LA</th><th align=\"left\">Comorbidities</th><th align=\"left\">Culture</th><th align=\"left\">Approach</th><th align=\"left\">Decortication</th><th align=\"left\">Debridement</th><th align=\"left\">Treatment for fistula</th><th align=\"left\">References</th></tr></thead><tbody><tr><td align=\"left\">1</td><td align=\"left\">3</td><td align=\"left\">M</td><td align=\"left\">RML</td><td align=\"left\">–</td><td align=\"left\">Negative</td><td align=\"left\">Thoracotomy</td><td align=\"left\"> + </td><td align=\"left\">ND</td><td align=\"left\">Rt middle lobectomy</td><td align=\"left\">[##REF##9766366##5##]</td></tr><tr><td align=\"left\">2</td><td align=\"left\">18</td><td align=\"left\">M</td><td align=\"left\">LLL</td><td align=\"left\">Smith-Magenis syndrome, DM</td><td align=\"left\">Negative</td><td align=\"left\">Thoracotomy</td><td align=\"left\"> + </td><td align=\"left\">ND</td><td align=\"left\">omental flap</td><td align=\"left\">[##UREF##1##6##]</td></tr><tr><td align=\"left\">3</td><td align=\"left\">40</td><td align=\"left\">M</td><td align=\"left\"><p>RLL,</p><p>LLL</p></td><td align=\"left\">DM</td><td align=\"left\"><p>Rt: <italic>Streptococcal sp.</italic></p><p>Lt: Negative</p></td><td align=\"left\">VATS</td><td align=\"left\"> + </td><td align=\"left\">ND</td><td align=\"left\"><p>Rt S9: direct suture</p><p>Rt S10: EWS → pedicled intercostal muscle flap</p><p>Lt S10: free intercostal muscle flap</p></td><td align=\"left\">[##REF##33620526##7##]</td></tr><tr><td align=\"left\">4</td><td align=\"left\">48</td><td align=\"left\">M</td><td align=\"left\">LUL</td><td align=\"left\">HT, DM</td><td align=\"left\">ND</td><td align=\"left\">Bronchoscopy</td><td align=\"left\">-</td><td align=\"left\">-</td><td align=\"left\">EWS, fibrin glue</td><td align=\"left\">[##UREF##2##8##]</td></tr><tr><td align=\"left\">5</td><td align=\"left\">51</td><td align=\"left\">M</td><td align=\"left\">LUL</td><td align=\"left\">ankylosing spondylitis, ARDS</td><td align=\"left\"><italic>Streptococcus constellatus</italic></td><td align=\"left\">VATS</td><td align=\"left\">ND</td><td align=\"left\">ND</td><td align=\"left\">wedge resection</td><td align=\"left\">[##REF##37153891##9##]</td></tr><tr><td align=\"left\">6</td><td align=\"left\">57</td><td align=\"left\">M</td><td align=\"left\">LLL</td><td align=\"left\">DM, RA</td><td align=\"left\"><italic>Mycobacterium avium</italic></td><td align=\"left\">Thoracotomy</td><td align=\"left\">–</td><td align=\"left\"> + </td><td align=\"left\">pedicled intercostal muscle flap</td><td align=\"left\">[##REF##21674316##10##]</td></tr><tr><td align=\"left\">7</td><td align=\"left\">57</td><td align=\"left\">M</td><td align=\"left\">RML</td><td align=\"left\">septic shock</td><td align=\"left\"><italic>Prevotella sp.</italic></td><td align=\"left\">Thoracotomy</td><td align=\"left\">–</td><td align=\"left\"> + </td><td align=\"left\">EWS</td><td align=\"left\">[##REF##28293000##11##]</td></tr><tr><td align=\"left\">8</td><td align=\"left\">58</td><td align=\"left\">M</td><td align=\"left\">RUL</td><td align=\"left\">RF, left pneumonia due to the inhalation of pus</td><td align=\"left\"><p><italic>Streptococcus intermedius,</italic></p><p><italic>prevotella buccae</italic></p></td><td align=\"left\">Bronchoscopy</td><td align=\"left\">–</td><td align=\"left\">–</td><td align=\"left\">EWS</td><td align=\"left\">[##UREF##3##12##]</td></tr><tr><td align=\"left\">9</td><td align=\"left\">60</td><td align=\"left\">M</td><td align=\"left\">RLL</td><td align=\"left\">asthma, arrythmia, RF</td><td align=\"left\"><italic>Prevotella loescheii</italic></td><td align=\"left\">Thoracotomy</td><td align=\"left\">ND</td><td align=\"left\">ND</td><td align=\"left\">Rt middle and lower lobectomy</td><td align=\"left\">[##REF##16281864##13##]</td></tr><tr><td align=\"left\">10</td><td align=\"left\">62</td><td align=\"left\">M</td><td align=\"left\">RML</td><td align=\"left\">DM</td><td align=\"left\"><italic>Streptococcus Intermedius</italic></td><td align=\"left\">Fenestration</td><td align=\"left\">–</td><td align=\"left\"> + </td><td align=\"left\">EWS → VAC</td><td align=\"left\">[##UREF##4##14##]</td></tr><tr><td align=\"left\">11</td><td align=\"left\">63</td><td align=\"left\">M</td><td align=\"left\">LLL</td><td align=\"left\">liver cirrhosis, DM</td><td align=\"left\"><p><italic>Peptococcus sp.,</italic></p><p><italic>Eubacterium sp</italic></p></td><td align=\"left\">VATS</td><td align=\"left\"> + </td><td align=\"left\"> + </td><td align=\"left\">EWS</td><td align=\"left\">[##UREF##5##15##]</td></tr><tr><td align=\"left\">12</td><td align=\"left\">63</td><td align=\"left\">M</td><td align=\"left\">RML</td><td align=\"left\">-</td><td align=\"left\">Negative</td><td align=\"left\">VATS</td><td align=\"left\">–</td><td align=\"left\"> + </td><td align=\"left\">direct suture, PGA sheet, fibrin glue</td><td align=\"left\">[##REF##18323196##16##]</td></tr><tr><td align=\"left\">13</td><td align=\"left\">20</td><td align=\"left\">F</td><td align=\"left\">RUL</td><td align=\"left\">septic shock, pulseless VT, MOF, DM, pulmonary tuberculosis</td><td align=\"left\"><italic>Mycobacterium tuberculosis</italic></td><td align=\"left\">VATS</td><td align=\"left\"> + </td><td align=\"left\">ND</td><td align=\"left\">Rt upper lobectomy</td><td align=\"left\">[##REF##35868131##4##]</td></tr><tr><td align=\"left\">14</td><td align=\"left\">30</td><td align=\"left\">F</td><td align=\"left\">LLL</td><td align=\"left\">asthma, ARDS</td><td align=\"left\">Negative</td><td align=\"left\">VATS</td><td align=\"left\"> + </td><td align=\"left\">ND</td><td align=\"left\">direct suture</td><td align=\"left\">[##REF##20715468##17##]</td></tr><tr><td align=\"left\">15</td><td align=\"left\">55</td><td align=\"left\">F</td><td align=\"left\">RUL</td><td align=\"left\">HT</td><td align=\"left\"><italic>Streptococcus angionosus group</italic></td><td align=\"left\">Thoracotomy</td><td align=\"left\"> + </td><td align=\"left\">ND</td><td align=\"left\">ND</td><td align=\"left\">[##REF##37187631##18##]</td></tr><tr><td align=\"left\">16</td><td align=\"left\">75</td><td align=\"left\">F</td><td align=\"left\">RUL</td><td align=\"left\">DM</td><td align=\"left\"><italic>MRSA</italic></td><td align=\"left\">Fenestration</td><td align=\"left\">–</td><td align=\"left\"> + </td><td align=\"left\">EWS → pedicled omental and muscle flap, thoracoplasty</td><td align=\"left\">[##REF##20214353##19##]</td></tr><tr><td align=\"left\">17</td><td align=\"left\">87</td><td align=\"left\">F</td><td align=\"left\">RLL</td><td align=\"left\">microscopic polyangiitis</td><td align=\"left\"><italic>Pseudomonas aeruginosa</italic></td><td align=\"left\">Bronchoscopy</td><td align=\"left\">–</td><td align=\"left\">–</td><td align=\"left\">EWS</td><td align=\"left\">[##REF##36548445##20##]</td></tr><tr><td align=\"left\">18</td><td align=\"left\">74</td><td align=\"left\">F</td><td align=\"left\">LLL</td><td align=\"left\">HT, AP, dental caries, periodonitis</td><td align=\"left\"><italic>Streptococcus intermedius</italic></td><td align=\"left\">VATS → Thoracotomy</td><td align=\"left\"> + </td><td align=\"left\">–</td><td align=\"left\">FPF inplantation, direct suture, fibrin glue</td><td align=\"left\">Our case</td></tr></tbody></table></table-wrap>" ]	[]	[]	[]	[]	[]	[]	[ "<table-wrap-foot><p>AP: angina pectoris, ARDS: acute respiratory distress syndrome, DM: diabetes mellitus, EWS: endobronchial Watanabe spigot, F: female, FPF: free pericardial fat, HT: hypertension, LA: lung abscess, LLL: left lower lobe, Lt: left, LUL: left upper lobe, M: male, MOF: murtiple orgean failure, ND: not described, PGA: polyglycolic acid, RA: rhumatoid arthritis, RF: respiratory failuer, RML: right middle lobe, RLL: right lower lobe, Rt: right, RUL: right upper lobe, VAC: vacuum-assisted closure, VATS: video assisted thoracic surgery, VT: ventricular tachycardia</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher's Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"40792_2024_1814_Fig1_HTML\" id=\"MO1\"/>", "<graphic xlink:href=\"40792_2024_1814_Fig2_HTML\" id=\"MO2\"/>", "<graphic xlink:href=\"40792_2024_1814_Fig3_HTML\" id=\"MO3\"/>" ]	[]	[{"label": ["3."], "surname": ["Semih", "LoCicero", "Feins", "Colson", "Rocco"], "given-names": ["H", "J", "RH", "YL", "G"], "article-title": ["Bacterial infections of the lungs and bronchial compressive disorders"], "source": ["Shields\u2019 general thoracic surgery"], "year": ["2018"], "edition": ["8"], "publisher-loc": ["Philadelphia"], "publisher-name": ["Lippincott Williams & Wilkins"], "fpage": ["1036"], "lpage": ["1054"]}, {"label": ["6."], "surname": ["Araki", "Soma", "Tofuku"], "given-names": ["T", "R", "Y"], "article-title": ["A case of Smith-Magenis syndrome associated with type 2 diabetes mellitus and thoracic empyema"], "source": ["J Jpn Diab Soc"], "year": ["2002"], "volume": ["45"], "fpage": ["33"], "lpage": ["38"]}, {"label": ["8."], "surname": ["Terashima", "Usami", "Ito", "Mizuno", "Horio", "Saito"], "given-names": ["T", "N", "K", "H", "M", "Y"], "article-title": ["Bronchial occlusion with endobronchial Watanabe spigot and fibrin glue for empyema due to a ruptured lung abscess"], "source": ["J Jpn Soc Respor Endosc"], "year": ["2017"], "volume": ["39"], "fpage": ["268"], "lpage": ["272"]}, {"label": ["12."], "surname": ["Kumeda", "Saito", "Hara", "Takagi", "Agatsuma", "Ideura"], "given-names": ["H", "G", "D", "Y", "T", "G"], "article-title": ["A successful case of treatment using extracorporeal membrane oxygenation and bronchial occlusion by endobronchial Watanabe spigot for severe bronchopleural fistula"], "source": ["J Jpn Soc Respor Endosc"], "year": ["2021"], "volume": ["43"], "fpage": ["498"], "lpage": ["503"]}, {"label": ["14."], "surname": ["Yamaoka", "Kikuchi", "Yanagihara", "Sakai", "Goto", "Sato"], "given-names": ["M", "S", "T", "M", "Y", "Y"], "article-title": ["EWS and vacuum-assisted closure (VAC) therapy for the early closure of open-window thoracostomy in the treatment of pleural empyema with a fistula"], "source": ["Jpn J Chest Surg."], "year": ["2018"], "volume": ["32"], "fpage": ["46"], "lpage": ["51"], "pub-id": ["10.2995/jacsurg.32.46"]}, {"label": ["15."], "surname": ["Hachisuka", "Uomoto"], "given-names": ["Y", "M"], "article-title": ["VATS for pneumopyothorax due to ruptured lung abscess: a case report"], "source": ["Jpn J Chest Surg"], "year": ["2007"], "volume": ["21"], "fpage": ["64"], "lpage": ["9"], "pub-id": ["10.2995/jacsurg.21.064"]}]	{ "acronym": [ "LA", "FPF", "MEPM", "CT", "POD" ], "definition": [ "Lung abscess", "Free pericardial fat", "Meropenem", "Computed tomography", "Postoperative day" ] }	22	CC BY	no	2024-01-13 00:02:19	Surg Case Rep. 2024 Jan 11; 10:15	oa_package/48/84/PMC10781657.tar.gz
PMC10781658	38200360	[ "<title>Background</title>", "<p id=\"Par19\">Despite advances in modern medicine, sepsis remains a major cause of morbidity and mortality. Sepsis accounts for 20% of global deaths [##UREF##0##1##], and survivors often endure long-term physical, psychological, and cognitive impairments.</p>", "<p id=\"Par20\">Reporting sepsis epidemiology accurately is challenging due to evolving definitions, variations in reporting, demographic disparities, and discrepancies in healthcare resources [##REF##26414292##2##, ##REF##33492864##3##]. Estimates of sepsis cases range widely, from 19 to 48.9 million yearly [##REF##33492864##3##, ##REF##31763206##4##].</p>", "<p id=\"Par21\">According to the Centers for Disease Control and Prevention, at least 1.7 million adults in the U.S. develop sepsis each year, resulting in nearly 270,000 deaths. Global sepsis data analysis [##UREF##0##1##] indicates a significant rise in sepsis cases, reaching 11 million deaths and 48.9 million incident cases in 2017 (Figs. ##FIG##0##1##, ##FIG##1##2##). While age-standardized sepsis incidence dropped by 37.0% and mortality by 52.8% between 1990 and 2017, substantial regional differences persist. The study highlights a decrease in global sepsis burden but emphasizes the urgent need for intervention, particularly in areas with the lowest Socio-Demographic Index.</p>", "<p id=\"Par22\">In 2021–22, England and Wales reported over 100,000 emergency admissions with sepsis, with a mean patient age of 71 years [##REF##37451817##5##]. In England, sepsis represented one-third of admissions to adult ICUs [##REF##29121281##6##] and in China sepsis affected a fifth of patients admitted to the ICU [##REF##31804299##7##].</p>", "<p id=\"Par23\">Sepsis affects all age groups, but its incidence and mortality notably increase with advancing age, particularly in older adults who face elevated risks [##REF##29149187##8##, ##REF##28793102##9##]. In a Taiwanese nationwide study on sepsis, the incidence of sepsis in the oldest old (≥ 85 years) was 31-fold greater than the adult incidence (18–64 years) and threefold greater than the old (65–84 years) [##REF##29533311##10##].</p>", "<p id=\"Par24\">Due to an aging population, sepsis incidences are expected to rise. By 2050, about 16% of the global population will be aged 65 and above [##UREF##1##11##]. The most rapid increases in older populations are happening in developed countries, with a projected 140% rise in individuals aged 65 years and older by 2030, and those aged 85 years and above being the fastest-growing group [##UREF##1##11##–##REF##27492269##13##].</p>", "<p id=\"Par25\">Today, three key factors stand out: a global increase in sepsis cases [##UREF##0##1##, ##REF##11445675##14##], significant healthcare challenges from sepsis-related mortality and morbidity [##REF##26414292##2##, ##UREF##3##15##] and a notably rise in very old patients with sepsis due to the aging of population [##REF##28238055##16##, ##REF##31135504##17##]. Addressing these challenges requires standardized definitions, improved data collection, and better healthcare access.</p>", "<p id=\"Par26\">In this review we will underscore the factors that contribute to the increased susceptibility to sepsis and higher mortality risk in older patients. The focus advocates for a comprehensive strategy in sepsis management, emphasizing a holistic approach and personalized care that considers individual factors, such as frailty, comorbidities, and patient values. We consider \"older adults\" as those surpassing 65 years with 'very old' individuals being those over 85 years.</p>", "<title>Risk factors for sepsis</title>", "<p id=\"Par27\">Older individuals are particularly vulnerable to developing sepsis due to pre-existing comorbidities, compromised immune function, sarcopenia, diminished physiological reserves associated with aging, malnutrition, and polypharmacy (Fig. ##FIG##2##3##). In the subsequent discussion, we will focus on the key factors, with additional details available in a recent review [##REF##37542186##18##].</p>", "<p id=\"Par28\"><italic>Immunosenescence and inflammaging</italic> play a crucial role in making the older individuals more susceptible to sepsis [##REF##27592340##19##, ##REF##31148100##20##]. Immunosenescence involves a gradual decline in the immune system, especially T-cell function and inflammaging is characterized by persistent low-grade inflammation. Both processes are interconnected, forming a cycle that heightens susceptibility [##REF##27592340##19##, ##REF##29375577##21##–##REF##31608061##23##]. The immune system’s interaction with other systems, such as the neural or endocrine system, links declining immune function to frailty, sarcopenia, and malnutrition [##REF##27592340##19##, ##REF##31148100##20##]. Reduced insulation and lower metabolism compromise the immune system, making older individuals more vulnerable to infections and illnesses.</p>", "<p id=\"Par29\">Geriatric syndromes arising from impairments in multiple systems, result from a combination of age-related changes, underlying medical conditions, and environmental influences and significantly impact quality of life and increase susceptibility to infection.</p>", "<p id=\"Par30\"><italic>Frailty,</italic> a clinically recognizable state of increased vulnerability resulting from aging-associated decline, becomes more prevalent with age, impacting 25% of those over 65 and over 50% of patients over 80. It affects approximately 40% of older ICU patients and significantly impacts mortality and morbidity [##REF##35119497##24##–##REF##32384336##30##]. Incorporating frailty assessment into risk stratification can identify a vulnerable population that may benefit from targeted interventions.</p>", "<p id=\"Par31\"><italic>Sarcopenia</italic>, characterized by muscle decline, has a prevalence rate ranging from 11 to 50% in those aged 80 years and above [##REF##25241753##31##]. Aging disrupts muscle balance, triggering mechanisms, such as anabolic resistance, reduced IGF-1 signalling, mitochondrial dysfunction, inflammation, and oxidative stress, leading to muscle loss. Anabolic resistance diminishes muscle responsiveness to stimuli, causing reduced protein synthesis and muscle wasting. Immobilization in hospitalized older individuals results in a daily muscle mass reduction (0.5%) and strength decline (0.3–4.2%), impacting functional status and quality of life [##REF##32234291##32##]. Sepsis worsens sarcopenia by promoting inflammation, muscle wasting, and potential mitochondrial dysfunction [##REF##35535373##33##, ##REF##34078275##34##]. Sarcopenia is linked to various pathophysiological processes, increasing mortality risk, especially in critical illness [##REF##35535373##33##].</p>", "<p id=\"Par32\"><italic>Malnutrition and dehydration</italic> are widespread in older people, and obesity is an increasing problem [##UREF##7##35##]. Malnutrition, linked to reduced food intake, underlying health issues, and nutrient absorption problems, contributes to functional decline, sarcopenia, slow wound healing, and adverse outcomes, such as increased infection rates and prolonged hospital stays [##REF##27703567##36##]. Prevalence rates vary but can exceed two-thirds in hospitalized patients [##UREF##7##35##]. Dehydration prevalence can rise to over one-third in more vulnerable individuals [##UREF##7##35##]. Preventive measures, ensuring adequate nutrition and hydration, are essential. In hospital settings interventions such as a protein-rich diet, nutritional supplements, sedation protocols with short-acting drugs and early mobilization can improve outcomes. Routine screenings for prompt identification of potential malnutrition risks in geriatrics patients are recommended [##UREF##7##35##].</p>", "<p id=\"Par33\"><italic>Cognitive impairment</italic>, is associated with brain changes, including reduced grey and white matter volume, impaired blood flow, altered neurotransmitter activity, and a more permeable blood–brain barrier [##REF##29278283##37##]. It involves memory, attention, and cognitive deterioration potentially progressing to dementia at a rate of 10–15% per year. Critical illness often induces psychological symptoms, sleep disturbances, delirium, and cognitive impairment, all associated with higher mortality rates [##REF##21617624##38##]. Delirium independently increases mechanical ventilation duration, ICU and hospital stays, health care costs, long-term cognitive impairment, and mortality risk. Non-pharmacological measures for delirium prevention are recommended [##REF##30113379##39##].</p>", "<p id=\"Par34\"><italic>The impact of comorbidities</italic> on septic patients is substantial. Malignancies, diabetes mellitus, and dysfunctions in cardiac, renal, liver, or pulmonary systems contribute to poorer outcomes. Notably, 78% of septic patients have at least one comorbidity [##REF##37642964##40##], and 60% exhibit three or more [##REF##32294254##41##]. On average, patients aged 65 to 84 have 2.6 ± 2.2 comorbidities, while those aged 85 or over have 3.6 ± 2.3 [##REF##22579043##42##].</p>", "<p id=\"Par35\">Moreover, older individuals face other vulnerabilities, including altered vaginal flora in women due to reduced estrogen levels, urinary issues from prostatic hypertrophy in men, compromised skin integrity, diminished cough reflex, and swallowing difficulties, all contributing to increased infection susceptibility. The use of <italic>medical instruments and institutionalization</italic> further heightens sepsis risk particularly due to the prevalence of multidrug-resistant (MDR) pathogens in healthcare settings.</p>", "<p id=\"Par36\">Finally, aging shows significant individual heterogeneity, with some maintaining resilience and an active lifestyle, while others face higher susceptibility to diseases and disabilities. Understanding resilience, the ability to withstand and recover from stressors, is crucial for addressing chronic diseases and promoting healthy aging [##REF##35796977##43##]. Lifestyle interventions, such as personalized exercise, and nutrition may help older individuals better adapt to the biological changes associated with aging and potentially reduce their susceptibility to infections [##REF##32234291##32##, ##REF##29580886##44##].</p>", "<title>Diagnosis of sepsis</title>", "<p id=\"Par37\">Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection [##REF##26903338##45##]. Organ dysfunction is defined as an acute increase of two or more points in the Sequential Organ Failure Assessment (SOFA) score [##REF##8844239##46##]. Septic shock, a severe form of sepsis with circulatory, cellular, and metabolic dysfunction, carries a higher risk of mortality compared to sepsis alone [##REF##26903338##45##, ##REF##26903335##47##].</p>", "<p id=\"Par38\">Diagnosing sepsis in older individuals can be challenging due to atypical presentations and subtle symptoms [##REF##21450180##48##–##UREF##8##50##] (Fig. ##FIG##3##4##). Timely recognition is crucial for proper management and prevention of adverse outcomes. Therefore, a comprehensive evaluation, including a detailed history, thorough physical examination, and a heightened suspicion for infections is necessary. Biomarkers can provide fast and accurate early diagnosis compared to traditional microbiology tests, reducing the risk of negative results due to prior antibiotic treatment.</p>", "<p id=\"Par39\">In older individuals, potentially life-threatening infections may manifest through various behavioural changes, including sudden confusion, perception disorders, psychomotor agitation, or lethargy. Physical symptoms such as loss of appetite, dehydration, dizziness, falls, and incontinence can serve as sole indicators. Notably, fever, a common sign of infection, is absent in 30–50% of older adults, who may exhibit a reduced febrile response to infections, such as bacteraemia, pneumonia, endocarditis, and meningitis [##UREF##9##51##–##UREF##10##53##]. The conventional definition of fever may not apply due to the lower baseline body temperature in older adults, influenced by diminished cytokine production, reduced hypothalamic receptor sensitivity, and impaired adaptation of peripheral thermoregulation [##UREF##9##51##, ##UREF##11##54##]. The use of medications, such as non-steroidal anti-inflammatory drugs, corticosteroids, beta-receptor blockers, antihistamines, and ranitidine, further dampens the inflammatory response. Therefore, assessing temperature changes from their baseline proves more useful than relying on absolute values.</p>", "<p id=\"Par40\">Regarding organ dysfunction related to sepsis, the SOFA score serves as a tool to assess organ failure and functions both diagnostically and prognostically [##REF##26903338##45##]. It is crucial to consider the interplay between pre-existing comorbidities and acute organ dysfunction when evaluating organ failure.</p>", "<p id=\"Par41\">Biomarkers contribute to antibiotic stewardship by minimizing unnecessary prescriptions. However, their performance may differ in older patients due to comorbidities and chronic inflammation. Clinical judgment and comprehensive assessment are necessary when using biomarkers in this population [##REF##36995460##55##].</p>", "<p id=\"Par42\"><italic>Lactate measurements</italic> indicate tissue hypoperfusion and sepsis severity. Levels of 2 mmol/L and higher predict mortality regardless of age, but factors such as dehydration and anaemia, common in older individuals, can also increase lactic acid levels [##REF##34184576##56##, ##REF##29595662##57##]. Comorbidities, such as heart, liver, renal or respiratory dysfunction, may contribute to increased lactate levels due to factors, such as reduced cardiac output, impaired liver function, compromised kidney clearance, and inadequate tissue oxygenation elevating the risk of developing type B lactic acidosis or hinder lactate clearance.</p>", "<p id=\"Par43\"><italic>C-reactive protein</italic> (CRP) has low specificity in this population. There is growing evidence suggesting that CRP is not only an inflammatory biomarker but is also associated with age-related conditions, such as cardiovascular disease, hypertension, diabetes mellitus, and kidney disease [##REF##28378496##58##].</p>", "<p id=\"Par44\"><italic>Serum procalcitonin</italic> (PCT) is a valuable biomarker for bacterial infections and sepsis prognosis [##REF##36517740##59##–##REF##32148921##62##]. It can be applied in older patients using similar cutoff values as in younger patients [##REF##33993243##63##] demonstrating comparable performance and higher diagnostic accuracy than other markers [##REF##36995460##55##]. Serial PCT measurements can guide antibiotic therapy duration, reducing exposure without compromising recovery. However, clinical and microbiological assessments should complement PCT levels due to potential false results [##REF##32986609##64##].</p>", "<p id=\"Par45\">An ideal biomarker with high clinical accuracy for sepsis diagnosis is still needed. Novel biomarkers, such as <italic>Pancreatic Stone Protein (PSP)</italic> [##REF##33879189##65##–##UREF##12##67##]<italic>, Presepsin, and Mid-regional Pro-adrenomedullin (MR-proADM)</italic> [##REF##37147598##68##], showing early elevation in response to sepsis, are under study. Future clinical trials are necessary to further verify their utility in clinical practice.</p>", "<title>Sources of infection</title>", "<p id=\"Par46\">Infections are more prevalent in older individuals correlating with increased hospitalization and mortality, particularly in those over 85 years [##REF##18500201##69##]. Lower respiratory tract and urinary tract infections (UTIs) are predominant both in community and health care associated infections (HAI) [##REF##29079157##49##, ##REF##34067797##70##]. Among 308 elderly individuals, respiratory tract infections represented 49.7%, urinary tract infections (UTIs) 33.8%, blood stream infections (BSIs) 21.1%, and surgical site infections 4.9% [##REF##28793102##9##].</p>", "<p id=\"Par47\">Pneumonia, a severe respiratory tract infection, can be challenging to diagnose because of atypical symptoms and difficulty in obtaining accurate chest radiographs due to physical limitations. Lung ultrasound and CT scanner can aid in the diagnosis, while bronchoscopy and BAL are recommended for immunocompromised and more critical patients. Aspiration pneumonia, with a higher mortality rate, is prevalent among older adults especially if impaired swallowing, intubation or in general anaesthesia’s postoperative phase. About 76% of aspiration pneumonia-related deaths occur in patients aged 75 years or older [##REF##35099619##71##]. Common microorganisms include Streptococcus pneumoniae, Staphylococcus aureus, Haemophilus influenzae, and Enterobacteriaceae. In case of poor dental health, anaerobic microorganisms should also be considered [##UREF##13##72##]. In HAI the pathogens involved are mainly gram-negative bacteria (many of which are MDR) [##REF##34067797##70##]. COVID-19 was a major complication in the older population, leading to high mortality rates, particularly among those requiring invasive ventilation [##REF##34176479##73##].</p>", "<p id=\"Par48\">UTI diagnosis is challenging due to overlapping symptoms, the presence of asymptomatic bacteriuria (ASB) and difficulties in obtaining uncontaminated urine. Approximately 15–50% of patients aged 80 and older have ASB, and over 50% of antibiotic treatments for ASB are unnecessary [##REF##12409046##74##–##REF##30220239##76##]. In urinary sepsis, E. coli is the most common microorganism, but catheter-associated infections are polymicrobial, including <italic>Proteus</italic> spp., <italic>Klebsiella</italic> spp., <italic>E. faecalis</italic> and <italic>Pseudomonas</italic> spp. [##REF##37630518##77##].</p>", "<p id=\"Par49\">BSIs are common and fatal in older patients, with around half of all cases occurring in this age group. Case fatality rates peak at 50–60% for individuals over 85 years. Older people face increased risks for Gram-negative infections, urinary source infections, and antimicrobial resistance, frequently healthcare-associated [##REF##26684392##78##–##REF##33413134##80##]. MDR microorganisms, pose significant challenges and may lead to treatment failures. In a Spanish cohort with healthcare-associated bacteremic UTIs, over 61% had MDR microorganisms, and over 75% were elderly [##REF##34626347##81##]. Removing unnecessary urinary catheters could reduce a significant portion of these BSIs.</p>", "<p id=\"Par50\">Skin and soft tissue infections (SSTIs) are prevalent in older adults, exhibiting a wide clinical spectrum from mild infections to life-threatening diseases. Prognosis worsens with comorbidities, such as heart failure, diabetes mellitus, and malnutrition. SSTIs pose a notable challenge in treatment, especially in acute care hospitals and long-term care facilities, where their prevalence is significant (10.9% and 17%, respectively) [##REF##34067797##70##, ##REF##33676996##82##, ##REF##36718942##83##]. Common bacteria associated with SSTIs in this demographic include Streptococcus spp., Staphylococcus spp., and Pseudomonas aeruginosa. Screening for risk factors associated with methicillin-resistant <italic>Staphylococcus aureus</italic> (MRSA) is crucial.</p>", "<p id=\"Par51\">Hospital-acquired infections (HAIs) pose serious health risks to the older population, resulting in longer hospital stays, extended antibiotic therapy, significant mortality, and higher healthcare costs. HAIs are the primary cause of death in one-third of individuals aged 65 and over. MDR microorganisms make infection prevention and control measures crucial [##REF##34067797##70##].</p>", "<title>Management of sepsis</title>", "<p id=\"Par52\">Treatment of older individuals with sepsis/septic shock should adhere to the Surviving Sepsis Campaign (SSC) International Guidelines [##REF##34599691##84##], but the following items require special attention.</p>", "<title>Antibiotic therapy</title>", "<p id=\"Par53\">Empirical antibiotic should consider common pathogens, their susceptibility to antimicrobials and resistance patterns. The risk of infections by MDR microorganisms is notable in this demographic due to frequent healthcare exposure [##REF##34067797##70##]. In addition, older individuals face an elevated risk of fungal infections due to age-related changes, compromised immune status, catheter use, prolonged antibiotic use, and treatments, such as corticosteroids and chemotherapy [##REF##24043935##85##, ##REF##29228380##86##].</p>", "<p id=\"Par54\">Selecting and dosing antibiotics is challenging due to factors, such as comorbidities, drug pharmacokinetics (PK) and pharmacodynamics (PD), polypharmacy and risk of drug interactions [##REF##32165285##87##, ##REF##37166074##88##]. Age-related changes in organ function, body composition, renal clearance, hepatic metabolism, and drug distribution significantly influence antibiotic PK and PD [##REF##29079157##49##, ##REF##17631229##89##, ##REF##36976501##90##]. With aging, the decrease in body water percentage reduces the distribution volume for hydrophilic drugs (e.g., β-lactams, glycopeptides, aminoglycosides, azoles), leading to a faster increase in plasma concentrations. Conversely, a relative increase in adipose tissue raises the distribution volume for lipophilic drugs (e.g., macrolides, fluoroquinolones), prolonging their half-life and leading to lower tissue concentrations [##REF##36976501##90##]. Age-related liver and renal declines affect drug half-life and elimination [##REF##32165285##87##, ##UREF##15##91##–##REF##26687340##94##]. Adjustments for antibiotics in reduced renal function involve considering bacterial killing type. For concentration-dependent antibiotics, increase dosing intervals to prevent overdosing; for time-dependent ones, reduce the dose while maintaining the interval.</p>", "<p id=\"Par55\">Morphological and functional changes such as delayed gastric emptying, reduced splanchnic blood flow and altered gastric pH can affect the bioavailability of orally administered drugs [##REF##32165285##87##, ##REF##17631229##89##, ##REF##36976501##90##, ##UREF##16##92##].</p>", "<title>Resuscitation</title>", "<p id=\"Par56\">In fluid resuscitation and hemodynamic support, careful fluid management is crucial, considering comorbidities and age-related changes in autoregulation. While guidelines propose a target mean arterial pressure (MAP) of ≥ 65 mm Hg, older patients with chronic hypertension may require higher MAP targets to prevent acute kidney injury [##REF##24635770##95##, ##REF##30535664##96##]. Dehydration is common in older adults, often necessitating an initial 500 mL crystalloid bolus. However, protocolized resuscitation, such as 30 mL/kg of intravenous crystalloid within 3 h, may be detrimental in patients with cardiac impairment or chronic kidney disease [##REF##37451817##5##]. Excessive fluid therapy can lead to impaired outcomes, emphasizing the need for a dynamic evaluation of fluid response. Customized assessment of perfusion indicators, including mental status, diuresis, circulatory assessment, pulse rate, blood pressure, capillary refill, and point-of-care echocardiography, is crucial for monitoring and treatment decisions. Initiating de-resuscitation promptly with diuretics is essential.</p>", "<p id=\"Par57\">The ideal hemoglobin transfusion threshold in older septic patients is undefined and may differ from that in young adults. Anemia is increasingly prevalent in the aging population, affecting over 10% of those aged 65 and older, with nearly two-thirds of critically ill patients in ICUs experiencing anemia. In sepsis, anemia's multifactorial causes include reduced red blood cell production, stress-induced bleeding, hemodilution, recurrent blood withdrawal, impaired iron metabolism and hemolysis. A study of 815 older septic patients revealed over 20% had hemoglobin levels below 10 g/dL on admission, doubling during the first week. Although initial hemoglobin strongly correlated with in-hospital mortality, blood transfusions, administered to 8.3% of patients, were not an independent predictor of mortality [##REF##27737630##97##] A recent meta-analysis focusing on older adults suggests higher hemoglobin thresholds result in lower mortality and fewer cardiac complications, considering age-related declines in cardiac output affecting oxygen delivery [##REF##31129009##98##]. Ongoing debates and trials explore anemia management, transfusion thresholds, and frequency.</p>", "<title>Additional factors</title>", "<p id=\"Par58\">Individualized sedation protocols, short-acting drugs, and nonpharmacologic approaches for managing pain, agitation and delirium significantly enhance outcomes in critically ill adults [##REF##30113379##39##]. The PADIS guidelines, crucial for all patients, are especially important for older individuals. They advocate for shorter mechanical ventilation (MV), early mobilization, and notably contribute to reducing sarcopenia and delirium incidence in older patients. Delirium rates can reach 80% in ventilated older patients, compared to 33% in general medical units, significantly increasing the risk of persistent cognitive impairment post-discharge. Up to 70% may experience prolonged cognitive impairment within a year post-hospitalization, with around 10% developing dementia [##UREF##17##99##, ##REF##25665067##100##]. Ventilated patients face a 30% higher likelihood of needing assistance with activities of daily living (ADLs) compared to non-ventilated individuals.</p>", "<p id=\"Par59\">Non-invasive ventilation (NIV) reduces risks associated with mechanical ventilation and eases discomfort in critically ill older patients. While guidelines primarily recommend NIV for acute COPD exacerbation with hypercapnia and acute respiratory failure due to pulmonary oedema, it is not the preferred initial therapy for hypoxemic respiratory failure from pneumonia, because the potential need for intubation post-NIV failure carries severe clinical implications and a high risk of death. An analysis [##REF##37698708##101##] compared NIV as the primary mode of respiratory support in two large observational studies with 1986 patients aged ≥ 80 (1292 from the VIP2 study, pre-pandemic era and 694 from the COVIP study, during pandemic). Those with COVID-19 ARDS treated primarily with NIV were less likely to survive 30 days after ICU admission, despite being less frail. This discrepancy may be linked to the study population, as VIP2 included patients with respiratory failure from COPD or pulmonary oedema. In addition, the risk of NIV failure quadrupled during the COVID-19 pandemic.</p>", "<p id=\"Par60\">Steroid use in sepsis is a subject of debate due to conflicting evidence regarding its impact on mortality [##REF##31808551##102##–##REF##30575845##104##]. The SSC guidelines recommend intravenous hydrocortisone at a dose of 200 mg per day if adequate fluid resuscitation and vasopressor therapy fail to restore hemodynamic stability or if adrenal impairment is suspected. Gradual reduction is advised when vasopressor support is no longer needed. The decision to use steroids in septic older patients should be individualized, considering the patient's overall health, comorbidities, and the specific circumstances of their sepsis.</p>", "<p id=\"Par61\">Finally, Impaired glucose control, thrombotic events and stress ulcers are more frequent in the older population. Therefore, glucose control should be monitored, and insulin therapy should be initiated promptly if hyperglycaemia is detected, although optimal target levels are not well-defined. Pharmacologic thromboembolic prophylaxis with LMWH, considering renal function and bleeding risks, as well as stress ulcer prophylaxis [##REF##31950977##105##], is recommended for older patients with sepsis.</p>", "<p id=\"Par62\">In addition to specific sepsis treatments, incorporating multidisciplinary interventions is crucial [##REF##34800286##106##–##REF##37817072##108##]. Utilizing a comprehensive geriatric assessment to understand an older patient’s medical, psychosocial, and functional capabilities can enhance their functional status, prevent institutionalization, and reduce mortality for those admitted to the hospital. High-quality geriatric nursing, including falls prevention, nutrition, and physiotherapy, remains important beyond the acute illness phase.</p>", "<title>Outcomes</title>", "<p id=\"Par63\">Sepsis has a profound impact on the senior population, leading to significant morbidity and mortality [##REF##32294254##41##, ##REF##37630518##77##, ##REF##26925430##109##]. The financial strain on healthcare systems is significant, with extensive healthcare resource utilization both before and after ICU admission [##REF##21963582##110##–##REF##28114505##115##].</p>", "<p id=\"Par64\">In patients aged ≥ 65, in-hospital mortality ranges from 30 to 60%, escalating to 40–80% in those aged 80 and above [##REF##29079157##49##]. A systematic review of very old septic patients in the ICU reports mortality rates of 43% in the ICU, 47% in the hospital, and 68% one year after ICU admission [##UREF##20##116##]</p>", "<p id=\"Par65\">An analysis of the Intensive Care Over Nations (ICON) database, focusing on patients above 50 years, reveals age-related differences in sepsis outcomes. Hospital mortality increases with age, doubling in patients over 80 compared to those under 50 years (49.3% vs. 25.2%, <italic>p</italic> < 0.05). Mortality sees a maximum rate increase of about 0.75% per year between the ages of 71 and 77 years. Multilevel analysis confirms age > 70 years as an independent risk factor for mortality [##REF##30802758##117##].</p>", "<p id=\"Par66\">Despite age often being considered an independent risk factor for mortality and morbidity [##REF##12700374##118##–##REF##21436163##120##], emerging research underscores the crucial roles of other factors, such as frailty, disease severity, and comorbid conditions [##REF##28936626##26##, ##REF##33744918##121##–##REF##36565961##125##]. Post-hoc analyses of the VIP-1 and VIP-2 studies, examining patients aged 80 and over admitted to the ICU with sepsis, show ICU mortality rates of 31% and 41%, with 30-day and 6-month mortality rates of 45% and 54%, respectively [##REF##33744918##121##, ##REF##32406016##122##] (Table ##TAB##0##1##). Sepsis as admission diagnosis did not maintain an independent link to mortality after adjusting for organ dysfunction. Frailty, advanced age, and SOFA score emerged as key independent prognostic factors for adverse outcomes (Table ##TAB##1##2##).</p>", "<p id=\"Par67\">Advancements in sepsis management have led to a reduction in sepsis-associated mortality. [##REF##26414292##2##, ##REF##29533311##10##, ##REF##18492971##126##–##REF##31870317##131##], even among the older population [##REF##37572240##132##]. However, older sepsis survivors face worse long-term outcomes, including greater cognitive and functional decline, an increase risk of hospital readmission, and a higher likelihood of discharge to long-term care facilities [##REF##37542186##18##, ##REF##28114505##115##, ##REF##23765236##133##–##REF##29297082##135##].</p>", "<p id=\"Par68\">Post-intensive care syndrome (PICS) symptoms, prevalent among older sepsis survivors, include muscle weakness, fatigue, cognitive decline, sleep disturbances, emotional distress, and swallowing problems [##REF##36565961##125##, ##UREF##23##136##, ##UREF##24##137##]. Another term, possibly more specific, is post-sepsis syndrome (PSS) [##REF##37795206##138##]. Factors such as pre-existing co-morbidity and frailty, polypharmacy, delirium during hospitalization and injury induced by sepsis [##REF##30478708##134##, ##REF##37795206##138##] can exacerbate outcomes.</p>", "<p id=\"Par69\">Ongoing efforts to improve sepsis management, including early recognition, prompt source control, and timely antibiotic administration are crucial. In addition, adopting a multi-faceted approach to improve long-term outcomes for survivors is essential.</p>", "<title>Goals of care</title>", "<p id=\"Par70\">Predicting survival or future quality of life for older individuals poses challenges due to the substantial biological and functional heterogeneity in this demographic. Ethical and legal frameworks vary globally, influencing diverse management approaches among healthcare professionals shaped by geographical locations and cultures. In the absence of robust evidence guiding patient management, decisions regarding the proportionality of intensive care often stem from personal preferences and experience [##REF##37884827##139##].</p>", "<p id=\"Par71\">Key criteria for ICU admission include the condition's reversibility, emphasizing both survival and maintaining a similar quality of life. Medical treatment should align with the patient's wishes and prioritize their well-being, considering the burden vs. the chance of recovery. Recognizing the patient’s perspective on aging, health, and disease is crucial, as some prioritize quality of dying over life-prolonging measures [##REF##32406016##122##, ##REF##28220233##127##, ##REF##37572240##132##]. In uncertain cases, a therapeutic trial is recommended, with its duration remaining undefined and contingent on the patient’s response. If irreversibility becomes clear, discussions with the patient, surrogates, and colleagues guide decisions on excluding treatments causing suffering. Divergent opinions require additional time for clarity.</p>", "<p id=\"Par72\">Decisions to limit life-sustaining treatment (LST) should account for baseline status, quality of life, survival potential, functional outcomes, and treatment burden. Mousai et al. [##REF##37162595##140##] illustrate that integrating clinical phenotypes with cultural factors and information about critical care course enhances predictive discrimination accuracy for LST in very old ICU patients. Clinicians can make these decisions either before ICU admission or as the patient’s condition evolves. Family involvement and regular discussions about the patient’s condition are essential. A framework encompassing physical and cognitive status, quality of life, survival likelihood, functional performance, preferences, and treatment burden guides decisions for intensive care in older patients (Fig. ##FIG##4##5##) [##REF##37542186##18##, ##REF##34599691##84##, ##REF##30478708##134##].</p>" ]	[]	[]	[]	[ "<title>Conclusions</title>", "<p id=\"Par73\">Sepsis poses a significant threat to the senior population. However, current research on this demographic remains insufficient. It is imperative to raise awareness, educate healthcare professionals, implement preventive measures, and deliver timely and appropriate care to improve outcomes.</p>", "<p id=\"Par74\">The insights from the VIP-1 and VIP-2 studies prompt a reassessment of sepsis as a standalone contributor to mortality, emphasizing the importance of understanding and addressing comorbid geriatric conditions to enhance patient resilience and overall prognosis. In addition, it is crucial to inquire about the patient's preferences and establish a personalized treatment plan that considers their potential for recovery with an acceptable HRQoL and functional outcomes.</p>", "<p id=\"Par75\">The aim ahead is to recognize the gaps and limitations in current research while determining short- and long-term priorities. These priorities should extend beyond merely reducing sepsis mortality to gaining insights into, and enhancing, the HRQoL of sepsis survivors.</p>" ]	[ "<p id=\"Par1\">Sepsis is a significant public health concern, particularly affecting individuals above 70 years in developed countries. This is a crucial fact due to the increasing aging population, their heightened vulnerability to sepsis, and the associated high mortality rates. However, the morbidity and long-term outcomes are even more notable. While many patients respond well to timely and appropriate interventions, it is imperative to enhance efforts in identifying, documenting, preventing, and treating sepsis. Managing sepsis in older patients poses greater challenges and necessitates a comprehensive understanding of predisposing factors and a heightened suspicion for diagnosing infections and assessing the risk of sudden deterioration into sepsis. Despite age often being considered an independent risk factor for mortality and morbidity, recent research emphasizes the pivotal roles of frailty, disease severity, and comorbid conditions in influencing health outcomes. In addition, it is important to inquire about the patient's preferences and establish a personalized treatment plan that considers their potential for recovery with quality of life and functional outcomes. This review provides a summary of the most crucial aspects to consider when dealing with an old critically ill patient with sepsis.</p>", "<title>Keywords</title>" ]	[]	[ "<title>Acknowledgements</title>", "<p>We thank all the researchers who have dedicated their time to the older population.</p>", "<title>Author contributions</title>", "<p>MI, AC and LH together drafted, wrote, and revised the manuscript. AA contributed to the writing. All authors read and approved the final manuscript.</p>", "<title>Funding</title>", "<p>AC acknowledges receiving financial support from Instituto de Salud Carlos III (ISCIII; Sara Borrell 2021: CD21/00087).</p>", "<title>Availability of data and materials</title>", "<p>Not applicable.</p>", "<title>Declarations</title>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par76\">Not applicable.</p>", "<title>Consent for publication</title>", "<p id=\"Par77\">Not applicable.</p>", "<title>Competing interests</title>", "<p id=\"Par78\">None of the authors have conflicts of interest; both authors have read the current “Instructions to authors” and accept the conditions posed therein. This manuscript is original and has not been and will not be simultaneously submitted elsewhere for publication. None of the material from this study is included in another manuscript, has been published previously, or has been posted on the internet.</p>" ]	[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Incident sepsis cases by age group and underlying cause category, both sexes, all locations, 2017. Bars represent 95% uncertainty intervals. Reproduced from (1). Published under the CC BY 4.0 license</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>Percentage of all sepsis-related deaths in each underlying cause category, by age group and for both sexes, in 2017. Bars represent 95% uncertainty intervals. Reproduced from (1). Published under the CC BY 4.0 license</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><p>Risk factors for sepsis in older adults. Older adults face an elevated risk of sepsis due to several factors, including aging itself, comorbidities, and a weakened immunity. The interplay between their general health and sepsis severity significantly influences both short- and long-term outcomes, emphasizing the need for comprehensive assessment and personalized treatment strategies</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><p>Clinical picture in older patients may be ambiguous</p></caption></fig>", "<fig id=\"Fig5\"><label>Fig. 5</label><caption><p>Triage considerations for the older septic patient. <italic>HRQoL</italic> Health-Related Quality of Life, <italic>TLT</italic> Time Limited (ICU) Trial</p></caption></fig>" ]	[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Characteristics of older patients (≥ 80 years) admitted to the ICU with sepsis diagnosis in VIP-1 and VIP-2 studies</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Cohorts</th><th align=\"left\">Sepsis VIP-1</th><th align=\"left\">Sepsis VIP-2</th></tr></thead><tbody><tr><td align=\"left\"><italic>N</italic> (%)</td><td align=\"left\">493/3869 (12.7%)</td><td align=\"left\">532/3596 (14.8%)</td></tr><tr><td align=\"left\">Age (years)</td><td align=\"left\">83 (81–86)</td><td align=\"left\">84 (81–86)</td></tr><tr><td align=\"left\">Gender (male)</td><td align=\"left\">265 (53.8%)</td><td align=\"left\">298 (56%)</td></tr><tr><td align=\"left\">SOFA score at admission</td><td align=\"left\">9 (6–12)</td><td align=\"left\">9 (6–11)</td></tr><tr><td align=\"left\">ICU LOS (days)</td><td align=\"left\">3.54 (1.5–8)</td><td align=\"left\">4.77 (2–9)</td></tr><tr><td align=\"left\" colspan=\"3\">Frailty (CFS)</td></tr><tr><td align=\"left\"> Fit (CFS 1–3)</td><td align=\"left\">165 (33.5%)</td><td align=\"left\">195 (36.7%)</td></tr><tr><td align=\"left\"> Vulnerable (CFS 4)</td><td align=\"left\">76 (15.4%)</td><td align=\"left\">89 (16.7%)</td></tr><tr><td align=\"left\"> Frail (5–9)</td><td align=\"left\">252 (51.1%)</td><td align=\"left\">248 (46.6%)</td></tr><tr><td align=\"left\" colspan=\"3\">ICU interventions</td></tr><tr><td align=\"left\"> Mechanical ventilation</td><td align=\"left\">234 (47.5%)</td><td align=\"left\">260 (49%)</td></tr><tr><td align=\"left\"> Non-invasive ventilation</td><td align=\"left\">108 (21.9%)</td><td align=\"left\">86 (16.2%)</td></tr><tr><td align=\"left\"> Vasoactive drugs</td><td align=\"left\">405 (82.2%)</td><td align=\"left\">456 (85.9%)</td></tr><tr><td align=\"left\"> Renal replacement techniques</td><td align=\"left\">86 (17.4%)</td><td align=\"left\">109 (20.6%)</td></tr><tr><td align=\"left\" colspan=\"3\">Limitations of care</td></tr><tr><td align=\"left\"> Withholding</td><td align=\"left\">108 (21.9%)</td><td align=\"left\">186 (35.6%)</td></tr><tr><td align=\"left\"> Withdrawing</td><td align=\"left\">76 (15.4%)</td><td align=\"left\">79 (15.1%)</td></tr><tr><td align=\"left\" colspan=\"3\">Mortality</td></tr><tr><td align=\"left\"> ICU</td><td align=\"left\">154 (31.2%)</td><td align=\"left\">166 (41.4%)</td></tr><tr><td align=\"left\"> 30 days</td><td align=\"left\">220 (44.6%)</td><td align=\"left\"/></tr><tr><td align=\"left\"> 6 months</td><td align=\"left\"/><td align=\"left\">286 (54%)</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Multivariate analysis (Cox). Predictors of 30-day mortality (VIP-1 study) and 6-month mortality (VIP-2 study) in older patients (≥ 80 years), admitted to the ICU with sepsis</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\">30-day mortality<break/>HR (95% CI)</th><th align=\"left\"><italic>P</italic> value</th><th align=\"left\">6-month mortality<break/>HR (95% CI)</th><th align=\"left\"><italic>P</italic> value</th></tr></thead><tbody><tr><td align=\"left\">Age (per 5-year increase)</td><td char=\"–\" align=\"char\">1.16 (1.09–1.25)</td><td char=\".\" align=\"char\">< 0.0001</td><td char=\"–\" align=\"char\">1.16 (1.09–1.25)</td><td char=\".\" align=\"char\">< 0.0001</td></tr><tr><td align=\"left\">Frailty (CFS > 4)</td><td char=\"–\" align=\"char\">1.47 (1.31–1.66)</td><td char=\".\" align=\"char\">< 0.0001</td><td char=\"–\" align=\"char\">1.34 (1.18–1.51)</td><td char=\".\" align=\"char\">< 0.0001</td></tr><tr><td align=\"left\">SOFA score (per one-point increase)</td><td char=\"–\" align=\"char\">1.13 (1.12–1.14)</td><td char=\".\" align=\"char\">< 0.0001</td><td char=\"–\" align=\"char\">1.16 (1.14–1.17)</td><td char=\".\" align=\"char\">< 0.0001</td></tr><tr><td align=\"left\">Sepsis</td><td char=\"–\" align=\"char\">0.99 (0.86–1.15)</td><td char=\".\" align=\"char\">0.92</td><td char=\"–\" align=\"char\">0.89 (0.77–1.02)</td><td char=\".\" align=\"char\">0.09</td></tr></tbody></table></table-wrap>" ]	[]	[]	[]	[]	[]	[]	[ "<fn-group><fn><p><bold>Publisher's Note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"13613_2023_1233_Fig1_HTML\" id=\"MO1\"/>", "<graphic xlink:href=\"13613_2023_1233_Fig2_HTML\" id=\"MO2\"/>", "<graphic xlink:href=\"13613_2023_1233_Fig3_HTML\" id=\"MO3\"/>", "<graphic xlink:href=\"13613_2023_1233_Fig4_HTML\" id=\"MO4\"/>", "<graphic xlink:href=\"13613_2023_1233_Fig5_HTML\" id=\"MO5\"/>" ]	[]	[{"label": ["1."], "surname": ["Rudd", "Johnson", "Agesa", "Shackelford", "Tsoi", "Kievlan"], "given-names": ["KE", "SC", "KM", "KA", "D", "DR"], "article-title": ["Global, regional, and national sepsis incidence and mortality, 1990\u20132017: analysis for the Global Burden of Disease Study"], "source": ["Lancet Lond Engl"], "year": ["2020"], "volume": ["395"], "fpage": ["200"], "lpage": ["211"], "pub-id": ["10.1016/S0140-6736(19)32989-7"]}, {"label": ["11."], "mixed-citation": ["Nations U. 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Goal-directed resuscitation for patients with early septic shock. N Engl J Med. 2014;371:1496\u2013506."]}, {"label": ["136."], "surname": ["Wang", "Allen", "Kheir", "Campbell", "Khan"], "given-names": ["S", "D", "YN", "N", "B"], "article-title": ["Aging and post-intensive care syndrome: a critical need for geriatric psychiatry"], "source": ["Am J Geriatr Psychiatry Off J Am Assoc Geriatr Psychiatry"], "year": ["2018"], "volume": ["26"], "fpage": ["212"], "lpage": ["221"], "pub-id": ["10.1016/j.jagp.2017.05.016"]}, {"label": ["137."], "surname": ["Lee", "Kang", "Jeong"], "given-names": ["M", "J", "YJ"], "article-title": ["Risk factors for post-intensive care syndrome: a systematic review and meta-analysis"], "source": ["Aust Crit Care Off J Confed Aust Crit Care Nurses"], "year": ["2020"], "volume": ["33"], "fpage": ["287"], "lpage": ["294"]}]	{ "acronym": [ "ASB", "COPD", "CFS", "HRQoL", "ICU", "IMV", "LOS", "LST", "MDR", "NIV", "PCT", "PICS", "RRT", "SOFA", "TLT", "UTI", "VIPs" ], "definition": [ "Asymptomatic bacteriuria", "Chronic obstructive pulmonary disease", "Clinical Frailty Scale", "Health-related quality of life", "Intensive care unit", "Invasive mechanical ventilation", "Length of stay", "Life sustaining treatment", "Multi-drug resistant", "Non-invasive ventilation", "Procalcitonin", "Post-intensive care syndrome", "Renal replacement therapy", "Sequential Organ Failure Assessment", "Time Limited (ICU) Trial", "Urinary tract infection", "Very Old Intensive Care Patients" ] }	140	CC BY	no	2024-01-13 00:02:19	Ann Intensive Care. 2024 Jan 10; 14:6	oa_package/80/60/PMC10781658.tar.gz
PMC10781659	0	[]	[]	[]	[]	[]	[ "<title>Abstract</title>", "<p>This scientific commentary refers to ‘Inhibition of insulin-degrading enzyme in human neurons promotes amyloid-β deposition’ by Rowland et al. (<ext-link xlink:href=\"https://doi.org/10.1042/NS20230016\" ext-link-type=\"uri\">https://doi.org/10.1042/NS20230016</ext-link>). Insulin-degrading enzyme (IDE) and neprilysin (NEP) have been proposed as two Aβ-degrading enzymes supported by human genetics and <italic toggle=\"yes\">in vivo</italic> data. Rowland et al. provide complementary evidence of a key role for IDE in Aβ metabolism in human-induced pluripotent stem cell (iPSC)-derived cortical neurons.</p>" ]	[ "<title>The value of a complementary mechanistic approach to manipulate Aβ deposition</title>", "<p>Alzheimer’s disease (AD) is a devastating, progressive neurodegenerative disease characterised by the aggregation and deposition of Aβ to form Aβ plaques in the brain. This has been proposed as the initiation event triggering downstream biochemical and cellular dysfunction ultimately leading to the clinical phase of the disease. Although we are living in an exciting age with disease-modifying Aβ-targeting immunotherapies reaching the clinic, the effects are modest and are associated with severe side effects in some patients. Whilst we may expect improvements to these treatments over time, there nevertheless remains an unmet need to substantially reduce the progression of neurodegeneration in AD, with a view to developing complementary approaches promoting Aβ degradation.</p>", "<title>Previous evidence for IDE and NEP involvement in Aβ metabolism</title>", "<p>Aβ clearance is mediated through proteolytic degradation, which is driven by the action of multiple proteases. Two such enzymes with a strong evidence base as being the key players involved are the zinc metalloproteases insulin-degrading enzyme (IDE) and neprilysin (NEP).</p>", "<p>Human genetic studies have implicated IDE in late-onset AD [##UREF##0##1##] and age of AD onset [##REF##11875758##2##]. Extracellular Aβ has also been shown to be modulated by IDE-mediated proteolysis in primary rat cortical neurons [##REF##10684867##3##], whilst IDE has been shown to be a major Aβ-degrading enzyme <italic toggle=\"yes\">in vivo</italic> with <italic toggle=\"yes\">Ide<sup>−/−</sup></italic> mice reported to have a higher load of endogenous Aβ, albeit modest [##REF##12634421##4##]. Additionally, IDE overexpression in APP transgenic mice lowered brain Aβ levels abrogating Aβ plaque formation [##REF##14687544##5##].</p>", "<p>There is also a wide range of literature supporting the role of NEP in Aβ clearance. NEP levels and activity are reportedly lower in AD patient brain (reviewed in [##REF##24391587##6##]) and there is human genetics evidence that variants at the <italic toggle=\"yes\">MME</italic> locus (encoding NEP) increase AD risk in certain human populations [##REF##26362309##7##,##REF##15548496##8##]. Interestingly, AD risk is further increased if individuals simultaneously harbour risk variants at the <italic toggle=\"yes\">IDE</italic> locus [##REF##19864659##9##]. There is also substantial <italic toggle=\"yes\">in vivo</italic> evidence for a role of NEP in Aβ metabolism with mice lacking NEP exhibiting an increase in Aβ [##UREF##1##10##]. Concurrently, overexpression of NEP in AD mouse models has been shown to lead to lower brain Aβ levels, reduced plaques and increased survival [##REF##14687544##5##,##REF##12657655##11–13##].</p>", "<p>Taken together, these and similar studies provide strong evidence for a role of IDE and NEP in Aβ degradation. However, one question that remains unaddressed is whether these enzymes contribute to Aβ metabolism specifically in human neurons, which would provide complementary data to the evidence base.</p>", "<title>IDE modulates Aβ clearance in human iPSC-derived neurons</title>", "<p>Rowland et al. [##REF##37808160##14##] provide complementary evidence to extend this work with the authors demonstrating that IDE is the major contributor to Aβ degradation in human-induced pluripotent stem cell (iPSC)-derived cortical neurons. They not only show this in human iPSC-derived neuronal lysates, but further validated these findings using an elegant 3D extracellular matrix (ECM) model with embedded human iPSC-derived cortical neurons, allowing the visualisation of Aβ deposition with the resulting plaques showing similar immunological properties to deposits in human AD brain. This experimental iPSC model was derived from individuals without AD and without familial AD mutations and interestingly shows a low baseline level of plaque formation.</p>", "<p>They provide convincing evidence that IDE inhibition induced by three different inhibitors with distinct mechanisms of action promoted Aβ deposition in neurons derived from two independent iPSC lines. Considering the IDE inhibitors reduced Aβ degradation by >65%, this clearly nominates IDE as the predominant Aβ-degrading protease in this system, providing experimental support that enhancing IDE activity could be harnessed therapeutically as a complementary Aβ-lowering approach in human neurons.</p>", "<title>No effect of NEP inhibition on Aβ metabolism in human iPSC-derived neurons</title>", "<p>Despite the strong evidence for a role of NEP in Aβ metabolism from <italic toggle=\"yes\">in vivo</italic> studies, Rowland et al. [##REF##37808160##14##] show that NEP does not appear to have a major role in Aβ degradation in iPSC-derived cortical neurons. Notably, NEP has been reported to robustly degrade disease-associated oligomeric forms of Aβ40 as well as oligomeric forms of Aβ42 <italic toggle=\"yes\">in vitro</italic> [##REF##12972166##15##]. Therefore, further work is warranted to investigate metabolism of longer, more aggregation prone Aβ forms in the human cellular context, including using models more relevant to the disease context such as using iPSCs derived from aged AD patients or patients with familial AD mutations. Finally, it is well established that non-neuronal cell types provide significant contributions in neurodegeneration; thus, further work is, therefore, also needed to decipher Aβ metabolism in more diverse human cellular contexts.</p>", "<title>Implications for therapeutic strategies</title>", "<p>This study provides a human neuronal cellular context promoting the case for modulating IDE activity as a therapeutic strategy for AD and patients with cerebral β-amyloid angiopathy. Promoting Aβ clearance via modulating endogenous metabolic pathways has the potential to clear build-up of abnormal pathological Aβ assemblies and/or to slow the progress of pathological Aβ templated misfolding and propagation via lowering levels of non-disease associated Aβ substrate. The capacity to visualise and quantify Aβ in this system provides a platform for investigating a wider scope of disease contexts and modulating factors for Aβ lowering.</p>" ]	[ "<title>Data Availability</title>", "<p>NA</p>", "<title>Competing Interests</title>", "<p>The authors declare that there are no competing interests associated with the manuscript.</p>", "<title>Open Access</title>", "<p>Open access for this article was enabled by the participation of University College London in an all-inclusive <italic toggle=\"yes\">Read & Publish</italic> agreement with Portland Press and the Biochemical Society under a transformative agreement with JISC.</p>", "<title>CRediT Author Contribution</title>", "<p><bold>Elizabeth Hill:</bold> Writing—original draft, Writing—review & editing. <bold>Thomas J. Cunningham:</bold> Writing—original draft, Writing—review & editing.</p>" ]	[]	[]	[]	[]	[]	[]	[]	[]	[]	[]	[]	[{"label": ["1."], "surname": ["Bertram", "Blacker", "Mullin", "Keeney", "Jones", "Basu"], "given-names": ["L.", "D.", "K.", "D.", "J.", "S."], "etal": ["et al"], "year": ["2000"], "article-title": ["Evidence for genetic linkage of Alzheimer\u2019s disease to chromosome 10q"], "source": ["Science (New York, NY"], "volume": ["290"], "fpage": ["2302"], "lpage": ["2303"], "pub-id": ["10.1126/science.290.5500.2302"]}, {"label": ["10."], "surname": ["Iwata", "Tsubuki", "Takaki", "Shirotani", "Lu", "Gerard"], "given-names": ["N.", "S.", "Y.", "K.", "B.", "N.P."], "etal": ["et al"], "year": ["2001"], "article-title": ["Metabolic regulation of brain A\u03b2 by neprilysin"], "source": ["Science (New York, NY"], "volume": ["292"], "fpage": ["1550"], "lpage": ["1552"], "pub-id": ["10.1126/science.1059946"]}]	{ "acronym": [ "AD", "ECM", "IDE", "iPSC", "NEP" ], "definition": [ "Alzheimer’s disease", "extracellular matrix", "insulin-degrading enzyme", "induced pluripotent stem cell", "neprilysin" ] }	15	CC BY	no	2024-01-13 00:02:19	Neuronal Signal. 2024 Jan 11; 8(1):NS20230020	oa_package/9d/20/PMC10781659.tar.gz
PMC10781660	38134421	[ "<title>1 Introduction</title>", "<p>Cellular Potts Models (CPMs) are a popular method to simulate multiscale cellular behaviors because they retain some spatial information like cellular geometry but avoid the computational complexity of a full physics simulation. Their applications are general, but many researchers have focused on using these models to study cancer (e.g. angiogenesis, tumor growth, and cancer cell migration) (##REF##23596570##Szabó and Merks 2013##). CPMs work by defining an integer grid where adjacent sites with the same value comprise an individual cell and locations with a value of zero represent empty regions where no cell is present (##FIG##0##Fig. 1A##). The model uses a Metropolis–Hastings algorithm to update grid sites to match their neighbors. This process depends on penalties which can, for example, encourage cells to adhere together or maintain their size (##FIG##0##Fig. 1B##). As these steps are applied to the grid, patterns observed in real cellular systems begin to emerge. The original CPM paper demonstrated how cells can sort themselves given the correct penalties (##REF##10046374##Graner and Glazier 1992##). Over the next 30 years, this modeling paradigm has been updated to include behaviors like cell migration (##REF##34022237##Wortel et al. 2021##), chemotaxis (##REF##31940735##Savill and Hogeweg 1997##), and intracellular forces (##REF##31825952##Rens and Edelstein-Keshet 2019##).</p>", "<p>The goal of this package is to provide a framework to develop CPMs using a graph-based approach. As compared to other software (Morpheus (##REF##24443380##Starruß et al. 2014##), Artistoo (##REF##33835022##Buttenschoen et al. 2021##), CompuCell3D (##REF##22482955##Swat et al. 2012##)), CellularPotts.jl takes a unique approach to handle common pitfalls observed when simulating these models. Specifically, our main contributions include the ability to simulate these models with arbitrary geometries, avoid cell fragmentation, and integrate with state-of-the-art differential equation libraries to facilitate the creation of multiscale models.</p>" ]	[ "<title>2 Methods and results</title>", "<title>2.1 Main workflow</title>", "<p>CellularPotts.jl requires three pieces of information to model a cellular system, the first being a domain that defines the space where cells exist. The default space is a rectangular grid with periodic boundary conditions, but options for a three-dimensional space or closed boundaries are available. Additionally, users can provide an image (e.g. a radiological image) to define spaces with more complex geometry.</p>", "<p>The second requirement is a table describing what cells will be placed in the domain. Users define the names of the cells, their desired sizes (area/volume), and quantities. Positional information can be provided, otherwise cells will be placed randomly throughout the space. Custom properties like cell division rate can also be added to further inform and influence the simulation.</p>", "<p>Finally, the user needs to specify which penalties to include in the model to encourage desired cell behaviors. In general, adhesion and volume penalties are typically added to ensure cell size and shape are maintained. Other penalties available can help maintain cell perimeter, encourage random cell migration, and move cells along concentration gradients. CellularPotts.jl is designed so that users can build their own custom penalties and expand the software’s capabilities.</p>", "<p>With the fully defined model, users can simulate cellular behaviors by creating an animation, save the model specification for later use, or record how models evolve over time. By default, models do not save every past state because this would lead to prohibitively large save files. However, when recording is desired, CellularPotts.jl only saves how the model changes over time as opposed to a full copy of the model at each timepoint. This makes recording larger models possible at the cost of retrieving past states slower. Future directions for this package will focus on making sensitivity analysis workflows because these are difficult to perform for stochastic models.</p>", "<title>2.2 Graph data structures are advantageous for CPM</title>", "<p>CPMs are typically described using multi-dimensional arrays of integers which have several benefits including constant look-up times for a given index and contiguous memory storage to minimize cache misses. However, relying solely on one data structure for every aspect of a complex model has its disadvantages. Stepping a CPM forward in time at a specific index relies heavily on knowing information about neighboring locations, which is problematic for array data structures. A method to describe adjacent locations would be a graph data structure, which directly encodes this information through edge connections. As an example, periodic boundary conditions are solved by connecting nodes on opposite boundaries with an edge.</p>", "<p>One critical benefit to using a graph-based approach is the identification of articulation points. CPMs are notorious for allowing cells to fragment (##UREF##0##Durand and Guesnet 2016##). This is usually addressed by lowering a modeling parameter called “temperature” which only decreases the probability of disconnections from occurring. By encoding cell space as a graph, we can simply test for articulation points (##UREF##1##Hopcroft and Tarjan 1973##) and avoid locations that would disconnect a cell. This method is guaranteed to work at any model temperature and is independent of how the user defines the geometry of the space. Additionally, for some geometries (e.g. connected and planar), we can determine articulation points independent of cell size by testing if the Euler characteristic (##UREF##4##Wilson 1972##) changes after a node is removed. This process is efficient because we only need to consider local changes to the graph (e.g. how many edges are removed).</p>", "<p>Another benefit to using a graph-based approach can be seen with cellular division. When a cell divides, the resulting daughter cells are roughly equal in size which is difficult to simulate because cells are irregularly shaped. One method that attempts to solve this issue involves finding a line that optimally divides the cell in half. However, this method is not guaranteed to work if the cell’s boundary is concave. Conversely, by using a graph partitioning algorithm we can handle any cell shape. In general, graph partitioning is an NP hard problem, but because we are only performing a 2-way partition, polynomial time heuristic algorithms remain tractable (##UREF##2##Karypis and Kumar 1998##).</p>", "<title>2.3 Easy integration with other software</title>", "<p>CPMs operate on a discretized space and over discrete time intervals which make them difficult to combine with continuous time models like systems of ordinary differential equations (ODEs). One would typically want to couple these models together to simulate cellular and sub-cellular processes (i.e. multiscale modeling). For example, each cell could contain some theoretical protein governed by an ODE which triggers a cell division event after reaching a certain threshold (##FIG##0##Fig. 1C–E##). Most CPM software solves ODEs using Runge-Kutta or Euler methods that run in-sync with the CPM’s Monte Carlo steps. This can lead to unstable trajectories that deviate from the true solution. Julia’s DifferentialEquations.jl library (##UREF##3##Rackauckas and Nie 2017##) (and more broadly the SciML ecosystem) offers a best-in-class suite of ODE solvers that can uniquely handle discontinuous jumps and variable state systems. ODEs can evolve independently of the CPM and can even adapt if the error does not meet a specified tolerance.</p>", "<p>Beyond solving ODEs, CellularPotts.jl can seamlessly integrate with the entire Julia library ecosystem to meet the needs of almost any user. Visualization is accomplished using the Plots.jl library, physical units could be added using the Unitful.jl library, and models could even be run in R or Python using the JuliaConnectoR or PyJulia packages, respectively. This interoperability is key to making CellularPotts.jl successful because users can customize the software to meet their specific needs.</p>" ]	[ "<title>2 Methods and results</title>", "<title>2.1 Main workflow</title>", "<p>CellularPotts.jl requires three pieces of information to model a cellular system, the first being a domain that defines the space where cells exist. The default space is a rectangular grid with periodic boundary conditions, but options for a three-dimensional space or closed boundaries are available. Additionally, users can provide an image (e.g. a radiological image) to define spaces with more complex geometry.</p>", "<p>The second requirement is a table describing what cells will be placed in the domain. Users define the names of the cells, their desired sizes (area/volume), and quantities. Positional information can be provided, otherwise cells will be placed randomly throughout the space. Custom properties like cell division rate can also be added to further inform and influence the simulation.</p>", "<p>Finally, the user needs to specify which penalties to include in the model to encourage desired cell behaviors. In general, adhesion and volume penalties are typically added to ensure cell size and shape are maintained. Other penalties available can help maintain cell perimeter, encourage random cell migration, and move cells along concentration gradients. CellularPotts.jl is designed so that users can build their own custom penalties and expand the software’s capabilities.</p>", "<p>With the fully defined model, users can simulate cellular behaviors by creating an animation, save the model specification for later use, or record how models evolve over time. By default, models do not save every past state because this would lead to prohibitively large save files. However, when recording is desired, CellularPotts.jl only saves how the model changes over time as opposed to a full copy of the model at each timepoint. This makes recording larger models possible at the cost of retrieving past states slower. Future directions for this package will focus on making sensitivity analysis workflows because these are difficult to perform for stochastic models.</p>", "<title>2.2 Graph data structures are advantageous for CPM</title>", "<p>CPMs are typically described using multi-dimensional arrays of integers which have several benefits including constant look-up times for a given index and contiguous memory storage to minimize cache misses. However, relying solely on one data structure for every aspect of a complex model has its disadvantages. Stepping a CPM forward in time at a specific index relies heavily on knowing information about neighboring locations, which is problematic for array data structures. A method to describe adjacent locations would be a graph data structure, which directly encodes this information through edge connections. As an example, periodic boundary conditions are solved by connecting nodes on opposite boundaries with an edge.</p>", "<p>One critical benefit to using a graph-based approach is the identification of articulation points. CPMs are notorious for allowing cells to fragment (##UREF##0##Durand and Guesnet 2016##). This is usually addressed by lowering a modeling parameter called “temperature” which only decreases the probability of disconnections from occurring. By encoding cell space as a graph, we can simply test for articulation points (##UREF##1##Hopcroft and Tarjan 1973##) and avoid locations that would disconnect a cell. This method is guaranteed to work at any model temperature and is independent of how the user defines the geometry of the space. Additionally, for some geometries (e.g. connected and planar), we can determine articulation points independent of cell size by testing if the Euler characteristic (##UREF##4##Wilson 1972##) changes after a node is removed. This process is efficient because we only need to consider local changes to the graph (e.g. how many edges are removed).</p>", "<p>Another benefit to using a graph-based approach can be seen with cellular division. When a cell divides, the resulting daughter cells are roughly equal in size which is difficult to simulate because cells are irregularly shaped. One method that attempts to solve this issue involves finding a line that optimally divides the cell in half. However, this method is not guaranteed to work if the cell’s boundary is concave. Conversely, by using a graph partitioning algorithm we can handle any cell shape. In general, graph partitioning is an NP hard problem, but because we are only performing a 2-way partition, polynomial time heuristic algorithms remain tractable (##UREF##2##Karypis and Kumar 1998##).</p>", "<title>2.3 Easy integration with other software</title>", "<p>CPMs operate on a discretized space and over discrete time intervals which make them difficult to combine with continuous time models like systems of ordinary differential equations (ODEs). One would typically want to couple these models together to simulate cellular and sub-cellular processes (i.e. multiscale modeling). For example, each cell could contain some theoretical protein governed by an ODE which triggers a cell division event after reaching a certain threshold (##FIG##0##Fig. 1C–E##). Most CPM software solves ODEs using Runge-Kutta or Euler methods that run in-sync with the CPM’s Monte Carlo steps. This can lead to unstable trajectories that deviate from the true solution. Julia’s DifferentialEquations.jl library (##UREF##3##Rackauckas and Nie 2017##) (and more broadly the SciML ecosystem) offers a best-in-class suite of ODE solvers that can uniquely handle discontinuous jumps and variable state systems. ODEs can evolve independently of the CPM and can even adapt if the error does not meet a specified tolerance.</p>", "<p>Beyond solving ODEs, CellularPotts.jl can seamlessly integrate with the entire Julia library ecosystem to meet the needs of almost any user. Visualization is accomplished using the Plots.jl library, physical units could be added using the Unitful.jl library, and models could even be run in R or Python using the JuliaConnectoR or PyJulia packages, respectively. This interoperability is key to making CellularPotts.jl successful because users can customize the software to meet their specific needs.</p>" ]	[]	[]	[ "<title>Abstract</title>", "<title>Summary</title>", "<p>CellularPotts.jl is a software package written in Julia to simulate biological cellular processes such as division, adhesion, and signaling. Accurately modeling and predicting these simple processes is crucial because they facilitate more complex biological phenomena related to important disease states like tumor growth, wound healing, and infection. Here we take advantage of Cellular Potts Modeling to simulate cellular interactions and combine them with differential equations to model dynamic cell signaling patterns. These models are advantageous over other approaches because they retain spatial information about each cell while remaining computationally efficient at larger scales. Users of this package define three key inputs to create valid model definitions: a 2- or 3-dimensional space, a table describing the cells to be positioned in that space, and a list of model penalties that dictate cell behaviors. Models can then be evolved over time to collect statistics, simulated repeatedly to investigate how changing a specific property impacts cellular behavior, and visualized using any of the available plotting libraries in Julia.</p>", "<title>Availability and implementation</title>", "<p>The CellularPotts.jl package is released under the MIT license and is available at <ext-link xlink:href=\"https://github.com/RobertGregg/CellularPotts.jl\" ext-link-type=\"uri\">https://github.com/RobertGregg/CellularPotts.jl</ext-link>. An archived version of the code (v0.3.2) at time of submission can also be found at <ext-link xlink:href=\"https://doi.org/10.5281/zenodo.10407783\" ext-link-type=\"uri\">https://doi.org/10.5281/zenodo.10407783</ext-link>.</p>" ]	[]	[ "<title>Conflict of interest</title>", "<p>None declared.</p>", "<title>Funding</title>", "<p>This work was supported by the National Institute of Health (NIH) through grants T15LM007059 (R.W.G.), R01HL127349 (P.V.B.), and R01HL159805 (P.V.B.).</p>", "<title>Data availability</title>", "<p>No new data were generated or analysed in support of this research.</p>" ]	[ "<fig position=\"float\" id=\"btad773-F1\"><label>Figure 1.</label><caption><p>Overview of CellularPotts.jl. (A) CPMs operate on an integer grid where numbers represent cell identifiers. Here, three cells are represented on the grid. (B) The grid is updated by changing cell IDs to match neighboring IDs. Penalties are enforced on these updates (such as adhesion and volume constraints) to encourage favorable changes. The total penalty H is calculated by summing over neighbors with differing cell IDs and comparing the total cell size to a target cell size. (C–E) Give an example Cellular Potts/differential equation model which describes a growing cell population. When a theoretical protein X reaches a concentration of one, the cell divides and the protein is randomly distributed to the daughter cells. (C) Shows a snapshot of the simulation. (D) Graphs the total cell count over time. (E) Records the dynamics of the theoretical protein from one cell.</p></caption></fig>" ]	[]	[]	[]	[]	[]	[]	[]	[]	[ "<graphic xlink:href=\"btad773f1\" position=\"float\"/>" ]	[]	[{"mixed-citation": ["\n"], "person-group": ["\n"], "string-name": ["\n"], "surname": ["Durand", "Guesnet"], "given-names": ["M", "E."], "article-title": ["An efficient cellular Potts model algorithm that forbids cell fragmentation"], "source": ["Comput Phys Commun"], "year": ["2016"], "volume": ["208"], "fpage": ["54"], "lpage": ["63"]}, {"mixed-citation": ["\n"], "person-group": ["\n"], "string-name": ["\n"], "surname": ["Hopcroft", "Tarjan"], "given-names": ["J", "R."], "article-title": ["Algorithm 447: efficient algorithms for graph manipulation"], "source": ["Commun ACM"], "year": ["1973"], "volume": ["16"], "fpage": ["372"], "lpage": ["8"]}, {"mixed-citation": ["\n"], "person-group": ["\n"], "string-name": ["\n"], "surname": ["Karypis", "Kumar"], "given-names": ["G", "V."], "article-title": ["A fast and high quality multilevel scheme for partitioning irregular graphs"], "source": ["SIAM J Sci Comput"], "year": ["1998"], "volume": ["20"], "fpage": ["359"], "lpage": ["92"]}, {"mixed-citation": ["\n"], "person-group": ["\n"], "string-name": ["\n"], "surname": ["Rackauckas", "Nie"], "given-names": ["C", "Q."], "article-title": ["DifferentialEquations.jl\u2014a performant and feature-rich ecosystem for solving differential equations in Julia"], "source": ["JORS"], "year": ["2017"], "volume": ["5"], "fpage": ["15"]}, {"mixed-citation": ["\n"], "person-group": ["\n"], "string-name": ["\n"], "surname": ["Wilson"], "given-names": ["RJ."], "source": ["Introduction to Graph Theory"], "publisher-loc": ["New York"], "publisher-name": ["Academic Press"], "year": ["1972"]}]	{ "acronym": [], "definition": [] }	13	CC BY	no	2024-01-13 00:02:19	Bioinformatics. 2023 Dec 22; 40(1):btad773	oa_package/42/e3/PMC10781660.tar.gz
PMC10781662	38213739	[ "<title>Introduction</title>", "<p>After the biomaterial is implanted in the body, some specific proteins (such as fibronectin, vitronectin and laminin) are adsorbed on the surface of the implant before the cells attach to it, which further regulates the behavior of the cells and affects tissue regeneration [##UREF##0##1##]. The presence of surface nanostructures on biomaterials profoundly alters their interaction with proteins. Studies have shown that biomaterials with surface nanostructures can promote greater protein secretion and stimulate new bone growth more effectively than conventional ones [##REF##10880091##2##, ##REF##11429149##3##]. After the nanoparticles (NPs) enter the living system, proteins in body fluids (such as blood) are rapidly adsorbed to the surface of the NPs, forming the protein corona (PC) [##REF##33340622##4##]. It is the PC, rather than the size, shape and surface chemistry of the NPs, that determines their physical and chemical identity. Analyzing the impact of NP’s physical and chemical properties on PC composition is crucial for rational utilization of the PC and ultimately improving the physiological functions of NPs. Significant progress has been made in the research of the PC, utilizing various analysis methods [##REF##23646069##5##, ##REF##25475529##6##]. Mature protein structure analysis techniques include dynamic light scattering (DLS), differential centrifugal sedimentation [##REF##29954482##7##, ##REF##28513153##8##] and transmission electron microscopy (TEM) [##REF##36018335##9##]. The relative protein abundance (RPA) can be quantitatively assessed using methods like bicinchoninic acid assay and Bradford method of determination [##REF##35460823##10##, ##REF##34343481##11##]. Conformational changes in the PC can be detected through Fourier transform infrared spectroscopy and so on [##REF##35951303##12##, ##REF##34242104##13##]. Duan <italic toggle=\"yes\">et al.</italic> [##UREF##1##14##] introduced fluorescamine to label fluorescence change as a novel descriptor for engineering nanoparticles (ENPs). They combined it with conventional descriptors of protein (such as isoelectric point and hydrophobicity value) and then used Random Forest (RF) regression model to predict changes in the PRA. Findlay MR applied ENPs properties, proteins characteristics and solution conditions for PC formation to classify whether ENPs are associated with proteins, achieving an F1-score of 0.81 [##REF##29881624##15##]. Helma <italic toggle=\"yes\">et al.</italic> [##REF##28670277##16##] employed a PC dataset as descriptors to predict the toxicities of NPs, achieving an <italic toggle=\"yes\">R</italic><sup>2</sup> of 0.68. Ban <italic toggle=\"yes\">et al.</italic> [##REF##32332167##17##] utilized the RF model to predict the RPA of individual proteins, achieving good <italic toggle=\"yes\">R</italic><sup>2</sup>. However, the researches above mainly focus on the prediction model, less on the imbalanced distribution of data itself. We notice that the quality of data actually affects the model performance to a great extent. Resampling technology can be used to change the distribution of dense and sparse samples. This article applies this technology to predict PC to solve the problem of imbalanced data distribution with high accuracy.</p>" ]	[ "<title>Materials and methods</title>", "<p>In this section, we will provide a detailed description of our data source and the resampling methods employed. Subsequently, we will present our machine learning (ML) models for predicting the RPA of individual proteins. Next, we will introduce the evaluation methods utilized to assess the performance of our model. Finally, we will conduct validation experiments involving four distinct NPs to validate the efficacy of our method.</p>", "<title>Data</title>", "<p>A total of 652 data were obtained from Ban Z’s open data [##REF##32332167##17##], where each record having 21 factors as model input parameters, and was complete without any missing data. In our study, we specifically focused on the RPA values of 60 proteins within this dataset. The 21 factors, as illustrated in ##FIG##1##Figure 1##, including NP properties (NP type, NP core, surface modification, modification type, size measured by DLS (size<sub>TEM</sub>), size measured by DLS (size<sub>DLS</sub>), zeta potential and polydispersity index (PDI, NP shape, dispersion medium, dispersion medium pH). Additionally, it encompassed factors related to PC isolation, such as centrifugation speed, centrifugation time, centrifugation temperature and centrifugation repetitions. Furthermore, factors associated with PC formation are included, such as protein source, incubation culture, incubation plasma concentration, incubation NP concentration, incubation time and incubation temperature.</p>", "<p>We used two approaches to process the data of the 21 factors. The eight category factors were encoded by OneHot-Encoding. The remaining 13 numerical factors were transferred by Max-Min Normalization maintaining the proportion relationship of the original data to a certain extent, as flows form the formula:\nwhere is the normalized value and is the original data. is the maximum value and is the minimum value in the original data.</p>", "<p>To ensure almost identical distributions between the train set and test set, we employ stratified sampling to divide the data into the 9:1 ratio, creating training and testing datasets.</p>", "<title>Resampling methods</title>", "<p>To address the issue of data imbalance, we applied resampling techniques, which involves using certain algorithms to generate new samples from the existing data. In this article, three resampling methods were applied: Random Oversampling, Synthetic Minority Oversampling Technique for Regression (SmoteR) and Weighted Relevance-based Combination Strategy (WERCS) [##UREF##2##18–20##].</p>", "<p>The relevance function serves as the foundation for data resampling. The relevance function [##UREF##3##19##] (Y):Y → [0,1] is a continuous function that expresses the application-specific bias concerning the target variable domain Y by mapping it into a [0, 1] scale of relevance, where 0 and 1 represent the minimum and maximum relevance, respectively. Using the relevance function can distinguish between sparse and dense “classes”. For imbalanced data, given the potentially infinite nature of the target variable domain, it is impractical to calculate all values, requiring an approximation. Two essential parts are necessary:</p>", "<p>A set of data points where relevance is known (control points). The set must be given as input to an interpolation algorithm. The element in the set S is (i) the target value , (ii) its relevance value and (iii) the first derivative of the relevance function at the point . By default, control points are assumed as local minimum or maximum of the relevance, thus the derivative values are equal to zero.</p>", "<p>A decision on which interpolation method to use. As in early work by Ribeiro, the use of Piecewise Cubic Hermite Interpolating Polynomials [##UREF##5##21##] (<italic toggle=\"yes\">pchip</italic>) to approximately gain the relevance function. ##TAB##0##Table 1## shows how <italic toggle=\"yes\">pchip</italic> does on a set of control points S [##UREF##3##19##].</p>", "<p>The Random Oversampling method randomly selects some samples from the original data and adds them to train. These replicas are only introduced in the most important ranges of the target variable, i.e. in the ranges where the relevance is a threshold. The aim is to better balance the number of the majority and minority samples.</p>", "<p>The SmoteR is a random method combining <italic toggle=\"yes\">K</italic> nearest neighbors. The basic process is to randomly select a sample X in a minority class () and calculate <italic toggle=\"yes\">K</italic> nearest neighbors of the samples; and then stochastically select a sample O from <italic toggle=\"yes\">K</italic> nearest neighbors, calculating the product of the random number between it and (0,1) as a new sample without repetition. This algorithm will oversample the observations in , thus leading to a new train set with a more balanced distribution of the values. ##SUPPL##0##Supplementary Tables S1 and S2## show the main details of SmoteR [##UREF##7##23##, ##UREF##8##24##].</p>", "<p>The WERCS method uses the correlation function as the probability of resampling. The high correlation data will likely be added to the original samples as new data. In oversampling, examples with higher relevance have a higher probability of being replicated. In Undersampling, examples are randomly selected to be removed with probability 1−Φ(y), the higher the relevance value of an example, the lower will be the probability of being removed. The parameters of three resampling are in ##SUPPL##0##Supplementary Table S3##.</p>", "<title>Random Forest model</title>", "<p>We tried three nonlinear ML models: SVR, RF and MLP models, and RF to gain better performance with the lowest root-mean-square deviation (RMSE). The details of the models are in ##SUPPL##0##Supplementary Tables S4–S6##. RF is first proposed in 2001 and can be used to solve classification and regression problems. It is an integrated learning model. For regression problems, the final prediction results come from the mean of the predicted values of all trees in the model. The idea of RF model is to combine bagging strategy and decision trees. Bagging is derived from the bootstrap method, which uses bootstrap to sample the train set samples and then combines them with the decision tree to decide the final output through the voting of the decision tree shown in ##FIG##2##Figure 2##.</p>", "<title>Evaluation metrics</title>", "<p>To evaluate the regression prediction performance of various ML models, three evaluation metrics are employed: <italic toggle=\"yes\">R</italic><sup>2</sup>, RMSE and learning curves. In the subsequent calculation formulas (2) and (3), the unified parameters are defined as follows: the model predictive value is denoted as <italic toggle=\"yes\">p</italic>, the true value as <italic toggle=\"yes\">r</italic> and <italic toggle=\"yes\">n</italic> represents the number of samples. Specifically, represents the true value of the <italic toggle=\"yes\">i</italic>-th sample, represents the predictive value of <italic toggle=\"yes\">i</italic>-th sample, signifies the average of the true value across all <italic toggle=\"yes\">n</italic> samples and represents the average of the predictive values of all <italic toggle=\"yes\">n</italic> samples.\n</p>", "<p>Learning curves can be used to determine whether the model is overfitting or underfitting the current data. These curves plot the score changes as the number of cross-validation data increases. For a well-trained model, the cross-validation score shows a certain trend improvement with the increase of data.</p>", "<p>Variance and entropy are metrics used to characterize data distribution. Variance quantifies the extent of data dispersion, with higher values indicating greater dispersion and a lack of significant data concentration. On the other hand, entropy serves measure as a measure of uncertainty or confusion within variables. A higher entropy value suggests a more uniform distribution of data.</p>", "<title>Validation experiment</title>", "<p>We prepared four NPs including the most common metal oxide NP classes: SiO<sub>2</sub>, TiO<sub>2</sub> and most common metal class of NP: Ag, and the non-metal class of NP: hydroxyapatite (HA), which played an important role in stimulating tissue regeneration to validate our model. And HA NP was not in the original dataset so as to verify the generalization ability of our model for the new sample.</p>", "<p>Our testing method for the adsorbed proteins was label-free quantification (supported by Allwegene Tech.). This technology emerged as an important mass spectrometry (MS) quantitative method [##REF##24942700##25##, ##REF##21254760##26##], which analyzed changes in the amount of protein in samples from different sources by comparing the intensity of MS peaks. The frequency with which peptides are captured and detected by MS is positively correlated with their abundance in a mixture, so the number of counts of proteins detected by MS reflects their abundance, and the proteins are quantified.</p>", "<p>The experiment conditions were as follows: the HA sample was supplied by Sichuan Baiamung Bioactive Materials Co., Ltd. And TiO<sub>2</sub>, SiO<sub>2</sub> and Ag samples were supplied by Nanjing XFNANO Materials Tech Co., Ltd. The Kel was supplied by College of Biomedical Engineering of Sichuan University. Kel serum was thawed from −20 to 4°C as a backup the day before the experiment. A 10 mg of each kind of NPs were incubated with 10 ml of Kel serum at 37°C for 2 h and then centrifuged at room temperature with a 15 970×<italic toggle=\"yes\">g</italic> centrifuge (Shuke TG-18) at high speed to remove the supernatant, resuspended with water, centrifuged, washed again, centrifuged twice in total and finally stored at −80°C after being extremely cooled by liquid nitrogen for 15 min, finally used as the sample for Label-free analysis for standby. The material was characterized using transmission electron microscopy (HRTEM was conducted on a Hitachi H-800 operating at 200 kV), and the zeta potential was measured using Malvern instruments.</p>" ]	[ "<title>Results and discussion</title>", "<p>In this section, we will present the results of resampling on training data, the performance of the RF model, the outcomes of four NPs experiments involving model prediction, and the feature importance analysis of the models.</p>", "<title>Training data resampling results</title>", "<p>Detailed parameters of three resampling methods are shown in ##SUPPL##0##Supplementary Table S3##. Here, ##FIG##3##Figure 3## shows the results of the three resampling techniques applied to the training data. As depicted in ##FIG##3##Figure 3##, the distribution of data becomes more balanced after the three resampling methods. In ##FIG##3##Figure 3A, C and E##, the <italic toggle=\"yes\">x</italic>-axis represents 60 individual proteins, while the <italic toggle=\"yes\">y</italic>-axis represents the sample size of each protein after resampling. ##FIG##3##Figure 3B, D and F## displays the Kernel Density Estimation (KDE) of 60 individual proteins’ training set after undergoing the three resampling methods, with the red line representing the resampled data and the green line representing the original training data. As shown in ##FIG##3##Figure 3##, the original distribution of RPA values is skewed with positive skewness and a long tail extending to the right. After implementing the three resampling methods, the distribution of this data has been improved, leading to an increase of data points with low density and a decrease in data points with high density in some way, achieving a more balanced effect than the original training data.</p>", "<p>Two statistical indicators were used to compare the distribution of PC before and after resampling. The variance of the resampled PC increased, suggesting a more dispersed and less concentrated distribution of the PC data. Additionally, the entropy indicator increased, and more evenly distributed PC data. These findings are illustrated in ##TAB##1##Table 2##.</p>", "<title>Model predicting results</title>", "<p>In this section, we showed the evaluation results of the RF model after resampling. We used the results of RF model without resampling as the baseline. ##FIG##4##Figure 4## shows the 10-fold cross-validation learning curves of RF model on the training data of 60 individual proteins. It could be seen that the baseline RF model with resampling achieving better performance, especially Random Oversampling resampling, and the RMSE indicator on the cross-validation set basically decreased with the increase of train samples >600, which indicated the model trending to be stable.</p>", "<p>As shown in ##FIG##4##Figure 4##, the RMSE on the model with Random Oversampling, SmoteR and WERCS had been decreased due to the weight of training data after resampling, which was the possible reason why the model had a better performance. And ##TAB##2##Table 3## shows the mean values of RMSE and <italic toggle=\"yes\">R</italic><sup>2</sup> of RF model before and after three resampling for 60 individual proteins, and ##FIG##5##Figure 5## shows the results of each protein which could also show the improvement of using resampling method with ∼10% improvement on <italic toggle=\"yes\">R</italic><sup>2</sup> and 10% reduction on RMSE.</p>", "<title>Feature importance analysis</title>", "<p>In order to further know the important features, we use RF model to rank the importance of features. RF model calculates the importance of features through the Gini index to score the importance of 21 influencing factors after pretreatment. We filter out features with importance >0.01 of 21 features on 60 target individual proteins. Then, the selected characters of the 21 features are classified. Finally, the most important factors affecting 60 proteins RPAs are shown in ##TAB##3##Table 4##. The most important features are incubation plasma concentration, PDI and surface modification, which importance are 0.27, 0.26 and 0.23, respectively.</p>" ]	[ "<title>Results and discussion</title>", "<p>In this section, we will present the results of resampling on training data, the performance of the RF model, the outcomes of four NPs experiments involving model prediction, and the feature importance analysis of the models.</p>", "<title>Training data resampling results</title>", "<p>Detailed parameters of three resampling methods are shown in ##SUPPL##0##Supplementary Table S3##. Here, ##FIG##3##Figure 3## shows the results of the three resampling techniques applied to the training data. As depicted in ##FIG##3##Figure 3##, the distribution of data becomes more balanced after the three resampling methods. In ##FIG##3##Figure 3A, C and E##, the <italic toggle=\"yes\">x</italic>-axis represents 60 individual proteins, while the <italic toggle=\"yes\">y</italic>-axis represents the sample size of each protein after resampling. ##FIG##3##Figure 3B, D and F## displays the Kernel Density Estimation (KDE) of 60 individual proteins’ training set after undergoing the three resampling methods, with the red line representing the resampled data and the green line representing the original training data. As shown in ##FIG##3##Figure 3##, the original distribution of RPA values is skewed with positive skewness and a long tail extending to the right. After implementing the three resampling methods, the distribution of this data has been improved, leading to an increase of data points with low density and a decrease in data points with high density in some way, achieving a more balanced effect than the original training data.</p>", "<p>Two statistical indicators were used to compare the distribution of PC before and after resampling. The variance of the resampled PC increased, suggesting a more dispersed and less concentrated distribution of the PC data. Additionally, the entropy indicator increased, and more evenly distributed PC data. These findings are illustrated in ##TAB##1##Table 2##.</p>", "<title>Model predicting results</title>", "<p>In this section, we showed the evaluation results of the RF model after resampling. We used the results of RF model without resampling as the baseline. ##FIG##4##Figure 4## shows the 10-fold cross-validation learning curves of RF model on the training data of 60 individual proteins. It could be seen that the baseline RF model with resampling achieving better performance, especially Random Oversampling resampling, and the RMSE indicator on the cross-validation set basically decreased with the increase of train samples >600, which indicated the model trending to be stable.</p>", "<p>As shown in ##FIG##4##Figure 4##, the RMSE on the model with Random Oversampling, SmoteR and WERCS had been decreased due to the weight of training data after resampling, which was the possible reason why the model had a better performance. And ##TAB##2##Table 3## shows the mean values of RMSE and <italic toggle=\"yes\">R</italic><sup>2</sup> of RF model before and after three resampling for 60 individual proteins, and ##FIG##5##Figure 5## shows the results of each protein which could also show the improvement of using resampling method with ∼10% improvement on <italic toggle=\"yes\">R</italic><sup>2</sup> and 10% reduction on RMSE.</p>", "<title>Feature importance analysis</title>", "<p>In order to further know the important features, we use RF model to rank the importance of features. RF model calculates the importance of features through the Gini index to score the importance of 21 influencing factors after pretreatment. We filter out features with importance >0.01 of 21 features on 60 target individual proteins. Then, the selected characters of the 21 features are classified. Finally, the most important factors affecting 60 proteins RPAs are shown in ##TAB##3##Table 4##. The most important features are incubation plasma concentration, PDI and surface modification, which importance are 0.27, 0.26 and 0.23, respectively.</p>" ]	[ "<title>Conclusion</title>", "<p>The resampling method has significantly improved the prediction error of the RMSE indicator by 10% points, suggesting the data resampling has a substantial positive impact on the prediction model. This means the quality of data greatly affecting the performance of the model. Additionally, the feature analysis showed that incubation plasma concentration, PDI and surface modification are the three most influential features affecting the RPA values for individual proteins calculated by the prediction model. Our model, trained using resampled data, demonstrated a good performance in predicting PC compositions, a validation further supported by the label-free proteomics experiment on four NPs: TiO<sub>2</sub>, SiO<sub>2</sub>, HA and Ag. The prediction results for the RPA of proteins using these four NPs showed an <italic toggle=\"yes\">R</italic><sup>2</sup> value >0.70.</p>" ]	[ "<title>Abstract</title>", "<p>Biomaterials with surface nanostructures effectively enhance protein secretion and stimulate tissue regeneration. When nanoparticles (NPs) enter the living system, they quickly interact with proteins in the body fluid, forming the protein corona (PC). The accurate prediction of the PC composition is critical for analyzing the osteoinductivity of biomaterials and guiding the reverse design of NPs. However, achieving accurate predictions remains a significant challenge. Although several machine learning (ML) models like Random Forest (RF) have been used for PC prediction, they often fail to consider the extreme values in the abundance region of PC absorption and struggle to improve accuracy due to the imbalanced data distribution. In this study, resampling embedding was introduced to resolve the issue of imbalanced distribution in PC data. Various ML models were evaluated, and RF model was finally used for prediction, and good correlation coefficient (<italic toggle=\"yes\">R</italic><sup>2</sup>) and root-mean-square deviation (RMSE) values were obtained. Our ablation experiments demonstrated that the proposed method achieved an <italic toggle=\"yes\">R</italic><sup>2</sup> of 0.68, indicating an improvement of approximately 10%, and an RMSE of 0.90, representing a reduction of approximately 10%. Furthermore, through the verification of label-free quantification of four NPs: hydroxyapatite (HA), titanium dioxide (TiO<sub>2</sub>), silicon dioxide (SiO<sub>2</sub>) and silver (Ag), and we achieved a prediction performance with an <italic toggle=\"yes\">R</italic><sup>2</sup> value >0.70 using Random Oversampling. Additionally, the feature analysis revealed that the composition of the PC is most significantly influenced by the incubation plasma concentration, PDI and surface modification.</p>", "<title>Graphical Abstract</title>", "<p>\n\n</p>" ]	[ "<title>Four NPs experiments</title>", "<p>As described in Validation experiment section, we selected the seven individual proteins with high abundance as the model predictive targets. As shown in ##FIG##6##Figure 6##, the validation results showed that our model achieved a good prediction effect with <italic toggle=\"yes\">R</italic><sup>2</sup> >0.70.</p>", "<p>However, for those proteins with low RPA values of individual proteins, our model performance is not good, which may be caused by the competitive adsorption between different proteins in our proteomics experiment. In our experiment, we did not analyze many proteins and chose the seven representative proteins (ID: P01834, P08603, P02655, Q14520, P01008, P01857 and P0C0L4) which were associated with the immune response [##REF##32343153##27##] and possibly affect tissue regeneration. In terms of these proteins as extra verified samples, our model could predict their RPAs accurately with the <italic toggle=\"yes\">R</italic><sup>2</sup> >0.70. In a word, the prediction score gained in the experiment of NPs largely resulted from the fact that we reached a better training performance using the resampling method. Ablation experiments on resampling could increase the <italic toggle=\"yes\">R</italic><sup>2</sup> by 0.06 and reduce the RMSE by 0.11 to the greatest extent possible, which again illustrated the importance of data processing.</p>", "<p>Moreover, in order to achieve better prediction results, we should start from the following points in the future: increase the collection of the data based on this; second, with enough data, we can consider using the deep learning model of tabular data [##UREF##9##28–30##], which may have higher accuracy under a large amount of data.</p>", "<p>The formation of PC over a long period of time could affect the biological responses of nanomaterials, such as biosorption and biotoxicity [##REF##28412403##31##]. PC might also attenuate the primitive cytotoxicity of NPs [##REF##33736249##32##, ##UREF##12##33##]. This was mainly due to the protective effect of PC against NP-induced cellular damage. The interaction of naked NPs with the cell disrupted the integrity of the plasma membrane and led to cell rupture, whereas the protein coverage made the surface of NPs more biocompatible and reduced the damage of NPs to the cell membrane [##REF##28133653##34–36##]. Therefore, our work would consider collecting new data from published literature on the NPs and biological effects to further validate the effectiveness of our resampling method in the future.</p>", "<title>Supplementary Material</title>" ]	[ "<title>Supplementary data</title>", "<p>\n##SUPPL##0##Supplementary data## are available at <italic toggle=\"yes\">Regenerative Biomaterials</italic> online.</p>", "<title>Funding</title>", "<p>This work was sponsored by the National Key Research and Development Program of China (2021YFB3802100, 2021YFB3802105), the Major Project of Sichuan Science and Technology Department (2022ZDZX0029) and the Miaozi Project of Sichuan Science and Technology Department (2023JDRC0097)</p>", "<p>\n<italic toggle=\"yes\">Conflicts of interest statement</italic>. None declared.</p>" ]	[ "<fig position=\"float\" id=\"rbad082-F7\"></fig>", "<fig position=\"float\" id=\"rbad082-F1\"><label>Figure 1.</label><caption><p>Twenty-one factors of the datasets. The features of NPs dataset consist of three parts: the properties of NPs, isolation of PC and formation of PC.</p></caption></fig>", "<fig position=\"float\" id=\"rbad082-F2\"><label>Figure 2.</label><caption><p>The prediction flow of RF model. The prediction of the RF model was the average of the regression results for all trees.</p></caption></fig>", "<fig position=\"float\" id=\"rbad082-F3\"><label>Figure 3.</label><caption><p>The left column (<bold>A</bold>) (<bold>C</bold>) (<bold>E</bold>) shows the number of training set for 60 individual proteins resampled by Random Oversampling, SmoteR and WERCS, respectively. The right column (<bold>B</bold>) (<bold>D</bold>) (<bold>F</bold>) shows the KDE of the 1st protein in all data of the original and resampled data by Random Oversampling, SmoteR, and WERCS, respectively.</p></caption></fig>", "<fig position=\"float\" id=\"rbad082-F4\"><label>Figure 4.</label><caption><p>The RMSE Index curves of RF model from the original and resampled data by Random Oversampling, SmoteR and WERCS on cross-validation data, respectively.</p></caption></fig>", "<fig position=\"float\" id=\"rbad082-F5\"><label>Figure 5.</label><caption><p>The Comparison of RMSE and <italic toggle=\"yes\">R</italic><sup>2</sup> before and after resampling. (<bold>A</bold>) is the RMSE of each protein and (<bold>B</bold>) is the <italic toggle=\"yes\">R</italic><sup>2</sup> of each protein.</p></caption></fig>", "<fig position=\"float\" id=\"rbad082-F6\"><label>Figure 6.</label><caption><p>The Performance on <italic toggle=\"yes\">R</italic><sup>2</sup> of four extra NPs experiments. The diagrams in the first row represented the four particle schematics of our experiments, and the diagrams in the second and third rows showed the high-magnification and low-magnification TEM images of these NPs. And the grams in the last row showed the model performance of <italic toggle=\"yes\">R</italic><sup>2</sup>. HA, TiO<sub>2</sub> and Ag exhibited significant crystal structures with obvious streaks, while the SiO<sub>2</sub> exhibited a significant amorphous structure.</p></caption></fig>" ]	[ "<table-wrap position=\"float\" id=\"rbad082-T1\"><label>Table 1.</label><caption><p>Piecewise cubic hermite interpolating polynomial</p></caption><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col valign=\"top\" align=\"left\" span=\"1\"/></colgroup><thead><tr><th rowspan=\"1\" colspan=\"1\">Algorithm 1 <italic toggle=\"yes\">pchip</italic>(S) Piecewise Cubic Hermite Interpolating Polynomial.</th></tr></thead><tbody><tr><td rowspan=\"1\" colspan=\"1\">\n<list list-type=\"simple\"><list-item><p>\n<bold>Input:</bold>\n,set of control points with < , their relevance values , and first derivative .</p></list-item><list-item><p>\n<bold>Output:</bold> Φ(y): Piecewise Cubic Hermite Interpolating Polynomial.</p></list-item></list>\n</td></tr><tr><td rowspan=\"1\" colspan=\"1\">\n<list list-type=\"simple\"><list-item><p>1: <bold>for</bold> k 1 <bold>to</bold> s-1 <bold>do</bold></p></list-item><list-item><p>2: </p></list-item><list-item><p>3: </p></list-item><list-item><p>4: )</p></list-item><list-item><p>5: <bold>end for</bold></p></list-item><list-item><p>6: check_slopes (), ) check_slopes [##UREF##6##22##]</p></list-item><list-item><p>7: <bold>for</bold> k 1 <bold>to</bold> s-1 <bold>do</bold></p></list-item><list-item><p>8: </p></list-item><list-item><p>9: </p></list-item><list-item><p>10: <bold>end for</bold></p></list-item><list-item><p>11: <bold>return</bold></p></list-item></list>\n</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"rbad082-T2\"><label>Table 2.</label><caption><p>The mean values of the variance and entropy indicators before and after resampling on 60 individual proteins</p></caption><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col valign=\"top\" align=\"left\" span=\"1\"/><col valign=\"top\" align=\"char\" char=\".\" span=\"1\"/><col valign=\"top\" align=\"char\" char=\".\" span=\"1\"/></colgroup><thead><tr><th rowspan=\"1\" colspan=\"1\">Original + resampled</th><th rowspan=\"1\" colspan=\"1\">Variance</th><th rowspan=\"1\" colspan=\"1\">Entropy</th></tr></thead><tbody><tr><td rowspan=\"1\" colspan=\"1\">Original</td><td rowspan=\"1\" colspan=\"1\">2.71</td><td rowspan=\"1\" colspan=\"1\">5.06</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Original + Random Oversampling</td><td rowspan=\"1\" colspan=\"1\">\n<bold>5.62</bold>\n</td><td rowspan=\"1\" colspan=\"1\">\n<bold>6.23</bold>\n</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Original + SmoteR</td><td rowspan=\"1\" colspan=\"1\">\n<bold>4.91</bold>\n</td><td rowspan=\"1\" colspan=\"1\">\n<bold>5.59</bold>\n</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Original + WERCS</td><td rowspan=\"1\" colspan=\"1\">\n<bold>5.84</bold>\n</td><td rowspan=\"1\" colspan=\"1\">\n<bold>5.80</bold>\n</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"rbad082-T3\"><label>Table 3.</label><caption><p>The mean values of RMSE and <italic toggle=\"yes\">R</italic><sup>2</sup> of RF model before and after three resampling for 60 individual proteins</p></caption><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col valign=\"top\" align=\"left\" span=\"1\"/><col valign=\"top\" align=\"center\" span=\"1\"/><col valign=\"top\" align=\"center\" span=\"1\"/></colgroup><thead><tr><th rowspan=\"1\" colspan=\"1\">Model + resampling</th><th rowspan=\"1\" colspan=\"1\">\n<italic toggle=\"yes\">R</italic>\n<sup>2</sup>\n</th><th rowspan=\"1\" colspan=\"1\">RMSE</th></tr></thead><tbody><tr><td rowspan=\"1\" colspan=\"1\">RF (baseline)</td><td rowspan=\"1\" colspan=\"1\">0.62</td><td rowspan=\"1\" colspan=\"1\">1.01</td></tr><tr><td rowspan=\"1\" colspan=\"1\">RF + SmoteR</td><td rowspan=\"1\" colspan=\"1\">\n<bold>0.65</bold>\n</td><td rowspan=\"1\" colspan=\"1\">\n<bold>0.97</bold>\n</td></tr><tr><td rowspan=\"1\" colspan=\"1\">RF + WERCS</td><td rowspan=\"1\" colspan=\"1\">\n<bold>0.64</bold>\n</td><td rowspan=\"1\" colspan=\"1\">\n<bold>0.99</bold>\n</td></tr><tr><td rowspan=\"1\" colspan=\"1\">RF + Random Oversampling</td><td rowspan=\"1\" colspan=\"1\">\n<bold>0.68 (+0.06)</bold>\n</td><td rowspan=\"1\" colspan=\"1\">\n<bold>0.90 (−0.11)</bold>\n</td></tr></tbody></table></table-wrap>", "<table-wrap position=\"float\" id=\"rbad082-T4\"><label>Table 4.</label><caption><p>Feature importance of 60 individual proteins</p></caption><table frame=\"hsides\" rules=\"groups\"><colgroup span=\"1\"><col valign=\"top\" align=\"left\" span=\"1\"/><col valign=\"top\" align=\"char\" char=\".\" span=\"1\"/></colgroup><thead><tr><th rowspan=\"1\" colspan=\"1\">Feature</th><th rowspan=\"1\" colspan=\"1\">Importance</th></tr></thead><tbody><tr><td rowspan=\"1\" colspan=\"1\">Incubation plasma concentration</td><td rowspan=\"1\" colspan=\"1\">\n<bold>0.27</bold>\n</td></tr><tr><td rowspan=\"1\" colspan=\"1\">PDI</td><td rowspan=\"1\" colspan=\"1\">\n<bold>0.26</bold>\n</td></tr><tr><td rowspan=\"1\" colspan=\"1\">Surface modification</td><td rowspan=\"1\" colspan=\"1\">\n<bold>0.23</bold>\n</td></tr><tr><td rowspan=\"1\" colspan=\"1\">NP without modification</td><td rowspan=\"1\" colspan=\"1\">0.22</td></tr></tbody></table></table-wrap>" ]	[ "<disp-formula id=\"E1\"><label>(1)</label><mml:math id=\"M1\" display=\"block\" overflow=\"scroll\"><mml:mtable><mml:mtr><mml:mtd><mml:mrow><mml:maligngroup/><mml:mo> </mml:mo><mml:mrow><mml:msup><mml:mrow><mml:mi>X</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\"></mml:mi></mml:mrow></mml:msup></mml:mrow><mml:mo>=</mml:mo><mml:mrow><mml:mfrac><mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mi>X</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo>-</mml:mo><mml:mrow><mml:msub><mml:mrow><mml:mi>X</mml:mi></mml:mrow><mml:mrow><mml:mi 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overflow=\"scroll\"><mml:mrow><mml:msub><mml:mrow><mml:mi>D</mml:mi></mml:mrow><mml:mrow><mml:mi>r</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math></inline-formula>", "<inline-formula id=\"IE31\"><mml:math id=\"IM31\" display=\"inline\" overflow=\"scroll\"><mml:mrow><mml:msub><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math></inline-formula>", "<inline-formula id=\"IE32\"><mml:math id=\"IM32\" display=\"inline\" overflow=\"scroll\"><mml:mrow><mml:msub><mml:mrow><mml:mi>p</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math></inline-formula>", "<inline-formula id=\"IE33\"><mml:math id=\"IM33\" display=\"inline\" overflow=\"scroll\"><mml:mrow><mml:mrow><mml:mover accent=\"true\"><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mo>¯</mml:mo></mml:mover></mml:mrow></mml:mrow></mml:math></inline-formula>", "<inline-formula id=\"IE34\"><mml:math id=\"IM34\" display=\"inline\" 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</mml:mi></mml:mrow></mml:mfrac></mml:mrow><mml:mo> </mml:mo></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:math></disp-formula>", "<disp-formula id=\"E3\"><label>(3)</label><mml:math id=\"M3\" display=\"block\" overflow=\"scroll\"><mml:mtable><mml:mtr><mml:mtd><mml:mrow><mml:maligngroup/><mml:mo> </mml:mo><mml:mi mathvariant=\"italic\">RMSE</mml:mi><mml:mo>=</mml:mo><mml:msqrt><mml:mrow><mml:mfrac><mml:mrow><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mi>n</mml:mi></mml:mrow></mml:mfrac></mml:mrow><mml:mrow><mml:mrow><mml:munderover><mml:mo stretchy=\"true\">∑</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn>0</mml:mn></mml:mrow><mml:mrow><mml:mi>n</mml:mi></mml:mrow></mml:munderover></mml:mrow><mml:mrow><mml:mrow><mml:msup><mml:mrow><mml:mrow><mml:msub><mml:mrow><mml:mo>(</mml:mo><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo>-</mml:mo><mml:mrow><mml:msub><mml:mrow><mml:mi>p</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:mrow></mml:mrow></mml:mrow></mml:msqrt></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:math></disp-formula>" ]	[]	[]	[]	[]	[ "<supplementary-material id=\"sup1\" position=\"float\" content-type=\"local-data\"><label>rbad082_Supplementary_Data</label></supplementary-material>" ]	[ "<table-wrap-foot><fn id=\"tblfn1\"><p>The bolded values in this table indicate that our method is improving and effective on the original metrics.</p></fn></table-wrap-foot>", "<table-wrap-foot><fn id=\"tblfn2\"><p>The bolded values in this table indicate that our method is improving and effective on the original metrics.</p></fn></table-wrap-foot>", "<table-wrap-foot><fn id=\"tblfn3\"><p>The bolded values in this table indicate that our method is improving and effective on the original metrics.</p></fn></table-wrap-foot>" ]	[ "<graphic xlink:href=\"rbad082f7\" position=\"float\"/>", "<graphic xlink:href=\"rbad082f1\" position=\"float\"/>", "<graphic xlink:href=\"rbad082f2\" position=\"float\"/>", "<graphic xlink:href=\"rbad082f3\" position=\"float\"/>", "<graphic xlink:href=\"rbad082f4\" position=\"float\"/>", "<graphic xlink:href=\"rbad082f5\" position=\"float\"/>", "<graphic xlink:href=\"rbad082f6\" position=\"float\"/>" ]	[ "<media xlink:href=\"rbad082_supplementary_data.zip\"><caption><p>Click here for additional data file.</p></caption></media>" ]	[{"label": ["1"], "mixed-citation": ["\n"], "person-group": ["\n"], "string-name": ["\n"], "surname": ["Webster"], "given-names": ["T."], "article-title": ["Nanophase ceramics: the future orthopedic and dental implant material"], "source": ["Adv Chem Eng"], "year": ["2001"], "volume": ["27"], "fpage": ["125"], "lpage": ["66"]}, {"label": ["14"], "mixed-citation": ["\n"], "person-group": ["\n"], "string-name": ["\n"], "surname": ["Duan", "Coreas", "Liu", "Bitounis", "Zhang", "Parviz", "Strano", "Demokritou", "Zhong"], "given-names": ["Y", "R", "Y", "D", "Z", "D", "M", "P", "W."], "article-title": ["Prediction of protein corona on nanomaterials by machine learning using novel descriptors"], "source": ["Nanoimpact"], "year": ["2020"], "volume": ["17"], "fpage": ["100207"]}, {"label": ["18"], "mixed-citation": ["\n"], "person-group": ["\n"], "string-name": ["\n"], "surname": ["Branco", "Ribeiro", "Torgo"], "given-names": ["P", "R", "L."], "italic": ["UBL: An R Package for Utility-Based Learning"], "year": ["2016"]}, {"label": ["19"], "mixed-citation": ["\n"], "person-group": ["\n"], "string-name": ["\n"], "surname": ["Ribeiro", "Moniz"], "given-names": ["R", "N."], "article-title": ["Imbalanced regression and extreme value prediction"], "source": ["Mach Learn"], "year": ["2020"], "volume": ["109"], "fpage": ["1803"], "lpage": ["35"]}, {"label": ["20"], "mixed-citation": ["\n"], "person-group": ["\n"], "string-name": ["\n"], "surname": ["Branco", "Torgo", "Ribeiro"], "given-names": ["P", "L", "RP."], "article-title": ["Pre-processing approaches for imbalanced distributions in regression"], "source": ["Neurocomputing"], "year": ["2019"], "volume": ["343"], "fpage": ["76"], "lpage": ["99"]}, {"label": ["21"], "mixed-citation": ["\n"], "person-group": ["\n"], "string-name": ["\n"], "surname": ["Dougherty", "Edelman", "Hyman"], "given-names": ["RL", "AS", "JM."], "article-title": ["Nonnegativity-, monotonicity-, or convexity-preserving cubic and quintic Hermite interpolation"], "source": ["Math Comp"], "year": ["1989"], "volume": ["52"], "fpage": ["471"], "lpage": ["94"]}, {"label": ["22"], "mixed-citation": ["\n"], "person-group": ["\n"], "string-name": ["\n"], "surname": ["Fritsch", "Carlson"], "given-names": ["FN", "RE."], "article-title": ["Monotone piecewise cubic interpolation"], "source": ["Soc Ind Appl Math"], "year": ["1980"], "volume": ["17"], "fpage": ["238"], "lpage": ["46"]}, {"label": ["23"], "mixed-citation": ["\n"], "person-group": ["\n"], "string-name": ["\n"], "surname": ["Pfahringer", "Ribeiro", "Torgo Luis", "Branco"], "given-names": ["B", "R", "P", "P."], "article-title": ["Resampling strategies for regression"], "source": ["Expert Syst"], "year": ["2015"], "volume": ["32"], "fpage": ["465"], "lpage": ["76"]}, {"label": ["24"], "mixed-citation": ["\n"], "person-group": ["\n"], "string-name": ["\n"], "surname": ["Torgo", "Ribeiro", "Pfahringer", "Branco"], "given-names": ["L", "R", "B", "P."], "collab": ["SMOTE for Regression"], "source": ["Progress in Artificial Intelligence"], "publisher-loc": ["Berlin, Heidelberg"], "publisher-name": ["Springer Berlin Heidelberg"], "year": ["2013"], "fpage": ["378"], "lpage": ["89"]}, {"label": ["28"], "mixed-citation": ["\n"], "person-group": ["\n"], "string-name": ["\n"], "surname": ["Arik", "Pfister"], "given-names": ["S\u00d6", "T."], "article-title": ["Tabnet: attentive interpretable tabular learning"], "source": ["AAAI"], "year": ["2021"], "volume": ["35"], "fpage": ["6679"], "lpage": ["87"]}, {"label": ["29"], "mixed-citation": ["\n"], "person-group": ["\n"], "string-name": ["\n"], "surname": ["Xu", "Skoularidou", "Cuesta-Infante", "Veeramachaneni"], "given-names": ["L", "M", "A", "K."], "article-title": ["Modeling tabular data using conditional GAN"], "source": ["Adv Neu Inf Process Syst"], "year": ["2019"], "volume": ["659"], "fpage": ["7335"], "lpage": ["45"]}, {"label": ["30"], "mixed-citation": ["\n"], "person-group": ["\n"], "string-name": ["\n"], "surname": ["Borisov", "Leemann", "Sessler", "Haug", "Pawelczyk", "Kasneci"], 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["97"], "lpage": ["112"]}]	{ "acronym": [], "definition": [] }	36	CC BY	no	2024-01-13 00:02:19	Regen Biomater. 2023 Dec 12; 11:rbad082	oa_package/3f/15/PMC10781662.tar.gz
PMC10781663	38049557	[]	[]	[]	[]	[]	[ "<title>Subject terms</title>" ]	[ "<p id=\"Par1\">Correction to: <italic>British Journal of Cancer</italic> 10.1038/s41416-023-02474-w, published online 30 October 2023</p>", "<p id=\"Par2\">In this article the unit of eotaxin blood concentration should be pg/mL, but there were two typos in ng/mL. This has been corrected.</p>", "<p id=\"Par3\">In section “Methods”, Study design and treatment, 3rd paragraph: Patients with high serum eotaxin levels (>81.02 ng/mL) were randomly assigned in a 2:1 ratio to receive either GemCap with GV1001 (GV1001 group) or GemCap (control group).</p>", "<p id=\"Par4\">It should read:</p>", "<p id=\"Par5\">Patients with high serum eotaxin levels (>81.02 pg/mL) were randomly assigned in a 2:1 ratio to receive either GemCap with GV1001 (GV1001 group) or GemCap (control group).</p>", "<p id=\"Par6\">Caption of figure 1:</p>", "<p id=\"Par7\">Flow diagram of patient disposition. A total of 511 pancreatic adenocarcinoma patients were screened, of 148 patients were enrolled.</p>", "<p id=\"Par8\">Patients with high serum eotaxin levels (>81.02 ng/mL) were randomly assigned in a 2:1 ratio to receive either Gemcitabine/Capecitabine with GV1001 (GV1001 group) or Gemcitabine/Capecitabine (control group). Finally, 148 patients were assigned to the GV1001 group (<italic>n</italic> = 75; all eotaxin-high) and control group (<italic>n</italic> = 73; 36 eotaxin-high and 37 eotaxin-low).</p>", "<p id=\"Par9\">It should read:</p>", "<p id=\"Par10\">Flow diagram of patient disposition. A total of 511 pancreatic adenocarcinoma patients were screened, of 148 patients were enrolled.</p>", "<p id=\"Par11\">Patients with high serum eotaxin levels (>81.02 pg/mL) were randomly assigned in a 2:1 ratio to receive either Gemcitabine/Capecitabine with GV1001 (GV1001 group) or Gemcitabine/Capecitabine (control group). Finally, patients were assigned to the GV1001 group (n = 75; all eotaxin-high) and control group (n = 73; 36 eotaxin-high and 37 eotaxin-low).</p>", "<p id=\"Par12\">The original article has been corrected.</p>" ]	[]	[]	[]	[]	[]	[]	[]	[]	[]	[]	[]	[]	[]	{ "acronym": [], "definition": [] }	0	CC BY	no	2024-01-13 00:02:19	Br J Cancer. 2024 Jan 31; 130(1):163	oa_package/bf/94/PMC10781663.tar.gz
PMC10781664	38199993	[ "<title>Introduction</title>", "<p id=\"Par3\">It is widely believed that large-scale machine learning might be one of the most revolutionary technologies benefiting society<sup>##REF##26017442##1##</sup>, including already important breakthroughs in digital arts<sup>##UREF##0##2##</sup>, conversation like GPT-3<sup>##UREF##1##3##,##UREF##2##4##</sup>, and mathematical problem solving<sup>##UREF##3##5##</sup>. However, training such models with considerable parameters is costly and has high carbon emissions. For instance, twelve million dollars and over five-hundred tons of CO<sub>2</sub> equivalent emissions have been produced to train GPT-3<sup>##UREF##4##6##</sup>. Thus, on the one hand, it is important to make large-scale machine-learning models (like large language models, LLM) sustainable and efficient.</p>", "<p id=\"Par4\">On the other hand, machine learning might possibly be one of the flag applications of quantum technology. Running machine learning algorithms on quantum devices, implementing readings of so-called <italic>quantum machine learning</italic>, is widely seen as a potentially very fruitful application of quantum algorithms<sup>##REF##28905917##7##</sup>. Specifically, many quantum approaches are proposed to enhance the capability of classical machine learning and hopefully find some useful applications, like<sup>##REF##25302877##8##,##UREF##5##9##</sup>. Despite rapid development and significant progress, current quantum machine learning algorithms feature substantial limitations both in theory and practice. First, practical applications of quantum machine learning algorithms for near-term devices are often lacking theoretical grounds that guarantee or at least plausibly suggest to outperform their classical counterparts. Second, for fault-tolerant settings of quantum machine learning problems<sup>##UREF##6##10##–##UREF##12##18##</sup>, rigorous super-polynomial quantum speedups can actually be proven<sup>##UREF##13##19##–##UREF##15##21##</sup> for highly structured problems. That said, these prescriptions are arguably still far from real state-of-the-art applications of classical machine learning. Some of them are primarily using quantum states as training data instead of classical data, which can be—highly encouraging as these approaches are—argued to be not the currently most important classical machine learning application<sup>##UREF##14##20##,##UREF##16##22##–##REF##35679419##25##</sup>. Efforts need to be made to extend our understanding of quantum machine learning, in the sense that we have to understand how they could have theoretical guarantees and how they could solve timely and natural problems, at least in principle, of classical machine learning. For instance, they should relate to scalable and sustainable natural problems in large-scale machine-learning.</p>", "<p id=\"Par5\">In this work, we take significant steps in this direction by designing end-to-end quantum machine learning algorithms that are expected to be timely for the current machine learning community and that are to an extent equipped with guarantees. Based on a typical large-scale (classical) machine-learning process (see Fig. ##FIG##0##1## for an illustration), we find that after a significant number of neural network training parameters have been pruned (sparse training)<sup>##UREF##18##26##–##UREF##21##29##</sup> and the classical training parameters compiled to a quantum computer, we suggest to find a quantum enhancement at the early state of training before the error grows exponentially. At its heart, the quantum algorithm part of the work includes suitable modifications of the quantum algorithm<sup>##REF##34446548##30##</sup> for solving differential equations to running (stochastic) gradient descent algorithms—presumably the primary classical machine learning algorithm—into a quantum processor after linearization. The expectation of a possible quantum enhancement is rooted in an application of a variant of the so-called <italic>Harrow-Hassidim-Lloyd</italic> (HHL) algorithm<sup>##REF##19905613##31##</sup>, an efficient quantum algorithm for sparse matrix inversion that solves the problem within time for suitably conditioned <italic>n</italic> × <italic>n</italic> sparse matrices. We find that our algorithm can solve large-scale model-dimension-<italic>n</italic> machine learning problems in or time, where <italic>T</italic> is the number of iterations. The scaling in <italic>n</italic> outperforms the scaling of any classical algorithms we know of. However, for a given machine learning problem with required performances, there is no guarantee that our hybrid quantum-classical algorithm will necessarily outperform all other conceivable classical algorithms for related, but different tasks (for instance, for algorithms that are not gradient-based). Thus, our result gives, to the best of our knowledge, rise to a potential substantial quantum speedup or enhancement of particular classical algorithms, instead of a quantum advantage over the entire problem class.</p>", "<p id=\"Par6\">From a quantum algorithms perspective, stochastic gradient descent processes are solved here using quantum <italic>ordinary differential equation</italic> (ODE) solvers derived from the findings of ref. <sup>##REF##34446548##30##</sup>, based on linearizing non-linear equations using so-called quantum Carleman linearization. We find that the corresponding differential equation solvers can, in principle, also be used in the discrete setting and for stochastic gradient descent in machine learning. However, in the discrete setting, the theoretical details are significantly different from those applicable in the small learning rate limit. In this work, we systematically establish a novel discrete Carleman linearization in the ##SUPPL##0##supplementary material##, including reformulations of the Carleman linearization theory, a tensor network diagrammatic notation for the discretization error, analytic derivations of higher-order corrections, and explicit examples for lower order expansions. Further details about the novelty of our algorithms beyond the findings of ref. <sup>##REF##34446548##30##</sup> are summarized in the ##SUPPL##0##supplementary material##.</p>", "<p id=\"Par7\">It is important to stress that the above algorithm has a number of requirements that do admit a quantum enhancement. First, both the machine learning model and the weight vectors have to be sufficiently <italic>sparse</italic>, which will ensure a fast interface between classical and quantum processors (this requirement could be relaxed in the presence of <italic>quantum random access memory</italic> (QRAM)<sup>##REF##18518173##32##</sup>, a fast uploader towards quantum data, but we stress that this is <italic>not</italic> required and there are no hidden resources in our scheme). Second, the model has to be sufficiently <italic>dissipative</italic>. For dissipative systems, the linearization error is well controlled, ensuring that the HHL algorithm can obtain reliable results even with non-linear machine learning models. We find dissipation happens generically in the early training process of large-scale machine learning.</p>", "<p id=\"Par8\">We corroborate the intuition developed here by a number of theorems, as well as extensive numerical experiments. The formal definition of dissipation, sparsity, and quantum speedups are rigorously proven in the ##SUPPL##0##supplementary material##. Informal readings of the main theorems are presented in “Results”, while solid numerical evidence up to 103 million training parameters are provided in “Numerical analysis”. Finally, a conclusion providing also an outlook will be provided in “Discussion”.</p>" ]	[]	[ "<title>Results</title>", "<title>Theorems</title>", "<p id=\"Par9\">In this section, we will lay out the informally formulated main theorems that are established in this work. Details can be found in the ##SUPPL##0##supplementary material##.</p>", "<p id=\"Par10\"><italic>Theorem 1</italic> (Informal). For a sparse machine learning model with model size <italic>n</italic>, running <italic>T</italic> iterations, with the algorithm being fully dissipative with small learning rates (whose formal definition is given in the ##SUPPL##0##supplementary material)##, there is a quantum algorithm that runs intime with precision <italic>ϵ</italic> > 0. The sparsity condition also ensures the efficiency of uploading and downloading quantum states towards classical processors.</p>", "<p id=\"Par11\"><italic>Theorem 2</italic> (Informal). For a sparse machine learning model with model size <italic>n</italic>, running in <italic>T</italic> iterations, and the algorithm being almost dissipative with small learning rates (whose formal definition is given in the ##SUPPL##0##supplementary material)##, then there is a quantum algorithm that runs intime with precision <italic>ϵ</italic> > 0. The sparsity condition also ensures the efficiency of uploading and downloading quantum states towards classical processors.</p>", "<p id=\"Par12\">In these expressions, takes the role of a system size of the quantum system. First, we describe the problem we are trying to solve. A machine learning model is defined partially by a function , called the <italic>loss function</italic>, as a function of weight vector (variational angle) , and the <italic>input training set</italic>. The weight vector has <italic>n</italic> components if an <italic>n</italic>-dimensional model. The task is to minimize the function by adjusting <italic>θ</italic> making use of <italic>T</italic> iterations.</p>", "<p id=\"Par13\">The presumably most widely utilized algorithm in machine learning is called (stochastic) gradient descent. Starting from the initial weight vector <italic>θ</italic>(<italic>t</italic> = 0), we implement the following ordinary differential equation from <italic>t</italic> = 0 to <italic>t</italic> = <italic>T</italic> with small, positive learning rate <italic>η</italic>,Variants of the gradient descent algorithms also include adding random noise <italic>ξ</italic><sub><italic>μ</italic></sub>(<italic>t</italic>) in each step, so-called stochastic gradient descent algorithms. One can show that in many cases, at the end of training, <italic>θ</italic><sub><italic>μ</italic></sub>(<italic>t</italic> = <italic>T</italic>) can make the loss function sufficiently small.</p>", "<p id=\"Par14\">The quantum algorithm with the promised efficiency in Theorem 1 and Theorem 2 is described in the following.<list list-type=\"bullet\"><list-item><p id=\"Par15\">Our starting point of the algorithm is given by a initial weight vector, <italic>θ</italic>(0), the maximal number of iterations <italic>T</italic>, and the machine learning architecture , with model size <italic>n</italic>.</p></list-item><list-item><p id=\"Par16\">In a first step, we use so-called <italic>quantum Carleman linearization</italic> introduced in ref. <sup>##REF##34446548##30##</sup>, to linearize the model with the matrix <italic>M</italic> (see the ##SUPPL##0##supplementary material## for more details).</p></list-item><list-item><p id=\"Par17\">Then, we need to upload the sparse weight vector <italic>θ</italic>(0) as a state vector in quantum devices, using tools of ref. <sup>##UREF##22##33##</sup> or alternatively more sophisticated and at the same time challenging architectures like <italic>quantum random access memory</italic> (QRAM)<sup>##REF##18518173##32##</sup>.</p></list-item><list-item><p id=\"Par18\">Then, in a further step, we use a variant of the HHL solver that has been introduced in ref. <sup>##REF##19905613##31##</sup> and the ##SUPPL##0##supplementary material##, to solve the state vector at the end <italic>t</italic> = <italic>T</italic>. The pipeline is runnable under the condition of sparsity and dissipation, which is satisfied by our models. Sparsity includes the sparsity of model themselves, and the sparsity of weight vectors (ensured by the assumptions of sparse training), while dissipation is a natural property of the early steps of training, extensively discussed in “Numerical analysis”.</p></list-item><list-item><p id=\"Par19\">Finally, we exploit tomographic methods described in, for instance, refs. <sup>##UREF##17##24##,##UREF##23##34##</sup> and the ##SUPPL##0##supplementary material##, to obtain the classical model parameters <italic>θ</italic>(<italic>T</italic>).</p></list-item></list></p>", "<p id=\"Par20\">Finally, we wish to mention that this potential enhancement from quantum computing is concerning the size of the model, not necessarily the precision. For instance, we use tomographic method to download the sparse quantum state at the end of quantum ODE solver with the precision scaling as 1/<italic>ϵ</italic><sup>2</sup>. This scaling might be optimal in the quantum setting<sup>##UREF##23##34##</sup>, but may not be ideal compared to purely classical algorithms (although the precise relationship between the error and the performances of classical machine learning models is generically not clear to date). We leave those interesting issues for future works.</p>", "<title>Linearizing classical neural networks</title>", "<p id=\"Par21\">In this section, we provide a short and heuristic description of how to solve stochastic gradient descent using HHL algorithms. For a given (stochastic) gradient descent process, the recursion relation is given bywith the initial condition <italic>u</italic>(0) = <italic>u</italic><sub>in</sub>. Here, <italic>u</italic>(<italic>t</italic>) = (<italic>θ</italic><sub><italic>μ</italic></sub>)(<italic>t</italic>) is a set of weight vectors at the iteration <italic>t</italic>, <italic>δ</italic><italic>o</italic> ≔ <italic>o</italic>(<italic>t</italic> + 1) − <italic>o</italic>(<italic>t</italic>) represents the discrete difference between two time steps (<italic>t</italic> + 1) and (<italic>t</italic>) for a variable <italic>o</italic> (see ref. <sup>##REF##34446548##30##</sup> for the continuous version), and <italic>u</italic><sup>⊗<italic>ℓ</italic></sup> is the <italic>ℓ</italic>-th order tensor product. Thus, Eq. (##FORMU##15##4##) characterizes the dynamics of <italic>q</italic>-th order non-linearity in classical neural networks. Now, we introduce a linear process designed to approximate the non-linear model (##FORMU##15##4##), called <italic>quantum Carleman linearization</italic>, asIn this linear process, the vector space (whose vectors could be denoted by ) is given by the weight vectors and all possible tensor products thereof, while <italic>A</italic> is a large matrix with matrix elements given by the <italic>F</italic><sub><italic>ℓ</italic></sub>, the so-called <italic>quantum Carleman matrix</italic> (QCM). In principle, this linear relation is infinitely dimensional, so we are replacing the original non-linear recursions to an infinite set of linear relations. If we wish to solve this infinite process by a digital system, we need to make truncation. In this work, we show that for dissipative systems (whose QCMs have enough negative eigenvalues, roughly corresponding to large enough positive eigenvalues for the Hessian of the loss functions in classical neural networks; the positive eigenvalues are called <italic>dissipative modes</italic> in the ##SUPPL##0##supplementary material##, while negative eigenvalues are called <italic>divergent modes</italic>), the truncation error can be well-controlled.</p>", "<p id=\"Par22\">For sparse, dissipative systems, Eq. (##FORMU##16##5##) can be treated as a matrix inversion problem, thus solved by the HHL algorithm using quantum computers,Here, we are considering <italic>T</italic> + 1 iterations in total from <italic>t</italic> = 0 to <italic>t</italic> = <italic>T</italic>, and the vector space has been further extended <italic>T</italic> + 1 times. <italic>I</italic> is the identity matrix, and is the initial weight vector corresponding to <italic>u</italic><sub>in</sub>, written as a tensor product. This quantum ODE solver is our primary strategy towards solving stochastic gradient descent equations using quantum computers.</p>", "<p id=\"Par23\">Although similar to its continuous version<sup>##REF##34446548##30##</sup>, the distinct differences between our algorithms and those of ref. <sup>##REF##34446548##30##</sup> are extensively discussed in the ##SUPPL##0##supplementary material##. More precisely, the discrete contributions will lead to higher order terms in the learning rate <italic>η</italic>. In fact, in the continuous case <italic>A</italic> is linearly depending on various <italic>F</italic>, while in the discrete case, <italic>A</italic> also has contributions scaling as <italic>η</italic><sup>2</sup><italic>F</italic><sup>2</sup> + <italic>η</italic><sup>3</sup><italic>F</italic><sup>3</sup> ⋯ (see the ##SUPPL##0##supplementary material## for detailed examples). In the limit where <italic>η</italic> → 0, the discrete contributions become identical to the continuous ones. It is also worth clarifying that those discrete contributions go beyond those considered in ref. <sup>##REF##34446548##30##</sup>. In fact, although one has to discretize the differential equations eventually in the ODE setup of ref. <sup>##REF##34446548##30##</sup>, the time derivative is computed before discretization for higher order Carleman linearization. For instance, <italic>d</italic>(<italic>u</italic><sup>⊗2</sup>)(<italic>t</italic>) is treated as <italic>d</italic><italic>u</italic> ⊗ <italic>u</italic> + <italic>u</italic> ⊗ <italic>d</italic><italic>u</italic> both at the time <italic>t</italic>, while in the discrete case one has to consider contributions both from the (<italic>t</italic> + 1)-st step and the <italic>t</italic>-th step. This contribution is the primary difference between the continuous and the discrete ODEs. Finally, in our problem setup, we always assume that an explicit form of the gradient descent equation is accessible, such that one can construct the Carleman linearization and make it available to quantum devices. This may not always be true for generic complicated classical neural networks whose complexity of analytic decomposition might grow with the size of the network. We leave a more thorough treatment to future research.</p>", "<title>Numerical analysis</title>", "<p id=\"Par24\">In this part of our work, we focus on providing numerical evidence of a potential quantum enhancement for large-scale machine-learning models. Commercial large-scale LLMs like GPT-3 can have billion parameters and even more, which is challenging as a starting point due to its tremendous computational costs. Instead, here we provide examples of classification and computer vision machine learning models, which are relatively small compared to language models used in industry. Our computational resources allow us to achieve the scale up to million, which is both practically minded and reachable. We expect that LLMs and other models will feature a similar behavior to those examples since our algorithm works in general as a replacement for stochastic gradient descent.</p>", "<p id=\"Par25\">Thus, in order to provide evidence of the functioning of our quantum algorithm in the context of practically minded machine learning, we perform numerical experiments on a state-of-the-art machine vision architectures, namely the so-called <italic>ResNet</italic>, to tentatively outline schemes with a potential quantum enhancement. First, we study a model with 7 million trainable parameters trained to distinguish images of 100 classes<sup>##UREF##24##35##</sup>. We first pre-train the neural network, use the largest 10% of learned parameters for initialization, and use the quantum ODE system to obtain a sparse output model. We record the Hessian spectra during sparse training, allowing us to track the evolution of an error bound related quantity, given bywhere <italic>ρ</italic> is the eigenvalue density, <italic>a</italic> is the negative of Hessian eigenvalues, and <italic>N</italic><sub><italic>c</italic></sub> is the renormalization constant implicitly defined byThis error proxy discards small magnitude Hessian eigenvalues because they are close to 0, extremely abundant, and renders the error proxy stationary.</p>", "<p id=\"Par26\">This numerical prescription is created according to criteria towards positivity of Hessian eigenvalues (dissipative modes). More dissipative systems have more positive Hessian eigenvalues, more negative <italic>a</italic>, and a better behaved error proxy. Specifically, the dissipative nature of the training dynamics initially leads to a reduction in this error proxy, which then gets overtaken by divergent modes and leads to an exponentially increasing error bound as shown in Fig. ##FIG##1##2##b. This motivates us to download the quantum trained model parameters sparsely and re-upload to the quantum computer to continue training every 100 steps. The effect of this procedure is that the exponentially increasing error restarts at 0 after re-uploading, with the side effect of Hessian broadening and accuracy reduction as shown in Fig. ##FIG##1##2##.</p>", "<p id=\"Par27\">There is another strategy assuming the existence of QRAM. To combat the effect of Hessian broadening on the error proxy, we train the model classically for 10 steps after download before re-uploading of the new dense parameters, during which no training error is accrued. Although classical training has a cost linear in <italic>n</italic>, it is a small fraction of the entire training process. The accuracy dips immediately after download improves as training progresses, so our quantum training scheme is capable of producing useful sparse models. Finally, we examine the Hessian of a 103 million parameter ResNet. We start with a pre-trained model and prune 90% of the parameters. Due to the immense computational cost of computing Hessian for a large machine learning model (a relatively large-scale model for computational vision based on our computational resources), we only benchmark the Hessian spectra to provide evidences of dissipation and potential quantum enhancements. Figure ##FIG##2##3## shows the initial Hessian, which clearly shows the dominance of dissipative modes over divergent modes similar to the 7 million parameter model. Since the Hessian improves with training for the 7 million parameter model, we believe this is evidence that the 103 million parameter model will have similarly manageable error growth.</p>" ]	[ "<title>Discussion</title>", "<p id=\"Par28\">In our work, we have provided quantum algorithm strategies that are presumably helpful for solving the (stochastic) gradient descent dynamics for large-scale classical machine learning models, like LLMs such as GPT-3. We identify certain precisely stated dissipative and sparse regimes of the model where quantum devices could meaningfully contribute, providing an end-to-end HHL-type quantum algorithm application that could outperform known classical algorithms. The observation that an efficient classical algorithm for efficiently solving all instances of non-linear dissipative differential equations would imply an efficient classical algorithm for any problem that can be solved efficiently by a quantum computer (is BQP hard)<sup>##REF##34446548##30##</sup> can be seen as an argument that our algorithm is implausible to be de-quantized by classical proposals along the lines of ref. <sup>##UREF##25##36##</sup>. Frankly, the core thesis of this work is that a main application of quantum computers may be in the <italic>training of classical neural networks</italic>.</p>", "<p id=\"Par29\">Indeed, we claim that our algorithm might significantly increase the scalability and sustainability of classical large-scale machine-learning models and provide evidence for our claims numerically up to 103 million training parameters. Our work provides solid theoretical guarantees and intersections with state-of-the-art classical machine learning research. It sharply deviates from the mindset of variational quantum algorithms, and instead aims at augmenting classical machine learning by a key quantum step that constitutes a bottleneck for the classical training. In a way, it can be seen as adding flesh to the expectation that quantum formulations of neural networks may lead to new computational tools<sup>##UREF##26##37##</sup>. Specifically, our model requires the sparsity to be kept as a constant (or feature a polynomial scaling) in the size of the model to maintain a potential enhancement, which is consistent with the so-called <italic>lottery ticket hypothesis</italic><sup>##UREF##27##38##</sup>. The setup is expected to be favorable in large-scale machine learning numerical experiments, although the sparsity ratio will generically decay.</p>", "<p id=\"Par30\">Our work is expected to open up several potential directions in the field of quantum machine learning where one can reasonably hope for algorithmic improvements. In the ##SUPPL##0##supplementary material##, we hint at a number of potentially particularly fruitful directions for future research. In short, they include the development of an alternative, time-dependent version during gradient descent trajectories, the identification of better formal criteria for dissipation, work on connections to diffusion models in classical machine learning and LLMs<sup>##UREF##28##39##</sup>, theoretical improvements on the truncated HHL algorithms, and the identification of mechanisms of possible quantum speedups beyond notions of dissipation. We hope that this work can provide some stimulus for this type of research.</p>" ]	[]	[ "<p id=\"Par1\">Large machine learning models are revolutionary technologies of artificial intelligence whose bottlenecks include huge computational expenses, power, and time used both in the pre-training and fine-tuning process. In this work, we show that fault-tolerant quantum computing could possibly provide provably efficient resolutions for generic (stochastic) gradient descent algorithms, scaling as , where <italic>n</italic> is the size of the models and <italic>T</italic> is the number of iterations in the training, as long as the models are both sufficiently dissipative and sparse, with small learning rates. Based on earlier efficient quantum algorithms for dissipative differential equations, we find and prove that similar algorithms work for (stochastic) gradient descent, the primary algorithm for machine learning. In practice, we benchmark instances of large machine learning models from 7 million to 103 million parameters. We find that, in the context of sparse training, a quantum enhancement is possible at the early stage of learning after model pruning, motivating a sparse parameter download and re-upload scheme. Our work shows solidly that fault-tolerant quantum algorithms could potentially contribute to most state-of-the-art, large-scale machine-learning problems.</p>", "<p id=\"Par2\">It is still unclear whether and how quantum computing might prove useful in solving known large-scale classical machine learning problems. Here, the authors show that variants of known quantum algorithms for solving differential equations can provide an advantage in solving some instances of stochastic gradient descent dynamics.</p>", "<title>Subject terms</title>" ]	[ "<title>Supplementary information</title>", "<p>\n\n\n</p>" ]	[ "<title>Supplementary information</title>", "<p>The online version contains supplementary material available at 10.1038/s41467-023-43957-x.</p>", "<title>Acknowledgements</title>", "<p>We thank Senrui Chen, Vedran Dunjko, Aram Harrow, Robert Huang, Daliang Li, John Preskill, Jonah Sherman, Umesh Vazirani, Carl Vondrick, Han Zheng, Sisi Zhou and Peter Zoller for many valuable discussions. This research used the resources of the Argonne Leadership Computing Facility, which is a U.S. Department of Energy (DOE) Office of Science User Facility supported under Contract DE-AC02-06CH11357. J.L. is supported in part by International Business Machines (IBM) Quantum through the Chicago Quantum Exchange, and the Pritzker School of Molecular Engineering at the University of Chicago through AFOSR MURI (FA9550-21-1-0209). M.L. acknowledges support from DOE Q-NEXT. J.-P.L. acknowledges the support by the NSF (grant CCF-1813814, PHY-1818914), an NSF QISE-NET triplet award (DMR-1747426), an NSF QLCI program (OMA-2016245), a Simons Foundation award (No. 825053), and the Simons Quantum Postdoctoral Fellowship. Y.A. acknowledges support from DOE Q-NEXT and the DOE under contract DE-AC02-06CH11357 at Argonne National Laboratory. J.E. acknowledges funding of the ERC (DebuQC), the BMBF (Hybrid, MuniQC-Atoms), the BMWK (PlanQK, EniQma), the Munich Quantum Valley (K-8), the QuantERA (HQCC), the Quantum Flagship (Millenion, PasQuans2), the DFG (The Berlin Mathematics Research Center MATH+ (EXC-2046/1, project ID: 390685689), CRC 183), and the Einstein Research Foundation (Einstein Research Unit on Quantum Devices). L.J. acknowledges support from the ARO (W911NF-23-1-0077), ARO MURI (W911NF-21-1-0325), AFOSR MURI (FA9550-19-1-0399, FA9550-21-1-0209), AFRL (FA8649-21-P-0781), DoE Q-NEXT, NSF (OMA-1936118, ERC-1941583, OMA-2137642), NTT Research, and the Packard Foundation (2020-71479).</p>", "<title>Author contributions</title>", "<p>J.L., M.L., J.-P.L., Z.Y., Y.A. and L.J. have initiated this work. All authors have contributed to pursuing the theoretical analysis and to proving the main claims. Y.W. has joined the team later for having made substantial contributions to the tensor network picture introduced. J.E. has in particular contributed to analysing sparsity and notions of tomographic recovery. J.L., M.L., J.-P.L., Z.Y. and Y.A. have performed the numerical analysis. All authors have contributed to discussions and to writing this manuscript.</p>", "<title>Peer review</title>", "<title>Peer review information</title>", "<p id=\"Par31\"><italic>Nature Communications</italic> thanks the anonymous reviewer(s) for their contribution to the peer review of this work. A peer review file is available.</p>", "<title>Funding</title>", "<p>Open Access funding enabled and organized by Projekt DEAL.</p>", "<title>Data availability</title>", "<p>The full data for this work is available at ref. <sup>##UREF##29##40##</sup>.</p>", "<title>Code availability</title>", "<p>The full code for this work is available at ref. <sup>##UREF##29##40##</sup>.</p>", "<title>Competing interests</title>", "<p id=\"Par32\">The authors declare no competing interests.</p>" ]	[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>A possible learning process in large-scale models, which might use sparse training, whose early stage in learning might admit possible quantum enhancement.</title><p>A dense neural network is pre-trained classically. The neural network weights are then pruned and only a small fraction is preserved. A quantum ordinary difference equation system that corresponds to the sparse training dynamics is created using the sparse network and the training data. To allow quantum enhancement, the system must be sparse and dissipative. Measurement on the solution state is performed to obtain the final trained parameters, used to construct a trained classical sparse neural network.</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>Numerical results on ResNet as a function of step (Each step corresponds to a step of stochastic gradient descent based on the derivatives of the loss computed from 2048 randomly selected training samples).</title><p><bold>a</bold> ResNet Hessian spectra during training. <bold>b</bold> Estimated error proxy during training. <bold>c</bold> Training accuracy evolution for ResNet.</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><p>Hessian of the pruned 103 million parameter model immediately after pruning without any additional training.</p></caption></fig>" ]	[]	[ "<inline-formula id=\"IEq1\"><alternatives><tex-math id=\"M1\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{{{{{{\\mathcal{O}}}}}}}}({T}^{2}\\times {{{{{{{\\rm{polylog}}}}}}}}(n))$$\\end{document}</tex-math><mml:math id=\"M2\"><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">O</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msup><mml:mrow><mml:mi>T</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup><mml:mo>×</mml:mo><mml:mi mathvariant=\"normal\">polylog</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>n</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq2\"><alternatives><tex-math id=\"M3\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{{{{{{\\mathcal{O}}}}}}}}(\\log n)$$\\end{document}</tex-math><mml:math id=\"M4\"><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">O</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>log</mml:mi><mml:mi>n</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq3\"><alternatives><tex-math id=\"M5\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{{{{{{\\mathcal{O}}}}}}}}({{{{{{{\\rm{polylog}}}}}}}}(n)\\times T)$$\\end{document}</tex-math><mml:math id=\"M6\"><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">O</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi mathvariant=\"normal\">polylog</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>n</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>×</mml:mo><mml:mi>T</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq4\"><alternatives><tex-math id=\"M7\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{{{{{{\\mathcal{O}}}}}}}}({{{{{{{\\rm{polylog}}}}}}}}(n)\\times {T}^{2})$$\\end{document}</tex-math><mml:math id=\"M8\"><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">O</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi mathvariant=\"normal\">polylog</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>n</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>×</mml:mo><mml:msup><mml:mrow><mml:mi>T</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:math></alternatives></inline-formula>", "<disp-formula id=\"Equ1\"><label>1</label><alternatives><tex-math id=\"M9\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\begin{array}{r}{{{{{{{\\mathcal{O}}}}}}}}\\left(T\\times {{{{{{{\\rm{poly}}}}}}}}\\left(\\log n,\\frac{1}{\\epsilon }\\right)\\right)\\end{array}$$\\end{document}</tex-math><mml:math id=\"M10\"><mml:mtable><mml:mtr><mml:mtd columnalign=\"right\"><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">O</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mi>T</mml:mi><mml:mo>×</mml:mo><mml:mi mathvariant=\"normal\">poly</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mi>log</mml:mi><mml:mi>n</mml:mi><mml:mo>,</mml:mo><mml:mfrac><mml:mrow><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mi>ϵ</mml:mi></mml:mrow></mml:mfrac></mml:mrow></mml:mfenced></mml:mrow></mml:mfenced></mml:mtd></mml:mtr></mml:mtable></mml:math></alternatives></disp-formula>", "<disp-formula id=\"Equ2\"><label>2</label><alternatives><tex-math id=\"M11\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\begin{array}{r}{{{{{{{\\mathcal{O}}}}}}}}\\left({T}^{2}\\times {{{{{{{\\rm{poly}}}}}}}}\\left(\\log n,\\frac{1}{\\epsilon }\\right)\\right)\\end{array}$$\\end{document}</tex-math><mml:math id=\"M12\"><mml:mtable><mml:mtr><mml:mtd columnalign=\"right\"><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">O</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:msup><mml:mrow><mml:mi>T</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup><mml:mo>×</mml:mo><mml:mi mathvariant=\"normal\">poly</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mi>log</mml:mi><mml:mi>n</mml:mi><mml:mo>,</mml:mo><mml:mfrac><mml:mrow><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mi>ϵ</mml:mi></mml:mrow></mml:mfrac></mml:mrow></mml:mfenced></mml:mrow></mml:mfenced></mml:mtd></mml:mtr></mml:mtable></mml:math></alternatives></disp-formula>", "<inline-formula id=\"IEq5\"><alternatives><tex-math id=\"M13\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$m:\\!\\!\\!={\\log }_{2}(n)$$\\end{document}</tex-math><mml:math id=\"M14\"><mml:mi>m</mml:mi><mml:mo>:</mml:mo><mml:mspace width=\"0.3em\"/><mml:mspace width=\"0.3em\"/><mml:mspace width=\"0.3em\"/><mml:mo>=</mml:mo><mml:msub><mml:mrow><mml:mi>log</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>n</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq6\"><alternatives><tex-math id=\"M15\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{{{{{{{\\mathcal{L}}}}}}}}}_{{{{{{{{\\mathcal{A}}}}}}}}}$$\\end{document}</tex-math><mml:math id=\"M16\"><mml:msub><mml:mrow><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">L</mml:mi></mml:mrow><mml:mrow><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">A</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq7\"><alternatives><tex-math id=\"M17\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\theta \\in {{\\mathbb{R}}}^{n}=({\\theta }_{\\mu })$$\\end{document}</tex-math><mml:math id=\"M18\"><mml:mi>θ</mml:mi><mml:mo>∈</mml:mo><mml:msup><mml:mrow><mml:mi mathvariant=\"double-struck\">R</mml:mi></mml:mrow><mml:mrow><mml:mi>n</mml:mi></mml:mrow></mml:msup><mml:mo>=</mml:mo><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:msub><mml:mrow><mml:mi>θ</mml:mi></mml:mrow><mml:mrow><mml:mi>μ</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq8\"><alternatives><tex-math id=\"M19\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{{{{{{\\mathcal{A}}}}}}}}$$\\end{document}</tex-math><mml:math id=\"M20\"><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">A</mml:mi></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq9\"><alternatives><tex-math id=\"M21\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{{{{{{{\\mathcal{L}}}}}}}}}_{{{{{{{{\\mathcal{A}}}}}}}}}$$\\end{document}</tex-math><mml:math id=\"M22\"><mml:msub><mml:mrow><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">L</mml:mi></mml:mrow><mml:mrow><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">A</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<disp-formula id=\"Equ3\"><label>3</label><alternatives><tex-math id=\"M23\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\theta }_{\\mu }(t+1)={\\theta }_{\\mu }(t)-{\\eta \\frac{d{{{{{{{{\\mathcal{L}}}}}}}}}_{{{{{{{{\\mathcal{A}}}}}}}}}}{d{\\theta }_{\\mu }} \\bigg\| }_{\\theta (t)}.$$\\end{document}</tex-math><mml:math id=\"M24\"><mml:msub><mml:mrow><mml:mi>θ</mml:mi></mml:mrow><mml:mrow><mml:mi>μ</mml:mi></mml:mrow></mml:msub><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>t</mml:mi><mml:mo>+</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:msub><mml:mrow><mml:mi>θ</mml:mi></mml:mrow><mml:mrow><mml:mi>μ</mml:mi></mml:mrow></mml:msub><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>t</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>−</mml:mo><mml:msub><mml:mrow><mml:mi>η</mml:mi><mml:mfrac><mml:mrow><mml:mi>d</mml:mi><mml:msub><mml:mrow><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">L</mml:mi></mml:mrow><mml:mrow><mml:mi class=\"MJX-tex-caligraphic\" 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(t=T))$$\\end{document}</tex-math><mml:math id=\"M26\"><mml:msub><mml:mrow><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">L</mml:mi></mml:mrow><mml:mrow><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">A</mml:mi></mml:mrow></mml:msub><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>θ</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>t</mml:mi><mml:mo>=</mml:mo><mml:mi>T</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq11\"><alternatives><tex-math id=\"M27\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{{{{{{{\\mathcal{L}}}}}}}}}_{{{{{{{{\\mathcal{A}}}}}}}}}$$\\end{document}</tex-math><mml:math id=\"M28\"><mml:msub><mml:mrow><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">L</mml:mi></mml:mrow><mml:mrow><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">A</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq12\"><alternatives><tex-math id=\"M29\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{{{{{{{\\mathcal{L}}}}}}}}}_{{{{{{{{\\mathcal{A}}}}}}}}}$$\\end{document}</tex-math><mml:math id=\"M30\"><mml:msub><mml:mrow><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">L</mml:mi></mml:mrow><mml:mrow><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">A</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<disp-formula id=\"Equ4\"><label>4</label><alternatives><tex-math id=\"M31\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\delta u \t=u(t+1)-u(t)=\\mathop{\\sum }\\limits_{\\ell=0}^{q}{F}_{\\ell }{u}^{\\otimes \\ell }(t)\\\\ \t={F}_{q}{u}^{\\otimes q}(t)+\\ldots+{F}_{2}{u}^{\\otimes 2}(t)+{F}_{1}u(t)+{F}_{0}\\,,$$\\end{document}</tex-math><mml:math id=\"M32\"><mml:mi>δ</mml:mi><mml:mi>u</mml:mi><mml:mo>=</mml:mo><mml:mi>u</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>t</mml:mi><mml:mo>+</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>−</mml:mo><mml:mi>u</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>t</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:munderover accent=\"false\" accentunder=\"false\"><mml:mrow><mml:mo>∑</mml:mo></mml:mrow><mml:mrow><mml:mi>ℓ</mml:mi><mml:mo>=</mml:mo><mml:mn>0</mml:mn></mml:mrow><mml:mrow><mml:mi>q</mml:mi></mml:mrow></mml:munderover><mml:msub><mml:mrow><mml:mi>F</mml:mi></mml:mrow><mml:mrow><mml:mi>ℓ</mml:mi></mml:mrow></mml:msub><mml:msup><mml:mrow><mml:mi>u</mml:mi></mml:mrow><mml:mrow><mml:mo>⊗</mml:mo><mml:mi>ℓ</mml:mi></mml:mrow></mml:msup><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>t</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:msub><mml:mrow><mml:mi>F</mml:mi></mml:mrow><mml:mrow><mml:mi>q</mml:mi></mml:mrow></mml:msub><mml:msup><mml:mrow><mml:mi>u</mml:mi></mml:mrow><mml:mrow><mml:mo>⊗</mml:mo><mml:mi>q</mml:mi></mml:mrow></mml:msup><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>t</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>+</mml:mo><mml:mo>…</mml:mo><mml:mo>+</mml:mo><mml:msub><mml:mrow><mml:mi>F</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub><mml:msup><mml:mrow><mml:mi>u</mml:mi></mml:mrow><mml:mrow><mml:mo>⊗</mml:mo><mml:mn>2</mml:mn></mml:mrow></mml:msup><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>t</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>+</mml:mo><mml:msub><mml:mrow><mml:mi>F</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:mi>u</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>t</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow><mml:mo>+</mml:mo><mml:msub><mml:mrow><mml:mi>F</mml:mi></mml:mrow><mml:mrow><mml:mn>0</mml:mn></mml:mrow></mml:msub><mml:mspace width=\"0.25em\"/><mml:mo>,</mml:mo></mml:math></alternatives></disp-formula>", "<disp-formula id=\"Equ5\"><label>5</label><alternatives><tex-math id=\"M33\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\delta \\left[\\begin{array}{c}u\\\\ {u}^{\\otimes 2}\\\\ {u}^{\\otimes 3}\\\\ \\vdots \\\\ {u}^{\\otimes (N-1)}\\\\ {u}^{\\otimes N}\\\\ \\vdots \\\\ \\end{array}\\right]=A\\left[\\begin{array}{c}u\\\\ {u}^{\\otimes 2}\\\\ {u}^{\\otimes 3}\\\\ \\vdots \\\\ {u}^{\\otimes (N-1)}\\\\ {u}^{\\otimes N}\\\\ \\vdots \\\\ \\end{array}\\right]+\\left[\\begin{array}{c}{F}_{0}\\\\ 0\\\\ 0\\\\ \\vdots \\\\ 0\\\\ 0\\\\ \\vdots \\\\ \\end{array}\\right]\\,.$$\\end{document}</tex-math><mml:math id=\"M34\"><mml:mi>δ</mml:mi><mml:mfenced close=\"]\" open=\"[\"><mml:mrow><mml:mtable><mml:mtr><mml:mtd columnalign=\"center\"><mml:mi>u</mml:mi></mml:mtd></mml:mtr><mml:mtr><mml:mtd columnalign=\"center\"><mml:msup><mml:mrow><mml:mi>u</mml:mi></mml:mrow><mml:mrow><mml:mo>⊗</mml:mo><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:mtd></mml:mtr><mml:mtr><mml:mtd columnalign=\"center\"><mml:msup><mml:mrow><mml:mi>u</mml:mi></mml:mrow><mml:mrow><mml:mo>⊗</mml:mo><mml:mn>3</mml:mn></mml:mrow></mml:msup></mml:mtd></mml:mtr><mml:mtr><mml:mtd columnalign=\"center\"><mml:mo>⋮</mml:mo></mml:mtd></mml:mtr><mml:mtr><mml:mtd columnalign=\"center\"><mml:msup><mml:mrow><mml:mi>u</mml:mi></mml:mrow><mml:mrow><mml:mo>⊗</mml:mo><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>N</mml:mi><mml:mo>−</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:msup></mml:mtd></mml:mtr><mml:mtr><mml:mtd columnalign=\"center\"><mml:msup><mml:mrow><mml:mi>u</mml:mi></mml:mrow><mml:mrow><mml:mo>⊗</mml:mo><mml:mi>N</mml:mi></mml:mrow></mml:msup></mml:mtd></mml:mtr><mml:mtr><mml:mtd columnalign=\"center\"><mml:mo>⋮</mml:mo></mml:mtd></mml:mtr><mml:mtr><mml:mtd columnalign=\"center\"/></mml:mtr></mml:mtable></mml:mrow></mml:mfenced><mml:mo>=</mml:mo><mml:mi>A</mml:mi><mml:mfenced close=\"]\" open=\"[\"><mml:mrow><mml:mtable><mml:mtr><mml:mtd columnalign=\"center\"><mml:mi>u</mml:mi></mml:mtd></mml:mtr><mml:mtr><mml:mtd columnalign=\"center\"><mml:msup><mml:mrow><mml:mi>u</mml:mi></mml:mrow><mml:mrow><mml:mo>⊗</mml:mo><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:mtd></mml:mtr><mml:mtr><mml:mtd columnalign=\"center\"><mml:msup><mml:mrow><mml:mi>u</mml:mi></mml:mrow><mml:mrow><mml:mo>⊗</mml:mo><mml:mn>3</mml:mn></mml:mrow></mml:msup></mml:mtd></mml:mtr><mml:mtr><mml:mtd columnalign=\"center\"><mml:mo>⋮</mml:mo></mml:mtd></mml:mtr><mml:mtr><mml:mtd columnalign=\"center\"><mml:msup><mml:mrow><mml:mi>u</mml:mi></mml:mrow><mml:mrow><mml:mo>⊗</mml:mo><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>N</mml:mi><mml:mo>−</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mrow></mml:msup></mml:mtd></mml:mtr><mml:mtr><mml:mtd columnalign=\"center\"><mml:msup><mml:mrow><mml:mi>u</mml:mi></mml:mrow><mml:mrow><mml:mo>⊗</mml:mo><mml:mi>N</mml:mi></mml:mrow></mml:msup></mml:mtd></mml:mtr><mml:mtr><mml:mtd columnalign=\"center\"><mml:mo>⋮</mml:mo></mml:mtd></mml:mtr><mml:mtr><mml:mtd columnalign=\"center\"/></mml:mtr></mml:mtable></mml:mrow></mml:mfenced><mml:mo>+</mml:mo><mml:mfenced close=\"]\" open=\"[\"><mml:mrow><mml:mtable><mml:mtr><mml:mtd columnalign=\"center\"><mml:msub><mml:mrow><mml:mi>F</mml:mi></mml:mrow><mml:mrow><mml:mn>0</mml:mn></mml:mrow></mml:msub></mml:mtd></mml:mtr><mml:mtr><mml:mtd columnalign=\"center\"><mml:mn>0</mml:mn></mml:mtd></mml:mtr><mml:mtr><mml:mtd columnalign=\"center\"><mml:mn>0</mml:mn></mml:mtd></mml:mtr><mml:mtr><mml:mtd columnalign=\"center\"><mml:mo>⋮</mml:mo></mml:mtd></mml:mtr><mml:mtr><mml:mtd columnalign=\"center\"><mml:mn>0</mml:mn></mml:mtd></mml:mtr><mml:mtr><mml:mtd columnalign=\"center\"><mml:mn>0</mml:mn></mml:mtd></mml:mtr><mml:mtr><mml:mtd columnalign=\"center\"><mml:mo>⋮</mml:mo></mml:mtd></mml:mtr><mml:mtr><mml:mtd columnalign=\"center\"/></mml:mtr></mml:mtable></mml:mrow></mml:mfenced><mml:mspace width=\"0.25em\"/><mml:mo>.</mml:mo></mml:math></alternatives></disp-formula>", "<inline-formula id=\"IEq13\"><alternatives><tex-math id=\"M35\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\hat{y}$$\\end{document}</tex-math><mml:math id=\"M36\"><mml:mover accent=\"true\"><mml:mrow><mml:mi>y</mml:mi></mml:mrow><mml:mrow><mml:mo>^</mml:mo></mml:mrow></mml:mover></mml:math></alternatives></inline-formula>", "<disp-formula id=\"Equ6\"><label>6</label><alternatives><tex-math id=\"M37\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\left[\\begin{array}{ccccc}I&&&&\\\\ -(I\\,+\\,A)&I&&&\\\\ &\\ddots &\\ddots &&\\\\ &&-(I\\,+\\,A)&I&\\\\ &&&-(I\\,+\\,A)&I\\\\ \\end{array}\\right]\\left[\\begin{array}{c}\\hat{y}(0)\\\\ \\hat{y}(1)\\\\ \\vdots \\\\ \\hat{y}(T-1)\\\\ \\hat{y}(T)\\\\ \\end{array}\\right]=\\left[\\begin{array}{c}{\\hat{y}}_{{{{{{{{\\rm{in}}}}}}}}}\\\\ b\\\\ \\vdots \\\\ b\\\\ b\\\\ \\end{array}\\right].$$\\end{document}</tex-math><mml:math id=\"M38\"><mml:mfenced close=\"]\" open=\"[\"><mml:mrow><mml:mtable><mml:mtr><mml:mtd columnalign=\"center\"><mml:mi>I</mml:mi></mml:mtd><mml:mtd columnalign=\"center\"/><mml:mtd columnalign=\"center\"/><mml:mtd columnalign=\"center\"/><mml:mtd columnalign=\"center\"/></mml:mtr><mml:mtr><mml:mtd columnalign=\"center\"><mml:mo>−</mml:mo><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>I</mml:mi><mml:mspace width=\"0.25em\"/><mml:mo>+</mml:mo><mml:mspace width=\"0.25em\"/><mml:mi>A</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mtd><mml:mtd columnalign=\"center\"><mml:mi>I</mml:mi></mml:mtd><mml:mtd columnalign=\"center\"/><mml:mtd columnalign=\"center\"/><mml:mtd columnalign=\"center\"/></mml:mtr><mml:mtr><mml:mtd columnalign=\"center\"/><mml:mtd columnalign=\"center\"><mml:mo>⋱</mml:mo></mml:mtd><mml:mtd columnalign=\"center\"><mml:mo>⋱</mml:mo></mml:mtd><mml:mtd columnalign=\"center\"/><mml:mtd columnalign=\"center\"/></mml:mtr><mml:mtr><mml:mtd columnalign=\"center\"/><mml:mtd columnalign=\"center\"/><mml:mtd columnalign=\"center\"><mml:mo>−</mml:mo><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>I</mml:mi><mml:mspace width=\"0.25em\"/><mml:mo>+</mml:mo><mml:mspace width=\"0.25em\"/><mml:mi>A</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mtd><mml:mtd columnalign=\"center\"><mml:mi>I</mml:mi></mml:mtd><mml:mtd columnalign=\"center\"/></mml:mtr><mml:mtr><mml:mtd columnalign=\"center\"/><mml:mtd columnalign=\"center\"/><mml:mtd columnalign=\"center\"/><mml:mtd columnalign=\"center\"><mml:mo>−</mml:mo><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>I</mml:mi><mml:mspace width=\"0.25em\"/><mml:mo>+</mml:mo><mml:mspace width=\"0.25em\"/><mml:mi>A</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mtd><mml:mtd columnalign=\"center\"><mml:mi>I</mml:mi></mml:mtd></mml:mtr><mml:mtr><mml:mtd columnalign=\"center\"/></mml:mtr></mml:mtable></mml:mrow></mml:mfenced><mml:mfenced close=\"]\" open=\"[\"><mml:mrow><mml:mtable><mml:mtr><mml:mtd columnalign=\"center\"><mml:mover accent=\"true\"><mml:mrow><mml:mi>y</mml:mi></mml:mrow><mml:mrow><mml:mo>^</mml:mo></mml:mrow></mml:mover><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mn>0</mml:mn></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mtd></mml:mtr><mml:mtr><mml:mtd columnalign=\"center\"><mml:mover accent=\"true\"><mml:mrow><mml:mi>y</mml:mi></mml:mrow><mml:mrow><mml:mo>^</mml:mo></mml:mrow></mml:mover><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mn>1</mml:mn></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mtd></mml:mtr><mml:mtr><mml:mtd columnalign=\"center\"><mml:mo>⋮</mml:mo></mml:mtd></mml:mtr><mml:mtr><mml:mtd columnalign=\"center\"><mml:mover accent=\"true\"><mml:mrow><mml:mi>y</mml:mi></mml:mrow><mml:mrow><mml:mo>^</mml:mo></mml:mrow></mml:mover><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>T</mml:mi><mml:mo>−</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mtd></mml:mtr><mml:mtr><mml:mtd columnalign=\"center\"><mml:mover accent=\"true\"><mml:mrow><mml:mi>y</mml:mi></mml:mrow><mml:mrow><mml:mo>^</mml:mo></mml:mrow></mml:mover><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mi>T</mml:mi></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:mtd></mml:mtr><mml:mtr><mml:mtd columnalign=\"center\"/></mml:mtr></mml:mtable></mml:mrow></mml:mfenced><mml:mo>=</mml:mo><mml:mfenced close=\"]\" open=\"[\"><mml:mrow><mml:mtable><mml:mtr><mml:mtd columnalign=\"center\"><mml:msub><mml:mrow><mml:mover accent=\"true\"><mml:mrow><mml:mi>y</mml:mi></mml:mrow><mml:mrow><mml:mo>^</mml:mo></mml:mrow></mml:mover></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">in</mml:mi></mml:mrow></mml:msub></mml:mtd></mml:mtr><mml:mtr><mml:mtd columnalign=\"center\"><mml:mi>b</mml:mi></mml:mtd></mml:mtr><mml:mtr><mml:mtd columnalign=\"center\"><mml:mo>⋮</mml:mo></mml:mtd></mml:mtr><mml:mtr><mml:mtd columnalign=\"center\"><mml:mi>b</mml:mi></mml:mtd></mml:mtr><mml:mtr><mml:mtd columnalign=\"center\"><mml:mi>b</mml:mi></mml:mtd></mml:mtr><mml:mtr><mml:mtd columnalign=\"center\"/></mml:mtr></mml:mtable></mml:mrow></mml:mfenced><mml:mo>.</mml:mo></mml:math></alternatives></disp-formula>", "<inline-formula id=\"IEq14\"><alternatives><tex-math id=\"M39\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\hat{y}}_{{{{{{{{\\rm{in}}}}}}}}}$$\\end{document}</tex-math><mml:math id=\"M40\"><mml:msub><mml:mrow><mml:mover accent=\"true\"><mml:mrow><mml:mi>y</mml:mi></mml:mrow><mml:mrow><mml:mo>^</mml:mo></mml:mrow></mml:mover></mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">in</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq15\"><alternatives><tex-math id=\"M41\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{{{{{{\\mathcal{O}}}}}}}}(100)$$\\end{document}</tex-math><mml:math id=\"M42\"><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">O</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mn>100</mml:mn></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq16\"><alternatives><tex-math id=\"M43\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{{{{{{\\mathcal{O}}}}}}}}(100)$$\\end{document}</tex-math><mml:math id=\"M44\"><mml:mi class=\"MJX-tex-caligraphic\" mathvariant=\"script\">O</mml:mi><mml:mrow><mml:mo>(</mml:mo><mml:mrow><mml:mn>100</mml:mn></mml:mrow><mml:mo>)</mml:mo></mml:mrow></mml:math></alternatives></inline-formula>", "<disp-formula id=\"Equ7\"><label>7</label><alternatives><tex-math id=\"M45\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\frac{1}{{N}_{c}}\\int\\nolimits_{-\\infty }^{-0.4}\\rho (a)\| {(1+a)}^{t}\| da+\\frac{1}{{N}_{c}}\\int\\nolimits_{0.4}^{\\infty }\\rho (a)\| {(1+a)}^{t}\| da,$$\\end{document}</tex-math><mml:math 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"<disp-formula id=\"Equ8\"><label>8</label><alternatives><tex-math id=\"M47\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\frac{1}{{N}_{c}}\\int\\nolimits_{-\\infty }^{-0.4}\\rho (a)da+\\frac{1}{{N}_{c}}\\int\\nolimits_{0.4}^{\\infty }\\rho (a)da=1.$$\\end{document}</tex-math><mml:math 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PMC10781665	38199995	[ "<title>Introduction</title>", "<p id=\"Par3\">Surface organs (organs with direct or indirect contact with the ambient air), including the oral cavity, gastrointestinal tract (GI), and skin, are the largest organs in human body with widespread involvement in physiological, metabolic, and immunological processes. Surface organs are inhabited by trillions of microbes and harbored diverse microbial communities<sup>##REF##27122046##1##,##REF##16497592##2##</sup>. The location and functional features of each surface organ create specific environmental conditions that gives rise to regional specificity in microbial populations<sup>##REF##31513770##3##</sup>. To date, emerging studies focusing on regional differences have unveiled numerous physiological roles of the microbiome that are crucial for human life<sup>##REF##22699609##4##,##REF##32066951##5##</sup>. Whereas few has reported the heterogeneity in microbiome among different body parts<sup>##REF##22699609##4##</sup>. Such inter-organ microbial disparity also contributes to variation and plasticity in the functional traits of gut microbiome<sup>##REF##29173869##6##</sup>. However, only a few investigations deciphered the microbiome along the digestive organs with limited sampling sites from the same individual<sup>##REF##30126990##7##</sup>. To fully uncover the human microbiome, it is necessary to assess microbiome composition in surface organs of digestive system (lumen and mucosa) and skin with much denser sampling as a whole.</p>", "<p id=\"Par4\">Microbiome in different regions/or sites of an organ could be varied, exemplified by the difference in microbiome between proximal and distal colon<sup>##REF##31513770##3##</sup>. We recently reported the presence of microbial heterogeneity in a single colorectal tumor<sup>##REF##33581124##8##</sup>. Understanding whether there are discernible patterns of microbial communities along the GI tract and elucidating regional microbial niches, are of great importance for complete mapping of the human microbiome. However, it has been challenging to collect samples from multiple sites within an organ, especially in the small intestine. Moreover, it remains undetermined whether microbiome in lumen samples is similar or different from microbiome in mucosa samples of the whole GI tract.</p>", "<p id=\"Par5\">The microbial crosstalk among organs is emerging as an essential indicator of human health<sup>##UREF##0##9##,##REF##32758418##10##</sup>. Yet, little is known about the routine inter-organ contacts of the microbiome. For instance, how the oral microbial community correlates with microbiome in other organs is completely unknown. Sampling a breath of intra-individual surface regions therefore offer an opportunity to decipher how microbial residents might translocate from one site/organ to another, respond to changing environments, and shape host physiology.</p>", "<p id=\"Par6\">In this study, lumen mucosa, gastric juice, and surface samples from 53 sites of 7 surface organs (oral cavity, stomach, esophagus, small intestine, appendix, large intestine, and skin) were collected from 33 subjects to give a total of 1608 samples. The large collection of samples facilitated the generation of a high-resolution biogeographical map of the human microbiome. Our findings revealed the differences in diversity, composition, interaction, and functional traits of microbiome among organs in the digestive system and all surface organs, as well as those among different sites within an organ. We also profiled the luminal- and mucosal-associated microbes along the GI tract. Using 16S full-length data from PacBio highly accurate long-read sequencing, we finally elucidated the inter-organ relations of microbes at species level in human body.</p>" ]	[ "<title>Methods</title>", "<title>Human subjects</title>", "<p id=\"Par23\">All the human donors were declared dead by cardiovascular death. Causes of death can be found in Supplementary Table ##SUPPL##0##1##. Subjects were excluded if they had tumour, infectious disease, or metabolic disease. Written informed consent was obtained from each enrolled donor via next-of-kin to permit the collection and banking of samples (consent rate: 24.2% (33/136)). Sample collection was conducted under the instruction and supervision of Organ Procurement Organization of First Hospital of Xi’an Jiaotong University and the Red Cross Society of Shaanxi Province (Supplementary Methods). Samples from multiple surface organs were collected right after the harvest of liver and kidney for organ transplantation in the First Affiliated Hospital of Xi’an Jiaotong University. Characteristics of recruited 33 subjects were provided in Supplementary Table ##SUPPL##0##1##. The study was approved by the Clinical Application Ethics Committee of First Affiliated Hospital of Xi’an Jiaotong University (Approval No. XJTU1AF2019LSK-059) and conducted in accordance with the Declaration of Helsinki.</p>", "<title>Surface organ collection</title>", "<p id=\"Par24\">The short duration (<1.5 h) of sample collection from the donor after death permits a thorough survey on microbial community of all human surface organs. Previous reports suggested that sampling within 2 h after death did not cause significant post-mortem changes in microbiome<sup>##REF##21385893##25##–##UREF##3##27##</sup>. Surface (swab/mucosa) samples and luminal samples of 53 sites from seven surface organs were collected per individual in the 100-level laminar operating room. Each sample was collected using disposable surgical instruments to avoid any external bacterial contamination, instrument-related contamination, and cross-site contamination. In addition, we parallelly introduced a set of environmental negative controls to measure the effect of laboratory-borne contamination (Supplementary Fig. ##SUPPL##0##1A##) during sample collection. These control samples were used to determine taxa that arise from contamination. All samples were frozen immediately using dry ice or liquid nitrogen and stored at −80 °C within 1.5 h for long-term storage.</p>", "<title>Detailed collection protocols from individual surface organs of human subjects</title>", "<p id=\"Par25\">Samples from the oral cavity and skin surface were collected first. Intestinal mucosal and luminal/surface samples were collected in sequence: esophagus (4 sites), stomach (5 sites), small intestine (14 sites), appendix (1 subsite) and large intestine (13 sites).</p>", "<title>Skin</title>", "<p id=\"Par26\">Ten skin sites representing a range of physiological characteristics were selected, including core/proximal body sites: chest, back, palmar (left and right), plantar (left and right), anterior leg (left and right), and anterior antebrachium (left and right). Skin samples were collected with wet cotton swabs soaked in sterile saline by wiping repeatedly more than 30 times.</p>", "<title>Oral cavity</title>", "<p id=\"Par27\">Oral samples were obtained by rubbing the buccal mucosa (left and right), hard palate (upper and lower), and inside of lips (upper and power) with wet cotton swabs soaked in sterile saline by wiping repeatedly more than 30 times. Samples were collected without touching the teeth to avoid contamination by microbes present on the tooth surface.</p>", "<title>Esophagus</title>", "<p id=\"Par28\">Thoracic esophagus was pulled carefully down to the peritoneal cavity through esophageal hiatus. A 1–1.5 cm longitudinal incision was made in the anterior wall to expose the mucosa of esophagus. Mucosal samples were collected from the thoracic part, abdominal part, zigzag line, and cardiac orifice using disposable surgical scissors and forceps. The incision was sutured immediately after sample collection.</p>", "<title>Stomach</title>", "<p id=\"Par29\">Gastric juice was retrieved before the collection of mucosal samples. Mucosa specimens were collected from four sites (cardia, fundus, antrum, and pylorus) according to their anatomical locations.</p>", "<title>Small intestine, appendix, and large intestine</title>", "<p id=\"Par30\">The sample collection procedure was as follows: 1) Small intestine: 9 sites including duodenal bulb, major duodenal papilla, duodenojejunal flexure, small intestine 1 m, small intestine 2 m, small intestine 3 m, small intestine 4 m, terminal part of ileum, and ileocaecal valve; 2) Appendix; 3) Large Intestine: 7 sites including cecum, ascending colon, transverse colon, descending colon, sigmoid colon, rectum, and annus. For each subsite of small and large intestines, luminal samples were retrieved before the collection of mucosal samples. Luminal samples were quickly transferred to a sterile 50 ml centrifuge tube (BD, USA). Mucosal samples were rinsed gently with sterile saline to avoid being contaminated by intestinal contents.</p>", "<title>DNA extraction</title>", "<p id=\"Par31\">Ultraclean kits and reagents were used to avoid exogenous DNA contamination. Mucosal samples (25–30 mg) were disrupted by bead-beating and digested in an enzymatic cocktail of mutanolysin and lysozyme (Sigma, St. Louis, MO) prior to DNA extraction with QIAamp DNA Mini Kit (Cat No.51306, Qiagen, Hilden, Germany). Swab sample was dissolved in a 2 ml RNase-free tube (Biosharp, China) with 500 μl sterile PBS and DNA was extracted using QIAamp DNA Mini Kit (No.51306, Qiagen). DNA from lumen samples was extracted using QIAamp Fast DNA Stool Mini Kit (No.51604, Qiagen). The negative control samples underwent identical processing procedures.</p>", "<title>16S ribosomal RNA (rRNA) gene sequencing</title>", "<p id=\"Par32\">The v3v4 regions of 16S rRNA genes were amplified using primers 341F [5′-CCTAYGGGRBGCASCAG-3′] and 806R [5′-GGACTACNNGGGTATCTAAT-3′] together with the adapters and barcode sequences, allowing directional sequencing covering the hypervariable region (Novogene, Nanjing, China). Sequencing libraries were generated using TruSeq® DNA PCR-Free Sample Preparation Kit (Illumina, USA), and sequenced on an Illumina NovaSeq platform (dual-index) to generate 250 bp paired-end reads.</p>", "<title>PacBio 16S rRNA gene full-length HiFi sequencing</title>", "<p id=\"Par33\">The full-length 16S rRNA genes were amplified by PCR using primers 27F [5′-AGRGTTTGATYNTGGCTCAG-3′] and 1492R [5′-TASGGHTACCTTGTTASGACTT-3′]. PCR products were purified using AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, USA). Amplicon pools were prepared for library construction using the Pacific Biosciences SMRTbellTM Template Prep kit 1.0 (PacBio, USA) and sequenced on PacBio RS II (LC-Bio Technology Co., Ltd., Hangzhou, China).</p>", "<title>Sequence curation and annotation</title>", "<p id=\"Par34\">For Illumina 16S v3v4 sequencing data, raw paired-end reads of 16S rRNA gene sequence were quality-filtered and analyzed using QIIME2 (version 2019.4.0; default parameters) software<sup>##REF##31341288##28##</sup>. Deblur algorithm was applied to reduce sequencing errors and dereplicate sequences with default parameters. Before dereplicating sequences that encoded amplicon sequence variants (ASVs), paired reads were joined and trimmed to 380 base pairs. After filtering chimera sequences, dereplicated sequences were classified taxonomically using Greengenes database at 99% identity cut-off. ASVs detected in negative controls were eliminated.</p>", "<p id=\"Par35\">For PacBio 16S full-length sequencing data, circular consensus sequence (CCS) reads were generated from raw subreads by SMRT Link (v6.0). CCS reads from different samples were distinguished by lima (v1.7.1). Primers of CCS reads were trimmed by cutadapt (v1.9; default parameters) to obtain the clean reads. The clean reads with length between 1200 bp to 1650 bp were kept for further analysis. DADA2 algorithm was applied to dereplicate the reads and filter chimeric sequences. The dereplicated sequences (ASVs) were classified taxonomically using Greengenes database, SILVA database, and NCBI database by BLAST tool kits. ASVs detected in negative controls were eliminated.</p>", "<p id=\"Par36\">Microbial community analysis, including α-diversity and β-diversity, were calculated using phyloseq R package. α-Diversity was evaluated by relative inverse Simpson index. β-iversity was measured by UniFrac distance, and principal coordinates analysis (PCoA) was used for ordination analysis. We applied two-tailed Wilcoxon signed-rank test for differential testing of α-diversity, and <italic>P</italic> < 0.05 was considered statistically significant. Community dissimilarities were tested by permutational multivariate analyses of variance (PERMANOVA) with 10,000 iterations. Differentially enriched microbes were analyzed using ANCOM-BC2 (v2.2.2; default parameters), a methodology for performing differential abundance (DA) analysis of microbiome count data<sup>##REF##32665548##29##</sup>. Differences with fold change >2 and adjusted <italic>P</italic> < 0.05 were considered statistically significant. We applied constrained correspondence analysis to evaluate microbial dissimilarities of intra-organ sites and that of the lumen and mucosa.</p>", "<title>Controls for contamination during all stages of experiment</title>", "<p id=\"Par37\">Several procedures were conducted to control for false-positive contaminating taxa. We reduced the read counts to 10,000 library size to reduce the variation of library depths of each sample. Taxa with relative abundance <0.1% in all samples were discarded, as their inclusion could introduce noise variation. We finally compared the taxa prevalence of real biological samples to that of negative controls using decontam method<sup>##REF##30558668##30##</sup> (threshold = 0.5), commonly used to discriminate true positives and contaminations.</p>", "<title>Luminal- and mucosal-related microbes by logistic regression</title>", "<p id=\"Par38\">We applied a logistic binomial regression with overdispersion to unravel luminal- or mucosal-related microbes:Where <italic>p</italic> represents the probability of observing taxa <italic>x</italic> after accounting for the overdispersion using beta distribution . The hyperparameters <italic>a</italic>, <italic>b</italic> were estimated automatically using the aod R package. <italic>R</italic><sub><italic>type</italic></sub> and <italic>S</italic><sub><italic>i</italic></sub> represent indicator variables for the sample type (lumen and mucosa/gastric juice) and human characteristics, respectively. <italic>C</italic> represents the read count of taxa <italic>x</italic> observed in this site, while <italic>N</italic> indicates the sequence depth of all taxa observed in this sample.</p>", "<title>Functional analysis</title>", "<p id=\"Par39\">Functional attributes of the microbiome associated with different surface organs were analyzed by PICRUSt2. Functional attributes were annotated by MetaCyc database, and the super-class of pathways was obtained using Pathway Tool (ver. 25.5). Differentially enriched pathways among organs were analyzed using ALDEx2 method, as suggested by PICRUSt2 pipeline. Differences with fold change >2 and FDR < 0.05 were considered statistically significant.</p>", "<title>Correlation network analysis</title>", "<p id=\"Par40\">SECOM method<sup>##UREF##4##31##</sup> was used to infer microbial interplay and was applied separately to each site. We also applied the SparCC method<sup>##UREF##5##32##</sup> to further validate the observations. Microbial correlations were selected if they have FDR < 0.05 and shared the same correlation type (positive/negative correlation) in all sites of an organ, and the average correlation was calculated. We then defined the organ-specific correlation, which met any of the following two conditions: 1) the difference in correlations between organs >0.6; 2) the correlation with strength >0.6 was present in this organ only. The selected correlations were visualized using Cytoscape (version 3.7.1).</p>", "<title>Statistical analysis</title>", "<p id=\"Par41\">Statistical significance tests, including Wilcoxon signed-rank test, ANOVA permutation test, ANCOM-BC2, SECOM, and SparCC correlation test, were performed using open-source R software. All statistical tests were two-sided, and <italic>P</italic> < 0.05 were considered statistically significant. For multiple comparisons, <italic>P</italic> values were adjusted using Benjamini-Hochberg False Discovery Rate (FDR) correction.</p>", "<title>Reporting summary</title>", "<p id=\"Par42\">Further information on research design is available in the ##SUPPL##5##Nature Portfolio Reporting Summary## linked to this article.</p>" ]	[ "<title>Results</title>", "<title>Sample collection for microbiome profiling</title>", "<p id=\"Par7\">We collected 1608 samples from 7 surface organs of oral cavity (6 sites), esophagus (4 sites), stomach (5 sites), small intestine (14 sites), appendix (1 site), large intestine (13 sites), and skin (10 sites), in total comprising of 53 sites in 33 subjects (Fig. ##FIG##0##1A##) who were dead due to vehicle accident, high-altitude falling, etc. (Supplementary Table ##SUPPL##0##1##). To minimize the post-mortem microbial changes, all samples were collected in a short duration (<1.5 h) after determination of death. Both luminal and mucosal samples were collected from the stomach, small intestine and large intestine. We parallelly introduced a set of negative controls to evaluate potential contamination (Supplementary Fig. ##SUPPL##0##1A##). All retrieved samples were subjected to microbial profiling by 16S v3v4 region sequencing, and samples from GI organs (<italic>n</italic> = 1030) were additionally analyzed by PacBio 16S full-length HiFi sequencing. After eliminating ASVs detected in negative controls (Supplementary Fig. ##SUPPL##0##1B## and Supplementary Table ##SUPPL##0##2##), we obtained a total of 9473 bacterial ASVs for downstream analysis (Fig. ##FIG##0##1B##). Key contaminating ASVs consist of environmental taxa (e.g., <italic>Propionibacterium</italic> (17.08%; relative abundance in negative controls), <italic>Phyllobacterium</italic> (6.12%), <italic>Deinococcus</italic> (4.87%)), and they were on average one order of magnitude higher than in mucosal samples as compared to luminal samples (Supplementary Table ##SUPPL##0##2##). We next applied permutational multivariate analysis of variance (PERMANOVA) to study the effect of subject’s characteristics (e.g., cause of death, length of hospitalization) on the microbiome communities. We found the length of hospitalization and antibiotic treatments had significant effects on microbiome in the oral cavity, small intestine, and large intestine, but not the cause of death (Supplementary Table ##SUPPL##0##3##).</p>", "<title>Microbial diversity varies among surface organs</title>", "<p id=\"Par8\">We first investigated the bacterial diversity in surface organs. Significant differences in bacterial α-diversity were identified among surface organs (Fig. ##FIG##1##2A## and Supplementary Fig. ##SUPPL##0##1C##). The α-diversity of skin, oral cavity, and esophagus was significantly higher compared to stomach, appendix, small or large intestines, respectively (<italic>P</italic> < 0.01, Wilcoxon signed-rank test). Among seven organs, the bacteria diversity in stomach was the lowest, attributed to its low pH that limits bacterial growth. Significantly higher α-diversity in the large intestine was observed when compared to stomach or small intestine (<italic>P</italic> < 0.05).</p>", "<p id=\"Par9\">Changes in α-diversity along the GI tract were then measured. In the upper GI tract (esophagus-stomach-duodenum), we observed that α-diversity initially falls in esophagus, reaching the bottom at the stomach, and subsequently rising at the duodenum (<italic>P</italic> < 0.05) (Fig. ##FIG##1##2B##). Meanwhile, significantly increasing trend of α-diversity was also identified in luminal samples along the lower GI tract (jejunum-Ileum-colon) (<italic>P</italic> < 0.05) (Fig. ##FIG##1##2B##), mainly attributed to the longer transit time in colon. When comparing α-diversity between mucosal and luminal samples, we observed disparities in α-diversity along the lower GI tract (Fig. ##FIG##1##2B##). Specifically, mucosal α-diversity was higher in jejunum/Ileum (<italic>P</italic> < 0.05) compared to luminal samples; while mucosal α-diversity was lower than that of luminal in the large intestine (<italic>P</italic> < 0.0001).</p>", "<p id=\"Par10\">The global microbial β-diversity was also significantly different among organs (<italic>P</italic> < 0.001, PERMANOVA) (Fig. ##FIG##1##2C##). The most different microbiome was found between the large intestine and oral cavity, whilst microbiome between the stomach and esophagus was the least different (Supplementary Table ##SUPPL##0##4##). In the small intestine, we observed drastic intra-organ variation (Fig. ##FIG##1##2C##), and showed that intra-organ variation in the small intestine spans between stomach and large intestine clusters according to the sampling location (Supplementary Fig. ##SUPPL##0##1D##). Additionally, appendix microbiome was similar to the small intestine microbiome (Fig. ##FIG##1##2C## and Supplementary Table ##SUPPL##0##4##).</p>", "<title>Inter-organ microbial communities are distinct</title>", "<p id=\"Par11\">We next measured the inter-organ microbial composition. Six phyla (<italic>Proteobacteria</italic>, <italic>Firmicutes</italic>, <italic>Bacteroidetes</italic>, <italic>Actinobacteria</italic>, <italic>Fusobacteria</italic>, and <italic>Tenericutes</italic>) together occupied >98% relative abundance in each organ (Fig. ##FIG##2##3A1## and Supplementary Fig. ##SUPPL##0##2A##). Abundances of these phyla were all significantly different among seven organs (Fig. ##FIG##2##3A2##). <italic>Bacteroidetes</italic>, <italic>Actinobacteria,</italic> and <italic>Fusobacteria</italic> were enriched in large intestine, skin, and oral cavity, respectively, while <italic>Proteobacteria</italic> and <italic>Firmicutes</italic> were enriched in esophagus, stomach, and small intestine. Microbial composition at genus level was then assessed (Fig. ##FIG##2##3B##). <italic>Bacteroides</italic> and <italic>Parabacteroides</italic> were predominantly enriched in small intestine, appendix, and large intestine; <italic>Porphyromonas, Prevotella</italic>, <italic>Streptococcus</italic> and <italic>Neisseria</italic> were enriched in the oral cavity; <italic>Fusobacterium</italic> was enriched in both oral cavity and appendix; and <italic>Staphylococcus</italic>, and <italic>Corynebacterium</italic> were the dominant genera in the skin. At the individual level, we observed a decreasing trend in the abundances of <italic>Staphylococcus</italic> and <italic>Corynebacterium</italic> from the skin to GI tract (Supplementary Fig. ##SUPPL##0##2B##). Conversely, increased abundances of <italic>Enterococcus</italic>, <italic>Ruminococcus</italic> and <italic>Bifidobacterium</italic> were observed along the GI tract. Moreover, <italic>Helicobacter</italic> was enriched in stomach and esophagus. These findings together suggested that microbial composition differs among surface organs.</p>", "<title>Intra-organ microbial communities are heterogenous</title>", "<p id=\"Par12\">As shown in Supplementary Fig. ##SUPPL##0##2##, microbes were not evenly distributed in each organ. We therefore investigated the microbiome of different intra-organ sites. β-diversity was significantly different among sites of each organ (Fig. ##FIG##3##4##). We identified the signature microbes specific to each site in an organ: <italic>Corynebacterium</italic> and <italic>Staphylococcus</italic> in the extremity cluster in skin (Fig. ##FIG##3##4A##); and <italic>Aggregatibacter</italic> in the jaws cluster of the oral cavity (Fig. ##FIG##3##4B##). In the esophagus, <italic>Helicobacter</italic> was increased from Thoracic Part (<italic>TP</italic>) to Cardiac orifice (<italic>CO</italic>), while its abundance was decreased from Fundus/Body to Pylorus (<italic>PY</italic>) in the stomach (Fig. ##FIG##3##4C, D##). In the small intestine, <italic>Prevotella</italic> were enriched in both the mucosa and lumen of duodenum, whereas <italic>Enterococcus</italic> and <italic>Bacteroides</italic> were enriched in both the mucosa and lumen of ileum (Fig. ##FIG##3##4E, F##). In the large intestine, we identified clear separation of microbiome between the right-sided and left-sided colon, attributed to the disparity in the enriched microbes (<italic>e.g., Klebsiella</italic> in the right-sided colon; <italic>Bifidobacterium</italic> and <italic>Oscillospira</italic> in the left-sided colon) (Fig. ##FIG##3##4G, H##). These data revealed the distinct microbial composition of intra-organ sites.</p>", "<title>Microbial community differences between lumen and mucosa</title>", "<p id=\"Par13\">The availability of paired lumen-mucosa samples allowed us to investigate the microbial difference between the two sample types. Using 16S v3v4 dataset, mucosal microbial communities were all significantly different from luminal microbial communities in stomach, small intestine and large intestine (<italic>P</italic> < 0.0001 for all, PERMANOVA) (Figs. ##FIG##1##2##B and ##FIG##4##5A##). To decipher the microbial relationships between lumen and mucosa, we used logistic regression and identified 33, 52, and 47 mucosal/luminal-associated microbes in stomach, small intestine and large intestine, respectively. In the stomach, 60% (9/15) of mucosal-enriched genera were members of <italic>Firmicutes</italic>; whilst major gastric juice-enriched microbes belonged to <italic>Firmicutes</italic> (47%, 7/15) and <italic>Proteobacteria</italic> (47%, 7/15; e.g., <italic>Helicobacter</italic>) (Fig. ##FIG##4##5B##). In the small intestine, <italic>Firmicutes</italic> occupied 50% (19/38) of mucosal-enriched microbes (e.g., <italic>Coprococcus</italic> and <italic>Clostridium</italic>), whereas 43% (6/14) of luminal-enriched microbes belonged to <italic>Proteobacteria</italic> (Fig. ##FIG##4##5C##). <italic>Akkermansia</italic> and <italic>Bifidobacterium</italic>, two beneficial microbes in humans, were also enriched in the intestinal mucosa. In the large intestine, 81% (13/16) of mucosal-enriched microbes belonged to <italic>Firmicutes</italic> (Fig. ##FIG##4##5D##). Among luminal-enriched microbes, 42% (13/31) were members of <italic>Firmicutes</italic>, followed by <italic>Bacteroidetes</italic> (29%, 9/31). We then conducted similar analysis using 16S full-length dataset (Supplementary Fig. ##SUPPL##0##3##) in order to validate the above observations. We found that consistent lumen/mucosa-enriched bacteria were identified along the GI tract, including the stomach, small intestine, and large intestine (Supplementary Fig. ##SUPPL##0##4##). Moreover, in both small intestine and large intestine, we observed nine mucosal-enriched genera and seven luminal-enriched genera that are mucosal- and luminal-associated microbes (Fig. ##FIG##4##5E##), respectively.</p>", "<title>Functional capacities of microbiome differ among organs</title>", "<p id=\"Par14\">Microbial functional attributes in surface organs were analyzed. Different pathways with significant enrichment were identified in each organ (Supplementary Fig. ##SUPPL##0##5A## and Supplementary Data ##SUPPL##3##1##): aerobic respiration in the skin; nucleoside and nucleotide biosynthesis/degradation (e.g., adenosine and guanosine) in the oral cavity; fatty acid metabolism (e.g., gondoate biosynthesis) in the esophagus, stomach, and small intestine; and pentose phosphate pathway including glucose/sugars catabolism in the appendix and large intestine. Comparative analysis of metabolic pathways revealed several carbohydrates degradation pathways that are significantly enriched in the small intestine (e.g., sucrose degradation) and large intestine (<italic>e.g</italic>., glycogen degradation of bacteria) (<italic>P</italic> < 0.05), respectively (Supplementary Fig. ##SUPPL##0##5B##). Amino acid synthesis (<italic>e.g</italic>., L-isoleucine, L-aspartate, L-histidine, and L-arginine) were significantly enriched in both the lower GI tract (appendix, small intestine and large intestine) and skin (<italic>P</italic> < 0.05) compared to other organs. Collectively, we revealed the differential microbial functional traits among surface organs.</p>", "<title>Intra-organ microbial interaction network reflects organ specificity</title>", "<p id=\"Par15\">To uncover microbial interplay in each organ, we calculated pairwise microbial interactions in each organ using SECOM method (Supplementary Fig. ##SUPPL##0##6##). We observed that each organ has its own patterns of microbial interactions (Supplementary Fig. ##SUPPL##0##7## and Supplementary Data ##SUPPL##4##2##). Significantly different microbial interactions were observed among organs, with more co-exclusive relationships in oral cavity and large intestine, and more co-occurrent relationships in other organs (Supplementary Fig. ##SUPPL##0##8A##). We also used <italic>SPARCC</italic> method, which showed consistent findings (data not shown). Twenty-eight organ-specific microbial interactions was identified by both <italic>SECOM</italic> and <italic>SparCC</italic> (Supplementary Table ##SUPPL##0##5##), showing that microbial correlations were different among GI organs, for example, <italic>Bacteroides</italic> showed strong co-exclusive relationship with other microbes in the large intestine but strong co-occurrence with the same microbes in the upper GI organs (Supplementary Fig. ##SUPPL##0##8B##). These results implied that the microbiome in each organ habitat exhibits distinct microbe-environment relationships, suggestive of impact from host factors such as pH level and nutrient availability.</p>", "<title>Microbial inter-organ relations exist in GI organs</title>", "<p id=\"Par16\">By sampling a large set of intra-individual sites, we attempt to characterize the microbial inter-organ relations (i.e., bacterial translocation) along the GI tract. We re-sequenced the samples using 16S full-length sequencing, which provides higher taxon resolution than 16S v3v4 region. We then measured the presence of bacterial ASVs (the exact sequence variants; relative abundance >0.1%) in the intra-individual organs using 16S v3v4 and full-length data, respectively. The ASVs were collapsed to species level and the species prevalence among individuals were calculated. Consistent results between the full-length sequencing and v3v4 region sequencing were found (Fig. ##FIG##5##6A## and Supplementary Fig. ##SUPPL##0##9A##). We discovered that oral pathogens (prevalence >50%; e.g., <italic>Neisseria</italic> spp. and <italic>F. nucleatum</italic>) were less prevalent in the GI tract (<50%), especially the lower GI tract (Fig. ##FIG##5##6A##). We then applied correlation analysis to indicate the co-enrichment or co-depletion of bacteria in multiple organs. We observed fewer bacteria with positive correlations (<italic>P</italic> < 0.05) between oral cavity and lower GI organs than that between oral cavity and upper GI organs (esophagus and stomach) (Fig. ##FIG##5##6B## and Supplementary Fig. ##SUPPL##0##9B##). These suggest that the oral-to-lower GI contribution is limited (5.5% ± 3.95% of oral bacteria) in healthy individuals. Moreover, Bacteria with positive correlations were more distinguishable within upper or lower GI organs (e.g., esophagus and stomach: ratio = 0.53; SI and LI: ratio = 0.51) than between upper and lower GI organs (e.g., esophagus and LI: ratio = 0.13) (Fig. ##FIG##5##6B##), supporting the evidence for the restricted bacteria translocation from the upper GI to lower GI organs in healthy individuals.</p>", "<p id=\"Par17\">Some high prevalent bacteria in an organ were also prevalent (>50%) in other organs as shown in Fig. ##FIG##5##6A##. We therefore asked if these bacteria were simultaneously present (relative abundance > 0.1%) in the upper GI or lower GI organs from the same individual (core microbial species, defined as species that coexisted in different organs of the same individual). Indeed, there were ASVs co-existed in all upper GI or lower GI organs intra-individually (Fig. ##FIG##5##6C##), which was independently verified by 16S v3v4 data (Supplementary Fig. ##SUPPL##0##10A##). On the other hand, unique bacterial signatures were found in the upper GI or lower GI tract. For example, <italic>S. salivarius</italic> and <italic>H. pylori</italic> in the upper GI, and <italic>Bacteroides spp</italic>. (e.g., <italic>B. vulgatus</italic> and <italic>B. caccae</italic>) and <italic>R. gnavus</italic> in the lower GI. Shared signatures including <italic>E. faecium</italic>, <italic>K. pneumoniae</italic>, and <italic>Enterobacteriaceae spp</italic>. (<italic>E. coli</italic>, <italic>E. flexneri</italic>, and <italic>E. sonnei</italic>) were also found between the upper GI and lower GI tract (Fig. ##FIG##5##6C##). Moreover, correlation analysis confirmed their inter-organ relations in the lower GI and upper GI organs, respectively (Supplementary Fig. ##SUPPL##0##10B##). Our result thus suggests a microbiome core with significant inter-organ relations co-existed in different organs of the intra-individual GI tract.</p>" ]	[ "<title>Discussion</title>", "<p id=\"Par18\">In this study, 1608 surface/mucosal and luminal samples were collected from 53 distinct sites of seven surface organs per subject (skin, oral cavity, esophagus, stomach, small intestine, appendix, and large intestine). To our knowledge, we provided a much denser sampling of each individual along the digestive tract and skin than previous publications<sup>##REF##30126990##7##,##REF##23861384##11##</sup>. Using 16S full-length data to provide taxon information at species level, we identified the microbiome core species with significant inter-organ relations in the human GI tract. Taxa with high prevalence in ≥2 distinct organs such as <italic>Streptococcus</italic> were reported<sup>##REF##22699609##4##,##UREF##1##12##</sup>, of which the species of this genus was also identified in our samples (e.g., <italic>S. salivarius</italic>). Meanwhile, some of our core species were recognized as signature taxa in previous organ-specific microbiome studies including <italic>R. gnavus</italic> and <italic>B. vulgatus</italic> in the lower GI organs<sup>##REF##22170420##13##,##REF##33462485##14##</sup>. Hence, our results indicate that microbes with wide-acknowledged organ specificity could also be present in different body habitats, highlighting the importance of simultaneous microbial profiling in multiple organs to obtain a more comprehensive mapping of the human microbiome.</p>", "<p id=\"Par19\">While the core species could co-exist in multiple organs, our results revealed that these species are specifically enriched in some organs (Fig. ##FIG##2##3##). Moreover, we analyzed differences in the microbial community among sites of an organ and revealed the intra-organ site-specific microbes. Across the skin sites, we identified the enrichment of <italic>Corynebacterium</italic> and <italic>Staphylococcus</italic>, of which these microbes are lipophilic and capable of utilizing sweat as nutrient source<sup>##REF##29332945##15##</sup>. When comparing functional characteristics among surface organs, we reported the domination of aerobic respiration in the skin, which could be solely attributed to the reduced capability of gut anaerobes in aerobic respiration. The oral cavity is known to harbor a diverse microbiome as it comprises of hard non-shedding surfaces of the teeth and epithelial surfaces of the mucosal membranes<sup>##REF##30301974##16##</sup>. We revealed that the oral mucosal microbiome could be clearly separated into distinct clusters including jaws/hard palates, buccal and lips with the respective enrichment of <italic>Neisseria</italic>, <italic>Peptostreptococcus,</italic> and <italic>Staphylococcus</italic> (Fig. ##FIG##3##4##), consistent with previous studies<sup>##REF##22699609##4##,##REF##22170420##13##,##REF##24800728##17##</sup>. As the oral microbiome is exposed to various environmental factors including diet and living habits, its diversity is readily influenced by host behaviors<sup>##REF##23416520##18##</sup>. The microbiome in the upper GI tract is less diverse since microbial growth is heavily limited by gastric acidity and peristalsis. Only a few microbes, particularly <italic>Helicobacter</italic> and <italic>Lactobacillus</italic> survive such harsh conditions<sup>##REF##31513770##3##</sup>. Moreover, our functional analysis revealed significant enrichment of fatty acid metabolism in the upper GI tract, inferring the contribution of gut commensal microbes to dietary fat digestion by gastric lipase. Microbiome diversity reaches the top in the lower GI tract. <italic>Streptococcus</italic> and <italic>Lactobacillus</italic> were enriched in the duodenum, and these microbes are involved in primary bile acids deconjugation to balance lipid and carbohydrate metabolism<sup>##REF##29018272##19##</sup>. In the jejunum, <italic>Oscillospira</italic> was found to be enriched which is a producer of butyrate, a crucial metabolite for maintaining gut homeostasis and energy metabolism<sup>##REF##34693878##20##</sup>. In the ileum, <italic>Bacteroides, Klebsiella,</italic> and <italic>Clostridium</italic> were enriched, and these genera contribute to bile acids recycling and re-entry into enterohepatic circulation<sup>##REF##29018272##19##,##REF##22258098##21##</sup>. Owing to the easier accessibility, multiple studies have reported the microbiome heterogeneity between right-sided/proximal and left-sided/distal colon in healthy subjects and patients<sup>##REF##30843732##22##</sup>. Consistently, we identified the enrichment of butyrate producers including <italic>Klebsiella, Enterococcus,</italic> and <italic>Lactobacillus</italic> in the right-sided colon, the major site of fermentation and microbes-mediated metabolism. <italic>Parabacteroides, Bifidobacterium</italic> and <italic>Dorea</italic> were enriched in the left-sided colon which is responsible for regulating gut motility<sup>##REF##31513770##3##</sup>. Additionally, as the primary site for nutrient absorption, enrichment of multiple metabolic pathways including amino acid synthesis, carbohydrate metabolism and energy production was observed in the lower GI tract. Overall, our findings provide solid evidence of the association of microbial taxa with key physiological functions in a site-specific manner.</p>", "<p id=\"Par20\">We collected paired mucosal and luminal samples from the GI tract. To date, most reports studied the mucosal microbiome by collecting endoscopic biopsies. Whereas some studies retrieved endoscopic aspirates or gastric juice samples to examine luminal microbiome particularly in the small intestine, which has thinner wall and lower tolerance for multiple biopsies than the large intestine<sup>##REF##31043597##23##</sup>. Here, we confirmed that the mucosal and luminal microbiome are distinct as evidenced by their significant differences in α and β-diversities. <italic>Firmicutes</italic> dominated the mucosal microbiome of small intestine (e.g., <italic>Staphylococcus</italic>, <italic>Ruminococcus</italic>) and large intestine (e.g., <italic>Clostridium</italic>, <italic>Lactobacillus</italic>), as well as the luminal microbiome of large intestine. Whereas <italic>Proteobacteria</italic> was enriched in the small intestine lumen. Only 20% and 18% (Jaccard index) of luminal-enriched and mucosal-enriched microbes were shared between small intestine and large intestine, respectively. Additionally, mucosal-enriched microbes account for 73% and 34% of total microbes in small intestine and large intestine, respectively. These results collectively reflect microbial heterogeneity between the small intestine and large intestine, which may be attributed to the differences in pH level, bile salt concentration, and mucin composition<sup>##REF##31513770##3##</sup>.</p>", "<p id=\"Par21\">Owing to our large collection of intra-individual samples, changes in microbiome diversity along intra-individual surface organs could be assessed. We discovered a gradual shift in microbial diversity along the GI tract (Fig. ##FIG##1##2C##). Microbial changes could be attributed to environmental factors (of each organ) and adaptation of microbes per se<sup>##REF##31513770##3##</sup>. It is therefore of interest to assess the microbial flow along the GI tract. Compared to OTU-based analysis, ASV-based analysis offers advantages such as a finer resolution down to the level of single-nucleotide differences<sup>##REF##28731476##24##</sup>. A single base difference in the 16S sequence will result in a unique ASV, thus providing a more detailed profiling of microbial diversity. Here, the ASVs with the exact sequence variant co-existed in different intra-individual organs. In addition, the inter-organ correlation of these ASVs revealed the species co-enrichment or co-depletion in multiple organs, indicative of the inter-organ bacterial translocation. Interestingly, we identified that the bacteria with significant inter-organ relations were clustered in the upper GI or lower GI organs; whilst only a few bacterial cross-contact between organs of the upper GI and lower GI were found, suggesting that the bacterial translocation were restricted between the upper GI and lower GI, thus explaining the limited contribution of oral microbiome to lower GI in healthy individuals. These results provide evidence that microbes in different organs could be carried by the luminal flow and accumulate in other organs, but could be constrained by the environmental factors in an organ-specific fashion.</p>", "<p id=\"Par22\">In conclusion, we have generated a comprehensive biogeographical microbial mapping of seven human surface organs. This allowed elucidation of microbes that are present in multiple organs or in a particular organ. We also revealed the microbiome in different sites within each surface organ, and linked these site-specific taxa to key functional characteristics. In addition, we explored crosstalks of microbiome among different organs by analyzing microbial inter-region relations along the GI tract. Overall, our discoveries enhance our current understanding of the human microbiome by unraveling the details of various features of the microbial communities in surface organs.</p>" ]	[]	[ "<p id=\"Par1\">The microbiome in a specific human organ has been well-studied, but few reports have investigated the multi-organ microbiome as a whole. Here, we aim to analyse the intra-individual inter-organ and intra-organ microbiome in deceased humans. We collected 1608 samples from 53 sites of 7 surface organs (oral cavity, esophagus, stomach, small intestine, appendix, large intestine and skin; <italic>n</italic> = 33 subjects) and performed microbiome profiling, including 16S full-length sequencing. Microbial diversity varied dramatically among organs, and core microbial species co-existed in different intra-individual organs. We deciphered microbial changes across distinct intra-organ sites, and identified signature microbes, their functional traits, and interactions specific to each site. We revealed significant microbial heterogeneity between paired mucosa-lumen samples of stomach, small intestine, and large intestine. Finally, we established the landscape of inter-organ relationships of microbes along the digestive tract. Therefore, we generate a catalogue of bacterial composition, diversity, interaction, functional traits, and bacterial translocation in human at inter-organ and intra-organ levels.</p>", "<p id=\"Par2\">Given that the human body is composed of many microbial niches, and there have been few reports on the biogeography of the microbiome, the authors analyse the intra-individual inter-organ and intra-organ microbiome of seven surface organs of deceased individuals.</p>", "<title>Subject terms</title>" ]	[ "<title>Supplementary information</title>", "<p>\n\n\n\n\n\n\n</p>", "<title>Source data</title>", "<p>\n\n</p>" ]	[ "<title>Supplementary information</title>", "<p>The online version contains supplementary material available at 10.1038/s41467-024-44720-6.</p>", "<title>Acknowledgements</title>", "<p>This study was supported by National Natural Science Foundation of China (81870380/82173394/82100554/82303941), Key Research and Development Program of Shaanxi Province (2020ZDLSF01-03), National Key R&D Program of China (No. 2020YFA0509200/2020YFA0509203), China Postdoctoral Science Foundation (2019M663748), Natural Science Basic Research Program of Shaanxi (S2023-JC-QN-2665), Integrative “Basic-Clinical” Innovation Program (YXJLRH2022043), International science and technology cooperation program of Shaanxi Province (2020KWZ-020), RGC Research Impact Fund (R4017-18F), RGC Theme-based Res Scheme Hong Kong (T21-705/20-N).</p>", "<title>Author contributions</title>", "<p>J.J.S. collected the samples and managed the study. W.L. performed all the computational analysis and drafted the manuscript. X.M.D. collected the samples. G.G. collected the samples, performed experiments, and revised the manuscript. J.H. provided bioinformatic support and revised the manuscript. H.C.H.L. and C.C.W. revised the manuscript. F.Y.S., C.G.D., W.J.X., W.S., G.X.L., Z.Z., C.H.H. and Y.C. collected the samples and performed DNA isolation. J.Y. designed, supervised the study and revised the manuscript.</p>", "<title>Peer review</title>", "<title>Peer review information</title>", "<p id=\"Par43\"><italic>Nature Communications</italic> thanks the anonymous, reviewers for their contribution to the peer review of this work. A peer review file is available.</p>", "<title>Data availability</title>", "<p>All the raw sequencing data generated in this study have been deposited in NCBI Sequence Read Archive (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ncbi.nlm.nih.gov/sra\">SRA</ext-link>) under BioProject PRJNA1049979. ASV sequences were classified taxonomically using Greengenes database at 99% identity cut-off. Remaining data are available within the Article or Supplementary Information. <xref ref-type=\"sec\" rid=\"Sec32\">Source data</xref> are provided with this paper.</p>", "<title>Code availability</title>", "<p>Source code and scripts performed for the study have now been uploaded to: <ext-link ext-link-type=\"uri\" xlink:href=\"https://github.com/WilsonYangLiu/DCD-bacteria.git\">https://github.com/WilsonYangLiu/DCD-bacteria.git</ext-link>.</p>", "<title>Competing interests</title>", "<p id=\"Par44\">The authors declare no competing interests.</p>" ]	[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>Body-wide microbiome profiling in human subjects.</title><p><bold>A</bold> A total of 1608 samples from 53 body sites of 7 surface organs were collected from 33 subjects and were subjected to microbiome profiling. <bold>B</bold> The amount of detectable phylotypes in each organ at different taxonomic levels.</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>Microbial diversity among seven organs.</title><p><bold>A</bold> α-diversity of samples was grouped by organs (<italic>n</italic> = 328, 198, 110, 150, 363, 32, 427 for skin, oral cavity, esophagus, stomach, small intestine, appendix, and large intestine, respectively) and measured using the relative inverse Simpson index at the genus level. Boxplots were colored by surface organs. <italic>P</italic> values were determined using two-sided Wilcoxon signed-rank test. <bold>B</bold> α-diversity of 53 body sites in surface organs (sample size <italic>n</italic> was indicated in the button of each boxplot). Boxplots and trendlines were colored by sample types (surface or lumen). <italic>P</italic> values were determined using two-sided Wilcoxon signed-rank test. <bold>C</bold> β-diversity was measured using PCoA based on UniFrac distance. Each point (sample) was colored by its belonged organ. Community dissimilarities were tested by PERMANOVA analysis. Data are shown as Box and whisker plots (<bold>A,</bold>\n<bold>B</bold>) to represent the median (center line), quartiles (box), and range (whiskers) of the α-diversity for each community, excluding outliers (points outside 1.5 times the interquartile range). Source data are provided as a ##SUPPL##6##Source Data## file.</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><title>Microbiome composition among seven surface organs.</title><p><bold>A1</bold> Abundances of six major phyla in seven organs: <italic>Proteobacteria</italic> (relative abundance: 41.31% ± 9.63%, Mean ± SD), <italic>Firmicutes</italic> (35.02% ± 8.36%), <italic>Bacteroidetes</italic> (14.10% ± 8.30%), <italic>Actinobacteria</italic> (6.21% ± 5.46%), <italic>Fusobacteria</italic> (1.65% ± 1.80%), and <italic>Tenericutes</italic> (0.37% ± 0.83%). <bold>A2</bold> Phylum with significantly different abundance among seven organs by ANCOM-BC2 method (<italic>n</italic> = 328, 198, 110, 150, 363, 32, 427 for skin, oral cavity, esophagus, stomach, small intestine, appendix, and large intestine, respectively). <bold>B</bold> Genus with significantly different abundance among seven organs by ANCOM-BC2 method. The colormaps represents the average bacterial abundance. Data are shown as Box and whisker plots (<bold>A</bold>2) to represent the median (center line), quartiles (box), range (whiskers), and outliers (points outside 1.5 times the interquartile range). Source data are provided as a ##SUPPL##6##Source Data## file.</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><title>Differentially enriched microbes in the intra-organ sites.</title><p>Intra-organ microbial communities were displayed in the <italic>left</italic> panel, measured using Constrained Correspondence Analysis (<bold>A</bold> skin; <bold>B</bold> oral cavity; <bold>C</bold> esophagus; <bold>D</bold> stomach; <bold>E</bold> mucosa of small intestine; <bold>F</bold> lumen of small intestine; <bold>G</bold> mucosa of large intestine; <bold>H</bold> lumen of large intestine and appendix). PERMANOVA analysis (adjusting for age, sex, BMI) was applied to test the significance of community dissimilarities. The arrow pointed to the direction of most rapid change towards the corresponding site. Differentially enriched microbes among intra-organ sites were displayed on the <italic>right</italic> panel. Selected microbes were colored based on their phyla. Source data are provided as a ##SUPPL##6##Source Data## file.</p></caption></fig>", "<fig id=\"Fig5\"><label>Fig. 5</label><caption><title>Association of microbial niches with mucosa or lumen.</title><p><bold>A</bold> Microbial dissimilarities between mucosal and luminal samples of the stomach, small or large intestines, measured using Constrained Correspondence Analysis. PERMANOVA analysis was applied to test the significance of mucosal samples compared to luminal samples. Each point represented an individual sample and was colored by sample types (mucosal or luminal sample) and shaped according to its originated site of the surface organ. Significant mucosa-enriched and lumen/gastric juice-enriched microbes in different sites of (<bold>B</bold>) stomach, (<bold>C</bold>) small or (<bold>D</bold>) large intestines, measured using logistic regression model. Beta values represented the magnitude of difference in relative abundance between paired luminal and mucosal samples, and the degree of consistency among subjects. Points were colored by sites. Selected microbes (FDR < 0.05) were colored based on their belonged phyla. <bold>E</bold> Shared mucosa-enriched (<italic>left</italic>) or lumen-enriched (<italic>right</italic>) microbes between the small intestine and large intestine. Source data are provided as a ##SUPPL##6##Source Data## file.</p></caption></fig>", "<fig id=\"Fig6\"><label>Fig. 6</label><caption><title>Microbial inter-region relations along the GI tract.</title><p><bold>A</bold> Bacterial prevalence in each organ by 16 S full-length sequencing. ASVs with relative abundance >0.1% (~10 sequencing reads) were considered as present on the organ. Red dot represents >50% prevalence. <bold>B</bold> The ratio of bacteria with positive correlations between each pair of organs among the prevalent bacteria (<italic>n</italic> = 76). The abundance of <italic>N. mucosa</italic> and <italic>F. nucleatum</italic> were plot in the right-side, with dash line links the same individual (<italic>P</italic> < 0.05, correlation analysis). Data are shown as Box and whisker plots to represent the median (center line), quartiles (box), range (whiskers), and outliers (points outside 1.5 times the interquartile range). Two-tailed Spearman correlation, Partial Spearman correlation, and two-tailed Pearson correlation were used simultaneously. <bold>C</bold> ASVs simultaneously present in the intra-individual upper GI (<italic>left</italic>) or intra-individual lower GI tract (<italic>right</italic>). Areas labeled in red represent the presence of ASV on all the organs from the same individual (relative abundance >0.1% for all). Light red color: one type of ASV of a particular species shared among organs from the same individuals. Dark red color: >1 ASVs of a particular species shared among organs from the same individuals. Bacterial prevalence in the oral cavity was displayed on left side of the plot. Source data are provided as a ##SUPPL##6##Source Data## file.</p></caption></fig>" ]	[]	[ "<disp-formula id=\"Equa\"><alternatives><tex-math id=\"M1\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\log \\frac{p}{1-p}={\\beta }_{0}+{\\beta }_{2}{R}_{{type}}+{\\beta }_{k}{S}_{1}+{\\beta }_{k+1}{S}_{2}+\\cdots$$\\end{document}</tex-math><mml:math id=\"M2\"><mml:mi>log</mml:mi><mml:mfrac><mml:mrow><mml:mi>p</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn><mml:mo>−</mml:mo><mml:mi>p</mml:mi></mml:mrow></mml:mfrac><mml:mo>=</mml:mo><mml:msub><mml:mrow><mml:mi>β</mml:mi></mml:mrow><mml:mrow><mml:mn>0</mml:mn></mml:mrow></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mrow><mml:mi>β</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mi>R</mml:mi></mml:mrow><mml:mrow><mml:mi>t</mml:mi><mml:mi>y</mml:mi><mml:mi>p</mml:mi><mml:mi>e</mml:mi></mml:mrow></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mrow><mml:mi>β</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mrow><mml:mi>β</mml:mi></mml:mrow><mml:mrow><mml:mi>k</mml:mi><mml:mo>+</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub><mml:mo>+</mml:mo><mml:mo>⋯</mml:mo></mml:math></alternatives></disp-formula>", "<disp-formula id=\"Equb\"><alternatives><tex-math id=\"M3\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$C \\sim {Binomial}\\,\\left(N,\\, p\\right)$$\\end{document}</tex-math><mml:math id=\"M4\"><mml:mi>C</mml:mi><mml:mo>~</mml:mo><mml:mi>B</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mi>o</mml:mi><mml:mi>m</mml:mi><mml:mi>i</mml:mi><mml:mi>a</mml:mi><mml:mi>l</mml:mi><mml:mspace width=\"0.25em\"/><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mi>N</mml:mi><mml:mo>,</mml:mo><mml:mspace width=\"0.25em\"/><mml:mi>p</mml:mi></mml:mrow></mml:mfenced></mml:math></alternatives></disp-formula>", "<inline-formula id=\"IEq1\"><alternatives><tex-math id=\"M5\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$p \\sim {Beta}\\,\\left(a,\\, b\\right)$$\\end{document}</tex-math><mml:math id=\"M6\"><mml:mi>p</mml:mi><mml:mo>~</mml:mo><mml:mi>B</mml:mi><mml:mi>e</mml:mi><mml:mi>t</mml:mi><mml:mi>a</mml:mi><mml:mspace width=\"0.25em\"/><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mi>a</mml:mi><mml:mo>,</mml:mo><mml:mspace width=\"0.25em\"/><mml:mi>b</mml:mi></mml:mrow></mml:mfenced></mml:math></alternatives></inline-formula>" ]	[]	[]	[]	[]	[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM3\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM4\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM5\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM6\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM7\"></supplementary-material>" ]	[ "<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>These authors contributed equally: Jun-Jun She, Wei-Xin Liu, Xiao-Ming Ding, Gang Guo, Jing Han.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"41467_2024_44720_Fig1_HTML\" id=\"d32e414\"/>", "<graphic xlink:href=\"41467_2024_44720_Fig2_HTML\" id=\"d32e469\"/>", "<graphic xlink:href=\"41467_2024_44720_Fig3_HTML\" id=\"d32e661\"/>", "<graphic xlink:href=\"41467_2024_44720_Fig4_HTML\" id=\"d32e771\"/>", "<graphic xlink:href=\"41467_2024_44720_Fig5_HTML\" id=\"d32e877\"/>", "<graphic xlink:href=\"41467_2024_44720_Fig6_HTML\" id=\"d32e1009\"/>", "<graphic xlink:href=\"41467_2024_44720_Article_Equa.gif\" position=\"anchor\"/>", "<graphic xlink:href=\"41467_2024_44720_Article_Equb.gif\" position=\"anchor\"/>", "<inline-graphic xlink:href=\"41467_2024_44720_Article_IEq1.gif\"/>" ]	[ "<media xlink:href=\"41467_2024_44720_MOESM1_ESM.pdf\"><caption><p>Supplementary Information</p></caption></media>", "<media xlink:href=\"41467_2024_44720_MOESM2_ESM.pdf\"><caption><p>Peer Review File</p></caption></media>", "<media xlink:href=\"41467_2024_44720_MOESM3_ESM.pdf\"><caption><p>Description of Additional Supplementary Files</p></caption></media>", "<media xlink:href=\"41467_2024_44720_MOESM4_ESM.xlsx\"><caption><p>Supplementary Data 1</p></caption></media>", "<media xlink:href=\"41467_2024_44720_MOESM5_ESM.xlsx\"><caption><p>Supplementary Data 2</p></caption></media>", "<media xlink:href=\"41467_2024_44720_MOESM6_ESM.pdf\"><caption><p>Reporting Summary</p></caption></media>", "<media xlink:href=\"41467_2024_44720_MOESM7_ESM.xlsx\"><caption><p>Source Data</p></caption></media>" ]	[{"label": ["9."], "mixed-citation": ["Park, S. Y. et al. Oral-gut microbiome axis in gastrointestinal disease and cancer. "], "italic": ["Cancers"], "bold": ["13"]}, {"label": ["12."], "mixed-citation": ["Huse, S. M. et al. A core human microbiome as viewed through 16S rRNA sequence clusters. "], "italic": ["Plos One"], "bold": ["7"]}, {"label": ["26."], "mixed-citation": ["Hyde, E. R. et al. The living dead: bacterial community structure of a cadaver at the onset and end of the bloat stage of decomposition. "], "italic": ["Plos One"], "bold": ["8"]}, {"label": ["27."], "mixed-citation": ["Pechal, J. L. et al. A large-scale survey of the postmortem human microbiome, and its potential to provide insight into the living health condition. "], "italic": ["Sci. Rep."], "bold": ["8"]}, {"label": ["31."], "mixed-citation": ["Friedman, J. & Alm, E. J. Inferring correlation networks from genomic survey data. "], "italic": ["Plos Comput. Biol."], "bold": ["8"]}, {"label": ["32."], "mixed-citation": ["Lin, H., Eggesbo, M. & Das Peddada S. Linear and nonlinear correlation estimators unveil undescribed taxa interactions in microbiome data. "], "italic": ["Nat. Commun."], "bold": ["13"]}]	{ "acronym": [], "definition": [] }	32	CC BY	no	2024-01-13 00:02:19	Nat Commun. 2024 Jan 10; 15:427	oa_package/03/3a/PMC10781665.tar.gz
PMC10781666	38200001	[ "<title>Introduction</title>", "<p id=\"Par2\">Post-traumatic stress disorder (PTSD) is a debilitating psychiatric disorder that occurs in some individuals as a result of exposure to a severe traumatic event. Presently, it is estimated to affect 1.1% of the global population within any 12-month period [##REF##23983056##1##]. PTSD prevalence is higher among those with combat trauma exposure [##REF##27189040##2##], and prevalence among Operation Iraqi Freedom (OIF) and Operation Enduring Freedom (OEF) veterans has been estimated to be 15.8% [##REF##25267288##3##]. Uncovering biomarkers clearly associated with PTSD or a subset of its symptoms could aid in more effective diagnosis, prognosis, and treatment as well as possibly refining our understanding of PTSD pathophysiology. Circulating cell-free mitochondrial DNA (ccf-mtDNA) is derived and released from cells into the systemic circulation following cellular injury or cellular stress. Its release can occur passively via different forms of cell death or actively from living cells through more regulated processes, which are not yet fully understood [##REF##33839318##4##]. Ccf-mtDNA has received increased attention as a biomarker for a variety of somatic pathologies. Increased blood ccf-mtDNA levels have been associated with conditions including diabetes [##REF##26299063##5##–##REF##31600210##9##], heart disease [##REF##26299063##5##, ##REF##26816608##6##], and inflammatory diseases [##REF##28299196##10##], as well as with aging [##REF##30861027##8##, ##REF##24470107##11##, ##REF##32919035##12##] and certain cancers [##REF##21562580##13##–##REF##31271962##15##]. Conversely, decreased ccf-mtDNA levels in cerebrospinal fluid (CSF) have been observed in some neurodegenerative conditions including some types of Parkinson’s disease and Alzheimer’s disease [##REF##23794434##16##–##REF##31143191##18##], where they have been suggested to possibly reflect mitochondrial loss in early stages of disease progression [##REF##31143191##18##].</p>", "<p id=\"Par3\">Although ccf-mtDNA has not been specifically investigated in PTSD, some studies have explored the relationship between ccf-mtDNA levels and other psychiatric disorders. Elevated plasma ccf-mtDNA levels have been associated with late-life depression (LLD) [##REF##34022472##19##], with LLD combined with frailty [##REF##34412934##20##], and with MDD in unmedicated individuals [##REF##29453441##21##]. Notably, antidepressant administration in individuals with depression altered plasma ccf-mtDNA levels differentially in antidepressant treatment responders vs. non-responders [##REF##29453441##21##], being associated with increased ccf-mtDNA levels in non-responders only. Lindqvist et al. [##REF##27922635##22##] found that plasma ccf-mtDNA was significantly increased in individuals who had recently attempted suicide compared to controls, and ccf-mtDNA levels were positively correlated with post-dexamethasone cortisol levels, suggesting an inverse relationship with glucocorticoid sensitivity in these individuals. A positive association was also found between acute psychosocial stress and ccf-mtDNA levels in plasma [##REF##30374018##23##] and serum [##REF##31029929##24##]. On the other hand, significantly lower plasma ccf-mtDNA has been reported in unmedicated [##REF##28633757##25##] and medicated [##REF##34735532##26##] individuals with MDD, in individuals with social anxiety disorder [##REF##36508952##27##], and in individuals with bipolar disorder [##REF##28633757##25##]. In addition, no significant differences in serum ccf-mtDNA levels relative to controls were found in studies of females with MDD [##REF##35532037##28##] and in individuals with bipolar disorder [##REF##25891376##29##, ##REF##32078836##30##], and a recent meta-analysis of ccf-mtDNA levels and brain disease that included studies of MDD, suicidality, bipolar disorder, and schizophrenia did not find a significant overall difference between psychiatric cases and controls [##REF##34096821##31##]. The reasons for the discrepant results thus far are unclear but raise the possibility that individual factors apart from the psychiatric diagnoses per se (e.g. comorbid psychiatric or medical conditions or medications, comorbid metabolic/inflammatory imbalances, or associated hypothalamic-pituitary-adrenal [HPA] axis alterations) may confound certain results [##REF##34735532##26##].</p>", "<p id=\"Par4\">A number of conditions closely associated with PTSD, including suicidal ideation [##REF##19539412##32##–##REF##23995037##34##], elevated inflammation [##REF##29628193##35##], and metabolic disorders such as type 2 diabetes [##REF##20484134##36##], were found to be associated with elevated ccf-mtDNA, although these have not been specifically studied in individuals with PTSD. However, increased glucocorticoid sensitivity has been reported in multiple studies of PTSD [##REF##27189040##2##, ##REF##15039004##37##, ##REF##16214171##38##], and based on the positive associations reported between ccf-mtDNA and glucocorticoid levels following physical stress [##REF##30374018##23##] and between post-dexamethasone suppression test (DST) cortisol levels and ccf-mtDNA levels in individuals who had attempted suicide [##REF##27922635##22##], it seems plausible that increased glucocorticoid sensitivity in individuals with PTSD could be associated with lower ccf-mtDNA levels.</p>", "<p id=\"Par5\">The main aim of this study was to test our hypothesis that ccf-mtDNA levels would be altered in individuals with PTSD compared to the PTSD negative controls, although we did not specify the direction of this alteration. We compared plasma ccf-mtDNA levels between individuals with and without PTSD in a large, well-characterized group of male veterans, all of whom had been exposed to combat trauma. We also conducted a sensitivity analysis to control for the influence of diabetes status, antidepressant medication, and age. Moreover, we analyzed ccf-mtDNA levels in relation to glucocorticoid sensitivity, antidepressant medication, and psychometric scores.</p>" ]	[ "<title>Methods</title>", "<title>Study participants</title>", "<p id=\"Par6\">Male combat trauma-exposed veterans who had served in active duty in Operation Iraqi Freedom (OIF) or Operation Enduring Freedom (OEF) were recruited by New York University (NYU), the Icahn School of Medicine at Mount Sinai (ISMMS), and the James J. Peters Veterans Administration Medical Center (JJPVAMC). Detailed descriptions of the recruitment procedure and exclusion criteria for study participants can be found in prior publications [##REF##24929195##39##–##REF##31501510##41##]. Briefly, inclusion criteria for the PTSD group were age 20–60 years and current diagnosis of war zone-related PTSD based on the Structured Clinical Interview for DSM-IV-<italic>TR</italic> [##REF##25551234##42##], which was the DSM version in use at the time of this study, and the Clinician-Administered PTSD Scale (CAPS) [##REF##7712061##43##, ##REF##11387733##44##]. Current PTSD diagnosis was determined by the <italic>DSM-IV-TR</italic> criteria. Combat trauma-exposed non-PTSD controls were also included if aged between 20 and 60, had previously served in war zones, were free from a lifetime history of PTSD, and had a current CAPS score less than or equal to 20. All participants (PTSD-positive and PTSD-negative) met DSM-IV diagnostic criterion A of the PTSD diagnosis for combat trauma exposure, and the time since the index trauma was 6 years (±2.8 years) on average. The exclusion criteria for the study included the following: prominent suicidal or homicidal ideation; a history of alcohol dependence within the past eight months or of substance abuse or dependence other than nicotine within the past year; a lifetime history of any psychiatric disorder with psychotic features, bipolar disorder, obsessive-compulsive disorder, any neurologic disorder, any systemic illness affecting central nervous system (CNS) function, or a moderate or severe traumatic brain injury (TBI) based on the Ohio State University TBI Identification Method––Short Form [##REF##18025964##45##]; and a history of anemia or recent blood donation in the past 2 months. In addition, individuals who were exposed to ongoing trauma or had been exposed to a traumatic event within the past three months and who were not stable for at least two months on psychiatric medication, anticonvulsants, antihypertensive medication, or sympathomimetic medication were excluded. Individuals with medical conditions that may affect systemic inflammation were also excluded. In total, 232 participants had available ccf-mtDNA data, and they were grouped into those with (<italic>n</italic> = 111) and those without (<italic>n</italic> = 121) PTSD. A power analysis using G*Power version 3.1.9.7 showed our sample size achieved 80% power for detecting a medium effect size of 0.4. The study was approved by the Institutional Review Board of the UCSF School of Medicine, the ISMMS, the JJPVAMC, the NYU Grossman School of Medicine, and the US Army Medical Research and Materiel Command. All participants provided written informed consent before participation in the study. The study was conducted in accordance with the Declaration of Helsinki (1989).</p>", "<title>Evaluation of psychiatric symptoms</title>", "<p id=\"Par7\">PTSD status of all participants was determined by doctoral-level psychologists using the Structured Clinical Interview for DSM-IV Axis I Disorders [##UREF##1##46##] and the Clinician Administered PTSD Scale (CAPS) [##REF##7712061##43##]. A review panel including senior experienced clinicians reviewed all diagnoses. Participants also self-reported their PTSD symptoms, depression symptoms, and perceived psychological stress via the PTSD Checklist for DSM-IV (PCL) [##REF##8870294##47##], the Beck Depression Inventory-II (BDI-II) [##REF##8991972##48##], and the Perceived Stress Scale-10 [##REF##6668417##49##], respectively.</p>", "<title>Blood collection</title>", "<p id=\"Par8\">Blood was drawn by venipuncture in the morning between 8am to 8:30am after a night of fasting in all participants. For plasma analyses, blood was collected into tubes containing EDTA. After being inverted 8–10 times, tubes were put on ice for a maximum of 30 minutes and then spun at 1100 x g for 15 minutes at 4 °C. Plasma was removed and divided into 500 μL aliquots, which were stored at −80 °C for later analysis.</p>", "<title>Ccf-mtDNA purification and quantification</title>", "<p id=\"Par9\">Plasma samples were stored at −80 °C after collection, thawed, and spun at 10,000 × <italic>g</italic> for 10 min to remove cellular debris. DNA was isolated from 200 μL of plasma using the QIAamp DNA Blood Mini Kit (Cat #51106, Qiagen, Valencia, CA, USA) according to the manufacturer’s instructions. The isolated DNA was eluted in 60 μL AE buffer and stored at −80 °C before being assayed. The quantitative analysis of cell free-mtDNA was performed using quantitative real-time polymerase chain reaction (qPCR) by amplifying a 161 bp product from the NADH:Ubiquinone Oxidoreductase Core Subunit 2 (MT-ND2) gene. Each 10 μL reaction was comprised of 5 μL QuantiFast SYBR Green PCR Kit (Qiagen, Valencia, CA, USA), 0.5 μL of each primer, and 2.5 μL of extracted DNA. Each reaction was run in triplicate on an LC480 (LightCycler, Roche, Mannheim, Germany) using the following program: Initial denaturation at 95 °C for ten minutes, and then 45 cycles of the following: melting at 95 °C for 10 s, annealing at 65 °C for 10 s, and extension at 72 °C for 11 s. Finally, a melting curve analysis measured fluorescence continuously from 60 °C to 97°. The forward primer sequence was 5′-CACACTCATCACAGCGCTAA-3′, and the reverse primer sequence was 5′-GGATTATGGATGCGGTTGCT-3′.</p>", "<p id=\"Par10\">A 7-point, 4-fold serial dilution of genomic DNA (Roche Life Science, Indianapolis, Indiana, United States) was created as the standard curve to quantify the mitochondrial DNA copy number relative to the genomic DNA using the Roche480’s absolute quantification maximum second derivative method. The highest concentration of the standard curve was 20 ng/μl. In order to convert the measured relative copy numbers to absolute copy numbers, the same primers were used to amplify a PCR product from the human genomic DNA (Roche Life Science, Indianapolis, Indiana, United States, cat # 1169111200). The PCR product was purified using the QIAquick PCR Purification Kit (Qiagen, Valencia, CA, USA) protocol. A PicoGreen Assay (Cat ThermoFisher Scientific Inc., Waltham, Massachusetts, USA) was conducted to obtain an accurate concentration of the product. The relative concentration of the PCR product was measured using the human genomic DNA as the reference standard, and the absolute mtDNA copy number concentration of each sample was then converted accordingly. The copy number of the PCR product was calculated first by dividing the mass by its molecular weight, calculated based on its unique sequence (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.bioinformatics.org/sms2/dna_mw.html\">http://www.bioinformatics.org/sms2/dna_mw.html</ext-link>), and multiplied by Avogadro’s constant. The final values are expressed as copy numbers/μL of plasma. The average inter-assay CV from 24 randomly picked samples was 3.8 ± 1.9%.</p>", "<title>Clinical labs</title>", "<p id=\"Par11\">Clinical labs were conducted by a CLIA-certified lab.</p>", "<title>Dexamethasone suppression test and neuroendocrine assays</title>", "<p id=\"Par12\">The dexamethasone suppression test (DST) was conducted as previously described (Somvanshi et al., 2020) to measure the negative feedback response of the HPA axis. Dexamethasone was administered orally at a dose of 0.5 mg at 11:00 pm the night before the second blood draw, which took place at 8:00 am the following day. Plasma cortisol was assayed with a Cortisol ELISA Kit (IBL-America, Minneapolis, MN), with intra- and inter-assay coefficients of variation of 5.3% and 9.8%, respectively. Assay sensitivity was 2.5 ng/mL. Plasma ACTH was assayed using an ACTH ELISA kit (ALPCO Diagnostics, Windham, NH), and the intra- and inter-assay coefficients of variation were 5.0% and 8.7%, respectively. Assay sensitivity was 0.5 pg/mL. Individuals with no dexamethasone values for day 2 (PTSD negative: <italic>n</italic> = 15; PTSD positive: <italic>n</italic> = 11) were excluded from analyses of post-dexamethasone measures. Declines of cortisol and ACTH from day 1 to day 2 were used as measures of dexamethasone suppression.</p>", "<title>Dexamethasone-induced lysozyme suppression (IC<sub>50-DEX</sub>) assay</title>", "<p id=\"Par13\">This test, which is used to determine the concentration of dexamethasone at which 50% of lysozyme activity is inhibited (IC<sub>50-DEX</sub>) in peripheral blood mononuclear cells (PBMCs), was performed as previously described in detail [##REF##15158431##50##, ##REF##32315214##51##]. Briefly, PBMCs were isolated on the day of blood collection via density gradient centrifugation using Ficoll-Paque media (Pharmacia), and the IC<sub>50-DEX</sub> assay was performed the same day. The turbidimetric method was used to determine lysozyme inhibition in cells that were incubated with <italic>Micrococcus lysodeikticus</italic> (Sigma) in dexamethasone concentrations of 0, .5, 1, 2.5, 5, 10, 50, and 100 nmol/L. IC<sub>50-DEX</sub> refers to the concentration of dexamethasone at which a 50% reduction in lysozyme activity was observed. Accordingly, higher IC<sub>50-DEX</sub> values correspond to lower glucocorticoid sensitivity. The intra-assay coefficient of variation was 6.9%, and the inter-assay coefficient of variation was 9.8%.</p>", "<title>Statistical analysis</title>", "<p id=\"Par14\">Statistical analyses were performed using the Statistical Package for the Social Sciences (SPSS), version 28.0 (SPSS Inc., Chicago, IL, USA). Variables were assessed for normality, and Blom transformation [##UREF##2##52##] was applied to those with non-normal distribution. Descriptive statistics were calculated for socio-demographic characteristics and biological markers. Continuous variables were summarized with means ± standard deviations (SD) and compared with independent t-tests, and categorical variables were summarized with frequencies and compared with chi-squared tests. To analyze group differences in ccf-mtDNA levels between PTSD-positive and PTSD-negative subjects, a two-tailed Student’s t-test was used.</p>", "<p id=\"Par15\">We then conducted a sensitivity analysis and ANCOVA to control for the confounding effects of age, diabetes status, and antidepressant use. These variables were selected based on significant associations reported in the literature between ccf-mtDNA levels and diabetes [##REF##26299063##5##–##REF##31600210##9##] and age [##REF##30861027##8##, ##REF##24470107##11##, ##REF##32919035##12##], and our previous findings in a cohort of MDD subjects suggesting higher ccf-mtDNA levels in individuals with MDD who did not respond to SSRIs relative to responders [##REF##29453441##21##]. Diabetes was defined as having hemoglobin A1c (HbA1c) values higher than 6.5% (PTSD, <italic>n</italic> = 6; control, <italic>n</italic> = 3). We excluded participants with diabetes and those who were taking antidepressants and performed an ANCOVA model with age as a covariate.</p>", "<p id=\"Par16\">For analysis of the relationship between ccf-mtDNA levels and glucocorticoid-related measures, two-tailed Pearson correlations were performed first between ccf-mtDNA levels and continuous variables. We then conducted multiple regression analysis including antidepressant use, age, and diabetes status as covariates.</p>", "<p id=\"Par17\">A two-tailed t-test was used to analyze differences in ccf-mtDNA levels between antidepressant users and non-users within the PTSD group, and ANCOVA was used to control for age and diabetes status.</p>", "<p id=\"Par18\">Two-tailed Pearson correlations were used for analyses of the association between ccf-mtDNA levels and age, body mass index (BMI), HbA1c, and psychometric scores. For all continuous variables analyzed other than age, multiple regression analysis was performed with age, diabetes status, and antidepressant use as covariates.</p>" ]	[ "<title>Results</title>", "<title>Characteristics of subjects and between-group differences</title>", "<p id=\"Par19\">Demographic and clinical characteristics of our sample are shown in Table ##TAB##0##1##. The PTSD and control groups did not differ significantly in terms of age. There were significant differences in BMI (<italic>t</italic> = −2.296, <italic>p</italic> = 0.023), ethnicity (<italic>χ</italic><sup>2</sup> = 12.423, <italic>p</italic> = 0.029), smoking status (<italic>χ</italic><sup>2</sup> = 17.125, <italic>p</italic> < 0.001), and years of education (<italic>t</italic> = 3.296, <italic>p</italic> < 0.001). Greater antidepressant use (<italic>χ</italic><sup>2</sup> = 24.000, <italic>p</italic> < 0.001) was found in PTSD positive subjects.</p>", "<title>PTSD status and ccf-mtDNA</title>", "<p id=\"Par20\">A between-group t-test did not show a significant difference in ccf-mtDNA levels between the PTSD and control groups (PTSD negative: <italic>M</italic> = 0.082 (standardized value), SD = 0.934, 95% CI [−0.086, 0.250]; PTSD positive: <italic>M</italic> = −0.089 (standardized value), SD = 1.053, 95% CI [−0.287, 0.109]; <italic>t</italic> = 1.312, df = 230, <italic>p</italic> = 0.191, Cohen’s <italic>d</italic> = 0.172). After excluding participants with diabetes and those who were taking antidepressants and controlling for age in an ANCOVA model, however, we found significantly lower ccf-mtDNA levels in the PTSD group (F(1, 179) = 5.971, <italic>p</italic> = 0.016, partial <italic>η</italic><sup>2</sup> = 0.033; PTSD negative: estimated marginal mean = 0.055, 95% CI [−0.121, 0.231]; PTSD positive: estimated marginal mean = −0.303, 95% CI [−0.532, −0.074]) (Fig. ##FIG##0##1##). Excluding those with a current MDD diagnosis instead of antidepressant use did not result in a significant between-group difference (<italic>F</italic>(1, 159) = 2.537, <italic>p</italic> = 0.113, partial <italic>η</italic><sup>2</sup> = 0.016; PTSD negative: estimated marginal mean = 0.075, 95% CI [−0.103, 0.254]; PTSD positive: estimated marginal mean = −0.191, 95% CI [−0.468, 0.086]), implicating antidepressant medication rather than the diagnosis of MDD.</p>", "<title>Ccf-mtDNA and glucocorticoid signaling</title>", "<p id=\"Par21\">Our analyses of DST measures showed that, across the entire group, but not within each individual group, ccf-mtDNA levels were negatively correlated with post-dexamethasone ACTH decline (<italic>r</italic> = −0.171, <italic>p</italic> = 0.020) and cortisol decline (<italic>r</italic> = −0.149, <italic>p</italic> = 0.034; Table ##TAB##1##2##). Specifically, lower ccf-mtDNA levels were associated with larger dexamethasone-associated decreases in ACTH and cortisol, indicating greater glucocorticoid sensitivity in the HPA axis. The correlations for ACTH decline and ccf-mtDNA levels and for cortisol decline and ccf-mtDNA levels remained significant when controlling for age, diabetes status, and antidepressant use (ACTH decline: <italic>β</italic> = −0.170, <italic>p</italic> = 0.020; cortisol decline: <italic>β</italic> = −0.143, <italic>p</italic> = 0.043) (Table ##TAB##2##3## and Fig. ##FIG##1##2a, b##).</p>", "<p id=\"Par22\">IC<sub>50-DEX</sub>, a measure of glucocorticoid sensitivity in PBMCs, was significantly lower in PTSD subjects relative to controls (PTSD negative: <italic>M</italic> = 5.295, SD = 3.239, 95% CI [4.688, 5.901]; PTSD positive: <italic>M</italic> = 4.328, SD = 2.429, 95% CI [3.866, 4.789]; <italic>t</italic> = 2.516, df = 205.743, <italic>p</italic> = 0.013, Cohen’s d = 0.337) (Table ##TAB##1##2##), indicating greater glucocorticoid sensitivity. Though IC<sub>50-DEX</sub> was not significantly associated with ccf-mtDNA levels in unadjusted analyses (<italic>r</italic> = 0.131, <italic>p</italic> = 0.51) (Table ##TAB##1##2##), after controlling for age, diabetes status, and antidepressant use, IC<sub>50-DEX</sub> was significantly positively associated with ccf-mtDNA levels across the entire group (<italic>β</italic> = 0.142, <italic>p</italic> = 0.038), but not within each individual group (Table ##TAB##2##3## and Fig. ##FIG##1##2c##).</p>", "<title>Ccf-mtDNA and antidepressant use in PTSD subjects</title>", "<p id=\"Par23\">Based on our ANCOVA results, we also explored the relationship between antidepressant use and ccf-mtDNA within the PTSD group. Antidepressant users had higher ccf-mtDNA levels, although the difference missed statistical significance (PTSD only: <italic>M</italic> = −0.199, SD = 1.056, 95% CI [−0.434, 0.036]; PTSD + Antidepressants: <italic>M</italic> = 0.211, SD = 1.020, 95% CI [−0.170, 0.592]; <italic>t</italic> = −1.832, <italic>p</italic> = 0.070, Cohen’s <italic>d</italic> = −0.392). The difference was significant (<italic>F</italic>(1, 100) = 4.082, <italic>p</italic> = 0.046; PTSD only: estimated marginal mean = −0.270, 95% CI [−0.513, −0.027]; PTSD + Antidepressants: estimated marginal mean = 0.200, 95% CI [−0.187, 0.586]), however, when controlling for age and diabetes status. Ccf-mtDNA levels did not differ significantly between PTSD subjects with concurrent MDD and those without concurrent MDD (PTSD only: <italic>M</italic> = −0.018, SD = 1.137, 95% CI [−0.326, 0.289]; PTSD + MDD: <italic>M</italic> = −0.159, SD = 0.969, 95% CI [−0.418, 0.101]; <italic>t</italic> = 0.699, <italic>p</italic> = 0.486, Cohen’s <italic>d</italic> = 0.133), including when controlling for age, diabetes, and antidepressant status (<italic>F</italic>(1, 101) = 0.311, <italic>p</italic> = 0.579; PTSD only: estimated marginal mean = −.076, 95% CI [−.0373, 0.220]; PTSD + MDD: estimated marginal mean = −0.192, 95% CI [−0.478, 0.093]), again implicating antidepressant medication rather than a comorbid MDD diagnosis.</p>", "<title>Exploratory correlation analyses</title>", "<p id=\"Par24\">Exploratory analyses were performed to examine potential associations between ccf-mtDNA levels, age, BMI, HbA1c, and psychometric test scores (Tables ##TAB##1##2## and ##TAB##2##3##). Ccf-mtDNA levels were significantly positively correlated with age across the entire group (<italic>r</italic> = 0.187, <italic>p</italic> = 0.004) and separately within the PTSD negative group (<italic>r</italic> = 0.181, <italic>p</italic> = 0.047) and the PTSD positive group (<italic>r</italic> = 0.204, <italic>p</italic> = 0.032). Ccf-mtDNA levels were also significantly associated with HbA1c across the entire group (<italic>r</italic> = 0.245, <italic>p</italic> < 0.001) and within each group separately (PTSD negative: <italic>r</italic> = 0.248, <italic>p</italic> = 0.006; PTSD positive: <italic>r</italic> = 0.249, <italic>p</italic> = 0.012). BMI was not significantly associated with ccf-mtDNA levels across the entire group or within either group.</p>", "<p id=\"Par25\">Within the PTSD group, there were no significant associations found between ccf-mtDNA and CAPS lifetime score, CAPS criterion B score, CAPS criterion C score, CAPS criterion D score, PCL score, time since worst event, BDI-II score, or PSS score, before and after controlling for age, diabetes status, and antidepressant use (Tables ##TAB##1##2## and ##TAB##2##3##). Within the PTSD negative group, ccf-mtDNA levels were significantly negatively correlated with current CAPS score (<italic>r</italic> = −0.221, <italic>p</italic> = 0.015), CAPS criterion D score (<italic>r</italic> = −0.192, <italic>p</italic> = 0.035), and time since worst event (<italic>r</italic> = 0.184, <italic>p</italic> = 0.043). After controlling for age, diabetes status, and antidepressant use, current CAPS score (<italic>β</italic> = −0.238, <italic>p</italic> = 0.009), CAPS criterion D score (<italic>β</italic> = −0.216, <italic>p</italic> = 0.018), and BDI-II score (<italic>β</italic> = −0.195, <italic>p</italic> = 0.039) were negatively associated with ccf-mtDNA levels within the PTSD negative group. Across the entire group, CAPS criterion D score was negatively correlated with ccf-mtDNA levels (<italic>r</italic> = 0.141, <italic>p</italic> = 0.031). After controlling for age, diabetes status, and antidepressant medication, CAPS current score (<italic>β</italic> = −0.192, <italic>p</italic> = 0.007), CAPS lifetime score (<italic>β</italic> = −0.194, <italic>p</italic> = 0.007), CAPS criterion B score (<italic>β</italic> = −0.168, <italic>p</italic> = 0.018), CAPS criterion C score (<italic>β</italic> = 0.155, <italic>p</italic> = 0.03, CAPS criterion D score (<italic>β</italic> = −0.226, <italic>p</italic> = 0.001), PCL score (<italic>β</italic> = −0.187, <italic>p</italic> = 0.008), and BDI-II score (<italic>β</italic> = −0.184, <italic>p</italic> = 0.011) were all negatively associated with ccf-mtDNA levels across the entire group.</p>" ]	[ "<title>Discussion</title>", "<p id=\"Par26\">To our knowledge, this is the first study to examine the relationship between ccf-mtDNA levels and PTSD. Ccf-mtDNA levels did not differ between PTSD positive subjects and PTSD negative controls in the unadjusted analysis. After controlling for age, diabetes status, and antidepressant use (all variables that may affect ccf-mtDNA levels), however, we found significantly lower ccf-mtDNA in the PTSD positive group. We also found an association between glucocorticoid sensitivity and ccf-mtDNA levels, such that higher glucocorticoid sensitivity was associated with lower ccf-mtDNA levels. Finally, we replicated previously reported correlations between ccf-mtDNA levels and age [##REF##24470107##11##, ##REF##32919035##12##] and HbA1c [##UREF##0##7##].</p>", "<p id=\"Par27\">The small number of studies that have examined ccf-mtDNA in psychiatric conditions have been inconclusive [##REF##35532037##28##, ##REF##6668417##49##], and comparing their results is complicated by differences in sample characteristics, exclusion criteria, and ccf-mtDNA purification protocols [##REF##33839318##4##]. Although there have been no prior studies of ccf-mtDNA in PTSD, there have been studies in MDD and in suicidality, which are common PTSD comorbidities and which were associated with elevated plasma ccf-mtDNA in two prior studies by our group, one in unmedicated MDD subjects [##REF##29453441##21##] and another in suicide attempters [##REF##27922635##22##]. In contrast, Fernström et al [##REF##34735532##26##]. reported that current depression and remitted depression were both negatively associated with ccf-mtDNA levels, though over 90% of subjects in that study were on psychotropic medication. Kageyama et al. [##REF##28633757##25##] also reported significantly lower ccf-mtDNA relative to controls in unmedicated MDD subjects. Although the relationship between MDD and ccf-mtDNA remains unclear, because MDD was overrepresented in the PTSD group (<italic>n</italic> = 56) relative to the control group (<italic>n</italic> = 4), it seemed plausible that MDD could have affected the association we found between PTSD and ccf-mtDNA. However, ccf-mtDNA levels did not differ significantly between PTSD subjects with and without concurrent MDD, including when controlling for age, diabetes status, and antidepressant use, suggesting that the association we observed between PTSD status and ccf-mtDNA was independent of MDD status. On the other hand, antidepressant users within the PTSD group had significantly higher ccf-mtDNA levels when controlling for age and diabetes status. This finding raising the possibility of a positive association between antidepressant use, itself, and increased ccf-mtDNA levels within the PTSD group may be consistent with prior work showing direct effects of antidepressants on mitochondrial function and integrity [##REF##21120605##53##–##REF##35355420##56##]. Antidepressants’ effects on mitochondrial function may include inhibition of key enzymes in the mitochondrial respiratory chain [##REF##21120605##53##], which could lead to apoptosis and thus ccf-mtDNA release. While several studies have reported that antidepressants may exert pro-apoptotic effects, others have reported anti-apoptotic effects [##UREF##3##57##]. One animal study [##REF##21414309##58##] suggested that pro- or anti-apoptotic effects may be dependent on the psychological state of the subject receiving the antidepressant. In a rodent model of chronic social isolation stress, this study found that SSRI treatment was associated with several hallmarks of apoptosis, but this was more pronounced in stressed animals than in non-stressed animals [##REF##21414309##58##]. This suggests that there may be synergistic effects at play in stressed individuals such as oxidative stress or HPA-axis hyperactivity that could lead to apoptosis following antidepressant use, which may have contributed to an apparent antidepressant-associated increase in ccf-mtDNA levels in the PTSD group. While our data do not offer any clues as to causality, they do suggest that antidepressant use should be considered a relevant variable in mitochondrial studies.</p>", "<p id=\"Par28\">PTSD has been associated with an increased risk of developing Type 2 diabetes [##REF##20484134##36##, ##REF##31433443##59##], and elevated HbA1c was reported to be a risk factor for developing PTSD [##REF##27981178##60##]. Type 2 diabetes is associated with elevated ccf-mtDNA levels [##REF##26299063##5##–##REF##31600210##9##], and HbA1c has been positively correlated with ccf-mtDNA levels in diabetic patients [##UREF##0##7##]. There was a higher number of subjects who met criteria for diabetes in the PTSD positive group than in the PTSD negative group, though the difference missed significance. Because of the increased prevalence of glucose dysregulation in PTSD, the results from our sensitivity and correlation analyses suggest that future studies of ccf-mtDNA should also take diabetes status and/or HbA1c levels into consideration.</p>", "<p id=\"Par29\">The results of our analyses of glucocorticoid sensitivity add support to the growing evidence that glucocorticoids and ccf-mtDNA may be related. Previous studies reported positive correlations between ccf-mtDNA levels and post-dexamethasone cortisol [##REF##27922635##22##] and salivary cortisol following exercise [##REF##30374018##23##]. PTSD is associated with a hypersensitive negative feedback response in the HPA axis, and previous findings have suggested it could be related to increased glucocorticoid receptor (GR) sensitivity [##REF##15158431##50##, ##REF##7598635##61##, ##REF##16123752##62##], which may contribute to the development of PTSD [##REF##27189040##2##]. However, it has also been hypothesized that the increased responsiveness to glucocorticoids is instead a product of trauma exposure [##REF##24011883##63##, ##REF##36162182##64##]. Increased dexamethasone-induced suppression of cortisol [##REF##35355420##56##, ##UREF##3##57##] and ACTH [##REF##21414309##58##, ##REF##31433443##59##] were reported in previous studies of PTSD. Consistent with this, we found significantly elevated glucocorticoid sensitivity in PBMCs from the PTSD group. Although the precise relationship between glucocorticoids and ccf-mtDNA levels remains unclear, our data are consistent with growing evidence of the importance of glucocorticoid interactions with mitochondria [##REF##19721888##65##–##REF##33609641##68##]. Based on our findings, investigating the associations between ccf-mtDNA and specific molecules involved in GR signaling would be of interest. GRs enter mitochondria and directly interact with mitochondrial DNA [##REF##21664385##69##–##UREF##4##72##], and molecules associated with GR entry and activity within mitochondria, such as Bag-1 [##REF##33025573##73##], Bcl-2 [##REF##19202080##66##], FKBP51 [##REF##21730050##74##], HDAC6 [##REF##27522966##75##], and Hsp90 [##REF##33025573##73##], could be interesting candidates.</p>", "<p id=\"Par30\">Several studies have suggested that acute psychological stress leads to an increase in ccf-mtDNA [##REF##33839318##4##, ##REF##30374018##23##, ##REF##31029929##24##], though the exact upstream mechanisms triggering ccf-mtDNA release in these cases are not fully understood. A study using cultured human fibroblasts found that glucocorticoid administration can trigger extrusion of mtDNA into the cytosol [##REF##27922635##22##], and oxidative stress, which can be induced by glucocorticoids [##REF##21416253##76##], has also been shown to induce the release of cytosolic mtDNA [##REF##21151103##77##], possibly via pores assembled by the protein voltage-dependent anion channel located on the outer mitochondrial membrane [##REF##31857488##78##]. Ccf-mtDNA can be released passively via different forms of cell death or actively via regulated processes [##REF##33839318##4##], and a clearer understanding of the mechanisms governing the release of cytosolic mtDNA from intact cells seems important for shedding light on the nature of the relationship between psychological stress, glucocorticoids, and ccf-mtDNA levels.</p>", "<p id=\"Par31\">Lower ccf-mtDNA levels have been found in the CSF of subjects in the early stages of Parkinson’s and Alzheimer’s disease [##REF##23794434##16##, ##REF##26343811##17##]. In both cases, the mechanisms underlying the lower CSF ccf-mtDNA levels are not currently understood, but it was hypothesized that the reduction in ccf-mtDNA in those cases could accompany a decline in mtDNA resulting from mitochondrial dysfunction that occurs prior to cell death [##REF##23794434##16##–##REF##31143191##18##]. Alternatively, pathologically decreased ccf-mtDNA levels could be a consequence of the drive to increase cellular mtDNA content by restricting the fraction of mtDNA that is released [##REF##32070373##79##]. PTSD has been associated with an increased risk of neurodegenerative disorders [##REF##32150226##80##], and mitochondrial dysfunction has been implicated in PTSD [##REF##29628193##35##, ##REF##18690294##81##–##REF##26120081##83##]. Kageyama et al. [##REF##34715189##84##] reported that transgenic mice whose forebrain neurons expressed a mutant form of <italic>Plog1</italic>, which results in an increase in mtDNA deletions, had a significantly lower C01/D-loop ratio in their plasma ccf-mtDNA relative to controls, suggesting brain-derived mtDNA can enter the plasma. If this is the case in humans, it is plausible that changes in mitochondrial function in the brain could have contributed to the differences observed here, though further research would be required to determine the impact of brain-derived ccf-mtDNA on plasma ccf-mtDNA levels. Importantly, the degree to which peripheral measures of ccf-mtDNA levels reflect central vs. other sources is unknown, and nothing in our data directly implicates central processes.</p>", "<p id=\"Par32\">The present study has several strengths. We recruited a relatively large, well-characterized sample of young and healthy participants, and our exclusion criteria reduced the likelihood that biochemical measurements were influenced by other medical comorbidities. Moreover, because the PTSD and control groups had all experienced combat trauma, we were able to control for the possibility that the differences observed were due to trauma experience itself. On the other hand, however, since the PTSD-negative controls had experienced significant combat trauma, they may have represented an especially resilient group of individuals. The relatively large size of our cohort relative to most other ccf-mtDNA studies to date increased the statistical power of our correlations, making this a significant contribution to the current ccf-mtDNA literature. Our replication of the positive associations between ccf-mtDNA levels and age and HbA1c suggests that these associations may be stable across various populations and should be considered in future studies of ccf-mtDNA levels. Finally, while there have been studies of ccf-mtDNA levels in other psychiatric disorders, this is the first study to investigate ccf-mtDNA levels in PTSD. Limitations to our study include having only male combat veterans. As a result, it is unclear whether our findings are generalizable to civilians or to females with PTSD. In addition, because studies thus far have had considerable variation in the blood fraction used and DNA purification protocols, our results can only be directly compared with studies using similar protocols. Moreover, ccf-mtDNA levels were only measured once in this study. Because there can be substantial variations in ccf-mtDNA levels within individual subjects over time and depending on psychological state [##REF##33839318##4##, ##REF##31112904##85##] and physical activity [##REF##33839318##4##, ##REF##30374018##23##, ##REF##34735532##26##, ##REF##28542490##86##, ##REF##32088743##87##], measurements at multiple time points along with a stress assessment and a record of physical activity at each blood draw would be ideal. In addition, although our exclusionary criteria included a history of moderate to severe traumatic brain injury, we did not collect data on injury characteristics in participants whose trauma involved physical injury. Increases in ccf-mtDNA have been reported in studies of hospital patients admitted for traumatic brain injury [##REF##33917426##88##], hip fracture [##REF##28073488##89##], blunt trauma [##REF##14709653##90##–##REF##25602756##92##], and trauma requiring ICU admission [##REF##23977360##93##]. Of the two studies that took multiple measurements over a 5-day period, both reported significantly elevated ccf-mtDNA levels in patients on day 1 [##REF##23787023##91##, ##REF##25602756##92##, ##REF##29633007##94##]. However, while one study found ccf-mtDNA levels remained significantly higher than healthy controls at all time points [##REF##25602756##92##, ##REF##29633007##94##], the other reported a significant decline following day 1 [##REF##23787023##91##, ##REF##29633007##94##]. In our study, ccf-mtDNA levels were measured over 6 years, on average, after index trauma, and further research is required to determine if acute injury has lasting effects on ccf-mtDNA levels. Moreover, though there were significantly more smokers in the PTSD group, many subjects were missing smoking data, so smoking status was not included in our analyses. Although the precise relationship between ccf-mtDNA and smoking is not yet clear, the limited number of available studies [##UREF##5##95##, ##REF##36598778##96##] suggest smoking may increase ccf-mtDNA, and this may have impacted ccf-mtDNA levels in our cohort. The influence of smoking, race/ethnicity, and other possible confounding variables on ccf-mtDNA levels should be assessed further in future studies. Another potential limitation to interpreting our results is that the primer set we used for amplifying mitochondrial DNA targeted a sequence that is also present in a nuclear mitochondrial pseudogene (NUMT) on chromosome 1. However, it does not seem likely that this significantly impacted our ccf-mtDNA data for the following reasons. We had attempted to amplify any nuclear DNA present our plasma samples using a Taqman assay (Thermo Fisher Scientific, Cat# 4403326) with RNase P as the target, but it did not yield any PCR product, suggesting that there was no detectable nuclear genome in the DNA from the plasma samples. Moreover, Meddeb et al. showed that in the plasma of healthy individuals, there are approximately 50,000 times more copies of ccf-mtDNA than circulating cell-free nuclear DNA (ccf-nDNA) [##REF##30914716##97##]. Given the low abundance of ccf-nDNA in plasma, even in the case that nuclear DNA had been present in our plasma samples, this could have elevated the levels of ccf-mtDNA in theory but likely not to a magnitude that would bias the results. Nevertheless, targeting mitochondrial sequences that are not shared by any NUMTs would be an important consideration for future assays of ccf-mtDNA levels. Finally, ccf-mtDNA comprises not only non-membrane bound DNA fragments but also mtDNA contained in cell-free intact mitochondria and in extracellular vesicles (EV) such as microvesicles and exosomes, each with potentially distinct mechanisms of release and physiological roles, and different purification protocols result in discrepancies in the type of ccf-mtDNA isolated [##REF##33839318##4##, ##REF##31957088##98##], potentially impacting results. Based on our protocol, our samples should have contained non-membrane bound ccf-mtDNA in addition to that contained in microvesicles and exosomes [##REF##15158431##50##], and we found a clear positive correlation with aging. On the other hand, Lazo et al. [##REF##33355987##99##] analyzed only EV-bound ccf-mtDNA and found a negative association. Future investigations of PTSD’s effect on each type of ccf-mtDNA separately would help clarify the clinical significance of our findings.</p>", "<p id=\"Par33\">In conclusion, using a relatively large, well-phenotyped veteran male sample, we found no overall between-group difference in ccf-mtDNA levels in unadjusted analyses. After controlling for age, diabetes status, and antidepressant use, however, those with PTSD showed lower ccf-mtDNA levels than those without PTSD. Thus, while PTSD per se is not associated with altered plasma ccf-mtDNA levels, a subgroup of individuals with PTSD who do not have diabetes or take antidepressants may show decreased levels. Our results also suggest that elevated glucocorticoid sensitivity may be associated with lower ccf-mtDNA levels. This observation may tie together the increased glucocorticoid sensitivity reported in PTSD with our observation of decreased ccf-mtDNA, at least in this subgroup. Finally, our results are consistent with literature suggesting mitochondrial involvement in PTSD [##REF##29628193##35##, ##REF##18690294##81##–##REF##26120081##83##], at least in individuals who are not diabetic or taking antidepressant medication, although the pathophysiological significance of low plasma ccf-mtDNA levels remains uncertain. Although ccf-mtDNA’s usefulness as a diagnostic biomarker of PTSD is doubtful, our results suggest that improving our understanding of ccf-mtDNA in PTSD could aid in elucidating the mechanisms underlying PTSD’s pathophysiology and the relationships between glucocorticoid signaling, antidepressants, and mitochondrial function.</p>" ]	[]	[ "<p id=\"Par1\">Circulating cell-free mitochondrial DNA (ccf-mtDNA) is a biomarker of cellular injury or cellular stress and is a potential novel biomarker of psychological stress and of various brain, somatic, and psychiatric disorders. No studies have yet analyzed ccf-mtDNA levels in post-traumatic stress disorder (PTSD), despite evidence of mitochondrial dysfunction in this condition. In the current study, we compared plasma ccf-mtDNA levels in combat trauma-exposed male veterans with PTSD (<italic>n</italic> = 111) with those who did not develop PTSD (<italic>n</italic> = 121) and also investigated the relationship between ccf mt-DNA levels and glucocorticoid sensitivity. In unadjusted analyses, ccf-mtDNA levels did not differ significantly between the PTSD and non-PTSD groups (<italic>t</italic> = 1.312, <italic>p</italic> = 0.191, Cohen’s d = 0.172). In a sensitivity analysis excluding participants with diabetes and those using antidepressant medication and controlling for age, the PTSD group had lower ccf-mtDNA levels than did the non-PTSD group (F(1, 179) = 5.971, <italic>p</italic> = 0.016, partial <italic>η</italic><sup>2</sup> = 0.033). Across the entire sample, ccf-mtDNA levels were negatively correlated with post-dexamethasone adrenocorticotropic hormone (ACTH) decline (<italic>r</italic> = −0.171, <italic>p</italic> = 0.020) and cortisol decline (<italic>r</italic> = −0.149, <italic>p</italic> = 0.034) (viz., greater ACTH and cortisol suppression was associated with lower ccf-mtDNA levels) both with and without controlling for age, antidepressant status and diabetes status. Ccf-mtDNA levels were also significantly positively associated with IC<sub>50-DEX</sub> (the concentration of dexamethasone at which 50% of lysozyme activity is inhibited), a measure of lymphocyte glucocorticoid sensitivity, after controlling for age, antidepressant status, and diabetes status (<italic>β</italic> = 0.142, <italic>p</italic> = 0.038), suggesting that increased lymphocyte glucocorticoid sensitivity is associated with lower ccf-mtDNA levels. Although no overall group differences were found in unadjusted analyses, excluding subjects with diabetes and those taking antidepressants, which may affect ccf-mtDNA levels, as well as controlling for age, revealed decreased ccf-mtDNA levels in PTSD. In both adjusted and unadjusted analyses, low ccf-mtDNA levels were associated with relatively increased glucocorticoid sensitivity, often reported in PTSD, suggesting a link between mitochondrial and glucocorticoid-related abnormalities in PTSD.</p>", "<title>Subject terms</title>" ]	[]	[ "<title>Acknowledgements</title>", "<p>Material has been reviewed by the Walter Reed Army Institute of Research. There is no objection to its presentation and/or publication. The opinions or assertions contained herein are the private views of the author, and are not to be construed as official, or as reflecting true views of the Department of the Army or the Department of Defense. The investigators have adhered to the policies for the protection of human subjects as prescribed in AR 70–25. This work was supported by funding from the U.S. Army Research Office, through award numbers W911NF-13-1-0376, W911NF-17-2- 0086, W911NF-18-2-0056, by the Army Research Laboratory under grant number W911NF-17-1-0069, and from the U.S. Department of Defense under W81XWH-10-1-0021, W81XWH-09-2-0044, and W81XWH-14-1-0043. Daniel Lindqvist is funded by the Swedish Research Council (grant number <ext-link ext-link-type=\"uri\" xlink:href=\"https://www.sciencedirect.com/science/article/pii/S0306453022003420?via%3Dihub#gs1\">#2020–01428</ext-link>) and Swedish governmental funding of clinical research (ALF).</p>", "<title>Author contributions</title>", "<p>ZB and GW designed the main conceptual ideas and carried out data analysis of the study. SM, OW, RY, MJ, CM, and FD devised the study and supervised the project. JL performed the ccf-mtDNA assay. SM, OW, DL, CT, JF, VR, RR, RH, and AG contributed to interpretation of the results. ZB took a lead in writing the manuscript. All authors provided critical feedback and helped shape the research, analysis, and manuscript.</p>", "<title>Data availability</title>", "<p>The data that support the findings of this study are available from the corresponding author, GW, upon reasonable request.</p>", "<title>Competing interests</title>", "<p id=\"Par34\">The authors declare no competing interests.</p>" ]	[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>Associations between PTSD and ccf-mtDNA levels.</title><p>After adjusting for age, diabetes status, and antidepressant medication, ccf-mtDNA levels were significantly lower in the PTSD group (PTSD negative: <italic>M</italic> = 0.136, SD = 0.085, 95% CI [−0.032, 0.304]; PTSD positive: <italic>M</italic> = −0.163, SD = 0.933, 95% CI [−0.368, 0.043]; <italic>t</italic> = 2.250, df = 220, <italic>p</italic> = 0.025, Cohen’s d = 0.303). Data represent residual values after adjusting for age, diabetes status, and antidepressant use.</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>Correlations between ccf-mtDNA levels and glucocorticoid sensitivity after adjusting for age, diabetes status, and antidepressant use.</title><p>Data represent residual values after adjusting for age, diabetes status, and antidepressant use. <bold>a</bold> Ccf-mtDNA levels were negatively correlated with ACTH decline across the entire group (<italic>r</italic> = −0.168, <italic>p</italic> = 0.024). <bold>b</bold> Ccf-mtDNA levels were also negatively correlated with cortisol decline across the entire group (<italic>r</italic> = −0.143, <italic>p</italic> = 0.045). <bold>c</bold> IC<sub>50-DEX</sub>, for which lower values indicate higher glucocorticoid sensitivity, was positively correlated with ccf-mtDNA levels across the entire group (<italic>r</italic> = 0.140, <italic>p</italic> = 0.043).</p></caption></fig>" ]	[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Demographic and clinical characteristics.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th/><th>Total (<italic>n</italic> = 232)</th><th>Control (<italic>n</italic> = 121)</th><th>PTSD (<italic>n</italic> = 111)</th><th>p value<sup>d</sup></th></tr></thead><tbody><tr><td>Age (years, mean ± s.d.)</td><td>33.44 ± 8.55</td><td>33.08 ± 8.61</td><td>33.84 ± 8.51</td><td>0.503</td></tr><tr><td>Education (years, mean ± s.d.)</td><td>14.45 ± 2.11</td><td>14.96 ± 2.15</td><td>13.9 ± 1.94</td><td><0.001</td></tr><tr><td>BMI (mean ± s.d.)</td><td>28.93 ± 5.00</td><td>28.19 ± 4.40</td><td>29.72 ± 5.48</td><td>0.023</td></tr><tr><td>Race/ethnicity</td><td/><td/><td/><td>0.029</td></tr><tr><td> Non-Hispanic Asian, <italic>n</italic> (%)</td><td>15 (6.5)</td><td>11 (9.1)</td><td>4 (3.6)</td><td>–</td></tr><tr><td> Non-Hispanic Black, <italic>n</italic> (%)</td><td>52 (22.4)</td><td>26 (21.5)</td><td>26 (23.4)</td><td>–</td></tr><tr><td> Non-Hispanic Native American, <italic>n</italic> (%)</td><td>2 (0.9)</td><td>2 (1.7)</td><td>0 (0)</td><td>–</td></tr><tr><td> Non-Hispanic Other, <italic>n</italic> (%)</td><td>7 (3.0)</td><td>4 (3.3)</td><td>3 (2.7)</td><td>–</td></tr><tr><td> Non-Hispanic White, <italic>n</italic> (%)</td><td>75 (32.3)</td><td>46 (38.0)</td><td>29 (26.1)</td><td>–</td></tr><tr><td> Hispanic, <italic>n</italic> (%)</td><td>81 (34.9)</td><td>32 (26.4)</td><td>49 (44.1)</td><td>–</td></tr><tr><td>Antidepressant users, <italic>n</italic> (%)<sup>a</sup></td><td>35 (15.1)</td><td>5 (4.1)</td><td>30 (27.0)</td><td><0.001</td></tr><tr><td>Smoking Status<sup>b</sup></td><td/><td/><td/><td><0.001</td></tr><tr><td> Not at all, <italic>n</italic> (%)</td><td>157 (80.5)</td><td>94 (88.7)</td><td>63 (70.8)</td><td>–</td></tr><tr><td> Some days, <italic>n</italic> (%)</td><td>15 (7.7)</td><td>8 (7.5)</td><td>7 (7.9)</td><td>–</td></tr><tr><td> Every day, <italic>n</italic> (%)</td><td>23 (11.8)</td><td>4 (3.8)</td><td>19 (21.3)</td><td>–</td></tr><tr><td>Current CAPS total score (mean ± s.d.)</td><td>34.88 ± 34.90</td><td>3.83 ± 5.09</td><td>68.72 ± 17.69</td><td><0.001</td></tr><tr><td>HbA1c (%, mean ± s.d.)</td><td>5.40 ± 0.75</td><td>5.33 ± 0.47</td><td>5.47 ± 0.99</td><td>0.766</td></tr><tr><td>Participants with Diabetes (HbA1c > 6.5%)<sup>c</sup></td><td>9</td><td>3</td><td>6</td><td>0.198</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Correlations of continuous variables with ccf-mtDNA and group comparisons for each variable.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th>Variable</th><th>Entire Group</th><th/><th>PTSD-</th><th/><th>PTSD+</th><th/><th>PTSD-</th><th>PTSD+</th><th/><th/><th/></tr><tr><th/><th>Pearson’s <italic>r</italic></th><th><italic>p</italic> value</th><th>Pearson’s <italic>r</italic></th><th><italic>p</italic> value</th><th>Pearson’s <italic>r</italic></th><th><italic>p</italic> value</th><th>mean ± s.d.</th><th>mean ± s.d.</th><th><italic>t</italic></th><th>df</th><th><italic>p</italic> value</th></tr></thead><tbody><tr><td>age (years)</td><td>0.187</td><td>0.004</td><td>0.181</td><td>0.047</td><td>0.204</td><td>0.032</td><td>33.08 ± 8.61</td><td>33.84 ± 8.51</td><td>−0.671</td><td>230</td><td>0.503</td></tr><tr><td>BMI</td><td>0.031</td><td>0.639</td><td>0.035</td><td>0.706</td><td>0.053</td><td>0.583</td><td>28.19 ± 4.40</td><td>29.72 ± 5.48</td><td>−2.296</td><td>206.997</td><td>0.023</td></tr><tr><td>HbA1c (%)<sup>a</sup></td><td>0.245</td><td><0.001</td><td>0.248</td><td>0.006</td><td>0.249</td><td>0.012</td><td>5.33 ± 0.47</td><td>5.47 ± 0.99</td><td>−0.299</td><td>221</td><td>0.766</td></tr><tr><td>pre-dex cortisol (µg/ml)<sup>a</sup></td><td>−0.038</td><td>0.567</td><td>−0.058</td><td>0.525</td><td>−0.015</td><td>0.881</td><td>14.13 ± 6.01</td><td>14.71 ± 7.47</td><td>−0.32</td><td>228</td><td>0.749</td></tr><tr><td>post-dex cortisol (µg/ml)<sup>a</sup></td><td>0.064</td><td>0.358</td><td>0.001</td><td>0.99</td><td>0.117</td><td>0.248</td><td>3.77 ± 4.36</td><td>3.76 ± 6.38</td><td>0.557</td><td>203</td><td>0.578</td></tr><tr><td>pre-dex ACTH (pg/ml)<sup>a</sup></td><td>−0.096</td><td>0.171</td><td>−0.081</td><td>0.409</td><td>−0.102</td><td>0.311</td><td>36.81 ± 26.95</td><td>38.21 ± 20.65</td><td>−1.508</td><td>194.866</td><td>0.133</td></tr><tr><td>post-dex ACTH (pg/ml)<sup>a</sup></td><td>0.086</td><td>0.234</td><td>0.127</td><td>0.208</td><td>0.039</td><td>0.71</td><td>14.76 ± 11.69</td><td>13.60 ± 10.45</td><td>0.897</td><td>184</td><td>0.371</td></tr><tr><td>ACTH decline (pg/ml)<sup>a</sup></td><td>−0.171</td><td>0.02</td><td>−0.146</td><td>0.157</td><td>−0.181</td><td>0.088</td><td>22.26 ± 26.91</td><td>24.50 ± 19.11</td><td>−1.686</td><td>183</td><td>0.094</td></tr><tr><td>cortisol decline (µg/ml)<sup>a</sup></td><td>−0.149</td><td>0.034</td><td>−0.183</td><td>0.061</td><td>−0.106</td><td>0.297</td><td>10.21 ± 6.17</td><td>11.24 ± 7.20</td><td>−1.188</td><td>201</td><td>0.236</td></tr><tr><td>IC<sub>50-DEX</sub> (nM)</td><td>0.131</td><td>0.051</td><td>0.143</td><td>0.131</td><td>0.102</td><td>0.29</td><td>5.29 ± 3.24</td><td>4.33 ± 2.43</td><td>2.516</td><td>219</td><td>0.013</td></tr><tr><td>Time since worst event (months)</td><td>0.074</td><td>0.264</td><td>0.184</td><td>0.043</td><td>−0.012</td><td>0.901</td><td>65.54 ± 35.20</td><td>77.77 ± 30.25</td><td>−2.827</td><td>230</td><td>0.005</td></tr><tr><td>BDI2 total</td><td>−0.107</td><td>0.111</td><td>−0.157</td><td>0.093</td><td>−0.024</td><td>0.809</td><td>5.94 ± 6.52</td><td>24.05 ± 10.96</td><td>−14.782</td><td>167.88</td><td><0.001</td></tr><tr><td>PCL total</td><td>−0.121</td><td>0.07</td><td>−0.168</td><td>0.069</td><td>−0.021</td><td>0.832</td><td>25.62 ± 9.27</td><td>59.54 ± 13.03</td><td>−22.224</td><td>187.485</td><td><0.001</td></tr><tr><td>CAPS total (current)</td><td>−0.107</td><td>0.105</td><td>−0.221</td><td>0.015</td><td>−0.042</td><td>0.659</td><td>3.83 ± 5.09</td><td>68.72 ± 17.69</td><td>−37.252</td><td>126.702</td><td><0.001</td></tr><tr><td>CAPS criterion B (current)</td><td>−0.081</td><td>0.217</td><td>−0.059</td><td>0.523</td><td>−0.014</td><td>0.885</td><td>0.29 ± 0.98</td><td>16.99 ± 7.46</td><td>−23.39</td><td>113.469</td><td><0.001</td></tr><tr><td>CAPS criterion C (current)</td><td>−0.085</td><td>0.197</td><td>−0.138</td><td>0.132</td><td>0.022</td><td>0.816</td><td>0.98 ± 2.55</td><td>27.35 ± 7.80</td><td>−34.001</td><td>131.55</td><td><0.001</td></tr><tr><td>CAPS criterion D (current)</td><td>−0.141</td><td>0.031</td><td>−0.192</td><td>0.035</td><td>−0.131</td><td>0.172</td><td>2.56 ± 3.73</td><td>24.38 ± 6.28</td><td>−31.796</td><td>175.769</td><td><0.001</td></tr><tr><td>CAPS total (lifetime)</td><td>−0.105</td><td>0.111</td><td>−0.139</td><td>0.127</td><td>−0.05</td><td>0.603</td><td>8.90 ± 7.88</td><td>89.22 ± 18.97</td><td>−41.438</td><td>144.249</td><td><0.001</td></tr><tr><td>Perceived Stress Scale</td><td>−0.077</td><td>0.258</td><td>−0.06</td><td>0.519</td><td>−0.013</td><td>0.897</td><td>1.85 ± 0.61</td><td>2.98 ± 068</td><td>−13.039</td><td>217</td><td><0.001</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab3\"><label>Table 3</label><caption><p>Associations with ccf-mtDNA controlling for age, diabetes status, and antidepressant use.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\">Variable</th><th>Entire group</th><th/><th>PTSD-</th><th/><th>PTSD+</th><th/></tr><tr><th><italic>β</italic></th><th><italic>p</italic> value</th><th><italic>β</italic></th><th><italic>p</italic> value</th><th><italic>β</italic></th><th><italic>p</italic> value</th></tr></thead><tbody><tr><td>BMI</td><td>−0.021</td><td>0.767</td><td>0</td><td>0.998</td><td>−0.008</td><td>0.941</td></tr><tr><td>HbA1c</td><td>0.203</td><td>0.006</td><td>0.21</td><td>0.029</td><td>0.285</td><td>0.084</td></tr><tr><td>Pre-dex cortisol (µg/ml)<sup>a</sup></td><td>−0.033</td><td>0.614</td><td>−0.06</td><td>0.511</td><td>0.011</td><td>0.91</td></tr><tr><td>Post-dex cortisol (µg/ml)<sup>a</sup></td><td>0.05</td><td>0.475</td><td>0.008</td><td>0.931</td><td>0.082</td><td>0.423</td></tr><tr><td>Pre-dex ACTH (pg/ml)<sup>a</sup></td><td>−0.116</td><td>0.097</td><td>−0.091</td><td>0.358</td><td>−0.113</td><td>0.28</td></tr><tr><td>Post-dex ACTH (pg/ml)<sup>a</sup></td><td>0.059</td><td>0.423</td><td>0.123</td><td>0.245</td><td>−0.005</td><td>0.962</td></tr><tr><td>ACTH decline (pg/ml)<sup>a</sup></td><td>−0.170</td><td>0.02</td><td>−0.143</td><td>0.168</td><td>−0.163</td><td>0.132</td></tr><tr><td>Cortisol decline (µg/ml)<sup>a</sup></td><td>−0.143</td><td>0.043</td><td>−0.18</td><td>0.069</td><td>−0.069</td><td>0.506</td></tr><tr><td>IC<sub>50-DEX</sub> (nM)</td><td>0.142</td><td>0.038</td><td>0.135</td><td>0.159</td><td>0.099</td><td>0.329</td></tr><tr><td>Time since worst event (months)</td><td>−0.051</td><td>0.449</td><td>0.147</td><td>0.119</td><td>0.012</td><td>0.904</td></tr><tr><td>BDI2 score</td><td>−0.184</td><td>0.011</td><td>−0.195</td><td>0.039</td><td>−0.019</td><td>0.854</td></tr><tr><td>pcl score</td><td>−0.187</td><td>0.008</td><td>−0.177</td><td>0.055</td><td>0.019</td><td>0.847</td></tr><tr><td>CAPS total score (current)</td><td>−0.192</td><td>0.007</td><td>−0.238</td><td>0.009</td><td>−0.062</td><td>0.533</td></tr><tr><td>CAPS B (current)</td><td>−0.168</td><td>0.018</td><td>−0.117</td><td>0.21</td><td>−0.059</td><td>0.552</td></tr><tr><td>CAPS C (current)</td><td>−0.155</td><td>0.03</td><td>−0.118</td><td>0.197</td><td>0.033</td><td>0.742</td></tr><tr><td>CAPS D (current)</td><td>−0.226</td><td>0.001</td><td>−0.216</td><td>0.018</td><td>−0.141</td><td>0.155</td></tr><tr><td>CAPS total score (lifetime)</td><td>−0.194</td><td>0.007</td><td>−0.165</td><td>0.079</td><td>−0.075</td><td>0.453</td></tr><tr><td>Perceived Stress Scale</td><td>−0.118</td><td>0.094</td><td>−0.069</td><td>0.468</td><td>0.031</td><td>0.758</td></tr></tbody></table></table-wrap>" ]	[]	[]	[]	[]	[]	[]	[ "<table-wrap-foot><p><italic>BMI</italic> body mass index, <italic>HbA1c</italic> hemoglobin A1c, <italic>CAPS</italic> Clinician-Administered PTSD Scale.</p><p><sup>a</sup>Data on antidepressant use was not available for 1 participant in the PTSD positive group.</p><p><sup>b</sup>Data on smoking status was not available for 37 participants (PTSD negative: <italic>n</italic> = 15; PTSD positive: <italic>n</italic> = 22).</p><p><sup>c</sup>Data on HbA1c was not availalble for 9 participants in the PTSD positive group.</p><p><sup>d</sup>For continuous variables, a t-test was used. For categorical variables, a <italic>χ</italic><sup>2</sup> test was used.</p></table-wrap-foot>", "<table-wrap-foot><p><italic>PTSD</italic> post-traumatic stress disorder, <italic>BMI</italic> body mass index, <italic>HbA1c</italic> hemoglobin A1c, <italic>ACTH</italic> adrenocorticotropic hormone, <italic>IC</italic><sub><italic>50-DEX</italic></sub> the concentration of dexamethasone at which 50% of lysozyme activity is inhibited, <italic>BDI2</italic> Beck Depression Inventory 2, <italic>PCL</italic> PTSD checklist, <italic>CAPS</italic> Clinician-Administered PTSD Scale.</p><p><sup>a</sup>Standardized values were used for correlations and <italic>t</italic>-tests, but means represent raw values.</p></table-wrap-foot>", "<table-wrap-foot><p><italic>PTSD</italic> post-traumatic stress disorder, <italic>BMI</italic> body mass index, <italic>HbA1c</italic> hemoglobin A1c, <italic>ACTH</italic> adrenocorticotropic hormone, <italic>IC</italic><sub><italic>50-DEX</italic></sub> the concentration of dexamethasone at which 50% of lysozyme activity is inhibited, <italic>BDI2</italic> Beck Depression Inventory 2, <italic>PCL</italic> PTSD Checklist, <italic>CAPS</italic> Clinician-Administered PTSD Scale.</p><p><sup>a</sup>Standardized values were used.</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>These authors contributed equally: Zachary N. Blalock, Gwyneth W. Y Wu.</p></fn><fn><p>A list of authors and their affiliations appears at the end of the paper.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"41398_2023_2721_Fig1_HTML\" id=\"d32e1215\"/>", "<graphic xlink:href=\"41398_2023_2721_Fig2_HTML\" id=\"d32e2304\"/>" ]	[]	[{"label": ["7."], "mixed-citation": ["Bae JH, Jo SI, Kim SJ, Lee JM, Jeong JH, Kang JS, et al. Circulating cell-free mtDNA contributes to AIM2 inflammasome-mediated chronic inflammation in patients with type 2 diabetes. Cells. 2019;8:328."]}, {"label": ["46."], "mixed-citation": ["First MB, Gibbon M. "], "italic": ["The Structured Clinical Interview For DSM-IV Axis I Disorders (SCID-I) And The Structured Clinical Interview For DSM-IV Axis II Disorders (SCID-II)"]}, {"label": ["52."], "mixed-citation": ["Blom G. "], "italic": ["Statistical Estimates and Transformed Beta-Variables"]}, {"label": ["57."], "surname": ["Caiaffo", "Oliveira", "de S\u00e1", "Ev\u00eancio Neto"], "given-names": ["V", "BD", "FB", "J"], "article-title": ["Anti-inflammatory, antiapoptotic, and antioxidant activity of fluoxetine"], "source": ["Pharm Res Perspect"], "year": ["2016"], "volume": ["4"], "fpage": ["e00231"], "pub-id": ["10.1002/prp2.231"]}, {"label": ["72."], "mixed-citation": ["Kokkinopoulou I, Moutsatsou P. Mitochondrial glucocorticoid receptors and their actions. Int J Mol Sci. 2021;22:6054."]}, {"label": ["95."], "mixed-citation": ["Giordano L, Gregory AD, P\u00e9rez Verdaguer M, Ware SA, Harvey H, DeVallance E, et al. Extracellular release of mitochondrial dna: triggered by cigarette smoke and detected in COPD. Cells. 2022;11:369."]}]	{ "acronym": [], "definition": [] }	99	CC BY	no	2024-01-13 00:02:19	Transl Psychiatry. 2024 Jan 10; 14:22	oa_package/9e/15/PMC10781666.tar.gz
PMC10781667	38200065	[ "<title>Introduction</title>", "<p id=\"Par2\">Rice (<italic>Oryza sativa</italic> L.) is the staple food for nearly half of the world’s population and is widely cultivated all over the world. Aroma is one of the most attractive characteristics of rice. The demand of aromatic rice is increasing globally<sup>##REF##19371779##1##</sup>. In International markets, aromatic rice is considered to be the premium group of rice in the view of its enchanting grain quality, unique flavor and higher nutritional profile<sup>##UREF##0##2##</sup>. Aroma is an innate trait<sup>##REF##27376959##3##</sup> and its development is mainly affected by the genetic factors, growing conditions and post-harvest handling methods<sup>##UREF##1##4##</sup>. There are above 200 different aromatic compounds including 2-Acetyl-1-pyroline (2-AP), 2-acetyl-pyrrole, pyrolidone, pyridine etc. which are responsible for aroma of rice. 2-Acetyl-1-pyrroline (2AP) is considered as the major chemical compound responsible for the fragrance of aromatic rice<sup>##REF##32785241##5##</sup>. The volatile aromatic compounds are synthesized in the aerial parts of rice seedling, in the early vegetative stage and final accumulation takes place in the seeds<sup>##UREF##2##6##</sup>. The main gene identified for regulating aroma in rice is fgr/badh2/Os2AP homologous to betaine aldehyde dehydrogenase (BADH) on chromosome number eight<sup>##UREF##3##7##</sup>. Proline is the main precursor of 2-AP, which is regulated by enzyme Δ1 -pyroline-5-carboxylic acid synthetase (P5CS). The 2AP synthesis in scented rice is attributed to the non-functionality of betaine aldehyde dehydrogenase (badh2) gene<sup>##REF##26743769##8##</sup>. Accurate quantification of expression levels has been done for many genes through various throughput techniques. One such technique is the use of quantitative reverse transcription—polymerase chain reaction (qRTPCR or real-time RT-PCR) which allows weakly expressed genes to be accurately quantified<sup>##REF##17207290##9##</sup>. Hinge et al.<sup>##REF##26481230##10##</sup> studied the expression analysis of major aroma volatile (2-AP)-related genes such as betaine aldehyde dehydrogenase 2 (badh2) and Δ1 -pyrolline-5-carboxylic acid synthetase (P5CS) using real-time PCR, in Basmati-370, Ambemohar-157 (non-basmati scented), and IR64 (non-scented) rice cultivars at vegetative and maturity stages. They reported maximum number of volatiles (72–58) at vegetative stage than at mature stage (54–39).</p>", "<p id=\"Par3\">Several investigators have used E-nose and gas chromatography mass spectroscopy (GC–MS) techniques for determining the aroma profile of rice<sup>##REF##35214407##11##,##REF##35804703##12##</sup>. E-nose is a combination of gas sensors which mimics human nose. There occurs an irreversible change in the electrical properties such as conductivity, when the volatile aromatic compounds react with sensing material of gas sensor. These changes are then detected and characterized by pattern recognition algorithms to perform discrimination or classification of aromatic compounds<sup>##UREF##4##13##</sup>. Jana et al.<sup>##UREF##5##14##</sup> successfully differentiated different aromatic rice samples using E-nose. Additionally, Zheng et al.<sup>##UREF##6##15##</sup> also studied the rapid identification of four rice samples using an electronic nose (Cyranose-320) unit consisting of 32 polymer sensors. The Cyranose-320 was able to differentiate between varieties of rice, which was helpful for obtaining an accurate training model to improve identification capability. Gas chromatography mass spectrometry (GC–MS) is an instrumental technique, comprising a gas chromatograph (GC) coupled to a mass spectrometer (MS), by which complex mixtures of chemicals are separated, identified and quantified. Also, seven major active volatile compounds (hexanal, octanal, nonanal, (E)-2-octenal, decanal, 1-heptanol, and 1-octanol were detected in Jasmine rice by GC-MS<sup>##REF##32466949##16##</sup>.</p>", "<p id=\"Par4\">Basmati and Jasmine rice are the major aromatic rice groups. However, India is a treasure trove of scented rice beyond the basket of Basmati and Jasmine rice as well. Various scented rice varieties (<italic>Ambemohar, Mullan Kazhama, Gobindo Bhog, Seerag Samba</italic>, <italic>Mushk Budiji, Radhuni Ragot and Chak Hao Amubijao</italic>) are grown across the India<sup>##UREF##7##17##</sup>. Out of the different scented rice varieties, <italic>Mushk Budiji</italic>- the native aromatic rice variety of Highland Himalayan regions has prodigious demand in the international market due to its unique flavor and organoleptic appeal. <italic>Mushk Budiji</italic> is a short bold rice variety cultivated over an area of 10,000 ha, at an altitude above 5000 ft in Highland regions of Himalayas<sup>##REF##29511225##18##</sup>. Genetic basis, variation in altitude, soil type and climatic conditions significantly effect the flavor profile of rice<sup>##REF##33792035##19##</sup>. However, no studies have been conducted so far on <italic>Mushk Budiji</italic> rice in this direction. Furthermore, genetic mechanism of flavor development w.r.t altitude variation in case of <italic>Mushk Budiji</italic> rice is yet to be explored.</p>", "<p id=\"Par5\">Therefore, in the present study flavor profiling of <italic>Mushk Budiji</italic> rice grown at eight different altitudes (ranging from 7053.80 to 5216.53 ft amsl) in Highland Himalayan regions was done using GC–MS and E-nose techniques. The novel transcriptomics approach was also used to understand the genetic basis of flavor development in <italic>Mushk Budiji</italic> rice, which identifies different pathways responsible for the production of volatile aromatic compounds contributing to the characteristic aroma in <italic>Mushk Budiji.</italic></p>" ]	[ "<title>Methods</title>", "<title>Materials</title>", "<p id=\"Par19\"><italic>Mushk Budiji</italic> seeds (an indigenious aromatic rice varietry) were procured from Mountain Research Centre for Field Crops (MRCFC), Khudwani, J&K, India and all the methods used in this work are in compliance with the institutional guidelines. The seeds were sown at 8 different locations of Jammu & Kashmir, India, viz, L1- (Arwah-Budgam; Altitude- 7053.80 ft), L2-(Sagam- Anantnag; Altitude- 6397.63 ft), L3-(Meeliyal- Kupwara; Altitude- 6328.74 ft), L4-(Satura- Pulwama; Altitude- 6299.21 ft), L5-(Chandilura- Baramulla; Altitude- 6167.97 ft), L6-(Khudwani-Anantnag; Altitude- 5314.96 ft), L7-(Kachwamuqam- Baramulla; Altitude- 5226 ft), L8-(Wadura- Sopore; Altitude- 5216.53 ft), in the month of June. The altitude range was chosen in order to identify the suitable location for the production of volatile aromatic compounds in <italic>Mushk Budiji</italic> rice. Transplanting was done using 25 days old seedling using 2–3 plants/hill with row to plant spacing of 15 × 15 cm. The fertilizer doses of nitrogen, phosphorous, potassium (NPK) & ZnSO<sub>4</sub> recommended as (N:P:K: ZnSO<sub>4</sub>) (70:90:35:15) kg/ha was applied. Soil status of all the locations along with the mean temperature, and mean rainfall from June (2021) to October (2021) is depicted in Table ##SUPPL##0##S1##. Harvesting was done at the initial physiological maturity stage, when rice grains turned into brown color. Standard agronomic practices were followed in collecting <italic>Mushk Budiji</italic> in all the selected locations. The paddy samples (Fig. ##SUPPL##0##S1##a) collected from eight different locations were dehusked in THU-34A Satake Testing rice husker (Satake, Japan) and brown rice obtained thereof was polished in a BS08A Satake Single pass friction rice pearler (Satake, Japan) for 1 min. The polishing of samples was done to remove the outer layers (bran and germ) of rice, since <italic>Mushk Budiji</italic> is consumed after polishing. The milled rice samples (Fig. ##SUPPL##0##S1##b) were packed in plastic containers and stored at 4 ± 1 °C in separate containers until analyzed. Chemicals, complementary DNA (CDNA) synthesis kit, primers and housekeeping genes used in the study were purchased from Sigma-Aldrich, USA.</p>", "<title>Gas chromatography mass spectroscopy analysis</title>", "<p id=\"Par20\">Gas chromatography mass spectroscopy (GC–MS) analysis for identification and quantification of volatile aroma compounds was done as per the method reported by Mahattanatawee et al.<sup>##REF##24518308##35##</sup>. Weighed mass of rice sample (2 g) was placed into head space vial and then 5 ml of type I water was added. The vial was then sealed with a silicon cap and was equilibrated for 30 min at 80 °C with shaking level-3 for further injection into GC–MS.</p>", "<p id=\"Par21\">GC–MS analysis was performed on GC–MS-TQ8040 instrument (GC–MS-TQ Shimadzu, Japan) using Stabilwax capillary column (internal diameter-30 m × 0.25 mm and0.25 µm film thickness). The column temperature was initially held at 50 °C for 1 min followed by 200 °C at 15 ° min<sup>−1</sup> and 220 °C at 5°Cmin<sup>-1</sup>. The column temperature was held at 220 °C for 5 min. The injector was maintained at 230 °C and 1 µl of sample was injected in splitless mode. Ultra-high purity helium (99.99%) was employed as carrier gas at a constant flow of 2.6 ml min<sup>−1</sup>. The transfer line and electron ionization (EI) source temperature was set at 230 °C, and quadrupole mass analyzer temperature was set at 150 °C. Data acquisition was performed in scan range of 35–500 m/z. The contents of volatiles components were quantified by measuring the peak areas in the total ion chromatogram (TIC).</p>", "<title>Gene expression analysis</title>", "<p id=\"Par22\">In order to analyze the effects of different locations on the gene expression in <italic>Mushk Budiji</italic> rice. Rice panicle/tissue from all the selected locations were collected and stored in RNA later till further analysis. The total RNA was extracted from 100 mg fresh weight of embryonic tissues using Trizol reagent (Invitrogen)<sup>##REF##2440339##36##</sup>. This was followed by treatment of RNase-free DNase I (Sigma-Aldrich, USA). CDNA synthesis was carried out by Thermo Fisher Scientific RevertAid First Strand CDNA Synthesis Kit using oligodT primers, as per the manufacturer’s protocol. The flavor controlling genes and their primer sequence used in the study is given in Table ##SUPPL##0##S2##. Relative quantification of genes was done by quantitative real-time PCR of normalized CDNA using Roche FastStart Universal SYBR Green Master (Rox) (Roche). Values were calculated using 2<sup>-△△CT</sup> relative quantification method<sup>##REF##11846609##37##</sup> for candidate genes and UBQ5 as reference gene. Relative expression studies were done for genes related to fatty acid, linoleic acid and ether lipid metabolism. C<sub>t</sub> (cycle threshold) curve was obtained from quantitative Real Time-PCR for all the eight selected locations. C<sub>t</sub> values were used to calculate ΔC<sub>t</sub> for both test and the positive calibrator (L3), where ΔC<sub>t</sub> equals [C<sub>t</sub> (target gene) − C<sub>t</sub> (reference gene)]. Afterward, ΔΔC<sub>t</sub> was calculated as: ΔΔC<sub>t</sub> = ΔC<sub>t</sub> gene (location) − ΔC<sub>t</sub> (location3). The ΔΔC<sub>t</sub>, so obtained, was translated to yield relative fold change (2 − ΔΔC<sub>t</sub>) in expression of target genes.</p>", "<title>E-nose analysis for aroma detection</title>", "<p id=\"Par23\">Electronic nose detector with metal-oxide semiconductor (MOS)-based gas analyzer array designed by Centre for Development of Advanced Computing (C-DAC, Kolkatta, India) was used in this study. This device contained an array of 8 different non specific commercial tin oxide semiconductor sensors from Figaro, Japan namely- Sensor 1-TGS-825 (sensitive for sulphur containing compounds), Sensor 2-TGS-816 (sensitive for hydrocarbarbons- alkanes), Sensor 3-TGS-823 (organic solvents such as alcohols), Sensor 4-TGS-832 (hydrocarbons- halocarbons), Sensor 5-TGS-830 (sensitive for hydrocarbons), Sensor 6-TGS-2600 (sensitive for hydrocarbons, alcohols, ketones), Sensor 7-TGS- 2620 (sensitive for hydrocarbons, organic solvents such as alcohols), and Sensor 8-TGS-821 (sensitive for hydrocarbons) to discriminate between the odour patterns of different aromatic compounds. The experimental set-up of electronic nose for aromatic rice system consists of: (1) a sensor array; (2) a micro-pump and solenoid valves with programmable sequence control; (3) PC-based data acquisition; and (4) olfaction software. Specially designed sample holders made of aluminum were used for experimental runs. An aluminum sample holder was fixed to the instrument by simple lock fitting. The entire sniffing cycle consisted of an automated sequence of internal operations, viz., (1) headspace generation, (2) sampling, and (3) purging (Fig. ##FIG##3##4##a).</p>", "<p id=\"Par24\">Rice samples (15 g) were placed in an aluminum sample holder to which 60 ml of distilled water was added. Samples were cooked in the E-nose setup at a temperature of 100 °C for 20 min. The cooked rice samples were placed for cooling in the sniffing chamber for 10 min. Prior to sampling the adequate accumulation of volatile compounds was ensured in head space of the sniffing chamber. The sensor array was then exposed to a constant flow (150 ml/min) of volatiles through pipelines inside the apparatus. During the purging operation, sensor heads were cleared with a blow of fresh air to bring the sensors back to the baseline values. The stepwise sequence of operations followed during the experimental sniffing cycle of E-nose are depicted in Table ##SUPPL##0##S3## and Fig. ##FIG##3##4##b.</p>", "<title>Free fatty acid content</title>", "<p id=\"Par25\">Free fatty acid (FFA) content of rice samples was determined using standard titrimetric method<sup>##UREF##17##38##</sup>. Ground rice flour (10 g) was placed in whattmann filter paper and oil extraction was done for 16 h using petroleum ether (200 ml) at room temp (24 ± 5 °C). The petroleum ether was evaporated by heating under fume. After evaporation of solvent (petroleum ether), approximately 50 ml of benzene alcohol phenolphthalein solution was added into the oil sample. The sample was titrated with 0.0178N potassium hydroxide (KOH) until faint pink color persisted for 1 min. The FFA content was calculated as:</p>", "<title>Peroxide value</title>", "<p id=\"Par26\">Peroxide value of rice samples was analyzed by method reported by Gichau et al.<sup>##UREF##18##39##</sup>. Soxhlet method was used to extract the lipids from rice samples. Two grams of samples were weighed into 250 ml stoppered conical flask. Thirty milliliter of acetic acid and chloroform solvent mixture (30:20) was added to the sample and swirled to dissolve. Afterwards, 0.5 ml saturated potassium iodide solution was added and left to stand for 1 min in the dark with occasional shaking, followed by addition of 30 ml of distilled water. The mixture was titrated with 0.01 N sodium thiosulphate solution, with vigorous shaking until yellow color disappeared. 0.5 ml starch solution was added as an indicator and titration was continued until the blue color disappeared. Peroxide value was calculated using the following formulae:where, W is the weight of the sample, Titre value = ml of sodium thiosulphate solution used, N = Normality of sodium thiosulphate solution.</p>", "<title>Catalase activity</title>", "<p id=\"Par27\">Catalase activity was determined using the method reported by Palmiano et al.<sup>##REF##16658546##40##</sup>. Grains were homogenised for 3 min with 10 ml of HCl buffer (pH 7). The homogenate was centrifuged at 10,000 rpm for 30 min at 4 °C. 5 ml of distilled water, 1 ml of Hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>) substrate, and 1 ml of enzyme extract was taken in a conical flask. The mixture was incubated in an incubator at 28 ± 1 °C for 15 min. Finally, the enzyme activity was stopped by adding 5 ml of 10% sulphuric acid (H<sub>2</sub>SO<sub>4</sub>) to the reaction mixture. The residual H<sub>2</sub>O<sub>2</sub> content of the mixture was estimated through titration against potassium permanganate (KMnO<sub>4</sub>) solution. A control set titration was also made without the addition of enzyme to the reaction mixture (6 ml distill water and 1 ml H<sub>2</sub>O<sub>2</sub> substrate). The control set was also incubated at 28 ± 1 °C for 15 min and the reaction was stopped by the addition of 5 ml of 10% H<sub>2</sub>SO<sub>4</sub> before titration against potassium permanganate (KMnO<sub>4</sub>) solution. The difference between the titration reading of the control set (without enzyme) and reaction mixture (with enzyme) provided the amount of H<sub>2</sub>O<sub>2</sub> decomposed by the enzyme action.</p>", "<title>Statistical models</title>", "<p id=\"Par28\">All the experiments were conducted in triplicates and results were expressed as mean ± standard deviation. The mean differences were analyzed by one way analysis of variance (ANOVA). Statistical significance of means was accessed using Duncan’s Multiple Range test (DMRT) test at p ≤ 0.05 level of significance using SPSS software. In addition, the output data from the E-nose was analyzed using the instrument software of the electronic nose (Lab VIEW). Principle component analysis was performed on the output data of E-nose by XLSTAT trial version (2019).</p>" ]	[ "<title>Results and discussion</title>", "<title>Flavor profiling of rice</title>", "<p id=\"Par6\">Flavor profile of <italic>Mushk Budiji</italic> rice grown at different altitudes is depicted in Table ##TAB##0##1##. Aroma volatiles usually include an oxygen-containing group, a nitrogen group, a sulfur group, and an aromatic group<sup>##UREF##8##20##</sup>. In this study, the identified species of volatile compounds (VOCs) in <italic>Mushk Budiji</italic> were mainly alcohols, aldehydes, hydrocarbons and other aromatic compounds. In general 35 volatile compounds were identified in <italic>Mushk Budiji</italic> rice, including 5 alcohols, 5 aldehydes, 1 ketone, 3 esters, 17 hydrocarbons and 4 other types. Among these VOC’s, aldehydes, and alcohols were more abundant in the aromatic rice samples grown at different altitudes. Aldehydes ranged from 6.33 to 29.09% and alcohols from 0.47 to 30.34% in rice samples grown at different locations (Table ##TAB##0##1##). Highest proportion of aldehydes and alcohols were recorded in L3 and L4 samples, respectively. Both these locations were characterized by different altitude and temperature (L3-6328.74 ft; T = 26.92 °C & L4-6299.21ft; T = 27.09 °C). Average rainfall received by L3 (67.94 mm) was also higher than the average rainfall received by L4 location (Table ##SUPPL##0##S1##). In general aldehydes, alcohols and ketone based compounds were found higher in L4 samples as compared to other samples. It has been reported that aldehydes like hexanal & octanal produce fruity flavors while as nonanal produces citrus & fatty flavor in rice<sup>##UREF##9##21##</sup>. Fatty alcohols, such as 1-hexanol etc. are the secondary products of polyunsaturated fatty acids and produce a soft and sweet flavor in rice<sup>##REF##34436469##22##</sup>. Among the fatty alcohols, flavor compounds like 1-hexanol (1.85%), propylene glycol (11.47%), 2-4-di-tert butylphenol (3.33%), silane diol dimethyl (13.69%) were recorded in aromatic rice grown at L4 (Table ##TAB##0##1##). Also hepatan-2-one, which is known to produce a characteristic fruity floral smell<sup>##REF##32156374##23##</sup> was found only in L4 (0.72%) sample. Acetoxyacetic acid-4- pentadecylester was found present only in L4 (1.49%) sample. However, 1,2-benzenedicarboxylic acid,bis (2-methylpropyl) ester was found highest (4.94%) in L7 followed by L6, L5, L8 and L1 sample while as 4-ethylbenzoicacid,cyclopentyl was found only in L3 (2.3%) and L8 (1.32%) samples. 2-pentyl furan which is known for its nutty and sweet aroma<sup>##REF##26743769##8##</sup> was found in all the samples except in L4 and L6 samples (Table ##TAB##0##1##).</p>", "<p id=\"Par7\">The highest number of hydrocarbons were identified in rice cultivated at L8. Also, aromatic rice grown at L8 exhibited higher concentration of total volatile compounds (total peak area percentage of 105.41%) in comparison to other samples. This area is located at an altitude of 5216.53 ft and received average rainfall of 41.10 mm with mean temperature of 29 °C. Prodhan et al.<sup>##UREF##0##2##</sup> also reported that the aromatic rice genotypes contained more volatile compounds and displayed the maximum aroma score at temperatures surpassing their ambient conditions. These hydrocarbons are positively related to aroma traits and nutritional qualities<sup>##REF##32438226##24##</sup> and the accumulation of these volatile compounds in aromatic rice is closely related to the characteristics of its growth environment, such as climate, soil conditions, and altitude<sup>##UREF##10##25##</sup>, which is bound to vary with different cultivation areas<sup>##REF##18363355##26##</sup>.</p>", "<p id=\"Par8\">2-AP content was found to be present only in <italic>Mushk Budiji</italic> grown at L1 (0.75%) & L3 (1%) locations. Higher percentage of 2-AP in L3 samples is attributed to the low rainfall of 67.94 mm received by L3 in comparison to 74.29 mm of rainfall received by L1 location. This suggested that 2-AP levels were more influenced by rainfall conditions than by the altitude. Yoshihashi et al.<sup>##UREF##11##27##</sup> also reported that 2AP levels in rice grains were highest when cultivated in regions with less water. Thus, it was observed that the amount of rainfall received by the samples affected the biosythesis, and accumulation of flavor volatiles in general and 2-AP in particular<sup>##UREF##12##28##</sup>. This could be due to the fact that rice leaves under water stress showed enhanced proline accumulation, which led to a considerable rise in 2-AP levels. Also, fragrant rice cultivars cultivated at different temperatures displayed varying degrees of 2AP accumulation<sup>##REF##30409674##29##</sup>. Sansenya et al.<sup>##REF##33792035##19##</sup> also reported that 2-AP accumulation takes place in rain-fed areas at higher elevations accompanied by low air temperature.</p>", "<p id=\"Par9\">Higher proportion of VOC’s were detected in aromatic rice grown at L8 (5216.53 ft) (Table ##TAB##0##1##), whereas the lowest number of volatiles were obtained from rice grown at L2 (6397.63 ft). This indicates that the aromatic rice samples harvested from locations at higher altitudes and receiving higher rainfall exhibited lower concentration of VOC’s than those from low altitudes with low rainfall. Rainfall is also an important factor in determining the yield and quality of aromatic rice. Furthermore, the mean temperatures and rainfall recorded for locations at higher altitudes was comparatively lower (26.1 °C; 69.21 mm) than the low altitude locations (29 °C; 41.10 mm), which indicated that high temperature and low rainfall was conducive to accumulate volatile aromatic compounds in <italic>Mushk Budiji</italic> rice. Thus, the accumulation of volatile compounds from aromatic rice samples harvested from different cultivation areas was potentially affected by different environmental conditions such as climate, soil conditions and altitude<sup>##UREF##10##25##</sup>. Indeed, previous authors have indicated that metabolite accumulation is substantially affected by environmental factors as well as by the genetic factors<sup>##UREF##13##30##</sup>.</p>", "<p id=\"Par10\">Soil nitrogen data of L1 (7053.80) & L3 (6328.74 ft) locations showed nitrogen levels of 420 & 265 kg/ ha which suggested that 2-AP biosynthesis can occur both under low and high nitrogen soil conditions. However, it was reported that higher nitrogen content promotes 2-AP accumulation in rice<sup>##REF##33792035##19##</sup>. Thus, this variation in our results can be attributed to different agro-climatic conditions and elevation levels tested.</p>", "<title>Gene expression of <italic>Mushk Budiji</italic></title>", "<p id=\"Par11\">In the present study relative expression of candidate genes responsible for fatty acid degradation, linoleic acid metabolism and ether lipid metabolism leading to flavor development in <italic>Mushk Budiji</italic> rice at different elevations was studied (Fig. ##FIG##0##1##). The results elucidated the role of altitude in regulating their expression and finally leading to variation in aroma content. It was observed that at higher elevation genes responsible for fatty acid degradation (Gene- 1 & Gene- 2) and linoleic acid metabolism (Gene-3, Gene-4, Gene-5) were up-regulated. The results suggested that altitude significantly stimulated the expression of these genes (G-1, G-2, G-3, G-4, & G-5) which promoted the lipase activity, and thus produced free fatty acids and heterocyclic compounds. Over expression of genes at higher altitudes may be triggered by low temperature and higher light intensity<sup>##REF##30722853##31##,##UREF##14##32##</sup>. Genes responsible for ether lipid metabolism (Gene-6, Gene-7, Gene-8 & Gene-9) and badh2 (Gene-10) were highly expressed in aromatic rice grown at lesser elevations (L2, L6, L7) revealing that the higher altitude inhibit the expression of these genes (G-6,G-7,G-8,G-9) which participate in the hydrolysis of lipases and degradation of phospholipases.</p>", "<title>Electronic nose analysis</title>", "<p id=\"Par12\">The volatile profile of <italic>Mushk Budiji</italic> rice grown at 8 different locations was characterized using electronic nose (E-nose). This device contains an array of sensors that respond to different types of volatile compounds present in the sample. Radar plot depicted in Fig. ##FIG##1##2##a shows almost a similar shape for all the samples, indicating that majority of aromatic compounds were present in <italic>Mushk Budiji</italic> rice samples grown at different altitudes. Rice samples of each location showed highest sensor response for sensor 2(TGS-816), detecting the hydrocarbons. The highest sensor response of 1.153 recorded by sensor 4 for L5, indicated the presence of higher percentage of aldehydes (hexanal), alcohols (propylene glycol) and hydrocarbons (n-hexane) in rice samples grown at location 5. These compounds possess relatively lower odour threshold and thus contribute to the rice flavor<sup>##UREF##8##20##</sup>. Proportionally higher VOC’s were present in rice grown at L5 which was in accordance with the highest sensor response of 2.173 recorded by sensor 6 which is sensitive to detect aldehydes, alcohols and ketones. Based on the different sensitivities and responses of the E-nose sensors, it was presumed that Sensor 6-TGS-2600 positively co-related with hydrocarbons, alcohols & ketones while Sensor 2-TGS-816 and Sensor 3-TGS-823 positively correlated with hydrocarbons (alkanes) and alcohols respectively. Overall the rice samples grown at L4 location showed highest sensor response, which was also validated by GC–MS technique. Aldehydes are mainly produced via lipid oxidation and their decomposition contribute to the overall flavor of aromatic rice because of their relatively lower odour threshold. Alcohols which are amongst the abundant volatiles possess lower odour threshold. They are regarded as the secondary products of unsaturated fatty acid oxidation, formed due to breakdown of aldehydes<sup>##UREF##8##20##</sup>.</p>", "<p id=\"Par13\">Overall higher E-nose scores were recorded for <italic>Mushk Budiji</italic> grown at lower altitudes as compared to higher altitudes, which indicated that flavor changes occurrence were more at higher altitudes as compared to lower altitudes<sup>##REF##30722853##31##</sup>. Out of all the eight tested samples highest E-nose score was recorded in L4 (2.52) followed by L3 (2.51) (as shown in Fig. ##FIG##1##2##b), which is in accordance to the flavor profiling results obtained through GC–MS.</p>", "<title>Principal component analysis of sensor response from E-nose</title>", "<p id=\"Par14\">Principal component analysis (PCA) was done to identify the differences in the flavor profile of <italic>Mushk Budiji</italic> grown at eight different locations. Data set obtained from E-nose comprising of sensor response from eight sensors and overall aroma score was analyzed in reduced dimension (Fig. ##FIG##1##2##c). Principal component 1 (Dimension 1) and 2 (Dimension 2), reflecting the abscissa and ordinate of the biplot in Fig. ##FIG##1##2##c accounted for 49.5% and 24.7%, of total variability respectively. The sensor response of location 4 was much higher than other locations as reflected by the numerical difference on the abscissa. All the sensors showed positive correlation with each other and sensor 5 (sensitive for hydrocarbons) showed higher variability. Sensor 3, 6 and 7 showed comparatively lesser variability as reflected by the PCA biplot.</p>", "<title>Free fatty acid content</title>", "<p id=\"Par15\">Lipases are naturally present in rice and can hydrolyze rice lipids to produce free fatty acids (FFA), which negatively impacts rice quality<sup>##UREF##15##33##</sup>. FFA content of the <italic>Mushk Budiji</italic> collected from 8 different locations showed significant (p ≤ 0.05) variation. As FFA content showed an increasing trend from L1 (1.3%) to L8 (4.6%) as shown in (Fig. ##FIG##2##3##a). Lesser amount of FFA content was recorded in lower altitudes samples as compared to higher altitude areas, possibly due to temperature difference. Biao et al.<sup>##REF##30722853##31##</sup> also reported that high temperature accelerates the free fatty acid generation in rice.</p>", "<title>Peroxide value</title>", "<p id=\"Par16\">Peroxide value (PV) indicates the amount of peroxides formed in fats and oils during oxidation<sup>##UREF##16##34##</sup> and is an important indicator of primary lipid oxidation. Although, PV is not a reliable index to judge the rancidity, but fat is generally considered rancid at PV of greater than 10. It has been reported that peroxide formation would increase in presence of high temperature, indicating that oxidation of fats in the product. The peroxide values of <italic>Mushk Budiji</italic> grown at eight different locations also showed significant (p ≤ 0.05) variation. PV increased from 0.13 meq/kg in L1 to 0.76 meq/kg in L8 (Fig. ##FIG##2##3##b) possibly due to increase in temeperature as higher peroxide fraction occurs at high temperature. This could be attributed to the fact that higher altitude has lesser temperature in comparison to lower altitudes. Biao et al.<sup>##REF##2440339##36##</sup> also reported that high temperature causes a surge in peroxide value. The range of peroxide value (0.13 meq/kg- 0.76 meq/ kg) recorded in this study was in accordance with the results reported previously by Ozkan et al.<sup>##UREF##16##34##</sup> for fats and oils.</p>", "<title>Catalase activity</title>", "<p id=\"Par17\">Catalase is an enzyme capable of decomposing hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>) into water (H<sub>2</sub>O) and oxygen (O<sub>2</sub>), which can reduce the ability of peroxides to promote lipid oxidation<sup>##REF##30722853##31##</sup>. In our study, catalase activity of <italic>Mushk Budiji</italic> rice significantly decreased (p ≤ 0.05) from L1 (4.083U/mg) to L8 (3.451U/mg) (Fig. ##FIG##2##3##c). This is due to the fact that lower altitude might inhibit the catalase activity present in rice due to the higher temperatures<sup>##REF##2440339##36##</sup>.</p>" ]	[ "<title>Results and discussion</title>", "<title>Flavor profiling of rice</title>", "<p id=\"Par6\">Flavor profile of <italic>Mushk Budiji</italic> rice grown at different altitudes is depicted in Table ##TAB##0##1##. Aroma volatiles usually include an oxygen-containing group, a nitrogen group, a sulfur group, and an aromatic group<sup>##UREF##8##20##</sup>. In this study, the identified species of volatile compounds (VOCs) in <italic>Mushk Budiji</italic> were mainly alcohols, aldehydes, hydrocarbons and other aromatic compounds. In general 35 volatile compounds were identified in <italic>Mushk Budiji</italic> rice, including 5 alcohols, 5 aldehydes, 1 ketone, 3 esters, 17 hydrocarbons and 4 other types. Among these VOC’s, aldehydes, and alcohols were more abundant in the aromatic rice samples grown at different altitudes. Aldehydes ranged from 6.33 to 29.09% and alcohols from 0.47 to 30.34% in rice samples grown at different locations (Table ##TAB##0##1##). Highest proportion of aldehydes and alcohols were recorded in L3 and L4 samples, respectively. Both these locations were characterized by different altitude and temperature (L3-6328.74 ft; T = 26.92 °C & L4-6299.21ft; T = 27.09 °C). Average rainfall received by L3 (67.94 mm) was also higher than the average rainfall received by L4 location (Table ##SUPPL##0##S1##). In general aldehydes, alcohols and ketone based compounds were found higher in L4 samples as compared to other samples. It has been reported that aldehydes like hexanal & octanal produce fruity flavors while as nonanal produces citrus & fatty flavor in rice<sup>##UREF##9##21##</sup>. Fatty alcohols, such as 1-hexanol etc. are the secondary products of polyunsaturated fatty acids and produce a soft and sweet flavor in rice<sup>##REF##34436469##22##</sup>. Among the fatty alcohols, flavor compounds like 1-hexanol (1.85%), propylene glycol (11.47%), 2-4-di-tert butylphenol (3.33%), silane diol dimethyl (13.69%) were recorded in aromatic rice grown at L4 (Table ##TAB##0##1##). Also hepatan-2-one, which is known to produce a characteristic fruity floral smell<sup>##REF##32156374##23##</sup> was found only in L4 (0.72%) sample. Acetoxyacetic acid-4- pentadecylester was found present only in L4 (1.49%) sample. However, 1,2-benzenedicarboxylic acid,bis (2-methylpropyl) ester was found highest (4.94%) in L7 followed by L6, L5, L8 and L1 sample while as 4-ethylbenzoicacid,cyclopentyl was found only in L3 (2.3%) and L8 (1.32%) samples. 2-pentyl furan which is known for its nutty and sweet aroma<sup>##REF##26743769##8##</sup> was found in all the samples except in L4 and L6 samples (Table ##TAB##0##1##).</p>", "<p id=\"Par7\">The highest number of hydrocarbons were identified in rice cultivated at L8. Also, aromatic rice grown at L8 exhibited higher concentration of total volatile compounds (total peak area percentage of 105.41%) in comparison to other samples. This area is located at an altitude of 5216.53 ft and received average rainfall of 41.10 mm with mean temperature of 29 °C. Prodhan et al.<sup>##UREF##0##2##</sup> also reported that the aromatic rice genotypes contained more volatile compounds and displayed the maximum aroma score at temperatures surpassing their ambient conditions. These hydrocarbons are positively related to aroma traits and nutritional qualities<sup>##REF##32438226##24##</sup> and the accumulation of these volatile compounds in aromatic rice is closely related to the characteristics of its growth environment, such as climate, soil conditions, and altitude<sup>##UREF##10##25##</sup>, which is bound to vary with different cultivation areas<sup>##REF##18363355##26##</sup>.</p>", "<p id=\"Par8\">2-AP content was found to be present only in <italic>Mushk Budiji</italic> grown at L1 (0.75%) & L3 (1%) locations. Higher percentage of 2-AP in L3 samples is attributed to the low rainfall of 67.94 mm received by L3 in comparison to 74.29 mm of rainfall received by L1 location. This suggested that 2-AP levels were more influenced by rainfall conditions than by the altitude. Yoshihashi et al.<sup>##UREF##11##27##</sup> also reported that 2AP levels in rice grains were highest when cultivated in regions with less water. Thus, it was observed that the amount of rainfall received by the samples affected the biosythesis, and accumulation of flavor volatiles in general and 2-AP in particular<sup>##UREF##12##28##</sup>. This could be due to the fact that rice leaves under water stress showed enhanced proline accumulation, which led to a considerable rise in 2-AP levels. Also, fragrant rice cultivars cultivated at different temperatures displayed varying degrees of 2AP accumulation<sup>##REF##30409674##29##</sup>. Sansenya et al.<sup>##REF##33792035##19##</sup> also reported that 2-AP accumulation takes place in rain-fed areas at higher elevations accompanied by low air temperature.</p>", "<p id=\"Par9\">Higher proportion of VOC’s were detected in aromatic rice grown at L8 (5216.53 ft) (Table ##TAB##0##1##), whereas the lowest number of volatiles were obtained from rice grown at L2 (6397.63 ft). This indicates that the aromatic rice samples harvested from locations at higher altitudes and receiving higher rainfall exhibited lower concentration of VOC’s than those from low altitudes with low rainfall. Rainfall is also an important factor in determining the yield and quality of aromatic rice. Furthermore, the mean temperatures and rainfall recorded for locations at higher altitudes was comparatively lower (26.1 °C; 69.21 mm) than the low altitude locations (29 °C; 41.10 mm), which indicated that high temperature and low rainfall was conducive to accumulate volatile aromatic compounds in <italic>Mushk Budiji</italic> rice. Thus, the accumulation of volatile compounds from aromatic rice samples harvested from different cultivation areas was potentially affected by different environmental conditions such as climate, soil conditions and altitude<sup>##UREF##10##25##</sup>. Indeed, previous authors have indicated that metabolite accumulation is substantially affected by environmental factors as well as by the genetic factors<sup>##UREF##13##30##</sup>.</p>", "<p id=\"Par10\">Soil nitrogen data of L1 (7053.80) & L3 (6328.74 ft) locations showed nitrogen levels of 420 & 265 kg/ ha which suggested that 2-AP biosynthesis can occur both under low and high nitrogen soil conditions. However, it was reported that higher nitrogen content promotes 2-AP accumulation in rice<sup>##REF##33792035##19##</sup>. Thus, this variation in our results can be attributed to different agro-climatic conditions and elevation levels tested.</p>", "<title>Gene expression of <italic>Mushk Budiji</italic></title>", "<p id=\"Par11\">In the present study relative expression of candidate genes responsible for fatty acid degradation, linoleic acid metabolism and ether lipid metabolism leading to flavor development in <italic>Mushk Budiji</italic> rice at different elevations was studied (Fig. ##FIG##0##1##). The results elucidated the role of altitude in regulating their expression and finally leading to variation in aroma content. It was observed that at higher elevation genes responsible for fatty acid degradation (Gene- 1 & Gene- 2) and linoleic acid metabolism (Gene-3, Gene-4, Gene-5) were up-regulated. The results suggested that altitude significantly stimulated the expression of these genes (G-1, G-2, G-3, G-4, & G-5) which promoted the lipase activity, and thus produced free fatty acids and heterocyclic compounds. Over expression of genes at higher altitudes may be triggered by low temperature and higher light intensity<sup>##REF##30722853##31##,##UREF##14##32##</sup>. Genes responsible for ether lipid metabolism (Gene-6, Gene-7, Gene-8 & Gene-9) and badh2 (Gene-10) were highly expressed in aromatic rice grown at lesser elevations (L2, L6, L7) revealing that the higher altitude inhibit the expression of these genes (G-6,G-7,G-8,G-9) which participate in the hydrolysis of lipases and degradation of phospholipases.</p>", "<title>Electronic nose analysis</title>", "<p id=\"Par12\">The volatile profile of <italic>Mushk Budiji</italic> rice grown at 8 different locations was characterized using electronic nose (E-nose). This device contains an array of sensors that respond to different types of volatile compounds present in the sample. Radar plot depicted in Fig. ##FIG##1##2##a shows almost a similar shape for all the samples, indicating that majority of aromatic compounds were present in <italic>Mushk Budiji</italic> rice samples grown at different altitudes. Rice samples of each location showed highest sensor response for sensor 2(TGS-816), detecting the hydrocarbons. The highest sensor response of 1.153 recorded by sensor 4 for L5, indicated the presence of higher percentage of aldehydes (hexanal), alcohols (propylene glycol) and hydrocarbons (n-hexane) in rice samples grown at location 5. These compounds possess relatively lower odour threshold and thus contribute to the rice flavor<sup>##UREF##8##20##</sup>. Proportionally higher VOC’s were present in rice grown at L5 which was in accordance with the highest sensor response of 2.173 recorded by sensor 6 which is sensitive to detect aldehydes, alcohols and ketones. Based on the different sensitivities and responses of the E-nose sensors, it was presumed that Sensor 6-TGS-2600 positively co-related with hydrocarbons, alcohols & ketones while Sensor 2-TGS-816 and Sensor 3-TGS-823 positively correlated with hydrocarbons (alkanes) and alcohols respectively. Overall the rice samples grown at L4 location showed highest sensor response, which was also validated by GC–MS technique. Aldehydes are mainly produced via lipid oxidation and their decomposition contribute to the overall flavor of aromatic rice because of their relatively lower odour threshold. Alcohols which are amongst the abundant volatiles possess lower odour threshold. They are regarded as the secondary products of unsaturated fatty acid oxidation, formed due to breakdown of aldehydes<sup>##UREF##8##20##</sup>.</p>", "<p id=\"Par13\">Overall higher E-nose scores were recorded for <italic>Mushk Budiji</italic> grown at lower altitudes as compared to higher altitudes, which indicated that flavor changes occurrence were more at higher altitudes as compared to lower altitudes<sup>##REF##30722853##31##</sup>. Out of all the eight tested samples highest E-nose score was recorded in L4 (2.52) followed by L3 (2.51) (as shown in Fig. ##FIG##1##2##b), which is in accordance to the flavor profiling results obtained through GC–MS.</p>", "<title>Principal component analysis of sensor response from E-nose</title>", "<p id=\"Par14\">Principal component analysis (PCA) was done to identify the differences in the flavor profile of <italic>Mushk Budiji</italic> grown at eight different locations. Data set obtained from E-nose comprising of sensor response from eight sensors and overall aroma score was analyzed in reduced dimension (Fig. ##FIG##1##2##c). Principal component 1 (Dimension 1) and 2 (Dimension 2), reflecting the abscissa and ordinate of the biplot in Fig. ##FIG##1##2##c accounted for 49.5% and 24.7%, of total variability respectively. The sensor response of location 4 was much higher than other locations as reflected by the numerical difference on the abscissa. All the sensors showed positive correlation with each other and sensor 5 (sensitive for hydrocarbons) showed higher variability. Sensor 3, 6 and 7 showed comparatively lesser variability as reflected by the PCA biplot.</p>", "<title>Free fatty acid content</title>", "<p id=\"Par15\">Lipases are naturally present in rice and can hydrolyze rice lipids to produce free fatty acids (FFA), which negatively impacts rice quality<sup>##UREF##15##33##</sup>. FFA content of the <italic>Mushk Budiji</italic> collected from 8 different locations showed significant (p ≤ 0.05) variation. As FFA content showed an increasing trend from L1 (1.3%) to L8 (4.6%) as shown in (Fig. ##FIG##2##3##a). Lesser amount of FFA content was recorded in lower altitudes samples as compared to higher altitude areas, possibly due to temperature difference. Biao et al.<sup>##REF##30722853##31##</sup> also reported that high temperature accelerates the free fatty acid generation in rice.</p>", "<title>Peroxide value</title>", "<p id=\"Par16\">Peroxide value (PV) indicates the amount of peroxides formed in fats and oils during oxidation<sup>##UREF##16##34##</sup> and is an important indicator of primary lipid oxidation. Although, PV is not a reliable index to judge the rancidity, but fat is generally considered rancid at PV of greater than 10. It has been reported that peroxide formation would increase in presence of high temperature, indicating that oxidation of fats in the product. The peroxide values of <italic>Mushk Budiji</italic> grown at eight different locations also showed significant (p ≤ 0.05) variation. PV increased from 0.13 meq/kg in L1 to 0.76 meq/kg in L8 (Fig. ##FIG##2##3##b) possibly due to increase in temeperature as higher peroxide fraction occurs at high temperature. This could be attributed to the fact that higher altitude has lesser temperature in comparison to lower altitudes. Biao et al.<sup>##REF##2440339##36##</sup> also reported that high temperature causes a surge in peroxide value. The range of peroxide value (0.13 meq/kg- 0.76 meq/ kg) recorded in this study was in accordance with the results reported previously by Ozkan et al.<sup>##UREF##16##34##</sup> for fats and oils.</p>", "<title>Catalase activity</title>", "<p id=\"Par17\">Catalase is an enzyme capable of decomposing hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>) into water (H<sub>2</sub>O) and oxygen (O<sub>2</sub>), which can reduce the ability of peroxides to promote lipid oxidation<sup>##REF##30722853##31##</sup>. In our study, catalase activity of <italic>Mushk Budiji</italic> rice significantly decreased (p ≤ 0.05) from L1 (4.083U/mg) to L8 (3.451U/mg) (Fig. ##FIG##2##3##c). This is due to the fact that lower altitude might inhibit the catalase activity present in rice due to the higher temperatures<sup>##REF##2440339##36##</sup>.</p>" ]	[ "<title>Conclusion</title>", "<p id=\"Par18\">The preamble of the results indicated that aroma profile of <italic>Mushk Budiji</italic> (an indigenous aromatic rice variety) varied with altitude and climatic conditions. In general, higher concentrations of aldehydes, alcohols, and esters, majorly recognized as aroma triggering compounds were recorded in <italic>Mushk Budiji</italic> grown at L8 location (altitude 5216.53 ft; temperature 29 °C), while 2-AP was detected only in <italic>Mushk Budiji</italic> samples grown at L1 & L3. Significant variation in E-nose score was also observed in <italic>Mushk Budiji</italic> samples grown at different altitudes and highest E-nose score (2.52) was recorded for L4. Principle component analysis method used accurately reflected the differences in the flavor profiles of <italic>Mushk Budiji</italic> grown at different altitudes. Therefore, it was presumed that altitude together with low temperature stimulates the accumulation of 2-AP in aromatic rice. Based on the findings of our study, it was concluded that besides 2-AP, several other VOC’s, particularly aldehydes, alcohols and hydrocarbons also contributed to the aromaticity of <italic>Mushk Budiji</italic> rice grown at various altitudes in Highland Himalayan region. Relationship between the flavor metabolic pathways and gene expression w.r.t altitudes was also explored in the study. High altitude was found to promote the over expression of fatty acid degradation and linoleic acid metabolism genes.</p>" ]	[ "<p id=\"Par1\"><italic>Mushk Budiji</italic>-an indigenous aromatic rice variety is usually grown at an altitude ranging from 5000 to 7000 ft above mean sea level in Highland Himalayas. This study was conducted to investigate the effects of altitude, soil nitrogen content and climatic conditions (temperature) of the selected locations on the flavor profile of <italic>Mushk Budiji</italic> using gas chromatography-mass spectroscopy (GC–MS) and electronic nose (E-nose). E-nose being rapid and non-destructive method was used to validate the results of volatile aromatic compounds obtained using GC–MS in <italic>Mushk Budiji.</italic> Around 35 aromatic compounds were identified in <italic>Mushk Budiji</italic> rice samples. Highest volatile peak area percentage (105.41%) was recorded for <italic>Mushk Budji</italic> grown at an altitude of 5216.53 ft. Highest E-nose score (2.52) was obtained at an altitude of 6299.21 ft. Over-expression of fatty acid degradation and linoleic acid metabolism genes was observed at higher altitudes, whereas lipid biosynthesis was negatively influenced by higher altitude. Fatty acid degradation and linoleic acid metabolism is responsible for the synthesis of volatile aromatic compounds in <italic>Mushk Budiji</italic>. This study will therefore be the path finder for investigating the intricate mechanism behind the role of altitude on aroma development in <italic>Mushk Budiji</italic> rice for future studies.</p>", "<title>Subject terms</title>" ]	[ "<title>Supplementary Information</title>", "<p>\n</p>" ]	[ "<title>Supplementary Information</title>", "<p>The online version contains supplementary material available at 10.1038/s41598-024-51467-z.</p>", "<title>Acknowledgements</title>", "<p>This work didn’t receive any funding. Authors are grateful for instrumentation support and Laboratory support provided by Centre for Development of Advanced Computing (C-DAC), Kolkata, India and Central Institute of temperate Horticulture (CITH), Rangreth, J&K.</p>", "<title>Author contributions</title>", "<p>U.F.: Investigation, writing original draft. S.Z.H.: Conceptualization and Supervision. B.N.: Writing, reviewing and editing. S.S.M.: Resources. J.I.M.: Resources. A.G.: Validation. A.J.: Software. N.R.W.: Methodology. A.J.: Validation. F.J.W.: Data curation. S.M.: Methodology.</p>", "<title>Data availability</title>", "<p>Raw data shall be made available on request to corresponding author.</p>", "<title>Competing interests</title>", "<p id=\"Par29\">The authors declare no competing interests.</p>" ]	[ "<fig id=\"Fig1\"><label>Figure 1</label><caption><p>Relative quantification of fatty acid degradation, linoleic acid metabolism, ether lipid metabolism and badh2 genes in <italic>Mushk Budiji</italic> rice grown at different locations.</p></caption></fig>", "<fig id=\"Fig2\"><label>Figure 2</label><caption><p>(<bold>a</bold>) E-nose sensor response of <italic>Mushk Budiji</italic> rice grown at different locations. (<bold>b</bold>) Aroma score of <italic>Mushk Budiji</italic> rice grown at different locations. (<bold>c</bold>) PCA plot of <italic>Mushk Budiji</italic> rice grown at different locations using E-nose.</p></caption></fig>", "<fig id=\"Fig3\"><label>Figure 3</label><caption><p>Effect of different locations on a) free fatty acid, b) peroxide value & c) catalase activity of <italic>Mushq Budiji</italic> rice.</p></caption></fig>", "<fig id=\"Fig4\"><label>Figure 4</label><caption><p>(<bold>a</bold>) Experimental set-up of electronic nose used in the study. (<bold>b</bold>) Various operations of electronic nose.</p></caption></fig>" ]	[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Relative contents of volatile compounds in <italic>MushkBudiji</italic> grown at different locations.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"3\">Compoud class</th><th align=\"left\" rowspan=\"3\">Compound name</th><th align=\"left\" colspan=\"16\">Locations</th></tr><tr><th align=\"left\" colspan=\"2\">L1</th><th align=\"left\" colspan=\"2\">L2</th><th align=\"left\" colspan=\"2\">L3</th><th align=\"left\" colspan=\"2\">L4</th><th align=\"left\" colspan=\"2\">L5</th><th align=\"left\" colspan=\"2\">L6</th><th align=\"left\" colspan=\"2\">L7</th><th align=\"left\" colspan=\"2\">L8</th></tr><tr><th align=\"left\">RT (min)</th><th align=\"left\">Peak area (%)</th><th align=\"left\">RT</th><th align=\"left\">Peak area(%)</th><th align=\"left\">RT</th><th align=\"left\">Peak area (%)</th><th align=\"left\">RT</th><th align=\"left\">Peak area (%)</th><th align=\"left\">RT</th><th align=\"left\">Peak area(%)</th><th align=\"left\">RT</th><th align=\"left\">Peak area(%)</th><th align=\"left\">RT</th><th align=\"left\">Peak area(%)</th><th align=\"left\">RT</th><th align=\"left\">Peak area (%)</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"5\">Aldehyde</td><td align=\"left\">Decanal</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">6.569</td><td align=\"left\">0.14</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td></tr><tr><td align=\"left\">Heptanal</td><td align=\"left\">3.341</td><td align=\"left\">0.43</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">3.341</td><td align=\"left\">0.29</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">3.342</td><td align=\"left\">0.77</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td></tr><tr><td align=\"left\">Octanal</td><td align=\"left\">4.431</td><td align=\"left\">0.45</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">2.356</td><td align=\"left\">11.9</td><td align=\"left\">4.432</td><td align=\"left\">0.35</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">4.433</td><td align=\"left\">1.27</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td></tr><tr><td align=\"left\">Nonanal</td><td align=\"left\">5.523</td><td align=\"left\">1.26</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">5.523</td><td align=\"left\">0.52</td><td align=\"left\">n.d</td><td align=\"left\">–</td><td align=\"left\">5.524</td><td align=\"left\">1.09</td><td align=\"left\">5.525</td><td align=\"left\">3.14</td><td align=\"left\">5.524</td><td align=\"left\">6.22</td><td align=\"left\">5.523</td><td align=\"left\">1.17</td></tr><tr><td align=\"left\">Hexanal</td><td align=\"left\">2.356</td><td align=\"left\">18.76</td><td align=\"left\">2.353</td><td align=\"left\">9.92</td><td align=\"left\">2.356</td><td align=\"left\">5.51</td><td align=\"left\">2.352</td><td align=\"left\">17.19</td><td align=\"left\">2.356</td><td align=\"left\">7.13</td><td align=\"left\">2.356</td><td align=\"left\">12.27</td><td align=\"left\">2.356</td><td align=\"left\">6.12</td><td align=\"left\">2.355</td><td align=\"left\">6.54</td></tr><tr><td align=\"left\" rowspan=\"5\">Alcohols</td><td align=\"left\">1-hexanol</td><td align=\"left\">3.018</td><td align=\"left\">0.84</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">3.019</td><td align=\"left\">0.3</td><td align=\"left\">3.015</td><td align=\"left\">1.85</td><td align=\"left\">3.018</td><td align=\"left\">0.61</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\">3.012</td><td align=\"left\">1.47</td><td align=\"left\">3.019</td><td align=\"left\">0.53</td></tr><tr><td align=\"left\">Propylene glycol</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">1.996</td><td align=\"left\">11.47</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td></tr><tr><td align=\"left\">2,4-di-tert butylphenol</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">9.338</td><td align=\"left\">3.33</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">9.337</td><td align=\"left\">3.33</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td></tr><tr><td align=\"left\">3-pentanol,3-(1,1-dimethyl)-2,2,4,4-tetramethyl</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">6.342</td><td align=\"left\">0.17</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">6.342</td><td align=\"left\">0.12</td><td align=\"left\">-</td><td align=\"left\">-</td><td align=\"left\">6.344</td><td align=\"left\">0.94</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td></tr><tr><td align=\"left\">Silane diol dimethyl</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">1.67</td><td align=\"left\">11.89</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">1.671</td><td align=\"left\">13.69</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">2.109</td><td align=\"left\">6.74</td><td align=\"left\">2.3</td><td align=\"left\">3.76</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td></tr><tr><td align=\"left\">Ketones</td><td align=\"left\">Heptan-2-one</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">3.214</td><td align=\"left\">0.72</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td></tr><tr><td align=\"left\" rowspan=\"3\">Esters</td><td align=\"left\">Acetoxyacetic acid,4-pentadecylester</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">5.523</td><td align=\"left\">1.49</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td></tr><tr><td align=\"left\">1,2- benzenedicarboxylicacid,bis(2-methylpropyl)ester</td><td align=\"left\">12.231</td><td align=\"left\">0.38</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">12.23</td><td align=\"left\">1.35</td><td align=\"left\">12.33</td><td align=\"left\">3.7</td><td align=\"left\">12.231</td><td align=\"left\">4.94</td><td align=\"left\">12.23</td><td align=\"left\">0.43</td></tr><tr><td align=\"left\">4-ethylbenzoicacid,cyclopentyl ester</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">12.231</td><td align=\"left\">2.3</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">3.558</td><td align=\"left\">1.32</td></tr><tr><td align=\"left\" rowspan=\"17\">Hydrocarbons</td><td align=\"left\">n-hexane</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">1.246</td><td align=\"left\">19.2</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td></tr><tr><td align=\"left\">cyclotrisiloxane,hexamethyl</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">5.166</td><td align=\"left\">1.3</td><td align=\"left\">6.89</td><td align=\"left\">0.60</td></tr><tr><td align=\"left\">cyclopentasilicoxane,decamethyl</td><td align=\"left\">5.79</td><td align=\"left\">0.74</td><td align=\"left\">5.792</td><td align=\"left\">8.56</td><td align=\"left\">5.789</td><td align=\"left\">0.69</td><td align=\"left\">5.793</td><td align=\"left\">2.35</td><td align=\"left\">5.79</td><td align=\"left\">0.65</td><td align=\"left\">5.79</td><td align=\"left\">8.64</td><td align=\"left\">5.79</td><td align=\"left\">6.89</td><td align=\"left\">5.79</td><td align=\"left\">5.79</td></tr><tr><td align=\"left\">cyclononasiloxane,octadecamethyl</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">11..428</td><td align=\"left\">0.76</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td></tr><tr><td align=\"left\">2,5-dimemethylhexane-2,5-dihydroperoxide</td><td align=\"left\">7.908</td><td align=\"left\">2.89</td><td align=\"left\">7.911</td><td align=\"left\">31.76</td><td align=\"left\">7.909</td><td align=\"left\">3.77</td><td align=\"left\">6.14</td><td align=\"left\">6.14</td><td align=\"left\">7.909</td><td align=\"left\">3.31</td><td align=\"left\">7.91</td><td align=\"left\">40.83</td><td align=\"left\">7.91</td><td align=\"left\">30.63</td><td align=\"left\">7.909</td><td align=\"left\">2.78</td></tr><tr><td align=\"left\">cycloheptasilicoxane,tetradecamethyl</td><td align=\"left\">8.931</td><td align=\"left\">0.46</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">8.932</td><td align=\"left\">0.6</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">8.932</td><td align=\"left\">2.62</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td></tr><tr><td align=\"left\">1,5-heptadien-3-yne</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">2.1</td><td align=\"left\">83.56</td></tr><tr><td align=\"left\">trisilicoxane,1,1,1,5,5,5-hexamethyl-3-[(trimethylsilyl)oxy]</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">4.154</td><td align=\"left\">1.2</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td></tr><tr><td align=\"left\">Toluene</td><td align=\"left\">2.103</td><td align=\"left\">80.92</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">2.102</td><td align=\"left\">83.65</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">2.102</td><td align=\"left\">82.56</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">2.101</td><td align=\"left\">3.14</td><td align=\"left\">-</td><td align=\"left\">-</td></tr><tr><td align=\"left\">Naphthalene</td><td align=\"left\">6.444</td><td align=\"left\">0.62</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">6.445</td><td align=\"left\">0.19</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">6.446</td><td align=\"left\">1.5</td><td align=\"left\">6.444</td><td align=\"left\">0.49</td></tr><tr><td align=\"left\"><sc>d</sc>-Limonene</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">4.794</td><td align=\"left\">2.04</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td></tr><tr><td align=\"left\">Dodecane</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">6.504</td><td align=\"left\">0.06</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td></tr><tr><td align=\"left\">Hexadecane</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">8.391</td><td align=\"left\">0.21</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td></tr><tr><td align=\"left\">Tetradecane</td><td align=\"left\">8.391</td><td align=\"left\">0.28</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">-</td><td align=\"left\">-</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">8.392</td><td align=\"left\">0.28</td></tr><tr><td align=\"left\">cyclooctasiloxane, hexadecamethyl</td><td align=\"left\">10.255</td><td align=\"left\">0.17</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">10.256</td><td align=\"left\">0.38</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">10.257</td><td align=\"left\">1.53</td><td align=\"left\">10.256</td><td align=\"left\">0.19</td><td align=\"left\">10.256</td><td align=\"left\">0.19</td></tr><tr><td align=\"left\">Cyclohexasilicoxanedodecamethyl</td><td align=\"left\">7.445</td><td align=\"left\">0.95</td><td align=\"left\">7.447</td><td align=\"left\">15.38</td><td align=\"left\">7.444</td><td align=\"left\">0.6</td><td align=\"left\">7.448</td><td align=\"left\">4.4</td><td align=\"left\">7.445</td><td align=\"left\">0.34</td><td align=\"left\">7.446</td><td align=\"left\">13.1</td><td align=\"left\">7.445</td><td align=\"left\">7.38</td><td align=\"left\">7.445</td><td align=\"left\">0.81</td></tr><tr><td align=\"left\">Cyclotetrasiloxane,octamethyl</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">4.156</td><td align=\"left\">1.48</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">4.154</td><td align=\"left\">1.94</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td></tr><tr><td align=\"left\" rowspan=\"4\">Others</td><td align=\"left\">2-pentyl furan</td><td align=\"left\">4.292</td><td align=\"left\">1.23</td><td align=\"left\">4.296</td><td align=\"left\">1.45</td><td align=\"left\">4.292</td><td align=\"left\">0.32</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">4.292</td><td align=\"left\">0.85</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">4.293</td><td align=\"left\">1.4</td><td align=\"left\">4.292</td><td align=\"left\">0.92</td></tr><tr><td align=\"left\">2-Acetyl-1-pyroline</td><td align=\"left\">3.561</td><td align=\"left\">0.75</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">3.561</td><td align=\"left\">1</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td></tr><tr><td align=\"left\">Qunoline-1,2-dihydro-2,2,4-trimethyl</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">8.824</td><td align=\"left\">6.38</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td></tr><tr><td align=\"left\">Oxime methoxy phenyl</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td><td align=\"left\">3.473</td><td align=\"left\">2.5</td><td align=\"left\">3.55</td><td align=\"left\">7.01</td><td align=\"left\">n.d</td><td align=\"left\">n.d</td></tr></tbody></table></table-wrap>" ]	[ "<disp-formula id=\"Equ1\"><label>1</label><alternatives><tex-math id=\"M1\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\text{FFA}}\\left(\\mathrm{\\%}\\right)=\\frac{10\\times \\left(mL\\, KOH \\,used\\right)\\times \\left(mL\\, KOH\\, blank\\right)}{100\\, (g\\, water\\, in\\, 100g\\, sample)}\\times 100$$\\end{document}</tex-math><mml:math id=\"M2\" display=\"block\"><mml:mrow><mml:mtext>FFA</mml:mtext><mml:mfenced close=\")\" open=\"(\"><mml:mo>%</mml:mo></mml:mfenced><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mn>10</mml:mn><mml:mo>×</mml:mo><mml:mfenced close=\")\" open=\"(\"><mml:mi>m</mml:mi><mml:mi>L</mml:mi><mml:mspace width=\"0.166667em\"/><mml:mi>K</mml:mi><mml:mi>O</mml:mi><mml:mi>H</mml:mi><mml:mspace width=\"0.166667em\"/><mml:mi>u</mml:mi><mml:mi>s</mml:mi><mml:mi>e</mml:mi><mml:mi>d</mml:mi></mml:mfenced><mml:mo>×</mml:mo><mml:mfenced close=\")\" open=\"(\"><mml:mi>m</mml:mi><mml:mi>L</mml:mi><mml:mspace width=\"0.166667em\"/><mml:mi>K</mml:mi><mml:mi>O</mml:mi><mml:mi>H</mml:mi><mml:mspace width=\"0.166667em\"/><mml:mi>b</mml:mi><mml:mi>l</mml:mi><mml:mi>a</mml:mi><mml:mi>n</mml:mi><mml:mi>k</mml:mi></mml:mfenced></mml:mrow><mml:mrow><mml:mn>100</mml:mn><mml:mspace width=\"0.166667em\"/><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>g</mml:mi><mml:mspace width=\"0.166667em\"/><mml:mi>w</mml:mi><mml:mi>a</mml:mi><mml:mi>t</mml:mi><mml:mi>e</mml:mi><mml:mi>r</mml:mi><mml:mspace width=\"0.166667em\"/><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mspace width=\"0.166667em\"/><mml:mn>100</mml:mn><mml:mi>g</mml:mi><mml:mspace width=\"0.166667em\"/><mml:mi>s</mml:mi><mml:mi>a</mml:mi><mml:mi>m</mml:mi><mml:mi>p</mml:mi><mml:mi>l</mml:mi><mml:mi>e</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mfrac><mml:mo>×</mml:mo><mml:mn>100</mml:mn></mml:mrow></mml:math></alternatives></disp-formula>", "<disp-formula id=\"Equ2\"><label>2</label><alternatives><tex-math id=\"M3\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\mathrm{PV\\, }({\\text{meq}}/{\\text{kg}}=\\frac{Titre\\, value\\times N\\times 100}{W}$$\\end{document}</tex-math><mml:math id=\"M4\" display=\"block\"><mml:mrow><mml:mrow><mml:mi mathvariant=\"normal\">PV</mml:mi><mml:mspace width=\"0.166667em\"/></mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mtext>meq</mml:mtext><mml:mo stretchy=\"false\">/</mml:mo><mml:mtext>kg</mml:mtext><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:mi>T</mml:mi><mml:mi>i</mml:mi><mml:mi>t</mml:mi><mml:mi>r</mml:mi><mml:mi>e</mml:mi><mml:mspace width=\"0.166667em\"/><mml:mi>v</mml:mi><mml:mi>a</mml:mi><mml:mi>l</mml:mi><mml:mi>u</mml:mi><mml:mi>e</mml:mi><mml:mo>×</mml:mo><mml:mi>N</mml:mi><mml:mo>×</mml:mo><mml:mn>100</mml:mn></mml:mrow><mml:mi>W</mml:mi></mml:mfrac></mml:mrow></mml:math></alternatives></disp-formula>" ]	[]	[]	[]	[]	[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>" ]	[ "<table-wrap-foot><p>n.d not detected.</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher's note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"41598_2024_51467_Fig1_HTML\" id=\"MO1\"/>", "<graphic xlink:href=\"41598_2024_51467_Fig2_HTML\" id=\"MO2\"/>", "<graphic xlink:href=\"41598_2024_51467_Fig3_HTML\" id=\"MO3\"/>", "<graphic xlink:href=\"41598_2024_51467_Fig4_HTML\" id=\"MO4\"/>", "<graphic xlink:href=\"41598_2024_51467_Article_Equ1.gif\" position=\"anchor\"/>", "<graphic xlink:href=\"41598_2024_51467_Article_Equ2.gif\" position=\"anchor\"/>" ]	[ "<media xlink:href=\"41598_2024_51467_MOESM1_ESM.docx\"><caption><p>Supplementary Information.</p></caption></media>" ]	[{"label": ["2."], "surname": ["Prodhan"], "given-names": ["ZH"], "article-title": ["Agronomic, transcriptomic and metabolomic expression analysis of aroma gene (badh2) under different temperature regimes in rice"], "source": ["Biotechnol. Adv."], "year": ["2017"], "volume": ["19"], "issue": ["3"], "fpage": ["569"], "lpage": ["576"]}, {"label": ["4."], "mixed-citation": ["Varatharajan, N. "], "italic": ["et al", "Integrative Advances in Rice Research"]}, {"label": ["6."], "surname": ["Ramtekey"], "given-names": ["V"], "article-title": ["Extraction, characterization, quantification, and application of volatile aromatic compounds from Asian rice cultivars"], "source": ["Rev. Anal. Chem."], "year": ["2021"], "volume": ["40"], "issue": ["1"], "fpage": ["272"], "lpage": ["292"], "pub-id": ["10.1515/revac-2021-0137"]}, {"label": ["7."], "surname": ["Routray", "Rayaguru"], "given-names": ["W", "K"], "article-title": ["2-Acetyl-1-pyrroline: A key aroma component of aromatic rice and other food products"], "source": ["Food Rev. Intl."], "year": ["2018"], "volume": ["34"], "issue": ["6"], "fpage": ["539"], "lpage": ["565"], "pub-id": ["10.1080/87559129.2017.1347672"]}, {"label": ["13."], "surname": ["Tan", "Jie"], "given-names": ["J", "Xu"], "article-title": ["Applications of electronic nose (e-nose) and electronic tongue (e-tongue) in food quality-related properties determination: A review"], "source": ["Artif. Intell. Agric."], "year": ["2020"], "volume": ["4"], "fpage": ["104"], "lpage": ["115"]}, {"label": ["14."], "surname": ["Jana"], "given-names": ["A"], "article-title": ["Fragrance measurement of scented rice using electronic nose"], "source": ["Int. J. Smart Sens. Intell. Syst."], "year": ["2015"], "volume": ["8"], "issue": ["3"], "fpage": ["1730"], "lpage": ["1747"]}, {"label": ["15."], "surname": ["Zheng"], "given-names": ["X-Z"], "article-title": ["Rapid identification of rice samples using an electronic nose"], "source": ["J. 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PMC10781668	38200205	[ "<title>Introduction</title>", "<p id=\"Par2\">Major depressive disorder is a debilitating and chronic illness with unknown etiology and pathophysiology<sup>##UREF##0##1##</sup>. Due to this lack of mechanistic understanding, current antidepressant treatments, in particular monoamine pharmacological approaches, have inconsistent responses and take weeks to exhibit clinical effects. There is an urgent need for the development of new, more rapidly acting antidepressants with fewer side effects. Neuroactive steroids (NAS) may be rising to fill this role, as the FDA-approved medication brexanolone, a formulation of the natural neurosteroid allopregnanolone (AlloP), persistently reduces depressive symptoms in women with postpartum depression upon a 60 h infusion<sup>##REF##30177236##2##,##REF##31649968##3##</sup>. Moreover, synthetic AlloP analogues currently in clinical trials may be useful for major depressive disorder<sup>##REF##31483961##4##,##UREF##1##5##</sup>. However, the mechanisms driving these rapid and durable therapeutic effects are not well understood.</p>", "<p id=\"Par3\">Macroautophagy (autophagy), a degradation pathway that is essential for preserving cellular homeostasis, may participate in the antidepressant effects of NAS. Dysregulated autophagy has been implicated in clinical depression, with multiple antidepressant drugs affecting this process<sup>##REF##31156481##6##</sup>. First, AlloP, a positive allosteric modulator of GABA-A receptors, increases autophagy<sup>##REF##32070183##7##</sup>. In addition, two conventional antidepressants (amitriptyline and fluoxetine) spur autophagy, linked causally to antidepressant benefit<sup>##REF##30038230##8##</sup>. Autophagy can directly affect synaptic function and intrinsic excitability<sup>##REF##31913125##9##,##UREF##2##10##</sup>, offering direct substrates to affect neuronal communication. Moreover, autophagy itself is modulated by synaptic activity<sup>##REF##33783472##11##</sup>. Finally, a human clinical study found that rapamycin (an autophagy inducer) prolonged the antidepressant effects of ketamine, suggesting an important role for autophagy in therapeutic outcomes<sup>##UREF##3##12##</sup>.</p>", "<p id=\"Par4\">The best known target of NAS are GABA-A receptors, and structure–activity relationships for NAS on GABA-A receptors have been well studied<sup>##REF##11744077##13##</sup>. However, structural and cellular requirements for autophagy remain mostly unexplored. Autophagy induction by AlloP in the rat retina fails to exhibit enantioselectivity<sup>##REF##35370641##14##</sup>, suggesting that NAS may act through unconventional pathways distinct from GABA-A receptor modulation. Both amitriptyline, a tricyclic, and fluoxetine, a selective serotonin reuptake inhibitor (SSRI), induce autophagy<sup>##REF##30038230##8##</sup> but share very little structural overlap. A common denominator may be lysosome, Golgi membrane, and ER interference. Uncharged NAS are known to accumulate in somatic Golgi of neurons<sup>##REF##27114255##15##</sup>, but likely also permeate lysosomes and the ER, targets relevant to autophagy, as a result of their hydrophobicity. We hypothesized that uncharged NAS, but not charged NAS, increase autophagy because of differential lysosome penetration. In addition, we predicted that uncharged NAS will not exhibit enantioselectivity if effects are driven strictly by lysosome accumulation and effects on membranes, rather than effects on chiral protein targets.</p>", "<p id=\"Par5\">Here, we examined the modulation of autophagy in response to structurally diverse NAS analogues using a stably expressed, ratiometric fluorescent probe, pcDNA3-GFP-LC3-RFP-LC3Δ3<sup>##REF##27818143##16##</sup> in human embryonic kidney 293 (HEK) cells. We quantified autophagy using microscopy and high-throughput microplate formats. We also expressed the probe in primary astrocytes. The adopted probe is not reliant on lysosomal inhibitors, and the RFP variant serves as an internal control for probe expression<sup>##REF##27818143##16##</sup>.</p>", "<p id=\"Par6\">Contrary to expectations and previous studies, we find that NAS do not strongly induce autophagy in HEK cells or astrocytes, even when tested in combination with known autophagy inducers (Torin1 and starvation). We conclude that NAS effects on autophagy are context-dependent and that this needs to be accounted for in screening assays.</p>" ]	[ "<title>Methods</title>", "<title>Human embryonic kidney 293 (HEK) cell culture</title>", "<p id=\"Par7\">HEK cells (ATCC CRL-1573) were stably transfected with 0.5 µg pcDNA3-GFP-LC3-RFP-LC3ΔG<sup>##REF##27818143##16##</sup> using Lipofectamine 2000 (Invitrogen, 11668030) for 4 h at 37 °C with 5% CO<sub>2</sub> in Opti-MEM (Gibco, 31985070) <sub>.</sub> Following incubation, the medium was exchanged for serum-containing medium. Cells were selected with 750 µg of G418 and grown in Dulbecco’s Modified Eagle’s medium (DMEM) 1 g/L glucose (Gibco, 11885084) with 10% heat-inactivated fetal bovine serum (FBS) (Gibco, 16140063), and 1% GlutaMAX (Gibco, 35050061). Cells were maintained with 400 µg of G418 in media at 37 °C with 5% CO<sub>2</sub>.</p>", "<title>Primary cortical astrocyte culture</title>", "<p id=\"Par8\">All procedures were carried out in accordance with National Institute of Health (NIH) guidelines and approved by the Washington University Institutional Animal Care and Use Committee, protocol 22-0344. Procedures involving animals are reported in accordance with ARRIVE guidelines (<ext-link ext-link-type=\"uri\" xlink:href=\"https://arriveguidelines.org\">https://arriveguidelines.org</ext-link>). Primary cortical astrocyte cultures were prepared from postnatal day 4–7 Sprague Dawley rat pups of either sex. Mouse pups were anesthetized with halothane and rapidly decapitated. Brains were carefully stripped of their meninges and digested with 1 mg/mL papain for 20 min at 37 °C with 5% CO<sub>2</sub>, then rinsed with 5–5 media (Minimum Essential medium (Gibco, 11090081) supplemented with heat-inactivated horse serum (5%) (Gibco, 26050070), fetal bovine serum (5%), 17 mM glucose, 400 µM glutamine, 50 µg/ml streptomycin, and 50 U/ml penicillin). The tissue was then triturated and plated into T-25 flasks which were pre-coated with 0.1 mg/mL poly-D-lysine hydrobromide (PDL) (Sigma-Aldrich, P7280). Astrocytes were kept at 37 °C with 5% CO<sub>2</sub>. The following day, media was aspirated and fresh 5–5 was added. Media was changed once again 3 days after plating and 7 days after plating, with the addition of 20 µL (6.7 µm) of cytosine beta-D-arabinofuranoside (AraC). After, media was changed once a week and AraC was added once every other week, until astrocytes reached around 20–30 days in vitro (DIV).</p>", "<p id=\"Par9\">Astrocytes were plated in 35 mm dishes pre-coated with PDL for transfection with 0.5 µg of pcDNA3-GFP-LC3-RFP-LC3ΔG, using Lipofectamine 2000. The transfection protocol consisted of 4 h incubation at 37 °C in Neurobasal medium (Gibco, 21103049) containing plasmids, 25 µM D-APV, 1 µM NBQX, and Lipofectamine 2000. Following the incubation, the medium was exchanged for 5–5.</p>", "<title>Treatments</title>", "<p id=\"Par10\">Cells were treated with drugs or starvation media Earl’s Balanced Salt Solution (EBSS) (Thermo Fisher, 24010043) 24 h after plating in 35 mm dishes or 96-well plates (50% confluence) for 24 h (unless specified) in an incubator at 37 °C with 5% CO<sub>2</sub>. For astrocyte experiments, cells were treated 24 h after transfection under the same conditions described above. Drug treatments were at 1:1000 dilutions, using 0.1% DMSO as a vehicle control, and media removal and replacement as a control for starvation.</p>", "<p id=\"Par11\">Drugs used were: rapamycin (LC—Laboratories, R-5000 or Tocris, 1292), Torin1 (LC—Laboratories, T7887), Bafilomycin A<sub>1</sub> (MilliporeSigma Calbiochem, 19600-010UG), non-commercial NAS were custom synthesized in house. We used NAS at 1 and 10 µM as having been shown effective in previous literature, including autophagy<sup>##REF##32070183##7##</sup> and GABA-A receptor modulation<sup>##REF##11744077##13##</sup>. Note that 10 µM of GABA-A receptor modulating NAS is supraphysiological and near the solubility limit for uncharged NAS.</p>", "<title>Microscopy</title>", "<p id=\"Par12\">Images were taken on a Nikon Eclipse TE2000-S microscope equipped with epi-fluorescence illumination and a camera (Photometrics, CoolSNAP ES<sup>2</sup>). Images were acquired with Micro-Manager 2.0 software. 10× images from 3 different fields were taken for each treatment. Oil-immersion 60× images were taken for GFP/RFP localization using cells that were plated on glass cover slips in a 35 mm dish. Following treatment, they were fixed for 10 min in 4% paraformaldehyde/0.02% glutaraldehyde in PBS at room temperature. Cells were washed 3 times with PBS and then cover slips were placed on slides and imaged. Image capture order: phase, GFP, RFP, Alexa Fluor 647 (when used).</p>", "<title>Image analysis</title>", "<p id=\"Par13\">GFP and RFP fluorescence were quantified using the analyze particles tool in FIJI. 200–500 cells were analyzed from each dish for HEK cell experiments, while ~ 6 cells were analyzed from each dish for astrocyte experiments. Intensity values from the entire cell were taken from the RFP image, whose ROI was then copied onto the GFP image for intensity values. Both channels were background subtracted, followed by the calculation of GFP/RFP.</p>", "<title>High-throughput screening using a microplate reader</title>", "<p id=\"Par14\">Stably transfected HEK cells < P10 were seeded on 96 well plates (Santa Cruz, sc-204468) coated with PDL at 17,000 cells/well. After 24 h, cells were treated and incubated for another 24 h (unless specified). Media was exchanged with HEPES-buffered live cell imaging solution (Invitrogen, A14291D), and cells were assayed using a microplate reader (Molecular Devices, Flexstation 3) with excitation/emission 488/509 nm GFP and 584/607 nm RFP. Every treatment (including controls) had 4 technical replicates (wells) in each experiment. Control values for each experiment were calculated by averaging the 4 technical replicates. Ratiometric values were expressed by normalizing replicates within a condition to the experiment’s mean control value (individual replicate/average controls in that experiment 100). Controls values exhibited little variability; in a representative 2 independent experiments (Fig. ##FIG##1##2##c) normalized individual values were 100 ± 3.5% (mean ± SEM, n = 8, 2 independent experiments).</p>", "<title>High-content imaging</title>", "<p id=\"Par15\">Stably transfected HEK cells < P10 were seeded into 96 well plates (Santa Cruz, sc-204468) coated with PDL at 15,000 cells/well and incubated at 37 °C with 5% CO<sub>2</sub>. After 24 h, cells were treated and incubated with drugs for another 24 h. After treatment, Hoescht stain was added to media and left in incubator for 10 min. Media was exchanged with HEPES-buffered live cell imaging solution, and cells were imaged at 10× using the InCell 2000 Analyzer (GE Healthcare) with excitation/emission 490/525 nm GFP, 579/624 nm RFP, 350/455 nm Hoescht. Each well had 4 fields imaged. Every treatment (including controls) had 4 technical replicates (wells) in each experiment.</p>", "<p id=\"Par16\">Images were analyzed using the Multi Target Analysis Module of the InCell Analyzer 1000 Workstation Software version 3.7). Individual cells (nuclei) were identified using the top-hat segmentation method in the Hoescht channel. A cytoplasmic sampling region was then established by dilating 2 µm from the nuclear region (i.e., collar segmentation). The average intensities in each cell were determined from the combined nuclear and cytoplasmic regions in both the GFP and RFP channels. GFP and RFP mean intensity from each cell were averaged for each well. Then, both channels were background subtracted, followed by the calculation of average GFP/RFP for each well. Around 15,000–18,000 cells were analyzed per well. Ratiometric values were expressed by normalizing replicates within a condition to the experiment’s mean control value (individual replicate/average controls in that experiment 100).</p>", "<title>Immunostaining</title>", "<p id=\"Par17\">Following treatment, astrocytes were fixed for 10 min in 4% paraformaldehyde/0.02% glutaraldehyde in PBS at room temperature. Cells were washed 3 times with PBS, blocked in 10% normal goat serum in PBS with 0.1% Triton X-100 for 15 min, incubated in primary rabbit anti-GFAP antibody (Millipore, AB 5804) diluted 1:1000 in block solution at 4 °C, shaken overnight and washed 3 times with PBS. Subsequently, cells were incubated in secondary Alexa Fluor 647 conjugated goat anti-rabbit antibody (Invitrogen, A-21245) diluted 1:500 in PBS for 1 h at room temperature, in the dark with shaking. Finally, cells were washed 4 times with PBS and maintained in PBS for imaging.</p>", "<title>Statistical analysis and figure preparation</title>", "<p id=\"Par18\">All image measurements were obtained from the raw data. GraphPad Prism was used to plot graphs and perform statistical analyses, including dose–response curves. Error bars represent mean ± standard error of mean (SEM). Each replicate was treated as an independent sample. For presentation of images, maximum and minimum gray values were adjusted in FIJI and assembled in InkScape.</p>" ]	[ "<title>Results</title>", "<title>Autophagy is reliably detected by low passage HEK cells stably expressing pcDNA3-GFP-LC3-RFP-LC3ΔG</title>", "<p id=\"Par19\">To assay NAS effects on autophagy, we stably transfected HEK cells with pcDNA3-GFP-LC3-RFP-LC3ΔG<sup>##REF##27818143##16##</sup>, a fluorescent ratiometric probe wherein GFP/RFP levels inversely correlate with autophagic flux. HEK cells were chosen because of their prominent role in studying neuronal ion channel function<sup>##REF##15862464##17##</sup> as well as other neuronal pathways, including autophagy<sup>##REF##24211851##18##,##REF##32523507##19##</sup>. GFP-LC3 is targeted for autophagic degradation through lysosomal quenching and protein degradation while RFP-LC3ΔG is an internal control that is not subject to autophagic degradation. High-magnification images of HEK cells transfected with the probe exhibited fluorescent puncta consistent with autolysosome formation (Supplementary Fig. ##SUPPL##0##1##). To confirm that the probe reliably detects pharmacological induction of autophagy, we treated cells with Torin1 (0.5 µM for 24 h), an mTOR inhibitor and potent autophagy inducer, and with allopregnanolone (AlloP; 1 µM for 24 h), a neurosteroid that induces autophagy in mammalian retina<sup>##REF##32070183##7##</sup>. Torin1 strongly decreased the GFP/RFP ratio as expected (Fig. ##FIG##0##1##a–c), mainly by decreasing GFP fluorescence (Fig. ##FIG##0##1##a, c). RFP fluorescence remained constant relative to vehicle control (Fig. ##FIG##0##1##a, c). RFP-LC3ΔG does not bind to the autophagosome membrane, thus it can be either retained within an autophagosome or exist freely in the cytoplasm. GFP fluorescence proved too dim to allow assessment of cellular distribution following Torin1 treatment. Compared with the effect of Torin1, 24 h treatment with 1 µM AlloP, a concentration believed to represent a therapeutic level<sup>##REF##25406290##20##</sup>, modestly reduced the GFP/RFP ratio, although this was not clearly associated with GFP quenching (Fig. ##FIG##0##1##b, c). Overall, the results suggest marginal AlloP effects on autophagic flux.</p>", "<p id=\"Par20\">The observations with AlloP led us to investigate factors that may affect sensitivity of the assay. During assay development, we observed a trend toward decreasing autophagy induction with Torin1 in successive experiments (Fig. ##FIG##1##2##a). We hypothesized that factors associated with cell division or age may modulate autophagic responses. Indeed, in a direct test of the hypothesis, we found that high-passaged cells (> passage 10, P10) showed an impaired Torin1 effect (Fig. ##FIG##1##2##b). The result of passage number was surprising given that HEK cells are immortalized cells<sup>##REF##886304##21##</sup>. Nevertheless, based on these results, we denoted < passage 10 as low-passage cells and > passage 10 as high-passage cells for subsequent experiments.</p>", "<p id=\"Par21\">We moved next to a high-throughput, microplate reader format, allowing us rapidly to evaluate several NAS structures, concentrations, and interacting factors. We first used this format to investigate the effect of passage on Torin1 potency versus efficacy. Across independent replicates, passage increased the EC<sub>50</sub> for Torin1 (Fig. ##FIG##1##2##c), thereby clarifying that passaging impairs potency of Torin1. Due to these observations, all subsequent experiments were conducted on low-passage cells < P10 to aid sensitivity of detecting autophagy induction.</p>", "<title>Little effect of NAS on autophagy in HEK cells; no substantial interaction with autophagy inducers</title>", "<p id=\"Par22\">We used the miniaturized microplate assay to re-evaluate AlloP and other NAS with diverse chemical structures. Although it is possible that the same NAS structural attributes important for positive actions at GABA-A receptors are required for effects on autophagy, we hypothesized that different structural attributes drive autophagy and that physiochemical properties are most important, for reasons described in the Introduction. Thus, we chose NAS with a variety of structures and charge status, with pregnenolone sulfate and MQ-221 bearing negative charge (Fig. ##FIG##2##3##a)<sup>##REF##10350561##22##,##REF##33115614##23##</sup>. Despite the weak effects of AlloP (Fig. ##FIG##0##1##), we suspected that uncharged NAS may be superior autophagy inducers due to their increased lysosomal penetration compared to charged NAS. We also expected a lack of enantioselectivity and stereoselectivity based on retinal results<sup>##REF##35370641##14##</sup>. However, NAS with these diverse structural attributes produced no reliable autophagy modulation at 1 and 10 µM (Fig. ##FIG##2##3##a), despite the reliable performance of the positive control Torin1 (0.3 µM for 24 h). In fact, <italic>ent</italic>-AlloP may inhibit autophagy in this assay, based on one-way ANOVA analysis (Fig. ##FIG##2##3##a legend). To ensure there was no ceiling effect in our assay’s ability to detect autophagy inhibition, we treated cells with bafilomycin A<sub>1</sub> (0.5 µM for 24 h; a potent autophagy inhibitor that blocks vacuolar H + ATPase) and observed robust GFP/RFP ratio increase relative to control, as expected (Fig. ##FIG##2##3##a). We next used a high-content imager, which allowed background subtraction, to evaluate AlloP effects. This high-sensitivity method revealed a strong effect of 500 nM Torin1 (GFP/RFP ratio 11.3 ± 1.4%) of control, but the AlloP (1 µM) effect was still negligible (101 ± 1.8% of control, mean ± SEM, n = 4 wells per condition from 1 experiment). The stronger Torin1 effect captured by the imager can be explained by background subtraction (non-background subtracted Torin1 effect, 84.5 ± 0.3% of control).</p>", "<p id=\"Par23\">Because NAS induce autophagy in the presence of retinal stress<sup>##REF##32070183##7##</sup>, we hypothesized that NAS may interact with known inducers of autophagy. We examined interaction with Torin1, rapamycin, and starvation using the high-throughput microplate reader. Although Torin1 (Fig. ##FIG##2##3##b) and starvation (Fig. ##FIG##2##3##c) produced dose and time dependent effects on autophagy, rapamycin did not strongly alter GFP/RFP ratio relative to control over a range of concentrations typically used in the literature (Supplementary Fig. ##SUPPL##0##2##A). Rapamycin’s weak effect was not necessarily a surprise, as others have found its efficacy to be cell-type dependent<sup>##REF##21576371##24##</sup>. The weak effect persisted across two lots from the same supplier and a separate lot from a second manufacturer, with which we saw no changes in autophagy (Supplementary Fig. ##SUPPL##0##2##B). Torin1 produced robust changes in GFP/RFP ratio in a dose-dependent manner (Fig. ##FIG##2##3##b). Starvation (1, 3, 12, and 24 h) also produced robust changes in GFP/RFP ratio, reaching 50% of control by 12 h (Fig. ##FIG##2##3##b). In interaction tests, dose–response curves of Torin1 failed to be affected by either AlloP or <italic>ent</italic>-AlloP (Fig. ##FIG##2##3##b,c). With the non-pharmacological induction, we did observe unexpected increase of the starvation-induced GFP/RFP ratio relative to control at 3 h (<italic>ent</italic>-AlloP) and at 12 h (AlloP and <italic>ent</italic>-AlloP) (Fig. ##FIG##2##3##c).</p>", "<title>AlloP effect in astrocytes</title>", "<p id=\"Par24\">Apart from inducing autophagy in retinal ganglion cells, AlloP has been shown to induce autophagy in astrocytes<sup>##REF##22154800##25##</sup>. To examine whether NAS effects are cell-type dependent, we transfected primary cortical astrocytes with the fluorescent probe. Transfected cells were verified as astrocytes with GFAP stain (Supplementary Fig. ##SUPPL##0##3##). We found that AlloP 10 µM did not reliably reduce GFP/RFP, although we cannot exclude a small effect, while Torin1 (500 nM) again robustly induced autophagy (Fig. ##FIG##3##4##).</p>" ]	[ "<title>Discussion</title>", "<p id=\"Par25\">Whether NAS with differing chemical structures differentially regulate autophagy is previously unexplored. Here, we attempted to adress this question by assaying several analogues with a fluorescent ratiometric probe. We first refined our assay to only use early passage cells (<italic>P</italic> < 10), as the potency of autophagy induction with Torin1 was dependent on cell passage. Despite optimizing sensitivity, contrary to expectations, we found that NAS failed to reliably increase autophagy in this cell model, including when tested for interaction in the context of known autophagy inducers Torin1 or EBSS starvation. Given evidence that AlloP induces autophagy in astrocytes<sup>##REF##22154800##25##</sup>, we tested NAS in this cell type, but failed to show reliable modulation of autophagy. Taken together, we conclude that NAS effects on autophagy may be context and cell-type specfic. Although cell lines could in principle rapidly identify active analogues, consideration of cell-type appears to be important in the case of NAS and autophagy.</p>", "<p id=\"Par26\">Passage effects on pharmacological autophagy induction were surprising, as HEK cells are an immortalized cell line and do not undergo senesence<sup>##REF##886304##21##</sup>. However, there is a large literature suggesting that autophagic activity declines with age in many organisms<sup>##REF##34901876##26##</sup>. In neurons, this could be due to decreased lysosomal function, as cells in the hypothalamus from aged wild-type mice have less autolysosomal fusion and impaired delivery of autophagy substrates to lysosomes<sup>##REF##22249165##27##</sup>. Factors associated with autophagic impairment during aging may be investigated in future experiments as potential contributors to the passage effects observed here. We also note that the cell based assay used here has advantages over other methods as described in the Introduction, particularly for our major goal of screening; however, for many experimenters Western blots remain the gold standard for autophagy assays. Although we had showed robust positive effects of Torin1, future work could extend the tests of passage effects and NAS to these standard assays of autophagy.</p>", "<p id=\"Par27\">AlloP produced small but statistically significant changes to GFP/RFP ratios in microscopy experiments (Fig. ##FIG##0##1##), whereas reliable changes were not detected in the high-throughput microplate format (Fig. ##FIG##1##2##). One might expect that the contribution of many cells in the high-throughput format might drive lower sensitivity to small differences if cells respond heterogenously to drugs. It is also possible that the higher cell density of the high-throughput format is associated with differences in responsiveness. Perhaps lack of background subtraction in the plate reader experiments contributed to an inability to detect small changes to fluorescence ratios, but background-subtracted values from high-content imaging also produced a negligible AlloP effect. Both microscopy and the plate reader detected similar changes to fluorescence ratios of Torin1 (~ 50%), so it remains unclear that assay sensitivity can explain the differences in AlloP effects. Statistical significance aside, both assays suggest a weak biological impact of AlloP.</p>", "<p id=\"Par28\">The context and cell type in which our experiments were executed may be crucial for reconciling our results with previous studies. Previously, AlloP protected rat retinal ganglion cells in a glaucoma model of elevated pressure, and protection was bafilomycin A1-sensitive<sup>##REF##32070183##7##</sup>. Thus, autophagy appeared to mediate the neuroprotective effects of AlloP applied for 24 h at 1 μM, under a condition of high cellular stress that resulted in histological neuronal and axonal damage. Although we tried to replicate a stress-like environment in the HEK cell model withTorin1 and EBSS (Fig. ##FIG##2##3##b,c), we observed no reduction of the GFP/RFP ratio by NAS. In fact, both AlloP and <italic>ent</italic>-AlloP interfered with the effect of starvation at 12 h. This interference was consistent with trends toward increased fluorescence ratio observed with <italic>ent</italic>-AlloP alone (Fig. ##FIG##2##3##a). Perhaps the stress of ocular pressure (retinal studies) may induce signaling pathways other than those induced by Torin1 or starvation and be critical for activating the autophagic protective effects of AlloP. Further, because a key difference between our experiment and the previous study is cell type, NAS may interact with pathways specific to retinal ganglion cells to modulate autophagy with cell-type specificity.</p>", "<p id=\"Par29\">Although we can exclude the possibility of a small effect, we also failed to observe strong AlloP induction of autophagy in primary rodent astrocytes (Fig. ##FIG##3##4##), contrary to an effect observed previously<sup>##REF##22154800##25##</sup>. Although we used rat astrocytes instead of mouse and utilized slightly different growth medium, both studies used astrocytes derived from the cortex grown until 30 days in vitro. Our studies extended exposure from 1 h<sup>##REF##22154800##25##</sup> to 24 h and increased AlloP concentration from 250 nM (previous work) to 10 μM to in hopes of optimizing induction. We also used a ratiometric probe to assay autophagy, instead of immunoblots. Although sensitivity of the ratiometric probe used in our studies is an advantage<sup>##REF##27818143##16##</sup>, it is possible that differences in the detection methods participate in the different outcomes. Overall, further work will be needed to reconcile these results, but our studies highlight the complexity inherent in replicating and extending even apparently straightforward results.</p>", "<p id=\"Par30\">Rapamycin’s lack of dose-dependent effects on autophagy in HEK cells (Supplementary Fig. ##SUPPL##0##2##) could provide insight into lack of NAS effects. Others have found that rapamycin’s potency and efficacy are cell-type dependent<sup>##REF##21576371##24##</sup>. This may result from high basal phosphatidic acid (PA) levels in HEK cells, which competes with rapamycin for a site on mTOR<sup>##REF##26916116##28##</sup>. Low sensitivity may also result from the high stability of mTORC1 in these cells<sup>##REF##21576371##24##</sup>. NAS may be subject to mitigating factors similar to rapamycin in HEK cells, despite likely different upstream mechanisms of action for initiating autophagy.</p>", "<p id=\"Par31\">In summary, our work examines NAS effects on autophagy in HEK cells using a previously validated ratiometric fluorescent probe. We failed to find significant induction of autophagy of varied NAS structures at therapeutic and supratherapeutic concentrations, including when tested as allosteric modulators. Additionally, passage number of HEK cells influenced the potency of pharmacological autophagy induction; thus, passage should be carefully considered in experimental design. We conclude that context and cell-type hinder the ability to screen for autophagy induction. Future studies should carefully consider cell type to maximize relevance to neuropsychiatric treatment.</p>" ]	[]	[ "<p id=\"Par1\">Neuropsychiatric and neurodegenerative disorders are correlated with cellular stress. Macroautophagy (autophagy) may represent an important protective pathway to maintain cellular homeostasis and functionality, as it targets cytoplasmic components to lysosomes for degradation and recycling. Given recent evidence that some novel psychiatric treatments, such as the neuroactive steroid (NAS) allopregnanolone (AlloP, brexanolone), may induce autophagy, we stably transfected human embryonic kidney 293 (HEK) cells with a ratiometric fluorescent probe to assay NAS effects on autophagy. We hypothesized that NAS may modulate autophagy in part by the ability of uncharged NAS to readily permeate membranes. Microscopy revealed a weak effect of AlloP on autophagic flux compared with the positive control treatment of Torin1. In high-throughput microplate experiments, we found that autophagy induction was more robust in early passages of HEK cells. Despite limiting studies to early passages for maximum sensitivity, a range of NAS structures failed to reliably induce autophagy or interact with Torin1 or starvation effects. To probe NAS in a system where AlloP effects have been shown previously, we surveyed astrocytes and again saw minimal autophagy induction by AlloP. Combined with other published results, our results suggest that NAS may modulate autophagy in a cell-specific or context-specific manner. Although there is merit to cell lines as a screening tool, future studies may require assaying NAS in cells from brain regions involved in neuropsychiatric disorders.</p>", "<title>Subject terms</title>" ]	[ "<title>Supplementary Information</title>", "<p>\n</p>" ]	[ "<title>Supplementary Information</title>", "<p>The online version contains supplementary material available at 10.1038/s41598-024-51582-x.</p>", "<title>Acknowledgements</title>", "<p>The authors thank members of the Taylor Family Institute for Innovative Psychiatric Research for discussion and input.</p>", "<title>Author contributions</title>", "<p>Conceptual study design: S.S., M.I., D.F.C., & S.M. Data collection and analysis: S.S., M.I., H.S., P.M.L., M.Q., & A.B. Original draft: S.S. Critical revisions: S.S., M.I., H.S., P.M.L., A.B., M.Q., D.F.C., C.F.Z., S.M. All authors reviewed the manuscript.</p>", "<title>Data availability</title>", "<p>Data that support the findings of this study are available from the corresponding author on reasonable request.</p>", "<title>Competing interests</title>", "<p id=\"Par32\">CFZ is a member of the Scientific Advisory Board for Sage Therapeutics and holds equity in Sage Therapeutics. DFC was a co-founder of Sage Therapeutics and holds equity in Sage Therapeutics. Sage Therapeutics had no role in the design or interpretation of the experiments herein. The remaining authors have nothing to disclose.</p>" ]	[ "<fig id=\"Fig1\"><label>Figure 1</label><caption><p>Autophagy is reliably detected by HEK cells stably expressing pcDNA3-GFP-LC3-RFP-LC3ΔG. (<bold>a</bold>) HEK cells in phase contrast, GFP, and RFP channels at 10× after a 24 h treatment with 500 nM Torin1 or 1 µM AlloP. Scale bar: 25 µm. (<bold>b</bold>) Background-subtracted mean fluorescence ratios relative to their vehicle-treated control. Each point represents a 35 mm dish (250–800 cells analyzed per dish) (n = 8 per group) from 3 independent experiments. Circles represent first experiment, squares second, and triangles third. A one-way ANOVA (<italic>F</italic> (2, 21) = 74.49, <italic>p</italic> < 0.0001) showed differences between treatments, and Dunnett’s multiple comparisons revealed a difference between control and Torin1 (**<italic>p</italic> < 0.0001) and between control and AlloP (<italic>p</italic> = 0.0056). (<bold>c</bold>) Separate GFP and RFP mean fluorescence for each treatment, 3 independent experiments. A repeated measures two-way ANOVA revealed a difference of fluorescent protein (<italic>F</italic> (1, 21) = 16.25, <italic>p</italic> = 0.0006) and no difference of treatment (<italic>F</italic> (2, 21) = 2.077, <italic>p</italic> = 0.1503), with a significant interaction of treatment x fluorescent protein (<italic>F</italic> (2, 21) = 7.819, <italic>p</italic> = 0.0029). Sidak’s multiple comparisons revealed a difference between control and Torin1 GFP (<italic>p</italic> = 0.0093).</p></caption></fig>", "<fig id=\"Fig2\"><label>Figure 2</label><caption><p>Passaging impairs pharmacological autophagy induction. (<bold>a</bold>) 500 nM Torin1 effect weakened with increasing cell passages. Overall, Torin1 reduced fluorescence ratio relative to control, unpaired <italic>t</italic>-test: <italic>t</italic>(16) = 10.54, <italic>p</italic> < 0.0001, n = 3 per condition in 3 replicates. 6 dishes from the hypothesis-generating studies in panel A were also those used for quantification of control and Torin1 in Fig. ##FIG##0##1##. (<bold>b</bold>) In independent microscopy experiments, we directly compared Torin1 effect in P5 + P7 versus P18 + P20 HEK cells to verify the observation (n = 3 per condition in 2 independent replicates, closed symbols first experiment open second). The Torin1 effect was impaired in higher passage cells compared to lower passage (<italic>p</italic> = 0.0004, two-way ANOVA with Sidak’s multiple comparisons). The two-way ANOVA also revealed a significant effect of treatment (<italic>F</italic> (1, 20) = 44.29, <italic>p</italic> < 0.0001) and of passage number (<italic>F</italic> (1, 20) = 10.28, <italic>p</italic> = 0.0044), with a significant interaction of treatment x passage number (<italic>F</italic> (1, 20) = 10.28, <italic>p</italic> = 0.0044). (<bold>c</bold>) Dose–response curves of Torin1 for low (P6 + P9) and high passage (P16 + P23) HEK cells determined using a microplate reader. Torin1 autophagy induction is less potent in higher passage cells (EC<sub>50</sub> = 97.6 nM) compared to low passage cells (EC<sub>50</sub> = 15.9 nM), n = 4 per condition, 2 independent experiments. An extra sum-of-squares <italic>F</italic>-test (<italic>F</italic> (3, 90) = 13.29, <italic>p</italic> < 0.0001) revealed a significant difference between the best-fit values (Bottom, HillSlope, EC<sub>50</sub>) for each curve. Control values from the microplate reader measurements are tightly clustered around 100%, (100 ± 3.5%, mean ± SEM), hence a gray dashed line at 100% represents control autophagy, and fits were constrained to a top value of 100%.</p></caption></fig>", "<fig id=\"Fig3\"><label>Figure 3</label><caption><p>Little effect of NAS on autophagy or interaction with autophagy inducers. (<bold>a</bold>) A variety of structurally diverse NAS tested for their effects on autophagy at 1 and 10 µM, along with known autophagy inducer, Torin1 0.3 µM, and blocker, Bafilomycin<sub>A1</sub> 0.5 µM. All treatments were for 24 h. Each point represents a well in a 96 well-plate (n = 4 replicates per condition from 3 separate experiments). Circles represent first experiment, squares second, and triangles third. Black asterisks represent significantly different conditions to the control based on results of separate one-way ANOVAs. 6 one-way ANOVAs (one per NAS) revealed an effect of <italic>ent</italic>-AlloP (<italic>F</italic> (2, 33) = 15.78, <italic>p</italic> < 0.0001). Dunnett’s multiple comparisons showed a difference between control and <italic>ent</italic>-AlloP 1 µM (<italic>p</italic> = 0.0134) and <italic>ent</italic>-AlloP 10 µM (**<italic>p</italic> < 0.0001). However when accounting for multiple drug comparisons, two nested one-way ANOVAs (one for each concentration) revealed no differences from control for any NAS at a given concentration. Two unpaired t-tests, one of Torin1 and the other of Bafilomycin<sub>A1,</sub> revealed significant effects of treatment (<italic>p</italic> < 0.0001), including a Bonferroni correction for multiple comparisons. (<bold>b</bold>) Dose–response curves of Torin1, Torin1 + AlloP 10 μM, Torin1 + <italic>ent</italic>-AlloP 10 μM revealed a dose-dependent effect of Torin1 on autophagy induction and no statistical interaction of NAS with Torin1. An extra sum-of-squares <italic>F</italic>-test (<italic>F</italic> (6, 204) = 0.6823, <italic>p</italic> = 0.6641) revealed no difference between the best-fit values (Bottom, HillSlope, EC<sub>50</sub>) for each curve. The values of Torin1 0.3 µM are represented in (<bold>a</bold>) as a positive control since these treatments were conducted on the same plates. (<bold>c</bold>) Time course study of EBSS showed time-dependent autophagy. A two-way ANOVA revealed a difference between time (<italic>F</italic> (4, 165) = 33.92, <italic>p</italic> < 0.0001) and treatment (<italic>F</italic> (2, 165) = 5.712, <italic>p</italic> = 0.0040), with no significant interaction (<italic>F</italic> (8, 165) = 1.082, <italic>p</italic> = 0.3780). Dunnett’s multiple comparison showed a difference between EBSS and <italic>ent</italic>-AlloP at 3 h <italic>p</italic> = 0.0328 (purple asterisk), and EBSS and AlloP <italic>p</italic> = 0.0259 and <italic>ent</italic>-AlloP <italic>p</italic> = 0.0134 at 12 h (brown asterisk). In all panels of this figure, fluorescence was measured using a microplate reader, n = 4 per condition, 3 independent experiments.</p></caption></fig>", "<fig id=\"Fig4\"><label>Figure 4</label><caption><p>AlloP has little effect on autophagy in astrocytes. (<bold>a</bold>) Primary rat cortical astrocytes in phase contrast, GFP, and RFP channels at 10× after a 24 h treatment with 500 nM Torin1 and 10 µM AlloP. Scale bar: 25 µm. (<bold>b</bold>) Background-subtracted mean fluorescence ratios relative to their vehicle-treated control. Each normalized point represents a 35 mm dish (3–8 cells analyzed per dish; 6 independent experiments). Lines connect independent dishes treated during the same experiment. A one-way ANOVA (<italic>F</italic> (2, 15) = 15.98) showed a main effect of treatment, <italic>p</italic> = 0.0002, and Dunnett’s multiple comparisons revealed a difference between control and Torin1 (*<italic>p</italic> = 0.0001).</p></caption></fig>" ]	[]	[]	[]	[]	[]	[]	[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>" ]	[ "<fn-group><fn><p><bold>Publisher's note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"41598_2024_51582_Fig1_HTML\" id=\"MO2\"/>", "<graphic xlink:href=\"41598_2024_51582_Fig2_HTML\" id=\"MO1\"/>", "<graphic xlink:href=\"41598_2024_51582_Fig3_HTML\" id=\"MO3\"/>", "<graphic xlink:href=\"41598_2024_51582_Fig4_HTML\" id=\"MO4\"/>" ]	[ "<media xlink:href=\"41598_2024_51582_MOESM1_ESM.pdf\"><caption><p>Supplementary Figures.</p></caption></media>" ]	[{"label": ["1."], "surname": ["Belmaker", "Agam"], "given-names": ["RH", "G"], "article-title": ["Major depressive disorder"], "source": ["N. Engl. J. Med."], "year": ["2009"], "volume": ["358"], "fpage": ["55"], "lpage": ["68"], "pub-id": ["10.1056/NEJMra073096"]}, {"label": ["5."], "surname": ["Walkery", "Leader", "Cooke", "Vandenberg"], "given-names": ["A", "LD", "E", "A"], "article-title": ["Review of allopregnanolone agonist therapy for the treatment of depressive disorders"], "source": ["Drug Des. Dev. Ther."], "year": ["2021"], "volume": ["15"], "fpage": ["3017"], "lpage": ["3026"], "pub-id": ["10.2147/DDDT.S240856"]}, {"label": ["10."], "surname": ["Tomoda", "Sumitomo", "Shukla", "Hirota-Tsuyada", "Miyachi", "Oh"], "given-names": ["T", "A", "R", "Y", "H", "H"], "article-title": ["BDNF controls GABA"], "sub": ["A"], "source": ["Neuropsychopharmacol"], "year": ["2021"], "volume": ["47"], "fpage": ["553"], "lpage": ["563"], "pub-id": ["10.1038/s41386-021-01116-0"]}, {"label": ["12."], "surname": ["Abdallah", "Averill", "Gueorguieva", "Goktas", "Purohit", "Ranganathan"], "given-names": ["CG", "LA", "R", "S", "P", "M"], "article-title": ["Modulation of the antidepressant effects of ketamine by the mTORC1 inhibitor rapamycin"], "source": ["Neuropsychopharmacol"], "year": ["2020"], "volume": ["45"], "fpage": ["990"], "lpage": ["997"], "pub-id": ["10.1038/s41386-020-0644-9"]}]	{ "acronym": [], "definition": [] }	28	CC BY	no	2024-01-13 00:02:19	Sci Rep. 2024 Jan 10; 14:1042	oa_package/2a/7b/PMC10781668.tar.gz
PMC10781669	38200048	[]	[ "<title>Materials and methods</title>", "<title>Statement concerning safety</title>", "<p id=\"Par29\">In this study highly toxic compounds have been used with LD<sub>50</sub> values well below 1 mg/kg.</p>", "<title>Surface sampling</title>", "<p id=\"Par30\">The surfaces of interest were firmly swabbed with a cotton swab. The swab was inserted in a tube with an extraction solution consisting of methanol/MilliQ (1:1 v/v) and vigorously shaken. The extraction efficiency of the swabs was estimated to be around 90%. They extraction solution was analyzed using LC–MS/MS (Waters Xevo TQ-S, with a Waters Acquity M-Class LC module, Waters, Milford, US).</p>", "<title>Filter extraction</title>", "<p id=\"Par31\">A flow of 0.4 NL/min was diverted from the exposure chamber and forced through a polyether sulfone filter (0.2 µm pore size) The filters were removed from the filter sampler and extracted with 4 mL methanol/MilliQ (1:1 v/v) solution using a vortex. The extraction efficiency of the filters was estimated at 94% The concentration of the extraction solution was determined using a LC–MS/MS (Waters Xevo TQ-S, with a Waters Acquity M-Class LC module, Milford, US).</p>", "<title>Vapor analysis</title>", "<p id=\"Par32\">A flow of 0.4 NL/min was diverted from the exposure chamber and forced through a Tenax TA 60/80 tube. The Tenax tubes were thermally desorbed and analyzed using a multimode inlet (OPTIC-4, GL Sciences, Eindhoven, The Netherlands) coupled to a GC–MS (an Agilent Technologies 7890A GC-system in combination with an Agilent Technologies 5975 inert XL MSD, Santa Clara, US). The injector was flash heated from 10 to 250 °C at a flow rate of 8 mL He/min. After a desorbing time of 90 s the column flow was reduced to 1.5 mL/min.</p>", "<title>TOP dispersion</title>", "<p id=\"Par33\">A disposable polypropylene syringe was filled with a stock solution of 17 mg/mL TOP in IPA and was placed in the syringe pump (KDS Legato, KD Scientific, Holliston, US) in the CHART. A set point in total particle count was manually chosen via the CHART’s software interface and the particle concentration was regulated automatically via the PI control mechanism.</p>", "<title>VX dispersion</title>", "<p id=\"Par34\">A disposable polypropylene syringe was filled with a stock solution of 20 mg/mL VX in IPA and was placed in the syringe pump (KDS Legato, KD Scientific, Holliston, US) in the CHART. A set point in total particle count was manually selected via the CHART’s software interface and the particle concentration was regulated automatically via the PI control mechanism.</p>" ]	[ "<title>Results</title>", "<p id=\"Par23\">To evaluate the accuracy and stability of the PI control mechanism an exposure with the low-volatile chemical TOP was performed. Figure ##FIG##5##6## left shows a dynamic mass concentration profile and the corresponding number geometric mean attained during these experiments. The aerosol mass concentration was adjusted several times while maintaining a constant number particle geometric mean (Fig. ##FIG##5##6## left panel). As expected the geometric mean becomes noisier at lower particle concentrations as the particle concentration in the measurement volume of the OPC becomes so small that normalized probabilistic variation in the sample intervals increases significantly. Figure ##FIG##5##6## (right panel) illustrates the stability of the particle size distribution in time.</p>", "<p id=\"Par24\">Important to note is that the CHART was capable of generating an airborne mass concentration of 6 µg/m<sup>3</sup>, which is well below the acute exposure guideline level of relevant CWAs, such as VX (AEGL 3, 10 min, 0.029 mg/m<sup>3</sup>), where serious to potential life threatening effects can be expected within 10 minutes<sup>##REF##10711394##20##</sup>.</p>", "<p id=\"Par25\">If a detector evaluation experiment is performed, delivering a detailed particle size distribution and mass concentration measurement is required. A constant concentration of TOP was generated and measured with the APS while three particle filters and Tenax tubes for vapor samples were collected over 60 min intervals. The results of the filter sampler and the mass determination of the APS are summarized in Table ##TAB##1##2##. A concentration of 1.15 mg/m<sup>3</sup> based on APS measurement was generated for 1 h in order to collect enough mass on the filter. The mass on the filter was quantified with LC–MS/MS (Supplementary Fig. ##SUPPL##0##S1##) as an orthogonal control of the APS measurement. Blank samples were prepared by extracting clean filters that have been placed in the CHART under the same process conditions without generating particles. LC–MS/MS analysis confirmed no presence of TOP on the filters (Supplementary Fig. ##SUPPL##0##S2##), this confirms that no re-aerosolization takes place during the operation of the CHART.</p>", "<p id=\"Par26\">As can be seen from Table ##TAB##1##2## less material was found with LC–MS/MS analysis than would be expected based on APS measurements. This can be explained with cumulative errors stemming from losses due to LC–MS/MS extraction efficiency, the APS measurement uncertainty<sup>##UREF##16##31##</sup>, and minor differences in gravitational and diffusional particle losses at the filter sampler and APS nozzle inlets.</p>", "<p id=\"Par27\">For a detector evaluation study the nerve agent VX was dispersed in the CHART for 3 h. The VX concentration was increased once step-wise and halted after 150 min and once the dispersion stopped the aerosol mass concentration dropped sharply (Fig. ##FIG##6##7##). During the first 20 min of the exposure the particle size distribution needs to stabilize as the nebulizer and tubing still needed to flush the cleaning solvent. From t = 20 min to t = 180 the geometric mean and standard deviation remain constant regardless of the steep increase in concentration at t = 100 min. Concentration can be changed quickly to simulate representative exposure scenarios. A dynamic exposure profile consisting of a two crested wave is shown in Supplementary Fig. ##SUPPL##0##S3##.</p>" ]	[]	[ "<title>Conclusion and future prospects</title>", "<p id=\"Par28\">The CHART has been successfully designed, adopted and validated for hot agent research within a dynamic mass range of 0.01 to 1 mg/m<sup>3</sup>, as inspired by the XM12 AVCAD program and the NATO D/100 requirements<sup>##UREF##4##14##,##UREF##5##15##</sup>. The CHART has been validated for future test and evaluation applications involving detectors against hot agent aerosols in a safe and controlled environment. Aerosols are generated in the nebulization chamber and detected in situ by an APS within the 0.5 to 5 µm particle range representing the thoracic fraction, relevant for detector evaluation. A virtual impactor was shown to be capable of tuning the particle size by shifting the number geometric mean with minimal impact on the geometric standard deviation. Samples of the aerosol can be taken from the exposure chamber using filters and Tenax tubes integrated into the sampling system for ex situ GC–MS and LC–MS/MS analysis. The mass concentration sampled on the filter and evaluated with the LC–MS/MS showed an average recovery of 75% compared to the measured concentration with the APS. Filter and APS measurements confirmed the absence of any re-aerosolization processes. Analysis of the Tenax samples verified that no traces of vapors stemming from the agent or its decomposition products were present. Altogether, the results proved the applicability of CHART for detector evaluation studies. The modular design allows the CHART to also be employed for performance assessment of protective materials and toxicology research in the future. In the near future CHART will be engineered for fabric testing against highly toxic aerosols. During the current study it has been proven already that a stable particle concentrations can be generated with steep increases in concentration with a constant particle size distribution for at least 3 h. A modular part will be developed where efficiency of protective materials can be evaluated against hot agents, while taking into account the compound specific aerosol properties.</p>" ]	[ "<p id=\"Par1\">Concern over the possibility of deliberate dispersion of chemical warfare agents and highly toxic pharmaceutical based agents as persistent aerosols has raised the need for experimental assessment of current and future defensive capabilities of armed forces and law enforcement agencies. Therefor we herewith present the design, realization and validation of the Chemical Hot Aerosol Research Tool (CHART) as a validated and safe experimental set-up for performance evaluation of chemical detection and identification equipment against chemical warfare agents and other highly toxic compounds. In the CHART liquid and solid compounds in solution or suspension are being dispersed as aerosols in a nebulization chamber. A broad dynamic particle size range can be generated, including particles known to be able to reach the lower respiratory tract. The aerosol generated is presented to the detection system-under-test while being monitored and characterized in real-time, using an optical particle counter and a time-of-flight aerosol analyzer, respectively. Additionally, the chemical composition of the aerosol is ex situ measured by analytical chemical methods. Evidently, in the design of the CHART significant emphasis was placed on laboratory safety and containment of toxic chemicals. The CHART presented in this paper has proven to be an indispensable experimental tool to study detectors and fieldable identification equipment against toxic chemical aerosols.</p>", "<title>Subject terms</title>" ]	[ "<p id=\"Par2\">Deliberate release of chemical warfare agents (CWAs) and pharmaceutical-based agents (PBAs) is of increasing concern, both from a civil security as from a military perspective<sup>##REF##26476351##1##–##REF##31602511##3##</sup>. Both CWAs and PBAs are toxic chemical substances with a potential to injure, incapacitate or kill<sup>##REF##21783898##4##</sup>. The primary categories of CWAs are nerve agents, blister agents, choking agents and blood agents, of which nerve agents are the most precarious<sup>##UREF##0##5##</sup>. PBAs include synthetic opioids, such as remifentanil and carfentanil with toxicities that even exceed those of nerve agents<sup>##REF##33482605##6##</sup>. In a briefing guide for first responders of the US Drug Enforcement Administration accidental exposure to these aforementioned synthetic opioids by first responders is considered a “real danger”<sup>##UREF##1##7##</sup>. Many reports have been composed detailing developed symptoms of police officers, fire-fighters and medical service providers responding to incidents in environments where illicit drugs were present<sup>##UREF##2##8##</sup>. Although nerve agent exposure is less common, terrorist organizations and rogue states have deployed nerve agents on civilians and armed forces<sup>##REF##16945386##9##,##REF##15141860##10##</sup>. Whereas, based on their intrinsic toxicity, many nerve agents and PBAs are capable of causing serious injury or death, it is the dispersion method and the accuracy of its delivery that determines the actual human exposure and thereby the overall effect<sup>##UREF##3##11##</sup>.</p>", "<p id=\"Par3\">Classical CWAs, including many nerve agents, typically are volatile liquids that are readily dispersed as vapors. Many of the conventional detection and identification systems are based on traditional analytical chemical analysis techniques, such as photoionization detectors, gas chromatography – mass spectrometry and ion mobility spectrometry<sup>##REF##36546880##12##</sup>. As such, these instruments are commonly reliant on the presence of a vapor<sup>##UREF##3##11##</sup>. In contrast, the clandestine development of more advanced nerve agents known as the fourth generation agents or Novichock agents has led to persistent agents of very low volatility. The low to non-volatility of these compounds ensure that they can proficiently be generated as an aerosol and will remain in the aerosol phase, potentially entering the body through inhalation, dermal exposure or ingestion<sup>##REF##36546880##12##</sup>.</p>", "<p id=\"Par4\">The new generation of detection systems designed to detect low-volatile aerosols frequently include identification capabilities. These systems go beyond mere presence detection of a general agent class and have the capacity to identify the chemical composition of the aerosols, allowing for more precise responses and appropriate countermeasures to be taken<sup>##REF##33881227##13##</sup>. As a consequence, these technologies rely on the substance-specific physicochemical properties of the corresponding compound for identification, such as molecular mass, ionizability or ion mobility<sup>##UREF##3##11##</sup>. Hence, the evaluation of detection devices has to be performed with the actual CWAs or PBAs, and aerosols of these compounds can rarely be substituted by less toxic simulants. This emphasizes the need to use the highly toxic compound itself, referred to henceforth as the “hot” agent. As a consequence, there is a corresponding need for new detector development and prototyping and the means to be able to evaluate the performance of aerosol detection equipment in a safe laboratory setting.</p>", "<p id=\"Par5\">The goal of this study has been the development and validation of a hot agent research tool, the Chemical Hot Aerosol Research Tool (CHART), that was designed for detector evaluation of military off-the-shelf and commercially off-the-shelf detectors. The CHART was designed with requirements aimed at emulating representative scenarios, inspired by the department of defense development XM12 AVCAD program and the NATO D/100 requirements<sup>##UREF##4##14##,##UREF##5##15##</sup>. As an approximation a dynamic concentration range between 0.01 and 1 mg/m<sup>3</sup> at a particle size distribution between 0.5 and 5 µm was chosen in this study<sup>##UREF##4##14##,##UREF##5##15##</sup>. A release of low-volatile CWAs, such as the nerve agent VX, typically results in an aerosol containing low volatile particles of various sizes<sup>##REF##16053290##16##</sup>. Likewise, an aerosol consisting of solid particles deployed in a deliberate or accidental release will result in a polydisperse size distribution<sup>##UREF##6##17##</sup>. For an aerosol consisting of a persistent CWAs, inhalation is considered to be the primary exposure route<sup>##REF##17188727##18##</sup>. From these polydisperse disseminations, health risk assessments estimate the thoracic fraction for adults at 50% cut-size to be at approximately 3 µm aerodynamic diameter<sup>##UREF##7##19##</sup>. For potent nerve agents, such as VX, even very low concentrations may cause severe and immediate health effects. The level at which VX causes irreversible health effects can vary based on factors such as exposure duration, individual susceptibility, and environmental conditions. The acute exposure guideline levels (AEGL) of the US environmental protection agency indicate that a 10-min exposure of 0.029 mg/m<sup>3</sup> already may result in life-threatening health effects<sup>##REF##10711394##20##</sup>.</p>", "<p id=\"Par6\">The CHART offers the means to evaluate detectors and fieldable identifiers against aerosolized hot agents of known and tuneable particle size distribution of relevant concentrations in a well-defined environment. The evaluation capabilities of CHART allows measurement of aerosol characteristics in a time-resolved fashion. During a test and evaluation experiment both analytical chemical and aerosol-physical methods are applied to measure compound-specific properties. Test and evaluation of detectors against hot agents requires a prominent structure of safety measures and monitoring capabilities. In this paper the design, realization and validation of the CHART, including its test and evaluation capabilities, are provided.</p>", "<title>Design and operating principle</title>", "<p id=\"Par7\">Detector evaluation against toxic chemical aerosols requires a test and evaluation facility that can simultaneously control and characterize the particle size distribution. This is achieved while measuring the challenge levels, in terms of mass per volume, both in real-time and off-line fashions for an extended amount of time to qualify detector response time and reliability. For detector evaluation mimicking a reproducible steep increase in aerosol concentration is required to enable determination of detector response time. In all cases particle size distributions should be reproducible in terms of geometric mean and standard deviation within the size range of 0.5–5 µm in order to simulate an aerosol that can form an inhalation hazard and to enable comparison of results. The particle concentration in the exposure chamber is monitored in-line in a time-resolved fashion to verify that both the desired concentration profile and the particle size distribution are generated. Offline fractions of both the vapor, solid and liquid phase need to be collected for quantitative chemical and gravimetric analysis as an orthogonal control. Vapors are of interest as some low or non-volatile chemicals might decompose during the path from the source to the detector forming volatile fragments.</p>", "<title>Device overview</title>", "<p id=\"Par8\">The CHART has been designed as a multi-applicable research tool for detector evaluation against hot agents with future extensions to study physical protection, agent fate and toxicology. The CHART is designed in a modular fashion offering experimental and safety benefits to the operators using it. Individual components can be exchanged and replaced to configure different modes of operation of CHART that can have conflicting experimental conditions. Additionally, inherent toxicity and persistency of the materials dispersed in the CHART may lead to trace contaminants that cannot be removed entirely from the CHART preventing users to declare parts “clean” or “safe” without extensive validation. In the design of CHART mainly replaceable components have been used in all parts that are in direct contact with the agents, so that the complete removal of contaminants is achieved by replacing contaminated components individually for their clean counterpart. A rigorous component replacement procedure is especially needed when a next series of experiments involves a new class of agent The CHART can be divided into three main functionalities: generation, conditioning and exposure, as illustrated in Fig. ##FIG##0##1## left, based on the functionality. In Fig. ##FIG##0##1## right, a picture of how the CHART is realized in the laboratory is provided.</p>", "<title>Generation</title>", "<p id=\"Par9\">Various commercially available options for aerosol generation have been considered. Solid aerosol generators can generate an aerosol from a solid substrate of known particle size distribution hence making the conditioning steps easier, because no drying is required, whereas liquid substrate aerosol generators may need additional drying steps<sup>##UREF##8##21##</sup>. The main disadvantage of a solid aerosol generator is the dependency on the size characteristics of the starting material on the aerosol that is eventually generated. Additionally, the majority of the nerve agents are viscous liquids excluding the use of a solid generator. For the CHART the PFA Meinhard liquid aerosol generator (PolyPro ST Nebulizer, Meinhard, Golden, US) was chosen to generate an aerosol from both solid and liquid substrates. Aerosols can be generated from a pure liquid, a suspension or a solution of dissolved agents that are loaded in a liquid syringe that is coupled to the Meinhard nebulizer with a microflow capillary. The Meinhard nebulizer generates a polydisperse aerosol consisting of droplets with an average mass median aerodynamic diameter (MMAD) of 12.9 µm in aqueous solution and 6.8 µm MMAD in organic solvents upon generation<sup>##REF##15732904##22##,##UREF##9##23##</sup>. The concentration of the agent in solution can be adjusted in order to obtain an aerosol with a particle size distribution representing the thoracic fraction of a polydisperse aerosol release (i.e. between 0.5 and 5 µm). Since the volume of a spherical particle with a diameter of 2 µm is 0.4% of the volume a 12.9 µm diameter particle, a concentration of 4 mg/mL need to be applied for solution of agents with unit density in aqueous solution to yield dry particles with a MMAD of approximately 2–3 µm. The CHART offers the capability to generate aerosols from two sides in a glass cylinder shaped aerosol nebulization chamber, enabling simultaneous aerosolization of two different compounds. The aerosol nebulization chamber consists of 3 glass parts mounted together with a chemical resistant plastic sealing and has a volume of approximately 40 L. The generated aerosol is distributed in the chamber with gas flows from the Meinhard nebulizer itself and two additional mixing flows located at the top of the aerosol nebulization chamber. The additional airstream is added to reduce coagulation of the generated aerosol and to provide additional drying capacity. The generated aerosol is fed to the aerosol conditioning unit where the particle size range as well as the humidity can be adjusted.</p>", "<title>Conditioning</title>", "<p id=\"Par10\">Upon generation aerosols do not necessarily have the particle size distribution of interest, i.e. a constant particle size distribution of 0.5–5 µm. The particle size distribution and the MMAD of the aerosol can be influenced by drying or humidifying the generated aerosol. Also certain size ranges of interest can be selected for experiments in the exposure chamber. The preceding interventions are referred to as aerosol conditioning. In the CHART aerosol conditioning is achieved by employment of a 90 cm Nafion humidity exchanger (MD-700, Perma Pure, Lakewood, US) followed by particle selection using an in house designed and developed virtual impactor. The humidity exchanger installed underneath the nebulization chamber is employed to actively control the drying speed of the aerosol droplets by either enhancing solvent evaporation from the aerosol to a dry stream or from a solvent saturated gas stream to the aerosol. A Nafion humidity exchanger opposed to a silica dryer was chosen, because only solvent molecules can permeate through the Nafion tube wall, hereby creating a clear division in a clean and ‘hot’ region. Solvent is stripped from the wet stream and exchanged to the dry stream, without contaminating the clean stream with hot agents. The Nafion humidity exchanger was chosen to be able to potentially achieve both a dry (RH = 0%) and wet (RH = 90%) aerosol stream without the need to regenerate consumables for drying. However, varying the relative humidity was not validated in this study, and all work was performed under dry conditions (RH = 0.5%). The conditioned aerosol thereafter arrives at the virtual impactor, where small particles can be extracted from the aerosol increasing the fraction of coarse particles. In the virtual impactor particles are accelerated in a small orifice called the acceleration nozzle to be collected in either the collection nozzle or discarded through the major flow orifice. A schematic view of the virtual impactor is provided in Fig. ##FIG##1##2##.</p>", "<p id=\"Par11\">As displayed in Fig. ##FIG##1##2##, the acceleration nozzle of the virtual impactor is positioned in a thread and its position can be manually adjusted with a hex key. Rotation of the acceleration nozzle will therefore increase or decrease its distance relative to the collection nozzle. Depending on the spacing between the collection nozzle and the acceleration nozzle certain particle sizes are collected more efficiently in the collection nozzle. The virtual impactor design is derived from the design of Loo et al<italic>.</italic> and iteratively modified to provide the desired separation characteristics<sup>##UREF##10##24##</sup>. The basis for this design concept lies in the impactor theory of Marple et al<italic>.</italic><sup>##UREF##11##25##</sup> where the cut-off characteristics are described with the Stokes number (Stk), which is given by Eq. (##FORMU##0##1##), where V<sub>0</sub>, ρ<sub>p</sub>, d<sub>p</sub>, C<sub>c</sub>, η and D<sub>0</sub> are gas velocity through the acceleration nozzle, particle density, particle diameter, Cunningham’s correction factor, air viscosity and the diameter of the particle acceleration nozzle, respectively. In order to concentrate coarse particles in the collection nozzle and removing submicron particles via the major flow nozzle a Stokes number of 0.5 was used to realize a cut-off diameter of 0.5 µm. The particle laden flow of 6 L/min enters the virtual impactor acceleration nozzle from the aerosol nebulization chamber where the stream is divided over the particle collection nozzle and the major flow. The minor flow of 1 L/min leaves the virtual impactor and enters the exposure chamber, whereas the major flow of 5 L/min exits the virtual impactor through a filter out of the CHART. Tris(2-ethylhexyl) phosphate (TOP) was used to characterize the particle selection effectivity of the virtual impactor, as it possesses similar physicochemical properties of liquid organophosphorus nerve agents such as a low volatility and high viscosity, without being extremely toxic. A concentration of 0.25 mg/m<sup>3</sup> was maintained in the exposure chamber for 40 min with and without a virtual impactor and with difference nozzle spacings of the virtual impactor. The cumulative normalized particle size distribution of TOP with and without employment of the virtual impactor are shown in Fig. ##FIG##2##3## left. The application of the virtual impactor resulted in extraction of a significant part of the small particles present in the aerosol leading to a shift of the particle size distribution. The particle selection effectivity was characterized by determining the particle aerodynamic cut-off diameter (d<sub>50</sub>). At the d<sub>50</sub> 50% of the particles of that specific size and above injected into the virtual impactor are collected in the collection nozzle. An increase in nozzle spacing, the distance between the accelerating nozzle and the collection nozzle, directly leads to an increase of the d<sub>50</sub> from 0.74 to 0.91 µm due to the extraction of small particles. The particle selection effectivity as function of nozzle spacing is shown in Fig. ##FIG##2##3## right.</p>", "<p id=\"Par12\">The effects on the particle size distribution in the exposure chamber due to the application of the virtual impactor have been studied in more detail by monitoring the change in the statistical properties of the resultant particle size distribution that is measured with an Aerodynamic particle sizer (APS) (model APS3321, TSI, St Paul, US) in the exposure chamber. The main purpose of the virtual impactor is shifting the geometric mean of the aerosol particle size distribution while maintaining the shape. As can be seen in Table ##TAB##0##1##, a 16% increase in number based geometric mean can be achieved with a minimal increase in geometric standard deviation of 2.5%.</p>", "<title>Exposure</title>", "<p id=\"Par13\">Once the desired particle size distribution is conditioned and selected, the aerosol enters the exposure chamber of the CHART. A filtered air stream is added to the aerosol if dilution is required to generate the desired concentration or to tune the velocity distribution to the sampling characteristics of the system under test (SUT). The exposure chamber consists of successive stainless steel 304 ISO-KF50 crosses with adaptors for a pressure sensor, temperature sensor, detectors, particle counters or sizers, and an aerosol/gas sampler. The CHART offers capabilities for simultaneously evaluating a SUT against a reference instruments such as an APS with or without an aerosol diluter 20:1 (aerosol diluter 3302A, TSI, St Paul, US), a Scanning mobility particle sizer (model electrostatic classifier 3082, SMPS spectrometer 3938, TSI, St Paul, US), aerosol spectrometer (Grimm 11-D, Grimm, Ainring, Germany), or an Electrical Low Pressure Impactor (ELPI) (model ELPI + , Dekati, Kangasala, Finland). Particle sizing and counting techniques such as the OPC and the APS report different “particle diameters”. Particle sizing using the OPC is based on elastic single particle light scattering according to the Mie theory, where diameters are reported in the optical diameter (d<sub>op</sub>). A sizing instrument such as the APS reports aerodynamic diameters (d<sub>ae</sub>) that are determined by the time-of-flight of particles traveling through a laser velocimeter<sup>##UREF##12##26##,##REF##20208803##27##</sup>. Because the aerodynamic diameters are considered more relevant for studies regarding health effects and particle inhalation, optical diameters were converted to aerodynamic diameters. When the refractive index (n) of the aerosolized compound is the same as polystyrene latex (PSL) optical diameters can assumed to be equal to volume equivalent diameters and optical diameters can be converted to aerodynamic diameters following Eq. (##FORMU##1##2##), shown below<sup>##UREF##12##26##,##UREF##13##28##</sup>.</p>", "<p id=\"Par14\">In Eq. (##FORMU##1##2##), is the unit density, C<sub>op</sub> and C<sub>ae</sub> are the Cunningham correction factors in terms of the optical and aerodynamic diameter and is the dynamic shape factor. However, the refractive indices of TOP (n = 1.44) and VX (n = 1.49) used in the current study are well below that of PSL (n = 1.60). Hence, for these compounds, the empirical equation derived by Chih-Hsiang et al<italic>.</italic> for oleic acid (n = 1.46) was used instead to convert optical diameters to aerodynamic diameters<sup>##UREF##12##26##</sup>, as outlined in Eq. (##FORMU##4##3##).</p>", "<p id=\"Par15\">In the validation of the CHART a solution of TOP in isopropanol was used to generate an aerosol. Because the used TOP is a pure liquid and the particles formed were < 1 mm in diameter a shape factor of 1 was assumed in the calculation<sup>##UREF##14##29##</sup>. In particle size range of interest of the CHART (0.5–5 µm) contributions from C<sub>op</sub>/C<sub>ae</sub> are assumed to be neglectable as well. In case suspensions or solutions are used that are known to form non-spherical particles, this conversion cannot be executed as a non-unit shape factor may have drastic effects on both aerodynamic and optical equivalent diameters complicating diameter conversion, underlining the prerequisite of employing an APS in detector evaluation studies. The APS offers a well-defined particle size distribution between 0.5 and 20 micron aerodynamic diameter with 52 individual software bins and is therefore the reference of choice for detector evaluation research. The operator of the CHART selects a setpoint for a desired particle concentration in the exposure chamber and a proportional-integral (PI) control loop determines the aerosol generation of the CHART according to a pulse-width modulation (PWM) scheme directing both the liquid pump controlling the agent containing syringe and the gas flow connected to the nebulizer. A high PWM results in both a gas flow of 1 NL/min (normal liter per minute) and a liquid flow of 100 µL/min though the nebulizer whilst a low PWM results in neither a gas flow nor a liquid flow through the nebulizer. The binary behavior ensures that an aerosol with reproducible particle size distribution is generated upon activation of the Meinhard nebulizer, as a change in the nebulizer flow is known to alter the aerosol particle size distribution<sup>##REF##15732904##22##</sup>. In the exposure chamber the conditioned aerosol is monitored with an OPC (OPC-N3, Alphasense, Essex, UK) to determine the total particle count and size distribution which is used as the process value for the PI control loop. The low cost OPC has limited aerosol characterization capabilities compared to the APS, but suffices for concentration determination for the control loop. The nebulization chamber and the exposure chamber of the CHART are spatially separated resulting in a delay of approximately 10 s between detection and particle generation. To simultaneously decrease the time required to reach the setpoint while correcting for the systems delay the Integral term was introduced in the PI controller that was optimized using the Ziegler-Nichols tuning formula<sup>##UREF##15##30##</sup>. The derivative term of the control loop could not be added since fluctuations in the OPC signal often yield an immense rate of change of the process value, which resulted in a uncontrollable generation.</p>", "<p id=\"Par16\">During an exposure aerosol and gas samples can be collected, the exposure chamber is equipped with an isokinetic sampler to extract a quantified volume of aerosol. The flow is first forced through a filter and subsequently through a Tenax tube. The filter and Tenax tube combination is adopted to verify the absence of a vapor whilst proving the presence of an aerosol by capturing aerosolized compounds on a filter in the exposure chamber. Aerosolized agents could potentially decompose during the exposure, and, additionally, detection and identification systems under test may not be able to differentiate agents in aerosol or vapor phases. Therefore, it is crucial to confirm the absence or presence of any vapors to accurately assess the detection and identification capabilities for aerosolized agents. The concentration determined with the filter sampler can be used as an orthogonal control value to verify the adequacy of the aerosol characterization instruments. During the validation stage of the design no traces of agent vapor nor decomposition products were found, while typically 75% of aerosol mass was recovered from the filter, confirming the hypothesis that only an aerosol was present in the exposure chamber.</p>", "<title>Safety</title>", "<p id=\"Par17\">Due to the nature of the compounds used in experiments, the CHART was designed in accordance with the “safety by design” principle. The safety considerations have been integrated into the design process from the very beginning, leading to a prominent structure of safety measures. To eliminate risks of leakage of toxic agent, the CHART is divided into two zones: the hot zone and the safety zone (Fig. ##FIG##3##4##). The hot zone includes the space where the aerosol is generated, conditioned and exposed, and its boundaries form the physical barrier that separates the hot agent containing compartment from the safety zone. As mentioned earlier, individual parts confining the hot zone are regularly replaced to prevent accumulation of hot agents as an alternative to a procedure involving in situ chemical decontamination. Replaced equipment is treated with high concentrations of bleach for at least 72 h before it is disposed as chemical waste. The safety zone is the space inside the CHART casing and encloses all equipment and technique that does not require direct contact with hot agents, such as pumps, mass flow controllers and detectors. It confines a space of 6.4 m<sup>3</sup> and has its own air ventilation system that streamlines an airflow into the containment zone through the admission gratings located at the bottom and side edges. The airflow is distributed evenly through the CHART components using a diffuser plate at the top and two additional exhausts at the side with a total flow of 170 m<sup>3</sup>/h yielding a refreshment rate of 26.5 h<sup>−1</sup>.</p>", "<p id=\"Par18\">Leak tightness of the containment of the hot zone has been validated with leakage test using a trace gas detector and by using a pressure compensated flow balance. The latter is continuously monitored and logged during operation. Isopropyl alcohol was nebulized in the CHART and a leak detection test was performed using a trace gas detector (MiniRAE 3000, RAE Systems, San Jose, US). A small amount of isopropyl alcohol was nebulized in the nebulization chamber and measured from the inside using a trace gas detector as 237 ppm. The measured concentration in the safety zone fluctuated between zero and 0.1 ppm which is at the Limit of Detection (LOD) of the trace gas detector, indicating that practically no measurable amount of solvent diffuses from the hot zone into the safety zone. Accounting for the LOD of the trace gas detector this translates to a maximal concentration of 0.4 ppb which is a factor 75 above the AEGL 3 (10 min) of VX of 0.029 mg/m<sup>3</sup><sup>##REF##10711394##20##</sup>. The safety zone serves to contain agents in the case of an incident like a leakage, containment breach or obstruction. The extraction system of the safety zone was evaluated by generating a dense aerosol with a smoke generator (Vesuvius, Haagen, Amersfoort, The Netherlands). The time needed to extract the aerosol from the outer containment to the extent that no traces were visually observable was less than 15 min. This confirms that the complete volume was refreshed and no dead spaces are present in the safety zone. TOP, a persistent low-volatile simulant of organophosphorus CWAs, was dispersed in the CHART for an extended period of time and surface samples were collected at points of increased leakage risk such as joints, valves and fittings using swabs. No traces of TOP were found inside the safety zone, confirming that the nebulized agents remain inside the hot zone. All gas flows entering and leaving the system pass through HEPA/Coal filters (carbon/HEPA filter capsule, Whatman®, Maidstone, UK) that will be replaced yearly. As a passive safety measure a central pump unit continuously purges the system, maintaining a relative negative pressure of 25 mbar at the aerosol nebulization chamber and a relative negative pressure of 50 mbar at the exposure chamber with respect to the outer containment. Due to the relative negative pressure in the hot zone, potential leaks will not immediately result in a release of hot agents to the safety zone. Pressure sensors situated in the aerosol nebulization chamber and the exposure chamber monitor the aforementioned pressures.</p>", "<p id=\"Par19\">Engineering controls will warn operators in the case of pressure or flow deviations and will automatically stop generation when a safety threshold is exceeded. Concentrations, flows, pressures and valve positions are visible to the operator at all time and can also be manipulated manually. Gas flows entering and exiting CHART are monitored with flow indicator controllers and analyzers (thermal mass flow controller F-201AV, Bronkhorst, Veenendaal, The Netherlands) with tolerances of 0.5% as can be seen in the piping and instrumentation diagram (Fig. ##FIG##4##5##).</p>", "<p id=\"Par20\">Flows Q̇<sub>1</sub>, Q̇<sub>2</sub> and Q̇<sub>3</sub> enter the aerosol nebulization chamber and in the exposure chamber the additional dilution flow Q̇<sub>4</sub> is mixed to the aerosol. The counter current flow of the humidity exchanger is not considered in the flow balance as this flow does not reach the hot agent part of CHART. Flows Q̇<sub>5</sub> to Q̇<sub>9</sub> exit CHART through the virtual impactor (Q̇<sub>5</sub>), the OPC (Q̇<sub>6</sub>), the main purge (Q̇<sub>7</sub>), sampling flow (Q̇<sub>8</sub>) or the system under test (Q̇<sub>9</sub>) respectively. When Q̇<sub>leakage</sub> > 0.3 NL/min the CHART will give a warning to the operator and when Q̇<sub>leakage</sub> exceeds 0.5 NL/min, CHART will automatically enter into safety modus. In safety modus aerosol generation stops by termination of the liquid pump and closure of the valves between the aerosol generator capillary and the syringe. Purging will continue until the particle concentration is zero in order to allow safe troubleshooting.</p>", "<p id=\"Par21\">In the case of a software crash a manual kill switch can be used to stop an experiment and in the case of a power blackout an uninterruptible power supply (UPS) will maintain the system long enough to finish termination mode. A battery bridges the time required for the UPS to start up and maintain the system. During this mode generation immediately stops, dilution air and the pump system remain operational, as a consequence agents present in the hot agent containment zone are diluted and flushed with clean air. The operator is also capable of flushing the hot zone with clean air manually. When particle generation ceases, the actual particle concentration decreases in accordance with the theoretical exponential decay rate of 0.15 min<sup>-1</sup>. As a result, the particle concentration in the exposure chamber decreases to 1% of the original concentration within 30 min.</p>", "<p id=\"Par22\">Taking into account the equipment design and the safety evaluation performed with the smoke generator, swabs and trace gas detector in accordance with the “safety by design” principle, CHART has been demonstrated to be an intrinsic safe system for detector evaluation studies.</p>", "<title>Supplementary Information</title>", "<p>\n</p>" ]	[ "<title>Supplementary Information</title>", "<p>The online version contains supplementary material available at 10.1038/s41598-023-50718-9.</p>", "<title>Author contributions</title>", "<p>D.D.J. and T.V. prepared the main manuscript text and DDJ prepared all figures. D.D.J., T.V. and D.A. performed all experimental work presented in this paper. A.W., M.B.H. and D.D.J. designed and constructed the CHART. AW and RB created the idea of CHART. All authors reviewed the manuscript.</p>", "<title>Funding</title>", "<p>This work was funded by the Dutch Ministry of Defence under grants V1802 and V2207.</p>", "<title>Data availability</title>", "<p>The datasets generated during and/or analyzed during the current study are not publicly available due to lab export control policies but are available from the corresponding author on reasonable request.</p>", "<title>Competing interests</title>", "<p id=\"Par35\">The authors declare no competing interests.</p>" ]	[ "<fig id=\"Fig1\"><label>Figure 1</label><caption><p>Left: The three main parts of the CHART: Generation, Conditioning and Exposure in the aluminum containment casing. Right: The CHART as realized in the laboratory. The external volume of the CHART is 7.50 m<sup>3</sup>.</p></caption></fig>", "<fig id=\"Fig2\"><label>Figure 2</label><caption><p>Schematic representation of the virtual impactor used in the CHART. Particles flow from the top through the acceleration nozzle with a flow of 9 L/min and are collected at the collection nozzle with a flow of 1 L/min or let through a filter via the Major flow orifice with a flow of 8 L/min.</p></caption></fig>", "<fig id=\"Fig3\"><label>Figure 3</label><caption><p>(Left) Cumulative normalized particle size distribution of TOP with and without employment of the virtual impactor with a nozzle spacing of 2.93 mm. (Right) The relation between the d<sub>50</sub> and nozzle spacing.</p></caption></fig>", "<fig id=\"Fig4\"><label>Figure 4</label><caption><p>Overview of the zones in the CHART. The hot zone comprises of the space where the aerosol is generated, conditioned and exposed and is kept at a negative pressure (25 to 50 mbar below the laboratory air pressure). The safety zone consist of an extracted enclosure which houses all the auxiliary equipment that does not come in direct contact with the hot agent.</p></caption></fig>", "<fig id=\"Fig5\"><label>Figure 5</label><caption><p>Schematic view of the components of the CHART, where the flows constituting the flow balance are indicated with Q̇.</p></caption></fig>", "<fig id=\"Fig6\"><label>Figure 6</label><caption><p>Left: Mass concentration and the moving average over 1 min in the exposure chamber of the CHART in a single dynamic run using the PI Control loop at 6 setpoints and the time resolved number particle geometric mean. Right: the corresponding particle size number distribution as function of time.</p></caption></fig>", "<fig id=\"Fig7\"><label>Figure 7</label><caption><p>Two step concentration profile of a VX exposure for detector evaluation at 0.75 mg/m<sup>3</sup> and 1 mg/m<sup>3</sup> where the concentration is slowly built to the first step and rapidly increased to the second concentration.</p></caption></fig>" ]	[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Number based geometric mean and geometric standard deviation as function of the nozzle spacing of the virtual impactor obtained with APS.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\">Geometric mean [µm]</th><th align=\"left\">Geometric standard deviation [-]</th></tr></thead><tbody><tr><td align=\"left\">No virtual impactor</td><td char=\".\" align=\"char\">0.87</td><td char=\".\" align=\"char\">1.53</td></tr><tr><td align=\"left\">Nozzle spacing = 1.04 mm</td><td char=\".\" align=\"char\">0.92</td><td char=\".\" align=\"char\">1.52</td></tr><tr><td align=\"left\">Nozzle spacing = 1.52 mm</td><td char=\".\" align=\"char\">0.95</td><td char=\".\" align=\"char\">1.55</td></tr><tr><td align=\"left\">Nozzle spacing = 1.95 mm</td><td char=\".\" align=\"char\">0.98</td><td char=\".\" align=\"char\">1.57</td></tr><tr><td align=\"left\">Nozzle spacing = 2.93 mm</td><td char=\".\" align=\"char\">1.01</td><td char=\".\" align=\"char\">1.57</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Mass concentrations according to the APS and LC–MS/MS during the 60-min intervals.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Time [min]</th><th align=\"left\">0–60</th><th align=\"left\">60–120</th><th align=\"left\">120–180</th><th align=\"left\">Average</th></tr></thead><tbody><tr><td align=\"left\">LC–MS/MS [mg/m<sup>3</sup>]</td><td align=\"left\">0.78</td><td align=\"left\">0.93</td><td align=\"left\">0.83</td><td align=\"left\">0.85</td></tr><tr><td align=\"left\">APS [mg/m<sup>3</sup>]</td><td align=\"left\">1.14</td><td align=\"left\">1.15</td><td align=\"left\">1.13</td><td align=\"left\">1.14</td></tr><tr><td align=\"left\">Recovery (%)</td><td align=\"left\">69</td><td align=\"left\">81</td><td align=\"left\">74</td><td align=\"left\">75</td></tr></tbody></table></table-wrap>" ]	[ "<disp-formula id=\"Equ1\"><label>1</label><alternatives><tex-math id=\"M1\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$Stk = \\frac{{V_{o} \\rho_{p} d_{p}^{2} C_{c} }}{{9\\eta D_{o} }}$$\\end{document}</tex-math><mml:math id=\"M2\" display=\"block\"><mml:mrow><mml:mi>S</mml:mi><mml:mi>t</mml:mi><mml:mi>k</mml:mi><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mi>V</mml:mi><mml:mi>o</mml:mi></mml:msub><mml:msub><mml:mi>ρ</mml:mi><mml:mi>p</mml:mi></mml:msub><mml:msubsup><mml:mi>d</mml:mi><mml:mrow><mml:mi>p</mml:mi></mml:mrow><mml:mn>2</mml:mn></mml:msubsup><mml:msub><mml:mi>C</mml:mi><mml:mi>c</mml:mi></mml:msub></mml:mrow><mml:mrow><mml:mn>9</mml:mn><mml:mi>η</mml:mi><mml:msub><mml:mi>D</mml:mi><mml:mi>o</mml:mi></mml:msub></mml:mrow></mml:mfrac></mml:mrow></mml:math></alternatives></disp-formula>", "<disp-formula id=\"Equ2\"><label>2</label><alternatives><tex-math id=\"M3\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$d_{ae} = d_{op} \\sqrt {\\frac{{\\rho_{{pC_{op} }} }}{{\\rho_{0} C_{ae} \\chi }}}$$\\end{document}</tex-math><mml:math id=\"M4\" display=\"block\"><mml:mrow><mml:msub><mml:mi>d</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">ae</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mi>d</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">op</mml:mi></mml:mrow></mml:msub><mml:msqrt><mml:mfrac><mml:msub><mml:mi>ρ</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:msub><mml:mi>C</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">op</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:msub><mml:mrow><mml:msub><mml:mi>ρ</mml:mi><mml:mn>0</mml:mn></mml:msub><mml:msub><mml:mi>C</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">ae</mml:mi></mml:mrow></mml:msub><mml:mi>χ</mml:mi></mml:mrow></mml:mfrac></mml:msqrt></mml:mrow></mml:math></alternatives></disp-formula>", "<inline-formula id=\"IEq1\"><alternatives><tex-math id=\"M5\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\rho }_{0}$$\\end{document}</tex-math><mml:math id=\"M6\"><mml:msub><mml:mi>ρ</mml:mi><mml:mn>0</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq2\"><alternatives><tex-math id=\"M7\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\chi$$\\end{document}</tex-math><mml:math id=\"M8\"><mml:mi>χ</mml:mi></mml:math></alternatives></inline-formula>", "<disp-formula id=\"Equ3\"><label>3</label><alternatives><tex-math id=\"M9\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$d_{ae} = - 0.03457d_{op}^{2} + 1.433d_{op} - 0.1157$$\\end{document}</tex-math><mml:math id=\"M10\" display=\"block\"><mml:mrow><mml:msub><mml:mi>d</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">ae</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mo>-</mml:mo><mml:mn>0.03457</mml:mn><mml:msubsup><mml:mi>d</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">op</mml:mi></mml:mrow><mml:mn>2</mml:mn></mml:msubsup><mml:mo>+</mml:mo><mml:mn>1.433</mml:mn><mml:msub><mml:mi>d</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">op</mml:mi></mml:mrow></mml:msub><mml:mo>-</mml:mo><mml:mn>0.1157</mml:mn></mml:mrow></mml:math></alternatives></disp-formula>", "<disp-formula id=\"Equ4\"><label>4</label><alternatives><tex-math id=\"M11\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\dot{\\text{Q}}}_{{{\\text{leakage}}}} = {\\dot{\\text{Q}}}_{{1}} + {\\dot{\\text{Q}}}_{{2}} + {\\dot{\\text{Q}}}_{{3}} + {\\dot{\\text{Q}}}_{{4}} {-}{\\dot{\\text{Q}}}_{{5}} {-}{\\dot{\\text{Q}}}_{{6}} {-}{\\dot{\\text{Q}}}_{{7}} {-}{\\dot{\\text{Q}}}_{{8}} {-}{\\dot{\\text{Q}}}_{{9}}$$\\end{document}</tex-math><mml:math id=\"M12\" display=\"block\"><mml:mrow><mml:msub><mml:mover accent=\"true\"><mml:mtext>Q</mml:mtext><mml:mo>˙</mml:mo></mml:mover><mml:mtext>leakage</mml:mtext></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mover accent=\"true\"><mml:mtext>Q</mml:mtext><mml:mo>˙</mml:mo></mml:mover><mml:mn>1</mml:mn></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mover accent=\"true\"><mml:mtext>Q</mml:mtext><mml:mo>˙</mml:mo></mml:mover><mml:mn>2</mml:mn></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mover accent=\"true\"><mml:mtext>Q</mml:mtext><mml:mo>˙</mml:mo></mml:mover><mml:mn>3</mml:mn></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mover accent=\"true\"><mml:mtext>Q</mml:mtext><mml:mo>˙</mml:mo></mml:mover><mml:mn>4</mml:mn></mml:msub><mml:mo>-</mml:mo><mml:msub><mml:mover accent=\"true\"><mml:mtext>Q</mml:mtext><mml:mo>˙</mml:mo></mml:mover><mml:mn>5</mml:mn></mml:msub><mml:mo>-</mml:mo><mml:msub><mml:mover accent=\"true\"><mml:mtext>Q</mml:mtext><mml:mo>˙</mml:mo></mml:mover><mml:mn>6</mml:mn></mml:msub><mml:mo>-</mml:mo><mml:msub><mml:mover accent=\"true\"><mml:mtext>Q</mml:mtext><mml:mo>˙</mml:mo></mml:mover><mml:mn>7</mml:mn></mml:msub><mml:mo>-</mml:mo><mml:msub><mml:mover accent=\"true\"><mml:mtext>Q</mml:mtext><mml:mo>˙</mml:mo></mml:mover><mml:mn>8</mml:mn></mml:msub><mml:mo>-</mml:mo><mml:msub><mml:mover accent=\"true\"><mml:mtext>Q</mml:mtext><mml:mo>˙</mml:mo></mml:mover><mml:mn>9</mml:mn></mml:msub></mml:mrow></mml:math></alternatives></disp-formula>" ]	[]	[]	[]	[]	[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>" ]	[ "<fn-group><fn><p><bold>Publisher's note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"41598_2023_50718_Fig1_HTML\" id=\"MO1\"/>", "<graphic xlink:href=\"41598_2023_50718_Fig2_HTML\" id=\"MO2\"/>", "<graphic xlink:href=\"41598_2023_50718_Article_Equ1.gif\" position=\"anchor\"/>", "<graphic xlink:href=\"41598_2023_50718_Fig3_HTML\" id=\"MO3\"/>", "<graphic xlink:href=\"41598_2023_50718_Article_Equ2.gif\" position=\"anchor\"/>", "<inline-graphic xlink:href=\"41598_2023_50718_Article_IEq1.gif\"/>", "<inline-graphic xlink:href=\"41598_2023_50718_Article_IEq2.gif\"/>", "<graphic xlink:href=\"41598_2023_50718_Article_Equ3.gif\" position=\"anchor\"/>", "<graphic xlink:href=\"41598_2023_50718_Fig4_HTML\" id=\"MO4\"/>", "<graphic xlink:href=\"41598_2023_50718_Fig5_HTML\" id=\"MO5\"/>", "<graphic xlink:href=\"41598_2023_50718_Article_Equ4.gif\" position=\"anchor\"/>", "<graphic xlink:href=\"41598_2023_50718_Fig6_HTML\" id=\"MO6\"/>", "<graphic xlink:href=\"41598_2023_50718_Fig7_HTML\" id=\"MO7\"/>" ]	[ "<media xlink:href=\"41598_2023_50718_MOESM1_ESM.pdf\"><caption><p>Supplementary Information.</p></caption></media>" ]	[{"label": ["5."], "surname": ["Jindal", "Mainuddin", "Veerabuthiran", "Razdan"], "given-names": ["MK", "M", "S", "AK"], "article-title": ["Laser-based systems for 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PMC10781670	38200219	[ "<title>Introduction</title>", "<p id=\"Par6\">Wasting refers to a condition when a child is very thin for his/her height; which is usually caused by a rapid loss in body weight and/or failure to gain weight that might lead to an increased risk of death if left untreated<sup>##UREF##0##1##</sup>. Severe wasting is the deadliest form of malnutrition, resulting from insufficient nutritious food and recurrent illnesses such as diarrhoea, measles, and malaria<sup>##UREF##1##2##</sup>. Weight-for-height nutritional index and mid-upper arm circumference (MUAC) are common ways to measure wasting. Moderate Acute Malnutrition (MAM) is defined by a weight-for-height index between −3 and < −2 Z-score or between 11.5 and < 12.5 cm by MUAC. Severe wasting, also known as severe acute malnutrition, is characterized by a Z score < −3 or MUAC < 11.5 cm<sup>##REF##30894145##3##,##UREF##2##4##</sup>.</p>", "<p id=\"Par7\">Children ssuffering from severe wasting are susceptible to long-term developmental delays due to weakened immunity and face an increased risk of death<sup>##UREF##3##5##</sup>. A severely wasted child is 11 times more likely to die of common childhood illnesses than a healthy child<sup>##UREF##1##2##</sup>. Globally, 13.6 million children under the age of 5 suffer from severe wasting, which is responsible for 1 in 5 deaths of children making severe wasting among the top threats to child survival<sup>##UREF##1##2##,##UREF##3##5##</sup>. Although low- and middle-income countries have less than half of the world's under-five children, they account for 75% of all children with wasting<sup>##UREF##3##5##</sup>. In 2020, 6% of all under-five children in Africa were wasted<sup>##UREF##3##5##</sup>.</p>", "<p id=\"Par8\">Ethiopia has a high prevalence of wasting, with 7.2% and 1.2% of under-five children moderately and severely wasted in 2019, respectively<sup>##UREF##4##6##</sup>. On top of that, socioeconomic and area-based disparities in wasting were observed among single studies in the country. For instance, 17.30%, 16.70%, 13.4%, 10.7%, 28.2%, 10% and 11.1% of under-five children were wasted in the east and west Gojjam<sup>##REF##26285047##7##</sup>, north Shewa<sup>##UREF##5##8##</sup>, Bule Hora<sup>##UREF##6##9##</sup>, Haramaya<sup>##REF##26675579##10##</sup>, Hawassa<sup>##UREF##7##11##</sup>, Northwest Ethiopia<sup>##REF##30606158##12##</sup> and Dilla<sup>##REF##32153946##13##</sup> respectively.</p>", "<p id=\"Par9\">The World Health Assembly has agreed to reduce severe wasting to less than 5% and 3% by the end of 2025 and 2030, respectively<sup>##UREF##3##5##</sup>. Ethiopia has notably reduced under-five mortality in the last decades through its multi-sectorial approaches to address malnutrition<sup>##REF##29309064##14##</sup>. Nevertheless, the existence of socioeconomic and area-based inequality could impede the country's progress toward the set goal<sup>##REF##34391432##15##</sup>. Hence, research evidence on identifying the potential inequality in severe wasting will provide evidence to design targeted interventions. However, as per our knowledge, a comprehensive assessment of the trend in socioeconomic and area-based inequality in severe wasting has not yet been conducted in Ethiopia using the World Health Organization (WHO) recommendation. The WHO recommends inequality be measured using absolute and relative measures using both complex and simple summary measures for the selected health indicator to compare the disparities across the inequality dimensions<sup>##REF##27760520##16##</sup>. Hence, employing the WHO-recommended inequality measure would give impactful evidence. Therefore, this study aimed at assessing the trends in socioeconomic and geographic inequalities in severe wasting in Ethiopia for the last two decades using the latest version of the WHO Health Equity Assessment toolkit.</p>" ]	[ "<title>Methods</title>", "<title>Study settings</title>", "<p id=\"Par10\">With over 120 million population, Ethiopia is the second most highly populated located in East Africa<sup>##UREF##8##17##,##UREF##9##18##</sup>. Administratively, Ethiopia is divided into nine regions and two administrative towns. Namely, Tigray, Amhara, Oromia, Southern Nation Nationalities and Peoples Region (SNNPR), Afar, Somalia, Gambela, Benishangul, Harari, Dire Dawa administrative town and Addis Ababa administrative town.</p>", "<p id=\"Par11\">Since the introduction of the second health sector development plan (HSDP II) in 2003, the country has implemented the three-tier health system. Primary health care contains the health posts, health centres and primary hospitals that deliver basic health services. The secondary level contains general hospitals that serve as a centre of referral for primary health care and tertiary healthcare serves the complex and sophisticated health care<sup>##UREF##10##19##</sup>. Ethiopia has a successful history of reducing maternal and child mortality through its innovative health extension program<sup>##UREF##11##20##</sup>. However, unacceptable socioeconomic and area-based inequality in basic maternal, and child health and nutrition services exist in Ethiopia<sup>##REF##28532407##21##,##UREF##12##22##</sup>.</p>", "<title>Data source</title>", "<p id=\"Par12\">This study used the secondary data obtained from the Ethiopian demographic and health survey (EDHS) data as part of the WHO HEAT software 2021 version from 2000 to 2019<sup>##UREF##13##23##</sup>. The HEAT software was developed to assess inequality in reproductive, maternal, neonatal and child health (RMNCH) services. Hence, 37 health indicators were part of the software with six inequality dimensions such as age, sex, economic status, education, place of residence and subnational region for more than 450 international household surveys conducted in 115 countries between 1991 and 2018. Hence, the software enables us to compare the health indicator’s inequality throughout time and across different countries. The software uses multiple nationally representative data such as Demographic and Health Surveys (DHS), Multiple Indicator Cluster Surveys (MICS) and Reproductive Health Surveys (RHS)<sup>##REF##27760520##16##</sup>.</p>", "<p id=\"Par13\">The EDHS is a regular cross-sectional survey conducted at the community level to represent the overall health status of the country’s population. 2000, 2005, 2011 and 2016 were the major DHS datasets whereas 2019 is the mini-DHS data, that is targeted only at maternal and child health indicators. Two-stage stratification was employed to select households at the country level. Firstly, two enumeration areas (EAs) were created with a proportional allocation depending on the Probability Proportion to Size (PPS). Next, newly created households were selected from the selected EAs systematically with an equal probability after a household listing operation was carried out in all selected enumeration areas. A brief description of each EDHS was presented elsewhere<sup>##UREF##14##24##–##UREF##17##27##</sup>.</p>", "<title>Study variable</title>", "<p id=\"Par14\">All children aged less than five in the selected household during the data collection period were included in the study. The outcome variable was severe wasting prevalence in children aged < 5 years which was defined as more than three standard deviations below the median weight-for-height of the WHO Child Growth Standards (yes/no).</p>", "<title>Measure of inequality</title>", "<p id=\"Par15\">The socioeconomic and geographic inequality in severe wasting was measured through different inequality disaggregation and summary measures. We have seen the inequality in severe wasting throughout households’ economic status, place of residence, mother’s educational status, and subnational regions. The economic status was classified in wealth quantile from poorest (quantile 1) to richest (quantile 2) subgroups. The place of residence was classified as urban and rural. The mother’s educational status is no education, primary education, and secondary and above education. The subnational regions were classified into nine regions and two city administrations as listed above. We presented the trend on socioeconomic and geographic inequality in severe wasting using tables and figures and the 95% uncertainty intervals (UIs) were calculated for each subgroup and survey years.</p>", "<p id=\"Par16\">The trend of inequality in severe wasting was analysed using the latest version of WHO’s HEAT software. Based on the WHO recommendation, we have used both relative and absolute measures of inequality that are simple and complex<sup>##REF##21166871##28##,##UREF##18##29##</sup>. Among the different summary measures incorporated in the software, we have used difference (D), ratio (R), slop index inequality (SII), relative concentration index (RCI) and population attributable risk (PAR) considering the nature of the outcome variable (favourable vs adverse, ordering vs non-ordering), the nature of the data and their more comprehensive application to the inequality assessment<sup>##REF##32520940##30##–##REF##31898494##32##</sup>.</p>", "<p id=\"Par17\">Difference and ratio are simple measures of inequality that do not consider the overall population size to calculate the inequality in severe wasting. Whereas the other three are complex measures of inequality that consider the average population size in calculating the proportion of severe wasting in each disaggregation group. On the other hand, D, SII and PAR are absolute measures of inequality that assess the absolute differences in severe wasting among the subgroups whereas R and RCI are relative measures of inequality that capture proportional differences between subgroups. Hence for ordered dimensions such as economic status and education status, we have calculated all the summary measures whereas, for non-ordered dimensions such as residence and subnational region, we have calculated the inequality using the simple measures of inequality such as D and R together with PAR.</p>", "<p id=\"Par18\">In general, the positive value of the summary measure was indicative of the disproportionately high prevalence of severe wasting among the disadvantageous group such as women who have no education, are poor or live in rural areas whereas the negative values indicate the high prevalence of severe wasting among the advantageous group.</p>", "<p id=\"Par19\">The detailed description of each summary measure was depicted in the technical note of the HEAT software<sup>##UREF##19##33##</sup>. But to briefly introduce each of the summary measures we have used in this study, D is the simple and absolute measure of inequality calculated as the mean percentage of severe wasting in the one group subtracted from the mean percentage of severe wasting in the other subgroup. Whereas R is the simple and relative measure of inequality calculated as the mean percentage of severe wasting divided by the mean percentage of severe wasting in the other subgroup. The simple measures of inequality were criticized for their ignorance of the middle subgroups and for not considering population size<sup>##UREF##18##29##,##UREF##20##34##</sup>.</p>", "<p id=\"Par20\">On the other hand, SII is the complex and absolute measure of inequality that applies to natural ordering subgroups with more than two subgroups like education and wealth. It calculates the difference in estimated values of severe wasting by ranking and subtracting from the most disadvantaged to the most advantaged subgroups using an appropriate regression model taking into consideration all the other subgroups. The positive value shows that severe wasting is more prevalent in disadvantageous subgroups. Besides, RCI is the complex and relative measure of inequality calculated for ordered dimensions that shows the extent to which severe wasting is concentrated among disadvantaged subgroups by considering all population subgroups. Hence positive values indicate a concentration of severe wasting among the advantaged, while negative values indicate a concentration of severe wasting among the disadvantaged group<sup>##UREF##21##35##</sup>.</p>", "<p id=\"Par21\">Inequality in severe wasting was further summarized with a PAR, which helps to give evidence on the contribution of a group of the population for the overall level change in severe wasting. Hence, PAR would help to know how much the national level severe wasting would be eliminated as the disparity in severe wasting in a certain group is changed maintaining the change in severe wasting the same as the reference population<sup>##UREF##21##35##</sup>.</p>", "<title>Statistical analysis</title>", "<p id=\"Par22\">The trends of socioeconomic and geographic inequality were disaggregated across the wealth, residence, mother’s educational status and subnational regions for the last two decades in Ethiopia based on the EDHS conducted from 2000 to 2019. A 95% uncertainty interval (UI) was calculated along with the point estimate in each survey year and represented in the table through error bars. Based on the software, inequality of severe wasting is significant if the absolute measure of inequalities (Difference, slope index inequality and population attributable risk) does not include 0 in their 95% uncertainty interval and if the relative measure of inequalities does not contain 1 in their 95% uncertainty interval. Additionally, when examining the trend of inequality between survey years, if the upper limit of the 95% uncertainty interval of one year does not overlap with the lower limit of the 95% uncertainty interval of the subsequent year, it is considered a significant change in the trend of inequality over the years<sup>##REF##32520940##30##–##REF##31898494##32##</sup>. Furthermore, to keep the scientific presentation of the study, we used the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guideline<sup>##UREF##22##36##</sup>.</p>", "<title>Ethical considerations</title>", "<p id=\"Par23\">As the data is publicly available as part of the WHO HEAT software, we have been exempted from providing an ethical clearance. All the ethical requirements were secured by the institution that conducted the survey. Hence, the Institutional Review Board of Ethiopia and the Inner-City Fund International approved the EDHS.</p>" ]	[ "<title>Results</title>", "<title>Trends of severe wasting throughout equity dimensions</title>", "<p id=\"Par24\">The proportion of severe wasting across the households’ economic status fluctuated between 2000 and 2019. In all subgroups the worst year was 2005 documenting the highest proportion of severe wasting with 5.8%, 6.0%, 5.2%, 2.8% and 3.0% among the poorest, poorer, middle, richer and richest subgroups respectively. Although a significant decline in the proportion of severe wasting was documented in all economic subgroups between the years 2000 to 2019, a decline in the poorest households was not significant (Fig. ##FIG##0##1##).</p>", "<p id=\"Par25\">Besides the percentage of severe wasting across mothers’ educational status fluctuated in all survey years taking the peak in 2005 among mothers with no education (5.1%) and mothers with primary education (3.6%) and in 2016 among secondary and above-educated mothers (1.8%). Between the 2005–2019 survey years, the proportion of severe wasting declined significantly among uneducated and primarily educated mothers from 5.1 to 1.7% and 3.6% to 0.6% but not among mothers with secondary and above education.</p>", "<p id=\"Par26\">Similarly, severe wasting significantly dropped from 4.0 to 1.1% among under-five children residing in rural settings but insignificantly declined from 2.0 to 1.0% among urban children between 2000 and 2019. Consistent with the wealth index, the highest proportion of severe wasting was documented in 2005 with 4.8% and 3.2% among rural and urban children, respectively.</p>", "<p id=\"Par27\">In Tigray and Somalia regions, the proportion of severe wasting peaked in 2000 at 3.5% and 10.2% subsequently decreasing significantly to 0.7%, and 5.8% in 2019, respectively. In Afar, severe wasting decreased insignificantly from 4.6% in 2000 to 1.9% in 2019 taking the peak in 2011 with 6.8%. In Amhara, Oromia, Benishangul and Dire Dawa subnational regions, the proportion of severe wasting peaked in 2005 at 6.0%, 4.5%, 8.7% and 7.8% respectively but the change through the survey years was significant in Oromia and Benishangul regions but insignificant in Amhara and Dire Dawa regions. But compared to the 2000 survey year, the proportion dropped in all the regions in 2019 to 1.6%, 0.3%, 0.8% and 0.7%, respectively. On the other hand, in SNNPR, Gambela, Harari and Addis Ababa, the proportion of severe wasting steadily declined from 2000 to 2019 from 4.8 to 0.8%, from 7.9 to 2.7%, 2.6% to 1.1% and from 2.6 to 0%, respectively. Among all the regions, the highest (10.2%) proportion of severe wasting was recorded in Somalia region in 2000 and the lowest (0%) proportion was in Addis Ababa in 2005 and 2019 (Fig. ##FIG##1##2##) (Table ##TAB##0##1##).</p>", "<title>Inequality in severe wasting by different inequality summary measures</title>", "<p id=\"Par28\">As indicated by RCI, significant fluctuation in wealth-related inequality was observed in all five survey years but a significant change in wealth-related inequality was observed in 2005 and 2019. Whereas the education-related inequality in RCI of severe wasting steadily increased from −8.8% in 2005 to −24.3% in 2019. And the change was significantly widened from 2011 to 2019. On the other hand, residence-related inequality of severe wasting was observed in 2000 in R, D and PAR summary measures but disappeared in 2019. Between 2000 and 2016, regional inequalities in severe wasting fluctuated between 8.7% in 2005 to 5.9% in 2016 taking D as a measure of inequality (Table ##TAB##1##2##).</p>" ]	[ "<title>Discussion</title>", "<p id=\"Par29\">This study assessed the trends in socioeconomic and geographic inequality in severe wasting in Ethiopia over the last two decades. Overall, the proportion of severe wasting fluctuated over time peaking at 4.7% in 2005 and dropping to 1.1% in 2019. However, a high proportion of severe wasting was concentrated among the poorest subgroups and children whose mothers had not attended formal education. Besides, the regional difference in severe wasting was also observed from 2000 to 2016. But successfully, the residential inequality in severe wasting disappeared in Ethiopia after the 2000 survey year.</p>", "<p id=\"Par30\">Our finding showed that over the last two decades in Ethiopia, the proportion of severe wasting decreased by more than a third from 3.8% in 2000 to 1.1% in 2019. This might be taken as a good track record to achieve the 2025 and 2030 targets to reduce the prevalence of wasting to less than 5% and 3%, respectively<sup>##UREF##3##5##</sup>. The rate of decline in severe wasting is also higher than in a study conducted in Guinea where the proportion of childhood wasting decreased by less than half from 10.1% in 1999 to 8.1% in 2016<sup>##UREF##23##37##</sup>. This might be due to the extensive application of maternal and child health services in primary health care units through the health extension program in the health systems of Ethiopia. In Ethiopia, the health extension workers are primarily responsible for monitoring the nutritional, child, and maternal health services provision for households in the frontline. The approach was also the success story of the Ethiopian Millennium Development goal<sup>##REF##29025635##38##</sup>. But the study conducted in Bale zone, Ethiopia showed that the prevalence of severe wasting increased between the years 2014 and 2017 from 3.6 to 4.7%, respectively<sup>##UREF##24##39##</sup>. It might be due to the difference in study settings and time horizon as the latter is a single site and short period study. but consistent with the study in Bale zone<sup>##UREF##24##39##</sup>, the highest peak of severe wasting was in 2005 in this study.</p>", "<p id=\"Par31\">Moreover, for the last two decades, wealth-related inequality has been persistent in Ethiopia in favour of the wealthiest households. In 2000, 2005, 2011, 2016 and 2019, the prevalence of severe wasting was concentrated among the poorest subgroups with RCI of −3.0%, −14.8%, −15.6%, −12.7%, and −32.3%, respectively. Besides, the inequality in severe wasting among the poorest and richest subgroups significantly widened in the 2005 and 2019 survey years. In 2005, the prevalence of severe wasting among the richest subgroups was 3% but it was 5.8% among the poorest subgroups the disparity declined in 2019 as the prevalence of severe wasting among the richest subgroups was 1.7% and among poorest subgroups was 2.4%. This can be supported by the study conducted in Ghana where the prevalence of malnutrition was 46.5% in the lowest wealth quintile, but only 8.4% in the highest quintile<sup>##REF##26603158##40##</sup>. The finding indicates the malnutritional problem is still the issue of the poorest<sup>##REF##18045499##41##,##REF##32984245##42##</sup>.</p>", "<p id=\"Par32\">Moreover, education-related inequality was observed in the 2005, 2011, 2016 and 2019 survey years with RCI of −8.8%, −1.7%, −12.5% and −24.3%, respectively. Besides, the education-related inequality in the prevalence of severe wasting has significantly increased since 2011 favouring children whose mothers were highly educated. In 2019, the prevalence of severe wasting was 1.7% among children whose mothers have not attended formal education and 0.4% among children whose mothers have attended secondary and above education. This is in line with the study conducted in Ghana, where mothers with no education account for more than 60% of malnourished<sup>##REF##26603158##40##</sup>.</p>", "<p id=\"Par33\">Furthermore, regional inequality in severe wasting was observed in 2000, 2005, 2011 and 2016 survey years with a difference of 7.6, 8.7, 7.1 and 5.9, respectively. The highest proportion of severe wasting was consecutively observed in Somalia region where 10.2%, 8.5%, 6.3%, and 5.8% of under-five children were severely wasted in 200, 2011, 2016 and 2019 survey years. on the other hand, the lowest proportion of severe wasting was documented consecutively among children living in Addis Ababa, where 2.6%, 0%, 1.4%, 0.4% and 0% of under-five children were severely wasted in 2000, 2005, 2011, 2016 and 2019 survey years. The spatial analysis conducted in Ethiopia also underlined that under-five children living in Somalia region were severely wasted compared to other parts of the country<sup>##REF##34900614##43##</sup>. But throughout time no significant change in inequality of severe wasting was observed among the regions. But this cannot be taken as good progress because the inequality should be narrow throughout time to disappear later. Surprisingly, the residence-related inequality in severe wasting was only observed in Ethiopia in the 2000 survey year. However, after 2000, the disparity in severe wasting among urban–rural residents disappeared in Ethiopia.</p>", "<p id=\"Par34\">Even though the study has employed the national and longitudinal data that represents the high level of sample populations and also observed the trends of inequality based on the WHO recommendation, it has the following limitations. Firstly, the findings of severe wasting are not representative of the current status of the children as the data is generated from the retrospective survey. Secondly, the retrospective nature of the survey might raise issues related to a recall bias. Thirdly, the factors leading to the disparity in severe wasting were not analysed in this study as the study was entirely conducted with the WHO HEAT, which does not allow to conduct a regression analysis. On top of that, the variation in sampling design in the mini-DHS of 2019 and other major DHSs is also one of the limitations of this study. Finally, for some summary measures and inequality dimensions, the toolkit lacks data. Therefore, they filled incomplete and were unable to generate evidence for the specified missed data.</p>" ]	[ "<title>Conclusion</title>", "<p id=\"Par35\">Overall, the proportion of severe wasting fluctuated over time peaking in 2005 and dropping in 2019. In reverse to the expectation, wealth-related inequality is significantly widened throughout time with under-five children from the richest households being less affected by severe wasting. On top of that, under-five children whose mothers did not attend formal education are highly affected by severe wasting the disparity also appeared in the most recent survey and no significant reduction of severe wasting was made among children with different educational status than their mother. On the other hand, the regional disparity in severe wasting is also exhibited in Ethiopia in all-around surveys favouring the metropolitan cities. Hence, unfairly, children from Addis Ababa were least affected whereas children from Somalia were highly affected by severe wasting. But there no significant disparity in the type of residence in severe wasting was revealed in Ethiopia.</p>", "<p id=\"Par36\">Therefore, the stakeholders in the health systems of Ethiopia should work with a focus on reducing wealth-related inequality, education-related inequality, and regional disparities. Hence, special attention should be paid to under-five children living in the poorest households, whose mothers did not attend formal education and children living in Somalia region.</p>" ]	[ "<p id=\"Par1\">Severe wasting is the deadliest form of wasting caused by a lack of nutritious food and repeated attacks of illness. The World Health Assembly has agreed to reduce severe wasting to less than 5% and 3% by the end of 2025 and 2030. Significant disparities were observed worldwide in progress towards the goal. However, limited evidence of disparity in severe wasting was available in Ethiopia. Therefore, this study aimed to assess trends in socioeconomic and geographic inequalities in severe wasting among under-five children in Ethiopia between 2000 and 2019. The trend in socioeconomic and geographic inequality was assessed using the World Health Organization Health Equity Assessment Toolkit, employing both absolute and relative measures of inequality. Difference (D), ratio (R), slope index inequality (SII), relative concentration index (RCI), and population attributable ratio (PAR) were utilized to assess disparity across wealth, education, residence, and subnational regions. The 95% uncertainty interval (UI) was used to declare the significant change in inequality through time. The proportion of severe wasting increased from 3.8% to 4.7% between 2000 to 2005 and dropped to 2.9% in 2011 to remain constant until 2016. However, the proportion of severe wasting significantly declined to 1.1% in 2019. As indicated by RCI, significant fluctuation in wealth-related inequality was observed in all five survey years but a significant change in wealth-related inequality was observed in 2005 and 2019. Whereas the education-related inequality in RCI of severe wasting steadily increased from −8.8% in 2005 to −24.3% in 2019. And the change was significantly widened from 2011 to 2019. On the other hand, residence-related inequality of severe wasting was observed in 2000 in ratio, difference and PAR summary measures but disappeared in 2019. Between 2000 and 2016, regional inequalities in severe wasting fluctuated between 8.7 in 2005 to 5.9 in 2016 taking the difference as a measure of inequality. Overall, Wealth-related inequality has significantly widened over time with under five children from the richest households being less affected by severe wasting. Education-related inequality was not changed with under five children whose mothers had not attended formal education highly affected by severe wasting. Regional disparity in severe wasting is also exhibited in Ethiopia in all-round surveys with children from Addis Ababa being least affected whereas children from Somalia were highly affected by severe wasting. However, no significant disparity in the type of residence in severe wasting was revealed in Ethiopia. Therefore, special attention should be paid to under-five children living in the poorest households, whose mothers did not attend formal education and children living in Somalia region.</p>", "<title>Subject terms</title>" ]	[]	[ "<title>Acknowledgements</title>", "<p>We are very thankful to the WHO for online and free access to the health equity assessment toolkit.</p>", "<title>Author contributions</title>", "<p>T.A.B., A.E., W.D.N. and T.B.B. developed the concept, reviewed the literature and drafted the original document. S.M.F., B.A., and A.A.K. conducted the statistical analysis and interpreted and discussed the results. A.F.Z., T.T.T., S.M.W., E.A.F., and D.B.A. revised the original document and drafted the manuscript. All authors reviewed the drafted manuscript and approved the final manuscript.</p>", "<title>Data availability</title>", "<p>The datasets supporting this article’s conclusions are available online as part of the WHO health monitoring database. The DHS data can be acquired online from the DHS database through a formal request by visiting <ext-link ext-link-type=\"uri\" xlink:href=\"https://dhsprogram.com/\">https://dhsprogram.com/</ext-link> or WHO HEAT software uploaded version at <ext-link ext-link-type=\"uri\" xlink:href=\"https://whoequity.shinyapps.io/heat/\">https://whoequity.shinyapps.io/heat/</ext-link>.</p>", "<title>Competing interests</title>", "<p id=\"Par37\">The authors declare no competing interests.</p>" ]	[ "<fig id=\"Fig1\"><label>Figure 1</label><caption><p>Trends of severe wasting among under-five children with different households' economic status in Ethiopia from 2000 to 2019 (NB: error bar shows the 95% CI).</p></caption></fig>", "<fig id=\"Fig2\"><label>Figure 2</label><caption><p>Trends of severe wasting among under-five children in different regions in Ethiopia from 2000 to 2019 (NB: error bar shows the 95% CI).</p></caption></fig>" ]	[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Severe wasting in under five children in Ethiopia across education, economic status, place of residence and region, 2000–2019.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"3\">Inequality dimensions</th><th align=\"left\" rowspan=\"3\">Category</th><th align=\"left\" colspan=\"15\">Survey years</th></tr><tr><th align=\"left\" colspan=\"3\">2000</th><th align=\"left\" colspan=\"3\">2005</th><th align=\"left\" colspan=\"3\">2011</th><th align=\"left\" colspan=\"3\">2016</th><th align=\"left\" colspan=\"3\">2019</th></tr><tr><th align=\"left\">Estimate</th><th align=\"left\">LB</th><th align=\"left\">UB</th><th align=\"left\">Estimate</th><th align=\"left\">LB</th><th align=\"left\">UB</th><th align=\"left\">Estimate</th><th align=\"left\">LB</th><th align=\"left\">UB</th><th align=\"left\">Estimate</th><th align=\"left\">LB</th><th align=\"left\">UB</th><th align=\"left\">Estimate</th><th align=\"left\">LB</th><th align=\"left\">UB</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"5\">Wealth index</td><td align=\"left\">Poorest</td><td align=\"left\">3.7</td><td align=\"left\">2.9</td><td align=\"left\">4.9</td><td char=\".\" align=\"char\">5.8</td><td char=\".\" align=\"char\">4.3</td><td char=\".\" align=\"char\">7.9</td><td char=\".\" align=\"char\">3.5</td><td char=\".\" align=\"char\">2.5</td><td char=\".\" align=\"char\">4.9</td><td char=\".\" align=\"char\">4.2</td><td char=\".\" align=\"char\">3.1</td><td char=\".\" align=\"char\">5.6</td><td align=\"left\">2.4</td><td align=\"left\">1.6</td><td align=\"left\">3.6</td></tr><tr><td align=\"left\">Poorer</td><td align=\"left\">4.0</td><td align=\"left\">3.1</td><td align=\"left\">5.2</td><td char=\".\" align=\"char\">6.0</td><td char=\".\" align=\"char\">4.0</td><td char=\".\" align=\"char\">8.8</td><td char=\".\" align=\"char\">4.1</td><td char=\".\" align=\"char\">2.9</td><td char=\".\" align=\"char\">5.7</td><td char=\".\" align=\"char\">2.9</td><td char=\".\" align=\"char\">2.1</td><td char=\".\" align=\"char\">4.2</td><td align=\"left\">1.1</td><td align=\"left\">0.6</td><td align=\"left\">2.2</td></tr><tr><td align=\"left\">Middle</td><td align=\"left\">4.1</td><td align=\"left\">3.2</td><td align=\"left\">5.3</td><td char=\".\" align=\"char\">5.2</td><td char=\".\" align=\"char\">3.7</td><td char=\".\" align=\"char\">7.2</td><td char=\".\" align=\"char\">2.6</td><td char=\".\" align=\"char\">1.9</td><td char=\".\" align=\"char\">3.8</td><td char=\".\" align=\"char\">3.1</td><td char=\".\" align=\"char\">1.9</td><td char=\".\" align=\"char\">4.9</td><td align=\"left\">0.5</td><td align=\"left\">0.2</td><td align=\"left\">1.5</td></tr><tr><td align=\"left\">Richer</td><td align=\"left\">4.3</td><td align=\"left\">3.3</td><td align=\"left\">5.7</td><td char=\".\" align=\"char\">2.8</td><td char=\".\" align=\"char\">1.6</td><td char=\".\" align=\"char\">4.8</td><td char=\".\" align=\"char\">2.0</td><td char=\".\" align=\"char\">1.1</td><td char=\".\" align=\"char\">3.6</td><td char=\".\" align=\"char\">2.0</td><td char=\".\" align=\"char\">1.3</td><td char=\".\" align=\"char\">3.2</td><td align=\"left\">0.6</td><td align=\"left\">0.1</td><td align=\"left\">2.7</td></tr><tr><td align=\"left\">Richest</td><td align=\"left\">2.6</td><td align=\"left\">1.9</td><td align=\"left\">3.5</td><td char=\".\" align=\"char\">3.0</td><td char=\".\" align=\"char\">1.6</td><td char=\".\" align=\"char\">5.5</td><td char=\".\" align=\"char\">1.6</td><td char=\".\" align=\"char\">0.9</td><td char=\".\" align=\"char\">2.6</td><td char=\".\" align=\"char\">2.4</td><td char=\".\" align=\"char\">1.5</td><td char=\".\" align=\"char\">3.8</td><td align=\"left\">0.6</td><td align=\"left\">0.1</td><td align=\"left\">2.5</td></tr><tr><td align=\"left\" rowspan=\"3\">Mother’s education</td><td align=\"left\">No education</td><td align=\"left\">NA</td><td align=\"left\">NA</td><td align=\"left\">NA</td><td char=\".\" align=\"char\">5.1</td><td char=\".\" align=\"char\">4.1</td><td char=\".\" align=\"char\">6.2</td><td char=\".\" align=\"char\">2.9</td><td char=\".\" align=\"char\">2.3</td><td char=\".\" align=\"char\">3.7</td><td char=\".\" align=\"char\">3.6</td><td char=\".\" align=\"char\">2.9</td><td char=\".\" align=\"char\">4.5</td><td align=\"left\">1.7</td><td align=\"left\">1.2</td><td align=\"left\">2.4</td></tr><tr><td align=\"left\">Primary</td><td align=\"left\">2.5</td><td align=\"left\">1.7</td><td align=\"left\">3.7</td><td char=\".\" align=\"char\">3.6</td><td char=\".\" align=\"char\">2.1</td><td char=\".\" align=\"char\">6.0</td><td char=\".\" align=\"char\">3.0</td><td char=\".\" align=\"char\">2.2</td><td char=\".\" align=\"char\">4.1</td><td char=\".\" align=\"char\">2.0</td><td char=\".\" align=\"char\">1.3</td><td char=\".\" align=\"char\">2.9</td><td align=\"left\">0.6</td><td align=\"left\">0.3</td><td align=\"left\">1.5</td></tr><tr><td align=\"left\">Secondary and above</td><td align=\"left\">1.2</td><td align=\"left\">0.5</td><td align=\"left\">3.3</td><td char=\".\" align=\"char\">0.1</td><td char=\".\" align=\"char\">0.0</td><td char=\".\" align=\"char\">0.3</td><td char=\".\" align=\"char\">1.2</td><td char=\".\" align=\"char\">0.3</td><td char=\".\" align=\"char\">4.1</td><td char=\".\" align=\"char\">1.8</td><td char=\".\" align=\"char\">0.7</td><td char=\".\" align=\"char\">1.8</td><td align=\"left\">0.4</td><td align=\"left\">0.1</td><td align=\"left\">1.7</td></tr><tr><td align=\"left\" rowspan=\"2\">Type of residence</td><td align=\"left\">Rural</td><td align=\"left\">4.0</td><td align=\"left\">3.5</td><td align=\"left\">4.6</td><td char=\".\" align=\"char\">4.8</td><td char=\".\" align=\"char\">3.9</td><td char=\".\" align=\"char\">5.8</td><td char=\".\" align=\"char\">3.0</td><td char=\".\" align=\"char\">2.4</td><td char=\".\" align=\"char\">3.7</td><td char=\".\" align=\"char\">3.1</td><td char=\".\" align=\"char\">2.6</td><td char=\".\" align=\"char\">3.8</td><td align=\"left\">1.1</td><td align=\"left\">0.8</td><td align=\"left\">1.6</td></tr><tr><td align=\"left\">Urban</td><td align=\"left\">2.0</td><td align=\"left\">1.3</td><td align=\"left\">3.1</td><td char=\".\" align=\"char\">3.2</td><td char=\".\" align=\"char\">1.4</td><td char=\".\" align=\"char\">7.1</td><td char=\".\" align=\"char\">2.1</td><td char=\".\" align=\"char\">1.2</td><td char=\".\" align=\"char\">3.6</td><td char=\".\" align=\"char\">2.1</td><td char=\".\" align=\"char\">1.1</td><td char=\".\" align=\"char\">3.7</td><td align=\"left\">1.0</td><td align=\"left\">0.4</td><td align=\"left\">2.6</td></tr><tr><td align=\"left\" rowspan=\"11\">Subnational regions</td><td align=\"left\">Tigray</td><td align=\"left\">3.5</td><td align=\"left\">2.7</td><td align=\"left\">4.6</td><td char=\".\" align=\"char\">3.1</td><td char=\".\" align=\"char\">1.9</td><td char=\".\" align=\"char\">5.1</td><td char=\".\" align=\"char\">2.9</td><td char=\".\" align=\"char\">2.1</td><td char=\".\" align=\"char\">3.9</td><td char=\".\" align=\"char\">3.4</td><td char=\".\" align=\"char\">2.2</td><td char=\".\" align=\"char\">5.1</td><td align=\"left\">0.7</td><td align=\"left\">0.2</td><td align=\"left\">2.9</td></tr><tr><td align=\"left\">Afar</td><td align=\"left\">4.6</td><td align=\"left\">3.0</td><td align=\"left\">7.0</td><td char=\".\" align=\"char\">6.7</td><td char=\".\" align=\"char\">3.8</td><td char=\".\" align=\"char\">11.6</td><td char=\".\" align=\"char\">6.8</td><td char=\".\" align=\"char\">5.5</td><td char=\".\" align=\"char\">8.3</td><td char=\".\" align=\"char\">5.4</td><td char=\".\" align=\"char\">4.1</td><td char=\".\" align=\"char\">7.1</td><td align=\"left\">3.1</td><td align=\"left\">1.9</td><td align=\"left\">5.0</td></tr><tr><td align=\"left\">Amhara</td><td align=\"left\">3.5</td><td align=\"left\">2.7</td><td align=\"left\">4.7</td><td char=\".\" align=\"char\">6.0</td><td char=\".\" align=\"char\">4.3</td><td char=\".\" align=\"char\">8.4</td><td char=\".\" align=\"char\">3.5</td><td char=\".\" align=\"char\">2.4</td><td char=\".\" align=\"char\">5.1</td><td char=\".\" align=\"char\">2.2</td><td char=\".\" align=\"char\">1.4</td><td char=\".\" align=\"char\">3.5</td><td align=\"left\">1.6</td><td align=\"left\">0.7</td><td align=\"left\">3.5</td></tr><tr><td align=\"left\">Oromia</td><td align=\"left\">3.3</td><td align=\"left\">2.6</td><td align=\"left\">4.3</td><td char=\".\" align=\"char\">4.5</td><td char=\".\" align=\"char\">3.2</td><td char=\".\" align=\"char\">6.4</td><td char=\".\" align=\"char\">2.7</td><td char=\".\" align=\"char\">1.8</td><td char=\".\" align=\"char\">4.1</td><td char=\".\" align=\"char\">3.7</td><td char=\".\" align=\"char\">2.8</td><td char=\".\" align=\"char\">4.8</td><td align=\"left\">0.3</td><td align=\"left\">0.1</td><td align=\"left\">1.1</td></tr><tr><td align=\"left\">Somalia</td><td align=\"left\">10.2</td><td align=\"left\">7.6</td><td align=\"left\">13.6</td><td char=\".\" align=\"char\">8.5</td><td char=\".\" align=\"char\">4.6</td><td char=\".\" align=\"char\">15.2</td><td char=\".\" align=\"char\">8.5</td><td char=\".\" align=\"char\">6.0</td><td char=\".\" align=\"char\">11.8</td><td char=\".\" align=\"char\">6.3</td><td char=\".\" align=\"char\">4.0</td><td char=\".\" align=\"char\">9.7</td><td align=\"left\">5.8</td><td align=\"left\">3.9</td><td align=\"left\">8.5</td></tr><tr><td align=\"left\">Benishangul</td><td align=\"left\">6.2</td><td align=\"left\">3.9</td><td align=\"left\">9.8</td><td char=\".\" align=\"char\">8.7</td><td char=\".\" align=\"char\">4.6</td><td char=\".\" align=\"char\">15.9</td><td char=\".\" align=\"char\">2.9</td><td char=\".\" align=\"char\">2.0</td><td char=\".\" align=\"char\">4.3</td><td char=\".\" align=\"char\">3.1</td><td char=\".\" align=\"char\">1.4</td><td char=\".\" align=\"char\">6.7</td><td align=\"left\">0.8</td><td align=\"left\">0.3</td><td align=\"left\">1.8</td></tr><tr><td align=\"left\">SNNPR</td><td align=\"left\">4.8</td><td align=\"left\">3.7</td><td align=\"left\">6.2</td><td char=\".\" align=\"char\">3.5</td><td char=\".\" align=\"char\">2.1</td><td char=\".\" align=\"char\">5.7</td><td char=\".\" align=\"char\">1.8</td><td char=\".\" align=\"char\">1.1</td><td char=\".\" align=\"char\">2.8</td><td char=\".\" align=\"char\">1.7</td><td char=\".\" align=\"char\">1.1</td><td char=\".\" align=\"char\">2.7</td><td align=\"left\">0.8</td><td align=\"left\">0.3</td><td align=\"left\">2.3</td></tr><tr><td align=\"left\">Gambela</td><td align=\"left\">7.9</td><td align=\"left\">5.7</td><td align=\"left\">11.0</td><td char=\".\" align=\"char\">5.0</td><td char=\".\" align=\"char\">2.7</td><td char=\".\" align=\"char\">9.2</td><td char=\".\" align=\"char\">3.3</td><td char=\".\" align=\"char\">1.5</td><td char=\".\" align=\"char\">6.9</td><td char=\".\" align=\"char\">3.5</td><td char=\".\" align=\"char\">2.2</td><td char=\".\" align=\"char\">5.6</td><td align=\"left\">2.7</td><td align=\"left\">1.0</td><td align=\"left\">7.4</td></tr><tr><td align=\"left\">Harari</td><td align=\"left\">2.6</td><td align=\"left\">0.9</td><td align=\"left\">7.4</td><td char=\".\" align=\"char\">1.4</td><td char=\".\" align=\"char\">0.5</td><td char=\".\" align=\"char\">4.2</td><td char=\".\" align=\"char\">1.5</td><td char=\".\" align=\"char\">0.8</td><td char=\".\" align=\"char\">2.8</td><td char=\".\" align=\"char\">3.0</td><td char=\".\" align=\"char\">1.7</td><td char=\".\" align=\"char\">5.2</td><td align=\"left\">1.1</td><td align=\"left\">0.4</td><td align=\"left\">3.5</td></tr><tr><td align=\"left\">Addis Ababa</td><td align=\"left\">2.6</td><td align=\"left\">1.4</td><td align=\"left\">4.7</td><td char=\".\" align=\"char\">0.0</td><td char=\".\" align=\"char\">0.0</td><td char=\".\" align=\"char\">0.0</td><td char=\".\" align=\"char\">1.4</td><td char=\".\" align=\"char\">0.6</td><td char=\".\" align=\"char\">3.5</td><td char=\".\" align=\"char\">0.4</td><td char=\".\" align=\"char\">0.1</td><td char=\".\" align=\"char\">1.6</td><td align=\"left\">NA</td><td align=\"left\">NA</td><td align=\"left\">NA</td></tr><tr><td align=\"left\">Dire Dawa</td><td align=\"left\">3.0</td><td align=\"left\">1.8</td><td align=\"left\">4.8</td><td char=\".\" align=\"char\">7.8</td><td char=\".\" align=\"char\">4.3</td><td char=\".\" align=\"char\">13.6</td><td char=\".\" align=\"char\">2.0</td><td char=\".\" align=\"char\">1.1</td><td char=\".\" align=\"char\">3.4</td><td char=\".\" align=\"char\">4.4</td><td char=\".\" align=\"char\">2.8</td><td char=\".\" align=\"char\">6.8</td><td align=\"left\">0.7</td><td align=\"left\">0.2</td><td align=\"left\">2.8</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Inequality in severe wasting by inequality summary measures across the various dimensions of inequality, 2000–2019.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"3\">Inequality dimensions</th><th align=\"left\" rowspan=\"3\">Category</th><th align=\"left\" colspan=\"15\">Survey years</th></tr><tr><th align=\"left\" colspan=\"3\">2000</th><th align=\"left\" colspan=\"3\">2005</th><th align=\"left\" colspan=\"3\">2011</th><th align=\"left\" colspan=\"3\">2016</th><th align=\"left\" colspan=\"3\">2019</th></tr><tr><th align=\"left\">Estimate</th><th align=\"left\">LB</th><th align=\"left\">UB</th><th align=\"left\">Estimate</th><th align=\"left\">LB</th><th align=\"left\">UB</th><th align=\"left\">Estimate</th><th align=\"left\">LB</th><th align=\"left\">UB</th><th align=\"left\">Estimate</th><th align=\"left\">LB</th><th align=\"left\">UB</th><th align=\"left\">Estimate</th><th align=\"left\">LB</th><th align=\"left\">UB</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"5\">Wealth index</td><td align=\"left\">D</td><td align=\"left\">1.2</td><td align=\"left\">−0.1</td><td align=\"left\">2.4</td><td align=\"left\"><bold>2.8</bold></td><td align=\"left\"><bold>0.3</bold></td><td align=\"left\"><bold>5.4</bold></td><td char=\".\" align=\"char\"><bold>1.9</bold></td><td char=\".\" align=\"char\"><bold>0.5</bold></td><td char=\".\" align=\"char\"><bold>3.4</bold></td><td char=\".\" align=\"char\"><bold>1.9</bold></td><td char=\".\" align=\"char\"><bold>0.2</bold></td><td char=\".\" align=\"char\"><bold>3.5</bold></td><td align=\"left\"><bold>1.8</bold></td><td align=\"left\"><bold>0.5</bold></td><td align=\"left\"><bold>3.1</bold></td></tr><tr><td align=\"left\">R</td><td align=\"left\">1.5</td><td align=\"left\">1.0</td><td align=\"left\">2.2</td><td align=\"left\">1.9</td><td align=\"left\">1.0</td><td align=\"left\">3.9</td><td char=\".\" align=\"char\">2.2</td><td char=\".\" align=\"char\">1.2</td><td char=\".\" align=\"char\">4.2</td><td char=\".\" align=\"char\">1.8</td><td char=\".\" align=\"char\">1.0</td><td char=\".\" align=\"char\">3.1</td><td align=\"left\">4.0</td><td align=\"left\">0.9</td><td align=\"left\">17.4</td></tr><tr><td align=\"left\">SII</td><td align=\"left\">−0.7</td><td align=\"left\">−2.0</td><td align=\"left\">0.6</td><td align=\"left\"><bold>−4.4</bold></td><td align=\"left\"><bold>−6.6</bold></td><td align=\"left\"><bold>−2.2</bold></td><td char=\".\" align=\"char\"><bold>−2.8</bold></td><td char=\".\" align=\"char\"><bold>−4.0</bold></td><td char=\".\" align=\"char\"><bold>−1.7</bold></td><td char=\".\" align=\"char\"><bold>−2.4</bold></td><td char=\".\" align=\"char\"><bold>−3.6</bold></td><td char=\".\" align=\"char\"><bold>−1.2</bold></td><td align=\"left\"><bold>−2.5</bold></td><td align=\"left\"><bold>−3.8</bold></td><td align=\"left\"><bold>−1.2</bold></td></tr><tr><td align=\"left\">RCI</td><td align=\"left\"><bold>−3.0</bold></td><td align=\"left\"><bold>−3.3</bold></td><td align=\"left\"><bold>−2.6</bold></td><td align=\"left\"><bold>−14.8</bold></td><td align=\"left\"><bold>−17.5</bold></td><td align=\"left\"><bold>−12.1</bold></td><td char=\".\" align=\"char\"><bold>−15.6</bold></td><td char=\".\" align=\"char\"><bold>−18.4</bold></td><td char=\".\" align=\"char\"><bold>−12.8</bold></td><td char=\".\" align=\"char\"><bold>−12.7</bold></td><td char=\".\" align=\"char\"><bold>−14.9</bold></td><td char=\".\" align=\"char\"><bold>−10.5</bold></td><td align=\"left\"><bold>−32.3</bold></td><td align=\"left\"><bold>−43.1</bold></td><td align=\"left\"><bold>−21.5</bold></td></tr><tr><td align=\"left\">PAR</td><td align=\"left\"><bold>−1.2</bold></td><td align=\"left\"><bold>−2.0</bold></td><td align=\"left\"><bold>−0.5</bold></td><td align=\"left\"><bold>−1.7</bold></td><td align=\"left\"><bold>−2.9</bold></td><td align=\"left\"><bold>−0.4</bold></td><td char=\".\" align=\"char\"><bold>−1.3</bold></td><td char=\".\" align=\"char\"><bold>−1.9</bold></td><td char=\".\" align=\"char\"><bold>−0.7</bold></td><td char=\".\" align=\"char\">−0.7</td><td char=\".\" align=\"char\">−1.4</td><td char=\".\" align=\"char\">0.1</td><td align=\"left\">−0.5</td><td align=\"left\">−1.0</td><td align=\"left\">0.0</td></tr><tr><td align=\"left\" rowspan=\"5\">Mother’s education</td><td align=\"left\">D</td><td align=\"left\">NA</td><td align=\"left\">NA</td><td align=\"left\">NA</td><td align=\"left\"><bold>5.0</bold></td><td align=\"left\"><bold>3.9</bold></td><td align=\"left\"><bold>6.0</bold></td><td char=\".\" align=\"char\"><bold>1.8</bold></td><td char=\".\" align=\"char\"><bold>0.1</bold></td><td char=\".\" align=\"char\"><bold>3.4</bold></td><td char=\".\" align=\"char\">1.9</td><td char=\".\" align=\"char\">0.0</td><td char=\".\" align=\"char\">3.8</td><td align=\"left\"><bold>1.3</bold></td><td align=\"left\"><bold>0.5</bold></td><td align=\"left\"><bold>2.1</bold></td></tr><tr><td align=\"left\">R</td><td align=\"left\"><bold>2.0</bold></td><td align=\"left\"><bold>1.3</bold></td><td align=\"left\"><bold>3.2</bold></td><td align=\"left\"><bold>61.8</bold></td><td align=\"left\"><bold>16.8</bold></td><td align=\"left\"><bold>227.3</bold></td><td char=\".\" align=\"char\">2.5</td><td char=\".\" align=\"char\">0.7</td><td char=\".\" align=\"char\">8.9</td><td char=\".\" align=\"char\">2.0</td><td char=\".\" align=\"char\">0.8</td><td char=\".\" align=\"char\">5.6</td><td align=\"left\">4.3</td><td align=\"left\">1.0</td><td align=\"left\">19.4</td></tr><tr><td align=\"left\">SII</td><td align=\"left\">NA</td><td align=\"left\">NA</td><td align=\"left\">NA</td><td align=\"left\"><bold>−6.0</bold></td><td align=\"left\"><bold>−9.9</bold></td><td align=\"left\"><bold>−2.0</bold></td><td char=\".\" align=\"char\">−0.5</td><td char=\".\" align=\"char\">−1.9</td><td char=\".\" align=\"char\">0.9</td><td char=\".\" align=\"char\"><bold>−3.9</bold></td><td char=\".\" align=\"char\"><bold>−5.7</bold></td><td char=\".\" align=\"char\"><bold>−2.1</bold></td><td align=\"left\"><bold>−2.6</bold></td><td align=\"left\"><bold>−4.4</bold></td><td align=\"left\"><bold>−0.9</bold></td></tr><tr><td align=\"left\">RCI</td><td align=\"left\">NA</td><td align=\"left\">NA</td><td align=\"left\">NA</td><td align=\"left\"><bold>−8.8</bold></td><td align=\"left\"><bold>−10.4</bold></td><td align=\"left\"><bold>−7.1</bold></td><td char=\".\" align=\"char\"><bold>−1.7</bold></td><td char=\".\" align=\"char\"><bold>−2.1</bold></td><td char=\".\" align=\"char\"><bold>−1.4</bold></td><td char=\".\" align=\"char\"><bold>−12.5</bold></td><td char=\".\" align=\"char\"><bold>−14.7</bold></td><td char=\".\" align=\"char\"><bold>−10.2</bold></td><td align=\"left\"><bold>−24.3</bold></td><td align=\"left\"><bold>−32.3</bold></td><td align=\"left\"><bold>−16.4*</bold></td></tr><tr><td align=\"left\">PAR</td><td align=\"left\">NA</td><td align=\"left\">NA</td><td align=\"left\">NA</td><td align=\"left\">−4.5</td><td align=\"left\">−9.4</td><td align=\"left\">0.4</td><td char=\".\" align=\"char\"><bold>−1.7</bold></td><td char=\".\" align=\"char\"><bold>−3.0</bold></td><td char=\".\" align=\"char\"><bold>−0.5</bold></td><td char=\".\" align=\"char\"><bold>−1.3</bold></td><td char=\".\" align=\"char\"><bold>−2.4</bold></td><td char=\".\" align=\"char\"><bold>−0.2</bold></td><td align=\"left\"><bold>−0.8</bold></td><td align=\"left\"><bold>−1.5</bold></td><td align=\"left\"><bold>−0.1</bold></td></tr><tr><td align=\"left\" rowspan=\"3\">Type of residence</td><td align=\"left\">D</td><td align=\"left\"><bold>2.1</bold></td><td align=\"left\"><bold>1.0</bold></td><td align=\"left\"><bold>3.1</bold></td><td align=\"left\">1.6</td><td align=\"left\">−1.1</td><td align=\"left\">4.3</td><td char=\".\" align=\"char\">0.9</td><td char=\".\" align=\"char\">−0.4</td><td char=\".\" align=\"char\">2.3</td><td char=\".\" align=\"char\">1.1</td><td char=\".\" align=\"char\">−0.3</td><td char=\".\" align=\"char\">2.4</td><td align=\"left\">0.2</td><td align=\"left\">−0.9</td><td align=\"left\">1.2</td></tr><tr><td align=\"left\">R</td><td align=\"left\"><bold>2.0</bold></td><td align=\"left\"><bold>1.3</bold></td><td align=\"left\"><bold>3.2</bold></td><td align=\"left\">1.5</td><td align=\"left\">0.7</td><td align=\"left\">3.4</td><td char=\".\" align=\"char\">1.5</td><td char=\".\" align=\"char\">0.8</td><td char=\".\" align=\"char\">2.7</td><td char=\".\" align=\"char\">1.5</td><td char=\".\" align=\"char\">0.8</td><td char=\".\" align=\"char\">2.8</td><td align=\"left\">1.2</td><td align=\"left\">0.4</td><td align=\"left\">3.2</td></tr><tr><td align=\"left\">PAR</td><td align=\"left\"><bold>−1.8</bold></td><td align=\"left\"><bold>−2.7</bold></td><td align=\"left\"><bold>−1.0</bold></td><td align=\"left\">−1.5</td><td align=\"left\">−3.3</td><td align=\"left\">0.4</td><td char=\".\" align=\"char\">−0.8</td><td char=\".\" align=\"char\">−1.6</td><td char=\".\" align=\"char\"><bold>−0.1</bold></td><td char=\".\" align=\"char\"><bold>−1.0</bold></td><td char=\".\" align=\"char\"><bold>−1.8</bold></td><td char=\".\" align=\"char\">−0.1</td><td align=\"left\">−0.1</td><td align=\"left\">−0.6</td><td align=\"left\">0.4</td></tr><tr><td align=\"left\" rowspan=\"3\">Subnational regions</td><td align=\"left\">D</td><td align=\"left\"><bold>7.6</bold></td><td align=\"left\"><bold>4.4</bold></td><td align=\"left\"><bold>10.7</bold></td><td align=\"left\"><bold>8.7</bold></td><td align=\"left\"><bold>3.7</bold></td><td align=\"left\"><bold>13.7</bold></td><td char=\".\" align=\"char\"><bold>7.1</bold></td><td char=\".\" align=\"char\"><bold>4.0</bold></td><td char=\".\" align=\"char\"><bold>10.1</bold></td><td char=\".\" align=\"char\"><bold>5.9</bold></td><td char=\".\" align=\"char\"><bold>3.1</bold></td><td char=\".\" align=\"char\"><bold>8.6</bold></td><td align=\"left\">NA</td><td align=\"left\">NA</td><td align=\"left\">NA</td></tr><tr><td align=\"left\">R</td><td align=\"left\"><bold>3.9</bold></td><td align=\"left\"><bold>2.1</bold></td><td align=\"left\"><bold>7.3</bold></td><td align=\"left\">NA</td><td align=\"left\">NA</td><td align=\"left\">NA</td><td char=\".\" align=\"char\"><bold>6.0</bold></td><td char=\".\" align=\"char\"><bold>2.3</bold></td><td char=\".\" align=\"char\"><bold>15.5</bold></td><td char=\".\" align=\"char\"><bold>16.3</bold></td><td char=\".\" align=\"char\"><bold>3.7</bold></td><td char=\".\" align=\"char\"><bold>71.5</bold></td><td align=\"left\">NA</td><td align=\"left\">NA</td><td align=\"left\">NA</td></tr><tr><td align=\"left\">PAR</td><td align=\"left\">−1.2</td><td align=\"left\">−4.0</td><td align=\"left\">1.5</td><td align=\"left\">−4.7</td><td align=\"left\">NaN</td><td align=\"left\">NaN</td><td char=\".\" align=\"char\">−1.5</td><td char=\".\" align=\"char\">−3.5</td><td char=\".\" align=\"char\">0.6</td><td char=\".\" align=\"char\"><bold>−2.6</bold></td><td char=\".\" align=\"char\"><bold>−4.3</bold></td><td char=\".\" align=\"char\"><bold>−1.0</bold></td><td align=\"left\">NA</td><td align=\"left\">NA</td><td align=\"left\">NA</td></tr></tbody></table></table-wrap>" ]	[]	[]	[]	[]	[]	[]	[ "<table-wrap-foot><p>Significant inequality.</p><p>*Significant change in inequality with 95% UI.</p><p><italic>D</italic> difference, <italic>R</italic> ratio, <italic>SII</italic> slope index of inequality, <italic>RCI</italic> relative concentration index, <italic>PAR</italic> population attributable risk, <italic>LB</italic> lower bound, <italic>UB</italic> upper bound.</p><p>Significant values are in bold.</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher's note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"41598_2023_51081_Fig1_HTML\" id=\"MO1\"/>", "<graphic xlink:href=\"41598_2023_51081_Fig2_HTML\" id=\"MO2\"/>" 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PMC10781671	38200067	[ "<title>Introduction</title>", "<p id=\"Par2\">The recent augmentation of artificial intelligence (AI) in oral radiology has allowed a more consistent and efficient approach towards classification, diagnostics and treatment planning<sup>##REF##33198554##1##–##REF##36115299##5##</sup>. The conventional time-consuming and observer dependent tasks are being constantly replaced by AI-based approaches which are able to either equal or surpass human accuracy<sup>##REF##31894144##6##</sup>. The most remarkable progress related to AI in oral radiology has been the introduction of deep learning in the form of convolutional neural networks (CNNs)<sup>##REF##30694159##7##</sup>. These networks mimic human cognition function in terms of learning and problem-solving and have been proven to be time-efficient and precise<sup>##UREF##0##8##</sup>. A task well suited for CNN in oral radiology is its assistance for the prediction of lower third molar eruption. However, the decision-making process on whether the tooth will erupt normally or stay impacted relies on regular clinical and radiological follow-up. An accurate prediction of an impacted tooth’s path might allow a clinician to perform a timely extraction at an early developmental stage, before it acquires a certain impacted position which might increase the risk of mandibular nerve injury and other complications<sup>##REF##22608199##9##</sup>.</p>", "<p id=\"Par3\">Previously, Vranckx et al.<sup>##REF##30734999##10##</sup> identified that third molar follicles with an initial angulation of greater than 27° relative to the second molar are predictive of a compromised eruption. In another study, the authors developed a CNN-based AI tool for assisting with the prediction process by allowing automated angulation measurements<sup>##UREF##1##11##</sup>. However, both studies were limited to the Belgian population and the timeframe between the two radiographs was narrow to allow for a precise prediction. Considering the aforementioned limitations, the rationale for conducting this study was to improve the generalizability of the AI tool and prediction model with a distinct population group having a broader age range. Furthermore, scarcity of evidence exists related to the incidence of third molars that can be effectively cleaned based on either clinical or radiological parameters<sup>##REF##30109309##12##</sup>. This concept, known as ‘hygienic cleansability’, can be radiologically defined as fully erupted lower third molars at the level of second molar’s occlusal plane, having the marginal bone situated beneath the cementoenamel junction (CEJ) on the distal side. This positioning facilitates cleanliness while preventing pathological conditions, such as pericoronitis, periodontitis and caries. Clinically, fully erupted lower third molars that are functional, symptom-free, caries-free, positioned with a healthy periodontium, and not associated with other pathological conditions, are deemed hygienically cleansable and do not require extraction<sup>##REF##30109309##12##</sup>. The documentation of such occurrences could enhance the decision-making process. If a population-based incidence of hygienic cleansability is low, it would imply that routine maintenance and periodic clinical and radiographic monitoring should be made mandatory for accommodating early preventive measures.</p>", "<p id=\"Par4\">The primary aim of the present study was to predict the eruption and uprighting of lower third molar in a Swedish population group with a wide age gap between the acquired radiographs using a CNN-based AI tool, in an attempt to enhance the decision-making process related to its timely extraction. The secondary aim focused on identifying the incidence of fully erupted lower third molars based on radiological features that can be effectively cleaned, in order to assess whether patients can maintain adequate hygienic cleansability or are at an increased risk of developing a pathological condition that warrants extraction. The null hypothesis was that the AI-based model’s performance for assessing lower third molar eruption and uprighting prediction would be similar, and no difference would exist between the incidence of fully erupted third molars with and without hygienic cleansability.</p>" ]	[ "<title>Methods</title>", "<title>Ethical declarations</title>", "<p id=\"Par5\">This radiological retrospective longitudinal study was approved by the Swedish Ethical Review Authority (Dnr 2019-04736). All methods were performed in accordance with the relevant guidelines and regulations<bold>.</bold> Informed consent was waived by the Swedish Ethical Review Authority.</p>", "<title>Data collection</title>", "<p id=\"Par6\">Panoramic radiographs of 10,921 patients having at least 2 radiographs were initially screened from the Electronic Healthcare system (T4 Practice Management Software; Carestream Dental; Altanta; GA; USA) of a Public Dental Service (Folktandvården Stockholm, Sweden). Radiographs and data including age and gender were extracted through Planmeca Romexis (Romexis 3.2.0; Planmeca; Helsinki; Finland). Data anonymization was achieved by removing the personal details of each patient and replacing them with a unique code number.</p>", "<p id=\"Par7\">Patients who underwent two panoramic radiographs with good contrast, high image definition and without any distortion, artefacts or positioning errors that could negatively affect the measurements, and with a time difference of at least one year between both acquisitions were included, where the first radiograph was acquired at 8–15 years of age (T1) and the second acquisition was between 16 and 23 years (T2). These longitudinal radiographs were taken depending on the patient’s diagnostic or clinical needs, such as restorative treatment of permanent teeth, dental screening, orthodontic alignment and restoration or extraction of deciduous teeth. In addition, inclusion criteria consisted of fully erupted lower dentition with the exception of third molars at T1 time-point and fully erupted lower dentition with either unerupted or fully erupted lower third molars at T2 time-point. Exclusion criteria were patients with supernumerary teeth and odontomas, previous history of maxillofacial trauma or reconstructive surgery, orthodontic extraction therapy and presence of craniofacial anomalies such as cleft lip and/or palate, hemifacial microsomia, craniosynostosis and other syndromic diseases.</p>", "<p id=\"Par8\">The selection of radiographs based on the inclusion criteria was performed using consecutive non-probability sampling technique by a single dentist having an experience of over 6 years, followed by reconfirmation by an oral radiologist. If a consensus could not be reached, a senior consultant oral and maxillofacial radiologist was consulted. All image data were anonymized prior to analysis An a priori power analysis was conducted using G*power 3.1, to determine the minimum sample size required for the study. The analysis was based on a mean angular difference of 2.1 ± 13.8°, with 80% power at a significance level of 5%, in accordance with a previous study<sup>##REF##30734999##10##</sup>. The minimum sample size was calculated to be 1072 lower third molars (536 patients).</p>", "<title>Recorded variables</title>", "<p id=\"Par9\">The recorded parameters for predicting third molar eruption and its uprighting included, third molar’s developmental stage, angulation difference between second and third molars, third molar eruption level and available retromolar space.</p>", "<p id=\"Par10\">Firstly, the level of third molar development was categorized as either having incompletely or fully formed roots. Thereafter, the panoramic radiographs at both T1 and T2 time-points were imported to a previously developed and validated AI tool for performing automated molars segmentation and angulation measurements. The angulation of third molars with fully formed roots were automatically measured by dividing the crown into two equal halves and then taking the midpoint of the widest diameter of the crown. The inclination line was then drawn perpendicularly (90°) against this line. In cases with incompletely developed roots the angulations were measured automatically by drawing a line at the region of largest diameter of the crown and the angulation line was made from the most apical part of the pulp chamber or the most coronal part of the bifurcation area. Angulations of the second and third lower molar were assessed relative to the horizontal plane of the radiograph. Finally, the AI tool provided the final angle of the third molar by assessing the angular difference between second and third molar (γ) (Fig. ##FIG##0##1##a–c). If the angle of third molar was equal to or less than 15° at T2 then it was classified as uprighted.</p>", "<p id=\"Par11\">The level of third molar eruption was further classified into four categories as follows; fully erupted with hygienic cleansability: erupted up to the level of occlusal plane of the second molar with the marginal bone situated beneath the CEJ at distal side suggesting hygienic cleansability, fully erupted without hygienic cleansability: erupted up to the level of occlusal plane of the second molar with the marginal bone situated above the CEJ at distal side without the ability to properly maintain oral hygiene, partially erupted: the height of tooth’s contour is above the level of surrounding alveolar bone, non-erupted: bony impaction, the tooth is completely encased in bone (Fig. ##FIG##1##2##a).</p>", "<p id=\"Par12\">Following third molar assessment, the available retromolar space was graded manually by an observer to assess the eruption space in accordance with a modified protocol described by Hattab and Alhaija. The criteria consisted of the following, sufficient space: widest mesiodistal crown width of the third molar fits the available space (measured between the distal side of second molar till anterior border of ramus) and reduced or insufficient space: available space is less than the third molar’s mesiodistal crown width (Fig. ##FIG##1##2##b).</p>", "<title>Statistical analysis</title>", "<p id=\"Par13\">Data were analyzed with S-plus 8.0 for Linux (S-plus 8.0; Tibco software; Palo Alto; CA). Descriptive statistics were applied to assess the incidence of lower third molar’s full eruption with and without hygienic cleansability and the impact of retromolar space on hygienic cleansability. A general linear mixed model with stepwise effect selection and fitting via the logit link was used to make prediction models for both third molar eruption and uprighting between T1 and T2. The decision for using this model was based on its ability to handle binary data. Moreover, a stepwise effect selection was added to observe which combination of the measured parameters made the strongest prediction model based on the Type III p-value. A threshold of 0.1 was used to put variables into the model and a p-value of 0.2 was used to leave variables out. Instead of relying on average combined data, the data from both left and right side from all the patients was used separately and the patient was modelled as a random factor. Moreover, Receiver operating characteristic (ROC) curves were generated for third molar eruption prediction models based on training and validation datasets.</p>" ]	[ "<title>Results</title>", "<p id=\"Par14\">Following the eligibility criteria, 771 patients (391 males, 380 females) with each patient having two panoramic radiographs were selected, accounting for a total of 1542 lower third molars at T1 and T2 time-points. The average time-interval between T1 and T2 was 4.5 ± 2.2 years, where the average age of patients at T1 time-point was 14.1 ± 1.0 years and 18.5 ± 2.0 at T2. Of the total third molar sample, 13.9% (214/1542) showed full eruption at T2, while 1.7% (26/1542) observed hygienic cleansability. Based on available retromolar space, 39% (16/41) of cases with sufficient space observed full eruption with hygienic cleansability, whereas only 0.06% (10/1501) of the patients were able to properly maintain oral hygiene with insufficient retromolar space.</p>", "<p id=\"Par15\">The stepwise effect selection showed that variables that most accurately predicted both third molar eruption (with and without hygienic cleansability) and uprighting were its angulation combined with retromolar space. As very few third molars reached full eruption, a clinically applicable model for full eruption could not be obtained based on third molar angulation at T1 combined with retromolar space.</p>", "<p id=\"Par16\">Prediction models for third molar uprighting at T2 based on third molar angulation and available retromolar space showed that when retromolar space was reduced or insufficient, an initial angle of γ < 21° at T1 predicted uprighting at T2. The positive predictive value (PPV) of the model was 67%, whereas the negative predictive value (NPV) was 85%. In terms of sensitivity and specificity, the model scored 75% and 79% respectively (Fig. ##FIG##2##3##a). With the model running on a validation dataset, it achieved a PPV and sensitivity of 67%, and NPV and specificity of 82% (Fig. ##FIG##2##3##b). When retromolar space was sufficient, an initial angle of γ < 32° at T1 predicted uprighting at T2. The PPV of this model was 91%, while the NPV was 71%. Furthermore, the model demonstrated a sensitivity and specificity of 83%. (Fig. ##FIG##2##3##c). Based on the validation set, the model demonstrated a PPV of 100%, NPV of 38%, along with a sensitivity and specificity of 74% and 100%, respectively. (Fig. ##FIG##2##3##d).</p>" ]	[ "<title>Discussion</title>", "<p id=\"Par17\">The null hypothesis was rejected as the predictive model for third molar eruption could not be established and a low incidence of patients existed having fully erupted teeth with radiological features suggesting hygienic cleansability.</p>", "<p id=\"Par18\">The prediction of third molar eruption is an area of great interest for clinicians since it can improve the current standard of patient care. Also, only a few studies have attempted to predict the lower third molar eruption over time<sup>##REF##22705213##13##,##REF##8284071##14##</sup>. The main limitation associated with these studies has either been the lack of longitudinal radiographic datasets or a small sample size owing to the removal of third molar during follow-up visits<sup>##REF##14762750##15##,##REF##11505260##16##</sup>. In addition, there is a paucity of evidence concerning the incidence of fully erupted third molars exhibiting radiological characteristics indicative of hygienic cleansability, a crucial determinant in the extraction decision-making process. This can be achieved by ensuring that the tooth is unobstructed by any soft or hard tissue, as most pathological diseases such as pericoronitis, periodontitis and caries are often associated with partially impacted lower third molars<sup>##REF##17619915##17##,##REF##19430626##18##</sup>. Therefore, the following study was conducted to predict the third molar eruption and uprighting based on different variables with the assistance of an AI tool and to report on the incidence of erupted third molars having radiological features suggestive of hygienic cleansability.</p>", "<p id=\"Par19\">The present study used an automated AI tool for measuring the third molar angulation, which has been technically and clinically validated in a prior study<sup>##UREF##1##11##</sup>. Hence, no further intra- or inter-examiner assessment was required. Gender discrimination was not taken into consideration as it has been previously reported that gender has no significant impact on third molar eruption<sup>##REF##26561441##19##</sup>. Several factors have been reported in literature which influence the probability of mandibular third molar eruption<sup>##REF##16876055##20##,##REF##8284073##21##</sup>. Amongst these factors, the availability of retromolar space is one of the most prime factors<sup>##REF##8284071##14##</sup>, which was also in accordance with our findings where patients with insufficient retromolar space were prone to a higher risk of impaction<sup>##REF##10503855##22##</sup>.</p>", "<p id=\"Par20\">An attempt was made to draw a prediction model based on third molar angulations at T1 combined with available retromolar space for full eruption. However, the limited sample size of fully erupted third molars precluded the development of such a model. This limitation can be attributed to the fact that the T1 radiographs were from patients aged between 8 and 15 years, a demographic that typically presents with orthodontic issues necessitating clinical examination supplemented with panoramic radiography, a common practice in orthodontics<sup>##REF##29607488##23##</sup>. This may have introduced a selection bias, as the majority of these patients could have crowded teeth due to a smaller mandible and higher risk of impacted mandibular third molars compared to the general population<sup>##REF##26561441##19##,##REF##22623930##24##,##REF##24380060##25##</sup>. Another potential factor contributing to the elevated occurrence of unerupted mandibular third molars could be the average age of the patients at the second time point (T2), which was 18.5 years. It has been observed that teeth impacted at the age of 18 years, may have a probability ranging from 30 to 50% of eventually fully erupting, provided they are not impacted in a horizontal orientation<sup>##REF##8284073##21##</sup>. Nevertheless, it was still feasible to predict the uprighting of the third molar, with full eruption occurring at angular cut-off angular values of < 21.28°–31.54° at T1, which was consistent with a prior study<sup>##REF##30734999##10##</sup>. Given that the average age at T2 was 18.5 years, and considering that many third molars tend to fully erupt later, upright angulation was employed as a marker for subsequent eruption. The predictions models for uprighting were considered strong, reaching values of around 75% and the results also proved to be robust and sustainable in the validation datasets. Hence, suggesting its clinical applicability for predicting third molar uprighting. An addition of larger sample with a higher age at T2 might improve the model’s prediction performance, which should be investigated in future studies. Previously, Vranckx et al.<sup>##REF##30734999##10##</sup> studied the relationship between third molar angulation and eruption and they reported that the recruited patients in their sample might have been too young at T2 to draw relevant conclusions related to the prediction. In contrast, the sample in present study included patients with a higher age at T2. Nevertheless, as the data of fully erupted molars was still scarce, more longitudinal data collection of patients having fully erupted teeth at T2 should be beneficial in improving the prediction workflow.</p>", "<p id=\"Par21\">The findings suggested a low incidence of patients having fully erupted teeth with hygienic cleansability, which was in accordance with prior studies where the risk of partial impaction was higher with a high degree of pericoronitis<sup>##REF##17619915##17##,##REF##19430626##18##</sup>. As the life-expectancy of Swedish population has been documented to increase by each generation<sup>##UREF##2##27##</sup> with a higher number of teeth being preserved<sup>##UREF##3##28##</sup>, the probability of pathology-free partially erupted lower third molars during one’s lifetime is limited. Hence, eruption prediction could allow to prophylactically extract these teeth with a less risk of complications<sup>##REF##17719384##29##,##REF##34479679##30##</sup>.</p>", "<p id=\"Par22\">The main strengths of the study were the application of an AI tool for assisting with the angulation measurement which increased the time-efficiency of the evaluation, while also helping to optimize the tool’s performance and its generalizability with the inclusion of heterogenous dataset at different time-intervals. Although AI assisted in only automatically calculating the third molar’s angulation, further studies are warranted to also include retromolar space assessment and other variables in an attempt to make the entire process of eruption prediction automated. It would be of valuable interest to iterate the proposed research set-up to a sample of higher age groups at T2, preferably at the age of 21–23 years old<sup>##REF##34620027##26##</sup>, which could improve the prediction model. Third molars might still fail to erupt even if all radiographic indicators are favorable, thereby, it is necessary to complement radiographic information with patient genomics which might shed some light onto the eruption physiology.</p>", "<p id=\"Par23\">The study had some limitations. Firstly, the eruption of the lower third molars was not clinically verified and hygienic cleansability was also only assessed radiologically. These findings should be interpreted with caution as the impact of soft tissue and other clinical parameters such as plaque index, bleeding, pericoronitis episodes were not investigated. Hence, it is recommended to perform further studies by focusing on a combination of both radiological and clinical parameters. Secondly, the initial selection of panoramic images was performed by one evaluator, which could have contributed towards selection bias. Finally, patients with a previous history of orthodontic teeth alignment therapy were also included in the study where alignment could have influenced the eruption path<sup>##REF##23904878##31##</sup>. Thereby, it is important to train the model based on different factors to improve its performance.</p>" ]	[ "<title>Conclusions</title>", "<p id=\"Par24\">Although it was not possible to predict the eruption of lower third molars, a strong prediction model was developed for predicting molar uprighting. This could help clinicians improve the decision-making process concerning extraction. Moreover, the likelihood of having lower third molars without any pathology throughout one’s life may be limited due to the low incidence of fully erupted third molars with radiological features that suggest hygienic cleansability.</p>" ]	[ "<p id=\"Par1\">Prediction of lower third molar eruption is crucial for its timely extraction. Therefore, the primary aim of this study was to investigate the prediction of lower third molar eruption and its uprighting with the assistance of an artificial intelligence (AI) tool. The secondary aim was identifying the incidence of fully erupted lower third molars with hygienic cleansability. In total, 771 patients having two panoramic radiographs were recruited, where the first radiograph was acquired at 8–15 years of age (T1) and the second acquisition was between 16 and 23 years (T2). The predictive model for third molar eruption could not be obtained as few teeth reached full eruption. However, uprighting model at T2 showed that in cases with sufficient retromolar space, an initial angulation of < 32° predicted uprighting. Full eruption was observed for 13.9% of the teeth, and only 1.7% showed hygienic cleansability. The predictions model of third molar uprighting could act as a valuable aid for guiding a clinician with the decision-making process of extracting third molars which fail to erupt in an upright fashion. In addition, a low incidence of fully erupted molars with hygienic cleansability suggest that a clinician might opt for prophylactic extraction.</p>", "<title>Subject terms</title>", "<p>Open access funding provided by Karolinska Institute.</p>" ]	[]	[ "<title>Author contributions</title>", "<p>S.C. collected data, analyzed results, drawed images and wrote the manuscript. M.V. contributed to the study design and data collection. A.O. contributed to the study design. P.Ö. contributed to the study design and data collection. C.K.-W. contributed to the study design and enabled access to data. D.B. contributed to the study design. S.S. co-wrote the manuscript. R.J. contributed to the study design. All authors reviewed the manuscript.</p>", "<title>Funding</title>", "<p>Open access funding provided by Karolinska Institute.</p>", "<title>Data availability</title>", "<p>The dataset analyzed during the current study are available from the corresponding author on reasonable request.</p>", "<title>Competing interests</title>", "<p id=\"Par25\">The authors declare no competing interests.</p>" ]	[ "<fig id=\"Fig1\"><label>Figure 1</label><caption><p>Calculation of lower third molar angulation. (<bold>a</bold>) Calculation of angulation on molars with fully formed roots where a line was drawn at region of largest coronal diameter and angulation line was drawn from most apical part of pulp chamber or most coronal part of bifurcation area; (<bold>b</bold>) calculation of angulation on molar with incompletely formed roots where the crown was divided into two equal halves, midpoint of widest diameter was taken and an inclination line was drawn perpendicularly (90°); (<bold>c</bold>) third molar angle defined based on angular difference (γ) between second (β) and third molar (α), represented by β − α = γ.</p></caption></fig>", "<fig id=\"Fig2\"><label>Figure 2</label><caption><p>Eruption levels of lower third molar and retromolar space. (<bold>a</bold>) 1: fully erupted with hygienic cleansability where third molar is at level of 2nd molar’s occlusal plane with marginal bone situated beneath the CEJ at distal side, 2: fully erupted without hygienic cleansability where third molar is at level of 2nd molar’s occlusal plane with marginal bone above CEJ at distal side, 3: partially erupted with height of third molar contour above level of surrounding alveolar bone, 4: unerupted with third molar completely encased in bone; (<bold>b</bold>) available retromolar space, where 1: sufficient space, widest mesiodistal crown width of third molar fits available space measured between distal side of second molar till anterior border of ramus, 2: insufficient space, widest mesiodistal crown width of third molar does not fit available space.</p></caption></fig>", "<fig id=\"Fig3\"><label>Figure 3</label><caption><p>Receiver operating characteristic (ROC) curves of third molar eruption prediction. (<bold>a</bold>) uprighting with reduced or insufficient retromolar space on training dataset; (<bold>b</bold>) uprighting with reduced or insufficient retromolar space on validation dataset; (<bold>c</bold>) uprighting with sufficient retromolar space on training dataset; (<bold>d</bold>) uprighting with sufficient retromolar space on validation dataset.</p></caption></fig>" ]	[]	[]	[]	[]	[]	[]	[]	[ "<fn-group><fn><p><bold>Publisher's note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"41598_2024_51393_Fig1_HTML\" id=\"MO1\"/>", "<graphic xlink:href=\"41598_2024_51393_Fig2_HTML\" id=\"MO2\"/>", "<graphic xlink:href=\"41598_2024_51393_Fig3_HTML\" id=\"MO3\"/>" ]	[]	[{"label": ["8."], "surname": ["Hashmi", "Katiyar", "Keskar", "Bokde", "Geem"], "given-names": ["MF", "S", "AG", "ND", "ZW"], "article-title": ["Efficient pneumonia detection in chest Xray images using deep transfer learning"], "source": ["Diagnostics"], "year": ["2020"], "volume": ["10"], "fpage": ["1"], "pub-id": ["10.3390/diagnostics10060417"]}, {"label": ["11."], "surname": ["Vranckx"], "given-names": ["M"], "article-title": ["Artificial intelligence (AI)-driven molar angulation measurements to predict third molar eruption on panoramic radiographs"], "source": ["Int. J. Environ. Res. Public Health"], "year": ["2020"], "volume": ["17"], "fpage": ["1"], "pub-id": ["10.3390/ijerph17103716"]}, {"label": ["27."], "mixed-citation": ["SCB. "], "italic": ["Medellivsl\u00e4ngden i Sverige"], "ext-link": ["https://www.scb.se/hitta-statistik/sverige-i-siffror/manniskorna-i-sverige/medellivslangd-i-sverige/"]}, {"label": ["28."], "mixed-citation": ["Socialstyrelsen. "], "italic": ["Statistik om tandh\u00e4lsa 2020"], "ext-link": ["https://www.socialstyrelsen.se/globalassets/sharepoint-dokument/artikelkatalog/statistik/2021-9-7565.pdf"]}]	{ "acronym": [], "definition": [] }	31	CC BY	no	2024-01-13 00:02:20	Sci Rep. 2024 Jan 10; 14:994	oa_package/88/db/PMC10781671.tar.gz
PMC10781672	38200019	[ "<title>Introduction</title>", "<p id=\"Par2\">Hepatocellular carcinoma (HCC) is a common malignant tumour in the Department of Gastroenterology. According to cancer statistics in 2020, approximately 906,000 people are diagnosed with HCC every year, representing 4.7% of cancer incidence, and about 830,000 people die of HCC, which seriously endangers human life, health, and safety<sup>##REF##33538338##1##</sup>. The proportion of HCC caused by hepatitis B virus infection in China is as high as 92.05%<sup>##REF##31912902##2##</sup>. Therefore, it is particularly important for the prevention and treatment of HCC. Non-pharmacological treatments such as hepatectomy resection, liver transplantation, and transarterial chemoembolization are beneficial for patients with early HCC. However, most patients with HCC have developed to the middle and advanced stage when diagnosed, and targeted drugs and immunocheckpoint inhibitor therapy with PD-1 / PD-L1 respond only in part of the dominant group, and treatment methods are very limited<sup>##REF##35804984##3##,##REF##28434648##4##</sup>. Given the shortcomings mentioned above of current clinical treatment approaches, the addition of Traditional Chinese Medicine (TCM) enhanced the comprehensive antitumor effect and therefore emerges as the focus of HCC treatment<sup>##REF##34538644##5##,##REF##35907976##6##</sup>.</p>", "<p id=\"Par3\">The <italic>Hedyotis diffusa</italic> and <italic>Scutellaria barbata herb pair</italic> (HD–SB) are often used for clinical cancer treatment with a definitive curative effect<sup>##UREF##0##7##</sup>. <italic>scutellaria barbata</italic> mainly contains flavonoids, while <italic>hedyotis diffusa</italic> mainly contains terpenoids and flavonoids<sup>##UREF##1##8##</sup>. Compared to the ethanol extract of a single drug, the HD–SB ethanol extract can significantly inhibit the growth of human colon cancer cell lines, indicating that the anticancer effect of the combined use of the two drugs will be enhanced<sup>##UREF##2##9##</sup>. Additionally, Studies have shown that the polysaccharides extracted from HD–SB inhibited the proliferation and the transformation from G1 phase to S phase of S180 tumor cell line<sup>##UREF##3##10##</sup>. Similarly, the studies have shown that HD–SB regulated the cell cycle of H22 hepatoma cells, and arrested cells in G1-S phase<sup>##UREF##4##11##,##UREF##5##12##</sup>. Importantly, network pharmacology reveals multiple mechanisms of HD–SB in colorectal cancer<sup>##REF##35707465##13##</sup> and antiovarian cancer<sup>##REF##34147618##14##</sup>. Therefore, Using network pharmacology to further study the mechanism of HD–SB in the treatment of HCC will contribute to improve the therapeutic effect.</p>", "<p id=\"Par4\">In this present study, network pharmacology was used to analyse active ingredients, potential targets, and main mechanisms of HD–SB in the treatment of HCC, and to construct the herb-ingredient-target-disease network, to provide a reference for the study of the specific mechanism of the drug in the treatment of HCC. The graphical abstract (Fig. ##FIG##0##1##) depicts the the research workflow.</p>" ]	[ "<title>Materials and methods</title>", "<title>Regents and materials</title>", "<p id=\"Par19\">Dulbecco's modified eagle medium (DMEM), double antibody, foetal bovine serum (FBS), and pancreatic enzyme were obtained from GENOM (Hangzhou, Zhejiang province, China). Phosphate buffered saline (PBS) was obtained from Wuhan Servicebio Technology Co., Ltd. (Wuhan, Hubei, China). Cell counting Kit-8 (CCK-8) and dimethyl sulfoxide (DMSO) were obtained from Bio-sharp (Hefei, Anhui province, China). Purifications of quercetin, luteolin, beta-sitosterol, and baicalein were purchased from MedChemExpress (Shanghai, China).</p>", "<title>Network pharmacology</title>", "<title>Target source of HD–SB and HCC</title>", "<p id=\"Par20\">The HD–SB compounds were obtained from the Database of Traditional Chinese Medicines for Systems Pharmacology (TCMSP, <ext-link ext-link-type=\"uri\" xlink:href=\"https://tcmspw.com/tcmsp.php\">https://tcmspw.com/tcmsp.php</ext-link>). Active compounds that meet the requirements are identified according to the screening conditions for oral bioavailability (OB) < 30% and drug similarity (DL) < 0.18. The target names of the compounds that met the requirements were recorded. HD–SB targets were collected using the TCMSP database. For compounds lacking targets, the PubChem database was used to search the compound ID number, the chemical structure of SMILES was queried for target complement through the SwissTarget Prediction database, and finally the obtained targets were converted into unified gene names using the Uniport database<sup>##REF##26673716##33##</sup>. HCC-relevant targets were obtained by searching the keyword 'Hepatoma', 'Hepatic Carcinoma', or 'Hepatocellular Carcinoma' from the GeneCards public database (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.genecards.org/\">https://www.genecards.org/</ext-link>).</p>", "<title>Data filtering and visual analysis</title>", "<p id=\"Par21\">A Venn diagram was used to draw data on the intersection of HD–SB compound targets and disease targets. The protein–protein interaction (PPI) data was obtained from STRING online (<ext-link ext-link-type=\"uri\" xlink:href=\"https://string-db.org\">https://string-db.org</ext-link>). The Bisogenet plug-in in Cytoscape3.8.2 software was used to analyse the PPI network for HD–SB HCC targets. Using CytoNCA plug-in to analyse topology parameters BC (Betweenness Centrality), CC (Closeness Centrality) and DC (Degree Centrality) in network, and screen key targets through topology parameters. The screening criterion is greater than the average value of DC, BC, and CC.</p>", "<title>Enrichment analysis</title>", "<p id=\"Par22\">Using the DAVID 6.8.0 database (<ext-link ext-link-type=\"uri\" xlink:href=\"https://david.Ncifcrf.gov/\">https://david.Ncifcrf.gov/</ext-link>) and setting thresholds < 0.05, GO function and the enrichment analysis of the KEGG pathway<sup>##REF##10592173##34##,##REF##31441146##35##</sup> of HCC targets treated with HD–SB were performed and the generated results were visualised.</p>", "<title>Molecular docking technology</title>", "<p id=\"Par23\">The main active components in the core target network diagram were selected for molecular docking with the key targets. PDB file for the target in the PDB database and SDF file for the active ingredient in the PubChem database. AutoDock Vina software was used for molecular docking and PyMOL software was used to visualise the results.</p>", "<title>Cell line and cell culture</title>", "<p id=\"Par24\">The HepG2 human hepatocarcinoma cell line and the Huh7 cell line were purchased from the American Type Culture Collection (ATCC). Cells were cultured in DMEM complete medium containing 10% foetal bovine serum at 37° C and 5%CO<sub>2</sub> and incubated in an incubator with saturated humidity. When the growth density of the cells reached 90%, subculture was carried out and the cells in the logarithmic growth stage were used for experimental research.</p>", "<title>Cell viability assay</title>", "<p id=\"Par25\">Cell viability was evaluated using a cell count kit-8 assay (CCK-8) following the manufacturer's protocol. We seeded 2 × 10<sup>3</sup> cells in 96-well plates and incubated at 37° C for 24 h. Cells were treated with various concentrations of quercetin, luteolin, baicalein, and beta-sitosterol for 24 h or 48 h, respectively, in a humidified chamber containing 5% CO<sub>2</sub>. Then we added 10 μl CCK-8 reagent to each well and incubated for 1.5 h at 37 °C. A microplate reader (Bio-Tech, USA) was used to determine the absorbance of cells at 450 nm (OD450). GraphPad Prim 9.0 software was adopted to calculate the value of 50% inhibitory concentration (IC50).</p>", "<title>Quantitative reverse transcription polymerase chain reaction</title>", "<p id=\"Par26\">Gene expression was determined by quantitative reverse transcription polymerase chain reaction (qRT-PCR) analysis. Total RNA was isolated from HepG2 cells and Huh7 cells using the SteadyPure Universal RNA Extraction Kit (AG21017; Invitrogen), and then the 1 μg sample of RNA was reverse transcribed using a PrimeScriptTM RT Master Mix (Perfect Real Time) (RR036A; Takara, Shiga, Japan) according to the manufacturer’s instructions. Relative gene levels were determined by RT-qPCR using the StepOnePlusTM real-time PCR system (Applied Biosystems, USA). All RT-qPCR mixtures were prepared using a TB Green® Premix Ex Taq™ II (Tli RNaseH Plus) (RR820A; Takaka) with specific primers (Table ##TAB##3##4##). The mRNA levels of all target genes were normalized to the expression of the housekeeping gene actin. Relative quantities were determined using the comparative ΔΔCt method.</p>", "<title>Western blot analysis</title>", "<p id=\"Par27\">Cells from each group were lysed with RIPA lysate for 30 min and then transferred to the centrifuge tube. After centrifuging at 12,000 rpm/min for 10 min, the supernatant was extracted. Quantitative protein concentration was detected by the BCA Protein Assay Kit. After being separated by SDS-PAGE, 50 µg protein samples were transferred. onto the PVDF membrane, sealed with 5% skim milk powder at room temperature for 1 h, and then washed with TBST solution. Rabbit anti-P53, XPO1, APP, and CDK2 monoclonal antibodies (1:1000) were added, respectively, to incubate overnight at 4 °C, then the membrane was washed again, the corresponding secondary antibody was added, and the ECL kit was used to stain. The grey value of each imaging protein band was analysed by the Gel Imaging System, and the relative expression change of each group protein was compared with Beta actin as an internal reference.</p>", "<title>Statistical analysis</title>", "<p id=\"Par28\">Data were expressed as mean ± SD. The results were analysed using GraphPad Prism 9.0 and SPSS 20.0 software. Student's t tests were developed to compare quantitative data between groups and <italic>p</italic> < 0.05 revealed a significant difference.</p>" ]	[ "<title>Results</title>", "<title>The active ingredients and the corresponding targets of HD–SB</title>", "<p id=\"Par5\">We applied a network-based pharmacology strategy to explore the mechanisms by which HD–SB affects HCC cells. The active components of HD and SB were recovered from the TCMSP. There were 7 species of HD and 31 species of SB. After screening with values of OB (30%) and DL (≥ 0.18) values, 7 species of HD and 29 species of SB were obtained (Table ##TAB##0##1##). An online search for targets for 36 compounds in the TCMSP and Uniprot database resulted in 217 targets collected.</p>", "<title>Target data analysis of “HD–SB–HCC”</title>", "<p id=\"Par6\">The target data of \"HD–SB–HCC\" were selected and analysed by GeneCards databases, and the relevance score of the screening condition was established < 15, and finally 1196 qualified targets were obtained. Through the Venn diagram, the target crossover of \"HD–SB–HCC\" was performed and 63 targets were obtained (Fig. ##FIG##1##2##A). These 63 targets are potentially effective targets for HD–SB for HCC, which is the main target of our next study.</p>", "<title>Visual analysis of the targets of HD–SB–HCC</title>", "<p id=\"Par7\">Cytoscape 3.8.2 was used to analyse the data of 63 common drugs and diseases targets, and the interaction diagram of the “drug compounds and disease targets” was obtained (Fig. ##FIG##1##2##B). It contained 18 active ingredient nodes, 62 target nodes, and 225 edges. The size of the node is set according to its degree value, and the larger the node, the larger its degree value. The network graph is analysed for topological parameters, where the degree value of the node is a key indicator of the importance of the nodes, the average degree value of the network graph is 9.1111 and the top three compounds in the degree value ranking were Quercetin, Luteolin, and Baicalein. The top compounds may be critical nodes in the network and possess an important anti-HCC effect.</p>", "<p id=\"Par8\">On the basis of the target protein interaction data obtained from the String database, the protein interaction network analysis diagram was obtained. Then the Bisogenet plug-in of Cytoscape3.8.2 software is used to analyse the PPI network interaction of 63 key targets, and a network graph with 4964 nodes and 126,489 edges is obtained. Then the topology parameters (DC, BC and CC) of the network graph are analyzed by the CytoNCA plug-in, and the mean value of DC > 2 times (100) is selected as the filtering condition to obtain the preliminary core target network graph, the network graph consisting of 614 nodes and 27,665 edges. Finally, a network graph of 111 nodes and 1981 edges with CC > 0.458 and BC > 48,777 with CC > 0.458 and BC > 48,777 was obtained with CC > 0.458 and BC > 48,777 as screening conditions for the HD–SB and HCC target network target network (Fig. ##FIG##1##2##C). As shown in Table ##TAB##1##2##, based on the 111 targets obtained from the topological analysis, a total of 15 core genes were obtained based on the condition of greater than the average value of DC, BC and CC, which were considered as possible key targets for the treatment of HCC.</p>", "<title>Visualisation of enrichment analysis</title>", "<p id=\"Par9\">GO enrichment analysis and the KEGG pathway enrichment analysis<sup>##REF##36300620##15##</sup> were performed to elucidate the function and enrichment pathways of HD–SB for HCC. GO functional enrichment analysis was performed on 63 potential targets through the DAVID database with <italic>p</italic> < 0.05 as the screening condition and 1887 items were obtained, including 1456 biological processes (BP) and 24 cell compositions (CC) and 60 molecular functions (MF). The top ten of GO–BP, GO–CC, and GO–MF were selected for visual analysis. As demonstrated in Fig. ##FIG##2##3##A, MFs include mainly ubiquitin-like protein ligase binding, ubiquitin protein ligase binding, and DNA-binding transcription activator activity. CCs mainly include the regulation of reactive oxygen metabolism, the RNA polymerase II transcription factor complex, and the transcription factor complex. BPs mainly include the oxidative stress response, cell response to oxidative stress, reactive oxygen metabolism, and cell response to external stimuli.</p>", "<p id=\"Par10\">Twenty KEGG pathways that were highly correlated with HCC research and their corresponding targets were selected for visual analysis (Fig. ##FIG##2##3##B). And the top 20 items mainly involve endocrine resistance, the P53 signaling pathway, cellular senescence, lipids and atherosclerosis, the AGE-RAGE signaling pathway in diabetic complications, and HCC. The results showed that HD–SB acted on multiple signal pathways to treat HCC.</p>", "<title>Active component-key target molecular docking</title>", "<p id=\"Par11\">To further verify whether the three active ingredients of HD–SB could effectively bind to the key target proteins of TP53, CDK2, XPO1 and APP, we performed molecular docking between the target proteins and the active ingredients by AutoDock Vina software. Generally, the binding energy is less than − 4.25 kcal/mol, indicating that the small ligand molecule had certain activity with the receptor protein, less than − 5 kcal/mol indicated good activity, and less than − 7 kcal/mol indicated strong activity. As shown in Table ##TAB##2##3##, the conformations of active compounds of Quercetin, Luteolin, Baicalein and the main protein targets showed good binding interactions, and the interactions were also reliable.</p>", "<p id=\"Par12\">The conformations of the minimal binding energies for the key active compounds and the major hub targets are shown in Fig. ##FIG##3##4##. The analysis showed that P53 and Baicalein formed three hydrogen bonds at Val-147 with bond lengths of 3.12, 2.88 and 3.24, respectively, and formed two hydrogen bonds with bond lengths of 2.75 and 2.89 at Thr-230 and 3.09 and 3.11 at Pro-151, respectively (Fig. ##FIG##3##4##A). As shown in Fig. ##FIG##3##4##B, APP and Baicalin formed two hydrogen bonds at Tyr369 with bond lengths of 2.92 and 2.97, respectively. Hydrogen bonds with bond lengths of 3.24, 2.96, 3.16, 2.8 and 2.75 were formed in Thr496, Asp7, Ala334, His365, and Glu389, respectively. As shown in Fig. ##FIG##3##4##C, CDK2 and quercetin form two hydrogen bonds of 3.28 and 3.08 at Asp145, 3.07 and 2.68 at Leu134, and 2.87 at Asp86. XPO1 and luteolin formed three hydrogen bonds at Val40 with bond lengths of 3.22, 2.74 and 2.92, respectively, and formed hydrogen bonds at Val-18 and Ala-31 with bond lengths of 3.09, 2.89, 3.08 and 2.97, respectively (Fig. ##FIG##3##4##D). Together, three representative compounds ( Quercetin, Luteolin and Baicalein) of HD–SB could bind well to four core targets of HCC (P53, CDK2, XPO1 and APP), all of which could play key roles in the treatment of HCC.</p>", "<title>Effect of three representative compounds of HD–SB on the proliferation of hepatoma carcinoma cells</title>", "<p id=\"Par13\">To determine whether the three representative compounds (quercetin, luteolin and baicalein) of HD–SB influence HCC cell proliferation, we first examined the effect of the three pharmaceutical ingredients above on HepG2 cell proliferation with the CCK-8 assay. HepG2 cells were treated with different concentrations of quercetin, luteolin, Baicalein for 24 and 48 h, respectively. Our results showed that Quercetin, Luteolin and Baicalein all decreased cell viability in a dose-dependent and time-dependent manner (Fig. ##FIG##4##5##A and B). And the same phenomenon was present in Huh7 cells (Fig. ##FIG##4##5##C and D). These findings suggest that HD–SB suppresses the proliferation of HCC cells.</p>", "<title>Three representative HD–SB compounds modulate the expression of TP53, XPO1, APP, and CDK2 in hepatoma carcinoma cells</title>", "<p id=\"Par14\">The transcription factor TP53 is an important suppressor of tumour development, which can reduce HCC cell proliferation by inhibiting CDK2 expression<sup>##REF##31804459##16##</sup>. XPO1 is the main mediator of nucleocytoplasmic transport and is overexpressed in a variety of human malignancies and closely related to tumour occurrence and development<sup>##REF##26048327##17##</sup>. APP inhibits apoptosis by mediating apoptosis proteins (Bcl-2)<sup>##REF##32133800##18##</sup>. Bioinformatic analysis has indicated that Quercetin, Baicalein, and Luteolin might play an important role in HCC by regulating the four genes mentioned above, and then we next investigated the effect of three representative compounds on the expression of TP53, XPO1, APP and CDK2 in HepG2 cells and Huh7 cells. The IC50 value at 48 h of drug treatment was selected as our subsequent experiment. Four representative compounds of HD–SB caused a significant decrease in mRNA levels of XPO1 and CDK2 and increase of TP53 and APP, compared with those in control group in HepG2 cells (Fig. ##FIG##5##6##A–D) and Huh7 cells (Fig. ##FIG##5##6##E–H). As shown in Fig. ##FIG##6##7##, Western blots also illustrated that pharmaceutical ingredients significantly decreased XPO1 and CDK2 protein expression and increased TP53 and APP. Taken together, our data further verified the network pharmacological results of the impotent functions of TP53, XPO1, APP, and CDK2 in HD–SB for HCC.</p>" ]	[ "<title>Discussion</title>", "<p id=\"Par15\">The efficacy of HD–SB is mainly to remove heat and detoxify the toxin and is slightly bitter. In clinical practise, this drug is commonly used in antitumor therapy, and the combination of HD–SB can exert a synergistic effect to promote endometrial cancer apoptosis, breast cancer cells, cervical cancer cells, gastric cancer cells, HCC cells and pancreatic cancer cells and inhibit tumour cell proliferation and differentiation<sup>##REF##29697746##19##,##UREF##6##20##</sup>. Some experiments have shown that HD–SB has a good tumour suppressor effect in HCC H22-bearing mice<sup>##UREF##7##21##,##UREF##8##22##</sup> and S18 sarcoma mice<sup>##UREF##9##23##,##UREF##10##24##</sup>. In this study, a total of 18 active ingredients were identified through network pharmacology, 63 key targets were analysed and 129 pathways related to HCC were involved, reflecting the mechanism of interregulation of HD–SB'multicomponent, multitarget, and multipathways’ to treat HCC.</p>", "<p id=\"Par16\">The studies confirmed that HD–SB could induce apoptosis in HCC cells by increasing the Bax/Bcl-2 ratio<sup>##UREF##11##25##</sup>. Furthermore, the active ingredients of HD–SB of Quercetin and Baicalein promoted the arrest of the HepG2 cell cycle by upregulating the expression of TP53 and P21<sup>##REF##28747003##26##,##REF##24760952##27##</sup>, and Quercetin and Luteolin inhibited CDK2 activity to arrest the HCC cell cycle<sup>##REF##25476752##28##</sup>. In our study, the analysis of the \"TCM compound-target\" network revealed that Baicalein, Luteolin, and Quercetin were the active ingredients of HD–SB in the treatment of HCC. The molecular docking results further confirmed that TP53, APP, CDK2 and XPO1 were able to bind to Quercetin, Baicalin and Luteolin and formed stable compounds. In vitro, we verified the network pharmacology results of the impotent functions of TP53, XPO1, APP and CDK2 in HD–SB for HCC.</p>", "<p id=\"Par17\">TP53 is an important oncogene, which can inhibit tumorigenesis by inducing cell cycle arrest or apoptosis. P53 can promote hepatocellular carcinoma cell apoptosis by increasing the expression of Bcl-2, P21, P14 and inhibiting the expression of BAX<sup>##REF##19049493##29##</sup>. In this study, the functional analysis of GO and KEGG also found that the P53 signaling pathway was closely related to HD–SB for HCC. XPO1 is a nuclear transport receptor protein responsible for the nuclear export of some growth regulators and tumour suppressors. Studies have shown that inhibition of XPO1 expression can activate TP53 and induce cancer cell apoptosis<sup>##REF##28819023##30##</sup>. APP expression is regulated by TP53 and induces apoptosis by inhibiting the mitochondrial apoptotic pathway and the expression of the apoptosis suppressor gene Bcl-2<sup>##REF##30947664##31##</sup>. CDK2 belongs to the CDK family and can phosphorylate a large number of transcription factors, involved in the regulation of a variety of cancer signaling pathways, and promote cancer development. Studies have shown that CDK2 expression is associated with HCC size and TNM stage<sup>##REF##25149358##32##</sup>. TP53 is involved in the regulation of CDK2 mRNA and protein expression levels<sup>##REF##31804459##16##</sup>. Our results showed that the three active components of HD–SB increased P53 and APP levels and decreased XPO1 and CDK2 levels in vitro. Based on the above results, we propose that HD–SB up-regulates TP53 expression by inhibiting XPO-1, thus increasing APP and inhibiting CDK2 levels, and inhibiting HCC cell proliferation (Fig. ##FIG##7##8##).</p>", "<p id=\"Par18\">In summary, this study used the network pharmacology and the molecular docking system to analyse the mechanism of action of HD–SB in the treatment of HCC, revealing that HD–SB acts on multiple targets to play a tumour suppressor role and the characteristics of HD–SB in the treatment of HCC through multiple components. Our study provides a theoretical basis for the treatment of tumours with HD–SB. Our subsequent studies will further study the specific mechanism of HD–SB regulation of liver cancer cells.</p>" ]	[]	[ "<p id=\"Par1\">The <italic>Hedyotis diffusa</italic>–<italic>Scutellaria officinalis pair</italic> (HD–SB) has therapeutic effects on a variety of cancers. Our study was to explore the mechanism of HD–SB in the treatment of hepatocellular carcinoma (HCC). A total of 217 active ingredients of HD–SB and 1196 HCC-related targets were reserved from the TCMSP and the SwissTarget Prediction database, and we got 63 intersection targets from GeneCards. We used a Venn diagram, and Cytoscape found that the three core ingredients were quercetin, luteolin, and baicalein. The PPI analysis showed that the core targets were TP53, CDK2, XPO1, and APP. Molecular docking results showed that these core ingredients had good binding potential with the core targets. HD–SB acts simultaneously on various HCC-related signaling pathways, including proteoglycans in cancer and the P53 signaling pathway. In vitro experiments confirmed that HD–SB can inhibit HepG2 cell proliferation by increasing TP53 and APP levels and decreasing XPO1 and CDK2 levels. This study analyzed active ingredients, core targets, and central mechanisms of HD–SB in the treatment of HCC. It reveals the role of HD–SB in targeting the P53 signaling pathway in the treatment of HCC. We hope that our research could provide a new perspective to the therapy of HCC and find new anticancer drugs.</p>", "<title>Subject terms</title>" ]	[]	[ "<title>Acknowledgements</title>", "<p>The paper was supported by the Hunan Provincial People’s Hospital (The First Affiliated Hospital of Hunan Normal University).This work was financially supported by the following funds: Hunan Province Natural Science Foundation (Grant No. 2020JJ5302), Hunan Province Traditional Chinese Medicine Research Program(Grant No. 2021097), Changsha Science and Technology Project (Grant No. kq2004118).</p>", "<title>Author contributions</title>", "<p>X.L. and Y.Z. designed the study. C.H. and X.G. cell experiments. S.Y. and Y.J. were responsible for network pharmacology. F.C. and Y.L. analyzed the data. C.H. prepared the manuscript. X.L. and Y.Z. reviewed and edited the work. All authors contributed to the discussion of the results, edited, and approved the final version of the manuscript.</p>", "<title>Data availability</title>", "<p>All data generated or analysed during this study are included in this published article.</p>", "<title>Competing interests</title>", "<p id=\"Par29\">The authors declare no competing interests.</p>" ]	[ "<fig id=\"Fig1\"><label>Figure 1</label><caption><p>The graphical abstract depicts the the research workflow.</p></caption></fig>", "<fig id=\"Fig2\"><label>Figure 2</label><caption><p>Screening of targets for cross-action between HD–SB and HCC. (<bold>A</bold>) 217 HD–SB targets intersected 1196 HCC targets for 63 common targets. (<bold>B</bold>) Relationship between the active ingredients of HD–SB and the common targets of HD–SB and HCC. (<bold>C</bold>) Get the protein–protein interaction (PPI) network from online STRING.</p></caption></fig>", "<fig id=\"Fig3\"><label>Figure 3</label><caption><p>Analysis of enrichment. (<bold>A</bold>) The top ten for GO–BP, GO–CC, and GO–MF enrichment. (<bold>B</bold>) Twenty KEGG pathways that were highly correlated with HCC research.</p></caption></fig>", "<fig id=\"Fig4\"><label>Figure 4</label><caption><p>The network of target drug compounds. The docking model of Baicalin, Quercetin and Luteolin with TP53, APP, CDK2 and XPO1, respectively.</p></caption></fig>", "<fig id=\"Fig5\"><label>Figure 5</label><caption><p>Three representative compounds of HD–SB inhibit the proliferation of hepatoma carcinoma cells. (<bold>A</bold> and <bold>B</bold>) HepG2 cells were treated with the indicated concentrations of Baicalin, Quercetin and Luteolin respectively (0, 25, 50, 75, 100, 125, 175 and 200 μM) for (<bold>A</bold>) 24 h and (<bold>B</bold>) 48 h. CCK-8 assays were used to measure cell viability. (<bold>C</bold> and <bold>D</bold>) Huh7 cells were treated with the indicated concentrations of Baicalin, Quercetin, and Luteolin, respectively (0, 25, 50, 75, 100, 125, 175 and 200 μM) for (<bold>C</bold>) 24 h and (<bold>D</bold>) 48 h. CCK-8 assays were used to measure cell viability. (Error bars represent the S.D. of the mean. n ≥ 6; <italic>p</italic> < 0.05;<italic>p</italic> < 0.01;*<italic>p</italic> < 0.001.).</p></caption></fig>", "<fig id=\"Fig6\"><label>Figure 6</label><caption><p>XPO1, TP53, APP and CDK2 are related to the inhibition of hepatoma carcinoma cells induced by three representative HD–SB compounds. HepG2 and Huh7 cells were treated with Baicalin, Quercetin, and Luteolin, respectively, or DMSO for 48 h. (<bold>A</bold>–<bold>D</bold>) Relative expression of XPO1, TP53, APP, and CDK2 mRNA in HepG2 cells compared to the control. (<bold>E</bold>–<bold>H</bold>) Relative expression of XPO1, TP53, APP, and CDK2 mRNA in Huh7 cells compared to the control. Graphs are representatives of three independent experiments (n ≥ 6; <italic>p</italic> < 0.05; <italic>p</italic> < 0.01; <italic>p</italic> < 0.001).</p></caption></fig>", "<fig id=\"Fig7\"><label>Figure 7</label><caption><p>XPO1, TP53, APP, and CDK2 are related to the inhibition of hepatoma carcinoma cells induced by three representative HD–SB compounds. HepG2 and Huh7 cells were treated with Baicalin, Quercetin, and Luteolin, respectively, or DMSO for 48 h. Western blot test of protein levels of XPO1, TP53, APP, and CDK2. Densitometry revealed the fold expression ofXPO1, TP53, APP, and CDK2 compared to that of β-actin, respectively. Graphs are representatives of three independent experiments (n ≥ 6; <italic>p</italic> < 0.05; <italic>p</italic> < 0.01; *<italic>p</italic> < 0.001).</p></caption></fig>", "<fig id=\"Fig8\"><label>Figure 8</label><caption><p>Schematic that illustrates the working principle of HD–SB-related signaling in HepG2 cells. We propose that HD–SB maintained XPO-1 expression, activating the TP53-dependent signaling pathway associated with HCC.</p></caption></fig>" ]	[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>The active ingredient in a drug.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Herb</th><th align=\"left\">MOL ID</th><th align=\"left\">Compound name</th><th align=\"left\">OB(%)</th><th align=\"left\">DL</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"7\">Hedyotis diffusa</td><td align=\"left\">MOL001646</td><td align=\"left\">2,3-dimethoxy-6-methyanthraquinone</td><td char=\".\" align=\"char\">34.85860475</td><td char=\".\" align=\"char\">0.26255</td></tr><tr><td align=\"left\">MOL001659</td><td align=\"left\">Poriferasterol</td><td char=\".\" align=\"char\">43.82985158</td><td char=\".\" align=\"char\">0.75596</td></tr><tr><td align=\"left\">MOL001663</td><td align=\"left\">(4aS,6aR,6aS,6bR,8aR,10R,12aR,14bS)-10-hydroxy-2,2,6a,6b,9,9,12a-heptamethyl-1,3,4,5,6,6a,7,8,8a,10,11,12,13,14b-tetradecahydropicene-4a-carboxylic acid</td><td char=\".\" align=\"char\">32.02801329</td><td char=\".\" align=\"char\">0.75713</td></tr><tr><td align=\"left\">MOL001670</td><td align=\"left\">2-Methoxy-3-methyl-9,10-anthraquinone</td><td char=\".\" align=\"char\">37.82770556</td><td char=\".\" align=\"char\">0.20517</td></tr><tr><td align=\"left\">MOL000449</td><td align=\"left\">Stigmasterol</td><td char=\".\" align=\"char\">43.82985158</td><td char=\".\" align=\"char\">0.75665</td></tr><tr><td align=\"left\">MOL000358</td><td align=\"left\">Beta-sitosterol</td><td char=\".\" align=\"char\">36.91390583</td><td char=\".\" align=\"char\">0.75123</td></tr><tr><td align=\"left\">MOL000098</td><td align=\"left\">Quercetin</td><td char=\".\" align=\"char\">46.43334812</td><td char=\".\" align=\"char\">0.27525</td></tr><tr><td align=\"left\" rowspan=\"29\">Sculellaria barbata</td><td align=\"left\">MOL001040</td><td align=\"left\">(2R)-5,7-dihydroxy-2-(4-hydroxyphenyl)chroman-4-one</td><td char=\".\" align=\"char\">42.36332114</td><td char=\".\" align=\"char\">0.21141</td></tr><tr><td align=\"left\">MOL012245</td><td align=\"left\">5,7,4′-trihydroxy-6-methoxyflavanone</td><td char=\".\" align=\"char\">36.62688628</td><td char=\".\" align=\"char\">0.26833</td></tr><tr><td align=\"left\">MOL012246</td><td align=\"left\">5,7,4′-Trihydroxy-8-methoxyflavanone</td><td char=\".\" align=\"char\">74.23522001</td><td char=\".\" align=\"char\">0.26479</td></tr><tr><td align=\"left\">MOL012248</td><td align=\"left\">5-Hydroxy-7,8-dimethoxy-2-(4-methoxyphenyl)chromone</td><td char=\".\" align=\"char\">65.81880606</td><td char=\".\" align=\"char\">0.32874</td></tr><tr><td align=\"left\">MOL012250</td><td align=\"left\">7-Hydroxy-5,8-dimethoxy-2-phenyl-chromone</td><td char=\".\" align=\"char\">43.7169646</td><td char=\".\" align=\"char\">0.25376</td></tr><tr><td align=\"left\">MOL012251</td><td align=\"left\">Chrysin-5-methylether</td><td char=\".\" align=\"char\">37.2683358</td><td char=\".\" align=\"char\">0.20317</td></tr><tr><td align=\"left\">MOL012252</td><td align=\"left\">9,19-cyclolanost-24-en-3-ol</td><td char=\".\" align=\"char\">38.68565906</td><td char=\".\" align=\"char\">0.78074</td></tr><tr><td align=\"left\">MOL002776</td><td align=\"left\">Baicalin</td><td char=\".\" align=\"char\">40.12360996</td><td char=\".\" align=\"char\">0.75264</td></tr><tr><td align=\"left\">MOL012254</td><td align=\"left\">Campesterol</td><td char=\".\" align=\"char\">37.57681789</td><td char=\".\" align=\"char\">0.71486</td></tr><tr><td align=\"left\">MOL000953</td><td align=\"left\">CLR</td><td char=\".\" align=\"char\">37.87389754</td><td char=\".\" align=\"char\">0.67677</td></tr><tr><td align=\"left\">MOL000358</td><td align=\"left\">Beta-sitosterol</td><td char=\".\" align=\"char\">36.91390583</td><td char=\".\" align=\"char\">0.75123</td></tr><tr><td align=\"left\">MOL012266</td><td align=\"left\">Rivularin</td><td char=\".\" align=\"char\">37.94023355</td><td char=\".\" align=\"char\">0.3663</td></tr><tr><td align=\"left\">MOL001973</td><td align=\"left\">Sitosteryl acetate</td><td char=\".\" align=\"char\">40.38964165</td><td char=\".\" align=\"char\">0.85102</td></tr><tr><td align=\"left\">MOL012269</td><td align=\"left\">Stigmasta-5,22-dien-3-ol-acetate</td><td char=\".\" align=\"char\">46.44190225</td><td char=\".\" align=\"char\">0.85814</td></tr><tr><td align=\"left\">MOL012270</td><td align=\"left\">Stigmastan-3,5,22-triene</td><td char=\".\" align=\"char\">45.02668769</td><td char=\".\" align=\"char\">0.71047</td></tr><tr><td align=\"left\">MOL000449</td><td align=\"left\">STIGMASTEROL</td><td char=\".\" align=\"char\">43.82985158</td><td char=\".\" align=\"char\">0.75665</td></tr><tr><td align=\"left\">MOL000173</td><td align=\"left\">Wogonin</td><td char=\".\" align=\"char\">30.68456706</td><td char=\".\" align=\"char\">0.22942</td></tr><tr><td align=\"left\">MOL001735</td><td align=\"left\">Dinatin</td><td char=\".\" align=\"char\">30.97205344</td><td char=\".\" align=\"char\">0.27025</td></tr><tr><td align=\"left\">MOL001755</td><td align=\"left\">24-Ethylcholest-4-en-3-one</td><td char=\".\" align=\"char\">36.08361164</td><td char=\".\" align=\"char\">0.75703</td></tr><tr><td align=\"left\">MOL002714</td><td align=\"left\">Baicalein</td><td char=\".\" align=\"char\">33.51891869</td><td char=\".\" align=\"char\">0.20888</td></tr><tr><td align=\"left\">MOL002719</td><td align=\"left\">6-Hydroxynaringenin</td><td char=\".\" align=\"char\">33.22920875</td><td char=\".\" align=\"char\">0.24203</td></tr><tr><td align=\"left\">MOL002915</td><td align=\"left\">Salvigenin</td><td char=\".\" align=\"char\">49.06592606</td><td char=\".\" align=\"char\">0.33279</td></tr><tr><td align=\"left\">MOL000351</td><td align=\"left\">Rhamnazin</td><td char=\".\" align=\"char\">47.14113124</td><td char=\".\" align=\"char\">0.33648</td></tr><tr><td align=\"left\">MOL000359</td><td align=\"left\">Sitosterol</td><td char=\".\" align=\"char\">36.91390583</td><td char=\".\" align=\"char\">0.7512</td></tr><tr><td align=\"left\">MOL005190</td><td align=\"left\">Eriodictyol</td><td char=\".\" align=\"char\">71.7926526</td><td char=\".\" align=\"char\">0.24372</td></tr><tr><td align=\"left\">MOL005869</td><td align=\"left\">Daucostero_qt</td><td char=\".\" align=\"char\">36.91390583</td><td char=\".\" align=\"char\">0.75177</td></tr><tr><td align=\"left\">MOL000006</td><td align=\"left\">Luteolin</td><td char=\".\" align=\"char\">36.16262934</td><td char=\".\" align=\"char\">0.24552</td></tr><tr><td align=\"left\">MOL008206</td><td align=\"left\">Moslosooflavone</td><td char=\".\" align=\"char\">44.08795959</td><td char=\".\" align=\"char\">0.25331</td></tr><tr><td align=\"left\">MOL000098</td><td align=\"left\">Quercetin</td><td char=\".\" align=\"char\">46.43334812</td><td char=\".\" align=\"char\">0.27525</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Information of 15 core targets in topological analysis.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">DC</th><th align=\"left\">BC</th><th align=\"left\">CC</th><th align=\"left\">Gene</th></tr></thead><tbody><tr><td align=\"left\">947</td><td align=\"left\">1,204,758</td><td char=\".\" align=\"char\">0.538</td><td align=\"left\"><bold>TP53</bold></td></tr><tr><td align=\"left\">848</td><td align=\"left\">1,220,727</td><td char=\".\" align=\"char\">0.532</td><td align=\"left\"><bold>APP</bold></td></tr><tr><td align=\"left\">793</td><td align=\"left\">423,543</td><td char=\".\" align=\"char\">0.522</td><td align=\"left\">CUL3</td></tr><tr><td align=\"left\">763</td><td align=\"left\">713,692</td><td char=\".\" align=\"char\">0.531</td><td align=\"left\">ESR1</td></tr><tr><td align=\"left\">681</td><td align=\"left\">508,968</td><td char=\".\" align=\"char\">0.518</td><td align=\"left\"><bold>XPO1</bold></td></tr><tr><td align=\"left\">662</td><td align=\"left\">298,221</td><td char=\".\" align=\"char\">0.511</td><td align=\"left\">MCM2</td></tr><tr><td align=\"left\">629</td><td align=\"left\">348,499</td><td char=\".\" align=\"char\">0.51</td><td align=\"left\">FN1</td></tr><tr><td align=\"left\">615</td><td align=\"left\">391,646</td><td char=\".\" align=\"char\">0.521</td><td align=\"left\">UBC</td></tr><tr><td align=\"left\">596</td><td align=\"left\">539,186</td><td char=\".\" align=\"char\">0.509</td><td align=\"left\">MYC</td></tr><tr><td align=\"left\">567</td><td align=\"left\">225,987</td><td char=\".\" align=\"char\">0.503</td><td align=\"left\"><bold>CDK2</bold></td></tr><tr><td align=\"left\">545</td><td align=\"left\">199,824</td><td char=\".\" align=\"char\">0.508</td><td align=\"left\">COPS5</td></tr><tr><td align=\"left\">536</td><td align=\"left\">338,340</td><td char=\".\" align=\"char\">0.521</td><td align=\"left\">HSP90AA1</td></tr><tr><td align=\"left\">530</td><td align=\"left\">174,515</td><td char=\".\" align=\"char\">0.496</td><td align=\"left\">RNF2</td></tr><tr><td align=\"left\">475</td><td align=\"left\">302,933</td><td char=\".\" align=\"char\">0.502</td><td align=\"left\">GRB2</td></tr><tr><td align=\"left\">465</td><td align=\"left\">189,006</td><td char=\".\" align=\"char\">0.508</td><td align=\"left\">YWHAZ</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab3\"><label>Table 3</label><caption><p>Virtual docking of three vital active compounds from HD–SB for hepatocellular carcinoma targets.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\">Compound</th><th align=\"left\" rowspan=\"2\">Structure</th><th align=\"left\" colspan=\"4\">Binding affinity (Kcal/mol)</th></tr><tr><th align=\"left\">TP53</th><th align=\"left\">APP</th><th align=\"left\">CDK2</th><th align=\"left\">XPO1</th></tr></thead><tbody><tr><td align=\"left\">Baicalein</td><td align=\"left\"></td><td align=\"left\"> − 7.3</td><td align=\"left\"> − 9.6</td><td align=\"left\"> − 8.7</td><td align=\"left\"> − 8.4</td></tr><tr><td align=\"left\">Luteolin</td><td align=\"left\"></td><td align=\"left\"> − 6.7</td><td align=\"left\"> − 8.5</td><td align=\"left\"> − 9.2</td><td align=\"left\"> − 9.4</td></tr><tr><td align=\"left\">Quercetin</td><td align=\"left\"></td><td align=\"left\"> − 6.8</td><td align=\"left\"> − 8.6</td><td align=\"left\"> − 9.3</td><td align=\"left\"> − 8.8</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab4\"><label>Table 4</label><caption><p>RT-PCR primer sequence.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Gene</th><th align=\"left\" colspan=\"2\">Primer sequence (5′-3′)</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"2\">TP53</td><td align=\"left\">Sense</td><td align=\"left\">GAGGTTGGCTCTGACTGTACC</td></tr><tr><td align=\"left\">Antisense</td><td align=\"left\">TCCGTCCCAGTAGATTACCAC</td></tr><tr><td align=\"left\" rowspan=\"2\">XPO1</td><td align=\"left\">Sense</td><td align=\"left\">AGCAAAGAATGGCTCAAGAAGT</td></tr><tr><td align=\"left\">Antisense</td><td align=\"left\">TATTCCTTCGCACTGGTTCCT</td></tr><tr><td align=\"left\" rowspan=\"2\">APP</td><td align=\"left\">Sense</td><td align=\"left\">TCTCGTTCCTGACAAGTGCAA</td></tr><tr><td align=\"left\">Antisense</td><td align=\"left\">GCAAGTTGGTACTCTTCTCACTG</td></tr><tr><td align=\"left\" rowspan=\"2\">CDK2</td><td align=\"left\">Sense</td><td align=\"left\">CCAGGAGTTACTTCTATGCCTGA</td></tr><tr><td align=\"left\">Antisense</td><td align=\"left\">TTCATCCAGGGGAGGTACAAC</td></tr><tr><td align=\"left\" rowspan=\"2\">β-actin</td><td align=\"left\">Sense</td><td align=\"left\">CGTAAAGACCTCTATGCCAACA</td></tr><tr><td align=\"left\">Antisense</td><td align=\"left\">AGCCACCAATCCACACAGAG</td></tr></tbody></table></table-wrap>" ]	[]	[]	[]	[]	[]	[]	[ "<table-wrap-foot><p>Significance values are in bold.</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher's note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"41598_2023_50696_Fig1_HTML\" id=\"MO1\"/>", "<graphic xlink:href=\"41598_2023_50696_Fig2_HTML\" id=\"MO2\"/>", "<graphic xlink:href=\"41598_2023_50696_Fig3_HTML\" id=\"MO3\"/>", "<inline-graphic xlink:href=\"41598_2023_50696_Figa_HTML.gif\" id=\"d32e959\"/>", "<inline-graphic xlink:href=\"41598_2023_50696_Figb_HTML.gif\" id=\"d32e974\"/>", "<inline-graphic xlink:href=\"41598_2023_50696_Figc_HTML.gif\" id=\"d32e989\"/>", "<graphic xlink:href=\"41598_2023_50696_Fig4a_HTML\" id=\"d32e1022\"/>", "<graphic xlink:href=\"41598_2023_50696_Fig4b_HTML\" id=\"d32e1023\"/>", "<graphic xlink:href=\"41598_2023_50696_Fig5_HTML\" id=\"MO5\"/>", "<graphic xlink:href=\"41598_2023_50696_Fig6_HTML\" id=\"MO6\"/>", "<graphic xlink:href=\"41598_2023_50696_Fig7_HTML\" id=\"MO7\"/>", "<graphic xlink:href=\"41598_2023_50696_Fig8_HTML\" id=\"MO8\"/>" ]	[]	[{"label": ["7."], "surname": ["Mo", "Wang", "Luo"], "given-names": ["ZC", "M", "XQ"], "article-title": ["Anti-tumor activity of combination of hedyotic diffusa and Scutellaria barbata"], "source": ["Nat. 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PMC10781673	38200136	[ "<title>Introduction</title>", "<p id=\"Par2\">The percutaneous transaxillary approach (PTAX) constitutes an alternative for large-bore access in patients without femoral access. In this technique, experts from the Society for Cardiovascular Angiography and Interventions (SCAI) and most operators advocate puncturing the second segment of the axillary artery located behind the pectoralis minor muscle, due to its potentially reduced risk of complications<sup>##UREF##0##1##</sup>. Indeed, there is limited data concerning the safety of the approach through the first axillary artery segment, situated between the lateral border of the first rib and the pectoralis minor muscle<sup>##UREF##0##1##,##REF##32926537##2##</sup>.</p>", "<p id=\"Par3\">The majority of the data demonstrating the efficiency of PTAX originates from transcatheter aortic valve replacement (TAVR), with a smaller portion coming from procedures involving mechanical circulatory support devices<sup>##REF##32926537##2##–##REF##33322918##7##</sup>. In fact, PTAX outcomes have never been directly compared between Impella-supported percutaneous coronary interventions (PCI) and TAVR. Consequently, our understanding of specific difficulties or complications associated with PTAX, particularly through the first axillary artery segment, in these two clinical contexts remains limited. The undertaking of such a direct comparison also presents challenges, given that PTAX for PCI with Impella and TAVR are typically conducted by distinct groups of operators, specializing either in high-risk PCI or structural heart diseases.</p>", "<p id=\"Par4\">In this study, we analyzed single-center data on PTAX during Impella-supported PCI versus TAVR, both performed through the first segment of the axillary artery by the same team of operators. Our objective was to identify differences in complications related to PTAX between these two clinical scenarios. Additionally, we outline a technique for puncturing the axillary artery “on the balloon” when standard imaging methods do not offer adequate guidance.</p>" ]	[ "<title>Methods</title>", "<title>Material</title>", "<p id=\"Par5\">The study group consisted of consecutive patients without femoral access who underwent high-risk PCI with Impella support or TAVR using PTAX. The vast majority of patients (96%) had a thorough examination for large-bore arterial access using CT scans, except for those who received the Impella pump for urgent indications, where the access site was chosen based on angiography. All patients presented with significant ilio-femoral arterial disease, which precluded femoral access. Additionally, nine had aortic aneurysms with intraluminal thrombus, and two patients had severely tortuous aortas. In each case, the decision about the treatment approach, particularly regarding the need for mechanical support and the access site, was made by the Heart Team. The procedures were performed by the same group of operators who dealt with both high-risk coronary interventions and structural cardiac procedures. Bleeding and vascular complications were assessed according to BARC and VARC-3 definitions<sup>##REF##21670242##8##,##REF##33871579##9##</sup>. The decrease in hemoglobin level within 3 days after the procedure was considered as bleeding associated with the intervention if no other probable cause existed. All patients provided their written informed consent for the procedures. The study was conducted in compliance with the principles of the Declaration of Helsinki, and the Institutional Ethics Committee of the University of Opole reviewed and approved the study.</p>", "<title>Technique of the percutaneous transaxillary approach</title>", "<p id=\"Par6\">To gain access to the axillary artery, first, a 7F or 6F sheath was placed into the ipsilateral radial artery, and a long 0.035\" guidewire was inserted into the ascending aorta, serving as a safety guidewire. Angiography was performed by retrograde contrast injection (diluted with saline 1:1) through the radial sheath to identify the first segment of the axillary artery, i.e., between the clavicle and the thoraco-acromial artery. The arterial puncture was done under ultrasound guidance at a shallow angle to facilitate the insertion of a large sheath. In cases where ultrasound imaging did not provide sufficient visualization, particularly in obese patients, the puncture was performed \"on the balloon,\" as shown in Fig. ##FIG##0##1## and the supplementary Video ##SUPPL##0##S1##. This technique facilitates puncture, as the balloon enhances both ultrasound and fluoroscopic visibility of the puncture site. Moreover, the balloon also enables puncturing a dissected artery, as inflating the balloon compresses the dissected layers, allowing for the needle insertion into the true arterial lumen.</p>", "<p id=\"Par7\">After gaining access, two Proglide/Prostyle sutures were deployed for later percutaneous closure, and then a large Impella or TAVR sheath was inserted. Upon completion of the procedure, a peripheral balloon was introduced through the radial sheath to the access site, with the goal of ensuring arterial tamponade during Impella or large sheath removal. The balloon not only prevented bleeding but also averted arterial stenosis while tightening the Proglide/Prostyle sutures. In case of vascular closure device failure, bleeding was resolved with Angioseal deployment or manual compression along with the balloon tamponade. Alternatively, instead of manual compression, a piece of hemostatic sponge was delivered directly to the bleeding site with the help of Proglide pusher<sup>##REF##36137702##10##</sup>. This double compression, i.e., from inside with the balloon and from outside manually (or with the pusher), for at least 10 min, usually resolved bleeding issues. If all measures failed, the vascular problem was addressed with a stent-graft or self-expanding stent implantation through the radial access<sup>##REF##33189644##11##</sup>.</p>", "<title>Endpoints</title>", "<p id=\"Par8\">The primary endpoints were the differences between the Impella and TAVR groups in bleeding according to BARC type 3 bleeding criteria and major vascular complications according to VARC-3 definition. The secondary endpoints included the differences in minor vascular complications according to VARC-3 definition, as well as various types of BARC and VARC bleeding.</p>", "<title>Statistical analysis</title>", "<p id=\"Par9\">Categorical variables are presented as numeric values and percentages, while continuous variables are given as median and interquartile range (IQR) or mean and standard deviation. The normality of the data distribution was assessed using Shapiro–Wilk test. Differences between variables were tested with either Student's t-test, Mann–Whitney test, or Fisher exact test as appropriate. To identify independent determinants of the primary endpoints, a stepwise multivariable logistic regression was employed, adjusted for age, sex, and BMI. Only procedural variables associated with the primary endpoints in the univariate analysis (with <italic>p</italic> < 0.1) were considered for inclusion in the multivariable models. Similarly, predictors of hemoglobin decline after procedures were determined using multivariable linear regression. The threshold probability of <italic>p</italic> < 0.05 was taken as the level of statistical significance. All analyses were performed using the Statistical Package for Social Sciences (SPSS, v. 22.0, Chicago, IL, USA).</p>" ]	[ "<title>Results</title>", "<p id=\"Par10\">The study population consisted of 46 patients who underwent PTAX between April 2020 and August 2023. Among them, 23 received the Impella CP (Impella group), while the remaining 23 patients underwent TAVR (TAVR group). Among the TAVR group, 15 subjects received Evolut R or Pro valves, and 8 subjects received Sapien 3 Ultra valves. All attempted PTAX procedures through the first axillary artery segment were successful without the need for conversion to any other approach.</p>", "<p id=\"Par11\">Most patients in the Impella group presented with compromised left ventricular function and Impella was used as mechanical circulatory support for high-risk PCI. While awaiting the procedure, two of them developed cardiogenic shock. Baseline patient characteristics showed significant differences between the Impella and TAVR groups (Table ##TAB##0##1##). The Impella patients were younger and had a significantly lower left ventricular ejection fraction (LVEF). Severe aortic stenosis was the primary issue among all TAVR subjects, with five patients in the Impella group having the same condition. More patients in the Impella group suffered from unstable angina or non-ST-segment elevation myocardial infarction. The prevalence of left main or three-vessel disease and a history of prior myocardial infarction was also higher among Impella patients. The Syntax Score and EuroScore II indicated a significantly higher surgical risk of death in the Impella group compared to the TAVR group. A greater number of subjects receiving Impella were on dual antiplatelet therapy and beta-blockers. Conversely, TAVR patients were more commonly treated with oral hypoglycemic drugs and antibiotics (for prophylaxis).</p>", "<p id=\"Par12\">Table ##TAB##1##2## presents procedural variables and outcomes. Specifically, all PCIs in the Impella group were completed without complications, and some TAVR patients also underwent successful PCI; however, this occurred prior to the valve procedure. The vast majority of TAVRs were carried out without problems. Nevertheless, in one case, a second valve had to be deployed due to partial dislodgement of the primary valve into the left ventricle. Balloon aortic valvuloplasty was commonly performed in the TAVR group. Additionally, five Impella patients with severe aortic stenosis underwent valvuloplasty to unload the left ventricle and facilitate Impella implantation. General anesthesia was more frequently used for TAVR patients, while the volume of intra-procedural fluids, heparin dose, contrast volume, radiation dose, and procedure time (i.e., implant duration) were higher in the Impella group. An increase in creatinine level after the procedure, but not the occurrence of acute kidney injury, was higher in Impella patients. They also had a substantially longer hospital stay.</p>", "<p id=\"Par13\">There was no need for vascular surgery, and neither group experienced stroke, brachial plexus injury, or pneumothorax. In the entire study population, only three stent-grafts and three self-expanding stents were implanted, as a result of vascular closure device failure and arterial dissection. Three deaths unrelated to PTAX occurred in the Impella group: one due to cardiogenic shock and two from heart failure (one of them after discharge).</p>", "<p id=\"Par14\">Figures ##FIG##1##2## and ##FIG##2##3## present the prevalence of bleeding and vascular complications according to BARC and VARC-3 definitions. Various types of bleeding were primarily identified based on the drop in hemoglobin level but not on the occurrence of overt bleeding. The median time for the greatest hemoglobin decline was 2 days (IQR: 1–2.5 days) after the procedure. Primary endpoints, namely BARC 3 bleeding and major vascular complications (according to VARC-3 criteria), were observed in the entire study population at rates of 57% and 17%, respectively. There was no significant difference between the Impella and TAVR groups in these outcomes. However, the Impella patients experienced a significantly higher incidence of BARC 3b bleeding. Table ##TAB##2##3## displays the independent determinants of BARC 3 bleeding, as well as the determinants of hemoglobin decline following the procedure. Since the number of major vascular complications was less than 10 (specifically, 8), conducting a reliable multivariable analysis for this outcome was not feasible.</p>" ]	[ "<title>Discussion</title>", "<p id=\"Par15\">In this study, PTAX was utilized in patients undergoing the Impella CP implantation or TAVR. All procedures were successfully conducted through the first segment of the axillary artery by the operators who performed both Impella-supported PCI and valve interventions. Bleeding complications, as per BARC 3 criteria, were frequent and led to the recognition of major vascular complications in some patients, as defined by VARC-3. No discernible difference was observed between the two groups in this respect. Due to inherent reasons, patients requiring mechanical circulatory support with the Impella pump needed to maintain the axillary access for a longer duration, resulting in a larger hemoglobin decline and consequently, a significantly higher incidence of BARC 3b bleeding. However, the blood loss did not lead to a worse prognosis, and there were no procedure-related deaths. Over the one-month observation period, three patients in the Impella group died due to heart failure.</p>", "<p id=\"Par16\">Several aspects of this study warrant further consideration. Specifically, the first segment of the axillary artery proved to be a secure access site for large bore devices. This approach was feasible in each case, with no need for conversion to alternative access sites. No patient required vascular surgery, nor did anyone experience brachial plexus injury or pneumothorax. Moreover, contrary to other reports that used the first axillary segment for endovascular aortic procedures, none of our patients suffered a stroke<sup>##REF##32089506##12##,##REF##34600031##13##</sup>. However, attributing such a difference to the specifics of aortic versus cardiac interventions is challenging, and it should rather be considered a matter of chance. Proglides/Prostyles were employed for access site closure in all patients. Additionally, Angioseals were used, mainly to stop oozing, in 26 subjects (57%). Three patients (7%) needed covered stent implantation due to the failure of vascular closure devices. This is a relatively low number compared to other reports where, for example, covered stents were used in up to 44% of cases, and indeed, they constituted an essential aspect of PTAX<sup>##REF##33281073##5##</sup>. However, our data shows that covered stenting should only remain a bailout procedure, to be employed when balloon and manual compression, or compression with a hemostatic sponge and Proglide pusher, cannot effectively address the bleeding issue<sup>##REF##36137702##10##</sup>.</p>", "<p id=\"Par17\">In 46% of the study population, hemoglobin loss ranged from 3.0 to 5.0 g/dl (BARC 3a). Furthermore, in 11% of the whole group, blood loss exceeded 5 g/dl (BARC 3b). However, overt bleeding from the axillary site occurred in only one case and was successfully managed with prolonged manual compression. In the multivariable analysis, the decrease in hemoglobin level was directly associated with the implant duration; conversely, the use of the left axillary access was linked to a smaller blood loss. The diagnosis of BARC 3 bleeding was independently determined by the hemoglobin decline and red blood cell (RBC) transfusion, and it was inversely related to patient age. The implant time in Impella patients was significantly longer than in TAVR subjects, and the hemoglobin decline was partially attributed to the PCI itself. Moreover, the volume of intra-procedural fluid infusion was larger in the Impella group, which might lead to some blood dilution. Additionally, more patients with Impella received dual antiplatelet therapy, and the heparin dose was also larger in this group. Therefore, the higher incidence of BARC 3b bleeding probably did not solely result from the Impella insertion through the percutaneous transaxillary access but was rather influenced by the aforementioned factors. This is supported by the fact that neither hemoglobin drop nor BARC 3 bleeding were independently associated with Impella usage itself. It should also be noted that two patients in the Impella group developed cardiogenic shock while awaiting Impella-supported PCI. As the analysis was conducted on an intention-to-treat basis, these patients were not excluded from the study. However, it is important to consider that patients with cardiogenic shock typically experience different complications and outcomes. According to our study, the left axillary access, instead of the right access, seems to be a safer way to avoid blood loss during PTAX.</p>", "<p id=\"Par18\">Compared to other reports, complications were more frequent in our material. In a recent systematic review, major bleeding and major vascular complications were reported at rates of 2.7% and 2.8%, respectively, while blood transfusion and stent-graft implantation occurred in 5.5% and 10.9%, respectively. The discrepancy between the complication rates and the therapeutic interventions in this review was attributed to the underreporting of clinical outcomes, heterogeneous outcome definitions, and a lack of adjudication <sup>##REF##32926537##2##</sup>. According to the VARC-2 and VARC-3 definitions, major vascular complications should be recognized when vascular events lead to major bleeding, i.e., when the hemoglobin decline associated with the procedure exceeds 3 g/dl<sup>##REF##33871579##9##,##REF##23036636##14##</sup>. Indeed, instances of bleeding and vascular complications seem to be underreported. In a study where 44.1% of patients received stent-grafts, major vascular complications were noted in 14.3% of cases<sup>##REF##33281073##5##</sup>. In a propensity-matched analysis of percutaneous versus surgical transaxillary access in TAVR, major vascular complications occurred in 3.0% versus 1.5% of the respective groups; however, the exact definition for these complications is not provided in the report<sup>##REF##34794935##4##</sup>. In another study where the average hospital stay was 1.2 days, with 83% of cases being discharged the following day, no bleeding or vascular complications were documented<sup>##UREF##1##6##</sup>. In our analysis, the median time for the highest hemoglobin decline was 2 days (IQR: 1–2.5 days) after the intervention. Therefore, accurate detection of the hemoglobin drop during a one-day hospitalization is not feasible. In a recent multicenter registry addressing PTAX for Impella pumps, a low rate of vascular complications was reported; however, the authors used the first edition of VARC definition, which did not include hemoglobin loss exceeding 3 g/dl as a criterion for major vascular complications<sup>##REF##33322918##7##,##REF##21216739##15##</sup>.</p>", "<p id=\"Par19\">In our study, we strictly adhered to the BARC and VARC-3 criteria, and a significant portion of bleeding complications were identified solely based on laboratory examinations leading to the recognition of major vascular complications in some subjects. Yet, a high prevalence of these complications in our patients did not impact their prognosis, challenging the usefulness of these criteria in subjects undergoing PTAX. The practicality of BARC and VARC scores in patients treated with mechanical circulatory support (i.e., Impella pumps) was questioned in a recent review. The authors highlighted a series of major limitations of these scores, particularly in the context of the intensive care unit<sup>##REF##37495347##16##</sup>. Indeed, the BARC and either VARC-2 or VARC-3 criteria are not limited to overt bleeding; they also include hematomas and procedural manipulations, such as the insertion and removal of large sheaths, which can lead to a decline in hemoglobin. However, as our study indicates, the high rate of recognizing these outcomes doesn't necessarily correlate with patient prognosis.</p>", "<p id=\"Par20\">Regarding other types of complications, it is worth mentioning the low prevalence of acute kidney injury, i.e., 13% in the entire group. The incidence of kidney injury was particularly low in the TAVR group (5%), despite a very high risk according to the Mehran risk score (median 57.3%; IQR 26.1–57.3). The renal protection offered by PTAX during TAVR was highlighted in a recent meta-analysis, where this approach reduced the risk of kidney damage by 43% compared to intrathoracic approaches (i.e., transaortic or transapical)<sup>##REF##34448652##17##</sup>.</p>", "<p id=\"Par21\">So far, PTAX was mainly compared with the surgical axillary approach<sup>##REF##32926537##2##</sup>. To the best of our knowledge, this is the first report directly comparing PTAX between two different invasive procedures, namely Impella implantation and TAVR. Furthermore, these two types of interventions were carried out through the first axillary artery segment by the same team of operators, allowing us to specifically assess whether the access complications differed between these two distinct clinical scenarios. According to our data, the only notable difference was the higher prevalence of BARC 3b bleeding in the Impella group; however, its cause is likely multifactorial, and it did not impact the prognosis. Despite the favorable outcomes presented in this manuscript, transaxillary access should not be overused, primarily due to the previously reported increased risk of stroke associated with this procedure in TAVR patients<sup>##REF##35512920##18##,##REF##36858659##19##</sup>.</p>" ]	[ "<title>Conclusion</title>", "<p id=\"Par23\">PTAX through the first segment of the axillary artery for Impella-supported PCI, when compared to TAVR, does not differ in terms of BARC 3 bleeding and major vascular complications. However, interventions with Impella present a higher incidence of BARC 3b bleeding, likely linked to PCI and the duration of axillary access maintenance. Left axillary access, in comparison to the right side, is associated with a lower risk of blood loss. In terms of short-term prognosis, PTAX through the first axillary artery segment yields favorable outcomes.</p>" ]	[ "<p id=\"Par1\">Percutaneous transaxillary approach (PTAX) through the first segment of the axillary artery is not widely recognized as a safe method. Furthermore, PTAX has never been directly compared between Impella-supported percutaneous coronary interventions (Impella-PCI) and transcatheter aortic valve replacement (TAVR). This study evaluated the feasibility and safety of PTAX through the first axillary segment in Impella-PCI versus TAVR. In cases where standard imaging guidance was insufficient, a technique involving puncturing the axillary artery “on-the-balloon” was employed. The endpoints were bleeding and vascular complications, as defined by BARC and VARC-3 criteria. PTAX was successfully performed in all 46 attempted cases: 23 for Impella-PCI and 23 for TAVR. Strict adherence to BARC and VARC-3 criteria led to the frequent identification of major bleeding (57%) and a moderately frequent diagnosis of vascular complications (17%). These incidences were primarily based on post-procedural hemoglobin reduction (> 3 g/dl) but not overt bleeding. The Impella group exhibited a higher rate of BARC 3b bleeding due to a greater hemoglobin decline resulting from the prolonged implant duration and PCI itself. Left axillary access was linked to smaller blood loss. Bleeding and vascular complications, as per BARC and VARC-3 definitions, did not affect short-term prognosis, with only 3 Impella patients succumbing to heart failure unrelated to the procedures during one-month follow-up period.</p>", "<title>Subject terms</title>" ]	[ "<title>Limitations</title>", "<p id=\"Par22\">The single-center retrospective design and short-term outcomes limit the broader generalizability of our findings. Further studies are needed to validate the safety of PTAX as an alternative large-bore access in patients without femoral access.</p>", "<title>Supplementary Information</title>", "<p>\n</p>" ]	[ "<title>Supplementary Information</title>", "<p>The online version contains supplementary material available at 10.1038/s41598-024-51552-3.</p>", "<title>Author contributions</title>", "<p>J.S., P.F., M.C., and M.G. planned and organized the procedures. J.S., K.K., W.G., P.L., W.M., and P.F. performed the procedures. J.S. analyzed the data and wrote the main manuscript text. All authors reviewed and discussed the manuscript.</p>", "<title>Data availability</title>", "<p>The data that support the findings of this study are available from the corresponding author upon reasonable request.</p>", "<title>Competing interests</title>", "<p id=\"Par24\">J.S. is an Impella proctor.</p>" ]	[ "<fig id=\"Fig1\"><label>Figure 1</label><caption><p>The puncture technique \"On the Balloon\". (<bold>A</bold>) The peripheral balloon is inserted through radial access and positioned at the puncture site. Subsequently, the artery is punctured together with the balloon under ultrasound guidance. (<bold>B</bold>) The guidewire is inserted into the balloon. (<bold>C</bold>) The guidewire and balloon are jointly advanced to the aorta. (<bold>D</bold>) The guidewire is removed from the balloon and advanced back to the aorta (afterward, the balloon is removed). Refer to supplementary Video ##SUPPL##0##S1##.</p></caption></fig>", "<fig id=\"Fig2\"><label>Figure 2</label><caption><p>The prevalence of different bleeding types according to the Bleeding Academic Research Consortium (BARC) in patients undergoing Impella implantation and transcatheter aortic valve replacement (TAVR).</p></caption></fig>", "<fig id=\"Fig3\"><label>Figure 3</label><caption><p>The prevalence of various types of bleeding (type 1–4), as well as major and minor vascular complications according to the Valve Academic Research Consortium 3 (VARC) in patients undergoing Impella implantation and transcatheter aortic valve replacement (TAVR).</p></caption></fig>" ]	[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Baseline patient characteristics and pharmacological treatment.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\">Characteristics</th><th align=\"left\">Whole group</th><th align=\"left\">Impella group</th><th align=\"left\">TAVR group</th></tr><tr><th align=\"left\">(n = 46)</th><th align=\"left\">(n = 23)</th><th align=\"left\">(n = 23)</th></tr></thead><tbody><tr><td align=\"left\">Age (years)</td><td align=\"left\">74 (68–81)</td><td align=\"left\">70 (66–78)</td><td align=\"left\">79 (73–83)</td></tr><tr><td align=\"left\">Male</td><td align=\"left\">29 (63)</td><td align=\"left\">16 (70)</td><td align=\"left\">12 (52)</td></tr><tr><td align=\"left\">BMI (kg/m<sup>2</sup>)</td><td align=\"left\">26.2 ± 4.5</td><td align=\"left\">25.7 ± 4.1</td><td align=\"left\">26.7 ± 4.9</td></tr><tr><td align=\"left\">Hypertension</td><td align=\"left\">42 (91)</td><td align=\"left\">19 (83)</td><td align=\"left\">23 (100)</td></tr><tr><td align=\"left\">Atrial fibrillation</td><td align=\"left\">16 (37)</td><td align=\"left\">8 (35)</td><td align=\"left\">8 (35)</td></tr><tr><td align=\"left\">Prior stroke/TIA</td><td align=\"left\">9 (20)</td><td align=\"left\">5 (22)</td><td align=\"left\">4 (17)</td></tr><tr><td align=\"left\">Heart failure</td><td align=\"left\">46 (100)</td><td align=\"left\">23 (100)</td><td align=\"left\">23 (100)</td></tr><tr><td align=\"left\">NYHA class</td><td align=\"left\">2 (2–3)</td><td align=\"left\">3 (2–4)</td><td align=\"left\">2 (2–3)</td></tr><tr><td align=\"left\">proBNP (ng/l)</td><td align=\"left\">7217 ± 9122</td><td align=\"left\">9257 ± 11,237</td><td align=\"left\">4947 ± 5451</td></tr><tr><td align=\"left\">Hemoglobin (g/dl)</td><td align=\"left\">12.4 ± 1.8</td><td align=\"left\">12.6 ± 1.6</td><td align=\"left\">12.1 ± 2.0</td></tr><tr><td align=\"left\">Creatinine (mg/dl)</td><td align=\"left\">1.22 ± 0.91</td><td align=\"left\">1.15 ± 0.5</td><td align=\"left\">1.3 ± 1.2</td></tr><tr><td align=\"left\">LVEF (%)</td><td align=\"left\">41 ± 16</td><td align=\"left\">32 ± 13‡</td><td align=\"left\">50 ± 13‡</td></tr><tr><td align=\"left\">Severe AVS</td><td align=\"left\">28 (61)</td><td align=\"left\">5 (22)§</td><td align=\"left\">23 (100)§</td></tr><tr><td align=\"left\">Chronic coronary syndrome</td><td align=\"left\">7 (15)</td><td align=\"left\">5 (22)</td><td align=\"left\">2 (9)</td></tr><tr><td align=\"left\">Unstable angina</td><td align=\"left\">5 (11)</td><td align=\"left\">5 (22)</td><td align=\"left\">0</td></tr><tr><td align=\"left\">NSTEMI</td><td align=\"left\">7 (15)</td><td align=\"left\">7 (30)</td><td align=\"left\">0</td></tr><tr><td align=\"left\">Cardiogenic shock</td><td align=\"left\">2 (4)</td><td align=\"left\">2 (9)</td><td align=\"left\">0</td></tr><tr><td align=\"left\">Left main disease</td><td align=\"left\">19 (41)</td><td align=\"left\">18 (78)§</td><td align=\"left\">1 (4)§</td></tr><tr><td align=\"left\">Three vessel disease</td><td align=\"left\">20 (43)</td><td align=\"left\">17 (74)‡</td><td align=\"left\">3 (13)‡</td></tr><tr><td align=\"left\">Prior myocardial infarction</td><td align=\"left\">21 (46)</td><td align=\"left\">15 (65)</td><td align=\"left\">6 (26)</td></tr><tr><td align=\"left\">Prior PCI</td><td align=\"left\">24 (52)</td><td align=\"left\">11 (48)</td><td align=\"left\">13 (57)</td></tr><tr><td align=\"left\">Prior CABG</td><td align=\"left\">5 (11)</td><td align=\"left\">4 (17)</td><td align=\"left\">1 (4)</td></tr><tr><td align=\"left\">LIMA graft</td><td align=\"left\">5 (11)</td><td align=\"left\">4 (17)</td><td align=\"left\">1 (4)</td></tr><tr><td align=\"left\">RIMA graft</td><td align=\"left\">1 (2)</td><td align=\"left\">1 (4)</td><td align=\"left\">0</td></tr><tr><td align=\"left\">Dyslipidemia</td><td align=\"left\">43 (93)</td><td align=\"left\">21 (91)</td><td align=\"left\">22 (96)</td></tr><tr><td align=\"left\">Diabetes mellitus</td><td align=\"left\">24 (52)</td><td align=\"left\">11 (48)</td><td align=\"left\">13 (57)</td></tr><tr><td align=\"left\">Chronic kidney disease</td><td align=\"left\">30 (65)</td><td align=\"left\">15 (65)</td><td align=\"left\">15 (65)</td></tr><tr><td align=\"left\">COPD</td><td align=\"left\">7 (15)</td><td align=\"left\">1 (4)</td><td align=\"left\">6 (26)</td></tr><tr><td align=\"left\">Gastro-intestinal disease</td><td align=\"left\">12 (26)</td><td align=\"left\">8 (35)</td><td align=\"left\">4 (17)</td></tr><tr><td align=\"left\">Malignancy</td><td align=\"left\">9 (20)</td><td align=\"left\">7 (30)</td><td align=\"left\">2 (9)</td></tr><tr><td align=\"left\">Syntax Score</td><td align=\"left\">25.8 ± 20.3</td><td align=\"left\">44 ± 10.5‡</td><td align=\"left\">7.5 ± 6‡</td></tr><tr><td align=\"left\">EuroSCORE II</td><td align=\"left\">11.86 ± 15.4</td><td align=\"left\">17.91 ± 19.74</td><td align=\"left\">5.8 ± 4.3</td></tr><tr><td align=\"left\">STS Score</td><td align=\"left\">6.652 ± 8.698</td><td align=\"left\">9.133 ± 11.793</td><td align=\"left\">4.171 ± 1.667</td></tr><tr><td align=\"left\">Mehran Risk Score (%)</td><td align=\"left\">26.1 (26.1–57.3)</td><td align=\"left\">26.1 (26.1–57.3)</td><td align=\"left\">57.3 (26.1–57.3)</td></tr><tr><td align=\"left\">Aspirin</td><td align=\"left\">42 (91)</td><td align=\"left\">23 (100)</td><td align=\"left\">19 (83)</td></tr><tr><td align=\"left\">DAPT</td><td align=\"left\">37 (80)</td><td align=\"left\">23 (100)†</td><td align=\"left\">14 (61)†</td></tr><tr><td align=\"left\">NOAC</td><td align=\"left\">15 (33)</td><td align=\"left\">8 (35)</td><td align=\"left\">7 (30)</td></tr><tr><td align=\"left\">ACEI/ARB</td><td align=\"left\">37 (80)</td><td align=\"left\">20 (87)</td><td align=\"left\">17 (74)</td></tr><tr><td align=\"left\">B-blocker</td><td align=\"left\">38 (83)</td><td align=\"left\">22 (96)</td><td align=\"left\">16 (70)</td></tr><tr><td align=\"left\">Mineralocorticoid antagonist</td><td align=\"left\">28 (61)</td><td align=\"left\">16 (70)</td><td align=\"left\">12 (52)</td></tr><tr><td align=\"left\">Diuretics</td><td align=\"left\">35 (76)</td><td align=\"left\">19 (83)</td><td align=\"left\">16 (70)</td></tr><tr><td align=\"left\">Statins</td><td align=\"left\">44 (96)</td><td align=\"left\">23 (100)</td><td align=\"left\">21 (91)</td></tr><tr><td align=\"left\">SLGT-2 inhibitors</td><td align=\"left\">10 (22)</td><td align=\"left\">7 (30)</td><td align=\"left\">3 (13)</td></tr><tr><td align=\"left\">Oral hypoglycemic drugs</td><td align=\"left\">14 (30)</td><td align=\"left\">3 (13)</td><td align=\"left\">11 (48)</td></tr><tr><td align=\"left\">Insulin</td><td align=\"left\">9 (20)</td><td align=\"left\">5 (22)</td><td align=\"left\">4 (17)</td></tr><tr><td align=\"left\">Noradrenaline</td><td align=\"left\">5 (11)</td><td align=\"left\">4 (17)</td><td align=\"left\">1 (4)</td></tr><tr><td align=\"left\">Dobutamine</td><td align=\"left\">2 (4)</td><td align=\"left\">1 (4)</td><td align=\"left\">1 (4)</td></tr><tr><td align=\"left\">Adrenaline</td><td align=\"left\">6 (13)</td><td align=\"left\">5 (22)</td><td align=\"left\">1 (4)</td></tr><tr><td align=\"left\">Levosimendan</td><td align=\"left\">2 (4)</td><td align=\"left\">2 (9)</td><td align=\"left\">0</td></tr><tr><td align=\"left\">Antibiotics</td><td align=\"left\">36 (78)</td><td align=\"left\">14 (61)†</td><td align=\"left\">22 (96)†</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Procedural variables and outcomes.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\">Variables</th><th align=\"left\">Whole group</th><th align=\"left\">Impella group</th><th align=\"left\">TAVR group</th></tr><tr><th align=\"left\">(n = 46)</th><th align=\"left\">(n = 23)</th><th align=\"left\">(n = 23)</th></tr></thead><tbody><tr><td align=\"left\">PCI</td><td align=\"left\">32 (70)</td><td align=\"left\">23 (100)§</td><td align=\"left\">9 (39)§</td></tr><tr><td align=\"left\">PCI of left main</td><td align=\"left\">19 (41)</td><td align=\"left\">18 (78)§</td><td align=\"left\">1 (4)§</td></tr><tr><td align=\"left\">Balloon aortic valvuloplasty</td><td align=\"left\">21 (46)</td><td align=\"left\">5 (22)†</td><td align=\"left\">16 (70)†</td></tr><tr><td align=\"left\">General anesthesia</td><td align=\"left\">16 (37)</td><td align=\"left\">0§</td><td align=\"left\">16 (70)§</td></tr><tr><td align=\"left\">CTA for access assessment</td><td align=\"left\">44 (96)</td><td align=\"left\">21 (91)</td><td align=\"left\">23 (100)</td></tr><tr><td align=\"left\">Completely non-femoral approach</td><td align=\"left\">31 (67)</td><td align=\"left\">15 (65)</td><td align=\"left\">16 (70)</td></tr><tr><td align=\"left\">Left axillary access</td><td align=\"left\">31 (67)</td><td align=\"left\">12 (52)</td><td align=\"left\">19 (83)</td></tr><tr><td align=\"left\">Minimum axillary artery diameter (mm)</td><td align=\"left\">6.5 (6–8)</td><td align=\"left\">6.5 (5.5–8)</td><td align=\"left\">7 (6–8)</td></tr><tr><td align=\"left\">Subclavian-axillary artery angle (degree)</td><td align=\"left\">88 ± 16</td><td align=\"left\">91 ± 19</td><td align=\"left\">84 ± 13</td></tr><tr><td align=\"left\">Sheath size</td><td align=\"left\">14 (14–21)</td><td align=\"left\">14 (14–14)</td><td align=\"left\">14 (14–21)</td></tr><tr><td align=\"left\">Proglides/Prostyles usage</td><td align=\"left\">46 (100)</td><td align=\"left\">23 (100)</td><td align=\"left\">23 (100)</td></tr><tr><td align=\"left\">Angioseal usage</td><td align=\"left\">26 (57)</td><td align=\"left\">12 (52)</td><td align=\"left\">14 (61)</td></tr><tr><td align=\"left\">Vascular closure device failure</td><td align=\"left\">3 (7)</td><td align=\"left\">2 (9)</td><td align=\"left\">1 (4)</td></tr><tr><td align=\"left\">Covered stent implantation</td><td align=\"left\">3 (7)</td><td align=\"left\">2 (9)</td><td align=\"left\">1 (4)</td></tr><tr><td align=\"left\">Self-expanding stent implantation</td><td align=\"left\">3 (7)</td><td align=\"left\">2 (9)</td><td align=\"left\">1 (4)</td></tr><tr><td align=\"left\">Vascular surgery</td><td align=\"left\">0</td><td align=\"left\">0</td><td align=\"left\">0</td></tr><tr><td align=\"left\">Brachial plexus injury</td><td align=\"left\">0</td><td align=\"left\">0</td><td align=\"left\">0</td></tr><tr><td align=\"left\">Pneumothorax</td><td align=\"left\">0</td><td align=\"left\">0</td><td align=\"left\">0</td></tr><tr><td align=\"left\">Periprocedural MI</td><td align=\"left\">1 (2)</td><td align=\"left\">1 (4)</td><td align=\"left\">0</td></tr><tr><td align=\"left\">Intra-procedural fluid (ml)</td><td align=\"left\">1296 ± 557</td><td align=\"left\">1538 ± 660†</td><td align=\"left\">1028 ± 208†</td></tr><tr><td align=\"left\">Heparin dose (U)</td><td align=\"left\">10,000 (7500–12,500)</td><td align=\"left\">12,500 (10,000–13,500)‡</td><td align=\"left\">7500 (5875–10,000)‡</td></tr><tr><td align=\"left\">Protamine usage</td><td align=\"left\">20 (43)</td><td align=\"left\">8 (35)</td><td align=\"left\">12 (52)</td></tr><tr><td align=\"left\">Contrast volume (ml)</td><td align=\"left\">272 ± 113</td><td align=\"left\">327 ± 121‡</td><td align=\"left\">218 ± 72‡</td></tr><tr><td align=\"left\">Radiation dose (mGy)</td><td align=\"left\">1793 ± 1512</td><td align=\"left\">2659 ± 1709‡</td><td align=\"left\">431 ± 90‡</td></tr><tr><td align=\"left\">Implant duration (hours)</td><td align=\"left\">2.34 (1.74–3.23)</td><td align=\"left\">3.17 (2.5–4.3)‡</td><td align=\"left\">1.73 (1.44–2.27)‡</td></tr><tr><td align=\"left\">Hemoglobin decline (g/dl)</td><td align=\"left\">2.9 ± 1.4</td><td align=\"left\">3.4 ± 1.5</td><td align=\"left\">2.8 ± 1.1</td></tr><tr><td align=\"left\">Time for hemoglobin decline (days after procedure)</td><td align=\"left\">2 (1–2.5)</td><td align=\"left\">2 (1–3)</td><td align=\"left\">2 (1–2.25)</td></tr><tr><td align=\"left\">Bleeding from axillary access</td><td align=\"left\">1 (2)</td><td align=\"left\">1 (4)</td><td align=\"left\">0</td></tr><tr><td align=\"left\">Large hematoma (> 4 cm)</td><td align=\"left\">4 (9)</td><td align=\"left\">3 (13)</td><td align=\"left\">1 (4)</td></tr><tr><td align=\"left\">Red blood cell transfusion</td><td align=\"left\">16 (37)</td><td align=\"left\">11 (48)</td><td align=\"left\">5 (22)</td></tr><tr><td align=\"left\">Red blood cell units</td><td align=\"left\">0 (0–2)</td><td align=\"left\">0 (0–2)</td><td align=\"left\">0 (0–0.5)</td></tr><tr><td align=\"left\">Hemolysis</td><td align=\"left\">1 (2)</td><td align=\"left\">1 (4)</td><td align=\"left\">0</td></tr><tr><td align=\"left\">Creatinine increase after procedure (mg/dl)</td><td align=\"left\">0.02 ± 0.31</td><td align=\"left\">0.12 ± 0.33</td><td align=\"left\">-0.7 ± 0.25*</td></tr><tr><td align=\"left\">Acute kidney injury</td><td align=\"left\">5 (11)</td><td align=\"left\">4 (17)</td><td align=\"left\">1 (4)</td></tr><tr><td align=\"left\">PM/CRT/ICD implantation</td><td align=\"left\">7 (15)</td><td align=\"left\">5 (22)</td><td align=\"left\">2 (9)</td></tr><tr><td align=\"left\">Infection/sepsis</td><td align=\"left\">7 (15)</td><td align=\"left\">5 (22)</td><td align=\"left\">2 (9)</td></tr><tr><td align=\"left\">Hospital stay (days)</td><td align=\"left\">15 (7–25)</td><td align=\"left\">20 (14–26)†</td><td align=\"left\">7 (6–17)†</td></tr><tr><td align=\"left\">One-month MI/stroke/TIA</td><td align=\"left\">0</td><td align=\"left\">0</td><td align=\"left\">0</td></tr><tr><td align=\"left\">One-month mortality</td><td align=\"left\">3 (7)</td><td align=\"left\">3 (13)</td><td align=\"left\">0</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab3\"><label>Table 3</label><caption><p>Multivariable analysis for hemoglobin decline and BARC type 3 bleeding.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" colspan=\"4\">Hemoglobin decline</th></tr><tr><th align=\"left\" rowspan=\"2\">Variables</th><th align=\"left\" colspan=\"3\">Multivariable linear regression</th></tr><tr><th align=\"left\">B</th><th align=\"left\">95% CI</th><th align=\"left\"><italic>P</italic> value</th></tr></thead><tbody><tr><td align=\"left\">Implant time (hours)</td><td align=\"left\">0.041</td><td align=\"left\">0.014–0.067</td><td align=\"left\">0.003</td></tr><tr><td align=\"left\">Left axillary access</td><td align=\"left\">− 1.036</td><td align=\"left\">− 1.809 to − 0.264</td><td align=\"left\">0.01</td></tr><tr><td align=\"left\" colspan=\"4\">BARC type 3</td></tr></tbody></table><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\">Variables</th><th align=\"left\" colspan=\"3\">Multivariable logistic regression</th></tr><tr><th align=\"left\">OR</th><th align=\"left\">95% CI</th><th align=\"left\"><italic>P</italic> value</th></tr></thead><tbody><tr><td align=\"left\">HB decline after procedure (g/dl)</td><td align=\"left\">5.39</td><td align=\"left\">1.85–15.7</td><td align=\"left\">0.002</td></tr><tr><td align=\"left\">RBC transfusion (units)</td><td align=\"left\">3.13</td><td align=\"left\">1.15–8.55</td><td align=\"left\">0.026</td></tr><tr><td align=\"left\">Age (years)</td><td align=\"left\">0.93</td><td align=\"left\">0.9–0.98</td><td align=\"left\">0.002</td></tr></tbody></table></table-wrap>" ]	[]	[]	[]	[]	[]	[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>" ]	[ "<table-wrap-foot><p>Values are mean ± standard deviation, median (interquartile range) or n (%). <italic>ACEI</italic> indicates angiotensin-converting enzyme inhibitor; <italic>ARB</italic>, angiotensin receptor blocker; <italic>AVS</italic>, aortic valve stenosis; <italic>BMI</italic>, body mass index; <italic>CABG</italic>, coronary-artery by-pass grafting; <italic>COPD</italic>, chronic obstructive pulmonary disease; <italic>DAPT</italic>, dual antiplatelet therapy; <italic>LIMA</italic>, left internal mammary artery; <italic>LVEF</italic>, left ventricular ejection fraction; <italic>NOAC</italic>, novel oral anticoagulant; <italic>NSTEMI</italic>, non-ST-segment elevation myocardial infarction; <italic>NYHA</italic>, New York Heart Association; <italic>PCI</italic>, percutaneous coronary intervention; <italic>proBNP</italic>, pro B-type natriuretic peptide; <italic>RIMA</italic>, right internal mammary artery; <italic>SGLT-2</italic>, sodium-glucose cotransporter-2; <italic>STS</italic>, Society of Thoracic Surgeons; TIA, transient ischemic attack.</p><p><italic>p</italic> < 0.05; †<italic>p</italic> < 0.01; ‡<italic>p</italic> < 0.001; §<italic>p</italic> < 0.0001 for differences between Impella and TAVR group.</p></table-wrap-foot>", "<table-wrap-foot><p>Values are mean ± standard deviation, median (interquartile range) or n (%), and median (lower–upper limit) for sheath size. <italic>CRT</italic>, indicates cardiac resynchronization therapy; <italic>CTA</italic>, computed tomography angiography; <italic>ICD</italic>, implantable cardioverter-defibrillator; <italic>MI</italic>, myocardial infarction; <italic>PCI</italic>, percutaneous coronary intervention; <italic>PM</italic>, pacemaker; <italic>TIA</italic>, transient ischemic attack.</p><p><italic>p</italic> < 0.05; †<italic>p</italic> < 0.01; ‡<italic>p</italic> < 0.001; §<italic>p</italic> < 0.0001 for differences between Impella and TAVR group.</p></table-wrap-foot>", "<table-wrap-foot><p>The candidates for the multivariable models were selected based on the univariate analysis of all procedural variables from Table ##TAB##1##2##, where: (i) implant duration, bleeding from axillary access, left axillary access and balloon aortic valvuloplasty (BAV) were associated with hemoglobin (HB) decline; (ii) BAV, hemoglobin decline, red blood cell (RBC) transfusion, and left axillary access were linked to BARC type 3 bleeding. Each model was adjusted for age, sex, and BMI.</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher's note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"41598_2024_51552_Fig1_HTML\" id=\"MO1\"/>", "<graphic xlink:href=\"41598_2024_51552_Fig2_HTML\" id=\"MO2\"/>", "<graphic xlink:href=\"41598_2024_51552_Fig3_HTML\" id=\"MO3\"/>" ]	[ "<media xlink:href=\"41598_2024_51552_MOESM1_ESM.mp4\"><caption><p>Supplementary Video 1.</p></caption></media>" ]	[{"label": ["1."], "surname": ["Seto", "Estep", "Tayal"], "given-names": ["AH", "JD", "R"], "article-title": ["SCAI position statement on best practices for percutaneous axillary arterial access and training"], "source": ["J. Soc. Cardiovasc. Angiogr. Interv."], "year": ["2022"], "volume": ["1"], "fpage": ["100041"]}, {"label": ["6."], "surname": ["Wilkins", "Bielauskas", "Costa"], "given-names": ["B", "G", "G"], "article-title": ["Percutaneous transaxillary versus surgically-assisted transsubclavian TAVR: A single center experience"], "source": ["Struct. Heart"], "year": ["2021"], "volume": ["5"], "fpage": ["79"], "lpage": ["84"], "pub-id": ["10.1080/24748706.2020.1849882"]}]	{ "acronym": [], "definition": [] }	19	CC BY	no	2024-01-13 00:02:20	Sci Rep. 2024 Jan 10; 14:1016	oa_package/ee/a7/PMC10781673.tar.gz
PMC10781674	38200025	[ "<title>Introduction</title>", "<p id=\"Par2\">Despite control efforts that have reduced morbidity and mortality, malaria remains a major burden globally, with deaths and cases rising in recent years<sup>##UREF##0##1##</sup>. The SU vaccine RTS,S/AS01E, the only malaria vaccine approved by the World Health Organization (WHO), is composed of virus-like particles consisting of hepatitis B virus surface antigen (HBsAg) combined with a fragment of Pf CS protein. While approval of the RTS,S vaccine represents a significant step forward, it is unlikely to lead to eradication of malaria, given its modest efficacy, which declines further over years<sup>##REF##28199305##2##–##UREF##2##6##</sup>. More recently, the SU R21-MatrixM vaccine, also approved for use in Ghana and using the same CS protein as RTS,S but with an increased ratio of CS to HBsAg, has also exhibited significant efficacy in a seasonal malaria setting. However, the utility of both RTS,S, and R21 vaccines is severely limited by the requirement for 3–4 doses plus seasonal boosters to achieve the WHO’s strategic goal of 75% protective efficacy against clinical malaria.</p>", "<p id=\"Par3\">Development of SU vaccines against <italic>Plasmodium</italic> is challenging due to the parasite’s large genome and complex multi-host life cycle. Eliminating the parasite at the clinically-silent PE stage would prevent erythrocytic infection and thus halt both disease and transmission<sup>##REF##26488565##7##,##REF##29138320##8##</sup>. One of the primary candidate antigens for PE vaccine development is the CS protein, which is expressed by infectious spz and is required for parasite motility and hepatocyte invasion. Among other features, CS has an immunodominant central repeat region of four amino acids (NANP) that is the target of neutralizing antibodies, and a GPI-anchored C-terminal region containing a T-cell epitope<sup>##REF##23613845##4##,##REF##26488565##7##,##REF##29138320##8##</sup>. Numerous vaccine platforms using CS have been evaluated as malaria interventions, but have not met the critical benchmarks<sup>##REF##29138320##8##–##REF##32183833##15##</sup>. Other strategies focus on delivery of WO vaccines (such as RAS immunization) that can elicit high antibody titers, central memory and effector memory CD8 + T cells (Tcm and Tem respectively), and resident (non-circulating) memory T cells (Trm)<sup>##UREF##3##16##</sup>. These Tem and Trm have been shown to mediate protection in WO-immunized mice by eliminating infected hepatocytes and conferring sterilizing immunity<sup>##REF##34731605##17##–##REF##24936199##19##</sup>. Protection has been achieved in both animal models and humans using repeated immunizations with WO RAS. Vaccination strategies employing WO spz (RAS, genetically-attenuated parasites (GAP), or chemoprophylaxis combined with administration of Pf spz (known as CPS)) have successfully protected malaria-naïve volunteers<sup>##REF##28097230##20##–##REF##28053159##24##</sup>. The protection appears to be mediated by a combination of liver-resident Trm and antibodies<sup>##REF##27158907##25##</sup>. However, the WO RAS vaccination strategy was less effective in malaria-endemic regions, confirming the need for more robust approaches<sup>##REF##30619241##26##–##REF##35928033##29##</sup>.</p>", "<p id=\"Par4\">The vaccination method known as “prime-and-trap” is intended to generate cellular immunity, by inducing liver Trm-mediated protection, as well as humoral immunity, with the help of CD4 + T cells and antibody production by B cells. Previous pre-clinical vaccination studies have assessed both homologous and heterologous prime-boost and prime-target approaches<sup>##REF##28422178##9##,##REF##16493425##30##,##REF##31618655##31##</sup> that have been shown to induce both specific, functional antibodies and high numbers of Trm at the time of infection, but none of these has achieved the strong safety profile of WO vaccination. Other “prime-and-trap” approaches combine a priming dose of a nucleic acid with a heterologous trapping dose of WO spz that naturally home to the liver<sup>##UREF##5##32##,##UREF##6##33##</sup>. The resulting liver Trm are positioned to respond effectively to liver-stage parasites, leading to sterile protection. Prime-and-trap vaccination using a gene-gun-delivered nucleic-acid prime and WO RAS trap in BALB/cJ mice completely protected against WT rodent malaria (<italic>Py</italic>) spz challenge<sup>##UREF##5##32##</sup>. Importantly, this approach required only one dose of WO spz<sup>##UREF##6##33##</sup>. However, the prime-and-trap vaccines developed to date have generally relied on DNA vaccination by gene gun<sup>##UREF##5##32##</sup>, and there is no gene gun delivery device currently approved for clinical use.</p>", "<p id=\"Par5\">Lipid nanoparticles (LNPs) are highly effective means of delivering ribonucleic acid (RNA) vaccines. The use of antigen-encoding messenger RNA (mRNA) emerged as a promising strategy for vaccination during the COVID-19 pandemic: these vaccines express antigen upon delivery into tissue, stimulate the innate immune system, induce cellular immunity, and eliminate the need to support large-scale protein antigen production. When PfCS mRNA was combined with a lipid nanoparticle (LNP) and tested in mice, a three-dose regimen achieved up to 60% protection in a Pb(ANKA)-PfCS challenge model in BALB/c and C57Bl6<sup>##REF##34145286##34##</sup>. Long-term protection was not observed, again highlighting the limitations of SU-only approaches. More recently, mRNAs encoding the PfCS and Pfs25 proteins were formulated with LNP and used to elicit functionally effective immune responses in mice to both antigens. This formulation protected against spz challenge and reduced Pf transmission to mosquitoes after multiple immunizations<sup>##REF##36456563##35##</sup>. However, clinical trials with mRNA vaccines formulated with traditional LNPs<sup>##UREF##7##36##–##REF##33378609##38##</sup> have encountered challenges such as LNP/mRNA reactogenicity upon injection, biodistribution to multiple organs<sup>##UREF##8##39##,##UREF##9##40##</sup>, and instability during prolonged storage<sup>##UREF##10##41##</sup>.</p>", "<p id=\"Par6\">Self-replicating or replicon RNA (repRNA) is a new and promising alternative to mRNA for use in vaccines. Once in cells, repRNA initiates biosynthesis of antigen-encoding mRNA, raising and prolonging antigen expression and enhancing humoral and cellular immune responses<sup>##REF##32690628##42##,##REF##37403357##43##</sup>. Based on recent studies<sup>##REF##32690628##42##,##UREF##11##44##,##REF##32913878##45##</sup>, repRNA systems formulated in an oil-in-water emulsion nanocarrier exhibit greater efficacy at lower doses than mRNA vaccine platforms. Moreover, repRNA elicits more robust immune responses after a single dose, offering an attractive approach for emerging infectious diseases such as dengue<sup>##REF##32913878##45##</sup>, Zika<sup>##UREF##11##44##</sup>, Crimean-Congo hemorrhagic fever virus<sup>##REF##35907368##46##</sup>, Mycobacterium tuberculosis<sup>##REF##36679975##47##</sup> and SARS-CoV-2/COVID-19<sup>##REF##32690628##42##</sup>.</p>", "<p id=\"Par7\">Here, we leverage this LION/repRNA technology to generate a two-dose, same-day prime-and-trap vaccine against malaria in mice. This was achieved by intramuscular (IM) priming with repRNA encoding full-length CS of <italic>Plasmodium yoelii</italic> (Py) (repRNA-PyCS) formulated in the LION nanoparticle carrier, followed by an IV injection of WO RAS as the trapping dose. This two-component, same-day regimen conferred sterile protection in mice and engaged both humoral and cellular arms of the immune system, bringing us closer to a single-visit, highly effective malaria vaccine.</p>" ]	[ "<title>Methods</title>", "<title>Vaccine design</title>", "<p id=\"Par31\">The antigen chosen for our vaccine design is the full-length codon-optimized circumsporozoite (CS) protein (PyCS XP_728216.3; PfCS XP_001351122.1; PvCS VUZ95499.1) from <italic>Plasmodium</italic>, as described in detail in Supplementary Fig. ##SUPPL##0##1##. The coding sequences of the full-length CS protein from Pf and Pv were utilized as irrelevant RNA controls. The PyCS antigen has a CD8<sup>+</sup> T-cell epitope that is immunodominant and protective in the H2-K<sup>d</sup> (BALB/cJ)-restricted genetic background.</p>", "<title>RNA production and LION formulation</title>", "<p id=\"Par32\">Full-length CS coding sequences from <italic>Pf</italic>, <italic>Pv</italic>, and <italic>Py</italic> were cloned separately into a Venezuelan equine encephalitis (VEE) replicon vector (pT7-VEE-Rep). In vitro transcription was performed at 34 °C using a T7 MEGAscript T7 Transcription kit (Invitrogen). RNA was purified via lithium-chloride precipitation, followed by capping with a capping kit (New England Biolabs) as described<sup>##REF##32690628##42##</sup>. RNA was further purified and stored at -80° C until use. Denatured repRNAs were verified by electrophoresis in a 1% agarose gel. Briefly, 2 μg of each repRNA was denatured by glyoxal treatment (NothernMax-Gly, AM8551, ThermoFisher), and run in an agarose gel with NorthernMax-Gly gel prep/running buffer (AM8678, ThermoFisher). Ethidium bromide was premixed into the running buffer and the gel image was analyzed by a Biorad gel docXR+ Imaging system.</p>", "<p id=\"Par33\">To protect the RNA replicons from degradation, we combined them with LION nanoparticles obtained from HDT Bio<sup>##REF##32690628##42##</sup>. LION nanoparticles consist of a hydrophobic squalene oil core stabilized with Tween 80, Span 60, and the cationic lipid DOTAP. The oil (squalene, span 60 and DOTAP) and aqueous (Tween 80 in 10 mM sodium citrate) phases were homogenized using an L5M-A high-shear mixer (Silverson) and further processed by passaging through a microfluidizer to achieve an average hydrodynamic diameter of 60 nm and polydispersity index of 0.2 by dynamic light scattering. The microfluidized LION was terminally filtered with a 200-nm pore-size polyethersulfone filter and stored at 2° to 8 °C.</p>", "<title>Cells lines</title>", "<p id=\"Par34\">To qualify the vaccine candidates in vitro, BHK cells (American Type Culture Collection) were transfected with repRNA or mock-transfected using OptiMEM (Gibco) and Expifectamine transfection kit (ThermoFisher). Cells were scraped off and lysed with RIPA buffer 24–48 h later, and lysates were analyzed by SDS–polyacrylamide gel electrophoresis and Western blot.</p>", "<title>Western blots</title>", "<p id=\"Par35\">Cells lysates were analyzed by Western blot after transfer to nitrocellulose membranes. For detection, anti–rabbit polyclonal anti-CSP (Py, Pf, or Pv) antibodies (Pocono) were used (1/1000) followed by goat anti-rabbit IgG (H + L) alkaline phosphatase-linked secondary antibody (Invitrogen, T2191) (1/10,000).</p>", "<title>Ethics statement</title>", "<p id=\"Par36\">Animal studies were performed according to the regulations of the Institutional Animal Care and Use Committee of Bloodworks Northwest, and approval was obtained from this committee. Our studies meet the standards of the Guide for the Care and Use of Laboratory Animals and applicable Bloodworks Northwest policies and procedures. Bloodworks Northwest has an approved Animal Welfare Assurance (#A4659-01, D16-00862) on file with the NIH Office of Laboratory Animal Welfare (OLAW).</p>", "<title>Mice</title>", "<p id=\"Par37\">Female BALB/cJ and C57Bl/6 (B6) mice, six to eight weeks old, were purchased from The Jackson Laboratories (Bar Harbor, ME, USA). Mice were maintained under pathogen-free conditions in animal facilities and were fed autoclaved food ad libitum. Mice were housed and cared for in standard IACUC-approved animal facilities at Bloodworks Northwest and used in compliance with IACUC-approved protocol 5285-01, which adheres to the NIH Office of Laboratory Animal Welfare standards. Mice were anesthetized using isoflurane and final bleed was collected by cardiac puncture, before been cervically dislocated inside a biosafety cabinet. For organs collection, mice were then doused with 70% ethanol and opened using sterile scissors and forceps. All methods are reported in accordance with ARRIVE guidelines.</p>", "<title>LION/repRNA vaccination</title>", "<p id=\"Par38\">For all LION/repRNA vaccines, 5 μg of RNA was mixed with LION at a nitrogen:phosphate (N:P) ratio of 15 and injected IM into mice using a total of 50 µl (25 µl in each leg). A two-vial formulation method was performed as described<sup>##REF##32690628##42##</sup>.</p>", "<p id=\"Par39\">To determine the immunogenicity of homologous prime-boost LION/repRNA-CS vaccination with single or dual CS antigens, mice were immunized with an IM prime of 5 µg of LION/repRNA, followed by a homologous boost 14 days later. The mice received a LION/repRNA vaccine with either one of the antigens (PyCS or PfCS; 5 μg per antigen) or a combination of two antigens (PyCS and PfCS, 2.5 μg per antigen) (Supplementary Fig. ##SUPPL##0##2A##).</p>", "<p id=\"Par40\">Other immunization prime-and-trap protocols and timelines are presented in each respective figure.</p>", "<title>Sporozoite isolation, vaccination, and challenge</title>", "<p id=\"Par41\">Wild-type Py (17XNL strain) spz were prepared by cyclical transmission in BALB/cJ mice and <italic>Anopheles stephensi</italic> mosquitoes at the Seattle Children’s Center for Global Infectious Disease Research Insectary (Seattle, WA, USA). Female 6- to 8-week-old Swiss Webster (SW) mice were injected with blood-stage Py 17XNL WT parasites to begin the growth cycle and used to feed female <italic>Anopheles stephensi</italic> mosquitoes. At day 15 after blood meal, salivary-gland spz were isolated and harvested as previously described<sup>##REF##17624848##63##</sup>. RAS were generated by exposure to 10,000 rads using an X-ray irradiator (Rad-Source, Suwanee, GA, USA). RAS were resuspended in 100 μL Schneider and administered to the mice through tail-vein injection. Infectious spz for challenge were prepared in an equivalent manner but without irradiation. All experimental and control mice were challenged with live <italic>Py</italic> 17XNL spz. A summary of vaccination and challenge experiments is included in Table ##TAB##0##1##.</p>", "<title>Liver lymphocyte isolation and flow cytometry</title>", "<p id=\"Par42\">Livers were perfused with 10 ml PBS/2 mM EDTA by injection into the portal vein, with outlet drainage via the inferior vena cava, and mashed into a single-cell suspension. Intrahepatic lymphocytes were isolated as previously described<sup>##UREF##5##32##,##UREF##6##33##</sup>. Final pellets were resuspended in 150 μl 1x MACs buffer and transferred to a 96-well plate for blocking and staining prior to flow cytometry. All antibody characterizations and flow-cytometry analyses were performed as previously described<sup>##REF##34801112##28##,##UREF##6##33##</sup>, using a live/dead dye (Zombie NIR Fixable Viability Kit, BioLegend) to enable exclusion of dead cells from downstream analysis. In brief, liver lymphocytes were treated with an Fc block (anti-CD16/32, clone 2.4G2; BD Biosciences) and live/dead dye for 30 min, stained for 45 min (with antibody cocktail as described<sup>##UREF##6##33##</sup>), and fixed for 20 min (Cytofix/Cytoperm reagent; BD Biosciences). Cells were gated for CD8 + T cells (CD3e + , B220-, CD4-), CD44hi by CD62Llo, then assessed by either KLRG1lo by CD69hi or by CXCR6hi by CD69hi. Antigen specificity was then assessed by PyCSP-tetramer (SYVPSAEQI-specific H2-Kd tetramer, National Institutes of Health Tetramer Core) conjugated to streptavidin-allophycocyanin (ProZyme) per standard protocols. Cell count per gram of tissue was calculated based on a known concentration of counting beads per sample to normalize data. Flow cytometry was performed on an LSR II (BD Biosciences), and data analysed with FlowJo version 10.7.1 (BD Biosciences).</p>", "<title>Ex vivo IFNγ ELISPOT</title>", "<p id=\"Par43\">Spleens were harvested and splenocytes separated from BALB/cJ mice 28 days post-immunization. A total of 1x10E5 splenocytes were combined with SYVPSAEQI peptide (1 mg/ml final) (Genemed Synthesis) for murine IFNγ ELISPOT (eBioscience), cultured for 18 h at 37 °C, and developed following manufacturer guidelines. The percentage of antigen-specific T cells was calculated based on the spot-forming units counted in each well divided by the total number of splenocytes applied to each well.</p>", "<title>Blood stage</title>", "<p id=\"Par44\">Breakthrough to blood-stage patency was assessed by Giemsa-stained thin blood smear starting at day 4 after challenge and ending at day 21, at which time a negative smear was attributed to complete protection.</p>", "<p id=\"Par45\">Mice immunized with Pf- or Pv-repRNA-CS and challenged with live Py spz were used as controls. Sterile protection was defined as being blood-smear negative. The Kaplan-Meier curves illustrate the time to developing parasitemia during days 4–21 after challenge with 17XNL strain Py live spz.</p>", "<title>qRT-PCR</title>", "<p id=\"Par46\">Liver burden was detected by qRT-PCR from harvested livers 44 h post-challenge<sup>##UREF##5##32##,##REF##31477704##64##</sup>. Total RNA was extracted from Py-infected livers using TRIzol reagent (Thermo Fisher Scientific) and treated with Turbo DNase (Ambion). cDNA synthesis was performed using a SuperScript III Platinum two-step qRT-PCR kit (Thermo Fisher Scientific). Specific PCR primers (as listed below) were used to amplify <italic>Py</italic> 18 S rRNA and GAPDH (housekeeping) gene from cDNA derived from mouse liver. The primers used for amplification of 18 S rRNA from cDNA were 18S-fwd: (GGGGATTGGTTTTGACGTTTTTGCG) and 18S-rev: (AAGCATTAAATAAAGCGAATACATCCTTAT). Mouse GAPDH was amplified with cDNA using gapdh-fwd: (CCTCAACTACATGGTTTACAT) and gapdh-rev: (GCTCCTGGAAGATGGTGATG) primers. All qRT-PCR amplification cycles were performed at 95 °C for 30 s (DNA denaturation) and 60 °C for 4 min (primer annealing and extension). Samples are run in triplicate. Results are expressed as the difference ΔCT in threshold cycle number between the average of CT value of the Py 18 S parasite gene and the average of CT value of the GAPDH house-keeping gene, so a high ΔCT represents a low parasite burden.</p>", "<title>ELISA</title>", "<p id=\"Par47\">MaxiSorp plates were coated overnight with 100 μL CS peptide (PyCS QGPGAPQGPGAPQGPGAPQGPGAP, PfCS PNANPNANPNANPNANPNAN, Genscript) at 1μg/ml in PBS 4 °C. Plates were then washed with PBS + 0.05% Tween 20 (PBS-T) and blocked with 1% BSA in PBS-T for 2 h at RT. Murine serum samples were plated at a dilution of 1:50 in PBS-T + 0.1% BSA, serially titrated 1:3 for 6 wells, and incubated for 2 h at RT or overnight at 4 °C. Following washing steps, plates were incubated with goat anti-mouse secondary antibodies, HRP diluted 1:5000 (62-6520, Thermo Fisher Scientific) in PBS-T + 0.1% BSA for 1 hr at RT. After a second wash, 100 μL TMB (substrate 95059-286, VWR) was added per well and incubated 5–10 min before stopping with 50 μL 1 N sulfuric acid.</p>", "<title>Statistical analysis</title>", "<p id=\"Par48\">Comparisons of ELISA groups or flow-cytometry cell counts were done using the non-parametric two-tailed Mann–Whitney U test (<italic>p</italic> = 0.05, <italic>p</italic> = 0.01, <italic>p</italic> = 0.001, *p < 0.0001). ELISPOT assay comparisons were done by unpaired, two-tailed Student’s <italic>t</italic> tests. Statistical significance between groups of mice for their liver burden qRT-PCR was evaluated using one-way ANOVA followed by the Kruskal-Wallis test and Dunn’s multiple-comparisons test ( <italic>p</italic> < 0.05, <italic>p</italic>< 0.005). Protection data were evaluated using Fisher’s exact test. All groups were compared against the prime-boost cohort or the trap-alone cohort (repRNA-PfCS or repRNA-PvCS for priming, and RAS for trapping dose; ** <italic>p</italic> < 0.0001). Error bars are SEM of the mean with individual mouse samples shown. Statistical significance was defined as <italic>p</italic> < 0.05 using Prism Graph-Pad 9.4.1 Software (San Diego, CA).</p>", "<title>Reporting summary</title>", "<p id=\"Par49\">Further information on research design is available in the ##SUPPL##1##Nature Research Reporting Summary## linked to this article.</p>" ]	[ "<title>Results</title>", "<title>repRNA-CS vaccine formulation and prime-boost immunogenicity in BALB/cJ mice</title>", "<p id=\"Par8\">Using the attenuated Venezuelan equine encephalitis (VEE) virus TC-83 strain<sup>##REF##25269775##48##</sup>, we incorporated the coding sequence of the full-length CS protein from Py into the alphavirus expression vector to create a repRNA malaria vaccine. The coding sequences of the full-length CS protein from Pf and <italic>Plasmodium vivax</italic> (Pv) were incorporated into the same expression vector and were utilized as irrelevant RNA controls (Supplementary Fig. ##SUPPL##0##1A##). After RNA transcription and capping, integrity of repRNA-PyCS, -PvCS, and -PfCS was verified by denaturing gel electrophoresis (Supplementary Fig. ##SUPPL##0##1B##). The vectors were then transfected into mammalian cells for validation. Western-blot analysis showed expression of CS proteins at higher apparent molecular weights (MW) than expected (observed vs expected: PyCS ~99 Kd vs 44.7 Kd, PfCS ~70 Kd vs 43.4 Kd, PvCS ~60 Kd vs 36.9 Kd, respectively), probably due to glycosylation that impeded protein migration into the gel (Fig. ##FIG##0##1a##, Supplementary Fig. ##SUPPL##0##1C##).</p>", "<p id=\"Par9\">The repRNA-CS vaccines were formulated with the LION emulsion (Fig. ##FIG##0##1b##)<sup>##REF##32690628##42##</sup>. Unlike current mRNA vaccines, the LION/repRNA vaccine platform utilizes an admixture formulation of LION (Fig. ##FIG##0##1b##) that can be manufactured independently of the RNA component and combined with the repRNA 30 min prior to immunization. To determine the immunogenicity of homologous prime-boost LION/repRNA-CS vaccination with single or dual CS antigens, mice were immunized with an IM prime of 5 µg of either LION/repRNA-PyCS or LION/repRNA-PfCS (used as an irrelevant repRNA) as a single-antigen dose or a combination of both. Primed mice received a homologous boost 14 days later (Supplementary Fig. ##SUPPL##0##2A##). Final bleeds were collected three weeks post-boost and immune responses were analyzed by ELISA against the corresponding CS tandem-repeat region. Humoral immune responses against corresponding CS antigens as evaluated by ELISA exhibited little to no cross-reactivity against heterologous CS (Supplementary Fig. ##SUPPL##0##2B##). All mice seroconverted after immunization.</p>", "<title>Immunogenicity and efficacy of a two-dose prime-boost repRNA-PyCSP vaccine in BALB/cJ mice</title>", "<p id=\"Par10\">To confirm antibody responses to homologous prime-boost LION/repRNA-PyCS vaccination in BALB/cJ, 15 mice were immunized with IM injections 14 days apart (Fig. ##FIG##1##2a##, Table ##TAB##0##1##). The LION/repRNA-PfCS and LION/repRNA-PvCS vaccines were used as separate irrelevant repRNA controls in cohorts of 7 mice each. Antibody responses were evaluated by ELISA following the prime (day 13) and boost (day 29) against peptides containing their corresponding CS tandem-repeat sequences (Fig. ##FIG##1##2b##). The anti-PyCS total IgG antibody levels in primed mice (D13, <italic>p</italic> = 0.001, Fig. ##FIG##1##2b##) were significantly higher than in naïve mice and were enhanced by a second dose of LION/repRNA-PyCS (day 29, <italic>p</italic> = 0.028, Fig. ##FIG##1##2b##) with slight cross-reactivity observed against the PfCS antigen (Fig. ##FIG##1##2b##).</p>", "<p id=\"Par11\">To assess efficacy, immunized mice were challenged 3 weeks post-boost with an IV injection of 1000 wildtype (WT) Py 17XNL spz freshly dissected from mosquito salivary glands (Table ##TAB##0##1##) while the LION/repRNA-PfCS and LION/repRNA-PvCS prime-boost vaccines were used as irrelevant repRNA controls cohorts. None of the prime-boost cohorts, including the repRNA-PyCS vaccination-alone cohort, were protected against Py wild-type spz challenge in mice (Fig. ##FIG##1##2c##). However, ELISA at the endpoint (post-challenge, noted as “term” at day 48) showed that the antibody levels were recalled by the challenge dose of WT spz (<italic>p</italic> < 0.0001 relative to naïves, Fig. ##FIG##1##2b##). These repRNA-PyCS data show the consistency of the antibody responses induced by this repRNA platform, though full protection was not conferred by this homologous approach.</p>", "<title>Superior T-cell immunogenicity of repRNA-PyCS over gene-gun DNA priming in BALB/cJ mice</title>", "<p id=\"Par12\">To assess T-cell responses to LION/repRNA-PyCS and evaluate this candidate as a potential priming dose in prime-and-trap vaccination, additional cohorts of BALB/cJ mice were immunized with a single IM injection and responses were assessed by splenocyte IFNγ ELISPOTs four weeks later (Fig. ##FIG##1##2d##). Responses were compared to splenocytes from mice immunized with a single repRNA-PfCS dose or DNA encoding PyCS administered by gene gun (gg DNA-PyCS), as used for priming in the first-generation prime-and-trap vaccine using RAS<sup>##UREF##5##32##,##UREF##6##33##</sup>. As expected, naïve mice and mice primed with the control LION/repRNA-PfCS did not recognize the PyCS epitope, as assessed by ELISPOT. In comparison, the LION/repRNA-PyCS vaccine elicited over tenfold more IFNγ-producing T cells than gene-gun DNA-PyCS vaccination (<italic>p</italic> = 0.0085, Fig. ##FIG##1##2d##), indicating robust priming of CD8 + T cells following a single LION/repRNA-PyCS injection.</p>", "<title>Immunogenicity of the accelerated prime-and-trap repRNA-PyCS-RAS vaccination in BALB/cJ mice</title>", "<p id=\"Par13\">To assess LION/repRNA-PyCS as the priming dose in prime-and-trap vaccination, cohorts of BALB/cJ mice were immunized with LION/repRNA-PyCS followed by PyRAS, along with control cohorts receiving two doses of homologous prime-boost repRNA-PyCS, or PyRAS trap-only (consisting either of an irrelevant repRNA-PfCS control followed by PyRAS or a single dose of PyRAS alone). Naïve animals were used as additional controls. Previous prime-and-trap vaccine studies<sup>##REF##28053159##24##,##UREF##5##32##</sup> employed a 28-day interval between doses and utilized 20,000 to 50,000 RAS for the trapping dose. To investigate whether the schedule could be accelerated with the LION/repRNA prime and 25,000 PyRAS as the trapping dose, mice were primed IM with repRNA-PyCS (1 μg or 5 μg) 14 or 5 days prior to a PyRAS trapping dose (Fig. ##FIG##2##3a##).</p>", "<p id=\"Par14\">To assess CS-specific whole IgG levels and parasite burden post-trapping, we collected sera and livers from seven mice of each cohort within 5–6 h after the trapping dose. As expected, mice immunized with either 14-day regimen (1 μg or 5 μg) had higher antibody titers than mice immunized with a 5-day regimen of repRNA-PyCS (trapped with RAS) and the irrelevant repRNA-PfCS regimen-with-RAS cohort (Fig. ##FIG##2##3b##). The repRNA-PfCS-plus-RAS cohort and the 5-day cohort exhibit an inverse trend between parasite burden in the liver and high levels of IgG (Fig. ##FIG##2##3b##). These results suggest that anti-PyCS IgG generated beyond 14 days post-prime targeted the WO spz of the trapping dose, reducing their distribution to the liver target.</p>", "<p id=\"Par15\">Next, to determine if a balanced or skewed IgG subclass response was induced by repRNA-PyCS, we measured circulating IgG subclasses four weeks post-trapping dose in a separate cohort of mice, using CS peptide ELISA (Fig. ##FIG##2##3c##). There was no significant difference between the IgG1, IgG2a, and IgG3 levels, or in the IgG2a/IgG1 ratio between cohorts immunized with repRNA-PyCS as the priming dose, indicating that the two-dose regimen induces a balanced Th1/Th2 antibody response. However, the cohort receiving RAS alone as the trapping dose (i.e, 14-day repRNA-PfCS and RAS, Fig. ##FIG##2##3c##, dark-blue data) exhibited a skewed Th2-type humoral immune response. Our results implicate IgG2a in addition to the IgG1 and IgG3 subclasses as substantial components of the humoral response, as opposed to the narrow IgG1-biased response observed after administration of a WO-based vaccine such as RAS.</p>", "<p id=\"Par16\">Finally, to assess CD8<sup>+</sup> T-cell responses to LION/repRNA-PyCS prime-and-trap vaccination, mice were immunized under various regimens as described above (Fig. ##FIG##3##4a##). Livers were collected 28 days after the last immunization in two independent experiments, and lymphocytes were isolated and stained for flow cytometry as described previously<sup>##UREF##5##32##</sup>. Total CD8<sup>+</sup> T cells and activated CD8<sup>+</sup> T cells (CD44<sup>hi</sup> CD62L<sup>lo</sup>) in the livers of prime-and-trap (5 μg 5-day, 5 μg 14-day, 1 μg 14-day) or trap-only (i.e., control prime-and-trap (5 μg 14-day) or 25,000 RAS) immunized mice were significantly higher than in the homologous prime-boost repRNA-PyCS-immunized mice (Fig. ##FIG##3##4b##). To determine if our prime-and-trap vaccine can generate CS-specific liver-resident Trm, CS-tetramer-labeled CD8<sup>+</sup> T cells were identified by either CD69<sup>+</sup>/KLRG1lo or CD69<sup>+</sup>/CXCR6<sup>+</sup> expression. Both populations of CS-specific liver-resident Trm were larger in the 5-day prime-and-trap cohort and the RAS-immunized mice than in the 14-day prime-and-trap immunized mice (Fig. ##FIG##3##4c##). This result is consistent with the observation that post-trap liver parasite burden is lower in the 14-day group, as shown above, resulting in reduced Trm generation.</p>", "<title>Efficacy of accelerated prime-and-trap repRNA-PyCS-RAS vaccination in BALB/cJ mice</title>", "<p id=\"Par17\">To assess whether the immune responses induced by prime-and-trap immunization can confer sterile immunity, cohorts of mice immunized with LION/repRNA-PyCS as prime dose followed by RAS for the trapping dose (as described above in Fig. ##FIG##2##3##) were challenged three weeks later with 1000 freshly prepared WT Py spz delivered IV (Fig. ##FIG##4##5a##, Table ##TAB##0##1##). Upon challenge, 66% of trap-only and 80% of prime-and-trap mice were sterilely protected (Fig. ##FIG##4##5b##; naïve mice were not protected). These results (66% vs 80%) are statistically indistinguishable. However, in the mice of the immunized cohorts that experienced breakthrough parasitemia, we saw a consistent 2- to 3-day delay in the onset of blood-stage parasitemia and a reduced peak load (Fig. ##FIG##4##5c##), indicating reduced liver burden. Two weeks post-challenge, sera were collected and total IgG levels were quantified by ELISA. The total IgG titer post-challenge was similar in all immunized cohorts (prime-and-trap vs trap alone), suggesting a recall of the CS-specific humoral immune response following spz challenge (Fig. ##FIG##4##5d##).</p>", "<p id=\"Par18\">The four cohorts exhibiting partial sterile protection post-challenge were re-challenged 6 weeks after the first challenge (Table ##TAB##0##1##). All were protected (Fig. ##FIG##4##5b##) with high levels of circulating specific anti-CS IgG (Fig. ##FIG##4##5e##). On the whole, the data from these replicate experiments are consistent with previous observations of limited protection after a single dose of PyRAS, indicating that the prime-and-trap strategy is important for making the most of the LION/repRNA-primed responses.</p>", "<title>Prime-and-trap vaccination improves protection against more stringent challenge in BALB/cJ mice over trap alone</title>", "<p id=\"Par19\">To determine if prime-and-trap vaccination can protect against a more stringent challenge, the efficacy of a 2-dose 5-day prime-and-trap immunization was compared to a trap-only regimen (irrelevant prime repRNA-PfCS + RAS). BALB/cJ mice were vaccinated with 5 μg repRNA-PyCS or the irrelevant rep-RNA-PfCS once, followed by a dose of 25,000 RAS 5 days later (Table ##TAB##0##1##). As a control cohort, mice were immunized 5 days apart with 2 injections of 5 μg repRNA-PyCS (homologous prime-boost). Mice were challenged with 10,000 Py WT spz at 8 weeks post-trap (Fig. ##FIG##5##6a, b##). We observed results similar to those previously reported, i.e., the homologous prime-boost repRNA-PyCS cohort showed no protection following challenge (Fig. ##FIG##5##6##b, ##FIG##5##c##, orange data), and was indistinguishable from our infectivity-control cohort (Fig. ##FIG##5##6##b, ##FIG##5##c##, black data). Following this 8-week challenge with a high challenge dose, the efficacy of a trap-only (RAS) vaccine was just 30% (Fig. ##FIG##5##6c##, blue data) and was 50% with a prime-and-trap vaccine (Fig. ##FIG##5##6c##, green data). However, only the 5-day regimen prime-and-trap yielded a moderate but significantly (<italic>p</italic> = 0.0325) higher level of sterile protection than prime-boost immunization. After challenge, high titers of anti-CS IgG were detected by ELISA in all prime-and-trap cohorts and no difference was observed in IgG subclasses among the three repRNA-immunized cohorts (Fig. ##FIG##5##6d##).</p>", "<title>Prime-and-trap repRNA-PyCS-RAS vaccination does not elicit sterile protection in C57Bl6 mice</title>", "<p id=\"Par20\">To further demonstrate the importance of CS-specific CD8 + T cells for protection of BALB/cJ mice, C57BL/6 mice (MHC H2b) that are unable to present the protective MHC H2-Kd-restricted epitope SYVPSAEQI were immunized and challenged. C57BL/6 mice do not express the MHC-I allele needed to present this CS-specific epitope in infected hepatocytes. C57BL/6 mice were immunized according to a 5-day regimen with 5 μg repRNA-PyCS and 25,000 RAS (Supplementary Fig. ##SUPPL##0##3A##, Table ##TAB##0##1##). The prime-and-trap cohort (repRNA-PyCS + RAS) was compared to a prime-boost repRNA-PyCS cohort, a trap cohort (repRNA-PvCS + RAS), a double-trap (RAS + RAS) cohort, and an infectivity-control cohort. At day 35 after the second immunization, sera were collected and analyzed by ELISA against the PyCS peptide (Supplementary Fig. ##SUPPL##0##3B##). Both trap and prime-boost cohorts had significantly lower IgG titers than the double-RAS and prime-and-trap cohorts, which exhibited equivalent strong anti-CS antibody titers (Supplementary Fig. ##SUPPL##0##3b##).</p>", "<p id=\"Par21\">These animals were then challenged 8 weeks later by an IV dose of 5000 infectious WT Py spz. As anticipated, none of the C57BL/6 mice cohort had sterile protective immunity following challenge (Supplementary Fig. ##SUPPL##0##3C##), but the trap, double-trap, and prime-and-trap animals experienced a delay in the onset of parasitemia compared to the infectivity control and prime-boost cohorts (Supplementary Fig. ##SUPPL##0##3C##). Moreover, relative to the infectivity control cohort (Supplementary Fig. ##SUPPL##0##3D##, black data), animals immunized with either prime-and-trap (Supplementary Fig. ##SUPPL##0##3D##, green data) or double trap (Supplementary Fig. ##SUPPL##0##3D##, blue data) showed significantly greater control of the parasitemia (particularly at the day of peak infection) than the cohorts immunized with either irrelevant repRNA-PvCS and RAS (Supplementary Fig. ##SUPPL##0##3D##, purple data) or prime-boost double repRNA (Supplementary Fig. ##SUPPL##0##3D##, orange data). Interestingly, while the antibody response to the homologous prime-boost repRNA immunization was biased toward IgG2c (Supplementary Fig. ##SUPPL##0##3E##, orange data), all other regimens induced more balanced IgG2c:IgG1 ratios (Supplementary Fig. ##SUPPL##0##3E##, prime-and-trap in green, prime control-and-trap in purple and double trap (RAS) in blue).</p>", "<title>Sterile protection following same-day prime-and-trap repRNA-PyCS-RAS vaccination</title>", "<p id=\"Par22\">As described above, accelerating the prime-and-trap vaccination from a 14-day to a 5-day immunization schedule reduced levels of circulating CS-specific antibodies (Fig. ##FIG##2##3b##) and improved numbers of CS+ liver Trm (Fig. ##FIG##4##5##), while retaining strong efficacy (Fig. ##FIG##3##4e##). We next compared the protective efficacy in BALB/cJ mice of same-day and 5-day prime-and-trap regimens. Mice were vaccinated with 5 μg repRNA-PyCS and 25,000 RAS and challenged 3 weeks later by an IV dose of 1000 freshly dissected infectious WT Py spz (Fig. ##FIG##6##7a##, Table ##TAB##0##1##). These cohorts were compared to a trap-alone (repRNA-PfCS + 25,000 RAS) cohort and an infectivity-control cohort. While both the trap cohort (3/5 mice) and control cohort (7/7 mice) rapidly developed high levels of parasitemia, parasitemia appeared in only 2/10 and 1/9 of the 5-day and 0-day prime-and-trap cohorts, respectively (Fig. ##FIG##6##7b–d##). In the prime-and-trap cohorts, parasitemia appeared 7–8 days after infection and was cleared by day 16 (Fig. ##FIG##6##7b##), whereas in both control cohorts the parasitemia appeared 4–5 days post-infection, persisted for 15 days, and was cleared at day 22 post-challenge as anticipated.</p>", "<p id=\"Par23\">To determine if a same-day prime-and-trap vaccine can protect against a more stringent challenge, a cohort was immunized with 5 μg repRNA-PyCS and 25,000 RAS on the same day and challenged two months later with 20,000 cryopreserved WT Py spz (previously frozen in-house as described<sup>##REF##23244590##49##</sup> and thawed and injected within 30 minutes) (Fig. ##FIG##6##7e##). All the infectivity-control cohorts consistently developed parasitemia by day 5 to 6, while the prime-and-trap and trap cohorts showed a 2-3-day delay (Fig. ##FIG##6##7f–g##). Upon challenge two months post-immunization, 7/10 (70%) of the prime-and-trap immunized mice showed sterile immunity, whereas 3/10 (30%) of the trap cohort were protected (Fig. ##FIG##6##7g, h##). These results support the feasibility of an accelerated prime-and-trap immunization with 2 concurrent injections via IM and IV routes, respectively.</p>" ]	[ "<title>Discussion</title>", "<p id=\"Par24\">An effective malaria vaccine will save many lives and lift the global burden of this disease. Among the next-generation approaches that build on the recent success of vaccines in COVID-19 prevention are mRNA vaccines delivered with nanoparticles<sup>##REF##34145286##34##,##REF##36456563##35##</sup>. Our prime-and-trap approach combines two strategies that exhibit modest efficacy individually but confer promising levels of protection when combined. This approach also has key logistical advantages over other current candidates. We summarize all the mouse immunization and challenge studies performed in this manuscript with Py wild-type parasites in Table ##TAB##0##1##. We have shown that a homologous LION/repRNA-PyCS vaccine (prime-boost) is highly immunogenic at all doses evaluated, eliciting strong antibody responses when given in a 2-dose immunization regimen 2 weeks apart. Antibody levels are modest after the priming dose but increase subsequent to the booster dose. We have also demonstrated that homologous LION/repRNA-CS vaccination alone was insufficient to prevent blood-stage infection and did not provide protection in mice. To overcome these issues and enhance the practicability of vaccinating people in low-resource environments, our efforts have focused on minimizing the interval between injections, lowering dosages, and simplifying storage and preparation of the agents. We believe an effective vaccine based on a strategy such as that described herein, administered in a single clinic visit, will enhance the logistics of the vaccination schedule and reduce the cost of goods.</p>", "<p id=\"Par25\">Our results suggest that the priming dose of LION/repRNA-PyCS is highly immunogenic. We observed induction of a strong humoral response in BALB/cJ and C57Bl/6 mice when they were immunized on the 14-day, 5-day, or same-day regimens. We also observed a strain-specific CD8 + T-cell response in BALB/cJ mice. There was a 2-3-day delay in the onset of blood-stage parasitemia compared to the control groups, which indicates a reduction in liver burden. Our prime-and-trap approach has the advantage of inducing both humoral and cellular immunities without compromising the generation of protective liver-resident CD8<sup>+</sup> T cells. Since the effectiveness of the RAS trapping dose may be affected by the presence of CS-specific antibodies, future work will include evaluation of how our vaccine performs in the context of infection-induced or vaccine-induced immunity.</p>", "<p id=\"Par26\">Previous studies have shown that RAS vaccines can induce liver CD8<sup>+</sup> Trm cells in animal models, but they can only achieve high levels of sterile protection in humans with three or more IV doses<sup>##REF##28097230##20##,##REF##21669394##50##,##REF##23929949##51##</sup>. The requirement for several IV doses compounds the time, cost, and logistical problems inherent to WO vaccines<sup>##REF##28216244##27##,##REF##34801112##28##</sup>. Others studies employing later-arresting GAP vaccines<sup>##REF##28053159##24##,##REF##21669394##50##,##REF##29440367##52##</sup> have shown that expressing more antigens leads to improved protection over early-arresting GAP or RAS<sup>##UREF##3##16##,##REF##32484795##53##</sup>. Likewise, immunization with wild-type (WT) WO spz administered under blood-stage drug chemoprophylaxis (known as CPS, (PfSPZ-CVac)<sup>##REF##34194041##22##,##REF##19434915##54##</sup>) allows full liver-stage development and can induce high levels of protection at reduced doses, although CPS involves co-administration of drugs with the vaccine, presenting practical and regulatory challenges<sup>##REF##34194041##22##</sup>. These well-established immunizations with WO SPZ RAS<sup>##REF##6057225##55##,##REF##4583408##56##</sup>, GAP<sup>##UREF##3##16##,##REF##28053159##24##,##REF##29440367##52##,##REF##15699336##57##</sup>, or PfSPZ-CVac<sup>##REF##34194041##22##,##REF##19434915##54##</sup> are at least in part reliant on the generation of liver-resident memory CD8<sup>+</sup> T cell (Trm) responses<sup>##REF##24936199##19##,##REF##31618655##31##,##REF##24823625##58##–##REF##23594961##60##</sup>.</p>", "<p id=\"Par27\">In the present study, we show the contribution of CS-specific liver-resident memory CD8<sup>+</sup> T cells induced by our prime-and-trap regime with LION/repRNA-PyCSP. Given that the initial dose of RAS administered to naïve individuals appears to be the most immunogenic and effective in inducing liver Trm cells, a vaccination strategy employing a single dose of RAS<sup>##UREF##5##32##</sup> may be the most efficient and cost-effective means of using this valuable resource.</p>", "<p id=\"Par28\">Our approach of combining repRNA and LION has several advantages over mRNA-LNP vaccines: a reduced number of doses (2), the use of a single dose of the WO irradiated spz, a shorter (5-day or same-day) administration schedule, and simpler logistics<sup>##REF##34145286##34##</sup>. Nanoparticle formulations as carriers for nucleic acid delivery make it possible to optimize for specific target cells and tissue types. The balance of tissue targeting can be shifted by changing the nanoparticle composition and route of delivery (oral, subcutaneous, or IV)<sup>##REF##34381159##61##</sup>. We will initially focus future work on lyophilization of our repRNA malaria vaccines to eliminate cold-chain requirements prior to reconstitution, and secondly on direct targeting of the liver, in the hope of replacing spz administration with a more practical and cost-effective nanoparticle formulation.</p>", "<p id=\"Par29\">Our findings reinforce the concept that CS remains one of the most immunodominant and protective antigens expressed by spz<sup>##REF##17151604##62##</sup>, and that its CD8 + T cell epitope is probably involved in the protective effect against parasites in the liver of the BALB/cJ mice following RAS immunization. Indeed, when C57BL/6 mice were challenged, sterile protection was not achieved, since the SYIPSAEKI immunodominant epitope cannot be presented by the C57BL/6 MHCI. Although the CS protein still appears to be the best vaccine candidate antigen it is not clear that any single antigen will confer the robust and durable protection needed to eradicate malaria. Given the genetic diversity among parasite strains and the many variables in physiology, environment, and logistical capabilities involved in a broad vaccination campaign, it may be necessary to target multiple antigens from various stages of the parasite life cycle to eliminate malaria. We are currently evaluating the immunogenicity of repRNA presenting multiples antigens from various stages of the parasite life-cycle.</p>", "<p id=\"Par30\">In summary, this replicating RNA/LION vaccine for malaria to show greater than 70% sterile protection in mice. We have demonstrated a heterologous vaccination approach that concurrently induces humoral and T-cell immunities, using repRNA-CS formulated with LION nanoparticles and PyRAS targeting the liver. These promising preliminary data suggest that sterile protection in mice may be achievable by making further rational refinements to this approach. This prime-and-trap approach is currently being broadened in mouse studies to include antigens from other stages of malaria, which will be the focus of a future manuscript. Our prime-and-trap approach is also being tested in non-human primate models, potentially leading to immunogenicity and efficacy studies in a CHMI trial in the near future.</p>" ]	[]	[ "<p id=\"Par1\">Malaria, caused by <italic>Plasmodium</italic> parasites, remains one of the most devastating infectious diseases worldwide, despite control efforts to lower morbidity and mortality. Both advanced candidate vaccines, RTS,S and R21, are subunit (SU) vaccines that target a single <italic>Plasmodium falciparum</italic> (Pf) pre-erythrocytic (PE) sporozoite (spz) surface protein known as circumsporozoite (CS). These vaccines induce humoral immunity but fail to elicit CD8 + T-cell responses sufficient for long-term protection. In contrast, whole-organism (WO) vaccines, such as Radiation Attenuated Sporozoites (RAS), achieved sterile protection but require a series of intravenous doses administered in multiple clinic visits. Moreover, these WO vaccines must be produced in mosquitos, a burdensome process that severely limits their availability. To reduce reliance on WO while maintaining protection via both antibodies and Trm responses, we have developed an accelerated vaccination regimen that combines two distinct agents in a prime-and-trap strategy. The priming dose is a single dose of self-replicating RNA encoding the full-length <italic>P. yoelii</italic> CS protein, delivered via an advanced cationic nanocarrier (LION<sup>TM</sup>). The trapping dose consists of one dose of WO RAS. Our vaccine induces a strong immune response when administered in an accelerated regimen, i.e., either 5-day or same-day immunization. Additionally, mice after same-day immunization showed a 2-day delay of blood patency with 90% sterile protection against a 3-week spz challenge. The same-day regimen also induced durable 70% sterile protection against a 2-month spz challenge. Our approach presents a clear path to late-stage preclinical and clinical testing of dose-sparing, same-day regimens that can confer sterilizing protection against malaria.</p>", "<title>Subject terms</title>" ]	[ "<title>Supplementary information</title>", "<p>\n\n\n</p>" ]	[ "<title>Supplementary information</title>", "<p>The online version contains supplementary material available at 10.1038/s41541-023-00799-4.</p>", "<title>Acknowledgements</title>", "<p>In memory of Dr Anil Ghosh. We thank Tess Seltzer, Cecilia Kalhtoff, and Dr Alexis Kaushansky (Seattle Children’s Research Institute) for assistance and support of <italic>Plasmodium yoelii</italic>–infected mosquito production and dissection of the salivary glands. We thank Mint Laohajaratsang for her technical assistance and the vivarium staff of Bloodworks NW Center for assistance with the mouse facilities. This research was supported by MVX internal funds, partial HDT Bio internal funds and partial support from 1R01AI141857 (to S.C.M.).</p>", "<title>Author contributions</title>", "<p>M.A., J.H.E, A.P.K, B.W., S.C.M, S.G.R., J.W.D. conceived the research concepts. M.A., Z.M, J.H.E., A.P.K., B.W., M.J.S., S.C.M, S.G.R., J.W.D. designed this study. J.W.D secured the funding for this study. M.A., Z.M, K.H, M.L., A.S. M.J.S. and F.W., performed the in vitro experiments. M.A., Z.M, K.H, conducted the in vivo experiments. M.A., Z.M, K.H., J.H.E., A.P.K., B.W., M.J.S., S.C.M, J.W.D. participated in data analysis and interpretation. The manuscript was written by M.A. and was reviewed and edited by all authors.</p>", "<title>Data availability</title>", "<p>The authors declare that all data generated or analyzed during this study are included in this study are available within the main and supplemental figures. The data supporting this study’s findings are available from the corresponding authors upon reasonable request.</p>", "<title>Competing interests</title>", "<p id=\"Par50\">M.A., Z.M, K.H., J.D. are full-time employees of MalarVx, Inc. M.A. and Z.M. are co-inventors on international patent PCT/US2023/19674. J.D. has equity interests in MalarVx, Inc. A.S., J.H.E., A.P.K. and S.G.R. are full-time employees of HDT Bio. A.S., J.H.E., A.P.K and S.G.R. have equity interests in HDT Bio. J.H.E has consulting agreements with various life sciences companies. J.H.E. and A.P.K. are inventors on granted U.S. patents pertaining to HDT Bio’s proprietary cationic nanocarrier formulation. S. C. M. has a patent application on selected aspects of the prime-and-trap concept through the University of Washington and has equity in a startup company (Sound Vaccines, Inc.) that is negotiating with the University of Washington for rights to this intellectual property. All other authors declare that they have no competing interests.</p>" ]	[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>LION/repRNA-CS vaccine design.</title><p><bold>a</bold> After RNA transcription and capping, repRNA-PyCS or -PfCS or -PvCS was transfected into BHK cells. 24 to 48 h later, the lysate (R, reduced, NR, non-reduced) of transfected cells or null transfection used as control were analyzed by Western blot, using rabbit polyclonal antibodies for immunodetection. <bold>b</bold> Graphic representation of replicon repRNA-PyCS and LION formulation that was used for immunization after mixing.</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>Immunogenicity and efficacy of a prime-boost vaccine.</title><p>Vaccine designed with repRNA encoding either PyCS (orange circle), PfCS (opened circle) or PvCS (black circle) formulated with LION. <bold>a</bold> BALB/cJ mice immunization schedule. Mice were injected with 5ug of LION/repRNA-PyCS, -PfCS, or -PvCS in a 2-week interval prime-boost regimen. <bold>b</bold> Mouse sera were collected at post-prime (day 13), post-boost (day 29), and post-challenge (day 48, i.e., “term” as terminal bleed). PyCS and PfCS antibody responses were determined by PyCS or PfCS repeat region peptide titration ELISA, respectively. <bold>c</bold> Parasitemia, patency curves (>1% parasitemia) of mice post-challenge and protection post-challenge of the three prime-boost cohorts. Number of mice per cohort indicated above bar graph. Naïve cohort indicated with a triangle symbol and dash line. <bold>d</bold> IFNγ ELISPOT of CS-specific T cells (against the PyCS SYVPSAEQI epitope) four weeks after a single prime injection of LION/repRNA-PyCS. Cohorts receiving gene gun DNA encoding PyCS (ggDNA), LION/repRNA-PfCS, or no immunization were used as controls. The n value represents total number of mice tested per cohort, in two to three independent assays. Each data point represents an individual mouse and the bar represents the group mean. Asterisks represent significance as determined by the non-parametric two-tailed Mann–Whitney two-tailed test (<italic>p</italic> = 0.05, <italic>p</italic> = 0.01, <italic>p</italic> = 0.001, *<italic>p</italic> < 0.0001).</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><title>Immunogenicity of accelerated prime-and-trap immunization regimens in BALB/cJ mice.</title><p><bold>a</bold> Immunization schedule. 5ug 5-day (green data) or 5ug 14-day (red data) or 1ug 14-day regimen of repRNA-PyCS (light blue data) prime followed by trap dose of 25,000 RAS, were tested in mice. Control cohorts are prime-boost repRNA-PyCS (orange data) or trap cohort (repRNA-PfCS +RAS, dark blue data). <bold>b</bold> Liver and serum from seven mice per cohort were harvested 5 h post-trap injection. Total IgG titer was analyzed by ELISA, while liver parasite burdens were analyzed by qPCR quantification. Liver-burden qPCR was evaluated using one-way ANOVA followed by Kruskal-Wallis test and Dunn’s multiple comparisons test (<italic>p</italic> < 0.05, *<italic>p</italic> < 0.005). A lower ΔCT represents a higher parasite burden. Pearson correlation comparing the IgG titer and the liver burden for the four vaccine groups. For visualization purposes, results from all cohorts are displayed in the same graph, but each correlation coefficient was computed individually for each cohort. <bold>c</bold> Final-bleed sera were collected at endpoint, 4 weeks post-trap (day 42), to evaluate total IgG titer and IgG1, IgG2a, IgG3 subclasses for each cohort by ELISA. Ratio of IgG2a/IgG1 is indicated in bar graph. All others statistical analyses were performed using a Mann–Whitney test. The n value represents total number of mice tested per cohort, and the error bars represent SD of the mean in two independent assays. Each data point represents an individual mouse, and the bar represents the group mean.</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><title>5-day prime-and-trap and trap-only (RAS) regimens of immunization induce higher-frequency liver Trm cells than 14-day prime-and-trap regimen.</title><p><bold>a</bold> Schedule of immunization. 5ug 5-day (green data) or 5ug 14-day (red data) or 1ug 14-day regimen of repRNA-PyCS (light blue data) prime followed by trap dose of 25,000 RAS (dark blue data), were tested in mice. Control cohorts are prime-boost repRNA-PyCS (orange data) or trap cohort (repRNA-PfCS +RAS, black data). <bold>b</bold> Flow cytometric total CD8<sup>+</sup> T cells and activated (CD44hi/CD62Llo) CD8<sup>+</sup> T cells in perfused livers 28 days after the Trap dose of 25,000 RAS. <bold>c</bold> Flow cytometric analysis of tetramer-stained, CS-specific CD8<sup>+</sup> liver Trm cells (by CD69+ and either KLRG1lo (upper) or CXCR6+ (lower)). All error bars are SD of the mean. <italic>p</italic> = 0.05, <italic>p</italic> = 0.01, <italic>p</italic> = 0.001 by Mann–Whitney two-tailed test. The <italic>n</italic> value represents the total number of mice tested per cohort in two independent assays. Each data point represents an individual mouse and the bar represents the group mean, with error bars representing the standard error of the mean. Asterisks represent significance as determined by the non-parametric two-tailed Mann–Whitney U test (<italic>p</italic> = 0.05, <italic>p</italic> = 0.01, <italic>p</italic> = 0.001, *<italic>p</italic> < 0.0001).</p></caption></fig>", "<fig id=\"Fig5\"><label>Fig. 5</label><caption><title>Efficacy of accelerated prime-and-trap immunization regimens in BALB/cJ mice.</title><p><bold>a</bold> Immunization schedule. Prime dose of 5 μg 5-day (green data) or 5 μg 14-day (red data) or 1 µg 14-day regimen of repRNA-PyCS (light blue data), followed by trap dose (dark blue data) of 25,000 RAS, were tested in mice. Three weeks later, mice were challenged intravenously with 1000 live spz isolated from infected mosquitos. Control cohorts are the trap cohort (RAS alone, dark blue data) and naïve cohort. Cohort showing partial protection were rechallenged with 1000 live spz six weeks later. <bold>b</bold> Protection post-challenge and re-challenge per cohort. Number of mice per cohort indicated above bar graph as protected/non-protected ratio. <bold>c</bold> Parasitemia post-challenge of immunized mice cohorts and patency curves (>1% parasitemia) of mice post-challenge. <bold>d</bold> Sera from seven mice per cohort were harvested 20 days post-challenge (day 55) and CS-specific IgG titers were analyzed by ELISA. <bold>e</bold> Final bleeds (six weeks post-rechallenge, day 104) of the four cohorts of mice showing full protection. The n value represents the total number of mice tested per cohort, in two or three independent assays. Each data point represents an individual mouse and the bar represents the group mean. All others statistical analyses were performed using a Mann–Whitney two-tailed test.</p></caption></fig>", "<fig id=\"Fig6\"><label>Fig. 6</label><caption><title>Prime-and-trap vaccine improves protection against stringent challenge in BALB/cJ mice.</title><p><bold>a</bold> Schedule of immunization. Prime-and-trap vaccine composed of 5ug 5-day regimen of prime with repRNA-PyCS (green data) followed by trap dose of 25,000 RAS. Control cohort is a 5 μg 5-day prime-boost repRNA-PyCS (orange data) or a trap cohort (repRNA-PfCS +RAS, dark blue data). Two months later, mice were challenged intravenously with 10,000 live spz isolated from infected mosquitos. <bold>b</bold> Parasitemia post-challenge of all cohorts including the naive cohort. Emphasis indicates the parasitemia peak at day 12, each dot representing a mouse, and the bar is the mean of the cohort. <bold>c</bold> Patency curves of mice post-challenge per cohort. Number of mice per cohort indicated above bar graph as protected/non-protected ratio. *<italic>p</italic> < 0.0001 by Fisher exact test. <bold>d</bold> Final-bleed sera were collected at endpoint (day 77) to evaluate total IgG titer and IgG1, IgG2a, IgG3 subclasses from each cohort by ELISA. Ratio of IgG2a/IgG1 is indicated in bar graph. The n value represents the total number of mice tested per cohort, and the error bars represent SD of the mean in two independent experiments. Each data point represents an individual mouse and the bar represents the group mean. All statistical analyses were performed using a Mann–Whitney two-tailed test. <italic>p</italic> = 0.05, <italic>p</italic> = 0.01, <italic>p</italic> = 0.001 unless specified in panel.</p></caption></fig>", "<fig id=\"Fig7\"><label>Fig. 7</label><caption><title>Immunogenicity and efficacy of a same-day prime-and-trap vaccine in BALB/cJ mice.</title><p><bold>a</bold> Schedule of immunization. Prime-and-trap vaccine consisted of a 5ug 5-day regimen (green data) or 5ug same-day interval (pink data) of prime with repRNA-PyCS followed by trap dose of 25,000 RAS. Control cohort is 5 μg same-day interval of a trap cohort (repRNA-PfCS +RAS, dark blue data). Three weeks later, mice were challenged intravenously with 1000 live spz isolated from infected mosquitos. <bold>b</bold> Parasitemia post-challenge of all cohorts including the naive cohort (black data). <bold>c</bold> Patency curves (>1% parasitemia) of mice post-challenge. <bold>d</bold> Protection post-challenge per cohort. Number of protected mice per cohort indicated above bar graph. <bold>e</bold> Schedule of immunization of a same-day prime-and-trap vaccine (5 μg, 0-day regimen) with trap dose of 20,000 cryopreserved spz administered IM and IV, respectively, on the same day. Control cohort is naive. Two months later, mice were challenged intravenously with 20,000 cryopreserved spz isolated from infected mosquitos. <bold>f</bold> Patency curves (>1% parasitemia) of mice post-challenge. <bold>g</bold> Patency curves (>1% parasitemia) and protection (h) post-challenge per cohort. Number of protected mice per cohort indicated above bar graph. **<italic>p</italic> < 0.0001 by Fisher exact test. The <italic>n</italic> value represents the total number of mice tested per cohort, in two independent experiments. All other statistical analyses were performed using a Mann–Whitney two-tailed test <italic>p</italic> = 0.05, <italic>p</italic> = 0.01, *<italic>p</italic> = 0.001.</p></caption></fig>" ]	[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Summary of mouse immunization and challenge studies with <italic>P.yoelii</italic> wild-type parasites. Protection data were evaluated using Fisher’s exact test.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th colspan=\"5\">Immunization</th><th colspan=\"4\">Challenge</th><th rowspan=\"2\"><italic>p</italic> value</th><th rowspan=\"2\">Linked to</th></tr><tr><th>BALB/cJ mice</th><th>PRIME dose (LION/ repRNA)</th><th>Route</th><th>2nd dose (day given)</th><th>Route</th><th>IV dose (week given)</th><th>Blood-stage patency</th><th>Protected/ Challenged</th><th>% protection</th></tr></thead><tbody><tr><td>15</td><td>5μg PyCSP</td><td>IM</td><td>5 µg PyCSP (day 14)</td><td>IM</td><td>1000 WT SPZ (week 3)</td><td>4 days</td><td>0/13</td><td>0%</td><td rowspan=\"3\"/><td rowspan=\"3\">Fig. ##FIG##1##2##</td></tr><tr><td>7</td><td>5μg PfCSP</td><td>IM</td><td>5 µg PfCSP (day 14)</td><td>IM</td><td>1000 WT SPZ (week 3)</td><td>4 days</td><td>0/5</td><td>0%</td></tr><tr><td>7</td><td>5μg PvCSP</td><td>IM</td><td>5 µg PvCSP (day 14)</td><td>IM</td><td>1000 WT SPZ (week 3)</td><td>4 days</td><td>0/7</td><td>0%</td></tr><tr><td>13</td><td>5μg PyCSP</td><td>IM</td><td>25,000 RAS (day 14)</td><td>IV</td><td>1000 WT SPZ (week 3)</td><td>7 days</td><td>8/13</td><td>61%</td><td>0.0186</td><td rowspan=\"10\">Fig. ##FIG##4##5##</td></tr><tr><td>13</td><td>5μg PyCSP</td><td>IM</td><td>25,000 RAS (day 5)</td><td>IV</td><td>1000 WT SPZ (week 3)</td><td>7 days</td><td>10/13</td><td>77%</td><td>0.0033</td></tr><tr><td>11</td><td>1μg PyCSP</td><td>IM</td><td>25,000 RAS (day 14)</td><td>IV</td><td>1000 WT SPZ (week 3)</td><td>8 days</td><td>7/11</td><td>64%</td><td>0.0174</td></tr><tr><td>13</td><td>–</td><td>–</td><td>25,000 RAS</td><td>IV</td><td>1000 WT SPZ (week 3)</td><td>6 days</td><td>9/13</td><td>69%</td><td>0.0085</td></tr><tr><td>5</td><td>–</td><td>–</td><td>–</td><td>–</td><td>1000 WT SPZ (week 3)</td><td>5 days</td><td>0/5</td><td>0%</td><td/></tr><tr><td rowspan=\"5\" colspan=\"5\"/><td colspan=\"4\">Rechallenge</td><td rowspan=\"5\"/></tr><tr><td>1000 WT SPZ (week 6)</td><td>none</td><td>6/6</td><td>100%</td></tr><tr><td>1000 WT SPZ (week 6)</td><td>none</td><td>6/6</td><td>100%</td></tr><tr><td>1000 WT SPZ (week 6)</td><td>none</td><td>6/6</td><td>100%</td></tr><tr><td>1000 WT SPZ (week 6)</td><td>none</td><td>6/6</td><td>100%</td></tr><tr><td>10</td><td>5μg PyCSP</td><td>IM</td><td>25,000 RAS (day 5)</td><td>IV</td><td>10,000 WT SPZ (week 8)</td><td>7–8 days</td><td>5/10</td><td>50%</td><td rowspan=\"4\">0.0325</td><td rowspan=\"4\">Fig. ##FIG##5##6##</td></tr><tr><td>10</td><td>5μg PfCSP</td><td>IM</td><td>25,000 RAS (day 5)</td><td>IV</td><td>10,000 WT SPZ (week 8)</td><td>7–9 days</td><td>3/10</td><td>30%</td></tr><tr><td>10</td><td>5μg PyCSP</td><td>IM</td><td>5 µg PyCSP (day 5)</td><td>IM</td><td>10,000 WT SPZ (week 8)</td><td>5–7 days</td><td>0/10</td><td>0%</td></tr><tr><td>5</td><td>–</td><td>–</td><td>–</td><td>–</td><td>10,000 WT SPZ (week 8)</td><td>4–5 days</td><td>0/5</td><td>0%</td></tr><tr><td>10</td><td>5μg PyCSP</td><td>IM</td><td>25,000 RAS (day 5)</td><td>IV</td><td>1000 WT SPZ (week 3)</td><td>6–7 days</td><td>8/10</td><td>80%</td><td>0.023</td><td rowspan=\"7\">Fig. ##FIG##6##7##</td></tr><tr><td>9</td><td>5μg PyCSP</td><td>IM</td><td>25,000 RAS (day 0)</td><td>IV</td><td>1000 WT SPZ (week 3)</td><td>5 days</td><td>8/9</td><td>89%</td><td rowspan=\"3\">0.014</td></tr><tr><td>5</td><td>5μg PfCSP</td><td>IM</td><td>25,000 RAS (day 0)</td><td>IV</td><td>1000 WT SPZ (week 3)</td><td>6–7 days</td><td>2/5</td><td>40%</td></tr><tr><td>7</td><td>–</td><td>–</td><td>–</td><td>–</td><td>1000 WT SPZ (week 3)</td><td>4 days</td><td>0/7</td><td>0%</td></tr><tr><td>10</td><td>5μg PyCSP</td><td>IM</td><td>25,000 RAS (day 0)</td><td>IV</td><td>20,000 cryo SPZ (week 8)</td><td>7–8 days</td><td>7/10</td><td>70%</td><td rowspan=\"3\">0.0098</td></tr><tr><td>10</td><td>5μg PfCSP</td><td>IM</td><td>25,000 RAS (day 0)</td><td>IV</td><td>20,000 cryo SPZ (week 8)</td><td>7–8 days</td><td>3/10</td><td>30%</td></tr><tr><td>7</td><td>–</td><td>–</td><td>–</td><td>–</td><td>20,000 cryo SPZ (week 8)</td><td>5 days</td><td>0/7</td><td>0%</td></tr><tr><td colspan=\"10\"><bold>C57Bl6</bold></td><td/></tr><tr><td>10</td><td>5μg PyCSP</td><td>IM</td><td>25,000 RAS (day 5)</td><td>IV</td><td>5000 WT SPZ (week 8)</td><td>4 days</td><td>0/5</td><td>0%</td><td rowspan=\"5\"/><td rowspan=\"5\">Supplementary Fig. ##SUPPL##0##3##</td></tr><tr><td>10</td><td>5μg PvCSP</td><td>IM</td><td>25,000 RAS (day 5)</td><td>IV</td><td>5000 WT SPZ (week 8)</td><td>4 days</td><td>0/5</td><td>0%</td></tr><tr><td>10</td><td>25,000 RAS*</td><td>IV</td><td>25,000 RAS (day 5)</td><td>IV</td><td>5000 WT SPZ (week 8)</td><td>4 days</td><td>0/5</td><td>0%</td></tr><tr><td>10</td><td>5ug PyCSP</td><td>IM</td><td>5µg PyCSP</td><td>IV</td><td>5000 WT SPZ (week 8)</td><td>4 days</td><td>0/5</td><td>0%</td></tr><tr><td>10</td><td>–</td><td>–</td><td>–</td><td>–</td><td>5000 WT SPZ (week 8)</td><td>4 days</td><td>0/5</td><td>0%</td></tr></tbody></table></table-wrap>" ]	[]	[]	[]	[]	[]	[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>" ]	[ "<table-wrap-foot><p>Partially protected mice were rechallenged 6 weeks post-challenge.</p><p>*Mice were injected with RAS as priming dose.</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"41541_2023_799_Fig1_HTML\" id=\"d32e504\"/>", "<graphic xlink:href=\"41541_2023_799_Fig2_HTML\" id=\"d32e585\"/>", "<graphic xlink:href=\"41541_2023_799_Fig3_HTML\" id=\"d32e1263\"/>", "<graphic xlink:href=\"41541_2023_799_Fig4_HTML\" id=\"d32e1363\"/>", "<graphic xlink:href=\"41541_2023_799_Fig5_HTML\" id=\"d32e1409\"/>", "<graphic xlink:href=\"41541_2023_799_Fig6_HTML\" id=\"d32e1488\"/>", "<graphic xlink:href=\"41541_2023_799_Fig7_HTML\" id=\"d32e1605\"/>" ]	[ "<media xlink:href=\"41541_2023_799_MOESM1_ESM.docx\"><caption><p>Supplemental material</p></caption></media>", "<media xlink:href=\"41541_2023_799_MOESM2_ESM.pdf\"><caption><p>REPORTING SUMMARY</p></caption></media>" ]	[{"label": ["1."], "mixed-citation": ["WHO \| World malaria report 2018. 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PMC10781675	37741899	[ "<title>Introduction</title>", "<p id=\"Par5\">Socioeconomic inequalities in colorectal cancer survival have been reported in England for many decades [##REF##20588275##1##–##REF##29540358##6##], with a deprivation gap (measured as the absolute difference in 1-year net survival between the most and least deprived quintile) ranging from −10.6% to −6.8% in 2006 [##REF##20588275##1##], and there was no evidence of a reduction following the introduction of successive national cancer policies since 2000 [##REF##29540358##6##]. Considerable efforts have been spent on identifying factors behind inequalities, of which most studies have focused on individual and tumour factors such as age, comorbidities and tumour stage (a proxy of late diagnosis). Yet, previous work in our research group has demonstrated that these factors explain only part of socioeconomic inequalities in cancer survival [##REF##28859056##4##]. Besides, among patients who were recruited in a clinical trial and given equal treatments across socioeconomic status, the deprivation gap in colorectal cancer survival was much smaller than that in the general population [##REF##19034284##7##]. These observations suggested that differential management and treatment of colorectal cancer may also contribute to such inequalities, despite that the National Health Service (NHS) in England is based on universal healthcare coverage. Although regional variations in treatment among colorectal cancer patients have been studied [##UREF##0##8##, ##REF##32818319##9##], it remains largely unknown to what extent socioeconomic deprivation affects the probability and their timing of receiving treatments, accounting for the fact that some patients may not survive up to treatments.</p>", "<p id=\"Par6\">Using data from National Cancer Registration and Analysis Service (NCRAS) in England between 2012 and 2016, this study aimed to investigate socioeconomic inequalities in access to treatment in patients with colon or rectal cancer at different tumour stages using a multistate modelling approach [##REF##28872690##10##–##UREF##1##12##].</p>" ]	[ "<title>Methods</title>", "<title>Patient and public involvement</title>", "<p id=\"Par7\">Patients and members of the public were involved in prioritising the research questions, developing the application for funding, management of the research and will be involved in dissemination of research findings. In October 2021, April 2022, and February 2023, the planned research and relevant progress of Inequalities in Cancer Outcome Network (ICON) Programme was discussed with the ICON advisory group, comprising five people including one patient representative affected by cancer. Important contributions have related to refining or redefining our research questions to ensure that our research is relevant and translatable. Patient representatives will also help us to explain and present our research by contributing to lay summaries and to disseminate our findings by commenting on our visual outputs (such as infographics).</p>", "<title>Data sources and population</title>", "<p id=\"Par8\">We used NCRAS to identify a cohort of patients diagnosed with colon and rectal cancer in England. NCRAS routinely collects clinical information on all cancer cases in England [##UREF##2##13##]. NCRAS are linked to systemic anti-cancer therapy (SACT) [##REF##31340008##14##] and National Radiotherapy Dataset (RTDS) at patient- and tumour-level, and to Hospital Episodes Statistics Admitted Patient Care (HES APC) [##REF##28338941##15##] databases at patient-level. Each patient is also linked to the ecological deprivation measure—Index of Multiple Deprivation (IMD) of the Lower-layer Super Output Areas (LSOA—population ranging from 1000 to 3000) of their residence at the time of their cancer diagnosis. We followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement in reporting and conducting this study [##REF##18313558##16##].</p>", "<p id=\"Par9\">We followed data quality control processes for NCRAS described in Li et al. [##REF##24685409##17##]. For the purpose of this study, inclusion and exclusion criteria are described as below. We included patients with a first primary colon (ICD-10 codes: C18) or rectal cancer (C19–C20), aged between 18 and 99 years at diagnosis between 1st Jan 2012 and 31st Dec 2016. The index date was the date of colon or rectal cancer diagnosis. In 295 (0.35%) colon and 11 (0.02%) rectal cancer patients having the same cancer record in HES APC within 120 days before the date of diagnosis in NCRAS, we treated these as the same diagnosis and used the earliest date of diagnosis across two databases as the index date (i.e. the date of cancer diagnosis). We excluded patients diagnosed via death certificate only, without the exact month and year of diagnosis, or with improper dates (i.e. death before diagnosis).</p>", "<title>Exposures and covariates</title>", "<p id=\"Par10\">Each patient was allocated the income domain score of the IMD 2015 based on the proportion of people in receipt of means tested benefits in their LOSA [##UREF##3##18##]; this deprivation score was then categorised according to the quintiles of the national distribution of LSOAs. The quintiles of IMD 2015 income domain was used as the proxy of socioeconomic status as it is more comparable with measures of material deprivation [##REF##15650142##19##]. The stage of cancer diagnosis (I, II, III, IV, and missing) reported by NCRAS was complemented through a pre-defined algorithm using clinical and pathological TNM staging information collected by NCRAS [##REF##27328310##20##]. The presence of comorbidities, which may affect the treatment decision, including heart failure, myocardial infraction, chronic pulmonary disease, and diabetes with complications, were derived from HES APC [##REF##31987032##21##]. Age at cancer diagnosis, sex, ethnicity and route to diagnosis were also extracted from NCRAS. The route to diagnosis was determined by the NCRAS team using multiple electronic health records datasets [##REF##22996611##22##]; based on algorithms related to patient’s journey in the NHS during diagnostic periods, patients can be diagnosed via 2-week-wait route (whereby patients being urgently referred for suspected cancer by their GP can expect to be seen by a specialist within 2 weeks), screening, standard GP referral, emergency presentation, inpatient elective, and other outpatient.</p>", "<title>Outcomes</title>", "<p id=\"Par11\">Outcomes included the date of any cancer treatment and the date of death within 1 year after diagnosis, in which death can occur before or after treatment. We chose to follow up patients for 1 year after diagnosis as treatment activities should be initiated within 1 year. Treatment could be colon or rectal resection (surgery), chemotherapy and radiotherapy. Surgery was ascertained by the presence of relevant OPCS-4 procedure codes in NCRAS and/or HES APC. If multiple procedures were undergone for the same patient, we used the earliest of the most extensive resection as the date of surgery. The use of chemotherapy was defined as the presence of anti-cancer regimens (excluding supportive regimens) in SACT or NCRAS, or relevant OPCS-4 codes for chemotherapy delivery in HES APC [##REF##34225249##23##]. Similarly, the use of radiotherapy was determined by the record of radiotherapy in RTDS or NCRAS, or relevant OPCS-4 codes for radiotherapy delivery in HES APC.</p>", "<title>Statistical analysis</title>", "<p id=\"Par12\">We described the characteristics at diagnosis of included patients with colon or rectal cancer by stage, with the median and interquartile range (IQR) for continuous and the number and proportion for categorical variables.</p>", "<p id=\"Par13\">We used multistate models with three states: (1) diagnosis (alive and untreated), (2) treatment (alive and treated), and (3) death (i.e. the absorbing state), thus three transition intensities (h1: diagnosis to treatment, h2: diagnosis to death, and h3: treatment to death) to investigate the probability and the length of stay at each state [##REF##28872690##10##]. Figure ##FIG##0##1## illustrates three states and three possible transitions. All patients are followed-up from diagnosis to death or 365.24 days after diagnosis (at which time they were censored), and with an intermediate outcome, if present, the earliest date of receiving treatment. For those patients having a transition on the same day as the previous state (e.g. a patient was treated on the date of diagnosis), we manually added a partial day (a random number between 0.1 and 0.9) to their event time to include them in the analyses.</p>", "<p id=\"Par14\">For each transition, we fitted a Royston-Parmar flexible parametric survival model using the survival time (days) to each outcome [##UREF##4##24##]. This multistate modelling approach allows accounting for the immortal time bias from patients who died before receiving any treatment. We assumed that the probability to move to the next state only depends on the present state (i.e. Markov assumption) [##REF##28872690##10##]. The degree of freedom for the hazard function used in each model was determined by Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) [##UREF##5##25##].</p>", "<p id=\"Par15\">In all regression models, we included the main exposure i.e. income deprivation (5 quintiles from least to most deprived), and adjusted for age assuming a non-linear functional form (smooth function of age using restricted cubic splines with four knots placed at 5, 35, 65 and 95 percentiles), sex (men vs. women), ethnicity (White vs. Other), presence of heart failure, myocardial infarction, chronic pulmonary disease, and diabetes with complications (Yes vs. No), and route to diagnosis (emergency presentation, inpatient elective, other outpatient, screening, and 2-week-wait vs. standard GP referral). Methodological research on missing data is yet sparse in the context of multistate models, we therefore developed a complete-case analysis and only included those patients without missing data on covariates. Patients with missing stage (8.6% of colon and 6.6% of rectal cancer) were analysed separately. We also conducted sensitivity analyses by including more contemporary data—patients who were diagnosed between 2015 and 2016. We further stratified analyses by whether patients were diagnosed via screening, as screening is an unique diagnostic modality where patients did not seek medical attentions for symptoms. From the multistate models, we derived the probability and length of staying at each state by socioeconomic status assessing the differences between the most and least deprived cancer patients. We presented the results stratified by cancer sites (colon and rectum) and stages and reported all estimates with 95% confidence intervals (CIs). We conducted all analyses in Stata 16.1/MP (College Station, TX: StataCorp LLC). Clinical code lists and statistical codes used in the analyses are available at GitHub (<ext-link ext-link-type=\"uri\" xlink:href=\"https://github.com/supingling/colorectal_cancer\">https://github.com/supingling/colorectal_cancer</ext-link>).</p>" ]	[ "<title>Results</title>", "<title>Cohort characteristics</title>", "<p id=\"Par16\">The detailed flowchart of patients’ selection is shown in Supplemental Fig. ##SUPPL##0##S1##. Of 85,137 and 48,798 patients with colon and rectal cancer, respectively, between 2012 and 2016, 1.5% and 0.9% did not meet inclusion criteria, 7.4% and 7.0% were excluded due to missing on deprivation, ethnicity and route to diagnosis, and further 7.9% and 6.0% patients were missing on stage, leaving 70,705 and 41,991 with stage I to IV colon and rectal cancer, respectively, included in our final analysis. Patients missing on stage only (6695 colon and 2950 rectal) were analysed separately. Missing data patterns are shown in Supplemental Table ##SUPPL##0##S1##<bold>:</bold> the proportion of missing data is also higher in missing stages than others (nearly 20% in stage missing vs. <8% in other stages). The baseline characteristics of included and excluded patients are shown in Table ##SUPPL##0##S2##.</p>", "<p id=\"Par17\">The characteristics of included patients at cancer diagnosis, stratified by stage (I–IV), are shown in Table ##TAB##0##1##. Overall, the median age was 73.1 years (IQR: 64.4–80.6) for colon and 70.2 (IQR: 61.5–78.2) for rectal cancer. There were 32,751 (46.3%) and 15,187 (36.2%) women with colon and rectal cancer, respectively, and 95.1% of patients were White in both cancers. In total, 18.4% of colon cancer patients were diagnosed through emergency presentation but this figure was 7.0% for rectal cancer; The most common comorbidity was chronic pulmonary disease (>10%), followed by myocardial infarction and heart failure (<5%), and the least was diabetes with complications (<1%). Table ##SUPPL##0##S3## shows the characteristics of patients with missing stage of colon and rectal cancer. Compared to other stages, patients with missing stage, in both colon and rectal cancers, were considerably older, more deprived, more comorbid, and more likely to be diagnosed through emergency presentation.</p>", "<title>Descriptions of three transitions and models</title>", "<p id=\"Par18\">Figure ##FIG##0##1## shows an overview of three states and transitions and Fig. ##SUPPL##0##S2## presents the number of patients entering and staying at each state (i.e. diagnosis: alive and untreated, treatment: alive and treated, and dead) and experiencing each transition (h1: diagnosis to treatment, h2: diagnosis to death, and h3: treatment to death) by cancer and stage. In stage I and II, compared to patients who died after treatment (h3), both colon and rectal patients who died before the treatment (h2) were older (e.g. stage I colon cancer, median age 85.6 vs. 78.3 years old) and more comorbid than those who died after treatment (h3). More patients at advanced stages, compared with early stages, died before receiving any treatment, in both colon (33.7% of stage IV vs. 2.0% stage I) and rectal (21.3% stage IV vs. 1.3% stage I) cancer.</p>", "<p id=\"Par19\">The degree of freedom selection for each Royston-Parmar Flexible Parametric survival model by cancer and stage are shown in Table ##SUPPL##0##S4##. Transition-specific Hazard ratios (HRs) for socioeconomic status are shown in Fig. ##SUPPL##0##S3## and Table ##SUPPL##0##S5##. For both cancers, compared with the least deprived quintile, other patients had a decreased risk of receiving treatment in all stages, with a larger effect size in patients with stage IV cancers and a gradient across quintiles of income deprivation. We also observed an increased risk of death before and after receiving treatment, with a larger effect size for death after than before treatment for most stages (Fig. ##SUPPL##0##S3##; Table ##SUPPL##0##S5##).</p>", "<title>Probability of staying at each state</title>", "<p id=\"Par20\">Probabilities and differences (most vs. least deprived) in probability of staying alive and untreated, alive and treated, or dead, by months since diagnosis are shown in Fig. ##FIG##1##2## for stage I to IV colon cancer and Fig. ##FIG##2##3## for rectal cancer. These estimates were reported for the least and most deprived 75-year-old patients and all other covariates were set as reference group (i.e. male, White ethnicity, without any of these four comorbidities, and standard GP referral). Overall, we observed consistent deprivation gaps (i.e. the absolute difference in the probability comparing the most deprived to the least deprived) in treatment and death across cancer sites and stages. Compared to the least deprived, 75-year-old deprived patients with colon or rectal cancer, had a lower probability of receiving treatment, and a higher probability of staying untreated and of dying (Figs. ##FIG##1##2## and ##FIG##2##3##).</p>", "<p id=\"Par21\">In stage I– IV of colon cancer, the deprivation gap in remaining alive and treated dramatically increased within 1 month after diagnosis and stabilised thereafter. The gap at 6 months widened steadily with increasing stage from −2.4% (95% CI: −4.0, −0.8) in stage I to −7.4% (95% CI: −9.4, −5.3) for stage IV (Fig. ##FIG##1##2##; Table ##SUPPL##0##S6##). A similar pattern though less clear was observed in remaining alive and untreated at 1 month after diagnosis, with a gap below 3% in stage I and II and around 5% in stage III and IV, but this gap narrowed towards null at 1 year. The deprivation gap in death was, however, progressively increasing during the whole study period, to 2.3% (95% CI: 0.7, 3.9) for stage I and up to 5.5% (95% CI: 3.2, 7.9) for stage IV at 1 year (Fig. ##FIG##1##2##; Table ##SUPPL##0##S6##).</p>", "<p id=\"Par22\">Comparable patterns were observed for rectal cancer stage I–IV, with smaller deprivation gaps in the probability of remaining alive and untreated but similar in the other two states (Fig. ##FIG##2##3##; Table ##SUPPL##0##S6##). Differences (most vs. least deprived) in the probability of being alive and untreated at 1 month ranged between 1.1% (95% CI: −1.3, 3.5) in stage I and 4.4% (95% CI: 1.9, 6.9) in stage IV; of remaining alive and treated at 6 months between −2.0% (95% CI: −3.5, −0.4) and −6.2% (95% CI: −8.9, −3.5); and of death at 1 year between 2.7% (95% CI: 0.9, 4.5) and 6.1% (95% CI: 2.8, 9.4).</p>", "<title>Length of stay at each state</title>", "<p id=\"Par23\">Figure ##FIG##3##4## shows the length of stay at alive and untreated, alive and treated, and dead (days of life lost) in the least and most deprived patients with stage I–IV colon and rectal cancer. Consistent with estimates of probabilities, the most deprived patients spent less days being alive and treated, but more days being alive and untreated (waiting for the treatment), or had more days of life lost (died earlier), indicating a later enter to and an earlier exit from “treatment” state than the least deprived quintile. Differences between the most and least deprived quintiles increased over time since diagnosis in all tumour stages of both cancers, and were larger in colon than rectal cancer, and in more advanced than early stages (Fig. ##FIG##3##4##; Table ##SUPPL##0##S7##).</p>", "<p id=\"Par24\">Of 360 days after diagnosis, the most deprived patients with stage I colon cancer, compared to the least deprived, typically spent 8.5 days less (95% CI: −14.2, −2.7) being alive and treated, of which 3.8 days (95% CI: −1.8, 9.3) were due to the difference in length of stay at alive and untreated (delayed treatment), and 4.7 more days (95% CI: 1.4, 8.0) of life were lost. The difference in being alive and treated increased along with the stage; in stage IV, the most deprived spent 24.6 days less (95% CI: −31.4,−17.8) in this state (Fig. ##FIG##3##4##; Table ##SUPPL##0##S7##). In early stages, differences in alive and treated were related to similar number days of delayed treatment and days of life lost (3.8 delays vs. 4.7 days lost in stage I; 5.6 vs. 6.3 days in stage II), but more days of delays than lost in stage III (12.0 vs, 7.2 days), and vice versa in stage IV (9.0 vs. 15.6 days).</p>", "<p id=\"Par25\">In rectal cancer, the differences in days staying at alive and treated were −7.0 (95% CI: −12.7, −1.4) in stage I, −11.1 days (95% CI: −18.6, −3.6) in stage II, −9.1 days (95% CI: −14.0, −4.3) in stage III, and −21.7 days (95% CI: −31.0, −12.4) in stage IV (Table ##SUPPL##0##S7##) at 1 year after diagnosis. In contrast to colon cancer, more days of such deprivation gaps were due to premature death than delayed treatment at all stages (Fig. ##FIG##3##4##; Table ##SUPPL##0##S7##).</p>", "<title>Sensitivity analyses</title>", "<p id=\"Par26\">Figures ##SUPPL##0##S4##, ##SUPPL##0##S5## and Table ##SUPPL##0##S6##, ##SUPPL##0##S7## show results of patients with missing stage colon and rectal cancer between 2012 and 2016 in England. In patients with missing stage colon cancer, differences between the most and least deprived quintiles in probability and length of stay at three states were similar to those patients with stage IV, but with larger uncertainties due to a smaller sample size, except that the most deprived patients with missing stage rectal cancer had a higher probability of death (5.1%; 95% CI: 0.1, 10.1), and a lower probability of being alive, regardless of treated or untreated.</p>", "<p id=\"Par27\">Sensitivity analyses by including patients diagnosed only between 2015 and 2016 are shown in Fig. ##SUPPL##0##S6## (probabilities in colon cancer), Fig. ##SUPPL##0##S7## (probabilities in rectal cancer), and Fig. ##SUPPL##0##S8## (length of stay in colon and rectal cancer). These estimates were indistinguishable with that of the main analyses, except with larger uncertainties due to a smaller sample size in both cancers. Stratified analyses by whether patients were diagnosed via screening are shown in Fig. ##SUPPL##0##S9##, Fig. ##SUPPL##0##S10##, and Fig. ##SUPPL##0##S11##. Both stage I-III colon and rectal patients diagnosed via screening had much lower probability of death and higher probability of treatment than those diagnosed via other routes, and we found no clear evidence of inequalities in these screen-detected patients. As stage IV patients were rarely diagnosed via screening, inequalities in this subgroup was inconclusive due to the small sample size.</p>" ]	[ "<title>Discussion</title>", "<p id=\"Par28\">Using data from 70,705 and 41,991 patients diagnosed with stage I–IV colon and rectal cancer in England between 2012 and 2016, we found persistent socioeconomic inequalities in access to treatment and premature death in every stage of colon and rectal cancer after controlling for age at cancer diagnosis, sex, ethnicity, route to diagnosis and four major comorbidities. Compared to the least deprived quintile, the most deprived had a lower probability of staying alive and treated, and a higher probability of death during the year after diagnosis. These inequalities were greater in advanced than early stages during the whole study period. The most deprived also had a higher probability of being alive and untreated within 1 month after diagnosis, but such disparities narrowed towards null along with the follow-up time.</p>", "<p id=\"Par29\">These estimates translated into a smaller number of days remaining alive and treated in the most than the least deprived (e.g. at 1 year after diagnosis, 169.3 vs. 144.7 days, i.e. 24.6 days less, in stage IV colon cancer), and more days being alive and untreated (e.g. 9.0 days more in stage IV colon cancer) as well as an earlier death (15.6 days earlier in stage IV colon cancer). Taken together, our findings indicate that, the deprivation gaps in treatment (i.e. being alive and treated) was due to both delayed access to treatment right after diagnosis (later enter to the “treatment” state) and premature death (days of life lost, earlier enter to the “death” state). We also observed a gradient across quintiles of income deprivation for being alive and treatment in both cancers at all stages (not shown).</p>", "<p id=\"Par30\">This study has some strengths and limitations. We included a large sample of patients with colorectal cancer from the latest available data from cancer registries in England—a high-quality database with as high as 99% national coverage on cancer patients [##UREF##2##13##]. We also linked to hospital admission data, systemic treatment records, and radiotherapy data to capture complete treatment records for these patients [##UREF##0##8##, ##REF##31340008##14##, ##REF##28338941##15##]. It should be noted that some of these data were not collected for the research purpose and activities outside NHS were not recorded (~1%); as some variables (e.g. treatment) used in our analysis relied on clinical coding in electronic health records, we could not rule out misclassification. The multistate approach allows better description of the outcomes appearing over time (such as treatment) while tackling the potential issue of competing risks and immortal time bias [##REF##28872690##10##], and using flexible parametric models allows capturing a variety of complex hazard functions [##UREF##4##24##]. Regarding the missing data ( < 10%), we could only conduct complete-case analysis (83% and 86% of all cases, respectively) due to the lack of methodological research for missing data in multistate modelling. However, both sensitivity analyses (analysis restricted to patients with missing stages, and analyses on patients diagnosed between 2015 and 2016) did not alter our main conclusions. Results on missing stages were mostly consistent with those of stage IV, and the second sensitivity analyses provided consistent results while using a more contemporary population (2015–16). Lastly, we used small area-based income to determine socioeconomic status, which may not fully reflect the individual’s income [##REF##33243814##26##].</p>", "<p id=\"Par31\">Many previous epidemiological research and literature reviews have reported less favourable results on receiving treatment for deprived patients with colon and/or rectal cancer [##REF##20570136##27##–##REF##33055178##31##], though different data sources, definitions of exposures and outcomes, or statistical methods were used. Of note, many previous studies have analysed non-stage-specific populations or even combined colorectal cancer patients [##REF##20570136##27##–##REF##20378687##29##, ##REF##24119140##32##, ##REF##26540571##33##], while our analyses were stratified by cancer sites and stages and there were large sample sizes in each subgroup. Different proportions of patients with two cancers and/or different stages in previous studies may affect their observed inequalities. Indeed, in our study, we observed larger socioeconomic gaps in colon than rectal cancer and in advanced than early stages, possibly due to more complex treatment strategies and higher risk of death in advanced stages of colorectal cancer. Adjustments for age and stage (and sites if applicable) in previous studies were useful [##REF##20378687##29##, ##REF##12237766##34##], but we stratified by sites and stages and also adjusted for other important confounders such as sex and ethnicity, and other clinical factors (route to diagnosis and comorbidities).</p>", "<p id=\"Par32\">However, several studies from Europe, England and Scotland suggested no evidence of treatment delay associated with deprivation [##REF##24119140##32##, ##REF##26540571##33##, ##REF##33516139##35##], or even showed that deprived patients actually received quicker treatment [##REF##12237766##34##]. The key explanation for these findings is that deprived patients may be more likely to be diagnosed via emergency presentation route [##UREF##6##36##], which leads to immediate treatment intervention [##REF##20378687##29##, ##REF##33516139##35##]. Previous studies suggested that whether patient met cancer waiting time targets for treatment (i.e. no more than 62 days from the urgent referral to the start of treatment; no more than 31 days between a decision to treat and the start of treatment) does not affect their survival—“waiting time paradox” [##REF##30133466##37##], as sicker patients would be seen and treated more quickly and nevertheless had worse outcomes. However, our current analyses showed that, under similar demographic, clinical and tumour conditions, the deprived patients were treated later and died earlier than the affluent, except when stage I-III colorectal patients were diagnosed via screening, among whom we found no evidence of socioeconomic inequalities. Further, time to treatment was measured from cancer diagnosis (usually pathological diagnosis in NCRAS) to the initiation of the treatment in our study, while several studies used time from first symptoms (or contact/consultation/referral) to treatment, in which the time interval between first symptoms to confirmed diagnosis should reflect delays in diagnosis rather than treatment [##REF##26540571##33##, ##REF##33516139##35##, ##REF##30133466##37##].</p>", "<p id=\"Par33\">This is the first study, to our knowledge, investigating the probability of treatment along the patient’s clinical journey while taking the premature death into account and estimating time being alive and treated within the year after the diagnosis. Some previous studies merely compared the mean/median time to treatment across deprivation groups and ignored those who did not receive treatment [##REF##20582599##28##, ##REF##12237766##34##, ##REF##33516139##35##]; some categorised outcomes even if time was involved [##REF##20378687##29##, ##REF##33516139##35##], which may lead to loss of information, and patients did not survive up to treatment would be categorised into no treatment group. Although some studies also used time-to-event analyses [##REF##26540571##33##, ##REF##12237766##34##], it was unclear how the occurrence of death during the follow-up was handled. We use multistate survival models to account for competing risk of death and present both relative and absolute differences in the probability of treatment and death along the follow-up. We also translated our estimates into numbers of days spending in each state to visualise delays in access to treatment and premature death.</p>", "<p id=\"Par34\">Within universal healthcare systems like NHS, every patient expects to receive equal treatment regardless of their socioeconomic status, but we found more deprived patients with colorectal cancer spend less time being alive and treated than the least deprived within the year after diagnosis, even after adjusting for differential clinical and tumour factors. Direct explanations include waiting longer to get treatment and premature death. We speculate that tumour, individual and healthcare factors are contributing to these observed inequalities. First, although we stratified by stage and adjusted for comorbidities and other relevant confounding factors, some stage-independent tumour and individual factors may affect the treatment (e.g. microsatellite instability [##UREF##7##38##] and performance status [##REF##26335750##39##]), which were not captured in the databases or modelling. Second, as the availability of good medical care (including hospitals with diagnostic and treatment facilities and experienced clinicians) tends to vary inversely with the need for it in the population served—Inverse Care Law remains true within NHS [##REF##30065009##40##, ##REF##12133675##41##], patients from deprived areas are less likely to be in the right care centre in the first place [##REF##18535029##42##], which may cause delays in both diagnosis and treatment after being referred across several hospitals. Third, deprived patients might find it more difficult to navigate within the complex healthcare system and they might not have the same level of social support as their affluent counterparts [##REF##29904805##43##, ##UREF##8##44##], which will affect patient’s preferences for treatment and ultimately clinicians’ decision-making.</p>", "<p id=\"Par35\">Notably, the events of interest were access to treatment (initiation) and death due to any causes; whether the treatment was completed (in particular, long course radiotherapy or chemotherapy) or the death was the complication of the treatment itself are outside the scope of this study. We have investigated the time to any treatment (surgery, chemotherapy, and radiotherapy) but not the specific modality or quality of care. Future research can provide more insights regarding equitable access to the optimal treatment in patients with colorectal cancer. Use of small-area-based deprivation ranking as a continuous variable or individual income might also provide a better picture of the socioeconomic gradient. In addition, apart from individual factors such as knowledge of cancer, education level etc., accumulating evidence suggests healthcare factors also play a role in these observed inequalities. Therefore, systemic data collection on healthcare system factors could support more research in this area, thereby identifying suitable effective system-level interventions.</p>", "<p id=\"Par36\">In conclusion, our study suggests that, compared to the least deprived quintile, more deprived patients with colon and rectal cancer had a lower probability of receiving treatment and remaining alive, due to both delayed access to treatment and premature death, with larger inequalities in advanced than early stages, and in colon than rectal cancer. These socioeconomic inequalities in treatment may partly explain poorer survival in the more deprived, and should be considered in the cancer policies and other healthcare inequalities improvement programmes. Since COVID-19 pandemic, NHS has reported worst ever waiting time statistics [##UREF##9##45##], and a recent study showed that reductions in both 2-week-wait referrals and first treatments for cancer were largest in patients from the most deprived areas [##REF##35332047##46##]. These reports suggested that inequalities in access to treatment now are very likely much wider than what we observed in current study. In the general context of the continuing difficulties experienced by the NHS, the issue of care resources available to the most deprived populations deserves to be examined in more detail [##REF##36450403##47##].</p>" ]	[]	[ "<title>Background</title>", "<p id=\"Par1\">Individual and tumour factors only explain part of observed inequalities in colorectal cancer survival in England. This study aims to investigate inequalities in treatment in patients with colorectal cancer.</p>", "<title>Methods</title>", "<p id=\"Par2\">All patients diagnosed with colorectal cancer in England between 2012 and 2016 were followed up from the date of diagnosis (state 1), to treatment (state 2), death (state 3) or censored at 1 year after the diagnosis. A multistate approach with flexible parametric model was used to investigate the effect of income deprivation on the probability of remaining alive and treated in colorectal cancer.</p>", "<title>Results</title>", "<p id=\"Par3\">Compared to the least deprived quintile, the most deprived with stage I–IV colorectal cancer had a lower probability of being alive and treated at all the time during follow-up, and a higher probability of being untreated and of dying. The probability differences (most vs. least deprived) of being alive and treated at 6 months ranged between −2.4% (95% CI: −4.3, −1.1) and −7.4% (−9.4, −5.3) for colon; between −2.0% (−3.5, −0.4) and −6.2% (−8.9, −3.5) for rectal cancer.</p>", "<title>Conclusion</title>", "<p id=\"Par4\">Persistent inequalities in treatment were observed in patients with colorectal cancer at every stage, due to delayed access to treatment and premature death.</p>", "<title>Subject terms</title>" ]	[ "<title>Research in context</title>", "<title>What is already known on this topic</title>", "<p id=\"Par37\">\n<list list-type=\"bullet\"><list-item><p id=\"Par38\">Summarise the state of scientific knowledge on this subject before you did your study and why this study needed to be done</p></list-item></list>\n</p>", "<p id=\"Par39\">\n<list list-type=\"bullet\"><list-item><p id=\"Par40\">Inequalities in colorectal cancer survival were repeatedly reported in England in the past 20 years.</p></list-item><list-item><p id=\"Par41\">Individual and tumour factors such as age, stage and comorbidities only partially explain these inequalities.</p></list-item><list-item><p id=\"Par42\">Differential management and treatment of colorectal cancer may also contribute to such inequalities.</p></list-item></list>\n</p>", "<title>What this study adds</title>", "<p id=\"Par43\">\n<list list-type=\"bullet\"><list-item><p id=\"Par44\">Summarise what we now know as a result of this study that we did not know before</p></list-item></list>\n</p>", "<p id=\"Par45\">\n<list list-type=\"bullet\"><list-item><p id=\"Par46\">Compared to the least deprived quintile, the most deprived patients with colon or rectal cancer had a lower probability of being alive and treated (differences ranging from −2.4% to −7.4% in colon cancer and −2.0% to −6.2% in rectal cancer at 6 months after the diagnosis), and a higher probability of being untreated and dead.</p></list-item><list-item><p id=\"Par47\">The most deprived spent a smaller number of days being alive and treated (maximum differences observed at 1 year after diagnosis in stage IV colon cancer: 169.3 days in the least deprived vs. 144.7 days in the most deprived) but a greater number of days being untreated and a larger number of days of life lost (earlier death).</p></list-item><list-item><p id=\"Par48\">Persistent socioeconomic inequalities in treatment were observed in patients with colorectal cancer, due to both delayed access to treatment and premature death.</p></list-item></list>\n</p>", "<title>How this study might affect research, practice or policy</title>", "<p id=\"Par49\">\n<list list-type=\"bullet\"><list-item><p id=\"Par50\">Summarise the implications of this study</p></list-item></list>\n</p>", "<p>Socioeconomic inequalities in treatment may partly explain poorer colon and rectal cancer survival observed in patients from the deprived areas as compared those from the least deprived. Reasons for differential access to treatment should be studied and should be considered in the cancer policy and/or other healthcare inequalities improvement programmes within the National Health Service (NHS). In the general context of the continuing difficulties experienced by the NHS, the issue of care resources available to the most deprived populations deserves to be examined in more detail.</p>", "<title>Supplementary information</title>", "<p>\n\n\n</p>" ]	[ "<title>Supplementary information</title>", "<p>The online version contains supplementary material available at 10.1038/s41416-023-02440-6.</p>", "<title>Acknowledgements</title>", "<p>Authors thank all patients who have provided their data to NCRAS.</p>", "<title>Author contributions</title>", "<p>SL designed the study, conducted data analysis, and drafted the paper. MAL, MQ and AB supported the data analysis, BR acquired funding and data. All authors contributed to the interpretation of the data, critically revised the paper and approved the final version. SL has full access to all the data and is responsible for the integrity of the work as a whole.</p>", "<title>Funding</title>", "<p>Inequalities in Cancer Outcome Network is funded by Cancer Research UK programme (Grant No. EPNCZS34).</p>", "<title>Data availability</title>", "<p>Data access is permitted via authorisation from NHS digital only. Clinical code lists and statistical codes are available at GitHub (<ext-link ext-link-type=\"uri\" xlink:href=\"https://github.com/supingling/colorectal_cancer\">https://github.com/supingling/colorectal_cancer</ext-link>).</p>", "<title>Competing interests</title>", "<p id=\"Par52\">The authors declare no competing interests.</p>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par53\">The use of data has been approved by NHS Health Research Authority London – Central Research Ethics Committee (REC reference: 21/LO/0552; IRAS project ID: 279592) and this study protocol by LSHTM Ethics Online (reference: 27483). Current legislation (GDPR and the DPA 2018) makes it permissible to use individual and even sensitive personal data, without consent, for bona fide non-interventional public health research, provided the relevant statutory and ethical permissions have been acquired from HRA and an NHS Research Ethics Committee, respectively. The wishes of patients who have withheld or withdrawn their consent are respected for identifiable data by the data providers (NHS and PHE). Data received by ICON group have been anonymised.</p>" ]	[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>An overview of three states and three possible transitions.</title><p>N1: The number of patients entering state 1; i.e., the total sample of each stage. n1: The number of patients staying at state 1 at the end of follow-up; i.e., those who did not die nor receive treatment. x: The number of patients moved from state 1 to state 2; i.e., those who received treatment. y: The number of patients moved from state 1 to state 3; i.e., those who died before receiving any treatment. N2: The number of patients entering state 2: i.e., those who received treatment (same as x). n2: The number of patients staying at state 2 at the end of follow-up; i.e., those who survived after receiving treatment. z: The number of patients moved from state 2 to state 3; i.e., those who died after receiving treatments. N3: The number of patients entering state 3; i.e., those who died during the follow-up, equal to the sum of y and z. n3: The number of patients staying at state 3 at the end of follow-up; as state 3 dead is an absorbing state, n3 is the same N3.</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>Probability of at each state in the least and most deprived and their differences in patients with stage I–IV colon cancer in England between 2012 and 2016 (age of 75).</title><p>Three colours represent three states (blue: alive and untreated; green: alive and treated; red: dead). The probability of staying at each state by time since diagnosis (months) are shown for a white, male, 75-year-old patient without comorbidity, and with standard GP referral route who was in the least deprived quintile (first row) and in the most deprived quintile (second row), and differences between them (most vs. least deprived) in the probability (third row).</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><title>Probability of at each state in the least and most deprived and their differences in patients with stage I–IV rectal cancer in England between 2012 and 2016 (age of 75).</title><p>Three colours represent three states (blue: alive and untreated; green: alive and treated; red: dead). The probability of staying at each state by time since diagnosis (months) are shown for a white, male, 75-year-old patient without comorbidity, and with standard GP referral route who was in the least deprived quintile (first row) and in the most deprived quintile (second row), and differences between them (most vs. least deprived) in the probability (third row).</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><title>Length of stay at each state in the least and most deprived quintiles in patients with stage I–IV colon and rectal cancer in England between 2012 and 2016.</title><p>Three colours represent three states (blue: alive and untreated; green: alive and treated; red: dead). The length of staying at each state (days) by time since diagnosis (months) are shown for a white, male, 75-year-old patient without comorbidity, and with standard GP referral route who was in the least deprived quintile (dash line) and in the most deprived quintile (solid line) and in colon (top panel) and rectal (bottom panel) cancer.</p></caption></fig>" ]	[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Baseline characteristics of included patients with colon (<italic>N</italic> = 70,705) or rectal (<italic>N</italic> = 41,991) cancer in England between 2012 and 2016.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th><italic>Colon cancer</italic></th><th>Total</th><th>Stage I</th><th>Stage II</th><th>Stage III</th><th>Stage IV</th></tr><tr><th/><th><italic>N</italic> = 70,705</th><th><italic>N</italic> = 11,832</th><th><italic>N</italic> = 19,083</th><th><italic>N</italic> = 21,354</th><th><italic>N</italic> = 18,436</th></tr></thead><tbody><tr><td>Age at diagnosis, years</td><td>73.1 (64.4–80.6)</td><td>71.2 (62.9–78.7)</td><td>74.2 (66.1–81.3)</td><td>72.7 (64.2–80.4)</td><td>73.2 (63.8–81.4)</td></tr><tr><td colspan=\"6\">Age group, years</td></tr><tr><td> 18-44</td><td>2727 (3.9%)</td><td>734 (6.2%)</td><td>625 (3.3%)</td><td>707 (3.3%)</td><td>661 (3.6%)</td></tr><tr><td> 45-54</td><td>4333 (6.1%)</td><td>615 (5.2%)</td><td>977 (5.1%)</td><td>1398 (6.5%)</td><td>1343 (7.3%)</td></tr><tr><td> 55-64</td><td>11,741 (16.6%)</td><td>2198 (18.6%)</td><td>2709 (14.2%)</td><td>3744 (17.5%)</td><td>3090 (16.8%)</td></tr><tr><td> 65-74</td><td>21,435 (30.3%)</td><td>4076 (34.4%)</td><td>5739 (30.1%)</td><td>6512 (30.5%)</td><td>5108 (27.7%)</td></tr><tr><td> 74-84</td><td>21,875 (30.9%)</td><td>3198 (27.0%)</td><td>6522 (34.2%)</td><td>6549 (30.7%)</td><td>5606 (30.4%)</td></tr><tr><td> ≥85</td><td>8594 (12.2%)</td><td>1011 (8.5%)</td><td>2511 (13.2%)</td><td>2444 (11.4%)</td><td>2628 (14.3%)</td></tr><tr><td colspan=\"6\">Sex</td></tr><tr><td> Men</td><td>37,954 (53.7%)</td><td>6662 (56.3%)</td><td>10,148 (53.2%)</td><td>11,242 (52.6%)</td><td>9902 (53.7%)</td></tr><tr><td> Women</td><td>32,751 (46.3%)</td><td>5170 (43.7%)</td><td>8935 (46.8%)</td><td>10,112 (47.4%)</td><td>8534 (46.3%)</td></tr><tr><td colspan=\"6\">Ethnicity</td></tr><tr><td> White</td><td>67,221 (95.1%)</td><td>11,258 (95.1%)</td><td>18,258 (95.7%)</td><td>20,219 (94.7%)</td><td>17,486 (94.8%)</td></tr><tr><td> Other ethnicities</td><td>3484 (4.9%)</td><td>574 (4.9%)</td><td>825 (4.3%)</td><td>1135 (5.3%)</td><td>950 (5.2%)</td></tr><tr><td colspan=\"6\">Income 2015 quintile</td></tr><tr><td> 1 – Least deprived</td><td>15,612 (22.1%)</td><td>2655 (22.4%)</td><td>4211 (22.1%)</td><td>4840 (22.7%)</td><td>3906 (21.2%)</td></tr><tr><td> 2</td><td>16,374 (23.2%)</td><td>2769 (23.4%)</td><td>4505 (23.6%)</td><td>5005 (23.4%)</td><td>4095 (22.2%)</td></tr><tr><td> 3</td><td>14,919 (21.1%)</td><td>2454 (20.7%)</td><td>4098 (21.5%)</td><td>4435 (20.8%)</td><td>3932 (21.3%)</td></tr><tr><td> 4</td><td>12,946 (18.3%)</td><td>2144 (18.1%)</td><td>3498 (18.3%)</td><td>3861 (18.1%)</td><td>3443 (18.7%)</td></tr><tr><td> 5 – Most deprived</td><td>10,854 (15.4%)</td><td>1810 (15.3%)</td><td>2771 (14.5%)</td><td>3213 (15.0%)</td><td>3060 (16.6%)</td></tr><tr><td colspan=\"6\">Comorbidity</td></tr><tr><td> Heart failure</td><td>2248 (3.2%)</td><td>367 (3.1%)</td><td>635 (3.3%)</td><td>608 (2.8%)</td><td>638 (3.5%)</td></tr><tr><td> Myocardial infarction</td><td>2966 (4.2%)</td><td>488 (4.1%)</td><td>891 (4.7%)</td><td>857 (4.0%)</td><td>730 (4.0%)</td></tr><tr><td> Diabetes with complications</td><td>529 (0.7%)</td><td>94 (0.8%)</td><td>141 (0.7%)</td><td>139 (0.7%)</td><td>155 (0.8%)</td></tr><tr><td> Chronic pulmonary disease</td><td>8935 (12.6%)</td><td>1635 (13.8%)</td><td>2488 (13.0%)</td><td>2490 (11.7%)</td><td>2322 (12.6%)</td></tr><tr><td colspan=\"6\">Route to diagnosis</td></tr><tr><td> Emergency presentation</td><td>12,980 (18.4%)</td><td>1227 (10.4%)</td><td>3225 (16.9%)</td><td>3337 (15.6%)</td><td>5191 (28.2%)</td></tr><tr><td> GP referral</td><td>17,798 (25.2%)</td><td>3393 (28.7%)</td><td>4618 (24.2%)</td><td>5370 (25.1%)</td><td>4417 (24.0%)</td></tr><tr><td> Inpatient elective</td><td>2521 (3.6%)</td><td>492 (4.2%)</td><td>667 (3.5%)</td><td>802 (3.8%)</td><td>560 (3.0%)</td></tr><tr><td> Other outpatient</td><td>5221 (7.4%)</td><td>959 (8.1%)</td><td>1512 (7.9%)</td><td>1409 (6.6%)</td><td>1,341 (7.3%)</td></tr><tr><td> Screening</td><td>8754 (12.4%)</td><td>2870 (24.3%)</td><td>2357 (12.4%)</td><td>2762 (12.9%)</td><td>765 (4.1%)</td></tr><tr><td> TWW</td><td>23,431 (33.1%)</td><td>2891 (24.4%)</td><td>6704 (35.1%)</td><td>7674 (35.9%)</td><td>6162 (33.4%)</td></tr></tbody></table><table frame=\"hsides\" rules=\"groups\"><thead><tr><th><italic>Rectal cancer</italic></th><th>Total</th><th>Stage I</th><th>Stage II</th><th>Stage III</th><th>Stage IV</th></tr><tr><th/><th><italic>N</italic> = 41,991</th><th><italic>N</italic> = 9510</th><th><italic>N</italic> = 7504</th><th><italic>N</italic> = 16,346</th><th><italic>N</italic> = 8631</th></tr></thead><tbody><tr><td>Age at diagnosis, years</td><td>70.2 (61.5–78.2)</td><td>70.6 (63.0–78.2)</td><td>72.6 (63.7–80.1)</td><td>68.7 (60.2–77.0)</td><td>70.4 (60.6–78.8)</td></tr><tr><td colspan=\"6\">Age group, years</td></tr><tr><td> 18–44</td><td>1401 (3.3%)</td><td>232 (2.4%)</td><td>184 (2.5%)</td><td>646 (4.0%)</td><td>339 (3.9%)</td></tr><tr><td> 45–54</td><td>3723 (8.9%)</td><td>675 (7.1%)</td><td>521 (6.9%)</td><td>1677 (10.3%)</td><td>850 (9.8%)</td></tr><tr><td> 55–64</td><td>9076 (21.6%)</td><td>1969 (20.7%)</td><td>1392 (18.6%)</td><td>3890 (23.8%)</td><td>1825 (21.1%)</td></tr><tr><td> 65–74</td><td>13,217 (31.5%)</td><td>3398 (35.7%)</td><td>2237 (29.8%)</td><td>5113 (31.3%)</td><td>2469 (28.6%)</td></tr><tr><td> 74–84</td><td>11,067 (26.4%)</td><td>2460 (25.9%)</td><td>2341 (31.2%)</td><td>3982 (24.4%)</td><td>2284 (26.5%)</td></tr><tr><td> ≥85</td><td>3507 (8.4%)</td><td>776 (8.2%)</td><td>829 (11.0%)</td><td>1038 (6.4%)</td><td>864 (10.0%)</td></tr><tr><td colspan=\"6\">Sex</td></tr><tr><td> Male</td><td>26,804 (63.8%)</td><td>5844 (61.5%)</td><td>4815 (64.2%)</td><td>10,579 (64.7%)</td><td>5566 (64.5%)</td></tr><tr><td> Female</td><td>15,187 (36.2%)</td><td>3666 (38.5%)</td><td>2689 (35.8%)</td><td>5767 (35.3%)</td><td>3065 (35.5%)</td></tr><tr><td colspan=\"6\">Ethnicity</td></tr><tr><td> White</td><td>39,935 (95.1%)</td><td>9075 (95.4%)</td><td>7143 (95.2%)</td><td>15,522 (95.0%)</td><td>8195 (94.9%)</td></tr><tr><td> Other ethnicities</td><td>2056 (4.9%)</td><td>435 (4.6%)</td><td>361 (4.8%)</td><td>824 (5.0%)</td><td>436 (5.1%)</td></tr><tr><td colspan=\"6\">Income 2015 quintile</td></tr><tr><td> 1 – Least deprived</td><td>8815 (21.0%)</td><td>2138 (22.5%)</td><td>1614 (21.5%)</td><td>3389 (20.7%)</td><td>1674 (19.4%)</td></tr><tr><td> 2</td><td>9488 (22.6%)</td><td>2248 (23.6%)</td><td>1705 (22.7%)</td><td>3705 (22.7%)</td><td>1830 (21.2%)</td></tr><tr><td> 3</td><td>8965 (21.3%)</td><td>2046 (21.5%)</td><td>1564 (20.8%)</td><td>3520 (21.5%)</td><td>1835 (21.3%)</td></tr><tr><td> 4</td><td>7865 (18.7%)</td><td>1685 (17.7%)</td><td>1425 (19.0%)</td><td>3012 (18.4%)</td><td>1743 (20.2%)</td></tr><tr><td> 5 – Most deprived</td><td>6858 (16.3%)</td><td>1393 (14.6%)</td><td>1196 (15.9%)</td><td>2720 (16.6%)</td><td>1549 (17.9%)</td></tr><tr><td colspan=\"6\">Comorbidity</td></tr><tr><td> Heart failure</td><td>859 (2.0%)</td><td>239 (2.5%)</td><td>176 (2.3%)</td><td>245 (1.5%)</td><td>199 (2.3%)</td></tr><tr><td> Myocardial infarction</td><td>1349 (3.2%)</td><td>375 (3.9%)</td><td>269 (3.6%)</td><td>435 (2.7%)</td><td>270 (3.1%)</td></tr><tr><td> Diabetes with complications</td><td>259 (0.6%)</td><td>67 (0.7%)</td><td>50 (0.7%)</td><td>91 (0.6%)</td><td>51 (0.6%)</td></tr><tr><td> Chronic pulmonary disease</td><td>4292 (10.2%)</td><td>1141 (12.0%)</td><td>820 (10.9%)</td><td>1415 (8.7%)</td><td>916 (10.6%)</td></tr><tr><td colspan=\"6\">Route to diagnosis</td></tr><tr><td> Emergency presentation</td><td>2920 (7.0%)</td><td>356 (3.7%)</td><td>522 (7.0%)</td><td>791 (4.8%)</td><td>1251 (14.5%)</td></tr><tr><td> GP referral</td><td>11,226 (26.7%)</td><td>2969 (31.2%)</td><td>1935 (25.8%)</td><td>4193 (25.7%)</td><td>2129 (24.7%)</td></tr><tr><td> Inpatient elective</td><td>1557 (3.7%)</td><td>400 (4.2%)</td><td>261 (3.5%)</td><td>599 (3.7%)</td><td>297 (3.4%)</td></tr><tr><td> Other outpatient</td><td>2281 (5.4%)</td><td>730 (7.7%)</td><td>435 (5.8%)</td><td>687 (4.2%)</td><td>429 (5.0%)</td></tr><tr><td> Screening</td><td>5215 (12.4%)</td><td>1997 (21.0%)</td><td>858 (11.4%)</td><td>1927 (11.8%)</td><td>433 (5.0%)</td></tr><tr><td> TWW</td><td>18,792 (44.8%)</td><td>3058 (32.2%)</td><td>3493 (46.5%)</td><td>8149 (49.9%)</td><td>4092 (47.4%)</td></tr></tbody></table></table-wrap>" ]	[]	[]	[]	[]	[]	[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>" ]	[ "<table-wrap-foot><p>TWW: 2-week-wait referral; comorbidity: patients can have more than one condition listed.</p></table-wrap-foot>", "<fn-group><fn><p>The original online version of this article was revised: “In this article table 1 has been given erroneously”.</p></fn><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p><bold>Change history</bold></p><p>11/14/2023</p><p>A Correction to this paper has been published: 10.1038/s41416-023-02486-6</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"41416_2023_2440_Fig1_HTML\" id=\"d32e353\"/>", "<graphic xlink:href=\"41416_2023_2440_Fig2_HTML\" id=\"d32e1278\"/>", "<graphic xlink:href=\"41416_2023_2440_Fig3_HTML\" id=\"d32e1286\"/>", "<graphic xlink:href=\"41416_2023_2440_Fig4_HTML\" id=\"d32e1337\"/>" ]	[ "<media xlink:href=\"41416_2023_2440_MOESM1_ESM.pdf\"><caption><p>Supplemental Material</p></caption></media>", "<media xlink:href=\"41416_2023_2440_MOESM2_ESM.pdf\"><caption><p>Supplemental Material STROBE checklist</p></caption></media>" ]	[{"label": ["8."], "surname": ["Boyle", "Kuryba", "Cowling", "Aggarwal", "Hill", "van der Meulen"], "given-names": ["JM", "A", "TE", "A", "J", "J"], "article-title": ["Determinants of Variation in the Use of Adjuvant Chemotherapy for Stage III Colon Cancer in England"], "source": ["Clin Oncol (R Coll Radio)"], "year": ["2020"], "volume": ["32"], "fpage": ["e135"], "lpage": ["44"], "pub-id": ["10.1016/j.clon.2019.12.008"]}, {"label": ["12."], "mixed-citation": ["Geskus RB. 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Longitudinal analysis of English population-based data 2006\u20132013"], "source": ["J Epidemiol Commun Health"], "year": ["2019"], "volume": ["73"], "fpage": ["3"], "lpage": ["10"], "pub-id": ["10.1136/jech-2017-210371"]}, {"label": ["38."], "surname": ["Kawakami", "Zaanan", "Sinicrope"], "given-names": ["H", "A", "FA"], "article-title": ["Microsatellite instability testing and its role in the management of colorectal cancer"], "source": ["Curr Treat Options Oncol"], "year": ["2015"], "volume": ["16"], "fpage": ["1"], "lpage": ["15"], "pub-id": ["10.1007/s11864-015-0348-2"]}, {"label": ["44."], "surname": ["Haviland", "Sodergren", "Calman", "Corner", "Din", "Fenlon"], "given-names": ["J", "S", "L", "J", "A", "D"], "article-title": ["Social support following diagnosis and treatment for colorectal cancer and associations with health-related quality of life: Results from the UK ColoREctal Wellbeing (CREW) cohort study"], "source": ["Psycho-Oncol"], "year": ["2017"], "volume": ["26"], "fpage": ["2276"], "lpage": ["84"], "pub-id": ["10.1002/pon.4556"]}, {"label": ["45."], "mixed-citation": ["Price P, Sullivan R, Ahmed A, Wishart GC. UK cancer care: a watershed moment and the need for urgent intervention. Lancet Oncol. 2022;24:133\u20138."]}]	{ "acronym": [], "definition": [] }	47	CC BY	no	2024-01-13 00:02:20	Br J Cancer. 2024 Jan 31; 130(1):88-98	oa_package/fd/0f/PMC10781675.tar.gz
PMC10781676	38200014	[ "<title>Background & Summary</title>", "<p id=\"Par2\">The intended and unintended release of micropollutants to the environment, the exposure of humans and ecosystems to those chemicals and the related potential of causing serious problems for the ecosystem and human health at various scales, has been defined as one of nine planetary boundaries<sup>##REF##35038861##1##</sup>. More than 350,000 chemicals and mixtures thereof are in commerce<sup>##REF##32450690##2##</sup>, and many of them are known or expected to end up in aquatic environments. Targeted screening analysis of surface waters and wastewater treatment plant effluents revealed the occurrence of up to 2000 chemicals<sup>##REF##27299692##3##–##REF##32120059##5##</sup> in the aquatic environment. Some persistent substances, including the so-called “forever” perfluorinated compounds”, even end up far remote from production and use<sup>##REF##36458501##6##</sup>, e.g., in high mountain<sup>##UREF##0##7##</sup> or arctic<sup>##REF##35446578##8##</sup> areas.</p>", "<p id=\"Par3\">Data on environmental occurrence in terms of measured environmental concentrations as well as on biological effects in terms of effect concentrations are sparse, scattered and still seldom interoperable. Nevertheless, some databases provide such information; for example, the NORMAN network collects chemical concentrations measured in European monitoring campaigns<sup>##UREF##1##9##</sup>, and the ECOTOXicology Knowledgebase (ECOTOX) developed by the US Environmental Protection Agency (US EPA) provides toxicity data for environmental species<sup>##REF##35262228##10##</sup>. Such data is required for hazard and environmental risk assessment.</p>", "<p id=\"Par4\">In the ideal case, experimental toxicity data is available for all chemicals and species present in the (aquatic) environment. In reality, scarcity of the relevant data for many chemicals often limits the risk prediction power. To fill gaps in the (eco)toxicity data, quantitative structure-activity relationship (QSAR) models are utilized. Lacking experimental data, these models estimate a chemical’s bioactivity or toxicity based on its structure<sup>##REF##24351051##11##</sup>. While classical QSAR models are typically based on linear regression models to predict chronic or acute toxicity of chemicals from the calculated physico-chemical properties<sup>##UREF##2##12##,##UREF##3##13##</sup>, many of the modern QSARs apply machine-learning approaches to estimate effect concentrations based on molecular descriptors<sup>##REF##28234392##14##–##REF##33289523##16##</sup>. Furthermore, structure-based classification schemes exist to categorize chemicals based on their mode of toxic action (MoA). However, available tools to predict such MoA, for instance, the Verhaar scheme<sup>##UREF##5##17##</sup>, the EPA ASTER QSAR application<sup>##REF##1815380##18##</sup>, and the EPA MOAtox database<sup>##REF##25700118##19##</sup>, are limited in their ability to provide consistent predicted MoAs for a wide range of chemicals<sup>##REF##28759717##20##</sup>.</p>", "<p id=\"Par5\">Due to the current limitations in data availability and in the predictability of potential long-term effects with short-term acute toxicity testing, as well as the time lag and costs associated with chronic toxicity testing using animals, risk assessment is about to change towards evidence-based and integrated assessments considering mechanistic knowledge and evidence on a chemical’s mechanism of toxic action derived from <italic>in vitro</italic> bioassays or read across approaches<sup>##REF##35820522##21##–##REF##22528508##23##</sup>. The adverse outcome pathway (AOP) concept, introduced by Ankley <italic>et al</italic>.<sup>##REF##31965587##24##</sup> and implemented by the OECD<sup>##UREF##7##25##</sup> helps to organize such knowledge from any kind of chemical-biological interaction studies and to inform such evidence-based approaches. Established AOPs provide mechanistic information, e.g., from a chemical binding to a biological receptor molecule exerting physiological events to the development of an adverse outcome. With this, AOPs contain information on a chemical’s MoA and offer cross-species considerations as well as read across options<sup>##UREF##8##26##</sup>. Using mechanistic information for grouping of chemicals according to their, e.g., similar biological MoA, has been proposed for the performance of risk assessment by EFSA<sup>##REF##34976164##27##</sup> and ECHA<sup>##UREF##9##28##</sup>. Thereby, EFSA defines a biologically plausible sequence of events in an organism leading to an observed effect as MoA. It refers to the major steps leading to an adverse health effect following interaction of the chemical with biological targets, but does not imply full understanding of the mechanism of action at the molecular level<sup>##UREF##10##29##</sup>.</p>", "<p id=\"Par6\">In 2016, Busch <italic>et al</italic>.<sup>##REF##27299692##3##</sup> researched mechanisms of action of and knowledge on MoAs for more than 400 environmentally relevant chemicals and showed that diverse pesticides, pharmaceuticals, and industrial chemicals can have the same or similar MoAs, for example, acting on the nervous or endocrine system of vertebrates. This is of relevance, especially for non-target organisms that are unintended co-exposed to mixtures of such chemicals in the aquatic environment. Chemical regulation and authorization, however, does, in most cases, not consider co-exposures and is separated into regulatory silos according to the intended chemical use domain. Recent efforts discuss the implementation of a mixture assessment factor<sup>##UREF##11##30##</sup> or the development of cumulative assessment groups of chemicals<sup>##REF##34976164##27##</sup> to cope with this issue. MoAs or mechanistic information are, however, not considered yet within these recent environmental mixture risk assessment considerations.</p>", "<p id=\"Par7\">Here, we aimed to extend the previous list of 426 environmentally relevant chemicals to a comprehensive list of 3,387 compounds and provide information on their MoAs as well as on their toxicity to support environmental risk assessment. Therefore, we undertook the effort to research, merge, curate, and provide data on biological MoAs, as well as curated effect concentrations for each compound for three biological quality elements (BQE) of the European Water Framework Directive, namely algae, crustaceans, and fish (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.eea.europa.eu/themes/water/european-waters/water-quality-and-water-assessment/water-assessments/quality-elements-of-water-bodies\">https://www.eea.europa.eu/themes/water/european-waters/water-quality-and-water-assessment/water-assessments/quality-elements-of-water-bodies</ext-link>). The data should be provided in a FAIR format to enable standardization, comparability, reproducibility, and acceleration for ecotoxicological hazard and risk assessment<sup>##REF##26978244##31##</sup>.</p>", "<p id=\"Par8\">Based on information from databases, lists of regulatory directives, monitoring projects, and respective chemical suspect lists, we compiled a curated list of 3,387 compounds of freshwater environmental occurrences e.g.<sup>##REF##36284750##32##–##REF##18952330##36##</sup>. We researched all chemicals on this list for their use and product group, their biological MoAs, and compiled ecotoxicity data ready to be used in environmental risk assessment based on data from the US EPA ECOTOXicology Knowledgebase<sup>##REF##35262228##10##</sup> (ECOTOXDB) and QSAR predictions. Out of all 3,387 compounds, 2,890 were identified as parent substances, 374 as transformation products (TP), and 96 as both, parent and TP. Only in a few cases (27 chemicals) of mainly industrial chemicals (e.g., 3-dodecylbenzene-1-sulfonic acid.), such a classification could only be assumed, and was, therefore, not assigned (Tables A, B, and C, available on Zenodo)<sup>##UREF##14##37##</sup>. Further details on the curated use group classifications, the MoA categories, and the toxicity data for all compounds are explained and summarized below.</p>" ]	[ "<title>Methods</title>", "<title>Use groups, mode of action and accompanying information</title>", "<title>Overall strategy</title>", "<p id=\"Par9\">For each compound in the dataset, a systematic search in different data sources and databases was performed. All information was obtained from the following databases listed in Table ##TAB##0##1##.</p>", "<p id=\"Par10\">We did not use specific search terms within those databases but collected all information on MoA and use that was available for the respective compound. For compounds with uncertain or limited information available, results of a literature research were included to fill as many remaining gaps as possible. There, we searched the web of Science and PubMed databases and used the compound name in combination with “toxicity” or “mode of action”. Results of these searches were not systematically stored, but relevant information transferred to our collection. Data were retrieved between January 2021 and April 2023. All collected data were sorted and categorized at higher levels: based on the results, each compound was assigned as a parent or transformation product; assigned to a specific use group; and if possible, to a mode of action and target or non-target taxa, as pointed out in detail below. Overall, we followed a step-wise approach where all information for a respective compound was collected first, and sorted and annotated according to our systematic and standardized broad categories of use groups and MoAs in a second step. Finally, we curated all further information and developed standardized terms for detailed use information as well as for specific MoA information, although this was challenging and some chemicals remained with individual descriptions (Tables A and C, available on Zenodo)<sup>##UREF##14##37##</sup>.</p>", "<title>Use group classification</title>", "<p id=\"Par11\">Based on the collected information, all compounds were assigned either as parent compound or as transformation product (TP) in column “parent_or_TP” (Tables A and C, available on Zenodo)<sup>##UREF##14##37##</sup>. Respective parent compound(s) are listed for all TPs in column “TP_of”. In the context of this dataset, TPs comprise both, metabolites and environmental degradation products. Some chemicals are both, they occur as TP of another compound, but are also parent compounds in another context. These chemicals were labeled as ‘parent + TP’. In our statistical analyses, we counted them as parent substances. In the next step, the compounds were grouped according to their use into eight categories: (1) Industrial Chemical, (2) Pesticide, (3) Biocide, (4) Pharmaceutical, (5) Drug of abuse, (6) Natural, (7) Food additive, and (8) Metal (column”use_group”). The groups “drug of abuse” and “biocide” are listed as separate use groups in the dataset but were included into the use group categories of “Pharmaceutical/Drug of abuse” and “Pesticide/Biocide”, respectively, in the analyses and figures. As “drugs of abuse” we defined psychoactive drugs that are not used as pharmaceuticals such as, e.g., Nicotine, or 3,4-Methylenedioxyamphetamine (MDA). Multiple use groups can be assigned to a compound depending on its range of applications.</p>", "<p id=\"Par12\">The category of pharmaceuticals and drugs is the most common in the dataset with 1,162 compounds and additional 139 TPs, followed by pesticides and biocides with 696 compounds and 204 TPs, industrial chemicals with 726 and 19 TPs, naturally occurring compounds with 93 and 4 TPs, metals with 19 compounds and food additives with 11 compounds (Fig. ##FIG##0##1##). 279 compounds (8 TPs) were assigned to more than one use group and are summarized as multiple use compounds in the graphs. These include food additives which predominantly have an additional application, e.g., in the industrial sector as fragrance. Moreover, many naturally occurring compounds such as alkaloids or hormones are frequently applied as pesticides, pharmaceuticals or in industrial applications.</p>", "<p id=\"Par13\">Detailed information on the application and usage of all compounds in each use group category were collected and added to the table in additional columns (“use_group_details” and “additional_use_info”). Pesticides were further categorized systematically into insecticides (I), acaricides (A), herbicides (H), fungicides (F), and other specific use groups, e.g., molluscicides and nematicides. Detailed application areas of pharmaceuticals were included and annotated according to their area of action, ranging from antibiotics over contraceptives to psychotropic or radioactive agents, just to mention a few examples. This listing contains more than 100 terms and was curated based on the retrieved information, but does not necessarily consider medical term standards. Moreover, specific applications of industrial chemicals are listed and respective terms were taken from the collected information or curated by us. If chemicals belong to different use groups, this is indicated in the “use_group” column where all broad application domains are listed, separated with “,”. In the “use_group_details” applications in different sectors, e.g., as industrial chemical and insecticide, are listed with “ + ” while multiple uses within a sector are separated with “/”. This listing is far from being comprehensive but covers the most common use domains for the investigated chemicals, whereas “most common” was a subjective decision by the curator based on the retrieved information. All abbreviations used in the mentioned columns of Table C are explained next to the column header explanations in Table A (available on Zenodo)<sup>##UREF##14##37##</sup>. The “additional_use_info” column contains additional non-systematic and non-standardized information on the use of the chemical.</p>", "<p id=\"Par14\">Most industrial chemicals are used for multiple applications, e.g., as colorants, fragrances, plasticizers, additives, flame retardants, solvents, reagents, or intermediates in the manufacture of other products. Furthermore, 105 compounds within the industrial chemicals group have an application as personal care (PC) products such as stabilizer, surfactant or UV filter in sunscreen products e.g., benzophenone-3. Compounds that are found in diesel exhaust, cigarette smoke or which are generally released in combustion processes of natural compounds are summarized as combustion products (59 compounds) within the group of industrial chemicals. Contrary to the multiple applications of industrial chemicals, pesticides are more specific in their use. Within the group of pesticides, 126 (40 TPs) compounds are assigned as pure insecticides, and 99 compounds (10 TPs) as both, insecticide and acaricide. The dataset includes 251 (83 TPs) herbicides, 124 (41 TPs) fungicides, and 73 compounds (26 TPs) with multiple applications or a specific use, e.g., as nematicide, molluscicide, avicide, or as herbicide safener. 80 compounds are considered obsolete, and 49 pesticides are also used as veterinary substances. Pure biocides are rare because most compounds with biocidal action also serve as industrial chemicals, or have an application in the agricultural sector as pesticide. The group of pharmaceuticals can be further distinguished into various detailed use groups, with psychotropic drugs, antibiotics, antihypertensive, antihistamine, and anti-inflammatory drugs being the most prominent. There is a large overlap between the use groups of pharmaceuticals and drugs of abuse, e.g., for doping agents used in sports. Otherwise, the use group of drugs of abuse contains mostly cannabinoids and stimulating, psychedelic or hallucinogenic substances. Finally, even with additional literature research efforts, 27 compounds could not be assigned to a specific application sector and remain with an unknown use group. Consequently, these are the ones for which an assignment to parent and TP was not possible.</p>", "<title>Mode of action</title>", "<p id=\"Par15\">The amount of information available on MoAs varied significantly between use groups and chemicals. MoA information covers molecular targets (e.g., action on a specific receptor), a pathway (e.g., photosynthesis inhibition), and/or general information about a disturbance (e.g., endocrine disruption).</p>", "<p id=\"Par16\">Based on the collected data and information and on our previous work<sup>##REF##27299692##3##,##REF##35483182##4##</sup>, we aimed to provide systematic and harmonized broad MoA categories but also additional and more specific information about the biological actions of the chemicals. Therefore, we started to note down whether there are target and/or known non-target species for the respective chemicals, their biological molecular targets and/or specific ways of action as well as additional MoA information. We sorted the retrieved information, which was available for more than half of the compounds, and established harmonized terms for specific MoAs and the broad MoA categories systematically. We grouped chemicals that act on the same target or the same biological signaling pathway into a common “MoA_specific” also considering terms established by the HRAC, IRAC and FRAC databases. Different specific MoAs that could be assigned to a broader biological system, such as, e.g., the nervous system or the endocrine system, were then grouped into common “MoA_broad” categories (Table C, columns “MoA_broad” and “MoA_specific”, available on Zenodo)<sup>##UREF##14##37##</sup>. Abbreviations for biological molecules used within these terms, such as, e.g., AChE for acetylcholine esterase, are explained in the following columns that contain information on molecular targets and further MoA details. The contents of these additional columns were only partly harmonized (Table C, columns “molecular_target” and “further_MoA_info”, available on Zenodo)<sup>##UREF##14##37##</sup>. Finally, many compounds act on multiple molecular targets and can be assigned to more than one specific or broad MoA group. In these cases, all MoAs are listed with “,” in the respective columns.</p>", "<p id=\"Par17\">MoA information could be retrieved for 2,172 compounds (64%) for at least one of the three levels (broad, specific, or molecular target) (Table S3). Of those, 1,975 were assigned to one of the 32 broad MoA categories, 174 to two or more categories and 1,238 (including 337 TPs) could not be assigned to a broad MoA category. Considering compounds with one broad MoA, chemicals with a neuroactive mode of action build the most prominent group (579 compounds), followed by compounds acting on neuromuscular (200), endocrine (154), and cardiovascular systems (136) as exemplarily shown in Fig. ##FIG##1##2A##. While, for example, the MoA category ‘Antibiotics’ and ‘Synthetic auxins’ contain chemicals of only one-use group, pharmaceuticals and pesticides, respectively, most MoA categories contain chemicals of different use groups. 15 broad MoA categories could be discriminated within the group of industrial chemicals, led by the categories ‘Nucleic acid damage’ and ‘Endocrine’ with 55 and 38 chemicals, respectively. 25 broad MoA categories were identified among the pharmaceuticals/drugs, with ‘Neuroactive’ (458) and ‘Cardiovascular system’ (150) being those with the most assigned chemicals. 23 MoA broad categories were identified for the pesticides/biocides, with the largest number of chemicals known to act on the ‘Neuromuscular system’ (124) and as ‘Neuroactive’ compounds (85). Only for 10% of all TPs, a broad MoA category could be assigned (Fig. ##FIG##1##2B##). Although a transformation product is sometimes the actual acting chemical, MoA information is in most cases described for the parent compound. Therefore, details on the biological action of TPs are widely lacking. We did assign the MoA of a parent compound also to its TP only in cases, where the respective action of the TP was clearly indicated in a respective information sources, which is also given Table C (column “source”, available on Zenodo)<sup>##UREF##14##37##</sup>.</p>", "<p id=\"Par18\">Although most chemicals could be assigned to one broad MoA category, more specific considerations showed that they still can act on multiple molecular targets in the same or across species. Up to eight molecular targets or specific MoAs were found per compound. There are considerable differences between the numbers of specific MoAs or molecular targets within one broad MoA category. ‘Endocrine’ acting chemicals were found across all use groups (including industrial chemicals). This broad MoA category contains 48 specific MoAs, with action on the glucocorticoid, androgen, and estrogen receptors being the most abundant ones (Fig. ##FIG##2##3A##). The broad MoA category ‘Endocrine’ accounts for all organisms which includes action on the hormone system of animals or humans, e.g., androgen or estrogen receptors, as well as the plant hormone syntheses, e.g., the inhibition of the gibberellin biosynthesis. This indicates that the broad MoA categories help to summarize the biological actions exerted by chemicals in the environment, but for detailed analysis or species-specific assessments the specific MoAs need to be considered. Indeed, single MoA broad categories are overlapping, e.g., the category ‘Neuromuscular system’ is related to the category of ‘Neuroactive’ compounds. In Fig. ##FIG##2##3B## it is shown that these two categories alone comprise more than 100 specific MoAs. Again, it becomes obvious that some seem to be use group-specific (e.g., serotonin receptor antagonists are all pharmaceuticals) while other specific MoAs contain chemicals of different use groups (e.g., the specific MoA with the largest number of chemicals in this category: ‘Acetylcholinesterase inhibition’).</p>", "<p id=\"Par19\">Finally, multiple pharmaceuticals, e.g., cortisol, serve as pro-drugs and are only active at a specific target when transformed to their active metabolites. Even though the prodrug itself is inactive, we decided to include the MoA of the respective active metabolite in the dataset. Furthermore, a unique assignment of a molecular target to a single broad MoA category is often challenging. For instance, cannabinoids act on the endocannabinoid system and are declared as neuroactive in the dataset, but effects on the immune system have also been proposed for the cannabinoid receptor 2. Therefore, the classification of broad MoA categories serves as a guidance and can be further discussed and developed in the future.</p>", "<title>Target/non-target taxa and additional information</title>", "<p id=\"Par20\">Data about action on target species (e.g., insects in the case of insecticides) and non-target species (e.g., other aquatic species or humans) were included into the dataset in case such information occurred during the curation. However, we did not systematically search for non-target effects and information of all compounds. A target taxon could be assigned for pesticides, e.g., insects, plants, fungi, or nematodes as these chemicals are designed, respectively. Moreover, for most pharmaceuticals and drugs of abuse, humans can be declared as target species, while in the case of veterinary substances, the target group can be extended to mammals or even vertebrates. The target taxa of antibiotics, antiviral and antifungal pharmaceuticals were specified as bacteria, viruses and fungi in humans, respectively. No target species was determined for natural compounds, food additives, metals or industrial chemicals, with a few exceptions, such as disinfectants or antifungals with target taxa of bacteria or fungi.</p>", "<p id=\"Par21\">While MoAs of pesticides and pharmaceuticals are intentionally designed for specific target taxa, their toxicological MoA on vertebrates as non-target species might be entirely different. For most of the chemicals, little information about the mechanisms of action in non-target species was identified. However, in cases where such information was found during our research, alerts for non-target species were included into the dataset. Risks of pesticides for non-target species were retrieved from the PPDB<sup>##UREF##15##38##</sup> database, risks of potential carcinogenic compounds for humans were retrieved from the International Agency for Research on Cancer (IARC)<sup>##UREF##16##39##</sup> of the World Health Organization, and information on endocrine disruptors were retrieved from the endocrine disruptor lists of ECHA<sup>##UREF##13##35##</sup>. Therefore, Table C also contains the columns “nontarget_taxa” and “nontarget_taxa_alert” available on Zenodo<sup>##UREF##14##37##</sup>.</p>", "<title>Chemical data</title>", "<title>Data compilation and retrieval</title>", "<p id=\"Par22\">The list of chemicals was compiled based on existing collections of known environmental contaminants and amended by chemical information retrieved from US EPA Chemical Dashboard<sup>##UREF##17##40##</sup> and PubChem<sup>##REF##33151290##41##</sup>. The data were linked by unequivocal identifiers (i.e. InChiKey, PubChem CID, and DTXSID).</p>", "<title>Curation of chemical structures</title>", "<p id=\"Par23\">To improve the quality of the chemical structures (i.e., SMILES) and QSAR modeling, curation of all structures was applied. It has been proven that deploying the pure and neutralized chemical structures enhances the quality of machine learning based QSAR predictions<sup>##UREF##4##15##,##REF##27885861##42##</sup>. The curation included, for example, the canonicalization of the SMILES code, the desalting, de-aromatization, removal of stereochemistry, solving hyper valency, and other transformations to generate QSAR-ready SMILES described elsewhere utilizing OPERA 2.7<sup>##UREF##4##15##</sup>.</p>", "<title>Prediction of log S<sub>w</sub> values</title>", "<p id=\"Par24\">The water solubility at 25 °C of all included was predicted applying OPERA 2.7 based on QSAR-ready SMILES<sup>##REF##27885861##42##</sup>. OPERA exports the values in csv format. The unit of log S<sub>w</sub> is mol/L. The S<sub>w</sub> in mg/L was calculated using the average molecular mass in g/mol. The solubility was needed to estimate the solubility domain class of the ecotoxicity data described below.</p>", "<title>Ecotoxicity data</title>", "<title>US EPA ECOTOXicology Knowledgebase data</title>", "<p id=\"Par25\">Environmental risk assessment requires exposure as well as effect concentrations and is often challenged by the variety of experimentally determined effect concentrations that differ between species, endpoints, and experimental settings. In this study, we aimed to provide one value per compound and BQE (algae, crustaceans, and fish according to the WFD), derived from all available data in the ECOTOXDB<sup>##REF##35262228##10##</sup> using carefully selected and developed criteria that ensure that comparable data is selected and merged, and a robust and representative effect concentration for each respective species group is derived. The whole procedure of data retrieval and processing was performed using the R package REcoTox<sup>##UREF##18##43##</sup> version 0.4.1 (<ext-link ext-link-type=\"uri\" xlink:href=\"https://github.com/tsufz/REcoTox/tree/0.4.1\">https://github.com/tsufz/REcoTox/tree/0.4.1</ext-link>). The software and workflow is described briefly below and in detail in the vignette document<sup>##UREF##19##44##</sup>.</p>", "<title>Data retrieval, processing, and curation</title>", "<p id=\"Par26\">The ASCII file version <italic>ecotox_ascii_03_10_2022.zip</italic> of the ECOTOXDB<sup>##REF##35262228##10##</sup> was downloaded from US EPA website (<ext-link ext-link-type=\"uri\" xlink:href=\"https://cfpub.epa.gov/ecotox\">https://cfpub.epa.gov/ecotox</ext-link>). The database files were unzipped and the tables tests, results, species, chemicals, and references were selected for further processing. Data from the selected tables were joined and further filtered by the IDs test_id, cas_number, species_number, and reference_number, and under consideration of the following criteria:<list list-type=\"bullet\"><list-item><p id=\"Par27\">type of dosing (only water-based dosing was considered, e.g., mg/L); dosing_group = “water_concentration“</p></list-item><list-item><p id=\"Par28\">time unit of endpoint (e.g., d for days, h for hours); duration_d = c(“d“, “dph“, “dpf“); duration_h = c(“h“, “ht“, “hph“, “hpf“, “hv“)</p></list-item><list-item><p id=\"Par29\">duration range of the exposure (e.g., minimum and maximum hours); duration_m = mi; min_d = 0; min_h = 0; min_m = 0; max_d = 5; max_h = 120; max_m = 7200</p></list-item><list-item><p id=\"Par30\">species (e.g., species group (ecotoxgroup) such as <italic>algae</italic>, habitat, subsets of species like standard species); ecotoxgroup = c(“Algae“, “Crustacean“, “Fish“); habitat = “Water“; species_selection = “all“ for “Algae“; species_selection = “standard_test_species“ for “Crustacean“ and “Fish“</p></list-item><list-item><p id=\"Par31\">effects and measurements; effects = c(“MOR“, “GRO“, “POP“, “REP“, “MPH“, “DEV“) for “Algae“ and “Fish“; effects = c(“MOR“, “GRO“, “POP“, “REP“, “MPH“, “DEV“, “ITX“) for “Crustacean“</p></list-item></list></p>", "<p id=\"Par32\">The definitions of the ECOTOXDB terms are detailed elsewhere (ECOTOX Help. <ext-link ext-link-type=\"uri\" xlink:href=\"https://cfpub.epa.gov/ecotox/help.cfm?sub=term-appendix\">https://cfpub.epa.gov/ecotox/help.cfm?sub=term-appendix</ext-link>).</p>", "<p id=\"Par33\">According to Busch <italic>et al</italic>.<sup>##REF##27299692##3##</sup>, the selected endpoints were cover the whole distribution of the concentration-effect-relationships. This includes all effect or lethal levels recorded in the ECOTOXDB namely: effect concentrations - EC<sub>1–99</sub>, effective doses - ED<sub>1–99</sub>, effect levels - EL<sub>1–99</sub>, inductive concentrations - IC<sub>1–99</sub>, lethal concentrations - LC<sub>1–99</sub>, lethal doses - LD<sub>1–99</sub>, lethal levels - LL<sub>1–99</sub>, lethal thresholds - LT<sub>1–99</sub>, lowest observed effect concentrations - LOEC, and lowest observed lethal concentrations - LETC.</p>", "<p id=\"Par34\">Afterwards, the different dosing units were standardized to mg/L. In cases where mol/L are used in the database, the table ecotoxgroup_mol_weight.csv was applied to recalculate concentrations to mg/L.</p>", "<p id=\"Par35\">In this study, the percentile value was set to 0.05 (quantile = 0.05) to calculate the 5<sup>th</sup> percentile of the included effect data. Additionally, the average, the geometric average, the median, the minimum, and the maximum effect concentrations were calculated and rounded to four significant digits. The estimated values can exceed the solubility of the single compounds. In older studies, often the nominal concentration was reported without measurement of the real concentrations in the bioassays. Some reported values might be thus overestimated. To mitigate the risk of false positive results, the so-called solubility domain was calculated according to the ideas realized in ChemProp<sup>##REF##21491860##45##</sup> (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.ufz.de/index.php?en=34593\">https://www.ufz.de/index.php?en=34593</ext-link>).</p>", "<p id=\"Par36\">The solubility domain has three classes:Where <italic>EC</italic><sub><italic>x</italic></sub> is the respective ecotoxicity value and <italic>S</italic> is the solubility value in mg/L. The basis of <italic>log</italic> (<italic>S</italic>) is 10. The classes of the solubility domain were calculated for each of the aggregated values (e.g., geometrical mean) and amended to the results table. Furthermore, all raw ecotoxicity values, endpoints, effects, measurements, species, durations, and reference IDs are collapsed in one single field per category for later review.</p>", "<title>Prediction of effect concentrations</title>", "<p id=\"Par37\">Measured effect concentrations were successfully derived and curated for a maximum of 25% of all compounds. Therefore, we additionally predicted effect concentrations for all chemicals for which the respective QSAR models<sup>##UREF##4##15##,##REF##33289523##16##,##REF##27885861##42##</sup> were applicable. It was not possible to predict values for inorganic and metal-organic compounds, and complexes because existing QSAR models do not handle such structures. In addition, mixtures were excluded. The VEGA QSAR models<sup>##REF##33289523##16##</sup> (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.vegahub.eu/portfolio-item/vega-qsar\">https://www.vegahub.eu/portfolio-item/vega-qsar</ext-link>) were applied to QSAR-ready SMILES, including these QSAR models:<list list-type=\"bullet\"><list-item><p id=\"Par38\">Algae acute [EC50] toxicity model (IRFMN) v. 1.0.1</p></list-item><list-item><p id=\"Par39\"><italic>Daphnia magna</italic> acute [EC50] toxicity model (IRFMN) v. 1.0.1</p></list-item><list-item><p id=\"Par40\">Fish acute [EC50] toxicity model (IRFM) v. 1.0.1</p></list-item></list></p>", "<p id=\"Par41\">The IRFMN models report the toxicity values in -log(mmol/L). In addition, an a-dimensional predicted value is reported. Since the employed version of the models contained a software bug related to molecular weight estimation, the a-dimensional value was used to calculate correct predicted values (email correspondence with IRFMN). The correction requires a box-cox transformation of the a-dimensional values of the algae and fish models utilizing equations</p>", "<title>Summary of ecotoxicity dataset</title>", "<p id=\"Par42\">The distributions of measured (retrieved from the ECOTOXDB) and QSAR-predicted effect concentrations for the biological quality elements (BQE)<sup>##UREF##20##46##,##UREF##21##47##</sup> algae, crustaceans, and fish are shown in Fig. ##FIG##3##4## and Table ##TAB##1##2##. The measured values, which were considered in this dataset, cover 586 (17%), 858 (25%), and 855 (25%) out of the 3,387 chemicals included here, respectively, for algae, crustacean, and fish. The experimental data is based on 6,156, 9,760, and 19,416 data points with a mean of 5 ± 3, 4 ± 3, and 7 ± 4 data points per chemical, respectively, for algae, crustacean, and fish (Table ##TAB##2##3##).</p>", "<p id=\"Par43\">While the applied QSARs predict to which extent the processed chemical structure covers the application domains of the models, the quality and accuracy of the experimental data is difficult to discover. The QSAR derived application domain classes assigned to each chemical included in this dataset are listed in the respective Tables D-F (available on Zenodo)<sup>##UREF##14##37##</sup>.</p>", "<title>Data compilation</title>", "<p id=\"Par44\">The final tables were processed using an R script. The different tables are linked by an internal identifier (ID). The dataset includes mixtures or compounds without InChiKeys, PubChem CIDs and/or DTXSIDs, and thus no other unequivocal identifier was applicable.</p>" ]	[]	[]	[]	[ "<p id=\"Par1\">Chemicals in the aquatic environment can be harmful to organisms and ecosystems. Knowledge on effect concentrations as well as on mechanisms and modes of interaction with biological molecules and signaling pathways is necessary to perform chemical risk assessment and identify toxic compounds. To this end, we developed criteria and a pipeline for harvesting and summarizing effect concentrations from the US ECOTOX database for the three aquatic species groups algae, crustaceans, and fish and researched the modes of action of more than 3,300 environmentally relevant chemicals in literature and databases. We provide a curated dataset ready to be used for risk assessment based on monitoring data and the first comprehensive collection and categorization of modes of action of environmental chemicals. Authorities, regulators, and scientists can use this data for the grouping of chemicals, the establishment of meaningful assessment groups, and the development of <italic>in vitro</italic> and <italic>in silico</italic> approaches for chemical testing and assessment.</p>", "<title>Subject terms</title>" ]	[ "<title>Data Records</title>", "<p id=\"Par45\">All data is available on ZENODO<sup>##UREF##14##37##</sup> (10.5281/zenodo.10071824). The ZENODO repository contains the following six tables in csv format and one table in xlsx format, containing all six csv files. The first table contains explanations for all columns of the dataset:</p>", "<p id=\"Par46\">Table A_column_descriptions_for_Tables B to F.csv</p>", "<p id=\"Par47\">This table contains all column headers of the following tables B to F and respective explanations and descriptions.</p>", "<p id=\"Par48\">Table B_chemical_information.csv</p>", "<p id=\"Par49\">Table B contains chemical names and common identifiers, such as CAS numbers, PubChem IDs, DTXSIDs, InCHI and Smiles codes for all 3387 chemicals considered in this study.</p>", "<p id=\"Par50\">Table_C_use_groups_and_mode_of_action_information_for_chemicals.csv</p>", "<p id=\"Par51\">Table C contains the collected and curated information on the usage domains of all chemicals and their biological modes of action. The curation process and all data sources are described in the methods part.</p>", "<p id=\"Par52\">Table_D_ecotoxicity_data_algae.csv</p>", "<p id=\"Par53\">Table D contains measured and predicted acute toxicity data for algae species curated and derived for all chemicals as described in detail in the methods part.</p>", "<p id=\"Par54\">Table_E_ecotoxicity_data_algae.csv</p>", "<p id=\"Par55\">Table E contains measured and predicted acute toxicity data for the species group of crustaceans curated and derived for all chemicals as described in detail in the methods part.</p>", "<p id=\"Par56\">Table_F_ecotoxicity_data_fish.csv</p>", "<p id=\"Par57\">Table F contains measured and predicted acute toxicity data for fish species curated and derived for all chemicals as described in detail in the methods part.</p>", "<p id=\"Par58\">Table_X_Tables_A_to_F_chemicals_MoAs_ecotoxdata.xlsx</p>", "<p id=\"Par59\">Table X is provided as additional service and contains all csv tables in an Excel spreadsheet.</p>", "<title>Technical Validation</title>", "<title>Toxicity data and chemical solubility</title>", "<p id=\"Par60\">Using and curating experimental data is always biased by the availability of the data itself. There are different reasons for variations in the availability of experimental data for different chemicals: i) not all chemicals have been tested in bioassays, ii) some chemicals were only tested in a single study, in a single bioassay, with only one species, or with one specific exposure setting, iii) newer chemicals have been screened less often than older (legacy) compounds. For example, this dataset included 475 individual data points for the (legacy in Europe) herbicide atrazine tested in an algae bioassay, but only one datapoint for the herbicide mecoprop, which is currently used.</p>", "<p id=\"Par61\">For filling such data gaps, we used QSARs that are state-of-the art and compliant with the recommendations of the Organization for Economic Co-operation and Development (OECD)<sup>##UREF##22##48##</sup>. The QSAR predicted data cover 3,318 (98%) compounds for the three species groups. In theory, the property of each chemical could be predicted using QSARs. However, QSARs do not cover all structures or compounds (e.g., organometallic or inorganic compounds cannot be predicted) and some included substances in our research are mixtures which are as well not covered by our QSAR analysis. The distribution of effect concentrations in Fig. ##FIG##3##4## are broader for the experimental data compared with the predicted values by one to two log orders of magnitude, while the median effect concentrations across all chemicals are similar between the measured and predicted data.</p>", "<p id=\"Par62\">The quality of the experimental and computational ecotoxicity data was validated using a solubility domain classification to identify and annotate possible outliers in the dataset.</p>", "<p id=\"Par63\">To reveal the data quality of the measured and predicted data, we calculated the solubility-based application domain classes based on the ideas realized in ChemProp<sup>##REF##21491860##45##</sup>. In theory, a chemical cannot be tested above its intrinsic water solubility in bioassays. In older studies, only the nominal concentrations (the initially dosed concentrations), but not the real concentrations in the test media, were reported. Thus, the nominal effect concentration may overestimate the effect if the effect value is above the solubility or saturation of the chemical in the test medium. Though, in cases, a solvation agent was used, or the test medium improves solvation, the solubility can be enhanced. Neither the ECOTOXDB nor most of the QSARs address the solubility issue comprehensively. Figure ##FIG##4##5## shows the number of chemicals of our dataset within respective solubility domain classes. The majority of chemicals fall into class 3. This means that the determined effect concentrations are below the calculated solubility limits, and, therefore, valid to be used. Class 2 refers to effect concentrations that lay between the solubility limit and a half log step above. Class 2 compounds can be considered valid according to the solvation criterion mentioned above. No chemicals fall into class 1 and only few into class 0. The latter values should be maintained with caution and considered in cases where such chemicals are high-ranking as toxicity drivers in risk assessments based on QSAR predictions. Such effect concentrations might only be valid after experimental confirmations.</p>", "<title>Curated modes of chemical action in comparison to pre-existing data</title>", "<p id=\"Par64\">Modes of chemical action were defined and categorized differently during the last decades. While Verhaar <italic>et al</italic>.<sup>##UREF##5##17##</sup> proposed four categories, namely, ‘inert’, ‘less inert’, ‘reactive’, and ‘specifically acting’, based on the composition of chemical structures, later approaches included more categories that were either of general nature (e.g., ‘narcotic action’) or more specifically related to the interaction with a certain biological enzyme or receptor (e.g., ‘acetylcholinesterase inhibition’)<sup>##UREF##23##49##,##REF##33478211##50##</sup>. In this study, we did not build categories upfront but summarized the existing knowledge into meaningful groups based on biological modes of action. These groups, the broad and specific MoA categories as well as use groups were harmonized and standardized in their names. Duplicates and typos were removed, and harmonization was cross-checked by counting the number of chemicals per term. With this, we provide the largest and most systematic dataset on MoA categories for environmental chemicals. Figure ##FIG##5##6## illustrates this by showing all 32 broad MoA categories with respective specific categories within. The comprehensiveness was confirmed by comparing chemicals and assigned MoA categories with results obtained with the tool provided by Firman <italic>et al</italic>.<sup>##UREF##24##51##</sup>. We found that some MoAs are predicted correctly for many chemicals, e.g., ‘acetylcholinesterase inhibition’ or ‘fatty acid biosynthesis inhibition’ while other MoAs such as interactions of chemicals with endocrine nuclear receptors or biological structures of the nervous system are not well covered by the prediction tool. Hence, the here provided data and the resulting MoA categories can be used to extend tools and approaches that aim to predict MoAs based on chemical structure information as summarized in Firman <italic>et al</italic>.<sup>##UREF##24##51##</sup> and Kienzler <italic>et al</italic>.<sup>##REF##31269286##52##</sup>. In turn, the mentioned tools might be applied and evidence generated, especially for those chemicals for which no MoA could be assigned in this study.</p>", "<title>Usage Notes</title>", "<p id=\"Par65\">Data can be used with each software that reads csv. CSV can be imported to excel via the “data/import csv function”.</p>", "<title>Recommendations for the use of the dataset</title>", "<p id=\"Par66\">The dataset was carefully compiled and curated. Quality measures were applied to amend the dataset with quality control tags (i.e. the application domains) to ensure a good data quality and re-usability. The provided dataset should be applied in a scientific and/or regulatory context by experienced assessors or under supervision of an experienced person. To our knowledge, this is the first curation of its kind which provides a starting point and structure that could and should be updated repeatedly as experimental data as well as prediction models constantly evolve<sup>##REF##17561780##53##</sup>. The provided workflow and dataset fulfil the FAIR principles<sup>##REF##26978244##31##</sup> and therefore enable comprehensive and transparent risk assessments with harmonized data for the aquatic environment as full transparency and reproducibility was not given in so far published large-scale chemical risk assessments for aquatic environments (e.g., Malaji <italic>et al</italic>.<sup>##REF##24979762##54##</sup>, Rorije <italic>et al</italic>.<sup>##REF##35090913##55##</sup>). Furthermore, next to the identification of risk driving compounds or sites at risk due to mixture exposures, the comparison of the available effect data with environmental monitoring data can also provide guidance for the prioritization of chemicals for toxicity testing.</p>", "<p id=\"Par67\">The curated collection of MoAs represents the state of knowledge and is definitely not complete but provides guidance and evidence for risk assessors and scientists. Chemicals can be grouped according to their similar ways of action, novel compounds might be assigned to MoAs via read-across approaches, and machine learning or artificial intelligence approaches might use the data for model training. This means, the collection of MoAs and molecular biological targets of chemicals provided in this study provides guidance for the development of <italic>in vitro</italic> and <italic>in silico</italic> methods that should be applied to identify MoAs of novel future chemicals in high throughput screening approaches prior to animal testing. In this regard, especially methods for the detection of MoAs indicative of chronic toxicity, such as neuroactivity or endocrine action of chemicals need to be further developed as we show here that a substantial number of environmentally relevant chemicals can act respectively. The date collected in this study also indicates that chemicals of diverse use groups can act via the same MoA. However, so far, joint exposures to chemicals of the same use group or with the same MoA belonging to different regulatory silos (legislative sectors) are not considered in risk assessments. Currently, initiatives are under way to change this and consider chemicals in common assessment groups or introduce risk factors for mixture exposures<sup>##REF##34976164##27##,##UREF##11##30##,##REF##32626259##56##</sup>. The former approach, proposed by EFSA, aims to apply hazard-driven criteria for grouping of chemicals into assessment groups using MoA information on toxicity<sup>##REF##34976164##27##</sup>. Furthermore, ECHA applies MoA information within risk assessments in the frame of read-across and grouping of chemicals<sup>##UREF##9##28##</sup>. The here provided dataset supports all of these efforts as it can be used i) for read-across studies with novel compounds, ii) for the definition of meaningful MoAs and MoA categories for the grouping of chemicals, and iii) for qualitative and quantitative mixture risk assessments based on data of joint chemical occurrence. Future efforts need to integrate data on more species as are currently curated, e.g., within the EU project PrecisionTox<sup>##UREF##25##57##</sup>, and further harmonize environmental and human risk assessment frameworks across regulatory silos as it is anticipated, e.g., in the European Partnership for the Assessment of Risks of Chemicals (PARC)<sup>##UREF##26##58##</sup>.</p>", "<title>Limitations of the dataset</title>", "<p id=\"Par68\">MoA information was curated and researched systematically by using external data sources. For many chemicals, no MoA information was found in those sources, which does not mean that this information is not known or provided elsewhere. We tried to consider MoA knowledge for more than one species group. However, each chemical may act via other MoAs in other species that were not considered here. To our knowledge, there is no ontology for MoAs available and annotations of MoAs are less harmonized than biological molecules, for example. We tried to start MoA annotations considering existing knowledge. However, this dataset is far from being complete and, dependent on the context, the grouping of chemicals into use groups and MoA categories could also be done in other ways.</p>", "<p id=\"Par69\">Despite the careful curation of the ecotoxicity dataset, the input data was retrieved from an external database or QSAR estimations. The underlying data might contain data with quality issues (e.g., bad experimental design, or wrong rating due to reported nominal concentrations). Furthermore, in some cases, only a few data points are available, and thus the predictive power is lacking due to the scarcity of data. This also applies to QSAR models because a limited number of chemicals with similar structural features in the training dataset results in low reliability of the predictions.</p>", "<title>Peer review</title>", "<p id=\"Par70\">The data descriptor was peer reviewed in 2023 based on the data version 2 available on Zenodo<sup>##UREF##14##37##</sup>.</p>" ]	[ "<title>Acknowledgements</title>", "<p>This work was carried out in the framework of the European Partnership for the Assessment of Risks from Chemicals (PARC) and has received funding from the European Union’s Horizon Europe research and innovation program under Grant Agreement No 101057014. Views and opinions expressed are, however, those of the authors only and do not necessarily reflect those of the European Union or the Health and Digital Executive Agency. Neither the European Union nor the granting authority can be held responsible for them. The study was funded by the Helmholtz Earth and Environment Program, Topic 9 and the Chemicals in The Environment (CITE) integration fund at the UFZ. T.S. acknowledges funding by Deutsche Forschungsgemeinschaft (441958208) and the Development Bank of Saxony (100669418). The authors thank Ronny Krause for legal advises regarding data use and reuse.</p>", "<title>Author contributions</title>", "<p>R.A.: Conceptualization, Review & Editing. W.B.: Conceptualization, Data Curation, Investigation, Validation, Writing, Review & Editing. J.H.: Conceptualization, Review & Editing. N.K.: Writing, Review & Editing. L.K.: Data Curation, Investigation, Data Analyses, Visualization, Writing. M.K.: Data Curation, Investigation, Review & Editing. T.S.: Conceptualization, Data Curation, Investigation, Data Analyses & Validation, Visualization, Writing, Review & Editing</p>", "<title>Funding</title>", "<p>Open Access funding enabled and organized by Projekt DEAL.</p>", "<title>Code availability</title>", "<p>The R package REcoTox<sup>##UREF##18##43##</sup> version 0.4.1 for processing US EPA ECOTOX Knowledgebase files is available on GitHub (<ext-link ext-link-type=\"uri\" xlink:href=\"https://github.com/tsufz/REcoTox/releases/tag/0.4.1\">https://github.com/tsufz/REcoTox/releases/tag/0.4.1</ext-link>) under AGPL-3.0 license. The REcoTox and graphics processing scripts are available on Zenodo<sup>##UREF##19##44##</sup> under AGPL-3.0 license.</p>", "<title>Competing interests</title>", "<p id=\"Par71\">The authors declare no competing interests.</p>" ]	[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><p>Number of compounds in the dataset categorized as ‘parent’ or ‘parent + TP’ (left) and transformation products (right) per use group category.</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><p>(<bold>A</bold>) Curated broad mode of action (MoA) categories with the number of assigned compounds in the dataset; colors indicate the different use group categories, chemicals with multiple or unknown MoA broad are not considered in this figure; (<bold>B</bold>) Number of chemicals distributed across the ten most common broad MoA categories per use group in our dataset, among the parent compounds (left), and the transformation products (right).</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><p>Number of chemicals assigned to specific MoAs within two broad MoA categories in the dataset (<bold>A</bold>) ‘Endocrine’ and (<bold>B</bold>) ‘Neuroactive’ and ‘Neuromuscular system’ colored according to use groups. MoA specific categories with less than two chemicals in (<bold>A</bold>) and nine chemicals in (<bold>B</bold>) are, respectively, not shown.</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><p>Distributions of measured and predicted effect concentrations for the three BQE (algae, crustacean, and fish) according to the data availability listed in Table ##TAB##0##1##.</p></caption></fig>", "<fig id=\"Fig5\"><label>Fig. 5</label><caption><p>Distribution of solubility domain classes per BQE for all chemicals considered in this dataset with measured and predicted effect concentrations.</p></caption></fig>", "<fig id=\"Fig6\"><label>Fig. 6</label><caption><p>Overview of the 32 broad MoA categories, with their specific sub-categories sorted according to numbers of chemicals within a category. This figure was created using the “SankeyMatic” online tool (<ext-link ext-link-type=\"uri\" xlink:href=\"https://sankeymatic.com\">https://sankeymatic.com</ext-link>, accessed 15 May 2023).</p></caption></fig>" ]	[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>List of databases for the research on the use of chemicals and information about modes of action.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th>Database Name</th><th>Description</th><th>URL</th><th>Access dates</th><th>Ref.</th></tr></thead><tbody><tr><td>Pesticide Properties DataBase (PPDB)</td><td>A comprehensive relational database of pesticide chemical identity, physicochemical, human health and ecotoxicological data</td><td><ext-link ext-link-type=\"uri\" xlink:href=\"http://sitem.herts.ac.uk/aeru/ppdb/en/search.htm\">http://sitem.herts.ac.uk/aeru/ppdb/en/search.htm</ext-link></td><td>Jan 2021 – Apr 2023</td><td><sup>##UREF##15##38##</sup></td></tr><tr><td>Bio-Pesticides DataBase (BPDB)</td><td>A comprehensive relational database of data relating to pesticides derived from natural substances</td><td><ext-link ext-link-type=\"uri\" xlink:href=\"http://sitem.herts.ac.uk/aeru/bpdb/search.htm\">http://sitem.herts.ac.uk/aeru/bpdb/search.htm</ext-link></td><td>Jan 2021 – Apr 2023</td><td><sup>##REF##26047120##59##</sup></td></tr><tr><td>Veterinary Substances DataBase (VSDB)</td><td>A comprehensive relational database of physicochemical and toxicological data for veterinary substances</td><td><ext-link ext-link-type=\"uri\" xlink:href=\"http://sitem.herts.ac.uk/aeru/vsdb/search.htm\">http://sitem.herts.ac.uk/aeru/vsdb/search.htm</ext-link></td><td>Jan 2021 – Apr 2023</td><td><sup>##REF##26047120##59##</sup></td></tr><tr><td>Insecticide Resistance Action Committee (IRAC): Mode of Action classification</td><td>A global scheme on the mode of actions and target sites of acaricides and insecticides</td><td><ext-link ext-link-type=\"uri\" xlink:href=\"https://irac-online.org/mode-of-action/classification-online/\">https://irac-online.org/mode-of-action/classification-online/</ext-link></td><td>Jan 2021 – Apr 2023</td><td><sup>##REF##26047120##59##</sup></td></tr><tr><td>Herbicide Resistance Action Committee (HRAC): Global herbicide classification lookup</td><td>A global scheme on the mode of actions and target sites of herbicides</td><td><ext-link ext-link-type=\"uri\" xlink:href=\"https://hracglobal.com/tools/classification-lookup\">https://hracglobal.com/tools/classification-lookup</ext-link></td><td>Jan 2021 – Apr 2023</td><td/></tr><tr><td>Fungicide Resistance Action Committee (FRAC): Mode of Action classification</td><td>A global scheme mode of actions and target sites of fungicides</td><td><ext-link ext-link-type=\"uri\" xlink:href=\"https://www.frac.info/fungicide-resistance-management/by-fungicide-common-name\">https://www.frac.info/fungicide-resistance-management/by-fungicide-common-name</ext-link></td><td>Jan 2021 – Apr 2023</td><td/></tr><tr><td>Drugbank</td><td>A comprehensive, online database containing information on drugs and drug targets</td><td><ext-link ext-link-type=\"uri\" xlink:href=\"https://go.drugbank.com/\">https://go.drugbank.com/</ext-link></td><td>Jan 2021 – Apr 2023</td><td><sup>##REF##16381955##60##</sup></td></tr><tr><td>PubChem</td><td>A comprehensive collection of freely accessible chemical information</td><td><ext-link ext-link-type=\"uri\" xlink:href=\"https://pubchem.ncbi.nlm.nih.gov/\">https://pubchem.ncbi.nlm.nih.gov/</ext-link></td><td>Jan 2021 – Apr 2023</td><td><sup>##REF##33151290##41##</sup></td></tr><tr><td>Wikipedia</td><td>A free online encyclopedia</td><td><ext-link ext-link-type=\"uri\" xlink:href=\"https://en.wikipedia.org/wiki/Main_Page\">https://en.wikipedia.org/wiki/Main_Page</ext-link></td><td>Jan 2021 – Apr 2023</td><td/></tr><tr><td>Human metabolome database (HMDB)</td><td>An electronic database containing detailed information about small molecule metabolites found in the human body</td><td><ext-link ext-link-type=\"uri\" xlink:href=\"https://hmdb.ca/\">https://hmdb.ca/</ext-link></td><td>Jan 2021 – Apr 2023</td><td><sup>##REF##34986597##61##</sup></td></tr><tr><td>Chemical and Products Database (CPDat)</td><td>A database containing information on usage or function of consumer products and chemicals</td><td><p><ext-link ext-link-type=\"uri\" xlink:href=\"https://comptox.epa.gov/dashboard/\">https://comptox.epa.gov/dashboard/</ext-link></p><p><ext-link ext-link-type=\"uri\" xlink:href=\"https://epa.figshare.com/articles/dataset/Chemical_and_Product_Categories_CPCat_database/7871537\">https://epa.figshare.com/articles/dataset/Chemical_and_Product_Categories_CPCat_database/7871537</ext-link></p></td><td>Jan 2021 – Apr 2023</td><td><sup>##REF##29989593##62##</sup></td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Numbers and percentage of chemicals for which measured and predicted effect concentrations could be derived (according to the selection criteria of this study (# = numbers)).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th>BQE</th><th>Algae</th><th>Crustacean</th><th>Fish</th></tr></thead><tbody><tr><td><bold>Measured (#)</bold></td><td>586</td><td>858</td><td>855</td></tr><tr><td><bold>Measured (%)</bold></td><td>17.3</td><td>25.3</td><td>25.3</td></tr><tr><td><bold>Predicted (#)</bold></td><td>3,318</td><td>3,318</td><td>3,318</td></tr><tr><td><bold>Predicted (%)</bold></td><td>98</td><td>98</td><td>98</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab3\"><label>Table 3</label><caption><p>Total number, geometrical mean, and geometrical standard deviation of data points per chemical available in the ECOTOXDB and considered in this study.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th>BQE</th><th>Algae</th><th>Crustacean</th><th>Fish</th></tr></thead><tbody><tr><td><bold>Total number of data points</bold></td><td>6,156</td><td>9,760</td><td>19,416</td></tr><tr><td><bold>geoMean (data points per chemical)</bold></td><td>5</td><td>4</td><td>7</td></tr><tr><td><bold>geoSD (data points per chemical)</bold></td><td>3.1</td><td>3.5</td><td>4.2</td></tr></tbody></table></table-wrap>" ]	[ "<disp-formula id=\"Equa\"><alternatives><tex-math id=\"M1\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$E{C}_{x}\\le {S}_{w}$$\\end{document}</tex-math><mml:math id=\"M2\" display=\"block\"><mml:mi>E</mml:mi><mml:msub><mml:mrow><mml:mi>C</mml:mi></mml:mrow><mml:mrow><mml:mi>x</mml:mi></mml:mrow></mml:msub><mml:mo>≤</mml:mo><mml:msub><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mi>w</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></disp-formula>", "<disp-formula id=\"Equb\"><alternatives><tex-math id=\"M3\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${S}_{w} < E{C}_{x}\\le 1{0}^{5}\\times log\\left({S}_{w}\\right)$$\\end{document}</tex-math><mml:math id=\"M4\" display=\"block\"><mml:msub><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mi>w</mml:mi></mml:mrow></mml:msub><mml:mo><</mml:mo><mml:mi>E</mml:mi><mml:msub><mml:mrow><mml:mi>C</mml:mi></mml:mrow><mml:mrow><mml:mi>x</mml:mi></mml:mrow></mml:msub><mml:mo>≤</mml:mo><mml:mn>1</mml:mn><mml:msup><mml:mrow><mml:mn>0</mml:mn></mml:mrow><mml:mrow><mml:mn>5</mml:mn></mml:mrow></mml:msup><mml:mo>×</mml:mo><mml:mi>l</mml:mi><mml:mi>o</mml:mi><mml:mi>g</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:msub><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mi>w</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfenced></mml:math></alternatives></disp-formula>", "<disp-formula id=\"Equc\"><alternatives><tex-math id=\"M5\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$1{0}^{5}\\times log\\left(S\\right) < E{C}_{x}\\le 1{0}^{10}\\times log\\left({S}_{w}\\right)$$\\end{document}</tex-math><mml:math id=\"M6\" display=\"block\"><mml:mn>1</mml:mn><mml:msup><mml:mrow><mml:mn>0</mml:mn></mml:mrow><mml:mrow><mml:mn>5</mml:mn></mml:mrow></mml:msup><mml:mo>×</mml:mo><mml:mi>l</mml:mi><mml:mi>o</mml:mi><mml:mi>g</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mi>S</mml:mi></mml:mrow></mml:mfenced><mml:mo><</mml:mo><mml:mi>E</mml:mi><mml:msub><mml:mrow><mml:mi>C</mml:mi></mml:mrow><mml:mrow><mml:mi>x</mml:mi></mml:mrow></mml:msub><mml:mo>≤</mml:mo><mml:mn>1</mml:mn><mml:msup><mml:mrow><mml:mn>0</mml:mn></mml:mrow><mml:mrow><mml:mn>10</mml:mn></mml:mrow></mml:msup><mml:mo>×</mml:mo><mml:mi>l</mml:mi><mml:mi>o</mml:mi><mml:mi>g</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:msub><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mi>w</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mfenced></mml:math></alternatives></disp-formula>", "<disp-formula id=\"Equd\"><alternatives><tex-math id=\"M7\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$E{C}_{x} > 1{0}^{10}\\times {S}_{w}$$\\end{document}</tex-math><mml:math id=\"M8\" display=\"block\"><mml:mi>E</mml:mi><mml:msub><mml:mrow><mml:mi>C</mml:mi></mml:mrow><mml:mrow><mml:mi>x</mml:mi></mml:mrow></mml:msub><mml:mo>></mml:mo><mml:mn>1</mml:mn><mml:msup><mml:mrow><mml:mn>0</mml:mn></mml:mrow><mml:mrow><mml:mn>10</mml:mn></mml:mrow></mml:msup><mml:mo>×</mml:mo><mml:msub><mml:mrow><mml:mi>S</mml:mi></mml:mrow><mml:mrow><mml:mi>w</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></disp-formula>", "<disp-formula id=\"Eque\"><alternatives><tex-math id=\"M9\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$E{C}_{50,algae}\\left[mmol/L\\right]={\\left(E{C}_{a-dimentional,algae}\\times 0.07+1\\right)}^{\\frac{1.0}{0.07}}$$\\end{document}</tex-math><mml:math id=\"M10\" display=\"block\"><mml:mi>E</mml:mi><mml:msub><mml:mrow><mml:mi>C</mml:mi></mml:mrow><mml:mrow><mml:mn>50</mml:mn><mml:mo>,</mml:mo><mml:mi>a</mml:mi><mml:mi>l</mml:mi><mml:mi>g</mml:mi><mml:mi>a</mml:mi><mml:mi>e</mml:mi></mml:mrow></mml:msub><mml:mfenced close=\"]\" open=\"[\"><mml:mrow><mml:mi>m</mml:mi><mml:mi>m</mml:mi><mml:mi>o</mml:mi><mml:mi>l</mml:mi><mml:mo>/</mml:mo><mml:mi>L</mml:mi></mml:mrow></mml:mfenced><mml:mo>=</mml:mo><mml:msup><mml:mrow><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mi>E</mml:mi><mml:msub><mml:mrow><mml:mi>C</mml:mi></mml:mrow><mml:mrow><mml:mi>a</mml:mi><mml:mo>−</mml:mo><mml:mi>d</mml:mi><mml:mi>i</mml:mi><mml:mi>m</mml:mi><mml:mi>e</mml:mi><mml:mi>n</mml:mi><mml:mi>t</mml:mi><mml:mi>i</mml:mi><mml:mi>o</mml:mi><mml:mi>n</mml:mi><mml:mi>a</mml:mi><mml:mi>l</mml:mi><mml:mo>,</mml:mo><mml:mi>a</mml:mi><mml:mi>l</mml:mi><mml:mi>g</mml:mi><mml:mi>a</mml:mi><mml:mi>e</mml:mi></mml:mrow></mml:msub><mml:mo>×</mml:mo><mml:mn>0.07</mml:mn><mml:mo>+</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:mfenced></mml:mrow><mml:mrow><mml:mfrac><mml:mrow><mml:mn>1.0</mml:mn></mml:mrow><mml:mrow><mml:mn>0.07</mml:mn></mml:mrow></mml:mfrac></mml:mrow></mml:msup></mml:math></alternatives></disp-formula>", "<disp-formula id=\"Equf\"><alternatives><tex-math id=\"M11\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} 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PMC10781678	38200044	[ "<title>Introduction</title>", "<p id=\"Par2\">New magma ascending beneath active volcanoes requires the creation of space, which is primarily facilitated by deformation of the surrounding host rock. In the mid to upper crust, space for magma is accommodated by two mechanisms (Fig. ##FIG##0##1##): (1) the uplift of the host rock above the intruding magma (the magma reservoir ‘roof’) via laccolith emplacement<sup>##UREF##0##1##–##UREF##2##3##</sup>; and (2) the subsidence of the rocks below the intruding magma (the magma reservoir ‘floor’)<sup>##UREF##3##4##–##UREF##7##8##</sup>. The type of emplacement mechanism influences the ground deformation and seismic signals that can be detected via volcano monitoring equipment<sup>##UREF##8##9##</sup>. The capacity for space to be created both initially, and during the growth of a magma reservoir, also influences the potential for overpressure build-up within the magma reservoir and therefore a potential eruption<sup>##UREF##9##10##</sup>. Nowadays, surface deformation and seismicity prior to, and during eruptions, is monitored in unprecedented detail. However, the interpretation of these signals relies on our understanding of how magma migrates and is stored within the crust<sup>##UREF##8##9##,##UREF##10##11##,##UREF##11##12##</sup>. Reconstruction of the geometry of solidified magma reservoirs (also called plutons) and observations of structures indicating host-rock deformation offer a way to infer magma emplacement mechanisms<sup>##UREF##12##13##–##UREF##15##16##</sup>. Furthermore, the structures in the roof of a pluton may record the formation of conduits that fed eruptions<sup>##UREF##14##15##</sup>.</p>", "<p id=\"Par3\">Here, we use the Reyðarártindur pluton in Southeast Iceland as a case study to explore how space was created for the formation of a reservoir of silicic magma. Secondly, we investigate the deformation of the host rock associated with eruption, and conditions that led to eruption. To do this, we use field mapping and photogrammetry to analyse the orientation of the lava layers and fractures, faults and dykes in the host rock to the pluton. We then combine this information with the 3D pluton shape reconstruction from Rhodes et al. (2021) and suggest that the Reyðarártindur pluton was emplaced predominantly via floor subsidence. We show that the brittle roof structures were created by overpressure in the pluton rather than by regional tectonics and link the overpressure build-up and eruption potential to floor subsidence failure. In order to quantify what, if any, deformation would be expected at the Earth’s surface during eruption, we constructed a simple numerical model that replicates the field observations of subsidence towards one of the conduits. Finally, we discuss the likely detectable signals related to the emplacement, growth, and eruption of magma reservoirs comparable to Reyðarártindur. With this case study, we aim to improve the interpretation of geophysical signals and creation of models for periods of magma movement and volcanic unrest in shallow magmatic systems.</p>", "<title>Geological setting</title>", "<p id=\"Par4\">Volcanism in Iceland is caused by the Iceland mantle plume and the divergent Mid-Atlantic Ridge (MAR)<sup>##UREF##25##26##</sup>. Active volcanism occurs along (1) rift zones that coincide with the plate boundary and (2) flank or off-rift zones, which are not plate boundaries<sup>##UREF##26##27##–##UREF##28##29##</sup>. Rift zone segments are connected by WNW–ESE transform fault zones, such as the Tjörnes Transform Zone and the South Iceland Seismic Zone<sup>##UREF##29##30##</sup>. Within the rift zones, volcanism occurs in individual volcanic systems, which contain fissure swarms and (often) a central volcano<sup>##UREF##30##31##</sup>. Volcanic systems generally have a NNE–SSW trend, perpendicular to the spreading direction of the MAR, and parallel to the fissure swarms that consist of normal faults, extensional fractures and volcanic fissures<sup>##UREF##31##32##,##UREF##32##33##</sup>.</p>", "<p id=\"Par5\">The Reyðarártindur pluton is exposed in the mountains surrounding the Lón fjord in Southeast Iceland (ref.<sup>##UREF##33##34##</sup>; Fig. ##FIG##1##2##a). The geology of Lón is characterised by the juxtaposition of a number of Neogene volcanic systems, which comprise volcanic rocks deposited in and around central volcanoes. Dyke swarms representing the subsurface feeders of volcanic fissures mostly strike NNE–SSW and NE–SW, indicating the direction of the rift zone at the time of dyke emplacement<sup>##UREF##33##34##</sup>. Moreover, kilometre-sized silicic and mafic-silicic plutonic complexes crosscut the volcanic deposits. One of these intrusions is the Reyðarártindur pluton, which yields zircon crystallization ages of 7.40 Ma<sup>##UREF##34##35##</sup>. The plutons likely formed at a depth of 1–2 km beneath a paleo rift-zone<sup>##UREF##35##36##–##UREF##37##38##</sup> and are regarded as the solidified magma reservoirs that fed eruptions in younger central volcanoes<sup>##UREF##14##15##,##UREF##33##34##,##UREF##37##38##</sup>.</p>" ]	[ "<title>Methods</title>", "<title>Structural orientation data</title>", "<p id=\"Par38\">Structural orientation data of FFDs and lava beds were collected both in the field, and from virtual outcrops generated by structure-from-motion photogrammetry. In the field, the structural orientations were acquired using (a) the FieldMove Clino Pro application (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.mve.com/digital-mapping/\">www.mve.com/digital-mapping/</ext-link>) on two different Iphone 6® phones in the coordinate system UTM Zone 28N, or (b) analogue compasses accompanied by a Garmin GPSMAP handheld GPS. The Fieldmove Clino Pro application automatically corrected the Iphone measurements for the magnetic declination (–9.01), and the compasses were manually corrected prior to use.</p>", "<p id=\"Par39\">Structure-from-motion photogrammetry was performed on 9 areas in total. Overlapping photos of the zones were acquired using a DJI Phantom 4 Pro UAV (Unmanned Aerial Vehicle) with a photo resolution of 5472 × 3648 pixels. The images were then processed to create virtual outcrops using the default workflow in the Agisoft Photoscan™ software (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.agisoft.com/\">www.agisoft.com/</ext-link>). The internal GPS of the UAV was used for georeferencing, and low-quality model inputs were reduced using the “Estimate image quality”, “Reconstruction uncertainty” and “Projection accuracy” functions. The resulting .obj virtual outcrop was imported into the LIME v2.0 software (<ext-link ext-link-type=\"uri\" xlink:href=\"https://virtualoutcrop.com/lime/\">https://virtualoutcrop.com/lime/</ext-link>; ref.<sup>##UREF##59##61##</sup>), where the ‘structural data from 3 points’ tool was used to acquire the orientation of measurable FFDs and lava bedding. The tool can best constrain the orientation when the feature of measurement intersects 3D topography, i.e., a lava bed traces through a gully or around a ridge. In the case that the feature did not, we omitted to measure it. For this reason, the dips of many lava beds were not measured.</p>", "<p id=\"Par40\">After data acquisition, all the measurements were collated in the Petroleum Experts MOVE 2019.1 software, where we plotted and analysed the data in equal-area stereographic projections of the lower hemisphere.</p>", "<title>Map outline and pluton shape reconstruction</title>", "<p id=\"Par41\">The map outline and hence 3D pluton reconstruction were updated from Rhodes et al., 2021. Additional field mapping in the Steinasel area (Fig. ##FIG##1##2##) led to a revision of the pluton wall contact. Specifically, the wall contact at ca. − 1,645,000, 9,467,000 changes orientation from NNE–SSW to strike NNW–SSW, and now connects directly to the corner at Fagralág. The 3D pluton reconstruction was modified accordingly in the MOVE software and the change in volume was negligible (< 0.02 km<sup>3</sup>).</p>", "<title>COMSOL modelling of host rock subsidence around Rílutungnahamrar–Fálkahnaus dyke.</title>", "<p id=\"Par42\">A Finite Element Model (FEM) was constructed using COMSOL Multiphysics® version 5.5 (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.comsol.com\">www.comsol.com</ext-link>) which reproduces the ca. 25° tilt of the roof basalts towards the eastern side of the Rílutungnahamrar–Fálkahnaus dyke (Figs. ##FIG##2##3##, ##FIG##4##5##). The model assumes that the lava layers were sub-horizontal prior to dyke emplacement, which is consistent with the other field observations of the host rock (Fig. ##FIG##3##4##).</p>", "<p id=\"Par43\">The model domain is assumed to consist of a linearly elastic, homogeneous and isotropic material with a Young’s modulus of E = 30 GPa and a Poisson’s ratio of ν = 0.25 cf.<sup>##UREF##60##62##</sup>. A domain measuring 250 × 250 × 250 km was used to avoid any edge effects. The magma body was modelled as a cavity (e.g., refs.<sup>##UREF##61##63##–##UREF##63##65##</sup>) centred at 2 km depth with a box shape measuring 2.8 × 1.3 × 0.5 km<sup>##UREF##14##15##,##UREF##35##36##,##UREF##36##37##</sup>. To model the Rílutungnahamrar–Fálkahnaus dyke, the model domain was sliced in the Y direction by a plane. Where the dyke was modelled on this plane the model domain contacts are defined as disconnected (“contact pairs”). The dyke spans the short-axis of the magma body and extends from the pluton roof at 1.75 km depth to the surface of the model (Supplementary Material ##SUPPL##0##2##). For the remainder of the plane the contacts were modelled as connected (“identity boundary pairs”). The surface of the model domain is allowed to deform freely, while the base is fixed, and boundary-parallel motion is allowed at the sides of the model domain.</p>", "<p id=\"Par44\">A 250 m–wide part of the pluton roof adjacent to the right side of the dyke is forced to subside. The subsidence is a linear function of the distance to the dyke (x–direction) and a parabolic function of the distance from the edges of the pluton along the dyke (y–direction):where <italic>a</italic> describes the observed tilt of the lava layers (here we used <italic>a</italic> = 25°)<italic>, b</italic> is the subsidence of the roof at the edges of the part which is forced to subside (apart from the edge at the dyke) and <italic>c</italic> = <italic>−</italic> <italic>tan(a)∙250 m</italic> + <italic>b</italic> and corresponds to the subsidence at the Rílutungnahamrar outcrop (located at x,y = 0,0). The first term of Eq. (##FORMU##0##1##) describes linear subsidence of a 250 m wide part of the roof towards the dyke (largest deformation directly at the dyke and decreasing with increasing distance). The second term lets the subsidence vary with y in a way so that the subsided roof has the shape of a parabola in a yz-cross section. The term is normalized to ensure that it is equal to 1 at the Rílutungnahamrar-outcrop. It is important to note that this function only influenced the displacement in the z–direction. Deformation in the horizontal was not specified. Expanded methods are presented in Supplementary Material ##SUPPL##0##3##.</p>" ]	[ "<title>Results</title>", "<title>Results from previous studies</title>", "<p id=\"Par6\">The Reyðarártindur pluton was emplaced into sub-horizontal basaltic lava flows of Neogene age, and in the north-west of the study area, rhyolite lavas of the Lón Volcano (Fig. ##FIG##1##2##b). Mapping of the exposed pluton and its host rock by Rhodes et al. (2021) documented that adjacent roof exposures occur with vertical offsets of up to 200 m, which creates structural highs and lows. The 3D reconstruction of the pluton shape by Rhodes et al. (2021) shows a complex angular rhomboid with a long axis trending NW–SE, with steps in the roof and a minimum volume of 2.5 km<sup>3</sup>. Minor changes to the pluton outline and 3D reconstruction were made in this study (Fig. ##FIG##1##2##c). Analysis of the internal magmatic lithology showed that the pluton is mainly constructed from a single rock unit, the Main Granite<sup>##UREF##14##15##</sup>. Local zones of mingling between the Main Granite and two other related magmas (quartz monzonite to granite) are exposed in the Reyðará River zone (Fig. ##FIG##1##2##b). Furthermore, Rhodes et al<italic>.</italic> (2021) identified that the pluton also fed eruptions from three locations; Rílutungnahamrar, Fagralág and Goðaborg (Fig. ##FIG##1##2##b,c,e). While the paleo-surface is not exposed, evidence for eruptive activity was based on the exposure of prominent dykes originating from the pluton. These dykes contain rocks with pyroclastic, brecciated and tuffisitic textures and are associated with local subsidence. Additionally, the same magmatic rock units as in the Reyðará River zone are exposed within the Fagralág and Rílutungnahamrar conduits.</p>", "<title>Observations and orientations of the host rock and the roof contacts</title>", "<p id=\"Par7\">We quantified host rock deformation by mapping of lava orientations in the host rock. Our measurements of the lavas at a distance of 500 m from the pluton contact (North Skammá) show northerly dips of less than 7° (Fig. ##FIG##2##3##: stereonet II), providing a background for comparison with lava orientations near the pluton.</p>", "<p id=\"Par8\">Likewise, our measurements of the lavas above the pluton are generally sub-horizontal (0°–12°), although dip directions vary from site to site, and locally between faults (Figs. ##FIG##2##3##, ##FIG##3##4##). While the roof contact is usually concordant with the layering of the overlying basalts, a few discordant contacts occur (e.g. at NW Reyðarártindur; Fig. ##FIG##1##2##b). Large vertical offsets of the pluton roof of up to 200 m create both structural highs (e.g. at Goðaborg) and lows (e.g., at Toppar), resulting in a ‘stepped’ pluton roof contact in 3D (Fig. ##FIG##1##2##c). Notably, the magmatic rocks of the pluton do not vary or show evidence of faulting in the vicinity of the steps in the pluton roof, which rules out that the steps are the result of tectonic faulting after the pluton solidified. Sparse outcrops and the contact trace suggest that the pluton wall contacts are sub-vertical and discordant to the host lavas (Fig. ##FIG##3##4##b). The lava beds at the wall contacts are sub-horizontal, e.g., as measured within the Reyðarártindur peak dataset (Fig. ##FIG##2##3##: stereonet IV).</p>", "<p id=\"Par9\">We identified three dip anomalies of the lava layers in the pluton roof. The first dip anomaly occurs at Reyðarárklettur where the lavas locally dip ca. 22° to the south (Fig. ##FIG##2##3##: stereonet VI). The second dip anomaly occurs to the east of the Rílutungnahamrar conduit. Here the lava layers dip at ca. 32° NE towards the conduit, and are locally discordant at the pluton contact (Figs. ##FIG##2##3##: stereonet I; 5a). In contrast, to the west of the conduit at the locality labelled Karlsfjall, the lavas are mostly sub-horizontal (0°–10°: Figs. ##FIG##2##3##: stereonet VIII, 5a), although affected by faults (see below). Based on the 32° dip of lava layering, which extends ca. 250 m from the conduit in cross-sectional view, we estimate that the pluton roof east of the conduit has subsided by 295 m, while no or insignificant subsidence occurred west of the conduit. The third dip anomaly occurs at the locality labelled Fálkahnaus on Fig. ##FIG##2##3##. Here, a 20 m wide, 60° striking dyke we refer to as the Fálkahnaus dyke is exposed in the host rock (Fig. ##FIG##4##5##b), and the lavas on the southern side of the dyke dip up to 22° to the NNW, i.e., roughly towards the dyke (Figs. ##FIG##2##3##: stereonet VII, 5b). The 22° dip anomaly continues for 180 m to the south, thereby yielding 65 m of subsidence at the dyke plane. The Fálkahnaus dyke is exposed in along-strike prolongation from the Rílutungnahamrar dyke, leading us to conclude that they are connected (i.e. as per the orange dashed line on Fig. ##FIG##2##3##).</p>", "<title>Observations and orientations of fractures, faults and dykes in the host rock</title>", "<p id=\"Par10\">The basaltic lavas overlying the pluton roof are fractured, faulted and intruded by granitic dykes that extend upwards, and thus likely originate from, the pluton. This is in contrast to the site at North Skammá away from the pluton, which does not exhibit these features (Fig. ##FIG##1##2##b). Near the pluton roof, the fractures are steeply dipping to sub-vertical (70°–90°) and some of the fractures exhibit sub-vertical displacement of the lava layers of up to 5 m (i.e. they are faults) (Figs. ##FIG##2##3##, ##FIG##5##6##). Dykes are 0.5–10 m wide, follow fractures, and can be widely spaced (ca. 100 m between dykes) or occur in densely spaced (ca. 5 m) clusters, mimicking the distribution of fractures. One particular dyke cluster is associated with the highest topographical step in the pluton roof at Goðaborg peak, a locality that is also highly fractured (Fig. ##FIG##4##5##c). Another dyke-and-fracture cluster (80 m wide, NE–SW striking) can be traced through the ridgelines of Reyðarártindur peak and may be associated with the eastern pluton wall contact (cf. Fig. ##FIG##1##2##b).</p>", "<p id=\"Par11\">Most dykes occupy fractures and faults, and dyke identification is often obscured by scree. Thus, fracture, fault and dyke (FFD) orientations were not measured separately. The results are displayed in Fig. ##FIG##5##6## and show that multiple FFD sets occur across the pluton roof, which can be described as follows for the specific areas:<list list-type=\"bullet\"><list-item><p id=\"Par12\">NW Reyðarártindur displays three FFD sets (stereonet III). The first set is sub-vertical with polymodal distribution, and strikes NE–SW. The second set is also sub-vertical with polymodal distribution and strikes approximately E–W. The third FFD set is SE striking, SW dipping with normal displacement.</p></list-item><list-item><p id=\"Par13\">Reyðarártindur Peak also displays two FFD sets (stereonet IV). The first set, which is most dominant, has a radial pattern with a best-fit plane of 332/35. The second set is conjugate with subvertical orientation and strikes NW–SE.</p></list-item><list-item><p id=\"Par14\">At Goðaborg Peak (stereonet V), one primary FFD set is exposed which is steeply dipping and conjugate with NE–SW orientation. Two further individual faults were measured with N–S and NW–SE orientations (black and green poles to planes: Fig. ##FIG##5##6##).</p></list-item><list-item><p id=\"Par15\">At Karlsfjall North (stereonet I), where the lavas dip NW towards the Rílutungnahamrar conduit, two FFD sets are displayed. The first FFD set is polymodal and strikes NW–SE, and the second strikes NE and shows normal displacement. Displacement of up to 2 m was measured along both these FFD’s, with progressive subsidence to the north.</p></list-item><list-item><p id=\"Par16\">Karlsfjall (stereonet VIII) displays polymodal FFD sets oriented NW–SE and NE–SW. Alternatively, the first set could also be interpreted as quadrimodal, with sets oriented 135°–315° (NNW–SSE) and 170°–350° (NW–SE).</p></list-item><list-item><p id=\"Par17\">At Fálkahnaus (stereonet VII), where the lavas dip NNW towards the Fálkahnaus dyke, polymodal FFD sets were measured oriented E–W and NE–SW.</p></list-item><list-item><p id=\"Par18\">The Fagralág conduit site (stereonet IX) displays polymodal FFD sets oriented NE–SW, and approximately E–W. A conjugate set was additionally measured oriented NW–SE.</p></list-item><list-item><p id=\"Par19\">At Reyðarárklettur (stereonet VI), FFD orientations can be divided into (a) a major polymodal set oriented N–NE/S–SW with a strong cluster at 0°–30°/180°–210°, (b) a major conjugate set oriented E–W, and (c) other minor conjugate sets oriented NW–SE and WNW–ESE, which may be quadrimodal or polymodal to the E–W set.</p></list-item></list></p>", "<p id=\"Par20\">In summary, the FFD sets measured show conjugate or polymodal distributions with orthorhombic symmetry<sup>##UREF##39##40##,##UREF##40##41##</sup> (Fig. ##FIG##5##6##). The specific density and orientation of FFDs vary from site to site, but three main FFD sets are consistently measured which strike NE–SW, NW–SE, and E–W. Which of these FFD sets are represented at individual locations varies slightly, but the absence of one or more sets can generally be explained by sampling bias due to the shape of the outcrop.</p>" ]	[ "<title>Discussion</title>", "<p id=\"Par21\">The structures preserved at Reyðarártindur represent the sum of successive processes encompassing the establishment and growth of a magma reservoir, as well as eruption, and cooling. In the following, we will discuss which deformation features can be assigned to what stage in the pluton evolution. Then we infer what volcano monitoring signals would have corresponded to the deformation during each stage.</p>", "<title>Initial magma emplacement</title>", "<p id=\"Par22\">Generally, the shape of plutons, the relationship with primary host-rock structures, and structures related to emplacement deformation are proxies for the type of magma emplacement<sup>##UREF##12##13##,##UREF##16##17##,##UREF##41##42##</sup>. In the case of the Reyðarártindur pluton, distinguishing emplacement by either roof uplift or floor subsidence should account for:<list list-type=\"order\"><list-item><p id=\"Par23\">its rhomboid shape with highs and lows in the roof, concordant sub-horizontal roof and discordant steep wall contacts (Figs. ##FIG##1##2##, ##FIG##2##3##, ##FIG##3##4##),</p></list-item><list-item><p id=\"Par24\">the distribution of intrusive rocks inside the pluton with mingling in the Reyðarártindur River zone and the conduits (ref.<sup>##UREF##14##15##</sup>; Fig. ##FIG##1##2##b),</p></list-item><list-item><p id=\"Par25\">the absence of significant uplift or tilting of the overlying host rocks except in the Reyðarárklettur locality (Fig. ##FIG##2##3##, stereonet VI) and the continuity of roof rocks beyond the pluton boundaries (Fig. ##FIG##3##4##b), and</p></list-item><list-item><p id=\"Par26\">the existence, distribution, and orientation of multiple FFD sets above the pluton roof (Fig. ##FIG##5##6##).</p></list-item></list></p>", "<p id=\"Par27\">These observations demonstrate that roof uplift did not cause the emplacement of > 2.5 km<sup>3</sup> of magma. Only the minor (22° S) local tilt of the pluton roof in the south (Figs. ##FIG##1##2##, ##FIG##2##3##) may have been caused by roof uplift or uneven floor subsidence during magma emplacement. Moreover, the steep, discordant wall rocks support magma emplacement by floor subsidence along steeply-dipping ring faults/dykes (cf. refs.<sup>##UREF##4##5##,##UREF##5##6##,##UREF##23##24##,##UREF##24##25##,##UREF##42##43##</sup>). Traditional floor subsidence models include the subsidence of a single piston of rock with an intrusion roof defined by a singular either bell-shaped or a horizontal surface, defined by the first intrusion of magma<sup>##UREF##3##4##–##UREF##5##6##,##UREF##43##44##</sup>. At Reyðarártindur, however, we observe a stepped, horizontal roof, with offsets of 100’s of metres (Fig. ##FIG##1##2##c,d). The internal continuity of the magmatic rock, as well as the absence of faults with large displacements in the roof rocks point to the roof steps as primary, emplacement-related features (cf. ref.<sup>##UREF##23##24##</sup>). Consequently, we interpret the stepped roof as evidence that magma emplacement initiated simultaneously at several nearby localities corresponding to the roof steps (Fig. ##FIG##6##7##a). The resulting piecemeal subsidence implies that multiple blocks of the pluton floor subsided into the underlying magma reservoir, and magma was transferred between the blocks from the lower to the upper reservoir<sup>##UREF##23##24##,##UREF##24##25##</sup>. Continued magma supply would have promoted the thickening and subsequent merging of individual intrusions (Fig. ##FIG##6##7##b)<sup>##UREF##23##24##,##UREF##24##25##</sup>.</p>", "<title>Deformation during continued magma reservoir growth</title>", "<p id=\"Par28\">Magma emplacement by floor subsidence does, however, not explain the distribution and orientation of brittle deformation features (FFDs) in the roof rocks of the Reyðarártindur pluton, nor do tectonic stresses in the rift zone. As indicated by the orientation of regional dyke swarms (ref.<sup>##UREF##33##34##</sup>; Fig. ##FIG##1##2##a), rifting during the Neogene likely occurred in NNE–SSW and NE–SW striking volcanic zones and would have produced consistently oriented sets of extension fractures with this strike<sup>##UREF##44##45##–##UREF##46##47##</sup>. Indeed, NNE–SSW and NE–SW striking FFD sets occur in the roof of the Reyðarártindur pluton (Fig. ##FIG##5##6##) and may indicate the influence of the concurrent tectonic stress field (Fig. ##FIG##1##2##a). However, FFD sets with wide ranges of orientations are measured across the pluton roof, with polymodal fracture sets additionally measured striking NW–SE and E–W (Fig. ##FIG##5##6##). Furthermore, and locally, normal faulting and radial fault patterns occur (NW Reyðarártindur and Reyðarártindur Peak; Fig. ##FIG##5##6##: stereonet IV). Hence, we consider the orientation of FFDs reflect the existence of a local, magmatic stress field juxtaposed on the rift-related tectonic stresses<sup>##UREF##39##40##</sup>.</p>", "<p id=\"Par29\">Specifically, we consider that local stress fields created by magma pressure likely dominated during FFD formation, and that they attest to periods of overpressure in the magma reservoir. Polymodal fault sets point to the interaction between the local stress field created by the magma body and the regional stress field<sup>##UREF##47##48##</sup>. This is in agreement with other pluton studies that have attributed bimodal or quadrimodal fractures to pressure from magma exerted on the magma-chamber roof<sup>##UREF##12##13##</sup>. Alternatively, the FFDs may reflect preferential reactivation of a specific, pre-existing fracture set<sup>##UREF##13##14##</sup>. The fractures and faults provided pathways for magma as indicated by the numerous dykes, which follow the brittle roof discontinuities (refs.<sup>##UREF##47##48##,##UREF##48##49##</sup>; Fig. ##FIG##3##4##). Moreover, stress concentrations at the sharp roof-wall transitions of the magma reservoir may explain the increased density of FFDs at locations such at the wall contact (e.g. Reyðarártindur peak; cf. ref<sup>##UREF##49##50##</sup>; Fig. ##FIG##1##2##b). The build-up of magmatic overpressure implies that subsidence of the pluton floor was insufficient in accommodating subsequent magma recharge. We envisage that cooling and sealing of the faults and magma pathways between the underlying magma source and the Reyðarártindur magma reservoir inhibited continued subsidence of the reservoir floor (Fig. ##FIG##6##7##c).</p>", "<title>Transition from reservoir growth to eruption</title>", "<p id=\"Par30\">Once the overpressure was sufficient to allow dyke propagation all the way to the Earth’s surface, an eruption could occur (cf. refs.<sup>##UREF##9##10##,##UREF##50##51##</sup>). The mingling of magmatic units with compositional ranges from quartz monzonite to granite within the Rílutungnahamrar and Fagralág conduits suggests that injection of new magma into the reservoir triggered eruption<sup>##UREF##14##15##</sup>. The locations of the three conduits in the roof of the pluton show that eruptions originated from (1) a structural high in the centre (Goðaborg), (2) at the roof-wall transition (Fagralág), and (3) from dykes cutting across the roof (Rílutungnahamrar–Fálkahnaus; Fig. ##FIG##6##7##d). The NE–SW strike of the Rílutungnahamrar–Fálkahnaus conduit suggests that the dyke geometry and eruption location was likely controlled by regional tectonics or occurred along a pre-existing, tectonic weakness (cf. Fig. ##FIG##1##2##). In contrast, the eruption of magma at the other two localities may have been related to stress concentration at steps in the roof (Goðaborg), or at a roof-wall transition (Fagralág)<sup>##UREF##49##50##</sup>. Hence, eruption locations and configurations reflect the interplay between the local magmatic stress field, the regional tectonic stress field, as well as the deformation features produced during magma reservoir emplacement and growth. Moreover, since both the Fagralág and the Rílutungnahamrar–Fálkahnaus conduits contain rocks equivalent to the mingled magmatic suite in the Reyðará River, and since Fagralág is located adjacent to Fálkahnaus, we may speculate that the eruptions from both conduits were contemporaneous and related.</p>", "<p id=\"Par31\">Our mapping of the lava layering and FFDs in the pluton roof identified pronounced, local subsidence of the magma reservoir roof spatially associated with the Fagralág and Rílutungnahamrar–Fálkahnaus conduits. The type of subsidence observed at these locations is unlike that found at the structural highs and lows elsewhere in the pluton roof, where (1) roof layering is continuous, (2) the roof contact is mostly concordant, and (3) the rocks in the dykes show no evidence of explosive brecciation.</p>", "<p id=\"Par32\">At Fagralág, multiple blocks of the roof subsided vertically, ‘piecemeal-style’ up to 150 m into the magma reservoir (ref.<sup>##UREF##14##15##</sup>; Fig. ##FIG##4##5##d). Notably, no lava dip anomalies were observed in this zone. The pre-existing fractures and faults in the pluton roof were likely used as planes of weakness for dyke intrusion, and facilitated subsidence of the roof blocks. Reconstruction of the volume of subsidence at Fagralág as a rectangular prism with an average depth of 100 m (from mapped roof blocks), width and length of 300 m (area exposed in map view), gives a volume of 9 million cubic metres (Supplementary Material ##SUPPL##0##1##). This number should correspond to the minimum amount of magma erupted minus the volume of magma remaining in the conduit.</p>", "<p id=\"Par33\">At Rílutungnahamrar–Fálkahnaus, subsidence occurred in an asymmetric, trapdoor-like manner, flanked by a dyke that is exposed in both locations and widens upwards at Rílutungnahamrar. Tilting of the roof lavas by 20°–30° in a NW direction towards the dyke, and some additional reactivation of conjugate faults added up to between 65 and 295 m of subsidence of the previously flat reservoir roof (100 m on average). The trapdoor subsidence likely affected the entire width of the roof of Reyðarártindur, although with higher rates of subsidence at Rílutungnahamrar. Evidence for this is the continuation of the tilted lavas at Rílutungnahamrar all the way from the dyke exposure to the northern pluton wall contact (Fig. ##FIG##1##2##b). Hence, at the Rílutungnahamrar–Fálkahnaus conduit the minimum volume of magma erupted was 19 million cubic metres<sub>,</sub> (as calculated from the triangular prism formed by a conduit length of 1300 m, a subsidence width in map view of 250 m and lava dip of 25°; Supplementary Material ##SUPPL##0##1##).</p>", "<title>Volcanic unrest signals related to magma emplacement and eruption at Reyðarártindur</title>", "<p id=\"Par34\">Our conceptual model of the emplacement and eruption of magma at Reyðarártindur can be linked to volcanic unrest signals that would be recorded by monitoring at active volcanoes. Surface deformation and seismicity following the upwards propagation of dykes would likely be recorded (e.g. cf. refs.<sup>##UREF##51##52##–##UREF##52##54##</sup>; Fig. ##FIG##6##7##a). Seismic and deformation signals would then have changed as magma propagated laterally parallel to the host rock lava layers (Fig. ##FIG##6##7##a). Since in this stage magma propagates along pre-existing weaknesses in the rock (e.g. the contact surface between lava flows)<sup>##UREF##53##55##</sup>, seismicity may have been at a lower magnitude compared to the dyke-propagation stage. Broad surface uplift such as observed during sill formation<sup>##UREF##8##9##</sup> may have been recorded early on, before floor subsidence was fully established. However, the scale of the surface uplift would significantly underestimate the volume of the intruding reservoir, as most of the magma emplacement was accommodated for by the downward-displacement of the floor along the subvertical feeders lubricated by magma. Hence, further growth of the magma reservoir by floor subsidence would have likely been aseismic and without any significant surface deformation (Fig. ##FIG##6##7##b). Aseismic magma chamber recharge has been documented for example at Colli Albani, Italy, and Cordon Caulle, Chile, instead inferred from ground inflation<sup>##UREF##54##56##,##UREF##55##57##</sup>, which was minimal at Reyðarártindur. If a comparable process is operating at an active volcano, it may be hard to detect by volcano monitoring systems. Magma can thus accumulate at shallow depths inside volcanoes without producing signals easily detectable by volcano monitoring equipment.</p>", "<p id=\"Par35\">Following the establishment of the Reyðarártindur magma reservoir by floor subsidence, there were three post-emplacement processes that would have created detectable deformation and/or seismicity. Firstly, the build-up of overpressure in the chamber led to roof fracturing and faulting and/or fracture reactivation across the entire reservoir roof (Figs. ##FIG##3##4##,##FIG##5##6##,##FIG##6##7##c). Additionally, it may have produced the local tilting of the roof observed at Reyðarárklettur (Fig. ##FIG##3##4##). While the former would have likely been detected in terms of minor earthquakes, likely across the entire pluton roof, the latter would have caused slight localised surface deformation. Secondly, the propagation of the dykes into the chamber roof, and in some cases to the surface, would have likely caused both seismicity and surface deformation (as discussed above; Fig. ##FIG##6##7##d). Finally, the eruption of magma from at least three locations at the crest, the edge, and across the chamber roof would have been picked up by volcano monitoring (Fig. ##FIG##6##7##d).</p>", "<p id=\"Par36\">In order to simulate the surface deformation associated with the trapdoor subsidence during the Rílutungnahamrar–Fálkahnaus eruption, we implemented a 3D finite-element model in COMSOL Multiphysics®. The model results (Fig. ##FIG##7##8##) show that vertical subsidence of 100 m at the dyke centre can reproduce the ca. 25° lava dip by pure tilting, and the left side of the dyke is little affected by the forced subsidence. Because of the subsidence, the dyke is widest at the base and narrows towards the surface, which is in contrast to the upward-widening observed in the field (Fig. ##FIG##4##5##). However, the present-day geometry of the Rílutungnahamrar conduit is the result of post-diking processes, such as the establishment and evolution of a vent (e.g. ref.<sup>##UREF##56##58##</sup>). At the model surface, a half-graben structure is produced which has a length corresponding to the dyke length and a maximum depth closest to the dyke. Due to the linear-elastic properties of the host rock, the 100 m subsidence at the magma chamber roof only partially translates to the surface, where the maximum surface displacement is ca. 15% of the maximum subsidence at 1.75 km depth (Fig. ##FIG##7##8##). Interestingly, the surface deformation pattern is highly asymmetric and concentrates above the collapsing part of the roof, not the dyke. Subsidence of the magma-reservoir roof may have released significant seismic energy, especially if the subsidence occurred <italic>en mass</italic><sup>##UREF##57##59##</sup>. The field observations highlight localised and unsymmetrical deformation with respect to the location of eruptive vents. This suggests faulting is important to consider when interpreting volcano deformation patterns, rather than the use of the homogeneous uniform elastic halfspace commonly used to interpret volcano deformation<sup>##UREF##58##60##</sup>.</p>" ]	[ "<title>Conclusions</title>", "<p id=\"Par37\">The emplacement of silicic magma at Reyðarártindur was accommodated by piecemeal floor subsidence. While initial magma chamber emplacement would have likely been detectable via seismic and geodetic monitoring, reservoir growth may have been aseismic. Magma overpressure caused small-scale faulting and fracturing of the reservoir roof, and eventually led to at least one eruption. This eruption occurred from a fissure with an orientation consistent with the regional-tectonic setting. Simultaneously, the eruption was associated with localised roof subsidence, which would have been observable at the Earth’s surface and may have caused significant seismicity. Hence, the study highlights processes that can take place in the volcanic plumbing system, not accounted for in widely used models to interpret volcanic unrest.</p>" ]	[ "<p id=\"Par1\">How the Earth’s crust accommodates magma emplacement influences the signals that can be detected by monitoring volcano seismicity and surface deformation, which are routinely used to forecast volcanic eruptions. However, we lack direct observational links between deformation caused by magma emplacement and monitoring signals. Here we use field mapping and photogrammetry to quantify deformation caused by the emplacement of at least 2.5 km<sup>3</sup> of silicic magma in the Reyðarártindur pluton, Southeast Iceland. Our results show that magma emplacement triggered minor and local roof uplift, and that magma reservoir growth was largely aseismic by piecemeal floor subsidence. The occurrence and arrangement of fractures and faults in the reservoir roof can be explained by magmatic overpressure, suggesting that magma influx was not fully accommodated by floor subsidence. The tensile and shear fracturing would have caused detectable seismicity. Overpressure eventually culminated in eruption, as evidenced by exposed conduits that are associated with pronounced local subsidence of the roof rocks, corresponding to the formation of an asymmetric graben at the volcano surface. Hence, the field observations highlight processes that may take place within silicic volcanoes, not accounted for in widely used models to interpret volcanic unrest.</p>", "<title>Subject terms</title>", "<p>Open access funding provided by Uppsala University.</p>" ]	[ "<title>Supplementary Information</title>", "<p>\n</p>" ]	[ "<title>Supplementary Information</title>", "<p>The online version contains supplementary material available at 10.1038/s41598-023-50880-0.</p>", "<title>Acknowledgements</title>", "<p>This research was conducted as part of the first author's PhD research, funded by the Centre of Natural Hazards and Disaster Science (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.CNDS.se\">www.CNDS.se</ext-link>). The authors thank the owners of Reyðará Farm for allowing us to conduct fieldwork on their land. The study was financed by the Alice and Knut Wallenberg grant KAW2017.0153, and The Royal Swedish Academy of Sciences grant GS2019-0024. Permission was granted for sampling from the Icelandic Institute of Natural History.</p>", "<title>Author contributions</title>", "<p>S.B. conceived the project. E.R. undertook the fieldwork, analysed the field/photogrammetry data and produced the figures. Assistance with fieldwork was provided by S.B., S.H.M.G, T.M., T.S., A.B., and T.W. The numerical deformation model was constructed by S.H.M.G. with assistance from F.S. E.R. and S.B wrote the manuscript which was then reviewed by all the authors.</p>", "<title>Funding</title>", "<p>Open access funding provided by Uppsala University.</p>", "<title>Data availability</title>", "<p>The datasets generated and analysed during the current study available from the corresponding author on reasonable request or can be downloaded from 10.5281/zenodo.10428597.</p>", "<title>Competing interests</title>", "<p id=\"Par45\">The authors declare no competing interests.</p>" ]	[ "<fig id=\"Fig1\"><label>Figure 1</label><caption><p>Primary emplacement models for magma reservoirs in the mid-upper crust. (<bold>a</bold>,<bold>b</bold>) Magma emplacement accommodated by roof uplift. (<bold>a</bold>) Roof doming typically produces a ‘forced fold’ in the host rock with sets of moderately dipping normal faults above the centre. Shallow dipping thrusts flank the intrusion (cf. refs.<sup>##UREF##16##17##,##UREF##17##18##</sup>). (<bold>b</bold>) In the piston-uplift scenario, the host rock should largely retain its original inclination, and large steeply dipping faults that facilitate uplift should be visible at the edges of the intrusion<sup>##UREF##18##19##–##UREF##21##22##</sup>. Figure adapted from Schmiedel et al<italic>.</italic> (2019). (<bold>c</bold>,<bold>d</bold>) Magma emplacement by floor subsidence. (<bold>c</bold>) The traditional floor subsidence model is via the detachment and subsidence of a single piston of rock, with a roof geometry defined by the first intrusion of magma (in this example, horizontal). Magma is fed to the growing reservoir via ‘ring dykes’ that surround the subsiding block<sup>##UREF##3##4##–##UREF##5##6##,##UREF##22##23##</sup>. (<bold>d</bold>) Piecemeal floor subsidence is similar to piston subsidence, but occurs via multiple floor blocks bound by faults and multiple source dykes<sup>##UREF##23##24##,##UREF##24##25##</sup>.</p></caption></fig>", "<fig id=\"Fig2\"><label>Figure 2</label><caption><p>Background information and overview of the Reyðarártindur Pluton. (<bold>a</bold>) Map of the plutons exposed within the Lón fjord of Southeast Iceland, with generalized trends of basaltic dyke swarms (dashed lines) collated by Walker 1974. The major dyke trends are a proxy for the former rift axis. Pluton outlines are based on maps produced by refs.<sup>##UREF##12##13##,##UREF##14##15##,##UREF##35##36##,##UREF##38##39##</sup>. Inset: Map of Iceland with location of Lón fjord. (<bold>b</bold>) Map of the Reyðarártindur pluton showing the exposed contact, average strike and dip of the host rock, prominent granite dykes, and Rílutungnahamrar conduit. Site names have been adapted from the Landmælingar Íslands map viewer (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.lmi.is\">www.lmi.is</ext-link>). (<bold>c</bold>) 3D shape reconstruction of the pluton to − 50 m asl in aerial view, modified after Rhodes et al<italic>.</italic> (2021). The floor of the pluton is not exposed, therefore a minimum lower elevation of − 50 m asl was inferred based on outcrop exposure at sea level. The mapped contact is shown in yellow. (<bold>d</bold>) Cross section NW–SE through the pluton. Cross section trace marked in (<bold>b</bold>). (<bold>e</bold>) Overview photo of the Reyðarártindur Pluton, looking eastwards from Fálkahnaus. Maps a, b and c created in MOVE 2019.1 software (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.petex.com/products/move-suite/\">https://www.petex.com/products/move-suite/</ext-link>).</p></caption></fig>", "<fig id=\"Fig3\"><label>Figure 3</label><caption><p>Orientation of bedding in the host lavas to the Reyðarártindur Pluton. Planes and poles to bedding are displayed in Schmidt stereonet plots (equal area, lower hemisphere). Red plane indicates the mean lava orientation, which is additionally reported in strike/dip convention beside the relevant stereonet. <italic>n</italic> denotes the number of measurements in the stereonet plot. The outline of the Reyðarártindur Pluton is adapted from Rhodes et al<italic>.</italic> (2021). Map created in MOVE 2019.1 software (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.petex.com/products/move-suite/\">https://www.petex.com/products/move-suite/</ext-link>).</p></caption></fig>", "<fig id=\"Fig4\"><label>Figure 4</label><caption><p>Photos of host rock features to the Reyðarártindur Pluton. (<bold>a</bold>) A typical horizontal roof contact with conformable basalt lava flows. (<bold>b</bold>) Example of a sub-vertical wall contact where the conformable basalts continue across the contact. Unfortunately, the wall sides and the (inferred) underlying basalt are covered by scree. (<bold>c</bold>, <bold>d</bold>) Unmanned Aerial Vehicle (UAV) photo, and interpretation of features in a roof section exposed along Karlsfjall ridge. Two fault sets are clearly visible at this site. Lava layers can be traced across faults and dykes with up to 5 m offset.</p></caption></fig>", "<fig id=\"Fig5\"><label>Figure 5</label><caption><p>Key features of dykes and conduits exposed at Reyðarártindur (<bold>a</bold>) UAV photo of Rílutungnahamrar. On the SW side of the conduit, the host rock lavas are sub-horizontal, whereas on the NE side they dip towards the conduit at ca. 32°, and the roof contact is locally discordant to the pluton contact. The conduit widens upwards. (<bold>b</bold>) UAV photo of the Fálkahnaus dyke. On the south side, the lava layers are discordant to the pluton contact and locally dip ca. 22° towards the dyke. (<bold>c</bold>) UAV photo looking eastwards down on Goðaborg, where the roof is heavily intruded by dykes from the Reyðarártindur pluton. Lava layers are continuous across dykes and faults. (<bold>d</bold>) UAV photo of the Fagralág locality, where blocks of the downfaulted roof are exposed as isolated blocks.</p></caption></fig>", "<fig id=\"Fig6\"><label>Figure 6</label><caption><p>Orientation of fractures, faults and dykes (FFDs) in the host lavas to the Reyðarártindur pluton. Poles to planes of FFDs are displayed in Schmidt stereonet plots (equal area, lower hemisphere). The colours represent the different FFD sets described in the text. Rose plots (bidirectional, linear scaling, class size 10°) display the strike of the FFDs and are scaled to the Reyðarárklettur dataset. For the pluton outline legend, refer to Fig. ##FIG##3##4##. Map created in MOVE 2019.1 software (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.petex.com/products/move-suite/\">https://www.petex.com/products/move-suite/</ext-link>).</p></caption></fig>", "<fig id=\"Fig7\"><label>Figure 7</label><caption><p>Conceptual model for the emplacement to eruption of the Reyðarártindur pluton which links the geological observations and shows the corresponding volcanic unrest signals. (<bold>a</bold>) Magma ascends from the source reservoir via multiple dykes and is emplaced as sills at different depths. The upward propagation of dykes would likely be recorded in terms of seismicity following the trace of the propagating dykes and characteristic ‘trough and bulge’ surface deformation. Sill emplacement would cause characteristic surface uplift and minor seismicity during propagation. (<bold>b</bold>) Blocks of rock, dislodged by the dykes and sills subside into the underlying magma reservoir. Magma is transferred from there into the growing Reyðarártindur magma reservoir. As long as the subsidence of the upper reservoir can accommodate the magma transfer, this stage is aseismic and does not create surface deformation. (<bold>c</bold>) When floor subsidence stalls, magma recharge creates overpressure in the Reyðarártindur magma reservoir. This creates fault and fracture sets in the reservoir roof, some of which get intruded by dykes. Roof fracturing and faulting would have caused detectable minor earthquakes across the pluton roof. (<bold>d</bold>) Recharge of magma with slightly different composition caused dyking at several locations in the reservoir roof. Subsequent eruption at the Earth’s surface and associated magma withdrawal caused significant, local subsidence of the reservoir roof, which was likely seismogenic.</p></caption></fig>", "<fig id=\"Fig8\"><label>Figure 8</label><caption><p>Results of the COMSOL Multiphysics deformation model simulating the effects of the observed roof subsidence on the east side of the Rílutungnahamrar–Fálkahnaus conduit. (<bold>a</bold>) X–Z cross section. Subsidence is localized to a zone close to the dyke. Black lines indicate the tilt of previously horizontal lines (i.e. lava layers). (<bold>b</bold>) Subsidence of the pluton roof at the pluton roof interface. The black box marks the zone which was forced to subside. The dyke is located at the left edge of the black box. (<bold>c</bold>) Subsidence observed at the Earth’s surface (i.e. 1.75 km above the pluton roof). The black box indicates the pluton extents in the model.</p></caption></fig>" ]	[]	[ "<disp-formula id=\"Equ1\"><label>1</label><alternatives><tex-math id=\"M1\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$d\\left( {x,y} \\right) = \\left[ {{\\text{tan}}\\left( a \\right)\\left( {x - 250m} \\right) + b} \\right] \\cdot \\left[ {\\frac{{b - c}}{{{\\text{400}},{\\text{000}}m^{2} c}}y^{2} - 0.375\\frac{{b - c}}{{500mc}}y + 1} \\right]$$\\end{document}</tex-math><mml:math id=\"M2\" display=\"block\"><mml:mrow><mml:mi>d</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mi>x</mml:mi><mml:mo>,</mml:mo><mml:mi>y</mml:mi></mml:mrow></mml:mfenced><mml:mo>=</mml:mo><mml:mfenced close=\"]\" open=\"[\"><mml:mrow><mml:mtext>tan</mml:mtext><mml:mfenced close=\")\" open=\"(\"><mml:mi>a</mml:mi></mml:mfenced><mml:mfenced close=\")\" open=\"(\"><mml:mrow><mml:mi>x</mml:mi><mml:mo>-</mml:mo><mml:mn>250</mml:mn><mml:mi>m</mml:mi></mml:mrow></mml:mfenced><mml:mo>+</mml:mo><mml:mi>b</mml:mi></mml:mrow></mml:mfenced><mml:mo>·</mml:mo><mml:mfenced close=\"]\" open=\"[\"><mml:mrow><mml:mfrac><mml:mrow><mml:mi>b</mml:mi><mml:mo>-</mml:mo><mml:mi>c</mml:mi></mml:mrow><mml:mrow><mml:mtext>400</mml:mtext><mml:mo>,</mml:mo><mml:mtext>000</mml:mtext><mml:msup><mml:mi>m</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mi>c</mml:mi></mml:mrow></mml:mfrac><mml:msup><mml:mi>y</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mo>-</mml:mo><mml:mn>0.375</mml:mn><mml:mfrac><mml:mrow><mml:mi>b</mml:mi><mml:mo>-</mml:mo><mml:mi>c</mml:mi></mml:mrow><mml:mrow><mml:mn>500</mml:mn><mml:mi>m</mml:mi><mml:mi>c</mml:mi></mml:mrow></mml:mfrac><mml:mi>y</mml:mi><mml:mo>+</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:mfenced></mml:mrow></mml:math></alternatives></disp-formula>" ]	[]	[]	[]	[]	[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>" ]	[ "<fn-group><fn><p><bold>Publisher's note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"41598_2023_50880_Fig1_HTML\" id=\"MO1\"/>", "<graphic xlink:href=\"41598_2023_50880_Fig2_HTML\" id=\"MO2\"/>", "<graphic xlink:href=\"41598_2023_50880_Fig3_HTML\" id=\"MO3\"/>", "<graphic xlink:href=\"41598_2023_50880_Fig4_HTML\" id=\"MO4\"/>", "<graphic xlink:href=\"41598_2023_50880_Fig5_HTML\" id=\"MO5\"/>", "<graphic xlink:href=\"41598_2023_50880_Fig6_HTML\" id=\"MO6\"/>", "<graphic xlink:href=\"41598_2023_50880_Fig7_HTML\" id=\"MO7\"/>", "<graphic xlink:href=\"41598_2023_50880_Fig8_HTML\" id=\"MO8\"/>", "<graphic xlink:href=\"41598_2023_50880_Article_Equ1.gif\" position=\"anchor\"/>" ]	[ "<media xlink:href=\"41598_2023_50880_MOESM1_ESM.docx\"><caption><p>Supplementary Information.</p></caption></media>" ]	[{"label": ["1."], "mixed-citation": ["Gilbert, G. 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Res."], "year": ["2020"], "volume": ["390"], "fpage": ["106684"], "pub-id": ["10.1016/j.jvolgeores.2019.106684"]}, {"label": ["64."], "surname": ["Trasatti", "Giunchi", "Bonafede"], "given-names": ["E", "C", "M"], "article-title": ["Structural and rheological constraints on source depth and overpressure estimates at the Campi Flegrei caldera, Italy"], "source": ["J. Volcanol. Geotherm. Res."], "year": ["2005"], "volume": ["144"], "fpage": ["105"], "lpage": ["118"], "pub-id": ["10.1016/j.jvolgeores.2004.11.019"]}, {"label": ["65."], "surname": ["Hickey", "Gottsmann"], "given-names": ["J", "J"], "article-title": ["Benchmarking and developing numerical finite element models of volcanic deformation"], "source": ["J. Volcanol. Geotherm. Res."], "year": ["2014"], "volume": ["280"], "fpage": ["126"], "lpage": ["130"], "pub-id": ["10.1016/j.jvolgeores.2014.05.011"]}, {"label": ["66."], "mixed-citation": ["J\u00f3hannesson, H. & S\u00e6mundsson, K. "], "italic": ["Geological Map of Iceland, 1:600000. Tectonics"]}]	{ "acronym": [], "definition": [] }	66	CC BY	no	2024-01-13 00:02:20	Sci Rep. 2024 Jan 10; 14:962	oa_package/e8/09/PMC10781678.tar.gz
PMC10781679	38200033	[ "<title>Introduction</title>", "<p id=\"Par2\">Heavy metals are a significant concern for both human health and the environment. These elements can easily enter the human body through various pathways, including internal sources such as contaminated water and food, as well as external sources in the ambient environment<sup>##REF##36593249##1##–##REF##36593249##3##</sup>. This ease of transfer makes heavy metal contamination a serious concern for public health and environmental safety<sup>##UREF##0##4##,##UREF##1##5##</sup>. When heavy metals leach into water bodies or soil, they can contaminate the food chain. For instance, plants may absorb these metals from the soil, and then animals consume the contaminated plants, leading to bioaccumulation in their tissues. Eventually, humans may be exposed to these toxic metals by consuming contaminated plants or animals<sup>##REF##35208029##6##–##REF##35215683##8##</sup>. In addition to direct ingestion, heavy metals can also be inhaled through the air, particularly in industrial areas where emissions from factories and vehicles release metal particles into the atmosphere. This can lead to inhalation of airborne particulates containing heavy metals, further increasing the risk of exposure.</p>", "<p id=\"Par3\">The impact of heavy metal exposure on human health varies depending on the specific metal and the level of exposure. Heavy metals such as lead, mercury, cadmium, and arsenic are known to be highly toxic and can result in a wide range of health issues. These may include neurological disorders, organ damage, developmental abnormalities, and even cancer. Therefore, it is crucial to prioritize the development of sensitive and selective sensors for the detection and quantification of these metals in the environment and various matrices (such as water, soil, and food)<sup>##UREF##3##9##,##UREF##4##10##</sup>. Lead is a highly toxic substance that can affect humans. It is released into the environment through various human activities, such as burning fossil fuels, mining operations, and manufacturing processes. Lead is extensively used in industries including the production of lead-acid batteries, ammunition, metal products, X-ray shielding devices, paint oxides, glass, pigments, sheet lead, ceramic products, and pipe solder. In the USA, approximately 1.52 million metric tons of lead were used for industrial applications in 2004. Unfortunately, lead poisoning has become a significant concern, particularly for children. As a result, children living in homes with lead exposure can have blood lead concentrations of 20 µg/dL or highe<sup>##REF##22945569##11##</sup>. Lead toxicity affects various organs in the body, including the kidneys, liver, central nervous system, hematopoietic system, endocrine system, and reproductive system. Among these, the central nervous system is particularly vulnerable to the effects of lead poisoning.</p>", "<p id=\"Par4\">Today, sensors are becoming a vital component in environmental monitoring systems, providing valuable insights into heavy metal contamination and contributing to sustainable resource management and pollution control efforts. The detection and quantification of heavy metals present significant challenges for scientists and researchers. Various analytical techniques, such as gas chromatography, mass spectrometry, dispersive X-ray fluorescence spectrometry, and laser ablation have been employed for this purpose. However, these techniques often involve high costs and complex preparation processes<sup>##REF##30388535##12##,##UREF##5##13##</sup>. As a result, scientists are aiming to explore alternative sensing techniques that provide a balance of affordability, environmental friendliness, and ease of preparation. One promising approach is the development of porous thin film membrane sensors based on highly active materials. These sensors are designed to be simple, low-cost, and easy to prepare. The use of thin film technology allows for efficient sensing of heavy metals, as it provides a large surface area for interaction with the target ions. This high surface area enhances the sensitivity and responsiveness of the sensor, making it suitable for detecting trace amounts of heavy metals<sup>##UREF##6##14##–##REF##36233854##16##</sup>. Another advantage of thin film membrane sensors is their portability and versatility. These sensors can be integrated into various devices, such as handheld detectors or wearable sensors, enabling on-site and real-time monitoring of heavy metal levels. Their ease of use and rapid response make them valuable tools for field applications, where timely detection is critical for preventing potential hazards<sup>##UREF##8##17##,##UREF##9##18##</sup>.</p>", "<p id=\"Par5\">The porous Al<sub>2</sub>O<sub>3</sub> membrane proves to be a highly promising approach for the preparation of nanomaterials with well-defined shapes and sizes<sup>##UREF##10##19##,##UREF##11##20##</sup>. The porous Al<sub>2</sub>O<sub>3</sub> membrane was fabricated by electrochemical oxidation (anodization) of high-purity aluminum in acidic electrolytes<sup>##UREF##12##21##</sup>. The precise control over pore size, shape, spacing, and thickness during the anodization process allows for tailored properties and enhanced functionalities. This membrane exhibits excellent chemical properties, including high chemical stability and resistance to corrosion. It also possesses good mechanical strength and thermal stability, enabling its use in harsh environments and integration into diverse devices and systems. Moreover, this Al<sub>2</sub>O<sub>3</sub> membrane is chemically inert and non-toxic, making it suitable for biomedical applications. Also, it possesses optical transparency in the visible and near-infrared range, enabling effective manipulation and control of light<sup>##UREF##13##22##</sup>. This characteristic makes this template well-suited for the development of optical devices like waveguides, filters, and lenses. The ordered pore arrangement in this template can act as a photonic crystal, enabling the engineering of photonic bandgaps and manipulation of light propagation. By depositing thin films or nanoparticles onto the surface of this membrane template, additional functionalities such as plasmonic effects or enhanced light scattering can be introduced. Moreover, by preserving the open pores and depositing nanomaterial on the top surface of this template, the increased surface area creates more sites for chemical reactions and adsorption<sup>##UREF##14##23##</sup>. One of the key advantages of using the Al<sub>2</sub>O<sub>3</sub> membrane is the generation of nanomaterials with highly active sites, leading to increased reactivity. The combination of precise morphology control, high activity sites, and porous thin film behavior makes the Al<sub>2</sub>O<sub>3</sub> membrane a valuable tool in the design and synthesis of advanced nanomaterials with tailored properties. These properties open up new applications for nanomaterials with high performance in various industries. For example, the preparation of TiO<sub>2</sub> thin films using the Al<sub>2</sub>O<sub>3</sub> membrane opens up opportunities in fields such as sensors, electronic devices, and coatings.</p>", "<p id=\"Par6\">Our team possesses supplementary literature focusing on the identification of hazardous heavy metals, particularly Hg<sup>2+</sup>, Cd<sup>2+</sup>, and Pb<sup>2+</sup>. This literature explores the use of highly sensitive nanomaterials, specifically WO<sub>2</sub>I<sub>2</sub>/polypyrrole, poly(m-toluidine), and poly(m-cresol). The sensitivity for Pb<sup>2+</sup> ions was initially measured at 0.4 µA/M with the poly(m-toluidine) material<sup>##UREF##15##24##</sup> and subsequently elevated to 1.1 µA/M when employing the poly(m-cresol)/Pt sensor<sup>##UREF##16##25##</sup>.</p>", "<p id=\"Par7\">Herein, the fabrication process involves using the Ni-imprinting technique to obtain highly controlled hexagonal-shaped Al<sub>2</sub>O<sub>3</sub> membranes. These membranes serve as containers for the deposition of TiO<sub>2</sub> and TiN nanomaterials. This structure (TiON/TiO<sub>2</sub>/Al<sub>2</sub>O<sub>3</sub>) is then utilized as a sensor for the detection of Pb<sup>2+</sup> ions in aqueous solutions. The TiON/TiO<sub>2</sub> combination provides enhanced reactivity and catalytic capabilities, facilitating the electrochemical sensing of Pb<sup>2+</sup> ions. The Al<sub>2</sub>O<sub>3</sub> membrane acts as a stable and robust support for the nanomaterials, ensuring their longevity and performance during the sensing process. The sensing techniques are studied under a wide range of concentrations from 10<sup>–4</sup> to 10<sup>–1</sup> M using the cyclic voltammetry (CV) technique.</p>" ]	[ "<title>Experimental methods</title>", "<title>Materials</title>", "<p id=\"Par8\">Al foil (99.9%) was obtained from Sigma-Aldrich, USA. Ethylene glycol (C<sub>2</sub>H<sub>6</sub>O<sub>2</sub>) and chromic acid (H<sub>2</sub>CrO<sub>4</sub>) were acquired from Sigma Aldrich, USA. Perchloric acid (HClO<sub>4</sub>), ethanol (C2H5OH), phosphoric acid (H<sub>3</sub>PO<sub>4</sub>), and titanium tetrachloride (TiCl<sub>4</sub>) were obtained from the VWR company in Germany. N2 and Ar gases are obtained from El-Naser Company, Egypt.</p>", "<title>Characterization of synthesized materials</title>", "<p id=\"Par9\">Various techniques were employed to analyze the structural composition properties of the Al<sub>2</sub>O<sub>3</sub> membrane and TiON/TiO<sub>2</sub>. Energy dispersive X-ray spectroscopy (EDAX; Oxford Link ISIS 300) was utilized to accurately determine the chemical elements present in the samples. X-ray diffraction (XRD) analysis was conducted using a Panalytical (Empyrean) X-ray diffractometer with CuKα radiation (λ = 0.154 nm), applying 40 kV and 35 mA, and measuring 2θ values within the range of 25°–80°. Morphological analysis was performed using a transmission electron microscope (TEM; JOEL JEM-2010) as well as a scanning electron microscope (SEM; Axioskop 40 POL by Zeiss).</p>", "<title><bold><italic>Synthesizing of TiON/TiO</italic></bold><sub><bold><italic>2</italic></bold></sub><bold><italic> hexagonal using Al</italic></bold><sub><bold><italic>2</italic></bold></sub><bold><italic>O</italic></bold><sub><bold><italic>3</italic></bold></sub><bold><italic> membrane</italic></bold></title>", "<p id=\"Par10\">The synthesis of the Al<sub>2</sub>O<sub>3</sub> membrane involves the Ni imprinting technique. The utilization of the Ni-imprinting technique enables the transfer of a hexagonal pattern onto the Al foil, facilitating the creation of an Al<sub>2</sub>O<sub>3</sub> membrane with a range of desired shapes, such as square, triangular, diamond, and hybrid pattern pores. This technique involves applying pressure using a Ni mold before anodization, leading to the successful replication of the chosen shape on the surface of the Al foil.</p>", "<p id=\"Par11\">A hexagonal Ni mold is used to imprint its shape onto Al foil (purity 99.99%) under a pressure of 10 kN/cm<sup>2</sup>. The Al foil is then subjected to electropolishing in a two-electrode cell using an electrolyte solution of HClO<sub>4</sub> and C<sub>2</sub>H<sub>5</sub>OH (1:1) at a temperature of 2 °C for 3 min. Subsequently, the first and second anodization processes are carried out at the same temperature. The anodization was performed for 10 min followed by an additional 30 min using an electrolyte solution of H<sub>3</sub>PO<sub>4</sub>, ethylene glycol, and H<sub>2</sub>O in a ratio of 1:100:200. Afterward, the etching process was conducted using a solution of H<sub>3</sub>PO<sub>4</sub> (6 wt.%) and H<sub>2</sub>CrO<sub>4</sub> (1.2 wt.%) at a temperature of 60 °C for 12 h. Finally, the pore widening process is performed using H<sub>3</sub>PO<sub>4</sub> (6 wt.%) at 60 °C for 30 min to achieve the desired pore size,</p>", "<p id=\"Par12\">TiO<sub>2</sub> deposition is achieved through the atomic layer deposition (ALD) technique, in which this technique offers several advantages such as high-quality films, low-temperature processing, precise control over film thickness and stoichiometry, scalability for large-scale production, excellent repeatability, and low defect. For the preparation of TiO<sub>2</sub>, the utilization of TiCl<sub>4</sub> and H<sub>2</sub>O as precursor sources at 300 °C. The deposition process was carried out for 300 cycles to ensure an adequate TiO<sub>2</sub> thickness. On the other hand, TiON is prepared using DC magnetron sputtering. A mixture of N<sub>2</sub> and Ar gases with concentrations of 75 and 25 sccm, respectively, was used during the deposition process. A Ti target is utilized, and the deposition is performed under a low pressure of 13 × 10<sup>–3</sup> mbar. The actual deposition time of TiN is about few minutes, but the total time for this procedures takes about four h. Finally, the TiON film was subjected to combustion at 300 °C for 2 h to complete the film preparation.</p>", "<title>The potentiometric sensing</title>", "<p id=\"Par13\">The TiON/TiO<sub>2</sub>/Al<sub>2</sub>O<sub>3</sub> hexagonal heterostructure is utilized as a potentiometric sensor for the detection of Pb<sup>2+</sup> ions. The sensing process is conducted using a three-electrode cell, where the TiON/TiO<sub>2</sub> heterostructure serves as the working electrode. The intensity of the cyclic voltammetry signal corresponds to the concentration of Pb<sup>2+</sup> ions and provides information about the sensor's sensitivity towards Pb<sup>2+</sup> ions. This testing is carried out under both dark and light conditions to examine the influence of photon illumination on the sensing performance using the CHI608E workstation. The TiON/TiO<sub>2</sub> heterostructure serves as the main electrode. The Pt sheet and Ag/AgCl serve as counter and reference electrodes.</p>", "<title>Ethics approval</title>", "<p id=\"Par14\">This article does not contain any studies involving animals or human participants performed by any of the authors.</p>" ]	[ "<title>Results and discussion</title>", "<title>Analyses</title>", "<p id=\"Par15\">The XRD pattern of Al<sub>2</sub>O<sub>3</sub> in Fig. ##FIG##0##1##a confirms the crystalline nature of the Al<sub>2</sub>O<sub>3</sub> nanomaterial. This is evident from the appearance of three sharp peaks located at 47.7°, 65.1°, and 78.3°, corresponding to the growth directions (113), (214), and (119), respectively<sup>##UREF##17##26##</sup>. On the other hand, the XRD pattern of the TiON/TiO<sub>2</sub> heterostructure shows the presence of seven sharp peaks as seen in Fig. ##FIG##0##1##b. These peaks are located at 30.0°, 42.1°, 50.0°, 55.4°, 63.1°, 68.1°, and 73.2° correspond to the Miller indices (101), (103), (200), (201), (213), (115), and (215), respectively. These peaks are characteristic of TiO<sub>2</sub> nanomaterials. The TiON does not appear to have distinct peaks in the XRD chart due to the TiON layer in the heterostructure is very thin (approximately 5 nm) and may not exhibit a well-defined crystalline structure.</p>", "<p id=\"Par16\">The EDX analyses of prepared nanomaterials provide comprehensive insights into the material's elemental composition, as depicted in Fig. ##FIG##0##1##. The EDX analyses conducted on Al<sub>2</sub>O<sub>3</sub>, as depicted in Fig. ##FIG##0##1##c, provide conclusive evidence of the presence of oxygen (O) and aluminum (Al) elements in the material. This indicates that the prepared Al<sub>2</sub>O<sub>3</sub> membrane exhibits high purity without the presence of any significant impurities. This finding agrees with the XRD analysis, further supporting the assertion that the Al<sub>2</sub>O<sub>3</sub> material is of high quality and exhibits the expected chemical composition with good crystallinity. Similarly, for the TiON/TiO<sub>2</sub> heterostructure, as demonstrated in Fig. ##FIG##0##1##d, the presence of titanium (Ti), oxygen (O), and nitrogen (N) elements is confirmed. The EDX spectrum shows characteristic peaks corresponding to these elements, indicating their presence in the heterostructure material.</p>", "<p id=\"Par17\">The Al<sub>2</sub>O<sub>3</sub> membrane displayed in Fig. ##FIG##1##2##a exhibits remarkable morphological and topographical properties. It features a distinct porous hexagonal shape like a bee house with a diameter of 350 nm. The large diameter of the porous ensures a significant amount of analyte molecules can be accommodated within the nanotubes, facilitating enhanced sensing performance. Upon closer examination of the magnified image of Fig. ##FIG##1##2##a, the depth of the Al<sub>2</sub>O<sub>3</sub> membrane is approximately 1.2 µm. This depth indicates that the membrane possesses adequate thickness to support subsequent material depositions, ensuring stability and mechanical integrity. Additionally, the substantial length of 1.2 µm provides ample room for functionalization or modification with specific sensing elements or surface functional groups, further tailoring their sensing capabilities. This characteristic is crucial for the successful synthesis of functional nanomaterials and nanostructures based on the membrane. The regularity and uniformity of the hexagonal pattern provide an ideal scaffold for further material deposition or growth, enabling the creation of functional nanostructures with tailored properties. This high-order membrane was fabricated using the Ni-imprinting technique, which has shown promise in synthesizing membranes with precise and well-defined structures<sup>##UREF##18##27##,##UREF##19##28##</sup>. The Ni-imprinting technique involves the use of a nickel mold pressed onto the Al<sub>2</sub>O<sub>3</sub> surface under controlled conditions. This process enables the replication of intricate patterns with high fidelity and precision. After the nickel mold is removed, the desired hexagonal bee-house structure remains embedded in the Al<sub>2</sub>O<sub>3</sub>, ready for further exploration and applications.</p>", "<p id=\"Par18\">Figure ##FIG##1##2##b presents the TiO<sub>2</sub> layer deposited on the Al<sub>2</sub>O<sub>3</sub> membrane. It is characterized by high-order tubes with a diameter of 310 nm and a length of 1.2 µm. The TiO<sub>2</sub> nanotubes have unique properties that make them highly attractive for various technological and scientific pursuits including environmental monitoring, healthcare diagnostics, and industrial quality control. The hollow structure possesses several advantages for sensing applications due to the abundance of active sites present both inside and outside their walls. The highly active sites present within the walls of the TiO<sub>2</sub> nanotubes create an environment conducive to sensing interactions, resulting in rapid and efficient detection of target analytes. Also, the hollow structure allows for efficient diffusion of analytes and enhances the surface area available for interactions. The combination of active sites and high surface area allows for efficient binding and recognition of analyte molecules, promoting improved sensitivity and response.</p>", "<p id=\"Par19\">The deposition of TiON on the TiO2/Al<sub>2</sub>O<sub>3</sub> results in the formation of a highly fibrous structure that covers the TiO<sub>2</sub> nanotubes, as shown in Fig. ##FIG##1##2##c. This fibrous morphology, with a width of approximately 5 nm, contributes to the increased roughness of the heterostructure material. The roughness of the TiON/TiO<sub>2</sub> heterostructure is illustrated in Fig. ##FIG##1##2##d using the Gwydion program. The presence of irregularities and rough textures amplifies the available surface area and the number of active sites for interactions with analyte molecules. Additionally, the presence of roughness fibers on the surface of the TiON/TiO<sub>2</sub> heterostructure can have implications for light absorption due to multiple reflections and trapping light. Under light conditions, the increased roughness and active sites provide even greater opportunities for photon-induced charge separation. This can be beneficial for sensing applications, as it facilitates stronger sensing responses and improved sensitivity to target analytes, particularly under light conditions.</p>", "<title>Sensing properties</title>", "<p id=\"Par20\">The sensing performance of the TiON/TiO<sub>2</sub> heterostructure sensor towards Pb<sup>2+</sup> ions is evaluated using the cyclic voltammetry technique. The Pb<sup>2+</sup> ions are tested across a concentration range of 10<sup>–5</sup> to 10<sup>–1</sup> M in a three-electrode cell configuration. The investigations were conducted under both dark and light conditions. A metal halide lamp serves as the light source for illumination with a power intensity of about 100 mW/cm<sup>2</sup>. The photon illumination plays a crucial role in initiating surface interactions within TiON/TiO<sub>2</sub> semiconductor materials.</p>", "<p id=\"Par21\">The incorporation of the TiON layer into the TiON/TiO2 heterostructure offers numerous advantages for sensor performance. Firstly, it acts as a protective layer, safeguarding the underlying materials from environmental degradation and ensuring long-term stability. Additionally, the TiON layer introduces roughness textures, resulting in a larger surface area and an increased number of active sites. This, in turn, provides more opportunities for absorption and interactions with analyte molecules. The presence of roughness fibers on the TiON layer facilitates efficient light interaction by enabling multiple reflections and light trapping, thereby maximizing the chances for photon-induced charge separation. Ultimately, these benefits significantly enhance the sensitivity of the sensor in detecting target analytes or signals, particularly under light conditions.</p>", "<p id=\"Par22\">Figure ##FIG##2##3##a presents the cyclic voltammogram (CV) with the varying concentration of Pb<sup>2+</sup> ions. The main sensing mechanism relies on electrostatic attraction between the sensor and Pb<sup>2+</sup> ions present in the solution. In this case, the CV is performed under dark conditions, which means there is no light influence on the electrochemical reactions. The main reduction peaks for CV are illustrated at − 0.28 V. The area of CV increases with the rising concentration of Pb<sup>2+</sup> ions from 10<sup>–5</sup> to 10<sup>–1</sup> M. The main observation is that the area under the CV curve increases with increasing concentration of Pb<sup>2+</sup> ions. The area under the curve is related to the amount of charge exchanged during the electrochemical reaction. As the concentration of Pb<sup>2+</sup> ions increases, the reduction peaks in the CV also tend to increase in magnitude. This is because as the Pb<sup>2+</sup> ion concentration increases, more Pb<sup>2+</sup> ions are available to participate in the reduction reaction at the electrode surface during the CV experiment. Consequently, a larger reducing current response is generated, resulting in higher peaks in the CV. This behavior indicates the ability of the TiON/TiO<sub>2</sub> heterostructure to detect and respond to varying levels of Pb<sup>2+</sup> ions in the tested solution<sup>##REF##34992227##29##</sup>. To evaluate the sensitivity of the sensor, the pM value (negative logarithm of Pb<sup>2+</sup> ion concentration) is plotted against the corresponding peak current, as shown in Fig. ##FIG##2##3##b. The rate of change in the peak current with respect to the concentration of Pb<sup>2+</sup> ions is determined by linear fitting for experimental data. It is about 1 × 10<sup>–6</sup> A/M based on this analysis. This value represents the sensitivity of the TiON/TiO<sub>2</sub> heterostructure for the electroanalytical detection trace of Pb<sup>2+</sup> ions in aqueous solutions. The obtained result demonstrates the potential of the TiON/TiO<sub>2</sub> heterostructure as an efficient and reliable sensor in environmental monitoring and analytical applications.</p>", "<p id=\"Par23\">Figure ##FIG##2##3##c shows data of the CV for the detection of Pb<sup>2+</sup> ions under light illumination. The TiON/TiO<sub>2</sub> heterostructure sensor exhibits a significant enhancement in the reduction peaks compared to the dark condition when exposed to light. This increase in reduction peaks indicates the positive impact of light on improving the sensing behavior. The light intensity facilitates a faster and more efficient response to the presence of Pb<sup>2+</sup> ions in the solution. Under light conditions, the TiON/TiO<sub>2</sub> semiconductor materials are activated by photon energy, leading to the excitation of electrons to higher energy levels. Consequently, an excess of electrons accumulates on the surface of the TiO<sub>2</sub>/TiON photoelectrode. Hence, the TiO<sub>2</sub>/TiON photoelectrode acts as an efficient electron donor<sup>##UREF##20##30##–##UREF##22##32##</sup>. Moreover, the incorporation of the TiON layer brings about surface roughness, leading to an expanded surface area with an augmented quantity of active sites. Consequently, this creates a greater number of possibilities for the absorption and interaction of analyte molecules. The existence of rough fibers on the TiON layer promotes effective light interaction by allowing for multiple reflections and light trapping, ultimately maximizing the opportunities for photon-induced charge separation. Overall, these advantages substantially elevate the sensitivity of the sensor when it comes to detecting Pb<sup>2+</sup> analytes. The accumulation of electrons creates a favorable environment for electrostatic interactions with Pb<sup>2+</sup> ions, resulting in their adsorption onto the sensor material through strong electrostatic forces. The light-induced photocatalytic effect significantly amplifies the sensing performance of the sensor, enabling it to detect trace levels of Pb<sup>2+</sup> ions in the solution. In contrast, under dark conditions, the electron excitation process is not activated, and the accumulation of charge carriers on the surface is limited. As a result, the sensor sensitivity is low to detect Pb<sup>2+</sup> ions. The relationship between the peak current and the concentration of Pb<sup>2+</sup> ions follows an exponential behavior rather than a linear one, as depicted in Fig. ##FIG##2##3##d. This characteristic further emphasizes the significant influence of light on the performance of the TiON/TiO<sub>2</sub> heterostructure sensor. The exponential relationship indicates that the sensing process is highly sensitive to light illumination, offering the advantage of fine-tuning the sensor performance under light<sup>##UREF##23##33##</sup>. The sensor demonstrates a sensitivity of approximately 1 × 10<sup>–4</sup> A/M, which is higher than that observed under dark conditions. By leveraging the power of light activation, this sensor holds great promise as a precise and efficient tool for detecting Pb<sup>2+</sup> ions, with potential applications in environmental monitoring.</p>", "<p id=\"Par24\">The effect of scan rate on the sensing process is crucial in determining its efficiency. The scan rate represents the speed at which the potential is changed during CV measurements. The sensitivity of the TiON/TiO<sub>2</sub> heterostructure sensor towards Pb<sup>2+</sup> ions at a concentration of 10<sup>–3</sup> M was studied by varying the scan rate. Figure ##FIG##3##4##a displays the results of the study, where the scan rate was varied in the range of 50 to 300 mV/s. It was observed that the cyclic curve showed enhancement with higher scan rates. This indicates that as the scan rate is increased, the movement of Pb<sup>2+</sup> ions towards the TiON/TiO<sub>2</sub> heterostructure sensor also increases. Consequently, there is a corresponding increase in the peak current observed in the cyclic voltammogram<sup>##UREF##16##25##</sup>. The increase in peak current with higher scan rates demonstrates the sensitivity of the TiON/TiO<sub>2</sub> heterostructure sensor in detecting Pb<sup>2+</sup> ions at low concentrations.</p>", "<p id=\"Par25\">To evaluate the stability of the TiON/TiO<sub>2</sub> heterostructure sensor's response to Pb<sup>2+</sup> ions, the CV measurements were repeated several times at a concentration of 10<sup>–3</sup> M. Figure ##FIG##3##4##b displays the results obtained from five consecutive runs under light. The results demonstrate that the TiON/TiO<sub>2</sub> heterostructure sensor maintains a consistent current value with negligible variation. This remarkable stability indicates that the sensor's sensing behavior remains unchanged over multiple cycles, without any discernible performance degradation or drift. In other words, the sensor exhibits high sensitivity and reliability throughout the repetitive measurements, ensuring reliable and accurate detection of Pb<sup>2+</sup> ions. The stability demonstrated by the TiON/TiO<sub>2</sub> sensor is crucial for practical applications, especially in long-term monitoring and real-time detection scenarios<sup>##REF##31913322##2##,##REF##34992227##34##,##UREF##24##35##</sup>. The ability to maintain consistent and reproducible results over multiple measurement cycles makes the sensor a reliable and robust tool for electroanalytical detection of Pb<sup>2+</sup> ions. The results highlight that the TiON/TiO<sub>2</sub> heterostructure sensor possesses a favorable combination of eco-friendliness, affordability, high sensitivity, and reproducibility, making it a suitable solution for detecting Pb<sup>2+</sup> ions in drinking water within industrial settings. This advancement in water management significantly contributes to the improved protection of public health.</p>", "<p id=\"Par26\">Figure ##FIG##4##5## illustrates the impact of interfering ions, including Na<sup>+</sup>, K<sup>+</sup>, Mg<sup>2+</sup>, Ca<sup>2+</sup>, and Al<sup>3+</sup> at a concentration of 0.01 M, on the sensitivity of the developed TiON/TiO<sub>2</sub> heterostructure sensor. The absence of discernible oxidation or reduction peaks for these ions in the figure suggests that there is negligible interference from Na<sup>+</sup>, K<sup>+</sup>, Mg<sup>2+</sup>, Ca<sup>2+</sup>, and Al<sup>3+</sup> on the sensitivity of the TiON/TiO<sub>2</sub> heterostructure sensor. This lack of observable peaks indicates that these ions do not impede the sensor's ability to detect Pb<sup>2+</sup> ions effectively. The graph in Fig. ##FIG##4##5## serves as evidence that the fabricated TiON/TiO2 heterostructure sensor remains selective in its response to Pb<sup>2+</sup> ions in the presence of the specified interfering ions. This outcome is crucial for the reliability and accuracy of the sensor in real-world applications where diverse ionic environments may be encountered. Consequently, the TiON/TiO<sub>2</sub> heterostructure sensor exhibits promising potential for precise and selective detection of Pb<sup>2+</sup> ions in the presence of common interfering ions, making it a valuable tool for environmental monitoring and analytical applications.</p>" ]	[ "<title>Results and discussion</title>", "<title>Analyses</title>", "<p id=\"Par15\">The XRD pattern of Al<sub>2</sub>O<sub>3</sub> in Fig. ##FIG##0##1##a confirms the crystalline nature of the Al<sub>2</sub>O<sub>3</sub> nanomaterial. This is evident from the appearance of three sharp peaks located at 47.7°, 65.1°, and 78.3°, corresponding to the growth directions (113), (214), and (119), respectively<sup>##UREF##17##26##</sup>. On the other hand, the XRD pattern of the TiON/TiO<sub>2</sub> heterostructure shows the presence of seven sharp peaks as seen in Fig. ##FIG##0##1##b. These peaks are located at 30.0°, 42.1°, 50.0°, 55.4°, 63.1°, 68.1°, and 73.2° correspond to the Miller indices (101), (103), (200), (201), (213), (115), and (215), respectively. These peaks are characteristic of TiO<sub>2</sub> nanomaterials. The TiON does not appear to have distinct peaks in the XRD chart due to the TiON layer in the heterostructure is very thin (approximately 5 nm) and may not exhibit a well-defined crystalline structure.</p>", "<p id=\"Par16\">The EDX analyses of prepared nanomaterials provide comprehensive insights into the material's elemental composition, as depicted in Fig. ##FIG##0##1##. The EDX analyses conducted on Al<sub>2</sub>O<sub>3</sub>, as depicted in Fig. ##FIG##0##1##c, provide conclusive evidence of the presence of oxygen (O) and aluminum (Al) elements in the material. This indicates that the prepared Al<sub>2</sub>O<sub>3</sub> membrane exhibits high purity without the presence of any significant impurities. This finding agrees with the XRD analysis, further supporting the assertion that the Al<sub>2</sub>O<sub>3</sub> material is of high quality and exhibits the expected chemical composition with good crystallinity. Similarly, for the TiON/TiO<sub>2</sub> heterostructure, as demonstrated in Fig. ##FIG##0##1##d, the presence of titanium (Ti), oxygen (O), and nitrogen (N) elements is confirmed. The EDX spectrum shows characteristic peaks corresponding to these elements, indicating their presence in the heterostructure material.</p>", "<p id=\"Par17\">The Al<sub>2</sub>O<sub>3</sub> membrane displayed in Fig. ##FIG##1##2##a exhibits remarkable morphological and topographical properties. It features a distinct porous hexagonal shape like a bee house with a diameter of 350 nm. The large diameter of the porous ensures a significant amount of analyte molecules can be accommodated within the nanotubes, facilitating enhanced sensing performance. Upon closer examination of the magnified image of Fig. ##FIG##1##2##a, the depth of the Al<sub>2</sub>O<sub>3</sub> membrane is approximately 1.2 µm. This depth indicates that the membrane possesses adequate thickness to support subsequent material depositions, ensuring stability and mechanical integrity. Additionally, the substantial length of 1.2 µm provides ample room for functionalization or modification with specific sensing elements or surface functional groups, further tailoring their sensing capabilities. This characteristic is crucial for the successful synthesis of functional nanomaterials and nanostructures based on the membrane. The regularity and uniformity of the hexagonal pattern provide an ideal scaffold for further material deposition or growth, enabling the creation of functional nanostructures with tailored properties. This high-order membrane was fabricated using the Ni-imprinting technique, which has shown promise in synthesizing membranes with precise and well-defined structures<sup>##UREF##18##27##,##UREF##19##28##</sup>. The Ni-imprinting technique involves the use of a nickel mold pressed onto the Al<sub>2</sub>O<sub>3</sub> surface under controlled conditions. This process enables the replication of intricate patterns with high fidelity and precision. After the nickel mold is removed, the desired hexagonal bee-house structure remains embedded in the Al<sub>2</sub>O<sub>3</sub>, ready for further exploration and applications.</p>", "<p id=\"Par18\">Figure ##FIG##1##2##b presents the TiO<sub>2</sub> layer deposited on the Al<sub>2</sub>O<sub>3</sub> membrane. It is characterized by high-order tubes with a diameter of 310 nm and a length of 1.2 µm. The TiO<sub>2</sub> nanotubes have unique properties that make them highly attractive for various technological and scientific pursuits including environmental monitoring, healthcare diagnostics, and industrial quality control. The hollow structure possesses several advantages for sensing applications due to the abundance of active sites present both inside and outside their walls. The highly active sites present within the walls of the TiO<sub>2</sub> nanotubes create an environment conducive to sensing interactions, resulting in rapid and efficient detection of target analytes. Also, the hollow structure allows for efficient diffusion of analytes and enhances the surface area available for interactions. The combination of active sites and high surface area allows for efficient binding and recognition of analyte molecules, promoting improved sensitivity and response.</p>", "<p id=\"Par19\">The deposition of TiON on the TiO2/Al<sub>2</sub>O<sub>3</sub> results in the formation of a highly fibrous structure that covers the TiO<sub>2</sub> nanotubes, as shown in Fig. ##FIG##1##2##c. This fibrous morphology, with a width of approximately 5 nm, contributes to the increased roughness of the heterostructure material. The roughness of the TiON/TiO<sub>2</sub> heterostructure is illustrated in Fig. ##FIG##1##2##d using the Gwydion program. The presence of irregularities and rough textures amplifies the available surface area and the number of active sites for interactions with analyte molecules. Additionally, the presence of roughness fibers on the surface of the TiON/TiO<sub>2</sub> heterostructure can have implications for light absorption due to multiple reflections and trapping light. Under light conditions, the increased roughness and active sites provide even greater opportunities for photon-induced charge separation. This can be beneficial for sensing applications, as it facilitates stronger sensing responses and improved sensitivity to target analytes, particularly under light conditions.</p>", "<title>Sensing properties</title>", "<p id=\"Par20\">The sensing performance of the TiON/TiO<sub>2</sub> heterostructure sensor towards Pb<sup>2+</sup> ions is evaluated using the cyclic voltammetry technique. The Pb<sup>2+</sup> ions are tested across a concentration range of 10<sup>–5</sup> to 10<sup>–1</sup> M in a three-electrode cell configuration. The investigations were conducted under both dark and light conditions. A metal halide lamp serves as the light source for illumination with a power intensity of about 100 mW/cm<sup>2</sup>. The photon illumination plays a crucial role in initiating surface interactions within TiON/TiO<sub>2</sub> semiconductor materials.</p>", "<p id=\"Par21\">The incorporation of the TiON layer into the TiON/TiO2 heterostructure offers numerous advantages for sensor performance. Firstly, it acts as a protective layer, safeguarding the underlying materials from environmental degradation and ensuring long-term stability. Additionally, the TiON layer introduces roughness textures, resulting in a larger surface area and an increased number of active sites. This, in turn, provides more opportunities for absorption and interactions with analyte molecules. The presence of roughness fibers on the TiON layer facilitates efficient light interaction by enabling multiple reflections and light trapping, thereby maximizing the chances for photon-induced charge separation. Ultimately, these benefits significantly enhance the sensitivity of the sensor in detecting target analytes or signals, particularly under light conditions.</p>", "<p id=\"Par22\">Figure ##FIG##2##3##a presents the cyclic voltammogram (CV) with the varying concentration of Pb<sup>2+</sup> ions. The main sensing mechanism relies on electrostatic attraction between the sensor and Pb<sup>2+</sup> ions present in the solution. In this case, the CV is performed under dark conditions, which means there is no light influence on the electrochemical reactions. The main reduction peaks for CV are illustrated at − 0.28 V. The area of CV increases with the rising concentration of Pb<sup>2+</sup> ions from 10<sup>–5</sup> to 10<sup>–1</sup> M. The main observation is that the area under the CV curve increases with increasing concentration of Pb<sup>2+</sup> ions. The area under the curve is related to the amount of charge exchanged during the electrochemical reaction. As the concentration of Pb<sup>2+</sup> ions increases, the reduction peaks in the CV also tend to increase in magnitude. This is because as the Pb<sup>2+</sup> ion concentration increases, more Pb<sup>2+</sup> ions are available to participate in the reduction reaction at the electrode surface during the CV experiment. Consequently, a larger reducing current response is generated, resulting in higher peaks in the CV. This behavior indicates the ability of the TiON/TiO<sub>2</sub> heterostructure to detect and respond to varying levels of Pb<sup>2+</sup> ions in the tested solution<sup>##REF##34992227##29##</sup>. To evaluate the sensitivity of the sensor, the pM value (negative logarithm of Pb<sup>2+</sup> ion concentration) is plotted against the corresponding peak current, as shown in Fig. ##FIG##2##3##b. The rate of change in the peak current with respect to the concentration of Pb<sup>2+</sup> ions is determined by linear fitting for experimental data. It is about 1 × 10<sup>–6</sup> A/M based on this analysis. This value represents the sensitivity of the TiON/TiO<sub>2</sub> heterostructure for the electroanalytical detection trace of Pb<sup>2+</sup> ions in aqueous solutions. The obtained result demonstrates the potential of the TiON/TiO<sub>2</sub> heterostructure as an efficient and reliable sensor in environmental monitoring and analytical applications.</p>", "<p id=\"Par23\">Figure ##FIG##2##3##c shows data of the CV for the detection of Pb<sup>2+</sup> ions under light illumination. The TiON/TiO<sub>2</sub> heterostructure sensor exhibits a significant enhancement in the reduction peaks compared to the dark condition when exposed to light. This increase in reduction peaks indicates the positive impact of light on improving the sensing behavior. The light intensity facilitates a faster and more efficient response to the presence of Pb<sup>2+</sup> ions in the solution. Under light conditions, the TiON/TiO<sub>2</sub> semiconductor materials are activated by photon energy, leading to the excitation of electrons to higher energy levels. Consequently, an excess of electrons accumulates on the surface of the TiO<sub>2</sub>/TiON photoelectrode. Hence, the TiO<sub>2</sub>/TiON photoelectrode acts as an efficient electron donor<sup>##UREF##20##30##–##UREF##22##32##</sup>. Moreover, the incorporation of the TiON layer brings about surface roughness, leading to an expanded surface area with an augmented quantity of active sites. Consequently, this creates a greater number of possibilities for the absorption and interaction of analyte molecules. The existence of rough fibers on the TiON layer promotes effective light interaction by allowing for multiple reflections and light trapping, ultimately maximizing the opportunities for photon-induced charge separation. Overall, these advantages substantially elevate the sensitivity of the sensor when it comes to detecting Pb<sup>2+</sup> analytes. The accumulation of electrons creates a favorable environment for electrostatic interactions with Pb<sup>2+</sup> ions, resulting in their adsorption onto the sensor material through strong electrostatic forces. The light-induced photocatalytic effect significantly amplifies the sensing performance of the sensor, enabling it to detect trace levels of Pb<sup>2+</sup> ions in the solution. In contrast, under dark conditions, the electron excitation process is not activated, and the accumulation of charge carriers on the surface is limited. As a result, the sensor sensitivity is low to detect Pb<sup>2+</sup> ions. The relationship between the peak current and the concentration of Pb<sup>2+</sup> ions follows an exponential behavior rather than a linear one, as depicted in Fig. ##FIG##2##3##d. This characteristic further emphasizes the significant influence of light on the performance of the TiON/TiO<sub>2</sub> heterostructure sensor. The exponential relationship indicates that the sensing process is highly sensitive to light illumination, offering the advantage of fine-tuning the sensor performance under light<sup>##UREF##23##33##</sup>. The sensor demonstrates a sensitivity of approximately 1 × 10<sup>–4</sup> A/M, which is higher than that observed under dark conditions. By leveraging the power of light activation, this sensor holds great promise as a precise and efficient tool for detecting Pb<sup>2+</sup> ions, with potential applications in environmental monitoring.</p>", "<p id=\"Par24\">The effect of scan rate on the sensing process is crucial in determining its efficiency. The scan rate represents the speed at which the potential is changed during CV measurements. The sensitivity of the TiON/TiO<sub>2</sub> heterostructure sensor towards Pb<sup>2+</sup> ions at a concentration of 10<sup>–3</sup> M was studied by varying the scan rate. Figure ##FIG##3##4##a displays the results of the study, where the scan rate was varied in the range of 50 to 300 mV/s. It was observed that the cyclic curve showed enhancement with higher scan rates. This indicates that as the scan rate is increased, the movement of Pb<sup>2+</sup> ions towards the TiON/TiO<sub>2</sub> heterostructure sensor also increases. Consequently, there is a corresponding increase in the peak current observed in the cyclic voltammogram<sup>##UREF##16##25##</sup>. The increase in peak current with higher scan rates demonstrates the sensitivity of the TiON/TiO<sub>2</sub> heterostructure sensor in detecting Pb<sup>2+</sup> ions at low concentrations.</p>", "<p id=\"Par25\">To evaluate the stability of the TiON/TiO<sub>2</sub> heterostructure sensor's response to Pb<sup>2+</sup> ions, the CV measurements were repeated several times at a concentration of 10<sup>–3</sup> M. Figure ##FIG##3##4##b displays the results obtained from five consecutive runs under light. The results demonstrate that the TiON/TiO<sub>2</sub> heterostructure sensor maintains a consistent current value with negligible variation. This remarkable stability indicates that the sensor's sensing behavior remains unchanged over multiple cycles, without any discernible performance degradation or drift. In other words, the sensor exhibits high sensitivity and reliability throughout the repetitive measurements, ensuring reliable and accurate detection of Pb<sup>2+</sup> ions. The stability demonstrated by the TiON/TiO<sub>2</sub> sensor is crucial for practical applications, especially in long-term monitoring and real-time detection scenarios<sup>##REF##31913322##2##,##REF##34992227##34##,##UREF##24##35##</sup>. The ability to maintain consistent and reproducible results over multiple measurement cycles makes the sensor a reliable and robust tool for electroanalytical detection of Pb<sup>2+</sup> ions. The results highlight that the TiON/TiO<sub>2</sub> heterostructure sensor possesses a favorable combination of eco-friendliness, affordability, high sensitivity, and reproducibility, making it a suitable solution for detecting Pb<sup>2+</sup> ions in drinking water within industrial settings. This advancement in water management significantly contributes to the improved protection of public health.</p>", "<p id=\"Par26\">Figure ##FIG##4##5## illustrates the impact of interfering ions, including Na<sup>+</sup>, K<sup>+</sup>, Mg<sup>2+</sup>, Ca<sup>2+</sup>, and Al<sup>3+</sup> at a concentration of 0.01 M, on the sensitivity of the developed TiON/TiO<sub>2</sub> heterostructure sensor. The absence of discernible oxidation or reduction peaks for these ions in the figure suggests that there is negligible interference from Na<sup>+</sup>, K<sup>+</sup>, Mg<sup>2+</sup>, Ca<sup>2+</sup>, and Al<sup>3+</sup> on the sensitivity of the TiON/TiO<sub>2</sub> heterostructure sensor. This lack of observable peaks indicates that these ions do not impede the sensor's ability to detect Pb<sup>2+</sup> ions effectively. The graph in Fig. ##FIG##4##5## serves as evidence that the fabricated TiON/TiO2 heterostructure sensor remains selective in its response to Pb<sup>2+</sup> ions in the presence of the specified interfering ions. This outcome is crucial for the reliability and accuracy of the sensor in real-world applications where diverse ionic environments may be encountered. Consequently, the TiON/TiO<sub>2</sub> heterostructure sensor exhibits promising potential for precise and selective detection of Pb<sup>2+</sup> ions in the presence of common interfering ions, making it a valuable tool for environmental monitoring and analytical applications.</p>" ]	[ "<title>Conclusions</title>", "<p id=\"Par27\">Using the atomic layer deposition (ALD) technique, a hexagonal nanotube TiON/TiO<sub>2</sub> heterostructure has been successfully created on a porous Al<sub>2</sub>O<sub>3</sub> membrane. This heterostructure displays a well-organized hollow tube design with a diameter of 345 nm and a length of 1.2 µm. The application of TiON on the TiO<sub>2</sub>/Al<sub>2</sub>O<sub>3</sub> substrate leads to a highly fibrous structure, increasing active sites and surface area, and facilitating effective light interaction. Consequently, this heterostructure shows promise for sensing applications. Specifically, the TiON/TiO<sub>2</sub> membrane exhibits outstanding electrochemical sensing capabilities for Pb<sup>2+</sup> ions due to its high surface charge density. The electrostatic attraction between the sensor and Pb<sup>2+</sup> ions results in heightened sensitivity, particularly in the presence of light. For electrochemical sensing of Pb<sup>2+</sup> ions in water, a cyclic voltammetry device is utilized under both light and dark conditions, with Pb<sup>2+</sup> ion concentrations ranging from 10<sup>–5</sup> to 10<sup>–1</sup> M. Sensitivity values obtained from cyclic voltammetry for the sensor are 1.0 × 10<sup>–6</sup> in dark conditions and 1.0 × 10<sup>–4</sup> in light conditions. The notable increase in sensitivity under light is attributed to enhanced activity and efficient electron transfer facilitated by light. The proposed sensor's practical and scalable nature positions it as an appealing solution for widespread use in environmental monitoring, water quality assessment, and safety regulation. Its accurate and timely detection capabilities for heavy metal contamination in water systems empower the implementation of effective monitoring and mitigation strategies.</p>" ]	[ "<p id=\"Par1\">The detection of heavy metals in water, especially Pb<sup>2+</sup> ions, is important due to their severe hazardous effects. To address this issue, a highly controlled hexagonal TiON/TiO<sub>2</sub> heterostructure has been synthesized in this study. The fabrication process involved the utilization of atomic layer deposition and direct current sputtering techniques to deposit TiO<sub>2</sub> and TiON layers onto a porous Al<sub>2</sub>O<sub>3</sub> membrane used as a template. The resulting heterostructure exhibits a well-ordered hollow tube structure with a diameter of 345 nm and a length of 1.2 µm. The electrochemical sensing of Pb<sup>2+</sup> ions in water is carried out using a cyclic voltammetry technique under both light and dark conditions. The concentration range for the Pb<sup>2+</sup> ions ranges from 10<sup>–5</sup> to 10<sup>–1</sup> M. The sensitivity values obtained for the sensor are 1.0 × 10<sup>–6</sup> in dark conditions and 1.0 × 10<sup>–4</sup> in light conditions. The remarkable enhancement in sensitivity under light illumination can be attributed to the increased activity and electron transfer facilitated by the presence of light. The sensor demonstrates excellent reproducibility, highlighting its reliability and consistency. These findings suggest that the proposed sensor holds great promise for the detection of Pb<sup>2+</sup> ions in water, thereby facilitating environmental monitoring, water quality assessment, and safety regulation across various industries. Furthermore, the eco-friendly and straightforward preparation techniques employed in its fabrication provide a significant advantage for practical and scalable implementation.</p>", "<title>Subject terms</title>" ]	[]	[ "<title>Acknowledgements</title>", "<p>“This study/publication (Sensing heavy metals in drinking water using nanophotonic structure) is made possible by the generous support of the American people through the United States Agency for International Development (USAID). The contents are the responsibility of [Asmaa M. Abd-Elsayed] and do not necessarily reflect the views of USAID or the United States Government.”</p>", "<title>Author contributions</title>", "<p>A.M.E. conceived the designs. A.M.E., A.A., M.T., M.F. and A.A. designed and conducted the analyses. A.M.E., A.A., M.T., M.F. and A.A. analyzed the results. All authors reviewed the manuscript.</p>", "<title>Data availability</title>", "<p>Requests for materials should be addressed to Arafa H. Aly.</p>", "<title>Competing interests</title>", "<p id=\"Par28\">The authors declare no competing interests.</p>" ]	[ "<fig id=\"Fig1\"><label>Figure 1</label><caption><p>The XRD analyses of (<bold>a</bold>) Al2O3 membrane and (<bold>b</bold>) TiON/TiO<sub>2</sub> heterostructure. EDX analyses of (<bold>c</bold>) Al<sub>2</sub>O<sub>3</sub> membrane and (<bold>d</bold>) TiON/TiO2 heterostructure.</p></caption></fig>", "<fig id=\"Fig2\"><label>Figure 2</label><caption><p>SEM image of (<bold>a</bold>) Al<sub>2</sub>O<sub>3</sub> membrane (top-view and cross section), (<bold>b</bold>) TiO2 face and cross-section, (<bold>c</bold>) TiON/TiO<sub>2</sub>, and (<bold>d</bold>) Cross section roughness of TiON/TiO2.</p></caption></fig>", "<fig id=\"Fig3\"><label>Figure 3</label><caption><p>(<bold>a</bold>, <bold>c</bold>) The cyclic voltammetry technique for Pb<sup>2+</sup> ion electrolyte with varying concentrations from 10–5 to 10–1 M (<bold>b</bold>, <bold>d</bold>) and Corresponding peak current of the cyclic voltammogram against the pM value (negative logarithm of the Pb<sup>2+</sup> ion concentration) in dark condition under light.</p></caption></fig>", "<fig id=\"Fig4\"><label>Figure 4</label><caption><p>The effect of (<bold>a</bold>) scan rate from 50 to 300 mV s-1and (<bold>b</bold>) reproducibility (five runs) of the Pb<sup>2+</sup> ion detection by TiON/TiO<sub>2</sub> heterostructure sensor that estimated through the cyclic voltammetry technique under dark conditions.</p></caption></fig>", "<fig id=\"Fig5\"><label>Figure 5</label><caption><p>The cyclic voltammetry of TiON/TiO2 heterostructure sensor for some interfering ions (Na<sup>+</sup> , K<sup>+</sup> , Mg2<sup>+</sup> , Ca2<sup>+</sup> , and Al<sup>3+</sup>).</p></caption></fig>" ]	[]	[]	[]	[]	[]	[]	[]	[ "<fn-group><fn><p><bold>Publisher's note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"41598_2023_50428_Fig1_HTML\" id=\"MO1\"/>", "<graphic xlink:href=\"41598_2023_50428_Fig2_HTML\" id=\"MO2\"/>", "<graphic xlink:href=\"41598_2023_50428_Fig3_HTML\" id=\"MO3\"/>", "<graphic xlink:href=\"41598_2023_50428_Fig4_HTML\" id=\"MO4\"/>", "<graphic xlink:href=\"41598_2023_50428_Fig5_HTML\" id=\"MO5\"/>" ]	[]	[{"label": ["4."], "surname": ["Lan", "Fallatah", "Suiter", "Padalkar"], "given-names": ["T", "A", "E", "S"], "article-title": ["Size controlled copper (I) oxide nanoparticles influence sensitivity of glucose biosensor"], "source": ["Sensors"], "year": ["1944"], "volume": ["2017"], "fpage": ["17"], "pub-id": ["10.3390/s17091944"]}, {"label": ["5."], "surname": ["Zhan", "Chen", "Hu", "Chen"], "given-names": ["W", "Z", "J", "X"], "article-title": ["Vertical CuO nanowires array electrodes: Visible light sensitive photoelectrochemical biosensor of ethanol detection"], "source": ["Mater. 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Technol."], "year": ["2019"], "volume": ["8"], "fpage": ["4510"], "lpage": ["4520"], "pub-id": ["10.1016/j.jmrt.2019.07.064"]}, {"label": ["35."], "surname": ["Khalil", "Issa", "El Sayed"], "given-names": ["MM", "YM", "GA"], "article-title": ["Modified carbon paste and polymeric membrane electrodes for determination of hydroxychloroquine sulfate in pharmaceutical preparations and human urine"], "source": ["RSC Adv."], "year": ["2015"], "volume": ["5"], "fpage": ["83657"], "lpage": ["83667"], "pub-id": ["10.1039/C5RA16250E"]}]	{ "acronym": [], "definition": [] }	35	CC BY	no	2024-01-13 00:02:20	Sci Rep. 2024 Jan 10; 14:1015	oa_package/d0/51/PMC10781679.tar.gz
PMC10781680	38199981	[ "<title>Introduction</title>", "<p id=\"Par2\">Hepatocellular carcinoma (HCC) is the primary subtype of liver cancer, accounting for ~90% of all cases. HCC is associated with high rates of tumor recurrence and metastasis after primary hepatic resection, contributing to the most common cause of cancer-related mortality worldwide [##REF##28283421##1##, ##REF##33479224##2##]. Radiotherapy (RT) is a major modality used in treating HCC, particularly progressive HCC patients with tumors that are not amenable to resection or transplantation, or those with extrahepatic metastasis tumors [##REF##33479224##2##–##REF##27377923##4##]. However, the efficacy of RT is limited by the endogenous and therapy-induced radioresistance of HCC [##REF##28134935##5##–##REF##35995846##9##]. To improve the effectiveness of RT in the treatment of HCC, there is still a need to identify potential therapeutic targets associated with HCC radioresistance.</p>", "<p id=\"Par3\">Cellular senescence is a state of stable cell cycle arrest characterized by changes in morphology, macromolecule compositions, and the acquisition of pro-inflammatory phenotypes. Senescence can be induced by a variety of endogenous and exogenous stressors, such as telomere shortening, mitochondrial dysfunction, DNA damage, and oncogene activation. Moreover, cancer therapies, including chemotherapy, radiotherapy, and targeted therapy, can trigger therapy-induced senescence (TIS). The induction of cellular senescence is considered as a potential strategy for treating cancer by inducing tumor suppression and immune surveillance [##REF##31188495##10##–##REF##36639375##12##]. However, senescent cancer cells acquire pro-tumorigenic properties through activation of the senescence-associated secretory phenotype (SASP), which can modulate the tumor microenvironment and increase cancer stemness, invasion, migration, angiogenesis, and immune evasion [##REF##36045302##7##, ##REF##35241831##8##]. Nonetheless, the induction of cellular senescence remains a promising strategy for combination therapies in cancer treatment. Recent studies have revealed that pro-senescence therapy can increase the vulnerability of tumors to combination treatments, particularly with the use of senolytics and senomorphic agents. This approach provides new avenues for enhancing treatment outcomes and addressing challenges related to senescence-associated phenotypes [##REF##31578521##13##–##REF##35302667##15##].</p>", "<p id=\"Par4\">The ubiquitin-proteasome system (UPS) is an important protein homeostasis mechanism that targets substrates for ubiquitin-mediated degradation. This process is mediated by three enzymes: a ubiquitin activating enzyme (E1), a ubiquitin conjugating enzyme (E2), and a ubiquitin ligase (E3) [##REF##22524316##16##]. Dysregulation of E3 ligases is commonly observed in cancer: Changes in E3 ligase expression or activity can affect the progression, development, immune checkpoint regulation, and drug responses of various cancers [##REF##29242641##17##, ##REF##33974914##18##]. E3 ligases play a crucial role in determining the specificity and selectivity of ubiquitinated substrates, and they can have oncogenic or tumor-suppressive properties depending on their substrates [##REF##28967907##19##–##REF##37027301##24##]. Understanding the roles of E3 ligases in cancers can provide valuable insights into treating the disease. The activity of oncogenic E3 ligases can be inhibited by small molecules or peptides, such as PROTAC (proteolysis targeting chimeric) or molecular glues. For tumor-suppressive E3 ligases, on the other hand, potential therapeutic strategies include reinstating their expression or activity, exploring synthetic lethality, or targeting downstream oncogenic substrates [##REF##29242641##17##, ##REF##33974914##18##]. E3 ligases contribute to cellular senescence by suppressing or promoting DNA damage responses and cell cycle arrest [##REF##21795702##25##–##REF##32041778##28##]. The E3 ligases tripartite motif-containing 22 (TRIM22) is a TRIM protein family member and is characterized by the presence of RING, BBox, and Coiled-Coil domain regions at the N-terminus and a SPRY region at the C-terminus. TRIM22 has been implicated in regulating the progression and development of various cancers, including glioblastoma, osteosarcoma, and gastric cancer [##REF##32814880##29##–##REF##35636015##31##]. However, the potential role of TRIM22 in the cellular senescence of HCC remains completely unexplored.</p>", "<p id=\"Par5\">In this study, we demonstrate that TRIM22 is upregulated by p53 and its overexpression attenuates the AKT phosphatase PHLPP2 via the UPS, resulting in activation of AKT-p53-p21 senescence pathway. Our study suggests that TRIM22 can be a promising target for the cancer treatment.</p>" ]	[ "<title>Materials and methods</title>", "<title>Cell lines</title>", "<p id=\"Par23\">HCC cell lines HepG2, SK-Hep-1, SNU449, and PLC/PRF/5 were purchased from the ATCC. HepG2 and PLC/PRF/5 cells were cultured in Minimum Essential Medium Eagle (MEM; WelGene, Daegu, Korea). SK-Hep-1 cells were cultured in Dulbecco’s Modified Eagle Medium (DMEM; WelGene). SNU449 cells were cultured in Roswell Park Memorial Institute Medium 1640 (RPMI-1640; WelGene). All the cell lines, supplemented with 10% fetal bovine serum (FBS; Gibco, Grand Island, New York, USA) and 1% penicillin/streptomycin (WelGene) were incubated at 37 °C in a 5% CO<sub>2</sub> incubator.</p>", "<title>Transfection of siRNA and plasmids</title>", "<p id=\"Par24\">Transfection of siRNAs (Bioneer, Daejeon Korea) and plasmids were performed using RNAi-MAX (Invitrogen, Carlsbad, CA, USA) and Lipofectamine 2000 (Invitrogen). Transfection medium were exchanged by regular growth media 6 hrs after transfection. The sequences of siRNAs used in this study were listed in Table ##SUPPL##3##S1##.</p>", "<title>Plasmid constructs</title>", "<p id=\"Par25\">Full-length TRIM22 or TRIM22 mutants (TRIM22 ΔRING, ΔBBox, ΔCC or ΔSPRY) were obtained from pCMV6-TRIM22-Myc-DDK (RC207431, Origene, Rockville, MD, USA) and cloned into the pCMV-Myc vector. Full-length PHLPP2 was provided by Dr. KyeongJin Kim at Inha University, Korea and cloned into the p3xFlag vector. PHLPP2 ΔC-terminal (CTD) mutant were prepared based on p3xFlag-PHLPP2 Wt. All gene fragments were obtained by PCR amplification. The primers for plasmid construction used in this study were listed in Table ##SUPPL##4##S2##.</p>", "<title>Proximity ligation assay (PLA)</title>", "<p id=\"Par26\">HepG2 cells seeded onto the glass coverslips were transfected with indicated plasmids or treated with IR. After 48 hrs, the cells were treated with 20 μM MG132 for 4 hrs. Cells were fixed with 4% paraformaldehyde for 10 min at room temperature, permeabilized with 0.1% Triton X-100 for 10 min and washed with DPBS. Protein-protein interactions were detected using Duolink<sup>®</sup> PLA kit (Sigma–Aldrich, St Louis, MO, USA) according to the manufacturer’s instructions. The coverslips were mounted using Duolink<sup>®</sup> In Situ Mounting Medium with DAPI (Sigma–Aldrich). Immunofluorescence was detected and visualized using Zeiss LSM510 confocal microscope.</p>", "<title>RNA extract, reverse transcription and quantitative PCR (RT-qPCR)</title>", "<p id=\"Par27\">Total RNA was extracted from cells and tissues using TRIzol reagent (Molecular Research Center, Netherlands). RNA was reverse transcribed to cDNA using M-MLV reverse transcriptase (Thermo Fisher Scientific, Waltham, MA, USA) according to the manufacturer’s instruction, and qPCR was performed using iQTM SYBR<sup>®</sup> Green Supermix (BioRad Laboratories, Hercules, CA, USA) in a CFX ConnectTM RT-PCR Detection System (BioRad Laboratories). Housekeeping gene Actin was used as internal controls to normalize target mRNAs. The primer sequences for RT-qPCR were listed in Table ##SUPPL##5##S3##.</p>", "<title>Immunoprecipitation (IP) and Chromatin Immunoprecipitation (ChIP)</title>", "<p id=\"Par28\">For IP, cells were lysed in NET-2 buffer (1 M Tris–HCl (pH 7.4), 5 M NaCl, 10% NP-40, 0.1 M PMSF and 0.2 M Benzamidine) containing 2.5 mM sodium pyrophosphate, 1 mM β-glycerophosphate and 1 mM sodium orthovanadate. To remove non-specific bindings, cell lysates were pre-cleared using either protein A-Sepharose beads (GE Healthcare Bio-Science AB, Uppsala, Sweden) or G-Resin (GenScript, Piscataway, NJ, USA). Pre-cleared lysates were immunoprecipitated with specific antibodies or IgG for 4 hrs, followed by overnight incubation with protein A/G beads. The immunoprecipitants were washed with NET-2 buffer and eluted with 2× Laemmli sample buffer, followed by incubation at 95 °C for 10 min. For ChIP, IR-treated HepG2 cells were cross-linked with 1% formaldehyde for 5 min and stopped by 0.125 M glycine for 10 min at room temperature. The cells were washed with cold PBS, lysed with NET-2 buffer, and pre-cleared with protein A-Sepharose beads. The lysates were immunoprecipitated with either anti-p53 (Santa Cruz, FL-393) or rabbit IgG for 4 hrs, followed by incubation with protein A beads overnight. After washing with NET-2 buffer, DNAs bounds to proteins were purified by phenol-chloroform extraction and precipitated in ethanol. The DNA was then resuspended in DNase-and RNase-free water and analyzed by qPCR analysis. The proteins were detected using Western blot analysis.</p>", "<title>Western blotting</title>", "<p id=\"Par29\">The cells were lysed in RIPA buffer (50 mM Tris–HCl pH 8.0, 150 mM NaCl, 1% NP-40, 2 mM EDTA, 0.1% SDS, 0.5% sodium deoxycholate) containing protease inhibitor (Roche) and phosphatase inhibitor (Sigma–Aldrich). The protein concentration of whole cell lysates was determined using Bradford Protein Assay (BioRad Laboratories). Equal amounts of proteins were mixed with 2× Laemmli sample buffer and incubated for 5 min at 95 °C. The proteins were separated by SDS-PAGE, transferred to a nitrocellulose membrane (GE Healthcare, Buckinghamshire, UK), and blocked with 5% Bovine Serum Albumin (BSA). The membranes were then incubated with the primary antibodies for overnight at 4 °C. After washing with 1× TBST, the membranes were incubated with an HRP-linked secondary antibody and signals were detected using Pierce™ ECL Western Blotting Substrate (Thermo Fisher Scientific). Full and uncropped Western blot images have been uploaded in Supplemental Material.</p>", "<title>Caspase-3 activity</title>", "<p id=\"Par30\">Caspse-3 activity was analyzed with NucView 488 Caspase-3 (10403, BIOTIUM, San Francisco, CA, USA) according to the manufacturer’s instruction. Briefly, cells were seeded in 60 mm dishes, cultured for 24 hrs, and transfected with indicated plasmids or treated with IR. After 48 hrs, the cells were replaced medium with PBS containing 5 μM NucView® 488 substrate stock solution and incubated for 30 min at room temperature. Next, the cells were incubated with Hoechst (Invitrogen) for 10 min at room temperature. After incubation, the cells were washed with PBS and observed by using Olympus CKX41 light microscope (Olympus, Tokyo, Japan). Activity of caspase-3 was analyzed by the ImageJ software (version 1.52).</p>", "<title>Reagents and antibodies</title>", "<p id=\"Par31\">Reagents were obtained from the following suppliers: Doxorubicin (D1515, Sigma–Aldrich), MG132 (C2211, Sigma–Aldrich), Cycloheximide (CHX) (C7698, Sigma–Aldrich), Chloroquine (CQ) (C6628, Sigma–Aldrich), Sodium chloride (NaCl) (7647-14-5, Duchefa), Magnesium chloride (MgCl<sub>2</sub>) (M2670, Sigma–Aldrich), Citric acid (C1909, Sigma–Aldrich), Sodium phosphate (S3264, Sigma–Aldrich), Potassium ferrocyanide (P9387, Sigma–Aldrich), Potassium ferricyanide (P8131, Sigma–Aldrich), X-galactosidase (X-Gal) (7002, Beamsbio), Tris (T1801, Duchefa), β-glycerophosphate (G9422, Sigma–Aldrich), Dithiothreitol (DTT) (P2325, Invitrogen), Sodium orthovanadate (Na<sub>3</sub>VO<sub>4</sub>) (567540, Sigma–Aldrich), ATP (P0756, NEB), NP-40 (68987-90-6, USB), Ethylenediaminetetraacetic acid (EDTA) (03609, Sigma–Aldrich), Sodium dodecyl sulfate (SDS) (3771, Sigma–Aldrich), Sodium deoxycholate (D6750, Sigma–Aldrich), Phenylmethanesulfonylfluoride fluoride (PMSF) (52332, Millipore), Benzamidine (434760, Sigma–Aldrich), Sodium pyrophosphate (71501, Sigma–Aldrich), Sodium Fluoride (NaF) (201154, Sigma–Aldrich).</p>", "<p id=\"Par32\">Antibodies were obtained from the following suppliers: Myc-Taq (9B11) (2276, CST), p53 (DO7) (NCL-L-p53-DO7, Leica), p21 Waf1/Cip1 (12D1) (2947, CST), TRIM22 (ab224059, abcam), β-Actin (8H10D10) (3700, CST), P-AKT S473 (D9E) (4060, CST), P-AKT T308 (D25E6) (13038, CST), P-mTOR S2448 (D9C2) (5536, CST), P-mTOR S2481 (2974, CST), AKT (9272, CST), PHLPP1 (A300-660A, Bethyl), PHLPP2 (A300-661A, Bethyl), PHLPP2 (NBP2-13757, Novus), C-PARP (CST), Flag-M2 peroxidase (A8592, Sigma–Aldrich), HA-Tag (C29F4) (3724, CST), P-(Ser/Thr) Phe (ab17464, abcam), P-P70S6K T389 (9206, CST), P-IKKα/β S176/S180 (16A6) (2697, CST), P70S6K (9202, CST), IKKα (EPR464) (ab109749, abcam), IKKβ (D30C6) (8943, CST), P-IKKβ Y188 (bs-3233, Bioss).</p>", "<title>Senescence-associated β-galactosidase (SA-β-Gal) staining</title>", "<p id=\"Par33\">To evaluate SA-β-Gal activity, cells were stained with the method described previously [##REF##23085987##56##]. Briefly, cells were washed with DPBS and fixed with 3.7% formaldehyde for 5 min at room temperature. Fixed cells were incubated for 16 hrs with 1 mL staining solution (150 mM NaCl, 2 mM MgCl<sub>2</sub>, 40 mM citric acid/sodium phosphate pH 6.0, 5 mM potassium ferrocyanide, 5 mM potassium ferricyanide 1 mg/ml X-galactosidase). Images were acquired by Olympus CKX41 using TOMORO AcquPRO 2005.</p>", "<title>Phosphoprotein antibody array</title>", "<p id=\"Par34\">The Phospho Explorer Antibody Assay (Cat# PEX100, Fullmoon Biosystems, Sunnyvale, CA, USA) was used to measure phosphorylation status in EV- or TRIM22-transfected cells. This array was consisted of 1318 antibodies associated with various signaling pathways. Transfected cells were lysed with non-denaturing lysis buffer. Extracted proteins were biotinylated and then incubated on antibody array slides. After incubation, signals of proteins were detected by dye-labeled streptavidin. Raw signal intensity was normalized to all signals on the array slides. Fold changes between samples were calculated and proteins with fold changes above 1.2-fold and below 0.8-fold were included in the final dataset. Array experiments and analysis were performed as services by Ebiogen lnc.</p>", "<title>Cell viability and Edu assay</title>", "<p id=\"Par35\">Cultured cells were treated with Trypsin-EDTA and collected in order to assess cell viability. Suspended cells were diluted 1:1 with 0.4% (w/v) trypan blue solution (Gibco) and counted using a hemocytometer. To determine cell proliferation, cells were labeled with EdU Staining Proliferation Kit (iFluor488) (Abcam, ab219801) according to the manufacturer’s instruction. Briefly, cells were seeded on glass coverslips and transfected with EV or TRIM22. After 4 days, cells were incubated with the cell culture medium containing 20 μM EdU for 2 hrs at 37 °C incubator. After fixation and permeabilization, cells were stained with iFluor488 azide and Hoechst. Cells were visualized by using a fluorescent microscope Olympus IX83. Edu positivity was analyzed by the ImageJ software (version 1.52).</p>", "<title>In vitro kinase assay</title>", "<p id=\"Par36\">Recombinant proteins of PHLPP2-Myc/Flag and GST-IKKβ (Active) were supplied by Origene and SignalChem, respectively. For phosphorylation reaction, 0.5 μg PHLPP2-Myc/Flag was incubated with 0.2 μg GST-IKKβ (Active) in 20 μl kinase reaction buffer (25 mM Tris–HCl (pH 7.5), 5 mM β-glycerophosphate, 2 mM DTT, 0.1 mM Na<sub>3</sub>VO<sub>4</sub>, 10 mM MgCl<sub>2</sub>, 200 μM ATP) at 30 °C for 30 min. Kinase reaction was stopped by adding 2× Laemmli sample buffer and incubation for 10 min at 95 °C. Phosphorylation of PHLPP2 was analyzed by Western blotting.</p>", "<title>Human HCC tissue samples</title>", "<p id=\"Par37\">The biospecimens and data of patients with HCC used in this study were provided by the Biobank of InJe University Paik Hospital (InjeBiobank) and Keimyung University Dongsan Hospital Biobank, member of the Korea Biobank Network. This study was performed with the approval of institutional review board (IRB) of Inha University (IRB no. 210408-1AR).</p>", "<title>Immunohistochemistry</title>", "<p id=\"Par38\">Paraffin-embedded tissues were deparaffinized in xylene and rehydrated with a grade series of ethanol solution. Tissue immunostaining was performed using Rabbit specific HRP/DAB detection IHC Kit (abcam, ab64261) according to the manufacturer’s instruction. Antigen retrieval was performed with citrate buffer (pH 6.0) in a microwave. Tissue slides were blocked with hydrogen peroxide block and incubated with primary antibodies against TRIM22 (1:500 dilution, abcam, ab224059) and PHLPP2 (1:100 dilution, Novus, NBP2-13757) overnight at 4 °C. The slides were then incubated with biotinylated goat anti rabbit IgG(H + L) and streptavidin peroxidase for 10 min at room temperature, respectively. The slides were stained with diaminobenzidine (DAB) and then counterstained with hematoxylin. We used FIJI to quantify and analyze IHC scores of TRIM22 and PHLPP2.</p>", "<title>ICGC, TCGA, and CCLE database analysis</title>", "<p id=\"Par39\">RNA-Seq data and clinical information of HCC patients were collected from JP Project from International Cancer Genome Consortium (ICGC-LIRI-JP) and The Cancer Genome Atlas (TCGA) database, and these datasets were extracted from Database of Hepatocellular Carcinoma Expression Atlas (HCCDB) (<ext-link ext-link-type=\"uri\" xlink:href=\"http://lifeome.net/database/hccdb\">http://lifeome.net/database/hccdb</ext-link>) and Broad Institute GDAD Firehorse (<ext-link ext-link-type=\"uri\" xlink:href=\"https://gdac.broadinstitute.org/\">https://gdac.broadinstitute.org/</ext-link>). TCGA survival data was obtained from OncoLnc (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.oncolnc.org\">http://www.oncolnc.org</ext-link>). For the gene expression analysis in cancer cell lines, we downloaded the expression data from Cancer Cell Line Encyclopedia (CCLE) (<ext-link ext-link-type=\"uri\" xlink:href=\"https://portals.broadinstitute.org/ccle\">https://portals.broadinstitute.org/ccle</ext-link>).</p>", "<title>Quantification and statistical analysis</title>", "<p id=\"Par40\">All statistical analyses were performed using GraphPad Prism 9 software (version 9.2.0). Student’s <italic>t</italic>-test and one-way ANOVA test were employed, followed by Tukey’s post hoc analysis to determine the significance levels. Pearson’s correlation coefficient was used to assess correlations between gene expressions. Results are presented as the means ± SD from three independent experiments. <italic>P</italic> > 0.05 (#: non-significant); <italic>P</italic> < 0.05 was considered statistically significant; <italic>P</italic> < 0.05, <italic>P</italic> < 0.01, and **<italic>P</italic> < 0.001.</p>", "<title>Reporting summary</title>", "<p id=\"Par41\">Further information on research design is available in the ##SUPPL##6##Nature Research Reporting Summary## linked to this article.</p>" ]	[ "<title>Results</title>", "<title>TRIM22 induces cellular senescence in HCC</title>", "<p id=\"Par6\">To identify E3 ligases that contribute to the therapy-induced senescence of HCC cells, we analyzed an expression profiling array (GSE30240) for IR (ionizing radiation)-induced senescent HepG2 cells. We found that 26 E3 ligases were upregulated (fold change > 2) after IR treatment. When we validated these findings by RT-qPCR, we found that the mRNAs encoding MIB2, PML, TRIM22, TRIM38, HERC6, and TRIM21 were increased by more than 1.5 times in IR-treated HepG2 cells (Fig. ##FIG##0##1A##). To investigate which E3 ligases play critical roles in the cellular senescence of HCC, we transfected small interfering RNA (siRNA, Si) to knockdown each of the identified E3 ligases in IR-treated HepG2 cells, and monitored typical senescent traits. Only TRIM22 depletion was found to reduce SA-β-Gal positivity (Fig. ##FIG##0##1B##) and partially rescue the cell number (Fig. ##FIG##0##1C##) in IR-treated HepG2 cell cultures. We analyzed TRIM22 expression in HCC cell lines with wild-type p53 (Wt p53) (SK-Hep-1, HepG2) or mutant p53 (Mut p53) (SNU449, Huh7, PLC/PRF/5). TRIM22 was found to be markedly upregulated in HCC cells with Wt p53, but not Mut p53, after IR treatment (Fig. ##SUPPL##1##S1A##). To verify whether IR induces cell death in HCC cell lines with Wt p53 (SK-Hep-1 and HepG2), we conducted Western blot and caspase-3 activity analyses. The results revealed that the protein levels of p53 and p21 were increased in both SK-Hep-1 and HepG2 after IR treatment (Fig. ##SUPPL##1##S1B##). However, neither cleaved PARP (C-PARP) nor caspase-3 activity was increased in IR-treated SK-Hep-1 and HepG2 cells, indicating that IR did not induce cell death in SK-Hep-1 and HepG2 cells (Fig. ##SUPPL##1##S1B–D##). Next, we analyzed TCGA data and found that TRIM22 expression was higher in Wt p53 HCC tissues compared to Mut p53 HCC tissues (Fig. ##SUPPL##1##S1E##). Furthermore, knockdown of p53 in HepG2 cells resulted in the downregulation of TRIM22 at mRNA and protein levels (Fig. ##SUPPL##1##S1F##). A chromatin immunoprecipitation (ChIP) assay performed with a p53 antibody in IR-treated HepG2 cells revealed that p53 directly bound to the p53-response element in the intron 1 of TRIM22, and its binding affinity was enhanced upon IR treatment (Fig. ##SUPPL##1##S1G##). These findings indicated that TRIM22 is positively regulated by Wt p53, which directly binds to the p53-response element in the intron 1 of TRIM22.</p>", "<p id=\"Par7\">Next, we analyzed the correlation between TRIM22 and senescence-associated genes in the TCGA-LIHC database and found that p53, p21, p27, ISG15, and STAT1 were positively correlated with upregulated TRIM22 expression in HCC patient samples (Fig. ##FIG##0##1D##). These data suggested that TRIM22 might function as an upstream regulator of cellular senescence. To explore the possible biological function of TRIM22 in cellular senescence, we overexpressed TRIM22 in two HCC cell lines: HepG2 and SK-Hep-1 cells. Western blotting analysis confirmed that p53 and p21 are increased in TRIM22-overexpressed HCC cells (Fig. ##FIG##0##1E##). We observed that TRIM22 overexpression reduced cell proliferation, as indicated by Edu incorporation assay and cell counting (Fig. ##FIG##0##1F, G##). However, PARP cleavage and caspase-3 activity did not increase in both HepG2 and SK-Hep-1 cells following TRIM22 overexpression (Fig. ##SUPPL##1##S2A–D##). TRIM22 overexpression decreased cell proliferation without causing cell death (Fig. ##FIG##0##1F, G##, Fig. ##SUPPL##1##S2A–D##). Additionally, senescence-associated β-galactosidase (SA-β-Gal) positivity was increased in TRIM22-overexpressed HCC cells (Fig. ##FIG##0##1H##). Taken together, these results suggest that TRIM22 induces cellular senescence by activating the p53-p21 signaling pathway in HCC.</p>", "<title>TRIM22 modulates AKT phosphorylation through degradation of PHLPP2</title>", "<p id=\"Par8\">To explore the upstream signaling pathway of p53-p21 in TRIM22-mediated HCC senescence, we applied phosphoprotein array analysis. Our results showed that the phosphorylation levels of 278 proteins were changed by TRIM22 overexpression; proteins showing levels with changes of more than 1.2-fold (160 proteins) and less than 0.8-fold (118 proteins) were included for further analysis (Fig. ##FIG##1##2A##). We found that these phosphorylated proteins were involved in the PI3K-AKT signaling and cellular senescence pathways (Fig. ##FIG##1##2B##). Increased phosphorylation levels of AKT (T308 and S473) and mTOR (S2448 and S2481) were confirmed by Western blot analysis of TRIM22-overexpressed HepG2 cells (Fig. ##FIG##1##2C##).</p>", "<p id=\"Par9\">We next investigated whether AKT and p53 are critical as downstream molecules for TRIM22-mediated cellular senescence. Our results revealed that depletion of AKT or p53 decreased p21 accumulation and SA-β-Gal positivity and rescued cell proliferation in TRIM22-overexpressed HepG2 cells (Fig. ##FIG##1##2D–F##). However, overexpression of TRIM22 in Mut p53 SNU449 cells failed to induce cellular senescence and did not cause cell death (Fig. ##FIG##1##2G–I##, Fig. ##SUPPL##1##S2E, F##). These results indicate that AKT and p53 are critical downstream players in TRIM22-mediated cellular senescence.</p>", "<p id=\"Par10\">We hypothesized that the ability of the E3 ligase, TRIM22, to increase AKT phosphorylation could reflect a decrease in the levels and/or activities of AKT phosphatases, such as PHLPPs (PHLPP1 and PHLPP2), PTEN, PP1, and PP2A (Fig. ##FIG##1##2J##). We examined level changes of these phosphatases and found that TRIM22-overexpressed cells exhibited a specific decrease of PHLPP2 at the protein level, with no change in the mRNA level (Fig. ##FIG##1##2K, L##). These results suggest that TRIM22 increases AKT phosphorylation by reducing the protein level of the AKT phosphatase, PHLPP2.</p>", "<p id=\"Par11\">Two members of the PHLPP family, PHLPP1 and PHLPP2, function to dephosphorylate AKT [##REF##17386267##32##]. To further investigate the involvement of PHLPP1 and/or PHLPP2 in AKT activation for HCC senescence, we depleted PHLPP1 and PHLPP2 by using each specific siRNA (Si). These knockdown studies revealed that PHLPP2 specifically activated AKT-p53-p21 signaling, whereas PHLPP1 did not affect this signaling (Fig. ##SUPPL##1##S3A##). Cells with depletion of PHLPP2 showed decreased cell proliferation and increased SA-β-Gal positivity, whereas such senescence traits were not in observed in PHLPP1-depleted cells (Fig. ##SUPPL##1##S3B, C##). These results indicate that PHLPP2, but not PHLPP1, contributes to TRIM22-mediated cellular senescence as a downstream regulator of TRIM22 and an upstream regulator of AKT. A previous study [##REF##35414774##33##] reported that PHLPP2 expression is suppressed by Mut p53 in colorectal cancer. To investigate the expression of PHLPP2 in HCC cell lines, we analyzed the Cancer Cell Line Encyclopedia (CCLE)-Liver database, as well as conducted RT-qPCR and Western blot analyses. In the CCLE-Liver database, mRNA levels of PHLPP2 exhibited an increase in HCC cell lines with Mut p53 (SNU449, Huh7, and PLC/PRF/5) compared to those with Wt p53 (SK-Hep-1 and HepG2) (Fig. ##SUPPL##1##S4A##). Subsequently, we performed RT-qPCR and Western blotting to assess PHLPP2 mRNA and protein levels in the HCC cell lines we used. RT-qPCR analysis indicated higher mRNA levels of PHLPP2 in the Mut p53 HCC cell lines (SNU449, Huh7, and PLC/PRF/5) compared to the Wt p53 HCC cell lines (SK-Hep-1 and HepG2) (Fig. ##SUPPL##1##S4B##). Moreover, analysis of The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) database showed that the PHLPP2 expression was not lower in Mut p53 HCC tissues compared to Wt p53 HCC tissues (Fig. ##SUPPL##1##S4C##). Western blot analysis revealed similar protein levels of PHLPP2 in the Mut p53 HCC cell lines compared to the Wt p53 HCC cell lines (Fig. ##SUPPL##1##S4D##). In conclusion, our analysis suggests that the basal expression of PHLPP2 is independent with p53 status in HCC cell lines.</p>", "<title>TRIM22 physically interacts with PHLPP2 and promotes its ubiquitin-mediated degradation</title>", "<p id=\"Par12\">To examine whether TRIM22 directly regulates the level of PHLPP2, we used cycloheximide (CHX) to block de novo protein synthesis in TRIM22-overexpressed or IR-treated HepG2 cells. We found that TRIM22 overexpression significantly decreased the PHLPP2 protein levels in TRIM22-overexpressed and IR-treated cells (Fig. ##FIG##2##3A##, Fig. ##SUPPL##1##S5A, B##). The PHLPP2 protein level in this system was rescued by the proteasome inhibitor, MG132, whereas the lysosome inhibitor, chloroquine (CQ), had no effect on this protein level (Fig. ##FIG##2##3B##, Fig. ##SUPPL##1##S5C##). To assess whether TRIM22 regulates the PHLPP2 protein level through a physical association, we performed reciprocal immunoprecipitations (IP) in HepG2 cells transfected with TRIM22-Myc. TRIM22 and PHLPP2 could be reciprocally precipitated using either anti-Myc or anti-PHLPP2 in TRIM22-Myc-transfected cells, indicating that there is a physical interaction between TRIM22 and PHLPP2 (Fig. ##FIG##2##3C##). The cytoplasmic interaction between TRIM22 and PHLPP2 was further confirmed by the results of a proximity ligation assay (PLA) (Fig. ##FIG##2##3D##). Moreover, the interaction between TRIM22 and PHLPP2 was increased in IR-induced senescent HepG2 cells (Fig. ##SUPPL##1##S5D, E##). Together, these findings demonstrate that TRIM22 directly interacts with PHLPP2 and regulates its protein level to induce cellular senescence.</p>", "<p id=\"Par13\">To elucidate the TRIM22 domain responsible for the interaction with PHLPP2, we generated Myc-tagged TRIM22 truncation mutants with deletions in the RING domain (ΔRING), BBox domain (ΔBBox), CC domain (ΔCC) or SPRY domain (ΔSPRY) (Fig. ##FIG##2##3E##, Upper). Co-IP and Western blot analyses demonstrated that the SPRY domain of TRIM22 was essential for interaction with PHLPP2, while the other domains (RING, BBox, and CC) did not influence the interaction with PHLPP2 (Fig. ##FIG##2##3E##, Bottom). Substrate phosphorylation can influence the substrate-E3 ligase interaction, leading to ubiquitination and degradation of the substrate [##REF##18082598##34##]. Review of the PhosphoSite database (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.phosphosite.org\">www.phosphosite.org</ext-link>) revealed that PHLPP2 contains 10 phosphorylation sites at its C-terminal domain. To test whether phosphorylation of PHLPP2 might be responsible for its association with TRIM22, we generated PHLPP2 truncation mutants lacking the C-terminal domain (ΔCTD) (Fig. ##FIG##2##3F##, Upper). We found that PHLPP2 ΔCTD could not bind to TRIM22 (Fig. ##FIG##2##3F##, Bottom). This indicates that the SPRY domain of TRIM22 and the C-terminus of PHLPP2 are essential for the interaction between these proteins. Furthermore, TRIM22 Wt, ΔBBox, or ΔCC induced K48-linkage polyubiquitination of PHLPP2, whereas TRIM22 ΔRING or ΔSPRY had no effect on PHLPP2 ubiquitination (Fig. ##FIG##2##3G##). In IR-induced senescent HepG2 cells, the upregulation of TRIM22 expression increased the K48-linked polyubiquitination levels of PHLPP2, whereas TRIM22 depletion decreased this parameter (Fig. ##SUPPL##1##S5F##). TRIM22-induced the K48-linked polyubiquitination of PHLPP2 Wt, but not its C-terminal deleted mutant (PHLPP2 ΔCTD), which was validated using a K48-linkage specific polyubiquitin antibody (Fig. ##FIG##2##3H##). In TRIM22 Wt-overexpressed cells, the PHLPP2 protein level was decreased and AKT-p53-p21 signaling was activated, finally leading to cellular senescence (Fig. ##SUPPL##1##S6A–C##). However, mutant TRIM22 lacking the RING-finger or SPRY domain failed to regulate the PHLPP2 protein level, activate downstream AKT-p53-p21 signaling, and induce cellular senescence (Fig. ##SUPPL##1##S6A–C##).</p>", "<p id=\"Par14\">These results demonstrate that TRIM22 physically interacts with PHLPP2 and promotes its ubiquitin-mediated proteasomal degradation, ultimately leading to cellular senescence due to AKT activation in HCC cells.</p>", "<title>Phosphorylation of PHLPP2 mediated by IKKβ promotes the PHLPP2-TRIM222 interaction</title>", "<p id=\"Par15\">Next, we found that the phosphorylation level of PHLPP2 was increased upon TRIM22 overexpression (Fig. ##FIG##3##4A##). When we treated TRIM22-overexpressed cells with Lambda protein phosphatase (λ-PPase), dephosphorylated PHLPP2 failed to interact with TRIM22 (Fig. ##FIG##3##4B##), indicating that the binding of PHLPP2 to TRIM22 was occurred though PHLPP2 phosphorylation per se. Thus, we investigated which kinase is responsible for PHLPP2 phosphorylation in TRIM22-overexpressed HCC cells. Our phosphoprotein array analysis revealed that TRIM22 overexpression led to the phosphorylation of 21 kinases (Fig. ##FIG##3##4C##). Using the STRING software with a confidence cutoff of 0.5, we predicted that AKT1, P70S6K, mTOR, and IKKα/β could potentially interact with PHLPP2 (Fig. ##FIG##3##4D##). Phosphorylation status of those kinases were confirmed by Western blot analysis of TRIM22-overexpressed cells (Fig. ##FIG##3##4E##). To further clarify the result, we knock downed AKT1, mTOR, P70S6K, IKKα, and IKKβ, and found that depletion of IKKα or IKKβ restored the PHLPP2 protein level in TRIM22-overexpressed cells (Fig. ##FIG##3##4F##). Moreover, IKKα and IKKβ are interacted with PHLPP2 in TRIM22-overexpressed cells (Fig. ##FIG##3##4G##).</p>", "<p id=\"Par16\">To explore the roles of IKKα and/or IKKβ in PHLPP2 phosphorylation, we performed PHLPP2 IP in IKKα- or IKKβ-depleted cells. We found that the interaction between PHLPP2 and TRIM22 was reduced in IKKβ-depleted cells in a PHLPP2 phosphorylation status-dependent fashion, but that this was not seen in IKKα-depleted cells (Fig. ##FIG##4##5A##). Similar results were obtained from TRIM22 IP assays in IKKα- or IKKβ-depleted cells (Fig. ##FIG##4##5B##). An in vitro kinase assay revealed that PHLPP2 was directly phosphorylated by IKKβ (Fig. ##FIG##4##5C##). Consistently, the degradation of PHLPP2 by TRIM22 was decreased in IKKβ-depleted cells (Fig. ##FIG##4##5D##) and TRIM22-induced K48-linked polyubiquitination of PHLPP2 was reduced by IKKβ knockdown (Fig. ##FIG##4##5E##). As the phosphorylation of IKKβ was increased in TRIM22-overexpressed HCC cells, we investigated the potential role of TRIM22 in regulating IKKβ phosphorylation. We observed that upregulation of TRIM22 increased the phosphorylation of IKKβ at Y188 in HCC cells with Wt p53, but not Mut p53 (Fig. ##SUPPL##1##S7A##). Additionally, knockdown of TRIM22 in IR-treated HepG2 cells did not alter the phosphorylation level of IKKβ (Fig. ##SUPPL##1##S7B##). Furthermore, TRIM22 overexpression in HepG2 cells dose-dependently induced the phosphorylation of IKKβ at Y188, in correlation with PHLPP2 degradation and AKT-p53 signaling activation (Fig. ##SUPPL##1##S7C##).</p>", "<p id=\"Par17\">In summary, our results indicate that TRIM22 induces the IKKβ-mediated phosphorylation of PHLPP2 and the subsequent degradation of PHLPP2 via ubiquitin-mediated proteasomal degradation, and that this involves a direct interaction between TRIM22 and PHLPP2.</p>", "<title>TRIM22 expression is inversely correlated with PHLPP2 expression in HCC databases and patient specimens</title>", "<p id=\"Par18\">To investigate the clinical significance of TRIM22 and PHLPP2 in HCC, we analyzed their expression levels in patients with HCC, as deposited to the International Cancer Genome Consortium Liver Cancer-RIKEN Japan (ICGC-LIRI-JP) (Fig. ##FIG##5##6A##) and TCGA-LIHC (Fig. ##FIG##5##6B##) databases. Analyses of these databases revealed that TRIM22 expression was downregulated in both total and paired HCC tumor tissues compared to normal tissues, while PHLPP2 expression was upregulated in HCC tumor tissues (Fig. ##FIG##5##6A,####FIG##5##B##). Furthermore, our TCGA-LIHC database analysis showed that patients with high TRIM22 expression and low PHLPP2 expression had better overall survival (OS) rates (Fig. ##FIG##5##6C##). To support these findings, we collected 30 pairs of HCC and normal tissues and evaluated the protein levels of TRIM22 and PHLPP2. The results showed that TRIM22 protein levels were lower and PHLPP2 protein levels were higher in HCC tissues compared to normal tissues. Moreover, we observed that levels of phospho-IKKβ (Y188) in HCC tissues were lower than those in normal tissues (Fig. ##FIG##5##6D##). Furthermore, the IHC scores were negatively correlated between TRIM22 and PHLPP2 in both normal and tumor tissues (Fig. ##FIG##5##6E##). Taken together, our findings suggest that the expression levels of TRIM22 and PHLPP2 are inversely associated in HCC patient tissues.</p>" ]	[ "<title>Discussion</title>", "<p id=\"Par19\">The present study demonstrates that TRIM22 promotes HCC senescence by activating the AKT-p53-p21 signaling pathway. AKT, a serine and threonine kinase, is activated by phosphorylation at T308 or S473, and regulates cell survival, proliferation, growth, and glycogen metabolism [##REF##30808672##35##, ##REF##31686003##36##]. Our group and others reported that, although AKT is associated with tumor initiation and progression, its activation can induce cellular senescence in both non-transformed and cancer cells [##REF##16079851##37##–##REF##35758651##41##]. This AKT-induced cellular senescence (AIS) can be triggered by various conditions, such as overexpression of myristoylated-AKT (Myr-AKT) and activation of receptors upstream of AKT [##REF##30337688##39##]. AIS can serve as a fail-safe mechanism against tumorigenesis. Our group and others also demonstrated that the loss of phosphatase and tensin homolog (PTEN), a major negative regulator of PI3K/AKT signaling, can induce cellular senescence in a p53-dependent manner that is called PTEN-induced cellular senescence (PICS) [##REF##16079851##37##–##REF##30337688##39##, ##REF##21909130##42##]. These findings emphasize that AKT plays dual roles in critically regulating cell fate decisions between proliferation and senescence, depending on cellular context.</p>", "<p id=\"Par20\">The induction of cellular senescence is an important anticancer strategy, as it can suppress tumor growth and enhance the vulnerability to combination treatments. Senescence can be triggered by various cancer therapies, particularly IR treatment. To identify E3 ligases that are involved in regulating therapy-induced senescence, we herein conducted expression profiling analysis of IR-treated senescent HepG2 cells. We found that TRIM22 is upregulated in response to IR-exposure in HCC cells. TRIM22 is an E3 ligase that can exhibit both tumor-promoting and tumor-suppressive roles in different cancers. Several TRIM22 isoforms play crucial roles in cancer biology. For instance, TRIM47 has been implicated in promoting tumor progression in colon and pancreatic cancer by degrading SMAD4 and FBP1 [##REF##30979374##43##, ##REF##33529753##44##]. In HCC, TRIM25 enhances tumor cell survival by targeting Keap1 for degradation [##REF##31953436##20##]. Conversely, TRIM7 and TRIM50 have been reported to suppress HCC progression by directly targeting Src and SNAIL for degradation, respectively [##REF##29789583##45##, ##REF##31802035##46##]. In colorectal cancer, TRIM67 functions as tumor suppressor by inducing p53-induced apoptosis and inhibiting cell growth [##REF##31239268##47##]. TRIM22 is upregulated in glioblastoma (GBM) and promotes tumor growth and progression by modulating the stability of IKKγ and IkBα [##REF##32814880##29##]. Conversely, TRIM22 is downregulated in osteosarcoma (OS) and gastric cancer [##REF##34489426##30##, ##REF##35636015##31##]. Overexpression of TRIM22 suppresses the proliferation and metastasis of OS cells by targeting NRF2 for degradation and activating the ROS/AMPK/mTOR/autophagy signaling pathway [##REF##35636015##31##]. In gastric cancer cells, TRIM22 inhibits cancer cell proliferation and migration by reducing the phosphorylation of SMAD2 [##REF##34489426##30##]. The present study demonstrates that TRIM22 critically contributes to the therapy-induced senescence of HCC cells. Our data indicated that TRIM22 functions as a tumor suppressor by directly regulating the level of PHLPP2. Previous studies have reported that TRIM22 induces apoptosis in osteosarcoma, monocyte, and neuron cells [##REF##35636015##31##, ##REF##28079123##48##, ##REF##32335773##49##]. However, in this study, we revealed that TRIM22 is a critical factor in cellular senescence in HCC cells. TRIM22 overexpression was shown to suppress cell proliferation by inducing cellular senescence without causing cell death in HCC. Our study showed consistent results from IR-induced and TRIM22-overexpressed senescent HCC cells, and from HCC patient tissues.</p>", "<p id=\"Par21\">p53 is a transcription factor that plays crucial roles in suppressing tumor growth by promoting cellular senescence, apoptosis, DNA repair, and other important processes. Mutations in p53 that result in the loss of its transcriptional activity can lead to cells taking on oncogenic functions, chemo-resistance, and other aspects of tumorigenesis [##REF##33479224##2##, ##REF##30712844##50##, ##REF##36400749##51##]. Approximately 70% of HCC patients having Wt p53 indicates that the frequency of p53 mutations is relatively low in HCC compared to other types of human cancer [##REF##30712844##50##, ##REF##36400749##51##]. The present study demonstrated that TRIM22, which is induced by Wt p53 under the IR-exposed condition, triggers HCC cell senescence by activating the AKT-p53-p21 signaling pathway. These findings indicate that Wt p53 is essential both upstream and downstream of TRIM22 for the induction of HCC cell senescence (Fig. ##FIG##6##7##). Mechanistically, TRIM22 degrades the AKT phosphatase, PHLPP2, to increase AKT phosphorylation in HCC cells. PHLPP2 belongs to the Pleckstrin Homology Domain Leucine-Rich Repeat Protein Phosphatase (PHLPP) family, the members of which negatively regulate PI3K/AKT signaling by dephosphorylating AKT at T308 and S473 [##REF##17386267##32##]. Moreover, Tantai et al. previously reported that TRIM46 activates AKT signaling by promoting the ubiquitination of PHLPP2 in lung adenocarcinoma (LUAD) [##REF##35354796##52##]. In this study, we found that TRIM22 overexpression phosphorylates PHLPP2, and this phosphorylation is crucial for the interaction of PHLPP2 with TRIM22. Unlike PHLPP2, we evidenced that the other PHLPP family isoform, PHLPP1, failed to mediate senescence in this study. It is reported that TRIM22 overexpression increases the phosphorylation of IKKβ at S181 and Y188, resulting in IKKβ activation [##REF##20534585##53##]. Activated IKKβ induces the phosphorylation and subsequent degradation of substrates by recruiting E3 ligases [##REF##36435834##54##, ##REF##36923932##55##]. Consistent with these previous reports, we observed that PHLPP2 was phosphorylated by IKKβ activation in TRIM22-overexpressed cells, which is crucial for the recruitment of TRIM22. Furthermore, TRIM22 and IKKβ were negatively correlated with PHLPP2 in HCC patient samples.</p>", "<p id=\"Par22\">Conclusively, we reveal a novel mechanism in which TRIM22 regulates PHLPP2 to promote HCC senescence. Targeting TRIM22 may present a promising therapeutic approach for the treatment of cancers, offering a new avenue for intervention in cancer therapy.</p>" ]	[]	[ "<p id=\"Par1\">The ubiquitin-proteasome system is a vital protein degradation system that is involved in various cellular processes, such as cell cycle progression, apoptosis, and differentiation. Dysregulation of this system has been implicated in numerous diseases, including cancer, vascular disease, and neurodegenerative disorders. Induction of cellular senescence in hepatocellular carcinoma (HCC) is a potential anticancer strategy, but the precise role of the ubiquitin-proteasome system in cellular senescence remains unclear. In this study, we show that the E3 ubiquitin ligase, TRIM22, plays a critical role in the cellular senescence of HCC cells. TRIM22 expression is transcriptionally upregulated by p53 in HCC cells experiencing ionizing radiation (IR)-induced senescence. Overexpression of TRIM22 triggers cellular senescence by targeting the AKT phosphatase, PHLPP2. Mechanistically, the SPRY domain of TRIM22 directly associates with the C-terminal domain of PHLPP2, which contains phosphorylation sites that are subject to IKKβ-mediated phosphorylation. The TRIM22-mediated PHLPP2 degradation leads to activation of AKT-p53-p21 signaling, ultimately resulting in cellular senescence. In both human HCC databases and patient specimens, the levels of TRIM22 and PHLPP2 show inverse correlations at the mRNA and protein levels. Collectively, our findings reveal that TRIM22 regulates cancer cell senescence by modulating the proteasomal degradation of PHLPP2 in HCC cells, suggesting that TRIM22 could potentially serve as a therapeutic target for treating cancer.</p>", "<title>Subject terms</title>" ]	[ "<title>Supplementary information</title>", "<p>\n\n\n\n\n\n\n\n</p>" ]	[ "<title>Supplementary information</title>", "<p>The online version contains supplementary material available at 10.1038/s41419-024-06427-w.</p>", "<title>Acknowledgements</title>", "<p>The biospecimens and data of patients with HCC used in this study were provided by the Biobank of InJe University Paik Hospital (InjeBiobank) and Keimyung University Dongsan Hospital Biobank, member of the Korea Biobank Network. This research was supported by National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2021R1A5A2031612, 2023R1A2C2003673)</p>", "<title>Author contributions</title>", "<p>DK: Conceptualization, resources, investigation, methodology, formal analysis, validation, visualization, data curation, writing-original draft, review and editing. HJH: Conceptualization, resources and validation. JYS: Investigation, methodology and formal analysis. KJK: Conceptualization, resources and data curation. HJP: Conceptualization and resources. YGK: Conceptualization and resources. YNK: Conceptualization, resources, investigation, methodology and formal analysis. JSL: Conceptualization, resources, investigation, methodology, formal analysis, data curation, supervision, funding acquisition, project administration, writing-original draft and review and editing.</p>", "<title>Data availability</title>", "<p>All experimental datasets generated and analyzed during the current study are included in this published article and its supplementary information files. Additional data and further information are available from the corresponding author upon reasonable request.</p>", "<title>Competing interests</title>", "<p id=\"Par42\">The authors declare no competing interests.</p>", "<title>Ethics approval</title>", "<p id=\"Par43\">The biospecimens and data of patients with HCC used in this study were provided by the Biobank of InJe University Paik Hospital (InjeBiobank) and Keimyung University Dongsan Hospital Biobank, member of the Korea Biobank Network. This study was performed with the approval of institutional review board (IRB) of Inha University (IRB no. 210408-1AR).</p>" ]	[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>Upregulation of TRIM22 induces cellular senescence in HCC.</title><p><bold>A</bold> RT-qPCR of an expression profiling array (GSE30240) for IR-treated HepG2 cells. Data are presented as mean ± SD (unpaired two-tailed <italic>t</italic>-test, MIB2: <italic>P</italic> = 0.0044, <italic>t</italic> = 5.805; PML: <italic>P</italic> = 0.0042, <italic>t</italic> = 5.874; TRIM22: *<italic>P</italic> = 0.0008, <italic>t</italic> = 8.990; TRIM38: <italic>P</italic> = 0.01, <italic>t</italic> = 4.606; TRIM5: *<italic>P</italic> = 0.0001, <italic>t</italic> = 14.34; HERC6: <italic>P</italic> = 0.0013, <italic>t</italic> = 8.116; TRIM21: <italic>P</italic> = 0.0016, <italic>t</italic> = 7.667; <italic>n</italic> = 3). <bold>B, C</bold> HepG2 cells were transfected with specific siRNA against each E3 ligase candidate in HepG2 cells. SA-β-Gal assay (<bold>B</bold>) (one-way ANOVA with Tukey’s multiple comparison test, F(7,16) = 21.96, <italic>P</italic> < 0.0001; <italic>P</italic> = 0.0003, <italic>n</italic> = 3) and cell counting (<bold>C</bold>) were performed. Data are presented as mean ± SD (one-way ANOVA with Tukey’s multiple comparison test, F(7,16) = 92.09, <italic>P</italic> < 0.0001; <italic>P</italic> = 0.0002, <italic>n</italic> = 3). Positive control (PC) for dead cells, Doxorubicin 2 μg/mL. <bold>D</bold> Gene expression analysis of TRIM22 and senescence-associated genes in the TCGA-LIHC database (Pearson correlation, <italic>n</italic> = 369). <bold>E</bold>–<bold>H</bold> Western blotting (<bold>E</bold>), Edu incorporation assay. Scale bars, 100 μm (<bold>F</bold>) Data are presented as mean ± SD (unpaired two-tailed <italic>t</italic>-test, HepG2: <italic>P</italic> = 0.0059, <italic>t</italic> = 5.352, <italic>n</italic> = 3; SK-Hep-1: <italic>P</italic> = 0.0046, <italic>t</italic> = 5.724, <italic>n</italic> = 3), cell counting (<bold>G</bold>) Data are presented as mean ± SD (unpaired two-tailed <italic>t</italic>-test, HepG2: <italic>P</italic> = 0.0008, <italic>t</italic> = 9.097, <italic>n</italic> = 3; SK-Hep-1: <italic>P</italic> = 0.0020, <italic>t</italic> = 7.168, <italic>n</italic> = 3), and SA-β-Gal assay (<bold>H</bold>) Data are presented as mean ± SD (unpaired two-tailed <italic>t</italic>-test, HepG2: <italic>P</italic> < 0.0001, <italic>t</italic> = 23.00, <italic>n</italic> = 3; SK-Hep-1: <italic>P</italic> = 0.0012, <italic>t</italic> = 8.265, <italic>n</italic> = 3) were performed in TRIM22-overexpressing HepG2 and SK-Hep-1 HCC cells.</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>TRIM22 activates AKT-p53-p21 signaling by reducing the PHLPP2 protein level.</title><p><bold>A</bold> Phosphoprotein array analysis showing the fold change of phosphoproteins upon TRIM22 overexpression in HepG2 cells. The level of each phosphoprotein was normalized to the total protein level; those above 1.2-fold (160 proteins) and below 0.8-fold (118 proteins) were labeled in red and blue, respectively. <bold>B</bold> Heatmaps representing the phosphorylation sites of proteins enriched in PI3K/AKT signaling or cellular senescence. <bold>C</bold> Western blot analysis of HepG2 cells transfected with empty vector (EV) or TRIM22-expressing vector, as generated using the indicated antibodies. <bold>D</bold>–<bold>F</bold> HepG2 cells were transfected with Con Si, AKT Si, or p53 Si, and then transfected with EV or TRIM22-expressing vector. Western blotting (<bold>D</bold>), relative cell number (<bold>E</bold>) Data are presented as mean ± SD (one-way ANOVA with Tukey’s multiple comparison test, F(5,12) = 16.37, **<italic>P</italic> < 0.0001; <italic>P</italic> = 0.0314; <italic>P</italic> = 0.0015, <italic>n</italic> = 3), and SA-β-Gal positivity (<bold>F</bold>) Data are presented as mean ± SD (one-way ANOVA with Tukey’s multiple comparison test, F(5,12) = 130.2, <italic>P</italic> < 0.0001; <italic>P</italic> < 0.0001; <italic>P</italic> < 0.0001, <italic>n</italic> = 3) were analyzed. Positive control (PC) for dead cells, Doxorubicin 2 μg/mL. <bold>G</bold>–<bold>I</bold> SNU449 HCC cells (Mut p53) were transfected with EV or TRIM22-expressing vector. Cell counting (<bold>G</bold>) Data are presented as mean ± SD (unpaired two-tailed <italic>t-</italic>test, <italic>P</italic> = 0.0072, <italic>t</italic> = 5.066, <italic>n</italic> = 3), SA-β-Gal s<italic>t</italic>aining (<bold>H</bold>), and Western blotting (<bold>I</bold>) were performed. PC for dead cells, Doxorubicin 2 μg/mL. <bold>J</bold> Schematic representation of possible means by which TRIM22 induces cellular senescence through the AKT-p53-p21 pathway. <bold>K</bold> Western blot analysis of TRIM22-overexpressed HepG2 cells using the indicated antibodies (left). Phosphatase levels were quantified from protein bands and are presented as mean values of the ratio relative to the levels in EV-transfected HepG2 cells (right). Actin was used as an endogenous control for protein level normalization. Data are presented as mean ± SD (unpaired two-tailed <italic>t</italic>-test, PHLPP2: <italic>P</italic> = 0.0006, <italic>t</italic> = 9.806, <italic>n</italic> = 3; PHLPP1: <sup>#</sup><italic>P</italic> = 0.3344, <italic>t</italic> = 1.097, <italic>n</italic> = 3; PTEN: <sup>#</sup><italic>P</italic> = 0.1780, <italic>t</italic> = 1.632, <italic>n</italic> = 3; PP1: <sup>#</sup><italic>P</italic> = 0.8012, <italic>t</italic> = 0.2690, <italic>n</italic> = 3; PP2A: <sup>#</sup><italic>P</italic> = 0.5672, <italic>t</italic> = 0.6227, <italic>n</italic> = 3). <bold>L</bold> RT-qPCR of PHLPP2 mRNA in TRIM22-overexpressed HepG2 cells. Data are presented as mean ± SD (unpaired two-tailed <italic>t-</italic>test, <sup>#</sup><italic>P</italic> = 0.6045, <italic>t</italic> = 0.5613, <italic>n</italic> = 3).</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><title>TRIM22 directly binds to PHLPP2 and induces its ubiquitin-mediated degradation.</title><p><bold>A</bold> Analysis of PHLPP2 protein stability by Western blotting. TRIM22-overexpressed HepG2 cells were treated with cycloheximide (CHX) for the indicated times, harvested, and analyzed by Western blotting. Data are presented as mean ± SD (unpaired two-tailed <italic>t</italic>-test, <italic>P</italic> = 0.0047, <italic>t</italic> = 5.708; <italic>P</italic> = 0.0208, <italic>t</italic> = 3.700; <italic>P</italic> = 0.0021, <italic>t</italic> = 7.040, <italic>n</italic> = 3). <bold>B</bold> Western blotting analysis of TRIM22-overexpressed HepG2 cells treated with the proteasomal degradation inhibitor, MG132, or the lysosomal inhibitor, CQ. <bold>C</bold> Co-immunoprecipitation (Co-IP) assays for the interaction between TRIM22 and PHLPP2. HepG2 cells were transfected with EV or TRIM22-Myc and then treated with 20 μM MG132 for 4 hrs. The cells were subjected to IP using anti-Myc (left) or anti-PHLPP2 (right) antibodies. Immunoprecipitates were analyzed by Western blotting using the indicated antibodies. <bold>D</bold> Proximity ligation assay (PLA) for cytoplasmic interaction between TRIM22 and PHLPP2 using each antibody. TRIM22-Myc-overexpressing HepG2 cells were treated with 20 μM MG132 for 4 hrs and subjected to PLA. The red spots indicate TRIM22-PHLPP2 interactions. Nuclei were stained with DAPI. Scale bars, 20 μm. <bold>E</bold> Schematic showing the domain structures of TRIM22 Wt and Muts (ΔRING, ΔBBox, ΔCC, or ΔSPRY) (Upper). HepG2 cells were transfected with TRIM22 Wt or Muts (ΔRING, ΔBBox, ΔCC, or ΔSPRY). IP was performed and immunoprecipitates were subjected to Western blot analysis. <bold>F</bold> Schematic showing the domain structures of PHLPP2 Wt and PHLPP2 ΔC-terminal (ΔCTD) (Upper). Flag-tagged PHLPP2 Wt or PHLPP2 ΔCTD was co-transfected with TRIM22 Wt into HepG2 cells. PHLPP2 Wt or PHLPP2 ΔCTD was immunoprecipitated using Flag antibody, and the immunoprecipitates were analyzed using Western blotting (Bottom). <bold>G</bold> Ubiquitination assays of PHLPP2 in TRIM22 Wt or Muts (ΔRING, ΔBBox, ΔCC, or ΔSPRY)-overexpressed HepG2 cells. <bold>H</bold> Co-IP and Western blot analysis of PHLPP2 Wt or PHLPP2 ΔCTD K48 ubiquitination in TRIM22 Wt-overexpressed HepG2 cells.</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><title>PHLPP2 is regulated by IKKα and IKKβ in TRIM22-overexpressed cells.</title><p><bold>A</bold> IP using anti-PHLPP2 in TRIM22-overexpressed HepG2 cells. Immunoprecipitates were analyzed by Western blotting using the indicated antibodies. <bold>B</bold> IP was performed using anti-PHLPP2 antibody in TRIM22-overexpressed HepG2 cells. After IP, lysates were treated with λ-PPase and analyzed by Western blotting using the indicated antibodies. <bold>C</bold> Workflow of strategy used to identify kinase candidates that interact with and phosphorylate PHLPP2. <bold>D</bold> STRING analysis of PHLPP2-interacting kinases. <bold>E</bold> Western blot analysis of kinases in TRIM22-overexpressed HepG2 cells. <bold>F</bold> HepG2 cells were transfected with siRNA targeting each indicated kinase. After 48 hrs, the cells were harvested and analyzed by Western blotting using the indicated antibodies. <bold>G</bold> IP using anti-IKKα (left) or anti-IKKβ (right) in TRIM22-overexpressed HepG2 cells. Immunoprecipitates were analyzed by Western blotting using the indicated antibodies.</p></caption></fig>", "<fig id=\"Fig5\"><label>Fig. 5</label><caption><title>Phosphorylation of PHLPP2 by IKKβ facilitates the interaction between PHLPP2 and TRIM22 and promotes the degradation of PHLPP2 by TRIM22.</title><p><bold>A</bold>, <bold>B</bold> IP using anti-PHLPP2 (<bold>A</bold>) or anti-Myc (<bold>B</bold>) was performed in IKKα- or IKKβ-depleted HepG2 cells following TRIM22 overexpression. Immunoprecipitates were analyzed by Western blotting using the indicated antibodies. <bold>C</bold> Western blotting of an in vitro kinase assay performed between IKKβ and PHLPP2. <bold>D</bold> Analysis of PHLPP2 protein stability. Con Si- or IKKβ Si-transfected HepG2 cells were transfected with EV or TRIM22-expressing vector. The cells were treated with cycloheximide (CHX) for the indicated times, harvested, and analyzed by Western blotting. Data are presented as mean ± SD (one-way ANOVA with Tukey’s multiple comparison test, F(14,30) = 14.01, <italic>P</italic> = 0.0153; <sup>#</sup><italic>P</italic> > 0.9999; <italic>P</italic> = 0.0437, <italic>n</italic> = 3). <bold>E</bold> Ubiquitination assay of PHLPP2 in IKKβ-depleted HepG2 cells following TRIM22 overexpression.</p></caption></fig>", "<fig id=\"Fig6\"><label>Fig. 6</label><caption><title>TRIM22 and PHLPP2 levels are inversely correlated in HCC and paired normal patient tissues.</title><p><bold>A</bold>, <bold>B</bold> Comparison of TRIM22 and PHLPP2 mRNA levels in HCC and paired normal tissue samples from ICGC-LIRI database [Normal (N), <italic>n</italic> = 177; Tumor (T), <italic>n</italic> = 212. Paired samples, <italic>n</italic> = 177 (<bold>A</bold>)] (TRIM22 mRNA: unpaired two-tailed <italic>t</italic>-test, <italic>P</italic> < 0.0001, <italic>t</italic> = 8.059; paired two-tailed <italic>t</italic>-test, <italic>P</italic> < 0.0001, <italic>t</italic> = 9.223; PHLPP2 mRNA: unpaired two-tailed <italic>t</italic>-test, <italic>P</italic> < 0.0001, <italic>t</italic> = 4.756; paired two-tailed <italic>t</italic>-tes<italic>t</italic>, <italic>P</italic> < 0.0001, <italic>t</italic> = 5.125) and TCGA-LIHC database [N, <italic>n</italic> = 50; T, <italic>n</italic> = 369. Paired samples, <italic>n</italic> = 50 (<bold>B</bold>)] (TRIM22 mRNA: unpaired two-tailed <italic>t</italic>-test, <italic>P</italic> < 0.0001, <italic>t</italic> = 5.485; paired two-tailed <italic>t</italic>-test, <italic>P</italic> < 0.0001, <italic>t</italic> = 5.693; PHLPP2 mRNA: unpaired two-<italic>t</italic>ailed <italic>t</italic>-test, <italic>P</italic> = 0.0105, <italic>t</italic> = 2.572; paired two-tailed <italic>t</italic>-test, *<italic>P</italic> < 0.0001, <italic>t</italic> = 4.938). <bold>C</bold> Survival analysis between groups with different levels of TRIM22 and PHLPP2 in the TCGA-LIHC database. <bold>D</bold> Western blot analysis of TRIM22, P-IKKβ (Y188), and PHLPP2 in HCC and paired normal patient tissues. <italic>n</italic> = 30. Statistical analysis of the value intensity of TRIM22, PHLPP2, and P-IKKβ (Y188) normalized to those of Actin and Total IKKβ in patient tissues (TRIM22: paired two-tailed <italic>t</italic>-test, <italic>P</italic> = 0.0002, <italic>t</italic> = 4.206; P-IKKβ: paired two-tailed <italic>t-</italic>test, <italic>P</italic> = 0.0004, <italic>t</italic> = 4.003; PHL<italic>P</italic>P2: paired two-tailed <italic>t</italic>-test, *<italic>P</italic> = 0.0002, <italic>t</italic> = 4.327). The protein levels were quantified by densitome<italic>t</italic>ry using the ImageJ software and normalized to the protein level of Actin or Total IKKβ. <bold>E</bold> IHC analysis of TRIM22 and PHLPP2 in HCC and paired patient tissues (left). <italic>n</italic> = 16. Scale bars, 50 μm. Correlation of TRIM22 and PHLPP2 IHC scores (right). <italic>r</italic> is the Spearman’s rank correlation coefficient.</p></caption></fig>", "<fig id=\"Fig7\"><label>Fig. 7</label><caption><title>Schematic model proposed according to the findings of the present study.</title><p>A proposed model for the function of TRIM22 in the degradation of PHLPP2 and the induction of cellular senescence in HCC cells (created with BioRender.com).</p></caption></fig>" ]	[]	[]	[]	[]	[]	[]	[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM3\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM4\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM5\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM6\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM7\"></supplementary-material>" ]	[ "<fn-group><fn><p>Edited by Professor Boris Zhivotovsky</p></fn><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"41419_2024_6427_Fig1_HTML\" id=\"d32e606\"/>", "<graphic xlink:href=\"41419_2024_6427_Fig2_HTML\" id=\"d32e811\"/>", "<graphic xlink:href=\"41419_2024_6427_Fig3_HTML\" id=\"d32e941\"/>", "<graphic xlink:href=\"41419_2024_6427_Fig4_HTML\" id=\"d32e1036\"/>", "<graphic xlink:href=\"41419_2024_6427_Fig5_HTML\" id=\"d32e1106\"/>", "<graphic xlink:href=\"41419_2024_6427_Fig6_HTML\" id=\"d32e1308\"/>", "<graphic xlink:href=\"41419_2024_6427_Fig7_HTML\" id=\"d32e1428\"/>" ]	[ "<media xlink:href=\"41419_2024_6427_MOESM1_ESM.docx\"><caption><p>Supplementary Figure Legends</p></caption></media>", "<media xlink:href=\"41419_2024_6427_MOESM2_ESM.pptx\"><caption><p>Supplementary Figures</p></caption></media>", "<media xlink:href=\"41419_2024_6427_MOESM3_ESM.pptx\"><caption><p>Original Western Blots</p></caption></media>", "<media xlink:href=\"41419_2024_6427_MOESM4_ESM.xlsx\"><caption><p>Supplementary Table S1</p></caption></media>", "<media xlink:href=\"41419_2024_6427_MOESM5_ESM.xlsx\"><caption><p>Supplementary Table S2</p></caption></media>", "<media xlink:href=\"41419_2024_6427_MOESM6_ESM.xlsx\"><caption><p>Supplementary Table S3</p></caption></media>", "<media xlink:href=\"41419_2024_6427_MOESM7_ESM.pdf\"><caption><p>Reporting summary</p></caption></media>" ]	[]	{ "acronym": [], "definition": [] }	56	CC BY	no	2024-01-13 00:02:20	Cell Death Dis. 2024 Jan 10; 15(1):26	oa_package/22/81/PMC10781680.tar.gz
PMC10781681	38200018	[]	[]	[]	[]	[]	[ "<title>Subject terms</title>" ]	[ "<p id=\"Par1\">Correction to: <italic>npj Science of Learning</italic> 10.1038/s41539-023-00205-7, published online 28 November 2023</p>", "<p id=\"Par2\">The Acknowledgements section was incomplete from this article and should have read We thank the members of the DIPS lab at UC Davis for their input and Samuel Aragones for his help with the graphics.</p>" ]	[]	[]	[]	[]	[]	[]	[]	[]	[]	[]	[]	[]	[]	{ "acronym": [], "definition": [] }	0	CC BY	no	2024-01-13 00:02:20	NPJ Sci Learn. 2024 Jan 10; 9:1	oa_package/ae/7e/PMC10781681.tar.gz
PMC10781683	38200087	[]	[ "<title>Methods</title>", "<title>Design</title>", "<p id=\"Par11\">The aims were realized in the cross-sectional study in the following steps: (1) translation of the original Academic Resilience Scale-30 (ARS-30)<sup>##REF##32302537##9##</sup> and the General Academic Self-Efficacy Scale (GASE)<sup>##UREF##11##16##</sup> into national Ukrainian and Polish languages, (2) completing subsamples—one Polish and two Ukrainian. Polish (P21) and the first Ukrainian (U21) subsamples included respondents who experienced remote education caused pandemic COVID-19. The second Ukrainian (U22) subsample contained students who were studied online during Russian war aggression in Ukraine, (3) measuring students' resilience and their self-efficacy using adapted tools, (4) analysis of the collected data and comparison of the results with those obtained by Cassidy (2016).</p>", "<p id=\"Par12\">The instrument was translated into Ukrainian by two certified translators. Then, it was checked the consistency of meaning for the parallel versions of the questionnaires by English Studies students whose first language was Ukrainian. The Polish version was prepared using a similar procedure.</p>", "<title>Participants</title>", "<p id=\"Par13\">The subsamples were organized in accordance with voluntary sampling scheme<sup>##UREF##12##17##</sup>. First, employees of Polish and Ukrainian universities were contacted and asked to provide information about the study and encourage their students to fill out survey questionnaires. Links to electronic versions of the tools with instructions have been provided to university employees. Data collection was anonymous in order to improve the validity of responses and lasted from March 2021 to June 2022. For P21 and U21, the following selection criteria were followed: the individual had to be a university student, participate in classes remotely and had to give written consent to participate in the study. Regarding U22, in addition to the aforementioned criteria, an additional consideration was factored in; specifically, the individual had to reside in an area impacted by Russian military aggression.</p>", "<p id=\"Par14\">Finally, empirical material was obtained from 582 undergraduate university students (aged 18–20 years), of which P21 covered 259 individuals, while U21 and U22 included 105 and 218 participants, respectively. Descriptive statistics for the subsamples are summarized in supplementary Table ##SUPPL##0##1##.</p>", "<title>Measures</title>", "<p id=\"Par15\">Students' resilience was measured using the ARS-30 in translated versions. The questionnaire consists of 30 items based on student responses to academic adversity. In the original, validation study the instrument consists of a three-factor structure: Perseverance (F1), Reflective and Adaptive Help-seeking (F2), Negative Affectivity, and Emotional Response (F3).</p>", "<p id=\"Par16\">Participants were also asked to complete the GASE according to the procedure proposed by Cassidy (2016). Both ARS-30 and GASE use a Likert scale with a range of 1 (very inappropriate) to 7 (very appropriate).</p>", "<title>Data analysis</title>", "<p id=\"Par17\">The data analysis methods applied by the authors were analogous to those used by Cassidy in the original paper. This made it possible to compare the results and assess the applicability of the construct for data taken from various populations.</p>", "<p id=\"Par18\">To determine reliability and the factor validity of the ARS, its psychometric properties were analyzed. First, the internal consistency of the instrument was quantified. Then, to test factor structure of the instrument exploratory and confirmatory factor analyses were conducted. Moreover, descriptive statistical analyses were carried out. Data analysis in this study was performed using the IBM SPSS-28 and AMOS-28 software.</p>", "<p id=\"Par19\">The procedure, objectives and research tools were approved by the Research Ethic Committee of the home University of the corresponding author. All methods were carried out in accordance with relevant guidelines and regulations. Informed consent was obtained from all students participated in research.</p>", "<title>Ethical approval</title>", "<p id=\"Par20\">The procedure, objectives and research tools were approved by the Research Ethic Committee of the Dragomanov Ukrainian State University, Kyiv, Ukraine.</p>" ]	[ "<title>Results</title>", "<title>Reliability of the measurements</title>", "<p id=\"Par21\">The reliability of the resilience measurement for the entire tool was high and ranged from Cronbach Alphas 0.86 to 0.89. Slightly lower, though still satisfactory results were obtained for particular factors, ranging from Cronbach Alphas 0.70 to 0.84. Detailed data for the analyzed subsamples are provided two last lines of supplementary Tables ##SUPPL##0##2##–##SUPPL##0##3##. Similarly, the reliability of the GASE measurement was high and amounted Cronbach Alphas: 0.810, 0.849, 0.828 for P21, U21 and U22, respectively.</p>", "<title>Factor structure of resilience</title>", "<title>Results of the exploratory factor analyses (EFA): three-factor solution</title>", "<p id=\"Par22\">Statistical analysis were conducted using the maximum likelihood method of factor extraction. Supplementary Table ##SUPPL##0##2## shows factor loadings after promax rotation. Item clustering by Cassidy (2016) suggests that F1 includes items 1, 2, 3, 4, 5, 8, 9, 10, 11, 13, 15, 16, 17, and 30; F2 contains items 18, 20, 21, 22, 24, 25, 26, 27, and 29 and F3 covers items 6, 7, 12, 14, 19, 23, and 28<sup>##UREF##10##15##</sup>.</p>", "<p id=\"Par23\">Concerning Polish students, 83.4% of the items follow the solution proposed by Cassidi (2016). F1 included 10 from 14 items originally designated to this Factor (items #2, 3, 4, 5, 9, 10, 11, 15, 16, 17). Poles interpret perseverance similarly to original study, however there was also one item (#7) that according to Cassidy (2016) characterized F3<sup>##UREF##10##15##</sup>. This item allocation might indicate change in respondents' attitude towards previous life choices. Furthermore, item #24 originally assigned to F2 was moved to F1. Perhaps, this item for Polish students represented strategy of the perseverance applied by them in the difficult situation of remote learning during the COVID-19 pandemic. Other items originally characterized F1 (#8, 13, 30) moved to F2. The answers to these items were referred to reflecting and adaptive help-seeking strategy and represented a combination of cognitive-affective and behavioral responses. One item #1 originally belongs to F1 moved to F3. This shift can be explained by the emotional perception of its content.</p>", "<p id=\"Par24\">Regarding F2, it contained 8 items (#18, 20, 21, 22, 25, 26, 27, 29) out of 9 identified by Cassidy (2016)<sup>##UREF##10##15##</sup>. The result indicated almost complete agreement in the interpretation of this factor by Polish respondents with those in the original study. It may indicate similarities in the structure of academic resilience of Polish students with their peers from Western European countries. Furthermore, there were 3 items (#8, 13, 30) characterizing F1. This shift may represent respondents’ perception of these items not as referring to perseverance, but as the strategy of seeking help in a stressful situation.</p>", "<p id=\"Par25\">Concerning F3, it is necessary to emphasize nearly full correspondence of the obtained results with Cassidy (2016) findings. F3 included six items (#6, 12, 14, 18, 23, 28) out of seven originally identified. In general, Polish students interpreted F3 similarly to British respondents as a factor related to affect associated with problem situations and catastrophic thinking.</p>", "<p id=\"Par26\">Considering Ukrainian students during pandemic Covid in 2021, 40% of the items follow the solution proposed by Cassidi (2016). F1 included items which originally pertained to F1 (#2, 3, 4, 8, 11, 13, 15, 16, 30) and F2 (#18, 20, 21, 22, 25, 26, 27, 29). Furthermore, F2 partially took items from F1 <italic>(</italic>#5, 9, 10, 17<italic>)</italic> and F3 (#7, 12, 19, 23, 28). The result obtained is noticeably different from the Cassidy (2016) concept as well as from the factor structure recorded among P21.</p>", "<p id=\"Par27\">Regarding F3, out of 7 items originally assigned to F3, only two items (approximately 28%) were reproduced in U21. It seems that the Cassidy's proposal to isolate a 3-factor structure with negative mood seems as the last factor is inadequate for Ukrainian students.</p>", "<p id=\"Par28\">Analyzing the findings obtained for U22, only 27% of the items reproduced the factor structure proposed by Cassidy (2016). F1 was loaded by items originally associated with F1 (#2, 3, 8, 9, 11, 13, 16, 30) and F2 (#18, 20, 22, 24, 25, 26, 27, 29). Moreover, F2 did not include any original item but it covered some items both from F3 (#6, 7, 12, 14, 19, 23, 28) and F1 <italic>(</italic>#5, 10, 15).</p>", "<p id=\"Par29\">Finally, F3 was loaded by 3 items originally related to F1 (#1, 4, 17) and one item (#21) from F2. The results of U21 and U22 allow to assume that F1 representing perseverance captured the items of F2 corresponding to help-seeking<italic>.</italic> In other words, the EFA results showed specifics of Ukrainian participants' approach to the perseverance as a resilience component. In their view perseverance is perceived as an interpretation of the events combined with action.</p>", "<title>Results of the EFA: two-factor solution</title>", "<p id=\"Par30\">Considering the findings obtained for U21 and U22 i inconsistent with postulated by Cassidy (2016), a two-factor resilience structure was proposed. It was assumed that the new F1 will be loaded by the items originally assigned to Perseverance and Reflecting and Adaptive Help-Seeking factors, whereas the new F2 will capture the items previously related to Negative affect and emotional response factor.</p>", "<p id=\"Par31\">The two-factor structure was tested in consecutive EFAs. The findings are presented in supplementary Table ##SUPPL##0##3##.</p>", "<p id=\"Par32\">As presumed, the new F1 described Ukrainian students' experience of motivating themselves, putting more effort to achieve goals, treating failures as challenges, monitoring their own actions, seeking support from significant others etc. The new F1 for U21 was loaded by 11 items originally tied to Perseverance factor (#2, 3, 4, 8, 9, 11, 13, 15, 16, 17, 30) and 8 items associated with Reflecting and Adaptive Help-Seeking factor (#18, 20, 21, 22, 24, 25, 27, 29). Therefore, the discussed factor was named <italic>Perseverance in Overcoming Problems</italic>.</p>", "<p id=\"Par33\">The new F2 for U21 contained items describing negative emotions resulting from failures and depressive anticipation of lack of success in school and work life. Thus, the new F2 was named Negative affect and emotional response analogous to the original F3. This factor covered 10 items, of which 7 (#6, 7, 12, 14, 19, 23, 28) loaded the original F3. The three other items originated from F1, but the factor loading of item #1 was negligible, while the relations of item #5 and #10, due to their contents, are difficult to interpret clearly and sensibly.</p>", "<p id=\"Par34\">The EFA results for U22 were similar to above described for U21. The new F1 (<italic>Perseverance in Overcoming Problems)</italic> was loaded also by 19 items, of which ten (#2, 3, 4, 8, 9, 11, 13, 16, 17, 30) correlated with the original F1, whereas nine (#18, 20, 21, 22, 24, 25, 26, 27, 29) originated from F2. Similarly for the new F2 (<italic>Negative Affect and Emotional Response)</italic> it was loaded by seven items (#6, 7, 12, 14, 19, 23, 28) correlated with original F3. The four other items either correlated low with the new F2 (#1) or their relationships with the factor were difficult to interpret (#5, 10, 15).</p>", "<title>Results of the confirmatory factor analyses (CFA)</title>", "<p id=\"Par35\">To ascertain to what extent the 3 or 2-factor solutions are adequate, the CFAs were carried out for particular subsamples. The results obtained are presented in supplementary Tables ##SUPPL##0##4##–##SUPPL##0##6##.</p>", "<p id=\"Par36\">The results obtained for P21 show that the proposed model matched the data well: χ<sup>2</sup>(306) = 1.32; p = 0.056; RMSEA = 0.23; 90% CI = 0.00 – 0.034; CFI = 0.988, TLI = 0.984, NFI = 0.912, AGFI = 0.878). The ARS items were significantly and strongly linked to the extracted factors: for F1, F2, and F3 averaged values of <italic>βs</italic> were 0.605, 0.528, and 0.629 respectively.</p>", "<p id=\"Par37\">The hypothesized two-factor models for Ukrainian students were also fitted well to the data (for U21: χ<sup>2</sup>(349) = 1.119, p = 0.062, RMSEA = 0.034; 90% CI = 0.00–0.051, CFI = 0.970, TLI = 0.962, NFI = 0.783, AGFI = 0.764 and for U22: χ<sup>2</sup>(319) = 1.129, p = 0.055, RMSEA = 0.24; 90% CI = 0.00–0.036, CFI = 0.982, TLI = 0.975, NFI = 0.867, AGFI = 0.860). Again, the ARS items correlated with the extracted factors. Averaged values of <italic>βs</italic> for F1 and F2 were 0.518 and 0.637 (U21), and 0.488 and 0.569 (U22).</p>", "<title>The relationship of resilience to academic self-efficacy</title>", "<p id=\"Par38\">Considering the three-factor solution for all subsamples, the correlations between GASE and ARS30 were positive, and their power was medium except F3 among U21, where this link was weaker (see Table ##TAB##0##1##). For the two-factor solution in U21 and U22, the correlations between ARS30 and GASE also were positive with a medium power (see Table ##TAB##1##2##). This means that as the level of general self-efficacy increased, the level of resilience in general terms and in relation to the identified dimensions also improved.</p>" ]	[ "<title>Discussion and conclusions</title>", "<p id=\"Par39\">The study pursued two objectives. First, an attempt was made to assess to what extent the multidimensional resilience measurement tool proposed by Cassidy (2016) can be used to analyze this trait among Polish and Ukrainian students. In general, we found that the resilience structure postulated by Cassidy reproduced to a greater extent in P21 (83.4% similarity) than in U21 (40%) and U22 (27%).</p>", "<p id=\"Par40\">In P21 the three-factor solution was obtained, while in U21 and U22, it was two-factor. For P21 factor 1 was interpreted as <italic>perseverance</italic>, includes items featuring hard work and trying, not giving up, sticking to plans and goals, accepting and utilizing feedback, imaginative problem solving and treating adversity as an opportunity to meet challenges and improve as central themes<sup>##UREF##10##15##</sup>. Over the past thirty years, a number of studies have justified structure perseverance. Namely, willingness to continue to struggle and to practice self-discipline<sup>##REF##7850498##18##</sup>, personal control and tenacity<sup>##REF##12964174##19##</sup>, hard work and effective strategies<sup>##UREF##13##20##</sup>, and personal control and goal orientation<sup>##UREF##14##21##</sup>.</p>", "<p id=\"Par41\">Items loading on factor 2, <italic>reflecting and adaptive-help-seeking</italic>, features themes including reflecting on strengths and weakness, altering approaches to study, seeking help, support and encouragement, monitoring effort and achievements and administering reward and punishments<sup>##UREF##10##15##</sup>. This factor contains items related to belief in one’s capabilities and recognizing personal strengths and limitations<sup>##UREF##15##22##</sup>, adaptability<sup>##UREF##14##21##</sup> and adaptive help-seeking<sup>##UREF##16##23##</sup>.</p>", "<p id=\"Par42\">Finally, factor 3, <italic>negative affect and emotional response features</italic> themes including anxiety, catastrophizing, avoiding negative emotional responses, optimism, and hopelessness. The structure of this factor is similar to acceptance of negative affect<sup>##REF##12964174##19##,##UREF##14##21##</sup>, composure<sup>##UREF##13##20##</sup>, and meaningfulness<sup>##REF##7850498##18##</sup>.</p>", "<p id=\"Par43\">Among U21 and U22, a two-factor solution was obtained in both EFA and CFA analysis. Ukrainian students were characterized by a specific approach to perseverance as resilience component. This was perceived as an interpretation of the events combined with action. Interpreting the resilience as an action-oriented process emphasizes the modifiable properties rather than the fixed conditions of challenging situations that student veterans face. Consequently, military veterans are able to initiate necessary changes to achieve a better life<sup>##REF##29047199##3##</sup>. Stressful situations, such as armed conflicts, appear to serve some people as an opportunity for revealing useful coping strategies and resilience<sup>##UREF##17##24##,##UREF##18##25##</sup>.</p>", "<p id=\"Par44\">Changes in the structure of the resilience can be explained by the historical and cultural background of Ukraine as a state. The Ukrainian historical context is associated with permanent efforts and even fighting for their own (including independence). This might affect the mentality of the Ukrainian people, especially in the interpretation of perseverance<sup>##UREF##19##26##,##UREF##20##27##</sup>. In this way, perseverance transforms into permanent action (persevering overcoming problems). In other words, is was observed a cultural based mixing of the factor 1 (<italic>Perseverance</italic>) with factor 2 (<italic>Reflecting and Adaptive Help-Seeking</italic>). This creates a qualitatively new dimension—<italic>Perseverance in Overcoming Problems</italic>.</p>", "<p id=\"Par45\">The structural alteration of the resilience construct can also be explained within Folkman and Lazarus (1988) concept of stress coping styles. They distinguished three strategies and one of which is a problem strategy. Authors proposed four types of coping which are strongly associated with changes in emotion: planful problem-solving, positive reappraisal, confrontive coping, and distancing<sup>##UREF##21##28##</sup>.</p>", "<p id=\"Par46\">According to their study planful problem-solving was associated with an improved emotion state; it was associated with less negative emotion and more positive emotion. It cannot be ruled out that people can begin to feel better when they turn to the problem that is causing distress. Another explanation is that planful problem-solving, when effective, can result in an improved person-environment relationship, which should in turn lead to a more favorable cognitive appraisal and hence a more positive emotion response<sup>##UREF##21##28##–##UREF##23##30##</sup>.</p>", "<p id=\"Par47\">As for the relationship between resilience and cultural background, similar conclusions are proposed by Bogdanov et al. (2021). These authors point to the need for contextual, culturally relevant measures of resilience for war-affected adolescents in Eastern Ukraine what is in short supply in Eastern Europe<sup>##REF##35813349##31##</sup>. The authors point out that in the case of Ukrainian adolescents, the process of cultural adaptation as well as strength and difficulties as the resilience components should be taken into account<sup>##UREF##24##32##,##UREF##25##33##</sup>. In the research of Bogdanov et al. (2021) uses measure, which has a three-factor structure—individual, relational, and contextual<sup>##UREF##26##34##</sup>, includes a local functioning scale that offers the possibility of contextualizing it to specific cultures and environments.</p>", "<p id=\"Par48\">It was assumed that if historical and social experiences in the group of Ukrainian students actually lead to the formation of a pattern of <italic>Perseverance in Overcoming Problems</italic>, then in the factor analysis this pattern should be reproduced in the form of a single factor. At the same time, experiences about negative emotions should give a second factor <italic>Negative affect and emotional response</italic>. The results obtained confirmed this assumption.</p>", "<p id=\"Par49\">The second objective of the study was to estimate the relationship between resilience and students self-efficacy. GASE positively correlated with resilience in both Polish and Ukrainian respondents, confirming the concurrent validity of the scale. Research suggests that self-efficacy is an important contributory factor for resilience<sup>##UREF##10##15##,##UREF##13##20##,##UREF##27##35##</sup>. Self-efficacy can build academic resilience, and on the other hand, resiliency can enhance self-efficacy. The result obtained is corresponding to that reported by Cassidy (2016) and other authors analyzing the relationship between these two constructs<sup>##UREF##15##22##,##UREF##28##36##,##UREF##29##37##</sup>.</p>", "<p id=\"Par50\">Obtained results develop resilience theory proposed by Cassidy. They make the construct can be used in various populations. Moreover, they provide an impetus for further research in which the structure of resilience will be modified taking into account the specificity of the respondents' experiences, especially in difficult life situations, the solution of which requires the resources postulated by Cassidy.</p>", "<p id=\"Par51\">On the other hand, discussed findings have great practical value. An accurate diagnosis of resilience allows for the design of intervention programs with empirically confirmed effectiveness, as opposed to random or speculative, commonsense, anecdotal approaches.</p>", "<p id=\"Par52\">This study has some limitations. Three of them seem to be the most relevant. First, using in this study the resilience scale proposed by Cassidy (2016), the measurement was conducted according to a slightly different procedure compared to the original one. Participants in this study were diagnosed in natural situations (the COVID-19 pandemic and military conflict in Ukraine). In contrast, Cassidy (2016) measured resilience in a quasi-experimental procedure, previously presenting respondents with two independent versions of the academic adversity vignette.</p>", "<p id=\"Par53\">The second limitation is characteristic of cross-sectional surveys. The measurement was conducted once and the results obtained could be to some extent random, resulting from the influence of various uncontrolled contextual variables. For example, the group of such variables may include temperament, personality traits that influence people's resistance to various types of stressors, including the threat of disease or aggression from others<sup>##UREF##30##38##</sup>. It was not ruled out that different results could have been obtained in longitudinal studies, which would track the development of resilience in changing circumstances. The power of conclusions in this type of research would increase for randomized trials<sup>##UREF##31##39##</sup>.</p>", "<p id=\"Par54\">Third, the comparison groups in this study were not equal and participants were involved using not random but volunteer sampling scheme. Therefore, it cannot be ruled out that other factors motivated Ukrainian and other Polish respondents to participate in the survey. Ultimately, this may have affected the findings.</p>" ]	[ "<title>Discussion and conclusions</title>", "<p id=\"Par39\">The study pursued two objectives. First, an attempt was made to assess to what extent the multidimensional resilience measurement tool proposed by Cassidy (2016) can be used to analyze this trait among Polish and Ukrainian students. In general, we found that the resilience structure postulated by Cassidy reproduced to a greater extent in P21 (83.4% similarity) than in U21 (40%) and U22 (27%).</p>", "<p id=\"Par40\">In P21 the three-factor solution was obtained, while in U21 and U22, it was two-factor. For P21 factor 1 was interpreted as <italic>perseverance</italic>, includes items featuring hard work and trying, not giving up, sticking to plans and goals, accepting and utilizing feedback, imaginative problem solving and treating adversity as an opportunity to meet challenges and improve as central themes<sup>##UREF##10##15##</sup>. Over the past thirty years, a number of studies have justified structure perseverance. Namely, willingness to continue to struggle and to practice self-discipline<sup>##REF##7850498##18##</sup>, personal control and tenacity<sup>##REF##12964174##19##</sup>, hard work and effective strategies<sup>##UREF##13##20##</sup>, and personal control and goal orientation<sup>##UREF##14##21##</sup>.</p>", "<p id=\"Par41\">Items loading on factor 2, <italic>reflecting and adaptive-help-seeking</italic>, features themes including reflecting on strengths and weakness, altering approaches to study, seeking help, support and encouragement, monitoring effort and achievements and administering reward and punishments<sup>##UREF##10##15##</sup>. This factor contains items related to belief in one’s capabilities and recognizing personal strengths and limitations<sup>##UREF##15##22##</sup>, adaptability<sup>##UREF##14##21##</sup> and adaptive help-seeking<sup>##UREF##16##23##</sup>.</p>", "<p id=\"Par42\">Finally, factor 3, <italic>negative affect and emotional response features</italic> themes including anxiety, catastrophizing, avoiding negative emotional responses, optimism, and hopelessness. The structure of this factor is similar to acceptance of negative affect<sup>##REF##12964174##19##,##UREF##14##21##</sup>, composure<sup>##UREF##13##20##</sup>, and meaningfulness<sup>##REF##7850498##18##</sup>.</p>", "<p id=\"Par43\">Among U21 and U22, a two-factor solution was obtained in both EFA and CFA analysis. Ukrainian students were characterized by a specific approach to perseverance as resilience component. This was perceived as an interpretation of the events combined with action. Interpreting the resilience as an action-oriented process emphasizes the modifiable properties rather than the fixed conditions of challenging situations that student veterans face. Consequently, military veterans are able to initiate necessary changes to achieve a better life<sup>##REF##29047199##3##</sup>. Stressful situations, such as armed conflicts, appear to serve some people as an opportunity for revealing useful coping strategies and resilience<sup>##UREF##17##24##,##UREF##18##25##</sup>.</p>", "<p id=\"Par44\">Changes in the structure of the resilience can be explained by the historical and cultural background of Ukraine as a state. The Ukrainian historical context is associated with permanent efforts and even fighting for their own (including independence). This might affect the mentality of the Ukrainian people, especially in the interpretation of perseverance<sup>##UREF##19##26##,##UREF##20##27##</sup>. In this way, perseverance transforms into permanent action (persevering overcoming problems). In other words, is was observed a cultural based mixing of the factor 1 (<italic>Perseverance</italic>) with factor 2 (<italic>Reflecting and Adaptive Help-Seeking</italic>). This creates a qualitatively new dimension—<italic>Perseverance in Overcoming Problems</italic>.</p>", "<p id=\"Par45\">The structural alteration of the resilience construct can also be explained within Folkman and Lazarus (1988) concept of stress coping styles. They distinguished three strategies and one of which is a problem strategy. Authors proposed four types of coping which are strongly associated with changes in emotion: planful problem-solving, positive reappraisal, confrontive coping, and distancing<sup>##UREF##21##28##</sup>.</p>", "<p id=\"Par46\">According to their study planful problem-solving was associated with an improved emotion state; it was associated with less negative emotion and more positive emotion. It cannot be ruled out that people can begin to feel better when they turn to the problem that is causing distress. Another explanation is that planful problem-solving, when effective, can result in an improved person-environment relationship, which should in turn lead to a more favorable cognitive appraisal and hence a more positive emotion response<sup>##UREF##21##28##–##UREF##23##30##</sup>.</p>", "<p id=\"Par47\">As for the relationship between resilience and cultural background, similar conclusions are proposed by Bogdanov et al. (2021). These authors point to the need for contextual, culturally relevant measures of resilience for war-affected adolescents in Eastern Ukraine what is in short supply in Eastern Europe<sup>##REF##35813349##31##</sup>. The authors point out that in the case of Ukrainian adolescents, the process of cultural adaptation as well as strength and difficulties as the resilience components should be taken into account<sup>##UREF##24##32##,##UREF##25##33##</sup>. In the research of Bogdanov et al. (2021) uses measure, which has a three-factor structure—individual, relational, and contextual<sup>##UREF##26##34##</sup>, includes a local functioning scale that offers the possibility of contextualizing it to specific cultures and environments.</p>", "<p id=\"Par48\">It was assumed that if historical and social experiences in the group of Ukrainian students actually lead to the formation of a pattern of <italic>Perseverance in Overcoming Problems</italic>, then in the factor analysis this pattern should be reproduced in the form of a single factor. At the same time, experiences about negative emotions should give a second factor <italic>Negative affect and emotional response</italic>. The results obtained confirmed this assumption.</p>", "<p id=\"Par49\">The second objective of the study was to estimate the relationship between resilience and students self-efficacy. GASE positively correlated with resilience in both Polish and Ukrainian respondents, confirming the concurrent validity of the scale. Research suggests that self-efficacy is an important contributory factor for resilience<sup>##UREF##10##15##,##UREF##13##20##,##UREF##27##35##</sup>. Self-efficacy can build academic resilience, and on the other hand, resiliency can enhance self-efficacy. The result obtained is corresponding to that reported by Cassidy (2016) and other authors analyzing the relationship between these two constructs<sup>##UREF##15##22##,##UREF##28##36##,##UREF##29##37##</sup>.</p>", "<p id=\"Par50\">Obtained results develop resilience theory proposed by Cassidy. They make the construct can be used in various populations. Moreover, they provide an impetus for further research in which the structure of resilience will be modified taking into account the specificity of the respondents' experiences, especially in difficult life situations, the solution of which requires the resources postulated by Cassidy.</p>", "<p id=\"Par51\">On the other hand, discussed findings have great practical value. An accurate diagnosis of resilience allows for the design of intervention programs with empirically confirmed effectiveness, as opposed to random or speculative, commonsense, anecdotal approaches.</p>", "<p id=\"Par52\">This study has some limitations. Three of them seem to be the most relevant. First, using in this study the resilience scale proposed by Cassidy (2016), the measurement was conducted according to a slightly different procedure compared to the original one. Participants in this study were diagnosed in natural situations (the COVID-19 pandemic and military conflict in Ukraine). In contrast, Cassidy (2016) measured resilience in a quasi-experimental procedure, previously presenting respondents with two independent versions of the academic adversity vignette.</p>", "<p id=\"Par53\">The second limitation is characteristic of cross-sectional surveys. The measurement was conducted once and the results obtained could be to some extent random, resulting from the influence of various uncontrolled contextual variables. For example, the group of such variables may include temperament, personality traits that influence people's resistance to various types of stressors, including the threat of disease or aggression from others<sup>##UREF##30##38##</sup>. It was not ruled out that different results could have been obtained in longitudinal studies, which would track the development of resilience in changing circumstances. The power of conclusions in this type of research would increase for randomized trials<sup>##UREF##31##39##</sup>.</p>", "<p id=\"Par54\">Third, the comparison groups in this study were not equal and participants were involved using not random but volunteer sampling scheme. Therefore, it cannot be ruled out that other factors motivated Ukrainian and other Polish respondents to participate in the survey. Ultimately, this may have affected the findings.</p>" ]	[ "<p id=\"Par1\">Academic resilience explains how students overcome various challenges or negative experiences that can hinder the learning process. The COVID pandemic as well as war conflicts might be significant factors affecting the structure of the academic resilience of students. This study attempted to assess the extent to which the Cassidy’s construct of resilience can be used to interpret the behavior of other—Polish and Ukrainian samples, under remote education caused by the COVID-19 pandemic and Russian military aggression against the Ukrainian civils. Second, the relationships between resilience and students' self-efficacy were estimated. To test the factor structure of the resilience exploratory and confirmatory factor analyses were conducted. Assumed structure reproduced to a greater extent among Polish (83.4% similarity) than in Ukrainian respondents (from 27 to 40%) and it was three or two factors for Polish and Ukrainian students, respectively. General self-efficacy positively correlated with resilience both among Polish and Ukrainian respondents confirming the concurrent validity of the scale. The discovered differences were explained by differences in the historical and sociocultural experiences of the two nations. If among Ukrainian students historical and social experiences actually lead to the formation of a pattern of Perseverance in Overcoming Problems, then in the factor analysis, this pattern should be reproduced in the form of a single factor. At the same time, experiences with negative emotions should give a second-factor Negative affect and emotional response. The results obtained confirmed this assumption.</p>", "<title>Subject terms</title>" ]	[ "<p id=\"Par2\">Academic resilience is a significant factor related to students' ability to adapt to the university environment and helps them reduce the risk of stress. It involves students' enjoyment of meeting all academic requirements, enhancing their academic achievements, and facilitating effective coping strategies when they experience academic stress<sup>##UREF##0##1##</sup>. Academic resilience explains how students overcome various challenges or significant negative experiences that can hinder the learning process. This enables individuals to adapt effectively and successfully complete their academic responsibilities. Resilience can be explained as an ability and a process that allows an individual to develop positive adaptation despite challenges and adversities<sup>##UREF##1##2##,##REF##29047199##3##</sup>.</p>", "<p id=\"Par3\">Over the last few years, people have been experiencing difficult conditions on a large scale including the COVID pandemic as well as war conflicts in areas that have been living without armed conflicts. The recent COVID-19 pandemic has brought changes in various aspects of life, which have become new challenges for students<sup>##UREF##2##4##</sup>. Higher education students experience rates of depression and anxiety substantially higher than those found in the general population<sup>##UREF##3##5##</sup>. A great deal of resilience is needed by all students and educators to get through the pandemic and to adapt to the huge impact it is having on education<sup>##UREF##4##6##</sup>. Student involvement by the ability to survive and face academic challenges during the online learning process; also called academic resilience<sup>##UREF##0##1##</sup>.</p>", "<p id=\"Par4\">Regarding war conflicts, we mean first of all the Russian invasion of Ukraine, which has triggered an enormous humanitarian crisis, and has inflicted, and continues to inflict, deep and enduring harm on human health<sup>##UREF##6##8##</sup>. One of the groups most heavily affected, including the greatest impacts on health and well-being, is young people. The psychological impacts of the Russian invasion—triggered by sheltering from bombardment, migrating from homes, having families separated, witnessing the destruction of communities, and suffering the death of family members and friends—are hugely destabilizing.</p>", "<p id=\"Par5\">Armed conflict significantly damages a nation’s education sector. Such damage takes various forms, including both direct and indirect damages to all participants of the educational process. Upon the outbreak of war, all schools across Ukraine were immediately closed and classroom learning replaced with online instruction. The harmful impacts of such interruptions to the academic learning, students’ social development and wellbeing, were revealed by the lockdowns mandated in response to COVID-19<sup>##REF##32302537##9##</sup>. These problems now likely to be repeated and exacerbated by war. When students maintain schools activities during times of ongoing violence, and the school provides a positive emotional and physical climate, students demonstrate greater resilience<sup>##UREF##7##10##</sup>. Studies with the undergraduate students during pandemic lockdown reported on psychological impact of quarantine with following disorders: confusion, fear, numbness<sup>##REF##26650630##11##</sup>.</p>", "<p id=\"Par6\">Resilience and self-efficacy are very important individual resources to cope with these difficult conditions. Resilience refers to an ability and a process that allows individuals to thrive in the face of adversity<sup>##UREF##8##12##,##REF##10953923##13##</sup>. Self-efficacy aims at a broad and stable sense of personal competence to deal effectively with a variety of stressful situations. It might reflect a generalization across various domains of functioning in which people judge how efficacious they are<sup>##UREF##9##14##</sup>.</p>", "<p id=\"Par7\">One of the unique and novel approach to the measurement of academic resilience in university students is multidimensional construct which was proposed by Cassidy (2016), based on students’ specific adaptive cognitive-affective and behavioral responses to academic adversity<sup>##UREF##10##15##</sup>. Cassidy's process-based construct applies to the unique challenges faced by students during the COVID-19 pandemic and military aggression because it reflects the conceptual areas of self-efficacy and self-regulation together with the range of attributes, characteristics and factors commonly associated with resilience: confidence (self-efficacy), commitment (persistence), coordination (planning), control (how hard work and effective strategies impact achievement) and composure (low anxiety). Cassidy’s model of resilience is based on protective factors such as perseverance, help-seeking, emotional response that helps mitigate risk and adversity caused by unprecedented challenges caused by the threat to life and health during the Covid pandemic and the war in Ukraine.</p>", "<p id=\"Par8\">Given the theoretical assumptions and empirical evidence discussed above, in the presented study was made an attempt to answer the following research questions:<list list-type=\"order\"><list-item><p id=\"Par9\">in what extent the solution proposed by Cassidi (2016), the author of the multidimensional construct measure of academic resilience analyzed on a sample of British students, is reproduced considering data from Polish and Ukrainian students in difficult situations caused by the COVID-19 pandemic and the Russian military aggression against Ukraine?, and</p></list-item><list-item><p id=\"Par10\">what are the relationship of students’ resilience with their self-efficacy?</p></list-item></list></p>", "<title>Supplementary Information</title>", "<p>\n</p>" ]	[ "<title>Supplementary Information</title>", "<p>The online version contains supplementary material available at 10.1038/s41598-024-51388-x.</p>", "<title>Author contributions</title>", "<p>T.M. organized the study and substantively revised the work. N.D. performed the analyzes of the background, and was a major contributor in writing the manuscript. S.T. was responsible for analysis and interpretation of data and preparing of the discussion. All authors read and approved the final manuscript.</p>", "<title>Data availability</title>", "<p>The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.</p>", "<title>Competing interests</title>", "<p id=\"Par55\">The authors declare no competing interests.</p>" ]	[]	[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Correlation coefficients between ARS-30 and general academic self-efficacy scale (GASE) for 3 factors solution.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\">ARS 30</th><th align=\"left\" colspan=\"3\">GASE</th></tr><tr><th align=\"left\">P21 (N = 259)</th><th align=\"left\">U21 (N = 105)</th><th align=\"left\">U22 (<italic>N</italic> = 218)</th></tr></thead><tbody><tr><td align=\"left\">Global score</td><td align=\"left\">.647</td><td align=\"left\">.572</td><td align=\"left\">.573</td></tr><tr><td align=\"left\">F1</td><td align=\"left\">.641</td><td align=\"left\">.412</td><td align=\"left\">.473</td></tr><tr><td align=\"left\">F2</td><td align=\"left\">.483</td><td align=\"left\">.523</td><td align=\"left\">.433</td></tr><tr><td align=\"left\">F3</td><td align=\"left\">.473</td><td align=\"left\">.240</td><td align=\"left\">.420</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Correlation coefficients between ARS-30 and General Academic Self-Efficacy Scale (GASE) for 2 factors solution.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\">ARS 30</th><th align=\"left\" colspan=\"2\">GASE</th></tr><tr><th align=\"left\">U21 (N = 105)</th><th align=\"left\">U22 (<italic>N</italic> = 218)</th></tr></thead><tbody><tr><td align=\"left\">Global score</td><td align=\"left\">.572</td><td align=\"left\">.573</td></tr><tr><td align=\"left\">F1</td><td align=\"left\">.462</td><td align=\"left\">.508</td></tr><tr><td align=\"left\">F2</td><td align=\"left\">.479</td><td align=\"left\">.468*</td></tr></tbody></table></table-wrap>" ]	[]	[]	[]	[]	[]	[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>" ]	[ "<fn-group><fn><p><bold>Publisher's note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>These authors contributed equally: Nataliia Demeshkant and Sławomir Trusz.</p></fn></fn-group>" ]	[]	[ "<media xlink:href=\"41598_2024_51388_MOESM1_ESM.docx\"><caption><p>Supplementary Information.</p></caption></media>" ]	[{"label": ["1."], "surname": ["Pidgeon", "Rove", "Stapleton", "Magyar", "Lo"], "given-names": ["AM", "N", "P", "HB", "BCY"], "article-title": ["Examining characteristics of resilience among university students: An international study"], "source": ["Open J. 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PMC10781684	37884682	[ "<title>Introduction</title>", "<p id=\"Par2\">Despite the excitement that surrounds newer, more targeted agents, the reality for most people with advanced cancer is that chemotherapy will be used, and it will cause a degree of collateral damage to healthy tissues [##UREF##0##1##]. Clinically, this damage presents as a broad variety of diverse, individualised, and highly dynamic symptoms and side effects. Rarely do these side effects occur in isolation; instead, they present as clusters of related symptoms that are united by common underlying mechanisms, as well as physical and psychosocial/behavioural determinants [##REF##17938700##2##]. With increased accessibility of “big”, real-world data, these symptom clusters have been documented and characterised with greater precision [##REF##35929562##3##]. This has prompted new initiatives to identify early drivers of chronic treatment-related morbidity, with the goal of halting the self-perpetuating nature of inter-related symptom clusters.</p>", "<p id=\"Par3\">Of the many documented side effects of chemotherapy, the breakdown of the mucosal barrier of the gastrointestinal tract (“mucositis”) is one of the earliest and most common. Mucositis is initiated by rapid and extensive DNA damage in highly proliferative stem cells throughout the gastrointestinal mucosa [##REF##31286231##4##]. The resulting apoptosis and inflammation degrades the mucosa, leading to the formation of ulcerative lesions in the mouth, oesophagus, intestines and rectum which severely impair functional capacity. This dysfunction can lead to taste changes, dysphagia, pain and malabsorption; each of which drive anorexia, malnutrition and dehydration [##REF##18046994##5##]. On a cellular level, these breaches in the protective mucosa create an inhospitable environment for resident gut bacteria, leading to loss of commensal species and their protective metabolites including short-chain fatty acids (SCFAs). These changes further weaken the mucosal barrier and permit unrestricted communication between the underlying immune system and luminal compounds (e.g., danger signals). This results in profound local and systemic inflammation which leads to numerous extraintestinal consequences such as fever (“febrile mucositis”) [##REF##25196917##6##], cognitive impairment [##REF##24339912##7##, ##UREF##1##8##] and fatigue [##REF##34589770##9##]. As such, the destructive changes in the gastrointestinal microenvironment position mucositis as a catalyst for a range of secondary complications, and a key player in a range of symptom clusters.</p>", "<p id=\"Par4\">Despite the impact of mucositis on patients and the healthcare system, it remains without effective intervention, and its range of secondary symptoms/consequences are managed reactively and in isolation [##REF##31286233##10##]. Given the body of evidence that now suggests many symptoms and treatment consequences may be influenced by mucositis, there is an opportunity to control mucositis to mitigate the constellation of impactful symptoms with which it is associated. This review aims to outline a rationale for how, based on its already documented effects on the gastrointestinal microenvironment, medicinal cannabis could be used to control mucositis and prevent its associated symptom cluster. We will provide a brief update on the current state of evidence on medicinal cannabis in cancer care and outline the potential benefits (and challenges) of using medicinal cannabis during active cancer therapy.</p>" ]	[]	[]	[]	[ "<title>Conclusions</title>", "<p id=\"Par42\">Soon after the discovery of its chemical structure and ability to obtain various compounds from the plant in the late 1900s, as well as the description of the cannabinoid receptors and the endocannabinoid system in the 1990s, cannabis use for medical purposes has increased significantly with a steep rise in the last few years [##REF##33635777##31##, ##REF##16810401##144##]. It is evident that the majority of this use results from illegal access, however, this has been recognised and laws are in a fast-changing phase with several products approved and several others on a registered unapproved list. This provides new opportunities for users and prescribers to access MC products in a legal manner. In cancer care, self-reported cannabis use is prevalent, however, the evidence base is lacking due to inconsistencies in study design and outcomes. Moving forward, it is critical that research efforts integrate appropriate pharmacokinetic and mechanistic sub-studies to understand cannabis biology in the context of cancer and investigate its efficacy in a more holistic sense by considering its impact on clusters of related symptoms. In the context of mucositis, this is a compelling approach given the numerous symptoms that occur secondary to mucosal barrier injury and the already documented benefits medicinal cannabis has on gastrointestinal physiology, inflammation, and dysfunction.</p>" ]	[ "<p id=\"Par1\">The side effects of cancer therapy continue to cause significant health and cost burden to the patient, their friends and family, and governments. A major barrier in the way in which these side effects are managed is the highly siloed mentality that results in a fragmented approach to symptom control. Increasingly, it is appreciated that many symptoms are manifestations of common underlying pathobiology, with changes in the gastrointestinal environment a key driver for many symptom sequelae. Breakdown of the mucosal barrier (mucositis) is a common and early side effect of many anti-cancer agents, known to contribute (in part) to a range of highly burdensome symptoms such as diarrhoea, nausea, vomiting, infection, malnutrition, fatigue, depression, and insomnia. Here, we outline a rationale for how, based on its already documented effects on the gastrointestinal microenvironment, medicinal cannabis could be used to control mucositis and prevent the constellation of symptoms with which it is associated. We will provide a brief update on the current state of evidence on medicinal cannabis in cancer care and outline the potential benefits (and challenges) of using medicinal cannabis during active cancer therapy.</p>", "<title>Subject terms</title>" ]	[ "<title>Medicinal cannabis: illicit drug, plant, or medicine?</title>", "<p id=\"Par5\">Cannabis has been used medicinally for over 3000 years, primarily for its analgesic properties. The predominant phytocannabinoids in cannabis by amount are Δ<sup>9</sup>-tetrahydrocannabinol (Δ<sup>9</sup>-THC or THC) and cannabidiol (CBD), with mainstay medicinal cannabis (MC) preparations containing either or both of these compounds in varying ratios as the active ingredients, either as isolates or whole extracts. However, there remains a vast phytochemical complexity to cannabis aside from just THC and CBD, whereby whole extracts may contain over one hundred different minor phytocannabinoids and terpenes, all of which may vary in their relative expression across a number of cannabis chemotypes and displaying variable retention via different extraction processes [##REF##35588111##11##, ##UREF##2##12##]. Many of these compounds have been shown to exhibit selective bioactivities that may interact with the efficacy of THC and CBD in MC preparations and may be considerations particularly where full-spectrum botanical extracts are concerned, oft described through the popular term ‘entourage effect’ applied to medicinal cannabis [##UREF##3##13##, ##UREF##4##14##]. However, their contribution to the efficacy of most conventional MC formulations where THC and CBD predominate may only be marginal, with a paucity of investigative studies on these cannabis phytochemicals compared to THC and CBD.</p>", "<p id=\"Par6\">Today, over 40 countries have legalised MC, with different access pathways depending on jurisdictional legislation [##REF##32433013##15##]. Despite significant variation across jurisdictions, the medicinal use of cannabis is guided by the formulation (oil, sprays, tablets and flowers). Typically, MC requires prescription for a pre-defined indication or with clear clinical justification for why a certain condition may respond positively to MC. This ambiguity often results in lengthy and administratively burdensome reporting requirements; hence, MC use continues to be a challenging medico-legal entity. The combination of its often high cost, clinician hesitancy and logistic difficulties for supply compared to relative ease of access in the community, is a major driver of why people continue to self-medicate with uncontrolled and non-standardised cannabis products.</p>", "<p id=\"Par7\">Canada was one of the first countries to introduce a MC access programme in 1999. Between 2015 and 2019, the number of registered MC patients in Canada increased from 40,000 to nearly 400,000, an increase attributed to several policy changes that have gradually broadened access to a variety of medicinal cannabis formulations. In December 2015, the first cannabis oil product was launched in the Canadian market and in 2016, the Access to Cannabis for Medicinal Purposes Regulations allowed patients to grow cannabis for personal use [##REF##36952564##16##].</p>", "<p id=\"Par8\">The United States was another early adopter of medicinal cannabis, starting with state-level legalisation in California in 1996. Now, as of February 2023, medicinal cannabis has been legalised in 37 states, 3 territories, and the District of Columbia [##REF##35696691##17##]. In February 2019, Thailand became the first and only nation in Southeast Asia to legalise medicinal cannabis, offering three different categories of cannabis-based products: medicinal-grade cannabis-based medicines, Thai traditional medicine products that contain cannabis as the active ingredient, and folk medicine products prepared by registered folk healers [##REF##33009244##18##].</p>", "<p id=\"Par9\">In 2016, cannabis was rescheduled in Australia to enable access for medicinal purposes. Currently, the majority of cannabis products are classified as “unapproved” therapeutic goods, with two exceptions: Sativex (nabiximols), an oromucosal spray containing equivalent amounts of THC and CBD, and Epidyolex (also known as Epidiolex), a CBD solution with a concentration of 100 mg/mL (Therapeutic Goods Administration, 2020a). The situation in the UK is similar; even though medicinal cannabis was legalised in 2018, it remains challenging for patients to obtain access, with only a limited number of National Health Service prescriptions issued to date [##UREF##5##19##]. Medicinal products that meet safety, quality and efficacy standards are registered on the Australian Register of Therapeutic Goods (ARTG), however additional pathways such as Special Access Schemes (SAS) and an Authorised Prescriber (AP) scheme facilitate patient access to “unapproved” therapeutic goods, including many other types of MC products currently. This scheme is being increasingly adopted in Australia, with the MC Therapeutic Goods Administration (TGA) dashboard showing an increase in AP applications and a levelling off of SAS-B approvals in the last 2 years.</p>", "<title>The “endocannabinoidome” and its relevance to chemotherapy symptoms and side effects</title>", "<p id=\"Par10\">The endocannabinoidome or, in its broader sense, the endocannabinoid system (ECS), is an endogenous network of receptors, enzymes, transporters and ligands, that has largely been recognised for its role in regulating of neurotransmitter release [##REF##26698193##20##]. However, this original concept of the ECS is gradually being replaced by an increasingly sophisticated and complex network capable of regulating numerous biological pathways and functions across a range of organ systems. The interactions that exist within the ECS are critical to central nervous system development, synaptic plasticity and the homeostatic maintenance of cognitive, behavioural, emotional, developmental and physiological processes [##REF##26698193##20##, ##REF##32980261##21##]. These diverse mechanisms are mediated by endogenous cannabinoids which are produced on demand, both physiologically and patho-physiologically, from membrane lipids and are metabolised by fatty acid amide hydrolase (FAAH) and monoacylglycerol lipase (MAGL), respectively [##REF##18096503##22##]. They include the main known endocannabinoids, N-arachidonoylethanolamine (AEA) or anandamide and 2-arachidonoylglycerol (2-AG) produced by phospholipid precursors through activity-dependent activation of specific phospholipase enzymes; N-acyl-phosphatidylethanolamine-selective phosphodiesterase and phospholipase C subsequent production of diacylglycerol via diacylglycerol lipase, respectively [##REF##26698193##20##]. The broadly termed acylethanolamines and acylglycerols interact to a variable extent with cannabinoid receptors including the well-described CB1 and CB2 receptors (CNR1/CNR2) as well as other G-protein-coupled receptors such as GPR55, GPR18, GPR3, GPR6, GPR12, transient receptor potential channels such as TRP vanilloids TRPV1 to TRPV4, TRP ankyrin TRPA1, TRP M member TRPM8 and peroxisome proliferator-activated receptors such as PPAR2, and PPARγ [##REF##12037135##23##]. Although there are diverse receptors with which endocannabinoids interact, CNR1 and CNR2 are functionally characterised both physiologically and with respect to ECS dysfunction.</p>", "<p id=\"Par11\">CNR1 is a G-protein-coupled receptor that is highly abundant within the peripheral and central nervous system, largely present on axon terminals and pre-terminal axon segments [##REF##16344153##24##] (Fig. ##FIG##0##1##). It is highly abundant in medium spiny neurons in both the dorsal and ventral striatum, and is particularly high on the direct pathway axons as they enter the globus pallidus heading towards the substantia nigra [##REF##9460749##25##]. However, the expression of CNR1 is increasingly diverse, with a range of immune cells (including glia) now understood to express CNR1 [##REF##29108864##26##–##REF##36093055##28##] as well as the more immunologically abundant, CNR2. Indeed, there remains debate about the expression of CNR2 which has historically been considered to be mainly expressed in the periphery [##REF##29794855##29##]. Although emerging evidence suggests it is expressed on sensory nerve terminals and microglia in the brain [##UREF##6##30##].</p>", "<p id=\"Par12\">Although not the only components of cannabis, the most commonly studied and clinically utilised components, THC and CBD, are highly lipid soluble and have a poor bioavailability when inhaled (10–35%) or orally ingested and 11–45% [##REF##33635777##31##–##UREF##8##33##]. When THC is consumed by inhalation or mucosal sprays, it is absorbed through the lungs into the bloodstream and concentrations in the plasma will typically peak in less than 10 min [##REF##33635777##31##] and when it is orally congested, concentrations may peak at ~2 h. THC is distributed into well vascularises organs as well as the brain. THC and CBD are highly protein-bound and has a half-life of 25–36 and 18–32 h, respectively [##REF##33635777##31##, ##REF##37185752##34##], with some of the metabolites (THCCOOH) having a very long half-life up to 52 h [##REF##17529896##35##]. Chronic users have much longer half-lives.</p>", "<p id=\"Par13\">THC undergoes extensive first-pass metabolism in the liver by cytochrome P450 (CYP 450) isozymes; these enzymes are also responsible for the metabolism of many anti-cancer drugs and several other commonly used co-medication and are known to cause large inter-individual variability in plasma concentrations for majority of clinically used medications [##REF##27561659##36##]. Therefore, it is particularly important to investigate any potential drug–drug interactions to avoid toxicity or therapeutic failure for any of the medications. THC primarily is metabolised via oxidation by CYP2C9, CYP2C19 and CYP3A4 into several metabolites with 11‐hydroxy‐THC (11‐OH‐THC) and 11‐carboxy‐THC (11‐COOH‐THC) being the most abundant [##REF##37185752##34##, ##UREF##9##37##]. 11-OH-THC is metabolised by the UGT1A9 and UGT1A10 enzymes and 11-COOH-THC is metabolised mostly by the UGT1A3 and UGT2B7enzyme [##REF##37185752##34##, ##REF##17529896##35##]. CBD is also metabolised in the liver via mostly CYP3A4, CYP2C19 and CYP2C9 and other CYPs to a lesser degree via hydroxylation to form metabolites, 7-OH-CBD and 7-COOH-CBD, which is then undergoes glucuronidation by UGT1A9 and UGT2B7 [##REF##33635777##31##, ##REF##37185752##34##]. The remaining THC, CBD and metabolites can be taken up by fat tissue before it is redistributed into the circulation. More than 65% of THC and CBD will be excreted in faeces and ~20% in the urine [##REF##17529896##35##, ##UREF##9##37##]. Metabolites excreted in urine have been observed to vary up to fivefold between individuals when drug administration was controlled, demonstrating the variability in metabolism [##REF##17529896##35##].</p>", "<p id=\"Par14\">Inter-individual differences in pharmacogenomics, pharmacokinetics and pharmacodynamics may explain contradictory outcomes from previous studies and accounting for such differences will provide an opportunity for personalised medicine where efficacy can be maximised, and toxicity minimised for various conditions or diseases [##REF##28534260##38##].</p>", "<p id=\"Par15\">Reflecting the breadth of cells upon which ECS receptors are expressed, the ECS regulates a number of critical functions that are well-known to contribute to the side effects of chemotherapy. Most notable is its psychotropic properties, modulating mood, anxiety, cognition, appetite, sleep and pain [##UREF##10##39##, ##UREF##11##40##]; all of which are well-documented to be negatively impacted by chemotherapy [##UREF##0##1##]. Peripheral CB1 expression is also implicated in gastrointestinal inflammation, mucosal defences and gastric motility [##REF##26935536##41##–##UREF##12##44##], and thus by extension diarrhoea and constipation, due to its expression on presynaptic cell of sympathetic motor neurons innervating visceral organs leading to reduced noradrenalin release [##REF##11316486##45##]. Further to this, given the immunomodulatory capacity of the ECS, its ability to influence numerous symptoms and side effects of chemotherapy of which many are underpinned by aberrant inflammation, is vast. It is for these reasons that medicinal cannabis, and strategies to augment the ECS, have gained considerable momentum for their potential benefits in people with cancer.</p>", "<title>Medicinal cannabis use in cancer care: what is the evidence?</title>", "<p id=\"Par16\">Cannabis use in people with cancer is not uncommon, although it remains difficult to determine exact prevalence due to heterogeneous results published across numerous studies with varied designs. In a study published in 2018, 43% of respondents (at a Canadian cancer centre) reported using “illicit” cannabis for a variety of symptoms and side effects of their treatment [##REF##29962840##46##]. This is similar to a large population-based analysis from 2005 to 2014 in the US, which showed 40.3% of the 826 respondents with cancer having used cannabis in the last 12 months [##REF##31006849##47##]. However, in a larger survey of more than 200,000 people, results indicated that less than 10% of people were using cannabis [##REF##34081772##48##]. Irrespective of self-reported cannabis use, there is a high degree of interest in its potential benefits during cancer care, with 80% of healthcare professionals reporting that they have engaged in conversations with their patients about cannabis [##REF##29746226##49##]. Unfortunately, less than 30% feel equipped to guide their patients citing a lack of clear evidence on its safety and efficacy [##REF##29746226##49##]. This uncertainty undoubtedly stems from the inadequate and highly variable evidence base for cannabis in cancer care, which is dominated by largely observational studies that are subject to inherent biases, powerful placebo effects and diverse confounders, leading to a high rate of false positives [##UREF##13##50##–##REF##36872397##52##]. Similarly, of the limited number of randomised control trials, few are considered high quality and they remain near impossible to compare/synthesise due to inherent differences in design, outcome measures and cannabis products/doses/delivery/formulations used [##UREF##13##50##]. This has prevented replication and meta-analyses, and the resulting evidence base is therefore inconsistent and largely uninformative.</p>", "<p id=\"Par17\">Although challenging to compare studies, a large number of systematic reviews have been conducted in an attempt to synthesise data and determine its efficacy in symptom control. Notably, there have been few that have been able to perform meta-analysis, reflecting the heterogeneity of available data. The most recent review of cannabis in cancer care reviewed 42 studies (19 randomised, 23 non-randomised), focused on people with cancer receiving palliative care [##UREF##14##53##]. Among these studies, pain was the most commonly investigated symptom, with highly variable effects reported across the studies. This aligns with a recent systematic review and meta-analysis which investigated the effect of cannabis for pain management in people with cancer, which was unable to form any conclusive recommendation [##REF##31959586##54##]. Accordingly, recent guidelines from the Multinational Association for Supportive Care in Cancer (MASCC) do not recommend the use of cannabinoids for cancer pain, although, it is unclear how this relates to <italic>chemotherapy-induced</italic> pain which is diverse in its origins [##REF##36872397##52##].</p>", "<p id=\"Par18\">While pain has dominated the landscape for cannabis research in cancer care, emerging evidence exists for its role in chemotherapy-induced nausea and vomiting (CINV), anorexia, cachexia, sleep disturbance and psychological symptoms (depression/anxiety). Doppen and colleagues reviewed the evidence for CINV, reporting generally positive effects across multiple studies using various assessment tools [##UREF##14##53##]. This is consistent with recommendations from MASCC, which show THC and nabilone are both effective in controlling CINV, however, no more effective than current antiemetic medications [##REF##36525085##55##]. Despite these positive findings, MASCC was unable to form any guideline due to insufficient, high-quality evidence. This is echoed by the American Society for Clinical Oncology (ASCO) who question the quality of current evidence [##REF##32658626##56##]. Despite the lack of clinical recommendation from MASCC and ASCO, it appears that there is discordance between published clinical trial data and anecdotal reports of patient preferences, which tend to favour cannabis over existing antiemetic strategies, even when adverse effects were higher [##REF##33068314##57##, ##REF##32801017##58##].</p>", "<p id=\"Par19\">MASCC maintains a similar stance with respect to anorexia and cachexia, with limited evidence available to inform relevant guidelines. Doppen et al. reviewed 15 studies for cannabis and appetite, with both objective and self-reported benefits reported for nabinol and Marinol, however, several studies reported no or inconsistent effects [##UREF##14##53##]. Given the heterogeneity in data and approaches, it remains difficult to draw robust conclusions despite the popularised effects of cannabis on appetite stimulation. Similarly, for psychological effects (e.g., on sleep, anxiety, depression), MASCC were unable to make any recommendations with most studies investigating these outcomes as secondary analyses with inconsistent data across studies [##REF##36809575##51##].</p>", "<p id=\"Par20\">While the systematic review by Doppen et al. has provided insight on the current state of evidence regarding cannabis use in cancer care, it has been scrutinised for its methodology and over-simplification of data as “positive” and “negative” effects based on the null hypothesis significance test [##UREF##13##50##, ##UREF##14##53##]. As cautioned by Davis and Soni (2022), our approach to cannabis research should be guided by effect sizes that are deemed clinically significant, rather than statistically significant [##UREF##13##50##]. Furthermore, they highlight the need to be more holistic in our assessment of cannabis for symptom management, avoiding excessively large studies designed with highly restrictive outcomes guided by narrow-minded criteria [##UREF##13##50##]. With this in mind, and the growing appreciation for symptom clusters in people undergoing chemotherapy, there is a clear rationale to prioritise trial designs that address clusters of related symptoms, rather than single symptoms, to deliver meaningful impacts to the participant’s physical or psychosocial well-being. Critical to these approaches is the inclusion of consumers in cannabis research, to ensure research methodologies are informed by, and consistent with, consumers behaviours and preferences. In line with ensuring consumer engagement, trials should include relevant patient reported outcome measures (PROMs) to ultimately determine if cannabis has a meaningful impact on people with cancer.</p>", "<title>Gastrointestinal effects of cannabis: can they be adapted to control mucositis?</title>", "<p id=\"Par21\">Cannabinoids, including both CBD and THC, are increasingly documented for their capacity to modulate gastrointestinal function, owing to the immense control that the ECS has on gastrointestinal homeostasis. Both CNR1 and CNR2 are present in the gastrointestinal tract, largely on enteric nerves and the epithelium, but also on enteroendocrine cells and immune cells [##REF##27413788##42##]. This network of ECS receptors controls gastric motility, and as such, it is now understood that variants in the genes encoding for CNR1 are implicated in diseases characterised by altered motility including irritable bowel syndrome (IBS), particularly diarrhoea-predominant [##REF##27133395##59##]. Both CNR1 and CNR2 are expressed in the gut at medium to high degrees, respectively. Accordingly, it has been shown that agonists of the cannabinoid receptors (CNR1 and CNR2) [##REF##16574988##60##, ##REF##19408320##61##], as well as targeting endocannabinoid degradation [##REF##18493729##62##], minimises experimental colitis and associated visceral hypersensitivity [##REF##33863856##63##]. Furthermore, preclinical investigations have shown that potent agonists of CNR1 (without central nervous system effects) and CNR2 have been shown to control increased gastrointestinal motility caused by stress [##UREF##15##64##] and inflammation [##UREF##12##44##]. Similarly, inhibition of anti-diacylglycerol lipase (DAGL) and fatty acid amide hydrolase (FAAH)—two enzymes critical in endocannabinoid metabolism—has been shown to normalise transit time in the context of opioid-induced constipation [##UREF##16##65##]. Of particular interest to changes in the gastrointestinal microenvironment associated with mucositis is the ability of CNR2 activation [##UREF##17##66##] and FAAH inhibition [##UREF##18##67##] to control accelerated gastrointestinal motility induced by lipopolysaccharide—a bacterial product that is causally implicated in chemotherapy-induced diarrhoea.</p>", "<p id=\"Par22\">In addition to its effect on gastrointestinal motility, which has clear applications in controlling chemotherapy-induced diarrhoea, the ECS exerts potent immunomodulatory effects in the gastrointestinal tract controlling intestinal inflammation [##REF##26935536##41##]. Both synthetic CB receptor agonists and endocannabinoids have been shown to impair cellular and humoral immunity by reducing inflammatory cell recruitment, inducing T-cell apoptosis and suppressing the production of numerous pro-inflammatory cytokines and chemokines (e.g., TNF-α, IL-1β, IL-2, IL-6, IL-17, IFN-γ, CCL2 or CXCL10) [##REF##26935536##41##, ##REF##25877930##68##]. Therapeutically, both exogenous administration of cannabis and preventing endocannabinoid degradation by inhibiting FAAH have been shown to reduce colitis [##REF##16574988##60##–##REF##33863856##63##, ##REF##25275313##69##]. In fact, FAAH inhibition and CB receptor activation have shown efficacy in mouse models of colitis and FAAH knockout mice are less susceptible to experimentally induced colitis compared to wild-type mice [##REF##25275313##69##]. The ability of endocannabinoids (or exogenous cannabis products) to accelerate wound healing in the gut points to their ability to promote intestinal/mucosal barrier function, that is, the bonding of intestinal epithelial cells to create a uniform and restrictive barrier. This mechanism has been confirmed in vitro, with cannabinoids improving or maintaining paracellular permeability (i.e., leakiness) and tight junction protein expression in Caco-2 cells treated with <italic>Clostridium difficile</italic> toxin A and other barrier-directed insults (e.g., cytokines, EDTA) [##REF##26935536##41##, ##UREF##19##70##]. These mucoprotective effects have also been reported preclinically, with cannabinoids reported to decrease intestinal permeability (and increase regulatory T-cell recruitment) in experimental colitis induced by dextran sulfate sodium (DSS) [##REF##26935536##41##]. It has also been suggested that cannabinoids can modulate secretory processes in the intestinal epithelium which, when dysregulated, lead to altered osmotic forces and potentially diarrhoea [##REF##12949722##71##–##UREF##21##74##].</p>", "<p id=\"Par23\">An emerging area of interest with respect to the gastrointestinal microenvironment is the interaction between the ECS, cannabis and the gut microbiota [##REF##33337346##43##, ##REF##26678807##75##, ##UREF##22##76##]. The gut microbiota is a collection of micro-organisms (bacteria, viruses and fungi) that reside in the gastrointestinal lumen and mucosal niches, regulating host physiology and immune function. Importantly, these beneficial host-directed effects are best achieved when there is high microbial diversity and enrichment for commensal microbes. Chemotherapy indirectly impacts the diversity and composition of the gut microbiota, through the destruction of their mucosal niches and oxidative stress [##REF##34604043##77##]. As such, a highly dysbiotic microbiota is a hallmark trait of chemotherapy, and an event documented to drive a range of adverse effects including fever, infection, diarrhoea, cachexia, weight loss, anxiety, cognitive impairment, cardiotoxicity and fatigue. Although a relatively new concept, emerging data suggests an interaction between the ECS and the gut microbiota. Most recently, cannabis extracts (CN1, CN2, CN6) were shown to increase microbial diversity and richness in a mouse model of metabolic disease whilst promoting enrichment of microbial taxa associated with health [##UREF##23##78##]. Further suggesting an ECS-microbiota interaction is the finding that germ-free mice (mice without a microbiota) are deficient in a number of ECS components, including the CNR1 [##REF##31690638##79##]. A microbial taxa of particular interest is <italic>Akkermansia muciniphila</italic>, a mucus-degrading microbe implicated in gut inflammation [##REF##36312913##80##] and chemotherapy side effects [##REF##31889131##81##, ##REF##33963313##82##]. This microbe is reportedly elevated in response to CNR1 antagonism with the compound SR141716A, although this was only demonstrated in obese mice [##UREF##22##76##, ##REF##29142285##83##]. Despite this emerging evidence, it is unclear if medicinal cannabis influences the gut microbiota and if this mechanism underpins/delivers meaningful impacts for the host.</p>", "<p id=\"Par24\">Collectively, this body of evidence strongly demonstrates the profound control that the ECS exerts on gastrointestinal function, regulating motility, barrier function and repair, immune function, secretion and potentially the microbiota. These data underscore the potential mucoprotective effects of exogenous cannabinoid administration or augmentation of the ECS. In the context of cancer care, this therefore supports strategies targeting ECS (via direct cannabinoid administration or inhibition of degradation) to control mucositis and promote a more resilient gastrointestinal microenvironment. Despite the scientific strength of this rationale, and the prevalence of mucositis (occurs in ~60% of patients treated with standard chemotherapy), there have been few attempts to explore the mucoprotective properties of medicinal cannabis in cancer care [##REF##36525085##55##]. This may reflect the complexities of using/investigating medicinal cannabis during active cancer treatment.</p>", "<title>Using medicinal cannabis during active cancer treatment: precautions, challenges and potential benefits</title>", "<p id=\"Par25\">To minimise both the depth and duration of mucositis, supporting the gastrointestinal microenvironment and controlling the constellation of symptoms with which mucositis is associated, medicinal cannabis should be used during active chemotherapy treatment. Of course, this raises some concerns regarding the possibility of adverse drug interactions with anti-cancer therapies and potential loss of anti-tumour efficacy. There has been limited investigation of how cannabis influences the anti-tumour efficacy of cancer treatment. Cannabis has been investigated for its effect on the pharmacokinetics of irinotecan and docetaxel, with no effects observed [##REF##17405893##84##]. On the other hand, two recently reported observational studies indicate a negative impact of cannabinoids on immune checkpoint inhibitors (ICI) related cancer outcomes [##REF##32872248##85##, ##REF##30670598##86##]. Bar-Sela et al. highlighted in a prospective study that the concomitant use of prescribed cannabis was associated with lower response rates (39% vs 59%), median time to progression (3.4 months vs 13.1 months) and median overall survival (6.4 months vs 28.5 months) when compared to ICI therapy alone [##REF##32872248##85##]. Another retrospective observational study from the same research group reported inferior outcomes for cannabinoid use along with nivolumab (an ICI) when compared to nivolumab among selected solid cancers [##REF##30670598##86##]. While some evidence suggests that cannabis may impair the anti-tumour efficacy of immunotherapy [##UREF##24##87##, ##UREF##25##88##], recent evidence suggests cannabis may actually have a synergistic effect with immunotherapy. This has been shown both preclinically and clinically, with median survival in CT26 tumour-bearing mice treated with THC and an anti-PD1 antibody having significantly higher overall survival compared to controls [##REF##36535195##89##]. Authors also reported higher overall survival (numerical, failed to reach statistical significance) in 201 people with non-small cell lung cancer undergoing monotherapy with pembrolizumab who used medicinal cannabis. Although this does not confirm a synergistic effect, it does indicate no detrimental effect. The only study in which a definite synergy has been identified was preclinical, where a combination of cannabigerol and anti-PD-1 resulted in enhanced tumour clearance and increased survival compared to monotherapy in tumour-bearing mice [##REF##37325437##90##]. Given the evidence from low quality clinical trials, well conducted trials are required to assess the efficacy of cannabinoids as anti-cancer therapeutics either alone or in combination with other systemic cancer therapies.</p>", "<p id=\"Par26\">In the context of chemotherapy, although there is no concrete evidence that suggests cannabis may impair its anti-tumour efficacy, given the increasing evidence for immune-mediated mechanisms enhancing chemoefficacy [##REF##33243969##91##], this risk cannot be ignored and should be appropriately built into studies investigating medicinal cannabis in combination with standard chemotherapy. Although it remains exclusively experimental and prone to inflation, there is a growing body of evidence that suggests medicinal cannabis may in fact be a beneficial adjunct to standard chemotherapy, capable of inducing cell death or controlling proliferation by inducing endoplasmic reticulum (ER) stress [##UREF##26##92##, ##REF##19652543##93##], proteosome inhibition [##REF##27769052##94##], upregulation of matrix metalloproteinases [##REF##18159069##95##] and reactive oxygen species activation [##REF##21566064##96##]. In line with these findings, several studies have outlined the potential for cannabinoids to be used as anti-cancer agents either on their own or in combination with other systemic cancer therapies or radiotherapy [##UREF##27##97##, ##REF##34830856##98##]. A recent review summarised the anti-cancer effects of cannabinoids to be mediated through multiple pathways including anti-proliferative, pro-apoptotic, pro-autophagy, anti-invasion and metastasis, anti-angiogenesis, and immunomodulation [##REF##35277658##99##]. However, it is important to note that the majority of these findings have only been explored in vitro or in preclinical (animal) models and are subject to inflation in the public domain. As a result, these benefits have not been robustly translated into the clinical setting.</p>", "<p id=\"Par27\">Three human clinical trials reported the results on the role of cannabinoids on patients with recurrent glioma [##REF##16804518##100##–##REF##34094937##102##]. Guzman et al. demonstrated that only two of nine patients with recurrent Glioblastoma (GBM) had reduced tumour proliferation when treated with intracranial Δ<sup>9</sup>-THC alone as monotherapy [##REF##16804518##100##]. Another trial that combined Nabiximols (a mixture of plant-derived THC, CBD and non-cannabinoid compounds) with temozolomide (<italic>N</italic> = 27) resulted in a numerically higher 2-year overall survival (50% vs 22%, <italic>P</italic> = 0.13) when compared to placebo/TMZ [##REF##33623076##101##]. Schloss et al. compared two different doses of THC/CBD combination as adjunct to standard treatment of recurrent high-grade gliomas (<italic>N</italic> = 88) and demonstrated an improved quality of life and an imaging-assessed tumour response in 11% while 34% had stable disease when compared to historical controls [##REF##34094937##102##]. These clinical trials highlight that a small proportion of people with recurrent GBM may benefit from cannabinoids, however, there are no available predictive biomarkers that may identify responders and non-responders.</p>", "<p id=\"Par28\">In addition to the impact on anti-tumour efficacy, the other main risk associated with medicinal cannabis use in parallel to active treatment is drug–drug interactions. Given the predominant role of MC for pain control in cancer care, its interaction with other analgesics is of interest. Evidence suggests that cannabis may in fact enhance opioid-induced analgesia, with synergistic analgesia observed when opioid/cannabinoid ligands are co-administered. In animal studies, either morphine or codeine produces synergistic antinociception when combined with THC [##UREF##28##103##–##UREF##29##106##], and similar synergies have been documented in humans [##UREF##30##107##]. However, these benefits must be taken in light of evidence that suggests this combination may increase tolerance to both forms of analgesia and increase the risk of “drug-liking” effects [##UREF##25##88##, ##REF##29463913##108##]. There is also evidence to suggest drug–drug interactions between CBD and the non-steroidal anti-inflammatory, naproxen, although this is limited to in vitro evidence [##REF##26187180##109##]. However, both CBD and THC are metabolised by CYP2C9, suggesting the possibility of impaired drug clearance [##REF##26187180##109##] or renal/liver toxicity. However, there are no data to suggest CBD and THC cause renal/liver toxicity, and in fact there is data to suggest hepatoprotective effects [##UREF##31##110##] and prevention of cisplatin-induced renal toxicity [##UREF##32##111##]. However safety profiles of MC in the context of cancer care remains superficially addressed, underscoring the importance of regular renal and liver function tests, appropriately titrated dosing, and studies with co-primary endpoints that address efficacy and safety [##UREF##25##88##]. These studies should also endeavour to capture the patient experience with respect to milder adverse events such as dry mouth and fatigue/somnolence, given their association with both cancer therapy and MC.</p>", "<p id=\"Par29\">While medicinal cannabis use during active chemotherapy presents some challenges, if approached carefully, there are a number of benefits that can be achieved. These benefits largely relate to the ability to control multiple symptoms early in their aetiology, rather than therapeutically targeting single symptoms that may have developed and persisted well after treatment ends. This holistic approach to controlling multiple symptoms is in line with recent suggestions by the MASCC, and also aligns with the scientific evidence that underpins the clinical phenomenon of symptom clustering [##REF##35470503##112##–##REF##29031318##114##].</p>", "<p id=\"Par30\">We have outlined a clear rationale for how medicinal cannabis products, or augmentation of the ECS, modulates gastrointestinal function and exerts mucoprotective effects. It is therefore well positioned to target numerous mechanisms known to be involved in mucositis pathobiology and symptomatology. Given the documented role of mucositis and associated changes in the gastrointestinal microenvironment (e.g. gut microbiota dysbiosis) driving a range of intestinal and extraintestinal symptoms, medicinal cannabis in the active stages of chemotherapy may deliver broad-reaching benefits. This is particularly compelling when considering that medicinal cannabis, and the ECS, will likely have paralleled effects on these associated symptoms. For example, mucositis can cause taste changes and pain, leading to reduced oral intake (anorexia) and therefore clinically impactful weight loss. As such, targeting mucositis with cannabis, whilst simultaneously promoting food enjoyment and behaviours, will likely deliver meaningful impacts of weight maintenance and nutritional status. Similarly, diarrhoea and pain due to mucositis is anecdotally thought to cause sleep disturbances [##REF##29975400##115##]. Again, by addressing a biological cause and providing symptomatic relief, the potential for meaningful impacts on patient quality of life is enhanced. This same framework can be applied to numerous, interacting consequences of mucositis and associated symptoms (Fig. ##FIG##1##2##).</p>", "<p id=\"Par31\">By approaching symptom management in this manner, the magnitude of benefit is likely to be larger, and thus the health and well-being of people undergoing chemotherapy better maintained. This will deliver knock-on effects to patients, ensuring they remain in the workforce, thus reducing personal financial toxicity, and remain willing/capable of receiving their intended chemotherapy dosing ensuring optimal tumour response and progression-free/overall survival. This indirect effect on treatment efficacy is significant and should not be disregarded. For example, in a recent review of 874 women with advanced breast cancer, chemotherapy dose reductions of >15% significantly increased the risk of mortality [##REF##29622384##116##]. Similarly, a reduction in total cumulative dose of neoadjuvant FEC-D (where it is <85% of intended dosing) decreased the length of survival in women with breast cancer [##REF##31390594##117##]. Similar effects have been reported in ovarian cancer and colon cancer [##REF##29906734##118##–##UREF##33##120##].</p>", "<p id=\"Par32\">When considering the causes of dose reductions or modifications, adverse effects (i.e., side effects) are the most common cause, accounting for 82% of all dose reductions in a recent audit of 584 people undergoing adjuvant chemotherapy for stage III colon cancer [##REF##33995589##119##]. As such, the provision of proactive supportive care that tackles symptom clusters at multiple points in their aetiology and progression is critical. In fact, the provision of supportive care early in symptom aetiology is recommended, with evidence illustrating both quality of life and survival benefits when this approach is adopted [##REF##21976846##121##]. Furthermore, by addressing multiple symptoms concurrently, or targeting common underlying mechanisms of multiple symptoms, polypharmacy (5+ medications) can be reduced. More than 80% of people with advanced care report polypharmacy [##REF##33037903##122##]. This approach is fragmented and places substantial burden on the patient who must navigate multiple medications, increasing their risk of adverse drug interactions and medical misadventure [##UREF##34##123##]. With recent calls to address the fragmented approach to supportive cancer care and symptom control [##UREF##35##124##], the ability of MC to transcend multiple symptoms is compelling and advantageous.</p>", "<p id=\"Par33\">In considering how MC can be used to deliver substantive impacts for people undergoing chemotherapy, a number of practical matters must be carefully considered. Firstly, there are a range of cannabis extracts that are available for consumption in a variety of formulations ranging from dry leaves/buds which can be smoked or vaporised, to highly purified and processed isolates [##REF##32239248##125##, ##UREF##36##126##]. Typically, the more readily accessible cannabis products are in the community, the lower the degree of purification and quality assurance. In light of high rates of self-reported use [##REF##36952564##16##, ##REF##29962840##46##, ##REF##34081772##48##], this underscores the need to deliver evidence that (if shown to be beneficial) will ensure patients can access MC in more appropriate and safe formulations.</p>", "<p id=\"Par34\">Cannabis is commonly available as an oil, which contains or is enriched for CBD and THC, typically in combination with many other cannabinoids and phytochemicals (e.g., terpenes) at varying proportions. Oils are a convenient method of administration and can be directly administered to the oral cavity for rapid mucosal absorption, however, they do require a degree of dexterity and are prone to inaccurate dosing as people typically administer their dose as a number of “drops” [##UREF##25##88##]. Similarly, sublingual or oral–mucosal sprays can be used. While direct application to the oral mucosa is a common and easy method of administration, it is important to consider how factors like oral mucositis may influence tolerance, as some sprays are prepared in an ethanol diluent which would be painful to apply to an ulcerated oral cavity. Similarly, it is unclear how oral mucositis impacts the rate of absorption. Oils can also be encapsulated for ingestion; however, this method of administration must be considered in the context of dysphagia and nausea/vomiting, which may impact the patient’s ability to swallow a capsule, or intestinal mucositis which may also influence the rate of absorption [##REF##36648505##127##]. This underscores the need to conduct appropriate pharmacokinetic studies to understand how these unique factors associated with cancer, in particular active chemotherapy, impact the uptake and efficacy of cannabis. Further to this, there is evidence that suggests the presence of high-fat food impacts the bioavailability of cannabis, and thus this should be carefully considered in the design of capsules and other formulation strategies [##UREF##7##32##, ##REF##30051434##128##]. Importantly, these methods of administration result in different clinical effects, particularly with respect to the timing and duration of the response [##UREF##25##88##]. These should be considered when selecting the time of administration (i.e., time of day) and the symptom or side effect(s) of interest.</p>", "<p id=\"Par35\">In addition to the method of administration, the selection of specific cannabinoids and their relevant doses is critical. This decision needs to be guided by the specific symptom or side effect(s) of interest, with a clear scientific rationale for their use. When considering the MC intervention of interest, it is also important to acknowledge and respect the complexity of medicinal cannabis as an entire entity (i.e., a whole plant) in which the combination of numerous active compounds work cooperatively to elicit benefit. As such, while using synthesised isolates may be attractive from a pharmacological perspective, evidence suggests that the synergy of numerous cannabis compounds, a process referred to as the “entourage effect”, outweighs the benefits that can be gained from a single isolate or molecule.</p>", "<p id=\"Par36\">In the context of mucositis and its constellation of symptoms, the use of CBD and THC (with other cannabinoids and compounds such as terpenes) is likely to be best positioned to deliver meaningful benefits based on their unique yet synergistic effects [##UREF##3##13##, ##UREF##37##129##]. Of note, CBD is documented to counteract the <italic>undesirable</italic> psychotropic effects of THC [##REF##31570536##130##] and may therefore improve adherence. This synergy is in addition to the ability of these compounds to address different but related symptoms. For example, CBD is hypothesised to control self-perpetuating inflammatory pathways that ultimately dictate the depth and duration of mucosal injury to deliver clinically meaningful benefits [##REF##18493729##62##, ##REF##32333910##131##–##REF##33858011##133##]. In parallel, THC has the capacity to provide complementary effects to control anxiety, promote food intake/appetite and sleep quality [##REF##21343383##134##–##REF##31120284##137##]. However, it cannot be ignored that while THC provides potential benefits for the patient, it certainly introduces additional medico-legal complexity, with many countries enforcing strict no-tolerance laws with respect to operating heavy machinery or driving motor vehicles. This may negatively impact patient well-being by impacting their employment prospects, or ability to live independently. It also poses challenges for patients that may have caring duties. Further advice regarding dosing has been summarised by Cyr and colleagues [##UREF##25##88##].</p>", "<title>The future of medicinal cannabis</title>", "<p id=\"Par37\">The field of MC has been and continues to be difficult to navigate, reflecting the legislative challenges, variations in formulations and complexities of this emerging pharmacotherapy [##UREF##8##33##, ##REF##37185752##34##, ##REF##28534260##38##, ##UREF##39##138##]. To understand how to appropriately use this plant and its individual components in a therapeutic manner and avoid toxicity, well-designed in vitro and in vivo clinical pharmacology studies are required, particularly due to individual differences [##UREF##39##138##, ##REF##17712819##139##]. Understanding the clinical pharmacology involved with MC will take the ‘guesswork’ out of the current largely uncontrolled and uncertain approach, which could provide the long wanting ‘safeguard’ for many vulnerable [##UREF##40##140##] individuals who are not benefiting from current practices and already suffering from poor quality of life due to undergoing cytotoxic chemotherapy and/or many other unpleasant conditions.</p>", "<p id=\"Par38\">It is well-known that there are many cannabis species producing hundreds of compounds of which more than 100 chemicals are known as phytocannabinoids, with THC, CBD, terpenes and flavonoids being mostly abundant [##REF##33635777##31##, ##REF##37185752##34##]. Therefore, in order to provide this ‘safeguard’ with MC therapy, there are two key elements that need to be addressed. The first being, understanding the molecular mechanisms behind both the therapeutic and adverse effects of the various compounds within MC, and the second being, understanding the pharmacogenomics, pharmacokinetics and pharmacodynamics of the various products and formulations between individuals [##REF##37185752##34##, ##REF##28534260##38##, ##REF##17712819##139##].</p>", "<p id=\"Par39\">To achieve this, well-established genetic approaches should be adopted in the field of MC to facilitate precision medicine (‘selecting right drug’) [##REF##28534260##38##, ##REF##17712819##139##] and pharmacokinetic-guided approaches to facilitate precision dosing (‘selecting the right dose’) [##REF##33635777##31##, ##REF##37185752##34##, ##REF##34464454##141##]. For the latter, specifically, exposure-response studies via therapeutic drug monitoring or pharmacogenomics may enable individualised therapy [##REF##28534260##38##, ##REF##17712819##139##], similar to how it has been done for anti-cancer therapeutics [##UREF##41##142##]. In addition, to provide true individualised or personalised dosing, population pharmacokinetic–pharmacodynamic (POP-PK/PD) models need to be developed to better understand how the population and individuals respond to these compounds, and which covariates (e.g., age, weight, gender, genotype, comedications, etc.) drive any variability in the pharmacokinetics and thus responses [##UREF##40##140##].</p>", "<p id=\"Par40\">It has been well-documented that clinical trials are limited and of the available data, it is clear that varied design has resulted in the inability to compare and establish conclusive evidence. However, with personalised medicine currently in its peak in many research areas, such as antimicrobials, and anti-cancer drugs [##REF##37444404##143##], it is an ideal time to build on current research and investigate which compounds should be isolated or combined in the appropriate formulations and administered to truly enable personalised MC therapy [##REF##37185752##34##, ##REF##28534260##38##, ##REF##17712819##139##]. This will lead to a better understanding how exactly these compounds exert their effects and how this knowledge can be utilised to treat conditions such as mucositis which in turn has the potential to reduce symptom clustering and an array of treatments which cause additional challenges (polypharmacy, drug–drug interactions, adverse effects, poor quality of life and financial burden) for both the individuals and the healthcare systems. The need and opportunity to investigate how MC therapy can be personalised for individuals suffering from mucositis and other conditions is evident and brings on an exciting future for MC research.</p>", "<p id=\"Par41\">While trial data remain inconclusive, it is likely that individual factors that dictate tolerance and efficacy have contributed to the variable and often contradictory results observed. For example, previous cannabis use is associated with lower anxiety after THC. Similarly genomic factors have been shown to predict the efficacy and tolerability of CBD. In a recent genomic study of patients with treatment-resistant epilepsy, single-nucleotide polymorphisms in certain genes were identified which were associated with a lower response and greater side effects of CBD [##REF##34464454##141##]. The study also revealed genetic variants that were related to the likelihood of CBD-associated diarrhoea [##REF##34464454##141##]. These findings present an opportunity for personalised pharmacogenomics-guided strategies for precise MC treatment that could be particularly advantageous for patients undergoing chemotherapy, already at risk of gastrointestinal side effects such as diarrhoea [##REF##34464454##141##]. Understanding these factors will be critical in optimising the safe and effective use of cannabis in medical practice.</p>" ]	[ "<title>Acknowledgements</title>", "<p>The authors would like to acknowledge Ms Bronwyn Cambareri for her help in preparing this manuscript.</p>", "<title>Author contributions</title>", "<p>This review was conceived by the entire authorship team, with initial drafting and preparation of the manuscript led by HW. LW, OB, KC and CC assisted in the literature review and synthesis, with Scott Smid and Jaroslav Boublik providing additional input based on expertise in the field. Sepeh Shakib and MvD provided input on the pharmacological aspects of the review. MD provided expert input on the current evidence base for cannabis in cancer care, having led the most recent edition of the clinical practice guidelines. Joanne Bowen, GK, TP, AZ and GC provided content expertise with respect to the clinical use of cannabis, and practical considerations for its use in people undergoing active treatment.</p>", "<title>Funding</title>", "<p>HW is supported by a Hospital Research Foundation Group Fellowship. KC, OB and LW are supported by the Australian Government Research Training Program (RTP) Domestic Stipend Scholarship. The team is supported by a research grant from the Medical Research Future Fund. Open Access funding enabled and organized by CAUL and its Member Institutions.</p>", "<title>Data availability</title>", "<p>There is no other relevant data from this manuscript.</p>", "<title>Competing interests</title>", "<p id=\"Par43\">Jaroslav Boublik is CEO LeafCann Research & Advisory P/L & Chief Scientist LeafCann Group P/L. The remaining authors declare no competing interests.</p>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par44\">Not applicable.</p>", "<title>Consent for publication</title>", "<p id=\"Par45\">All images are our own and can be used freely assuming appropriate reference to the original work is included.</p>" ]	[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>Simplified scheme representing the expression pattern of the main cannabinoid receptors, CNR1 and CNR2.</title><p>In addition to CNR1 and CNR2, including GPCRs (GPR18, GPR55, GPR3, GPR6, and GPR12), the receptor potential (TRP) channels (TRPV1, TRPV2, TRPV3, TRPV4, TRPA1 and TRPM8) and peroxisome proliferator-activated receptors (PPAR2 and PPARγ).</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>The centrality of gastrointestinal mucositis to infectious (orange), gastrointestinal (green) and neuropsychological (purple) symptoms commonly reported in people with advanced cancer undergoing chemotherapy.</title><p>This positions gastrointestinal mucositis as an ideal therapeutic target.</p></caption></fig>" ]	[]	[]	[]	[]	[]	[]	[]	[ "<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"41416_2023_2466_Fig1_HTML\" id=\"d32e473\"/>", "<graphic xlink:href=\"41416_2023_2466_Fig2_HTML\" id=\"d32e909\"/>" ]	[]	[{"label": ["1."], "surname": ["Berman", "Laird", "Minton", "Monnery", "Ahamed", "Boland"], "given-names": ["R", "BJA", "O", "D", "A", "E"], "article-title": ["The rise of supportive oncology: a revolution in cancer care"], "source": ["Clin Oncol"], "year": ["2023"], "volume": ["35"], "fpage": ["213"], "lpage": ["5"], "pub-id": ["10.1016/j.clon.2023.01.015"]}, {"label": ["8."], "mixed-citation": ["Schroyen G, Blommaert J, van Weehaeghe D, Sleurs C, Vandenbulcke M, Dedoncker N, et al. 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PMC10781686	38199982	[]	[]	[]	[]	[]	[ "<title>Subject terms</title>" ]	[ "<p id=\"Par1\">Correction to: <italic>Oncogenesis</italic> 10.1038/s41389-020-0229-9, published online 07 May 2020</p>", "<p id=\"Par2\">Following the publication of this study, the authors noted the omission of grant number 20204BCJL23052 (Jiangxi Provincial Natural Science Foundation of China) from the acknowledgments section. This has now been added.</p>", "<p id=\"Par3\">The original article has been corrected.</p>" ]	[]	[]	[]	[]	[]	[]	[]	[]	[]	[]	[]	[]	[]	{ "acronym": [], "definition": [] }	0	CC BY	no	2024-01-13 00:02:20	Oncogenesis. 2024 Jan 10; 13(1):5	oa_package/b8/0e/PMC10781686.tar.gz
PMC10781687	38199986	[ "<title>Introduction</title>", "<p id=\"Par3\">Rapid transformation of ecosystems combined with anthropogenic climate change are driving global declines in biodiversity and nature’s contributions to people (NCP)<sup>##UREF##0##1##</sup>. While habitat conversion for economic development has raised standards of living for many, the loss of NCP has negatively impacted millions of people<sup>##REF##33288690##2##</sup>. In an attempt to prevent further losses of biodiversity and NCP, nearly 200 nations have recently committed to effectively conserving and managing 30% of lands and waters by 2030 under the Kunming-Montreal Global Biodiversity Framework<sup>##UREF##1##3##</sup> and similar national targets (e.g., “America the Beautiful”<sup>##UREF##2##4##</sup>). Other proposals call for conserving 50% of global land area, or “Half-Earth”, to safeguard biodiversity and avert the most devastating effects of climate change<sup>##REF##32917614##5##,##UREF##3##6##</sup>. National governments have also initiated efforts to achieve targets related to the Paris Climate Agreement and the UN Sustainable Development Goals. There is a growing recognition that biodiversity, climate, and development goals are intertwined: addressing climate change is necessary to avoid further losses of biodiversity<sup>##UREF##0##1##</sup>, nature-based solutions are essential for mitigating and adapting to climate change<sup>##REF##29078344##7##</sup>, and conserving natural assets and addressing climate are both foundational for achieving sustainable development<sup>##UREF##4##8##</sup>. At the same time, ensuring food, energy, and livelihood security would require carefully planned development of agriculture, energy, urban expansion, and other sectors<sup>##UREF##5##9##</sup>. Unfortunately, this kind of coordinated planning is rare; where development is not designed to optimize NCP and biodiversity, conservation and development goals will conflict.</p>", "<p id=\"Par4\">Building on recent work<sup>##REF##36443466##10##</sup>, we present maps of joint priorities for ten important NCP as well as terrestrial biodiversity. We include one NCP with global benefits, due to its importance for mitigating climate change: vulnerable terrestrial ecosystem carbon storage, defined as the proportion of total ecosystem carbon that could be lost in a typical disturbance event<sup>##UREF##6##11##</sup>. We also include nine NCP with local or regional benefits<sup>##REF##36443466##10##</sup>: coastal risk reduction, flood regulation, sediment retention (important for reducing erosion and improving water quality), nitrogen retention for water quality regulation, crop pollination, fodder production for livestock (including grazing), fuel wood production, timber production, and access to nature (important for recreation as well as physical and mental well-being). All NCP are realized, either as an end use or benefit (e.g., timber harvest or livestock fodder production), or, where possible given current data, weighted by number of beneficiaries (e.g., count of people downstream, or count of people with reduced risk from coastal storm surge). We define areas providing 90% of all ten NCP as critical natural assets<sup>##REF##36443466##10##</sup>.</p>", "<p id=\"Par5\">As a measure of biodiversity, we use Area of Habitat (AOH) for each of 26,709 terrestrial vertebrate species<sup>##REF##31324345##12##</sup>. We conducted a global spatial optimization at a spatial resolution of 10 km to identify prioritized areas that simultaneously achieve target levels of all ten NCP and also achieve minimum species representation targets, independent of protection status. Species targets were consistent between all scenarios and were intended to represent both restricted-range and wide-ranging species. Following previous studies<sup>##REF##31324345##12##–##REF##30988288##15##</sup> we set representation targets that require 10–100% of each species’ AOH be conserved, based on the total AOH area (see “Methods”). We did not set area constraints for each country. To identify potential areas of conflict between conservation and development objectives, we overlaid the prioritized areas with estimates of development potential across major sectors, including commercial agriculture, renewable energy, mining, oil and gas, and urban expansion<sup>##REF##31249308##16##</sup>. While past research has explored the sufficiency of the global protected area (PA) network for biodiversity<sup>##REF##32269340##13##</sup>, the sufficiency of the PA network for jointly representing many NCP and species has not been explored. We therefore calculated the percentage of prioritized areas that fall within PAs and other effective area-based conservation measure (OECM) areas. We also ran a separate prioritization analysis to identify areas required to achieve targets beyond the current system of protected areas and OECM, by locking in such areas to the prioritization results.</p>" ]	[ "<title>Methods</title>", "<title>Nature’s contributions to people</title>", "<p id=\"Par25\">We included global maps of ten NCP that were first mapped in a previous analysis<sup>##REF##36443466##10##</sup> in a new optimization framework which includes biodiversity. The NCP included here are vulnerable ecosystem carbon storage, coastal risk reduction, flood regulation, sediment retention, nitrogen retention, crop pollination, fodder production for livestock (including grazing), fuel wood production, timber production, and access to nature. Vulnerable ecosystem carbon storage is mapped as the above-ground and below-ground ecosystem carbon lost in a “typical” disturbance event<sup>##UREF##6##11##</sup>. This includes terrestrial and coastal (mangrove, salt marsh, seagrass) ecosystem carbon pools (above-ground, below-ground, and soils), based on how much carbon is likely to be released if the ecosystem were converted. Coastal protection, sediment retention, nitrogen retention, and crop pollination were modeled using InVEST models<sup>##UREF##31##46##</sup>, adapted to be run at global scales<sup>##REF##36443466##10##,##UREF##30##45##</sup>. Fodder production for livestock, timber production, and fuelwood production were modeled using Version 3 of Co$ting Nature<sup>##REF##36443466##10##,##UREF##32##47##</sup>. Flood regulation was modeled using Version 2 of WaterWorld<sup>##REF##36443466##10##,##REF##30909028##48##,##UREF##33##49##</sup>. Access to nature was modeled as the number of urban and rural people<sup>##REF##33431572##50##</sup> within one hour of travel of natural and semi-natural habitat, taking the least-cost path (by foot, road, rail or boat) across a friction surface developed using data on roads, railroads, rivers, bodies of water, elevation and slope, land cover, and national borders<sup>##REF##36443466##10##,##REF##29320477##51##</sup>. This layer may overestimate the accessibility of nature for people who don’t have access to cars, and it doesn’t account for access rights nor physical barriers such as fences. Data sources, units of measurement, and the original spatial resolution of each modeled NCP are summarized in Supplementary Table ##SUPPL##0##1##; additional datasets included in our analysis are summarized in Supplementary Table ##SUPPL##0##2##.</p>", "<p id=\"Par26\">All NCP are realized, either as an end use or benefit (e.g., timber harvest or livestock fodder production per unit area of land), or, where possible given current data, weighted by number of beneficiaries<sup>##REF##36443466##10##</sup>. Beneficiaries include people downstream of habitats providing flood regulation or water quality benefits, coastal populations protected from coastal storm surge, or people within a certain travel time of natural habitats. We attributed all NCP to the natural and semi-natural land cover classes providing the benefit, excluding developed lands (croplands and urban areas) and unvegetated areas (Supplementary Table ##SUPPL##0##3##). We excluded Antarctica due to lack of data on NCP from that continent.</p>", "<title>Biodiversity (area of habitat, AOH)</title>", "<p id=\"Par27\">As a measure of biodiversity, we used species area of habitat (AOH)<sup>##REF##31324345##12##</sup> for all 26,709 species of birds, mammals, reptiles and amphibians for which data was available. AOH are based on species range maps from IUCN but refined using habitat preferences and elevational limits from IUCN Red List data<sup>##REF##31324345##12##</sup>. AOH are more specific than extent-of-occurrence (EOO) which can overestimate species range sizes<sup>##REF##17686977##52##</sup>. AOH areas “exclude areas of unsuitable habitat from each species’ range, which reduces commission errors and more closely approximates the actual occurrence of the species”<sup>##UREF##34##53##</sup>.</p>", "<p id=\"Par28\">Species AOH ranges were produced for all terrestrial vertebrates for which IUCN range polygon data is available<sup>##REF##31324345##12##</sup>. This includes 10,774 species of birds, 5219 mammals, 4462 reptiles and 6254 amphibians. Species range polygons obtained from the IUCN Red List spatial data portal<sup>##UREF##35##54##</sup> and the Birdlife International spatial data zone<sup>##UREF##36##55##</sup> were first filtered for “extant” range then rasterized to a global one km grid in the Eckert IV equal area projection. Individual species range rasters were then modified to only include land cover classes that match the habitat associations for each species. Habitat associations were obtained from the IUCN Red List species habitat classification scheme and were matched to ESA land cover classes for the year 2018<sup>##REF##30653250##56##</sup>. ESA land cover classification data was aggregated from its native 300 m resolution to match the global ten km grid using a majority rule. Species ranges were additionally filtered so that only areas within a species' accepted elevational range were included. Global elevation data derived from SRTM was obtained from WorldClim v. 2<sup>##UREF##37##57##</sup>. For bird species, seasonal range codes 1–3 (1 = year-round; 2 = breeding range; 3 = non-breeding range) were processed individually and stored as separate range files where applicable. Species targets used for the spatial optimization are described below.</p>", "<title>Spatial optimization</title>", "<p id=\"Par29\">We used linear programming techniques<sup>##UREF##38##58##</sup> to estimate how much land area is needed to provide different levels of NCP and/or achieve species representation targets. We identified areas that provide the highest value across all ten NCP using spatial prioritization procedures. Specifically, we generated prioritizations using the minimum set formulation of the reserve selection problem, and completing optimization procedures using linear programming techniques<sup>##REF##30988288##15##</sup>. These procedures were completed using the prioritizr R package<sup>##REF##30988288##15##,##UREF##39##59##</sup> and Gurobi<sup>##UREF##40##60##</sup>. Because our global-scale optimization included a large number of planning units (more than 20 million) along with 26,709 species and ten NCP features, prioritizr stood out as both computationally efficient and, when combined with Gurobi, sufficiently powerful to solve large optimization problems<sup>##UREF##39##59##</sup>.</p>", "<p id=\"Par30\">The minimum set formulation of the reserve selection problem seeks to minimize the overall cost of the prioritization, whilst ensuring that representation targets are met for all of the conservation features. To define this formulation mathematically, let <italic>I</italic> denote the set of planning units (indexed by <italic>i</italic>) and <italic>J</italic> denote the set of features (indexed by <italic>j</italic>). Also, let <italic>c</italic><sub><italic>i</italic></sub> denote the cost of planning units <italic>i</italic> ∈ <italic>I</italic>, <italic>r</italic><sub><italic>ij</italic></sub> denote the amount of features <italic>j</italic> ∈ <italic>J</italic> in planning units i ∈ <italic>I</italic>, and <italic>T</italic><sub><italic>j</italic></sub> denote the targets for feature <italic>j</italic> ∈ <italic>J</italic>. Additionally, the decision variables are the <italic>x</italic><sub><italic>i</italic></sub> variables, which indicate if planning units <italic>i</italic> ∈ <italic>I</italic> are selected, or not, for prioritization (using values of one and zero, respectively). Given these variables, the problem can be formulated following:subject to</p>", "<p id=\"Par31\">The objective function (Eq. (##FORMU##0##1##)) is to minimize the cost of selected planning units. Constraints (Eq. (##FORMU##1##2##)) are used to ensure that the representation targets are met.</p>", "<p id=\"Par32\">To explore the land area required to maintain different levels of NCP provision, we ran the optimization using 19 different targets ranging from 5 to 95% of total NCP value, across all ten NCP, at 5% increments.</p>", "<p id=\"Par33\">To explore how much additional area is required to conserve biodiversity, we added species representation targets, using data on extent of suitable habitat, or area of habitat (AOH)<sup>##REF##31324345##12##</sup> data for all species of mammals, reptiles, amphibians, and birds for which data was available (26,709 species in total). We followed previous studies which established targets based on species’ habitat size, with the goal of ensuring that both restricted-range and wide-ranging species are represented<sup>##REF##31324345##12##–##REF##30988288##15##</sup>. We assigned a 100% threshold to species with less than 1000 km<sup>2</sup> of suitable habitat (2391 species of amphibians, 1024 birds, 680 mammals, and 1264 reptiles), a 10% threshold to species with more than 250,000 km<sup>2</sup> of suitable habitat (695 amphibians, 5600 birds, 1758 mammals, and 589 reptiles), and log-linearly interpolated thresholds for species with intermediate amounts of suitable habitat (2872 amphibians, 6296 birds, 2607 mammals, and 2168 reptiles; migratory bird species were assigned targets for each seasonal distribution separately). We also assigned a cap of 1,000,000 km<sup>2</sup> for species with a large amount of suitable habitat (>10,000,000 km<sup>2</sup>) (six amphibians, 148 birds, 57 mammals, and six reptiles). These targets should be considered minimum representation targets as they do not account for habitat connectivity, ecological intactness<sup>##UREF##27##42##</sup>, species traits<sup>##UREF##26##41##</sup>, evolutionary processes, ecosystem representation<sup>##UREF##28##43##</sup>, genetic diversity, or other important dimensions of biodiversity. Species targets are also summarized in Supplementary Table ##SUPPL##0##4##. Species targets were consistent across all scenarios (that is, NCP targets varied from 5–95% across scenarios, but species targets were achieved in all scenarios.)</p>", "<p id=\"Par34\">To develop the maps in Fig. ##FIG##1##2##, we combined (summed) the optimization results for NCP targets ranging from 5–90%. Darker blue areas in Fig. ##FIG##1##2## provide the highest levels of NCP per unit area (collectively providing 5% of current levels of all ten NCP in the least area) and lighter yellow areas provide lower levels of NCP per unit area (collectively, the dark blue to light yellow areas provide 90% of all ten NCP). Species targets were held constant across all scenarios.</p>", "<p id=\"Par35\">For subsequent analyses, we focused on prioritized areas, defined as areas providing 90% of current levels of all ten NCP which also meet all species targets. We overlaid prioritized areas with data on development potential to examine overall conversion risk as well as risks from major sectors.</p>", "<p id=\"Par36\">Separately, we also ran scenarios in which protected areas from the World Database on Protected Areas<sup>##UREF##8##17##</sup> were locked in to the spatial prioritization (that is, protected areas were required as part of the solution in each scenario). This allowed us to estimate how much additional land area would be required to achieve NCP and species representation targets, beyond the current system of protected areas (Figs. ##FIG##0##1## and ##FIG##1##2## and Supplementary Table ##SUPPL##0##5##).</p>", "<p id=\"Par37\">For the scenarios that included NCP (but not species), we ran the optimizations globally and at a spatial resolution of two km. Due to computational constraints and the large number of planning units (more than 20 million) and species (26,709), 10 km was the finest resolution at which we were able to run optimizations for scenarios that contained both NCP and species targets. After running the optimizations, we masked the 10 km optimization results using natural and semi-natural landcover data<sup>##UREF##41##61##</sup> at two km. This allowed us to more precisely map and quantify the natural and semi-natural habitats providing NCP, and to align our results with NCP-only scenarios run at a higher spatial resolution<sup>##REF##36443466##10##</sup>. The NCP models used here assume low or no provision of NCP in sparsely vegetated areas (such as the extremely arid deserts of the Sahara, the Australian outback, and the Arabian peninsula) and human-modified habitats (such as intensive croplands and developed urban areas.) Many species rely on deserts and modified landscapes, however. To address this, we re-included the prioritized areas for species (at 10 km), ensuring that species that rely on deserts and human-modified habitats were included.</p>", "<p id=\"Par38\">Computational limitations associated with optimizing across the large number of planning units (more than 20 million) and features (more than 26,000) prevented use of a contiguity criterion, which would have selected adjacent (contiguous) planning units when possible. Consequently, though our results are area efficient, they include prioritized areas that are not contiguous in certain regions and may thus require additional planning for implementation. Global priorities such as those provided here can be informative, but should always be combined with local information, including existing land use, to inform decision making.</p>", "<p id=\"Par39\">In the present study, solutions which achieved all targets in the least amount of land area (minimum land area) are used in place of a minimum cost objective. Globally available data on opportunity costs of conservation for agriculture (e.g., Naidoo and Iwamura<sup>##UREF##42##62##</sup>) have limitations (e.g., lack of information about potential land uses other than agriculture) and were considered unsuitable for this analysis. Proxy indicators such as gridded GDP were also considered a poor measure of cost since costs of safeguarding NCP and biodiversity may be poorly correlated with national or local estimates of economic productivity. Areas with high value for NCP and biodiversity that also have high suitability for development may have high opportunity costs of conservation, due to their potential value for alternative land uses. Instead of a measure of cost, therefore, we separately include data on development potential across major sectors<sup>##REF##31249308##16##</sup>.</p>", "<title>Development pressure</title>", "<p id=\"Par40\">We integrated spatially-explicit estimates of the potential for habitat conversion for development across several economic sectors<sup>##REF##31249308##16##</sup>. To create a development pressure map (Supplementary Fig. ##SUPPL##0##6##), we used published Development Potential Indices (DPIs)<sup>##REF##31249308##16##</sup> for renewable energy (concentrated and photovoltaic solar power, wind power, and hydropower), oil and gas (conventional and unconventional oil and gas), mining (coal, metallic and non-metallic mining), and commercial agriculture (crop and biofuels expansion). For urban expansion pressure, we created an Urban Pressure Index (UPI) following similar methodologies and categorization techniques as the DPIs using urban expansion probabilities<sup>##REF##31000723##63##</sup> (see Supplementary Materials for details on the UPI).</p>", "<p id=\"Par41\">DPIs are global, spatially explicit one km resolution maps that depict the suitability of land for potential expansion by agriculture, renewable energy, oil and gas, mining, and urbanization. Each DPI has standardized 0–1 values that account for sector-specific land constraints that restrict development (e.g., suitable land cover, slope); land suitability for sector expansion based on resource availability (sector-specific yields); and siting feasibility of new development (e.g., ability to transport resources or materials, access to demand centers, existing development, and other economic costs associated with resource siting). For each of the 14 DPIs, we binned the range of values represented into six categories based on standardized <italic>z</italic>-score ranges to characterize development pressure as very low (≤10th percentile), low (>10th–25th percentile), medium-low (>25th–50th percentile), medium-high (>50th–75th percentile), high (>75th–90th percentile), and very high (>90th percentile). We calculated <italic>z</italic>-scores by mean-standardizing values per country to capture national-level domestic demand coupled with global-level demand likely to drive national-level resource extraction to occur within each countries’ highest development suitability for that resource. To identify regions of high development pressure, we retained the highest value within the 14 classified DPIs (Supplementary Fig. ##SUPPL##0##6a##) and then selected the high and very high classified cells (i.e., values 5 and 6) (Supplementary Fig. ##SUPPL##0##6b##).</p>", "<title>Protected areas</title>", "<p id=\"Par42\">We evaluated the extent to which currently protected areas and other effective area-based conservation measures (OECM) might achieve targets and maintain NCP, and how much additional land area might require conservation or stewardship. For this step, we compiled spatial data to delineate the boundaries of protected areas and other conservation areas worldwide. To achieve this, we obtained the World Database on Protected Areas (WDPA) and the World Database on Other Effective Area-Based Conservation Measures (WDOECM)<sup>##UREF##8##17##</sup>. We prepared these data for analysis following standard practices (using the wdpar R package)<sup>##UREF##43##64##</sup>. Briefly, we (1) excluded sites within an unknown or proposed designation, (2) excluded UNESCO Biosphere Reserves<sup>##REF##23701641##65##</sup>, (3) transformed the site boundaries to avoid numerical issues associated with geometries that cross the dateline, (4) reprojected the data to an equal-area coordinate reference system (World Behrmann; ESRI:54017), (5) replaced sites represented as point localities with circular reserves matching their reported area<sup>##REF##23869663##66##</sup>, (6) removed slivers and (7) excluded marine protected areas. Additionally, throughout this process, we implemented routines to detect and repair invalid geometries.</p>", "<p id=\"Par43\">We included WDPA and OECM areas in our analysis in two different ways. First, to calculate the percentage of prioritized areas that are currently protected or effectively conserved, we overlaid prioritized areas with the combined WDPA and OECM areas (Supplementary Fig. ##SUPPL##0##2##). Second, to calculate how much additional land area would be required to achieve NCP and species representation targets, beyond the current system of protected areas, we ran separate spatial optimization scenarios in which WDPA and OECM areas were locked in (Fig. ##FIG##1##2b##). Because protected areas and OECM do not necessarily overlap with prioritized areas, locking them in to the prioritization solutions results in larger total land areas to achieve targets (Fig. ##FIG##0##1##).</p>", "<title>Spatial resolution</title>", "<p id=\"Par44\">To test the effect of spatial resolution on our results, we conducted prioritizations for NCP at four different resolutions: two, three, five, and 10 km. At coarser resolutions, more land area is required to achieve NCP targets (Supplementary Fig. ##SUPPL##0##7## and Supplementary Table ##SUPPL##0##6##), consistent with previous studies<sup>##REF##22268786##67##</sup>. Due to the global geographic scope and the large number of species (26,709), prioritizations at finer spatial resolutions for both NCP and species were beyond the scope of this analysis, which relied on traditional computational resources. To address this issue, and to bring our results in line with previous work<sup>##REF##36443466##10##</sup>, we masked the 10 km prioritization results to natural and semi-natural habitat data at a finer spatial resolution (two km), which more precisely identifies the two km habitat grid cells which provide NCP within each 10 km grid cell. Spatial analyses other than optimizations were conducted using R<sup>##UREF##44##68##</sup>, QGIS<sup>##UREF##45##69##</sup>, and ArcGIS Desktop<sup>##UREF##46##70##</sup>.</p>", "<title>Reporting summary</title>", "<p id=\"Par45\">Further information on research design is available in the ##SUPPL##5##Nature Portfolio Reporting Summary## linked to this article.</p>" ]	[ "<title>Results</title>", "<title>Prioritized areas for NCP and species</title>", "<p id=\"Par6\">Our results indicate that conserving 44% of global land area, excluding Antarctica, could provide 90% of current levels of ten NCP and meet minimum representation targets for 26,709 terrestrial vertebrate species, if spatially optimized and coordinated among nations (Figs. ##FIG##0##1## and ##FIG##1##2##). If the current network of PA and OECM sites are locked in to the optimization results, the percent of global land area required to achieve targets increases to 49%. If species targets are not included, 90% of NCP could be provided with 36% of global land area, but many of the areas required to achieve NCP and species targets overlap (Fig. ##FIG##2##3##), demonstrating the opportunity for synergies between maintaining NCP and conserving biodiversity.</p>", "<p id=\"Par7\">Prioritized areas for NCP and species are distributed unevenly across countries, including species-rich areas within the Amazon and Congo basins, Papua New Guinea and Indonesia, and southeastern Australia, which also contain high levels of vulnerable ecosystem carbon storage<sup>##UREF##6##11##</sup> (Fig. ##FIG##1##2a##). Other notable areas include regions with unusually high endemism or restricted-range species, including the Himalayas (also important for water quality, flood regulation, and fuelwood production), the Andes (grazing and water quality), New Zealand (water quality, grazing), eastern Madagascar (vulnerable carbon, sediment retention, fuelwood), the Caribbean islands (water quality, pollination, nature access, and grazing), montane regions of Central America (nature access, fuelwood, grazing, pollination), western India (multiple NCP), and islands in Oceania. Western Europe (nature access, pollination and grazing) and the Yangtze basin (water quality, flood regulation, pollination, fuelwood, and timber) also provide globally high levels of NCP and important habitat for many species.</p>", "<p id=\"Par8\">While species targets can be achieved with relatively little additional land area, when compared to areas prioritized solely for NCP, the inclusion of species targets changes the spatial distribution of priority areas slightly (Fig. ##FIG##2##3##). For example, sparsely vegetated arid lands (e.g., southwestern USA, western Australia) and northern latitudes (e.g., northern Canada and Russia), contain important biodiversity but relatively lower levels of the NCP modeled here due to sparse vegetation and/or lower human population densities (Supplementary Fig. ##SUPPL##0##1##). The NCP included in this analysis, which can be modeled with globally available data, tend to be concentrated in regions with dense vegetation and in areas accessible to, or upstream of, human populations<sup>##REF##36443466##10##</sup>.</p>", "<p id=\"Par9\">Only 18% of the prioritized areas for NCP and biodiversity are currently protected, based on the World Database on Protected Areas (WDPA), which includes other effective area-based conservation measures (OECM)<sup>##UREF##8##17##</sup> (Supplementary Fig. ##SUPPL##0##2##). We found that conserving or sustainably managing an additional 34% of land area beyond the current system of protected areas and OECM (49% of global land area) would be required to provide 90% of current levels of NCP and meet species representation targets (Fig. ##FIG##0##1##).</p>", "<title>Prioritized areas with high development potential</title>", "<p id=\"Par10\">More than one-third (37%) of areas prioritized for NCP and species also have high development potential for commercial agriculture, renewable energy, oil and gas, mining or urban expansion (equivalent to 16% of global land area) (Fig. ##FIG##3##4a##). Only 11% of such areas are currently protected, which may result in future conflicts between development and conservation objectives. The renewable energy sector (concentrated solar power, photovoltaic solar, wind, and hydropower) comprises the largest share of areas with high development potential globally<sup>##REF##31249308##16##</sup>, and overlaps with 10% of prioritized areas (4% of global land area) (Fig. ##FIG##3##4b##). Though renewable energy is needed to avert catastrophic effects of climate change and can be implemented in ways that are compatible with NCP<sup>##UREF##9##18##</sup> our findings underscore the need to carefully plan, site, and evaluate tradeoffs with other objectives<sup>##UREF##10##19##,##UREF##11##20##</sup>. Constraining new projects to already cleared or degraded lands, for example, would reduce conflicts between renewable energy and biodiversity conservation goals<sup>##UREF##9##18##,##UREF##11##20##,##UREF##12##21##</sup>.</p>", "<p id=\"Par11\">Areas with high suitability for commercial agriculture (including crops and biofuels) overlap with 7% of prioritized areas (3% of global land area) (Fig. ##FIG##3##4b##). While agricultural expansion can support food security, if not implemented sustainably, conversion of natural ecosystems to croplands may undermine nature’s other contributions<sup>##UREF##13##22##</sup>. These include benefits to existing agricultural systems such as pollination, sediment retention, and flood mitigation. Policies promoting food security should therefore consider the contributions of croplands as well as natural and semi-natural habitats to food systems.</p>", "<p id=\"Par12\">Mining, which overlaps with 6% of prioritized areas (3% of global land area), and oil and gas development (5% of prioritized areas, 2% of global land area) could create more localized but severe hazards for NCP and species, and are a cause for concern in parts of Western Asia, North America, and the Amazon.</p>", "<p id=\"Par13\">For six of the world’s fourteen biomes (broad habitat types), at least one-quarter of their areas contain prioritized areas and are highly suitable for development, making these habitats of special concern. These include mangroves, temperate broadleaf and mixed forests, flooded grasslands and savannas, tropical and subtropical dry broadleaf forests, temperate conifer forests, and temperate grasslands, savannas and shrublands (Supplementary Figs. ##SUPPL##0##3## and ##SUPPL##0##4## and Supplementary Data ##SUPPL##3##1## and ##SUPPL##4##2##). In these habitats, future development should be carefully sited and planned to avoid negatively impacting nature’s contributions for people and biodiversity.</p>", "<p id=\"Par14\">Geographically, prioritized areas overlap with areas of high development potential across 31% of the land area of Oceania, 25% of South America, 23% of Europe, 20% of North America, 17% of Africa, 15% of Australia, and 11% of Asia (Supplementary Data ##SUPPL##4##2##). More than half of the land area of certain countries such as Gambia (63%), Ireland (60%), and Jamaica (53%) contain globally prioritized areas with high development potential (Supplementary Fig. ##SUPPL##0##4## and Supplementary Data ##SUPPL##4##2##). These patterns are driven by the co-occurrence of NCP, species, and development pressures. For example, areas with dense vegetation (such as tropical forests) in proximity to, or upstream of, human populations may be simultaneously important for biodiversity, NCP (carbon storage, provision of timber and fuelwood, access for recreation), while also being highly suitable for certain kinds of development, such as palm oil.</p>", "<p id=\"Par15\">The co-occurrence of NCP, biodiversity and development pressures aren’t limited to forests, however. New Zealand, for example, contains large numbers of endemic species as well as extensive areas important for grazing, pollination, and sediment retention, all overlapping with areas of high potential for expansion by oil and gas, mining, and renewable energy. European countries such as Ireland, the UK, Estonia, Latvia, Finland, and the Netherlands contain extensive areas important for grazing, access to nature, and sediment and nitrogen retention that overlap with areas with high development potential for expansion by agriculture, renewable energy, and mining. Conversely, some countries have only a small fraction of their land area in globally prioritized areas suitable for development, including high income countries such as Denmark (1%), Saudi Arabia (4%), and Iceland (4%).</p>" ]	[ "<title>Discussion</title>", "<p id=\"Par16\">Our study offers a starting point to identify global targets and broad priority regions for conservation and sustainable use investments. We build on a history of efforts to define global biodiversity hotspots<sup>##REF##10706275##23##</sup> by adding two important new considerations: the diverse contributions of nature to people and potential conflicts with expansion of agriculture, energy, extractive industries, or urban development projects. To our knowledge, this is the most comprehensive effort to bring together data on NCP, biodiversity, and development pressures at a global scale. Our results lend support to proposals to conserve at least 30% of the planet by 2030, as well as proposals to conserve “Half-Earth” for biodiversity and its benefits to humanity. Our estimates of the proportion of global land area needed to achieve species and NCP targets (44–49%) are likely conservative, given that estimates based on biodiversity conservation alone range from 34–44%<sup>##REF##32269340##13##,##REF##35653463##24##</sup>. The logistical challenges of conserving or sustainably managing small non-contiguous priority areas, and the likelihood that not all areas would be effectively conserved, also imply that more land area would be needed to achieve species and NCP targets.</p>", "<p id=\"Par17\">By providing consistent and comparable data across countries, global maps can facilitate the establishment of international targets and highlight where broad action and investment may be most impactful<sup>##UREF##14##25##</sup>. Our work builds on previous efforts that focused on national-scale NCP priorities that accrue at local- to regional-scales<sup>##REF##36443466##10##</sup>. Here we also include areas required for conserving vulnerable ecosystem carbon stocks (a global benefit) as well as biodiversity. Our aim in this paper was to identify global-scale priorities to support processes such as the Global Biodiversity Framework, and to inform funding priorities of actors with a worldwide remit. Within priority areas, national and sub-national planning also can benefit from understanding the global significance of local conservation efforts. Global priorities also can support efforts of less wealthy nations to secure resources to achieve shared global targets.</p>", "<p id=\"Par18\">Many NCP (such as water quality regulation, flood mitigation, and carbon storage) cross national borders, as do many species (migratory birds, wide-ranging mammals) therefore identifying areas of global importance is a key first step. Nonetheless, we recognize that most development and conservation decision-making takes place at national and sub-national scales. In previous work, we provided national-scale priorities for NCP<sup>##REF##36443466##10##</sup>. Here, we provide globally optimized results disaggregated by country (Supplementary Figs. ##SUPPL##0##3## and ##SUPPL##0##4## and Supplementary Data ##SUPPL##3##1## and ##SUPPL##4##2##). We also provide spatial results from the global optimization scenarios to support finer-scale prioritization or decision making<sup>##UREF##15##26##</sup>. Other recent work has compared global and national-scale priorities for biodiversity and carbon<sup>##UREF##16##27##</sup>. In all cases, finer-scale information related to conservation feasibility, costs, and the rights and preferences of local people should be combined with global- or national-scale priorities to identify appropriate interventions for particular locations<sup>##UREF##14##25##</sup>. Furthermore, conservation and development projects should always be co-developed in partnership with Indigenous peoples and local communities to respect local perspectives and sovereignty, and to result in more effective and equitable outcomes<sup>##UREF##17##28##</sup>.</p>", "<p id=\"Par19\">While our maps identify areas in urgent need of conservation attention, they are not intended to define priorities for strict protection. Strictly protected areas preclude activities such as grazing or timber harvesting which are essential to the provision of certain NCP. Furthermore, the current PA and OECM networks are disproportionately located in remote areas with relatively low threat<sup>##REF##20011603##29##</sup>, and do not represent important areas for NCP particularly well (Supplementary Fig. ##SUPPL##0##2##), as NCP tend to be concentrated in areas with natural and semi-natural habitat in proximity to human populations. That said, due to data limitations for both NCP and biodiversity, we do not recommend degazetting currently protected areas on the basis of our maps alone. Other conservation measures, including OECMs, strengthening Indigenous and local land tenure, Payments for Ecosystem Services (PES), and sustainable management will be essential for conserving NCP and biodiversity outside of the current system of protected areas. For example, areas providing high levels of water quality, flood regulation, and timber production could be targeted for PES, certification, or other mechanisms. Areas required to achieve species targets that also contain vulnerable carbon could be candidates for Indigenous, local, or government protection; but methods other than protection can also be effective at maintaining biodiversity and carbon stocks. Our maps of prioritized areas include both natural and semi-natural (e.g., grazed pasture, commercial forestry) landcover classes. In such areas, the goal would be to maintain sustainable flows of NCP while also conserving biodiversity.</p>", "<p id=\"Par20\">Our optimization results represent a best-case scenario in which conservation efforts are internationally coordinated. Our findings indicate that conserving 30% of global land area could, if optimally allocated, represent areas supplying 65% of current levels of NCP while also meeting species representation targets. If the current system of PAs and OECM are locked in to the optimization scenario, 30% of land area only provides 45% of NCP while also achieving species targets. This provides a clue that expansion of protected areas, even if nations were to reach a 30% area target, will at best represent 45% of current levels of NCP. Also, given the many barriers to optimally targeting conservation action and investments, our estimates of the area required to achieve targets are likely conservative.</p>", "<p id=\"Par21\">Conversely, areas not identified as priorities in our analysis may contain valuable NCP and biodiversity that are not well represented in globally available data, therefore supplementing global-scale priorities with local data, as well as data on NCP and biodiversity not represented here, is essential<sup>##UREF##14##25##</sup>. Furthermore, our maps provide an indicator of areas where certain land uses may conflict with conservation in the future, but shifts in demand for energy and commodities, and ever-changing policies and incentives, make it very challenging to predict exactly which areas will actually be developed<sup>##UREF##10##19##</sup>. Our global estimates of areas with high suitability generally reflect patterns of expansion when production demands are considered<sup>##UREF##10##19##</sup>, and capture areas of projected tree cover loss<sup>##UREF##18##30##</sup> and urban and cropland expansion<sup>##UREF##19##31##</sup> by other studies (Supplementary Fig. ##SUPPL##0##5##). However, we recognize that our development pressure map may under- or over-estimate development threats in certain regions. Where possible, new development should be constrained to already cleared or degraded areas<sup>##UREF##9##18##–##UREF##11##20##</sup>. Certain forms of development, if appropriately located and carefully designed, may be compatible with the ongoing provision of NCP and biodiversity conservation. Examples include water-sensitive urban design that enhances biodiversity, such as green roofs and rain gardens<sup>##UREF##20##32##</sup> and solar energy farms that can double as livestock enclosures<sup>##UREF##21##33##</sup>, enhance crop production<sup>##UREF##22##34##</sup> or provide habitat for pollinators and other ecosystem services<sup>##UREF##23##35##</sup>.</p>", "<p id=\"Par22\">The large disparities between countries in terms of levels of NCP, biodiversity, and development potential highlights the importance of international cooperation<sup>##UREF##16##27##</sup>. Countries with larger conservation responsibilities but without sufficient domestic resources will require access to international funding from their wealthier peers, via mechanisms such as the Global Environmental Facility<sup>##UREF##24##36##</sup>. The Global Biodiversity Framework includes a target of increasing biodiversity-related funding from developed countries to developing countries to at least USD 30 billion per year by 2030<sup>##UREF##1##3##</sup>. An alternative approach is for all countries to set consistent targets, such as conserving thirty percent of their land area<sup>##UREF##2##4##</sup>. While resulting in more equitable distribution of land areas between countries, consistent area-based targets requires more land area overall to achieve targets<sup>##REF##36443466##10##,##UREF##16##27##</sup>, and risks missing the mark for NCP and species which are disproportionately concentrated in a minority of countries.</p>", "<p id=\"Par23\">While our analysis includes a large number of NCP and species, the areas we identified are still an underestimate of the true extent of natural ecosystems needed to sustain all life on earth. Advances in NCP modeling and data availability will soon make it possible to model additional NCP at global scales<sup>##UREF##25##37##</sup>. Biodiversity priorities will benefit from data on additional taxa such as plants<sup>##REF##34429536##38##</sup> and invertebrates, marine<sup>##REF##33731930##39##</sup> and freshwater<sup>##REF##32284631##40##</sup> species. More comprehensive biodiversity priorities should also incorporate other important dimensions of biodiversity such as evolutionary processes, species traits<sup>##UREF##26##41##</sup>, intactness of ecosystems<sup>##UREF##27##42##</sup>, and ecosystem representation<sup>##UREF##28##43##</sup>, many of which are represented in Key Biodiversity Areas<sup>##UREF##29##44##</sup>. Computational limitations constrained the spatial resolution of our analysis; future research to conduct prioritizations using more powerful computing resources could advance our understanding of finer-scale patterns and priorities. Projected future demand for NCP due to changes in climate, population, and consumption patterns, as well as species responses to climate change, could help identify ecosystems that will become critical in the future<sup>##UREF##30##45##</sup>.</p>", "<p id=\"Par24\">To date, international agreements such as the recently adopted Global Biodiversity Framework and the Paris Climate Agreement have largely ignored nature’s many other contributions to human life and well-being. The natural ecosystems we identify here underpin at least half of the UN Sustainable Development Goals, including providing clean water, reducing hunger, contributing to climate resilience, renewable energy, and supporting health and well-being<sup>##UREF##4##8##</sup>. Given that every person on the planet benefits from nature, and ~87% of the global population, 6.4 billion people, benefit locally from critical natural assets<sup>##REF##36443466##10##</sup>, conservation and climate targets should more explicitly incorporate the many other benefits that nature provides to humanity. Future global negotiations must move beyond considering biodiversity, climate, and sustainable development in isolation to jointly consider the multitude of nature’s contributions to life, livelihoods, and cultures on earth.</p>" ]	[]	[ "<p id=\"Par1\">Meeting global commitments to conservation, climate, and sustainable development requires consideration of synergies and tradeoffs among targets. We evaluate the spatial congruence of ecosystems providing globally high levels of nature’s contributions to people, biodiversity, and areas with high development potential across several sectors. We find that conserving approximately half of global land area through protection or sustainable management could provide 90% of the current levels of ten of nature’s contributions to people and meet minimum representation targets for 26,709 terrestrial vertebrate species. This finding supports recent commitments by national governments under the Global Biodiversity Framework to conserve at least 30% of global lands and waters, and proposals to conserve half of the Earth. More than one-third of areas required for conserving nature’s contributions to people and species are also highly suitable for agriculture, renewable energy, oil and gas, mining, or urban expansion. This indicates potential conflicts among conservation, climate and development goals.</p>", "<p id=\"Par2\">This study shows that conserving approximately half of global land area through protection or sustainable management could provide 90% of ten of nature’s contributions to people and could meet representation targets for 26,709 species of mammals, birds, amphibians, and reptiles. This finding supports recent commitments to conserve at least 30% of global lands and waters by 2030.</p>", "<title>Subject terms</title>" ]	[ "<title>Supplementary information</title>", "<p>\n\n\n\n\n\n\n</p>" ]	[ "<title>Supplementary information</title>", "<p>The online version contains supplementary material available at 10.1038/s41467-023-43832-9.</p>", "<title>Acknowledgements</title>", "<p>The authors gratefully acknowledge Christopher Barrett, Pamela Collins, Alexandra Goldstein, Catherine Kling, Jeffrey Milder, Monica Noon, and Will Turner for their insight and intellectual contributions. We gratefully acknowledge funding support from the Cornell University Department of Natural Resources and the Environment (R.A.N.) the Cornell Lab of Ornithology (R.A.N., A.D.R., M.S.M.), Betty and Gordon Moore (R.A.N., P.R.R., D.H.), Conservation International (R.A.N., P.R.R., D.H.), The Natural Capital Project (R.C.K., R.P.S.), SPRING (R.C.K., R.P.S.), John and Jody Arnhold (P.R.R.), Environment and Climate Change Canada (J.O.H.), Nature Conservancy of Canada (J.O.H., R.S.) The Nature Conservancy (C.M.K., J.R.O., J.K.), the Liber Ero Fellowship (R.S.), and One Earth (C.M.K., J.R.O.). We thank the National Science Foundation for funding through Graduate Research Fellowship DGE—2139899 (R.A.N.) and NSF awards 2225078 and 2225076 (P.R.R.). EC Horizon 2020 ReSET project grant 101017857 (M.M., A.v.S.), UKRI/F4B Nature Finance Seed Corn Grant (M.M., A.v.S.).</p>", "<title>Author contributions</title>", "<p>R.A.N., R.C.K., D.H., S.P., and A.D.R. conceptualized and designed the analysis. R.A.N., R.C.K., R.P.S., R.S., M.S.M., P.R.R., M.M., A.v.S., C.M.K., J.R.O., J.K., J.A.J., S.P., and J.O.H. contributed to the acquisition, analysis, and interpretation of data. R.P.S., M.M., J.A.J., and J.O.H. created new software and code used in the analysis. R.A.N., R.C.K., and A.D.R. drafted and substantially revised the manuscript. A.D.R. supervised the study. All authors contributed to reviewing and revising the manuscript and approved the final submitted version.</p>", "<title>Peer review</title>", "<title>Peer review information</title>", "<p id=\"Par46\"><italic>Nature Communications</italic> thanks Javier Nori and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available.</p>", "<title>Data availability</title>", "<p>The data on prioritized areas and high development potential areas generated in this study have been deposited in the Zenodo database<sup>##UREF##15##26##</sup>. The prioritized areas data disaggregated by country, continent, and biome generated in this study are provided in Supplementary Data files ##SUPPL##3##1## and ##SUPPL##4##2##. Data on nature’s contributions to people used in this study are available in the Open Science Framework database<sup>##UREF##47##71##</sup> and can be visualized at: <ext-link ext-link-type=\"uri\" xlink:href=\"https://bit.ly/3Jk8vDo\">https://bit.ly/3Jk8vDo</ext-link>. The biodiversity data used in this study are available under restricted access for non-commercial use, access can be obtained by request (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.iucnredlist.org/resources/spatial-data-download\">https://www.iucnredlist.org/resources/spatial-data-download</ext-link>). The WDPA and OECM data used in this study are available under restricted access for non-commercial use, access can be obtained by request (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.protectedplanet.net\">www.protectedplanet.net</ext-link>). The vulnerable carbon data used in this study are available in the Zenodo database<sup>##UREF##48##72##</sup>. The data on projected tree cover loss used in this study are also available in the Zenodo database<sup>##UREF##49##73##</sup>. The data on areas vulnerable to land cover change used in this study are available from ArcGIS Online (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.arcgis.com/home/item.html?id=645c280931ac486cadb92c828eac09e3\">https://www.arcgis.com/home/item.html?id=645c280931ac486cadb92c828eac09e3</ext-link>).</p>", "<title>Code availability</title>", "<p>Code is available on Zenodo: <ext-link ext-link-type=\"uri\" xlink:href=\"https://zenodo.org/record/8225989\">https://zenodo.org/record/8225989</ext-link>.</p>", "<title>Competing interests</title>", "<p id=\"Par47\">The authors declare no competing interests.</p>" ]	[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>Percentage of global land area required to provide different levels of NCP.</title><p>Prioritized areas for nature’s contributions to people (NCP) (blue squares) with species targets included (red circles) and with protected areas (PAs) and other effective area-based mechanisms (OECM) sites locked in (orange diamonds). Vertical dashed lines correspond to 30 and 50% of global land area. The horizontal dashed line corresponds to 90% of NCP.</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>Prioritized areas for nature’s contributions to people (NCP) and biodiversity.</title><p><bold>a</bold> Combined prioritization results for all species representation targets and NCP targets ranging from 5% (dark blue) to 90% (light yellow). <bold>b</bold> Combined prioritization results for NCP and species with the World Database of Protected Areas (WDPA) and other effective area-based conservation mechanisms (OECM) sites locked in to prioritization results. In all cases, dark blue areas represent areas required to achieve targets in the least amount of area. Collectively, dark blue to light yellow areas provide 90% of all ten NCP and meet species representation targets in the least amount of area. Prioritized areas achieve all species representation targets (see main text); only the level of NCP achieved varies.</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><title>Prioritized areas for nature’s contributions to people (NCP) only (90% of current levels of NCP, in blue), prioritized areas for both NCP (90% of current levels) and that also meet all species targets (red), and areas of overlap (purple).</title><p>Prioritized areas overlap over 33% of global land area (representing 94% of areas prioritized for NCP alone, or 75% of areas prioritized for NCP and species).</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><title>Prioritized areas for nature’s contributions to people and biodiversity (NCP) that also have high development potential across several economic sectors.</title><p><bold>a</bold> Prioritized areas (blue) represent areas providing 90% of current levels of NCP while also achieving all species targets. Areas with high and very high development potential (orange) and areas of overlap (green). <bold>b</bold> Prioritized areas with high development potential (areas of overlap), by economic sector. Sectors include agriculture (crops and biofuels expansion) (green); mining (metallic, non-metallic, and coal) (pink); oil and gas (conventional and unconventional) (yellow); renewable energy (concentrated solar power, photovoltaic solar, wind, and hydropower) (red); urban expansion (light blue), and “multiple sectors” where sectors overlap (dark blue).</p></caption></fig>" ]	[]	[ "<disp-formula id=\"Equ1\"><label>1</label><alternatives><tex-math id=\"M1\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{{{{\\rm{Minimize}}}}}}\\mathop{\\sum }\\limits_{i=1}^{I}{x}_{i}{c}_{i}$$\\end{document}</tex-math><mml:math id=\"M2\"><mml:mi mathvariant=\"normal\">Minimize</mml:mi><mml:munderover accent=\"false\" accentunder=\"false\"><mml:mrow><mml:mo>∑</mml:mo></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mi>I</mml:mi></mml:mrow></mml:munderover><mml:msub><mml:mrow><mml:mi>x</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mi>c</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></disp-formula>", "<disp-formula id=\"Equ2\"><label>2</label><alternatives><tex-math id=\"M3\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\mathop{\\sum }\\limits_{i=1}^{I}{x}_{i}{r}_{{ij}}\\ge {T}_{j}\\forall \\,j\\in J$$\\end{document}</tex-math><mml:math id=\"M4\"><mml:munderover accent=\"false\" accentunder=\"false\"><mml:mrow><mml:mo>∑</mml:mo></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mrow><mml:mi>I</mml:mi></mml:mrow></mml:munderover><mml:msub><mml:mrow><mml:mi>x</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mi>r</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mo>≥</mml:mo><mml:msub><mml:mrow><mml:mi>T</mml:mi></mml:mrow><mml:mrow><mml:mi>j</mml:mi></mml:mrow></mml:msub><mml:mo>∀</mml:mo><mml:mspace width=\"0.25em\"/><mml:mi>j</mml:mi><mml:mo>∈</mml:mo><mml:mi>J</mml:mi></mml:math></alternatives></disp-formula>" ]	[]	[]	[]	[]	[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM3\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM4\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM5\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM6\"></supplementary-material>" ]	[ "<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"41467_2023_43832_Fig1_HTML\" id=\"d32e518\"/>", "<graphic xlink:href=\"41467_2023_43832_Fig2_HTML\" id=\"d32e532\"/>", "<graphic xlink:href=\"41467_2023_43832_Fig3_HTML\" id=\"d32e540\"/>", "<graphic xlink:href=\"41467_2023_43832_Fig4_HTML\" id=\"d32e623\"/>", "<graphic xlink:href=\"41467_2023_43832_Article_Equ1.gif\" position=\"anchor\"/>", "<graphic xlink:href=\"41467_2023_43832_Article_Equ2.gif\" position=\"anchor\"/>" ]	[ "<media xlink:href=\"41467_2023_43832_MOESM1_ESM.pdf\"><caption><p>Supplementary Materials</p></caption></media>", "<media xlink:href=\"41467_2023_43832_MOESM2_ESM.pdf\"><caption><p>Peer Review File</p></caption></media>", "<media xlink:href=\"41467_2023_43832_MOESM3_ESM.pdf\"><caption><p>Description of Additional Supplementary Files</p></caption></media>", "<media xlink:href=\"41467_2023_43832_MOESM4_ESM.xlsx\"><caption><p>Supplementary Data 1</p></caption></media>", "<media xlink:href=\"41467_2023_43832_MOESM5_ESM.xlsx\"><caption><p>Supplementary Data 2</p></caption></media>", "<media xlink:href=\"41467_2023_43832_MOESM6_ESM.pdf\"><caption><p>Reporting Summary</p></caption></media>" ]	[{"label": ["1."], "mixed-citation": ["D\u00edaz, S. et al. 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PMC10781688	38200101	[ "<title>Introduction</title>", "<p id=\"Par2\">Emotional outbursts (EO) are dysfunctional behaviors highly prevalent in children and adolescents with severe neurodevelopmental disabilities such as Autism Spectrum Disorder (ASD) and intellectual disability (ID) that can require lifelong care interventions<sup>##REF##32471600##1##</sup> as they significantly impact adaptive functioning<sup>##REF##23006014##2##,##UREF##0##3##</sup>. Previous studies use other related terms for emotional outbursts such as 'meltdowns', 'crisis', 'behavioural breakdown', 'blips', 'rages', 'temper outbursts', 'tantrums', or 'tempers'<sup>##UREF##0##3##–##REF##12860775##5##</sup>. For standardization purposes, the term emotional outburst (EO) will be adopted in this article. The types of EO commonly reported in studies include behaviours such as crying, screaming, going limp, flailing, hitting, throwing items, breath-holding, pushing, biting, destroying property or injuring other people or themselves, among others<sup>##UREF##0##3##</sup>.</p>", "<p id=\"Par3\">In typical development, the frequency of EO is expected to decrease throughout childhood, mainly because of the acquisition of cognitive, communication and socio-emotional skills that provide an emotional and behavioral repertoire with better adaptive function according to the demands of the environment<sup>##REF##23006014##2##,##REF##33707806##6##</sup>. Previous studies reported that the highest rates of EO in typical development occur in children aged 1 to 4 years old, with an approximate frequency of 1 to 5 times a day and a duration of up to 15 min<sup>##REF##33743940##7##,##REF##12806226##8##</sup>. This frequency tends to decrease with increasing age. However, when the emotional outbursts persist after the age of four and are accompanied by aggressive behaviours or self-harm, it is recommended to assess expected developmental milestones or to screen for signs of neurodevelopmental or other psychiatric conditions<sup>##REF##23006014##2##</sup>.</p>", "<p id=\"Par4\">Emotional dysregulation is characterized by too much emotion, expressed too often, too quickly, and associated with antecedent triggering events<sup>##REF##35358662##9##</sup>. Negative emotional dysregulation is usually the main feature characteristic of emotional outbursts. Children with severe neurodevelopmental disabilities such as ID and ASD have high rates of EO, even after the age of five<sup>##REF##23006014##2##,##UREF##0##3##,##REF##26783943##10##,##REF##24444385##11##</sup>. In autism, the deficits in emotional dysregulation affect more than 80% of individuals with the diagnosis, which may be linked to specific patterns of functioning and connectivity in the amygdala/prefrontal cortex as well as difficulties in the processing of social information<sup>##REF##34682429##12##</sup>. These factors may influence emotional dysregulation increased EO and impaired adaptive functioning<sup>##REF##11814269##13##–##REF##31652032##15##</sup>.</p>", "<p id=\"Par5\">The assessment of emotional outbursts can be done using inventories, questionnaires, scales, behavioural observation and structured and semi-structured interviews<sup>##REF##31775528##16##</sup>. One of the advantages of using inventories, questionnaires and scales is the systematization of measurements in all parameters (for example, type or topography, frequency, duration, among others). Supporting this view, research identifying characteristics of the development of emotional self-regulation in individuals under the age of 18 years, mainly in natural contexts suggested that it is necessary for researchers to consider the richness involved in observing behavioural parameters such as duration and sequence of behaviours<sup>##REF##35686062##17##</sup>.</p>", "<p id=\"Par6\">Several instruments assess emotional dysregulation through behavioural parameters<sup>##REF##35358662##9##</sup>. The Emotion Dysregulation Inventory (EDI) evaluates emotional dysregulation within the past seven days using a Likert scale, covering emotional reactivity and dysphoria<sup>##REF##31910035##18##</sup>. The Emotion Regulation Checklist (ERC) measures emotional lability/negativity and emotion regulation through Likert scale responses<sup>##REF##9383613##19##</sup>. The Emotional Outburst Inventory (EMO-I) screens phasic irritability/severe emotional outbursts in clinical youth settings, evaluating outburst severity, frequency, and duration<sup>##UREF##1##20##</sup>. Additionally, the Preschool Age Psychiatric Assessment (PAPA)<sup>##UREF##2##21##</sup> and Child and Adolescent Psychiatric Assessment (CAPA)<sup>##REF##10638066##22##</sup>, both available through Duke University, gather specific information on tantrum severity, independent of mood. CAPA is designed for children aged 9 to 17, while PAPA is for children aged 2 to 8.</p>", "<p id=\"Par7\">Another instrument is the Emotional Outburst Questionnaire (EOQ), which was developed to fill gaps in the literature regarding the assessment of emotional outbursts and their associated contexts and mechanisms. The EOQ comprises 133 items that assess different behavioural indicators of EO such as their frequency, duration, emotional patterns during their occurrence, recovery time, physiological and environmental factors that trigger EO and the effectiveness of strategies used by parents, caregivers and professionals to reduce EO<sup>##REF##35523842##23##</sup>.</p>", "<p id=\"Par8\">In low and middle-income countries<sup>##REF##23006014##2##,##REF##35523842##23##</sup>, there are no instruments with appropriate psychometric properties tested for validity that can comprehensively assess EO indicators. In Brazil, for instance, there are only two instruments that can be used to assess severe behavioural problems in individuals with neurodevelopmental disabilities such as ASD and ID. These include the Behavior Problems Inventory (BPI-01)<sup>##REF##11814269##13##,##REF##25923392##24##</sup> and the Aberrant Behavior Checklist (ABC)<sup>##REF##21655842##25##</sup>. Both are designed to assess certain types of severe behavioural problems but are not specific instruments for the evaluation of different parameters of EO as they are measured in the EOQ.</p>", "<p id=\"Par9\">Given the negative impacts that EO can have, both for the person who experiences them and for parents, caregivers and other professionals, there is a need for culturally adapted instruments to evaluate them that have evidence of validity in the cultural and social contexts in which they will be used. To assure the quality and reliability of a measurement tool, it is important that when adapting instruments for use in different cultures, a methodologically rigorous process is used<sup>##UREF##3##26##–##UREF##5##28##</sup>.</p>", "<p id=\"Par10\">The objective of this study was to describe the translation and cross-cultural adaptation of the EOQ for use in the Brazilian context, and to verify predictive validity evidence based on external criteria of the instrument. We hypothesized that EO would increase during a period of interruption of mental health services due to the COVID-19 pandemic.</p>" ]	[ "<title>Methods</title>", "<p id=\"Par11\">This was a cross-sectional quantitative study to examine the content validity and validity based on external criteria of the Brazilian Portuguese version of the EOQ. The study sample was selected by convenience. In addition to translation and cultural adaptation of the instrument, feedback from members of the target population was adopted to increase content validity. For evidence of predictive validity, interruption of mental health services during the COVID-19 pandemic was used as an external criterion. We hypothesized that EO would increase due to a lack of access to mental health services. The project was approved by the Human Research Ethics Committee of the Mackenzie Presbyterian University (Process: CAAE-29428620.4.0000.0084) and all the caregivers provided informed consent prior to participating in this study and completing the Brazilian Portuguese version of the Emotional Outburst Questionnaire.</p>", "<title>Instrument</title>", "<p id=\"Par12\">Emotional Outburst Questionnaire: the instrument assesses EO based on the reports of informants, with the recommended informants being parents or caregivers<sup>##REF##35523842##23##</sup>. The average completion time for the sample of Brazilian caregivers is 1 h. The EOQ comprises 133 items grouped into six factors: (a) Types of EO: behaviours that occur during EO according to the level of severity, namely: aggression directed at others, property and oneself; vocalizations; motor agitation and avoidance, among others. The frequency of the behaviours during EO are coded as “does not apply/never/rarely” when they occur 0 to 3 times in every 10 outbursts; “sometimes” when they occur between 4 and 6 times in every 10 outbursts, and “often/always” when they occur between 7 and 10 times in every of 10 outbursts. The frequency of the occurrence of EO has the response options “never”, “less than once a month”, “once a month”, “two to three times a month”, “once a week”, “two to three times a week”, “once a day” and “more than once a day”; (b) EO duration: answered by means of a timeline, with options of \"less than 5 min\", \"5 to 15 min\" \"15 to 30 min\", \"30 min to 1 h\", \"1 to 2 h\", \"2 h to 1 day\" and \"one day or more\"; (c) Emotional patterns: evaluated through a scale scored from 1 to 7, with a variability between “not angry or upset at all” to “as angry or upset as I have ever seen them”; (d) EO recovery time: measures the time required to recover an EO, classified as: “less than 5 min”, “5 to 15 min”, “15 to 30 min”, “30 min to 1 h”, “1h to 2h”, “2h to 1 day” and “one day or more”; (e) Physiological and environmental factors that trigger EO: factors such as hunger, tiredness, pain, and stressful events such as changes in routine; (f) Control strategies used to calm the person during EO: evaluates the use of strategies such as persuasion, physical comfort, relaxation, use of visual aids, punishment, and negotiation, among others.</p>", "<p id=\"Par13\">A previous study was conducted to assess whether the contextual clusters of emotional outbursts evaluated using the EOQ that emerged from a sample of caregivers in Brazil would be comparable to the clusters identified in a study from the United Kingdom<sup>##REF##35984587##29##</sup>. To evaluate this cross-cultural comparison, the factor structure of the contextual items in the Brazilian Portuguese version of the questionnaire was validated and compared against the factors derived from the English version<sup>##REF##35984587##29##</sup>. Additionally, the EOQ can be completed by different informants, making it possible to provide information on clinical aspects of the emotion regulation of individuals with neurodevelopmental disabilities based on multiple informants.</p>", "<title>Procedures and participants</title>", "<p id=\"Par14\">The process of translation, cross-cultural adaptation, and content and predictive validity analysis was conducted in four stages. We adopted the recommendations of the International Test Commission in respect to the methodological procedures used for the cross-cultural adaptation of the instrument<sup>##UREF##5##28##,##UREF##6##30##</sup>:</p>", "<title>Stage 1—translation and cross-cultural adaptation</title>", "<p id=\"Par15\">Independent translations by two professionals: both translators are native speakers of Portuguese and are proficient in English (MCTV and RL), have master's and doctoral degrees and more than 30 years' experience in behavioural assessment and intervention, and neurodevelopmental disabilities. After the two translations were completed, they were synthesized by considering semantic, experiential, idiomatic and conceptual equivalence in respect to the items<sup>##UREF##4##27##</sup>. Semantic equivalence was determined by assessing whether the words had the same meaning and whether any item had more than one possible meaning, or whether there were grammatical errors in the translation. The determination of idiomatic equivalence assessed whether the items that were difficult to translate from the original instrument were adapted and translated using an equivalent expression that did not change the cultural meaning of the item. Experiential equivalence was determined by assessing whether a given item was appropriate in the new culture and, if not, it was replaced by an equivalent item. Finally, the determination of conceptual equivalence assessed whether a given term or expression, even when translated properly, evaluated the same aspect in different cultures.</p>", "<title>Stage 2—back-translation and final modifications</title>", "<p id=\"Par16\">The back-translation was carried out into the original language by a certified native English-speaking translator and a specialist in Portuguese-English translation. The author of the original version of the EOQ (JC) then evaluated the back-translation to verify that the content of the back-translated items was compatible with the content of the original items.</p>", "<title>Stage 3—evaluation by target audience</title>", "<p id=\"Par17\">Seven parents of children with ASD between 11 and 25 years old (mean = 18.28, <italic>SD</italic> = 5.34, sex male = 5), were selected by convenience to complete the EOQ to verify that they correctly understood the content of the items. This was based on the following questions: (a) Did you understand everything that was asked? (b) Could you explain what you understood with examples of behaviour of your child/student/adult in respect to each item? (c) Were there any specific words that you did not understand?</p>", "<title>Stage 4—preliminary evidence of predictive validity</title>", "<p id=\"Par18\">During the COVID-19 pandemic, around 70% of the individuals with ASD experience social isolated, when at least 30% of in-person treatments were completely interrupted in Latin America, including Brazil<sup>##UREF##7##31##</sup>. This atypical situation allowed us to test differences in the frequency and severity of EO during this period (Time 1) compared to when services were resumed in 2022 (with the improvement of the health situation in respect to COVID-19 - Time 2). Between June 2020 and January 2021, a sample of participants with developmental disabilities was selected by convenience. This sample of 25 parents of children and adolescents diagnosed with Down syndrome (DS), ID or ASD completed the EOQ (mean= 11 years old; <italic>SD</italic> = 4.89; range = 5–25 years old). Parents were informed about the study and invited to respond to the questionnaire through social media of parent support groups and during visits to mental health service clinics with their children. A total of 201 parents were invited to participate and all parents who participated in the present study accepted this invitation. This low response rate might have been due to reasons such as (a) change of email contact; (b) invitations being sent directly to spam. None of the parents who did not participate in the research responded by declining the invitation. Participants were invited in person (those who attended the clinic) or directly via email message (for those who had already completed the questionnaire directly via the RedCap platform link). For participants who were personally invited to the clinic and who agreed to participate, a tablet with the questionnaire was made available to fill out the questionnaire, while the participant waited for their child's intervention session. Participation in the study was voluntary and participants did not receive any financial reward for the study. However, a link to an online talk was provided to parents who completed the questionnaire. The theme of the talk was “Parental behavioural management of children with developmental disabilities”. Only one mother attended the talk.</p>", "<p id=\"Par19\">The sample size was determined based on convenience and practical constraints, primarily due to low response rates among participants who were already facing challenging circumstances as primary caregivers of individuals with neurodevelopmental conditions. To minimize any additional burden on them, we refrained from insisting on their participation. Given these constraints, a formal power analysis was not conducted. The study's findings should be interpreted with caution, particularly in terms of generalizability. Further discussion on this aspect is provided in the Limitations section.</p>", "<p id=\"Par20\">Time 1 of data collection corresponds to the worst period of the COVID-19 pandemic in Brazil, when mental health services were interrupted. Time 2 was in 2022 when Brazil adopted no restriction measures of social distancing. The caregivers completed the EOQ online on the Research Electronic Data Capture (REDCap) platform. After the improvement in the health situation in respect to COVID-19 and the resumption of face-to-face mental health interventions, between the months of May and August 2022, parents completed the EOQ again to evaluate whether the instrument could temporally predict outcomes in regards to the frequency and severity of EO using access to mental health services as an external independent variable.</p>", "<title>Data analysis</title>", "<p id=\"Par21\">In the analysis of the translations for the synthesis process, the two professionals compared the two translations and analysed idiomatic, conceptual/experiential, and semantic discrepancies. For the analysis of predictive validity, the EO indicators evaluated by the EOQ were compared between the two time points using the non-parametric Wilcoxon signed-rank test, adopting a statistical significance level of <italic>p</italic> ≤ 0.05. The instrument indicators that were evaluated were those that assessed the frequency of outbursts (most severe, least severe, and general), duration of outbursts (most severe and least severe), and intensity of outbursts (most severe and least severe). For the analyses, the response options were classified numerically: for example, in items relating to the frequency of the outburst, “never” was coded as “0”, and “more than once a day” was coded as “7”. Additionally, we asked the caregivers if they believed that their child's behaviours had changed after the relaxation of COVID-19 prevention measures, followed by a question on whether they believed the changes were for better or worse.</p>", "<title>Ethical approval</title>", "<p id=\"Par22\">The study procedures were carried out in accordance with the Code of Ethics of the World Medical Association (Declaration of Helsinki) and were approved by the Research Ethics Committee of the Mackenzie Presbyterian University (Process number: CAAE-29428620.4.0000.0084).</p>" ]	[ "<title>Results</title>", "<p id=\"Par23\">After carrying out the two translations, analyses were performed based on identifying idiomatic, conceptual/experiential, and semantic discrepancies for the synthesis composition. Out of a total of 133 items, 33 (24.81%) required revision (including the instructions). Table ##TAB##0##1## shows the 33 items that required revision according to the criterion used (semantic, experiential/conceptual and/or idiomatic). One item required partial modification and two total modifications. There was good evidence of content validity and the adequacy of the adaptation in respect to the conceptual, idiomatic, and semantic aspects of the items of the instrument.</p>", "<p id=\"Par24\">After the synthesis analysis, an analysis of the items was performed by seven mothers of children, adolescents, and adults with ASD (stage 3). None of the mothers reported any difficulties in understanding the items, instructions, and the response scale of the instrument, and completing the EOQ in approximately 1 h.</p>", "<title>Predictive validity of the EOQ</title>", "<p id=\"Par25\">To explore the predictive validity of the EOQ using an external criterion, we conducted an analysis of a two-time point evaluation comparison based on an external independent variable—with the COVID-19 pandemic restriction measures (Time 1) and without the restriction measures (Time 2). The sample of this analyses consisted of 25 respondents of children, adolescents, and young adults, most of the children being male (76%), with 72% of the sample diagnosed with ASD, followed by 24% with DS and one child with an exclusive diagnosis of ID. The mean age of the and use of medication is reported in Table ##TAB##1##2##.</p>", "<p id=\"Par26\">The Wilcoxon test showed that the frequency of more severe emotional outbursts increased over time, being higher at Time 2 than at Time 1 (<italic>Z</italic> = − 3.660; <italic>p</italic><0.001), as shown in figure ##FIG##0##1##, with a relatively strong negative effect size (<italic>r</italic>=− 0.73). The same effect was not observed in questions referring to less severe (<italic>Z</italic> = − 1.615; <italic>p</italic> = 0.10) and general (Z = − 1.341; <italic>p</italic> = 0.18) emotional outbursts, as shown in Table ##TAB##2##3##.</p>", "<p id=\"Par27\">The item referring to the frequency of more severe emotional outbursts averaged 0.52, but increased to 1.96, on a scale from 0 (never) to 7 (more than one time a day), with 8 children whose number of outbursts did not change between the two times (32%).</p>", "<p id=\"Par28\">Regarding the duration of the most severe emotional outbursts, there was a borderline increase in the time reported by caregivers (<italic>p</italic> = 0.05), with a moderate negative effect size (<italic>r</italic> = 0.38). At Time 1, parents (52%) reported that the outbursts lasted less than 5 min, with a maximum of 15 to 30 min in 3 cases (12%). At Time 2, three parents (12%) reported duration of outbursts from 30 min to 1 h, which was not observed in the first assessment, although most responses (36%) indicated outbursts of less than 5 min at Time 2 (Fig. ##FIG##1##2##).</p>", "<p id=\"Par29\">At both times, the use of mental health services was evaluated, with the types of care provided by professionals in psychology, speech and language therapy, occupational therapy, neurologists, psychiatrists, and others. At time 1 (during the worse period of the Pandemic COVID-19 lockdown) 72% (<italic>n</italic> = 18) of the children in the sample received mental health services, while at time 2 this number increased to 92% (<italic>n</italic> = 23).</p>", "<p id=\"Par30\">There was a significant increase in the frequency and duration of emotional outbursts in the present study sample at the Time 2. It is possible that this increase is associated with lack of access to mental health services in approximately 30% of the sample at Time 1.</p>", "<p id=\"Par31\">At Time 2, parents were asked about their general perception of their children's behaviours, with the question: “Do you believe that your child's behaviours have changed after the relaxation of COVID-19 prevention measures?”, followed by the question: “If yes, do you believe that the changes were for better or worse?”. As a result, 60% of parents reported observing changes in their children's behaviour from Time 1 to Time 2, and 52% reported that the changes were for better (six parents did not answer this question in the Time 2 evaluation).</p>" ]	[ "<title>Discussion</title>", "<p id=\"Par32\">The present study described the process of translation and cross-cultural adaptation of the EOQ for use in Brazil, and preliminary evidence with regards to its content and predictive validity<sup>##REF##35523842##23##</sup>. The EOQ assesses the frequency and duration of emotional outbursts (EO), types of EO, emotional patterns during the EO, recovery time from EO, environmental and physiological factors that trigger EO and the effectiveness of control strategies used to calm individuals with EO. It is important that EO assessment tools are available to monitor indicators of emotional dysregulation in people with severe neurodevelopmental disabilities. It is important to have culturally adapted instruments for specific contexts because many autism studies tend to overlook the complex interplay of several factors affecting this population and how professionals approach the diagnosis and intervention practices. Conducting research that is culturally, gender, racial, and ethnically sensitive could result in more accurate and valid procedures for a diverse range of autistic individuals<sup>##UREF##8##32##</sup>.</p>", "<p id=\"Par33\">The process of translation, cultural adaptation and synthesis showed that the Portuguese version was adequate since there were few errors in relation to the presence of complex and elaborate words or phrases. We verified that only minor adjustments to semantic, experiential/conceptual and/or idiomatic aspects were required. Despite the large number of items of the EOQ, only 24.81% of items/instructions required modification. This result demonstrates the quality of the translations and provides support for the content validity of the questionnaire<sup>##UREF##4##27##,##UREF##5##28##</sup>. As we hypothesized, the Brazilian Portuguese version of the EOQ maintained its measurement properties and is equivalent to the original version, indicating a successful adaptation. This process made the EOQ suitable for use in the research area in Brazil.</p>", "<p id=\"Par34\">For predictive validity, we used the interruption of mental health services during the COVID-19 pandemic as an external criterion. Our hypothesis was that EO would increase due to the lack of access to mental health services, and as expected, differences emerged between Time 1 and Time 2. This provides evidence of the EOQ's sensitivity in measuring changes influenced by an individual's environmental context. Specifically, at Time 2, we observed a significant increase in the frequency and duration of emotional outbursts. Approximately 30% of the present sample did not receive intervention for nearly two years, possibly leading to more pronounced impacts and heightened EO. An international study<sup>##REF##36008773##33##</sup> also observed increased intensity of behaviour problems in individuals with ID during lockdown, potentially due to reduced stimulation. Additionally, evidence also suggests that the interruption of mental health services had detrimental effects on ASD populations, with families noting behavioural setbacks during confinement<sup>##UREF##7##31##</sup>. Even with interventions resuming, EO levels had not fully returned to pre-pandemic levels, indicating no significant improvement in behaviour from the parents' perspective in our study. This corroborates recent literature, which indicates that challenging behaviours, such as emotional outbursts, are chronic in the clinical conditions of individuals with ID or ASD<sup>##UREF##9##34##</sup>.</p>", "<p id=\"Par35\">The indicators of frequency and intensity of emotional outbursts worsened or remained the same from Time 1 to Time 2, except for the duration of less severe emotional outbursts, which decreased by 1 point in the maximum score. This result does not compromise the predictive validity of the instrument, and it can be hypothesized that for this reduction in scores, there is evidence from previous studies showing that removing demands, especially social ones such as attending school<sup>##REF##35669990##35##</sup>, was a driver of well-being for autistic pupils and their parents/caregivers<sup>##UREF##10##36##</sup>.</p>", "<p id=\"Par36\">The increased use of medication from Time 1 to Time 2 corroborates the data found by Rauf et al.<sup>##REF##33736746##37##</sup>, who also reported an increase of medication use in a similar sample during the pandemic. In an attempt to support people with ID and their families in managing behavioural problems, a rise in requests for psychotropic medication was expected<sup>##REF##32349992##38##</sup>. Since the medications were mostly for behavioural purposes for Rauf et al.<sup>##REF##33736746##37##</sup> as well as our study, their use could have been for EO, which also increased from Time 1 to Time 2. Although medication use is not a direct question in the (EOQ), it is a variable to be considered when assessing behavioural problems and emotional outbursts, especially if the medication purpose is for the management of behavioural problems.</p>", "<p id=\"Par37\">Given the scarcity of scientific evidence on the contexts and mechanisms associated with emotional outbursts<sup>##REF##35523842##23##</sup>, the present study contributes to not only the verification of preliminary predictive validity of the Brazilian version of EOQ, but also the understanding of the influence of the environment on the emotional outbursts of neurodivergent individuals, such as ASD and ID populations. Our research paves the way for significant contributions in the realms of potential intervention and recommendations for practice, education, and management. It is essential to understand the factors contributing to emotional outbursts, particularly in populations with ASD and ID diagnoses<sup>##REF##32471600##1##,##UREF##9##34##,##REF##37515997##39##</sup>. This understanding is a fundamental step in designing and monitoring the effectiveness of targeted interventions, which may encompass behaviour management strategies, stress reduction techniques, and support systems for caregivers, educators, and healthcare professionals. Future research should focus on the design and evaluation of interventions that can mitigate emotional dysregulation in these populations.</p>", "<p id=\"Par38\">This study demonstrates that the Brazilian version of the EOQ is promising, but also has some limitations. The study's findings should be interpreted with caution, especially regarding their generalizability, as the sample size and heterogeneity in the age of the participants may limit the ability to detect smaller or more nuanced effects and can limit the interpretation of the data. Future studies may benefit from conducting a power analysis to determine an appropriate sample size, and we emphasize the importance of including larger and more representative samples in research efforts. While the sample size may not be ideal, it is worth noting that our research still provides valuable insights and contributes to the existing knowledge. In terms of future directions, further studies are recommended with the expansion of the sample and inclusion of new variables, such as interrater reliability, exposure to screens and relaxation of parental rules/exercise of parenting. It would also be interesting for future studies to replicate the EOQ in developing countries with similar cultural contexts, such as Latin America, as done by Chung et al.<sup>##REF##35523842##23##</sup> in developed countries.</p>", "<p id=\"Par39\">Another point to be studied is the association between the frequency and severity of emotional outbursts and the frequency and type of mental health services. Further research on the predictive validity of the EOQ could involve other criterion variables, such as estimating the predictive validity of the EOQ with other populations, especially ID, to verify its association with the level of impairment (e.g., repetitive behaviours, language, cognitive, function, language, and motor impairments). In addition, new studies on concurrent validity using other criteria should be conducted. It is also important to note that not all EOQ variables evidenced worsening in severity, and this relates to a limit of the measure, which does not comprise an overall severity score.</p>", "<p id=\"Par40\">In the last question, after Time 2 (general perception of change in their children's behaviours), it was possible to note that about 40% of the parents reported no change. However, the main results, based on the EOQ questions, have shown a significant increase reported by caregivers. This difference could be linked to memory biases or maybe a more general and less specific view of behaviours, because parents were asked about general changes and not specific behaviours or their severity, frequency or intensity.</p>" ]	[ "<title>Conclusion</title>", "<p id=\"Par41\">The present study described the process of translation and cross-cultural adaptation of the EOQ for use in Brazil, and preliminary predictive validity. It is important that EO assessment tools are available to monitor indicators of emotional dysregulation in people with severe neurodevelopmental disabilities. It is recommended to have studies of psychometric properties of instruments that were translated from other countries when considering the use of an instrument. The analysis of external validity revealed, even if preliminarily, that the EOQ is sensitive in measuring changes influenced by the individual's environmental context. However, even with these results on validity, the verification of predictive validity of the EOQ still requires further studies to explore the influence of other environmental and cultural factors as well as the assessment of other severe neurodevelopmental disabilities, e.g., intellectual disability. Other psychometric studies are required to verify the accuracy of validity and reliability of the EOQ in the Brazilian context. Nevertheless, this study brings not only psychometric evidence for the EOQ, but also contributes to the understanding of the influence of the environment on the emotional outbursts of neurodivergent individuals. In practice, the results of our study emphasize the importance of culturally adapted assessment tools for emotional outbursts and the understanding of environmental or biological factors that may contribute to emotional outbursts in neurodivergent individuals.</p>" ]	[ "<p id=\"Par1\">This study focuses on the cross-cultural adaptation of the Emotional Outburst Questionnaire (EOQ) to Brazilian Portuguese and preliminarily assesses its predictive validity. The EOQ evaluates aspects of emotional outbursts (EO), including frequency, duration, intensity, types, associated behaviours, recovery time, triggers, and effectiveness of calming strategies. Two independent translators performed the translation, with subsequent synthesis and analysis revealing that only 33 items (24.81%) required revision. Among these, one item needed partial modification, and two needed total modification. The study demonstrated strong content validity and adaptation in terms of conceptual, idiomatic, and semantic aspects. The EOQ's predictive validity was assessed by analysing the interruption of mental health services in Brazil due to Covid-19 (T1) compared to when services resumed after social distancing measures were lifted (T2). Parents of 25 individuals with developmental disabilities (ASD, DS and ID), with a mean of 11 y/o, mostly male (76%), completed the EOQ. Service interruption during T1 led to increased frequency and duration of severe emotional outbursts reported by caregivers compared to T2 (frequency: <italic>p</italic> < .001; duration: <italic>p</italic> = 0.05). This suggests that the EOQ exhibits predictive validity and sensitivity to changes influenced by individual contexts. These findings highlight the EOQ's potential as an outcome measure for intervention development.</p>", "<title>Subject terms</title>" ]	[]	[ "<title>Acknowledgements</title>", "<p>This work was undertaken with the support of the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) (the National Council for Technological Development)—Research Productivity Grant PQ-1C—Process Number 308665/2021− 0. This study was also financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES-PrInt)—Finance Code: CAPES/AUXPE: 2739/2018, Process: 88881.310344/2018-01 and by the CAPES/Coordination for the Improvement of Higher Education Personnel—CAPES)—PROEX—Process Number: 2020/07992-2.</p>", "<title>Author contributions</title>", "<p>M.C.T.V.T. conceived and conducted the experiments, analyzed the results and wrote the manuscript. T.L.T. conducted the experiments, analyzed the results and wrote the manuscript. R.L. conducted the experiment and wrote the manuscript. C.S.P. wrote the manuscript. B.B. conducted the experiment. C.M. reviewed the manuscript. JCYC conceived the experiments and wrote the manuscript. KAW conceived the experiments, analyzed the results and wrote the manuscript. All authors commented on previous versions of the manuscript and all authors read and approved the final manuscript.</p>", "<title>Funding</title>", "<p>This work was undertaken with the support of the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) (the National Council for Technological Development)—Research Productivity Grant PQ-1C—Process Number 308665/2021–0. This study was also financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES-PrInt)—Finance Code: CAPES/AUXPE: 2739/2018, Process: 88881.310344/2018–01 and by the CAPES/Coordination for the Improvement of Higher Education Personnel—CAPES)—PROEX—Process Number: 2020/07992–2.</p>", "<title>Data availability</title>", "<p>The data analysed in this study can be found at <ext-link ext-link-type=\"uri\" xlink:href=\"https://osf.io/qtser/\">https://osf.io/qtser/</ext-link>.</p>", "<title>Competing interests</title>", "<p id=\"Par42\">The authors declare no competing interests.</p>" ]	[ "<fig id=\"Fig1\"><label>Figure 1</label><caption><p>Difference between Time 1 and Time 2 comparing the frequency of the more severe emotional outbursts.</p></caption></fig>", "<fig id=\"Fig2\"><label>Figure 2</label><caption><p>Difference between Time 1 and Time 2 comparing the duration of the more severe emotional outbursts.</p></caption></fig>" ]	[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Results of the translation synthesis, back-translation, and final cross-cultural adaptation of the 33 items revised.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Original instruction or item</th><th align=\"left\">Translation synthesis</th><th align=\"left\">Back-translation</th><th align=\"left\">Final cross-cultural adaptation</th><th align=\"left\">Criterion and type of modification (TM, PM, UN)</th></tr></thead><tbody><tr><td align=\"left\">In this questionnaire, we want you to think about the most severe and least severe emotional outbursts within the past month that the individual you care for has displayed and the characteristics associated with each type of emotional outburst, such as behaviours, frequency, and duration. In terms of the severity of emotional outbursts, we are ref erring to how disruptive and negatively impactful they are to the person and/or those around them at the time of the emotional outburst</td><td align=\"left\">Neste questionário, nós queremos que você pense sobre as explosões emocionais mais graves e menos graves que durante o mês passado a pessoa da qual você é o cuidador apresentou e as características associadas com cada tipo de explosão emocional, tal como comportamentos, frequência e duração. Em relação à gravidade das explosões emocionais, nós estamos nos referindo ao grau de disrupção e ao impacto negativo que essas explosões têm para a pessoa e/ou àqueles ao redor dela no momento da explosão emocional</td><td align=\"left\">In this questionnaire, we would like you to think about the more and less serious emotional outbursts that the person f or whom you are the caregiver has displayed over the past month and the characteristics associated with each type of emotional outburst, such as behaviours, frequency, and duration. With regards to the seriousness of the emotional outbursts, we are ref erring to the level of disruption and to the negative impact that these outbursts have for the person and/or for those around them at the time of the emotional outburst</td><td align=\"left\">Neste questionário, nós queremos que você pense sobre as explosões emocionais mais graves e menos graves que durante o mês passado a pessoa da qual você é o cuidador apresentou e as características associadas com cada tipo de explosão emocional, tal como comportamentos, f requência e duração. Em relação à gravidade das explosões emocionais, nós estamos nos referindo ao grau de disrupção e ao impacto negativo que essas explosões têm para a pessoa e/ou àqueles ao redor dela no momento da explosão emocional</td><td align=\"left\">Idiomatic (UN)</td></tr><tr><td align=\"left\">Meltdowns</td><td align=\"left\">Colapso emocional</td><td align=\"left\">Emotional breakdown</td><td align=\"left\">Crise</td><td align=\"left\">Idiomatic, conceptual and semantic (TM)</td></tr><tr><td align=\"left\">Blips</td><td align=\"left\">Crise temporária</td><td align=\"left\">Temporary crisis</td><td align=\"left\">Crise transitória</td><td align=\"left\">Idiomatic and conceptual (PM)</td></tr><tr><td align=\"left\">Rages</td><td align=\"left\">Explosões de raiva</td><td align=\"left\">Outbursts of anger’</td><td align=\"left\">Explosões de raiva</td><td align=\"left\">Idiomatic and conceptual (UN)</td></tr><tr><td align=\"left\">Tempers</td><td align=\"left\">Mudanças de humor</td><td align=\"left\">Mood swings</td><td align=\"left\">Mudanças de humor</td><td align=\"left\">Idiomatic, conceptual and semantic (UN)</td></tr><tr><td align=\"left\">Name-calling</td><td align=\"left\">Xingamentos</td><td align=\"left\">Swearing</td><td align=\"left\">Xingamentos</td><td align=\"left\">Idiomatic and semantic (UN)</td></tr><tr><td align=\"left\">Screaming</td><td align=\"left\">Berros</td><td align=\"left\">Shouting</td><td align=\"left\">Berros</td><td align=\"left\">Idiomatic and semantic (UN)</td></tr><tr><td align=\"left\">Shouting</td><td align=\"left\">Gritos</td><td align=\"left\">Screaming</td><td align=\"left\">Gritos</td><td align=\"left\">Idiomatic and semantic (UN)</td></tr><tr><td align=\"left\">Swearing</td><td align=\"left\">Falar palavrões</td><td align=\"left\">Using bad language</td><td align=\"left\">F alar palavrões</td><td align=\"left\">Idiomatic and semantic (UN)</td></tr><tr><td align=\"left\">Slamming door</td><td align=\"left\">Bater porta</td><td align=\"left\">Knocking on the door,</td><td align=\"left\">Bater porta</td><td align=\"left\">Idiomatic (UN)</td></tr><tr><td align=\"left\">Throwing objects down</td><td align=\"left\">Jogar objetos com força</td><td align=\"left\">Throwing objects with force</td><td align=\"left\">Jogar objetos com força</td><td align=\"left\">Idiomatic (UN)</td></tr><tr><td align=\"left\">Smashing windows</td><td align=\"left\">Estilhaçar vidros</td><td align=\"left\">Smashing glass</td><td align=\"left\">Estilhaçar vidros</td><td align=\"left\">Idiomatic (UN)</td></tr><tr><td align=\"left\">Grabbing</td><td align=\"left\">Agarrar</td><td align=\"left\">Gripping,</td><td align=\"left\">Agarrar</td><td align=\"left\">Idiomatic (UN)</td></tr><tr><td align=\"left\">Picking skin</td><td align=\"left\">Cutucar a pele</td><td align=\"left\">Poking their skin</td><td align=\"left\">Cutucar a pele</td><td align=\"left\">Idiomatic (UN)</td></tr><tr><td align=\"left\">Picking rectum</td><td align=\"left\">Cutucar o reto</td><td align=\"left\">Poking their rectum</td><td align=\"left\">Cutucar o ânus</td><td align=\"left\">Idiomatic and experiential (UN)</td></tr><tr><td align=\"left\">Pacing</td><td align=\"left\">Andar sem parar</td><td align=\"left\">Walking without stopping</td><td align=\"left\">Andar sem parar</td><td align=\"left\">Idiomatic (UN)</td></tr><tr><td align=\"left\">Rushing about</td><td align=\"left\">Correr</td><td align=\"left\">Running</td><td align=\"left\">Correr</td><td align=\"left\">Idiomatic (UN)</td></tr><tr><td align=\"left\">Increased physiological arousal</td><td align=\"left\">Resposta fisiológica aumentada</td><td align=\"left\">Increased physiological responses</td><td align=\"left\">Resposta fisiológica aumentada</td><td align=\"left\">Idiomatic (UN)</td></tr><tr><td align=\"left\">Contextually inappropriate sexual behaviours</td><td align=\"left\">Comportamentos sexuais em locais inapropriados</td><td align=\"left\">Sexual behaviour in inappropriate locations</td><td align=\"left\">Comportamentos sexuais em locais inapropriados</td><td align=\"left\">Idiomatic (UN)</td></tr><tr><td align=\"left\">Grabbing</td><td align=\"left\">Agarrar</td><td align=\"left\">Snatching</td><td align=\"left\">Agarrar</td><td align=\"left\">Idiomatic (UN)</td></tr><tr><td align=\"left\">Making themselves sick</td><td align=\"left\">Induzir vômitos</td><td align=\"left\">Inducing vomiting</td><td align=\"left\">Induzir vômitos</td><td align=\"left\">Idiomatic (UN)</td></tr><tr><td align=\"left\">Retching</td><td align=\"left\">Regurgitação</td><td align=\"left\">Regurgitation</td><td align=\"left\">Regurgitação</td><td align=\"left\">Idiomatic (UN)</td></tr><tr><td align=\"left\">Angry or upset</td><td align=\"left\">Nervosa ou emburrada</td><td align=\"left\">Nervous or grumpy</td><td align=\"left\">Nervosa ou emburrada</td><td align=\"left\">Idiomatic and experiential (UN)</td></tr><tr><td align=\"left\">As angry or upset as I have ever seen them</td><td align=\"left\">Nada nervosa ou emburrada</td><td align=\"left\">More nervous or grumpy than I’ve ever seen before</td><td align=\"left\">Nada nervosa ou emburrada</td><td align=\"left\">Idiomatic and experiential (UN)</td></tr><tr><td align=\"left\">Whilst on holiday away from home</td><td align=\"left\">Durante as férias longe de casa</td><td align=\"left\">During holidays far from home</td><td align=\"left\">Durante as férias longe de casa</td><td align=\"left\">Idiomatic (UM)</td></tr><tr><td align=\"left\">A parent/caregiver</td><td align=\"left\">Um parente/cuidador</td><td align=\"left\">A relative/carer</td><td align=\"left\">Um parente/cuidador</td><td align=\"left\">Idiomatic (UN)</td></tr><tr><td align=\"left\">Disagreement with others</td><td align=\"left\">Desentendimentos com os outros</td><td align=\"left\">Misunderstandings with others</td><td align=\"left\">Desentendimentos com os outros</td><td align=\"left\">Idiomatic (UN)</td></tr><tr><td align=\"left\">Being told off, criticised, or accused of making a mistake</td><td align=\"left\">Ser chamado a atenção, icado, ou acusado de fazer alguma coisa errada</td><td align=\"left\">Having attention drawn, criticized, or accused of doing something wrong</td><td align=\"left\">Ser chamado a atenção, criticado, ou acusado de fazer alguma coisa errada</td><td align=\"left\">Idiomatic (UN)</td></tr><tr><td align=\"left\">Being teased</td><td align=\"left\">Ser provocado</td><td align=\"left\">Being provoked</td><td align=\"left\">Ser provocado</td><td align=\"left\">Idiomatic (UN)</td></tr><tr><td align=\"left\">Someone not understanding the individual you care for</td><td align=\"left\">Alguém de f ora que não entende a pessoa que você cuida</td><td align=\"left\">Someone uninvolved doesn’t understand the person you care for</td><td align=\"left\">Alguém de fora que não entende a pessoa que você cuida</td><td align=\"left\">Idiomatic (UN)</td></tr><tr><td align=\"left\">Light is too bright</td><td align=\"left\">Iluminação fica muito clara</td><td align=\"left\">Lighting is very bright</td><td align=\"left\">Iluminação fica muito clara</td><td align=\"left\">Idiomatic (UN)</td></tr><tr><td align=\"left\">Temperature is too hot or too cold</td><td align=\"left\">Temperatura está muito quente ou muito fria</td><td align=\"left\">Temperature is very hot or very cold</td><td align=\"left\">Temperatura está muito quente ou muito fria</td><td align=\"left\">Idiomatic (UN)</td></tr><tr><td align=\"left\">Appearing withdrawn</td><td align=\"left\">Mostrar-se introvertido</td><td align=\"left\">Acting introverted</td><td align=\"left\">Retrai-se</td><td align=\"left\">Idiomatic and semantic (TM)</td></tr><tr><td align=\"left\">Staying in a bad mood</td><td align=\"left\">Ficar de mau humor</td><td align=\"left\">Getting in a bad mood</td><td align=\"left\">Ficar de mau humor</td><td align=\"left\">Idiomatic (UN)</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Sample demographics among the evaluations in Time 1 and Time 2.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\">Time 1</th><th align=\"left\">Time 2</th></tr></thead><tbody><tr><td align=\"left\">Children's mean age (years old)</td><td align=\"left\">10.8 (<italic>SD</italic> = 4.89)</td><td align=\"left\">13 (<italic>SD</italic> = 5.02)</td></tr><tr><td align=\"left\">Medication use</td><td align=\"left\">44%</td><td align=\"left\">56%</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab3\"><label>Table 3</label><caption><p>Difference between the frequencies of the most severe, less severe and general emotional outbursts in times 1 and 2 for frequency, duration and intensity questions (<italic>n</italic> = 25).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\"/><th align=\"left\" rowspan=\"2\">Item</th><th align=\"left\" colspan=\"2\">Time 1</th><th align=\"left\" colspan=\"2\">Time 2</th><th align=\"left\" rowspan=\"2\">Difference between Times</th></tr><tr><th align=\"left\">Minimum score</th><th align=\"left\">Maximum score</th><th align=\"left\">Minimum score</th><th align=\"left\">Maximum score</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"3\">Frequency</td><td align=\"left\"><bold>Frequency of the most severe emotional outbursts (Question 24)</bold></td><td align=\"left\"><bold>0</bold></td><td align=\"left\"><bold>2</bold></td><td align=\"left\"><bold>0</bold></td><td align=\"left\"><bold>5</bold></td><td align=\"left\"><p><bold><italic>Z</italic></bold><bold> =− 3.660</bold></p><p><bold><italic>p</italic></bold><bold> < 0.001</bold></p><p><bold><italic>r</italic></bold><bold> = 0.73</bold></p></td></tr><tr><td align=\"left\">Frequency of the least severe emotional outbursts (Question 52)</td><td align=\"left\">0</td><td align=\"left\">7</td><td align=\"left\">0</td><td align=\"left\">7</td><td align=\"left\"><p><italic>Z</italic> =<bold>− </bold>1.615</p><p><italic>p</italic> = 0.10</p></td></tr><tr><td align=\"left\">Frequency of general emotional outbursts (Question 57)</td><td align=\"left\">0</td><td align=\"left\">7</td><td align=\"left\">0</td><td align=\"left\">7</td><td align=\"left\"><p><italic>Z</italic> = <bold>− </bold>1.341</p><p><italic>p</italic> = 0.18</p></td></tr><tr><td align=\"left\" rowspan=\"2\">Duration</td><td align=\"left\"><bold>Duration of the most severe emotional outbursts (Question 25)</bold></td><td align=\"left\"><bold>1</bold></td><td align=\"left\"><bold>3</bold></td><td align=\"left\"><bold>1</bold></td><td align=\"left\"><bold>4</bold></td><td align=\"left\"><p><bold><italic>Z</italic></bold><bold> = − 1.928</bold></p><p><bold><italic>p</italic></bold><bold> = 0.05</bold></p><p><bold><italic>r</italic></bold><bold> = 0.38</bold></p></td></tr><tr><td align=\"left\">Duration of the least severe emotional outbursts (Question 53)</td><td align=\"left\">1</td><td align=\"left\">4</td><td align=\"left\">1</td><td align=\"left\">3</td><td align=\"left\"><p><italic>Z</italic> = − 0.215</p><p><italic>p</italic> = 0.83</p></td></tr><tr><td align=\"left\" rowspan=\"2\">Intensity</td><td align=\"left\">Intensity of the most severe emotional outbursts (Question 26)</td><td align=\"left\">1</td><td align=\"left\">7</td><td align=\"left\">1</td><td align=\"left\">7</td><td align=\"left\"><p><italic>Z</italic> = − 0.430</p><p><italic>p</italic> = 0.66</p></td></tr><tr><td align=\"left\">Intensity of the least severe emotional outbursts (Question 54)</td><td align=\"left\">1</td><td align=\"left\">5</td><td align=\"left\">1</td><td align=\"left\">7</td><td align=\"left\"><p><italic>Z</italic> = − 0.057</p><p><italic>p</italic> = 0.95</p></td></tr></tbody></table></table-wrap>" ]	[]	[]	[]	[]	[]	[]	[ "<table-wrap-foot><p>PM, partially modified; TM, totally modified; UN, unmodified.</p></table-wrap-foot>", "<table-wrap-foot><p>The purpose of the medications was predominantly for behaviour management at both times, followed by attention stimulants, reported only at Time 2.</p></table-wrap-foot>", "<table-wrap-foot><p>The scored number are explained at the “<xref rid=\"Sec7\" ref-type=\"sec\">Data analysis</xref>” section. * = <italic>p</italic> ≤ 0.05. Significant for 95% confidence.</p><p>Significant are in value [bold].</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher's note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"41598_2023_49834_Fig1_HTML\" id=\"MO1\"/>", "<graphic xlink:href=\"41598_2023_49834_Fig2_HTML\" id=\"MO2\"/>" ]	[]	[{"label": ["3."], "mixed-citation": ["Sisterhen, L.L., & Wy, P.A.W. Temper Tantrums. "], "italic": ["StatPearls [Internet]"], "ext-link": ["https://www.ncbi.nlm.nih.gov/books/NBK544286/"]}, {"label": ["20."], "surname": ["Carlson", "Silver", "Klein"], "given-names": ["GA", "J", "DN"], "article-title": ["Psychometric properties of the emotional outburst inventory (EMO-I)"], "source": ["J. Clin. Psychiatry"], "year": ["2022"], "volume": ["83"], "fpage": ["39382"], "pub-id": ["10.4088/JCP.21m14015"]}, {"label": ["21."], "mixed-citation": ["Egger, H.L., & Angold, A. The preschool age psychiatric assessment (PAPA): A structured parent interview for diagnosing psychiatric disorders in preschool children. In: "], "italic": ["Handbook of Infant and Toddler Mental Health Assessment"]}, {"label": ["26."], "surname": ["Vijver", "Leong", "Bartram", "Cheung"], "given-names": ["FJR", "RTL", "D", "FM"], "article-title": ["Test adaptations"], "source": ["The ITC international handbook of testing and assessment"], "year": ["2016"], "publisher-name": ["Oxford University Press"], "fpage": ["364"], "lpage": ["377"]}, {"label": ["27."], "surname": ["Borsa", "Dam\u00e1sio", "Bandeira"], "given-names": ["JC", "BF", "DR"], "article-title": ["Adapta\u00e7\u00e3o e valida\u00e7\u00e3o de instrumentos psicol\u00f3gicos entre culturas: Algumas considera\u00e7\u00f5es"], "source": ["Paid (Ribeir\u00e3o Preto)"], "year": ["2012"], "volume": ["22"], "fpage": ["423"], "lpage": ["432"], "pub-id": ["10.1590/S0103-863X2012000300014"]}, {"label": ["28."], "mixed-citation": ["International Test Commission (ITC). "], "italic": ["The ITC Guidelines for Translating and Adapting Testes (2nd edn)"], "ext-link": ["https://www.intestcom.org/"]}, {"label": ["30."], "surname": ["Hambleton", "Zenisky", "Matsumoto", "van de Vijver"], "given-names": ["RK", "AL", "D", "FJR"], "article-title": ["Translating and adapting tests for cross-cultural assessments"], "source": ["Cross-Cultural Research Methods in Psychology"], "year": ["2011"], "publisher-loc": ["Cambridge"], "publisher-name": ["Cambridge University Press"], "fpage": ["46"], "lpage": ["70"]}, {"label": ["31."], "mixed-citation": ["Valdez, D., Montiel Nava, C., Silvestre Paula, C., "], "italic": ["et al", "Siglo Cero Rev Espa\u00f1ola sobre Discapac Intelect"]}, {"label": ["32."], "surname": ["Montiel-Nava", "Montenegro", "Ramirez"], "given-names": ["C", "MC", "AC"], "article-title": ["One world, one autism? A commentary on using an intersectionality framework to study autism in low-resourced communities"], "source": ["Revista Psicologia: Teoria e Pr\u00e1tica"], "year": ["2023"], "pub-id": ["10.5935/1980-6906/ePTPCP15905.en"]}, {"label": ["34."], "surname": ["Thompson", "Walker", "LeBoeuf"], "given-names": ["TJ", "MW", "JB"], "article-title": ["Chronicity of challenging behaviors in persons with severe/profound intellectual disabilities who received active treatment during a 20-year period"], "source": ["J. Policy Pract. Intellect. Disabil."], "year": ["2022"], "volume": ["19"], "fpage": ["162"], "lpage": ["170"], "pub-id": ["10.1111/jppi.12395"]}, {"label": ["36."], "surname": ["Dal Pai", "Wolff", "Aranchipe"], "given-names": ["J", "CG", "CS"], "article-title": ["COVID-19 pandemic and autism spectrum disorder, consequences to children and adolescents\u2014A systematic review"], "source": ["Rev. J. Autism. Dev. Disord."], "year": ["2022"], "pub-id": ["10.1007/s40489-022-00344-4"]}]	{ "acronym": [], "definition": [] }	39	CC BY	no	2024-01-13 00:02:20	Sci Rep. 2024 Jan 10; 14:984	oa_package/25/e1/PMC10781688.tar.gz
PMC10781689	38200069	[ "<title>Introduction</title>", "<p id=\"Par2\">Brainstorming is a method for idea generation commonly employed across disciplines. Other methods to systematically increase the efficiency of idea generation have been explored in the fields of psychology and engineering. Much of this work has focussed on targeting insight problems—those whose solutions may require a change in approach, or a restructuring of the initial problem<sup>##UREF##0##1##</sup>. The Obscure Features Hypothesis posits that innovative solutions to a problem are based upon at least one previously ‘obscure’—novel or rarely noticed—feature of that problem<sup>##REF##22318998##2##</sup>. However, many human habits, biases, and heuristics hinder the noticing of obscure features (Table ##TAB##0##1##). This line of research has led to the development of several Innovation-Enhancing Techniques (IETs) that assist in the identification of obscure features and so can be applied to enhance the ideation stage of solving engineering and design problems<sup>##UREF##1##3##,##UREF##2##4##</sup>.</p>", "<p id=\"Par3\">Given their origin in engineering contexts, these IETs have so far been used to target problems which require the use of tangible materials and resources (such as stone, bricks, and cement) to solve practical goals (such as building a bridge). However, many important problems in biomedical and scientific contexts concern goals that are conceptual or abstract in nature and may not necessarily be easily measurable, such as ‘benefitting patients’ or ‘decreasing health disparities’. Equally, such problems may require the use of intangible resources, such as software and data, that lack physical instantiation and are better characterised by their functions and affordances than their material composition.</p>", "<p id=\"Par4\">Despite the frequent occurrence of problems in scientific research and healthcare requiring creative restructuring and insight, no studies to date have tested the efficacy of IETs to facilitate insight, innovation, or creativity in problems featuring intangible objects or conceptual goals. Enhancing creativity and idea generation in biomedicine has the potential to catalyse scientific and medical progress in highly cost-effective and efficient ways. This potential is particularly important at a time when research demonstrates a decreasing rate of innovation across scientific fields by multiple measures over several decades<sup>##REF##36600070##9##</sup>.</p>", "<p id=\"Par5\">We hypothesised that the IETs shown to enhance innovation and idea generation in engineering and design contexts can usefully be applied or adapted to problems featuring conceptual goals or intangible resources. We tested this by applying two IETs in parallel to a case study which involved identifying potential solutions to a problem involving a conceptual goal (furthering bioethical principles of beneficence, justice, autonomy, and non-maleficence) using an intangible resource (blockchain technology).</p>" ]	[ "<title>Methods</title>", "<p id=\"Par16\">Our aims were:<list list-type=\"order\"><list-item><p id=\"Par17\">To evaluate whether IETs can usefully be applied to identify potential solutions to problems involving (a) conceptual goals, and (b) intangible materials as opposed to material objects.</p></list-item><list-item><p id=\"Par18\">To use these techniques to identify specific innovative applications of blockchain technologies in biomedicine that improve ethical outcomes. We aimed to identify 100 such potential applications.</p></list-item></list></p>", "<title>Goals and Resources for BrainSwarming graph</title>", "<p id=\"Par19\">We began by populating our BrainSwarming graph with our chosen ultimate conceptual goal (furthering ethical goals in biomedicine), and our initially defined resources (blockchain technology).</p>", "<p id=\"Par119\">\n<italic>Population of refined goals</italic>\n</p>", "<p id=\"Par20\">Having chosen our conceptual goal of furthering bioethics, we defined our refined goals as corresponding to the four classical principles of biomedical ethics as described in the classic textbook by Beauchamp & Childress<sup>##UREF##8##11##</sup> (Table ##TAB##1##2##). We further broke down these principles as much as possible following the analyses by Beauchamp and Childress themselves. Where necessary, we complemented these with our own conceptual analysis to create more detailed levels of refined goals. Thus, it bears emphasis that others carrying out the same exercise would likely choose other refinements; however, for the purposes of applying innovation-enhancing techniques, we considered this essentially pragmatic approach to be sufficient.</p>", "<p id=\"Par120\">\n<italic>Population of resources</italic>\n</p>", "<p id=\"Par21\">In order to further populate the Resources (bottom) section of our BrainSwarming graph, we broke down blockchain technology (a type of cryptographic technology used principally to store records) into its necessary components and features (IT artefacts): Immutable Audit Trail, Consensus Mechanism, Encryption Mechanism, Distributed Ledger, and Smart Contracts, as described in the literature on blockchain affordances<sup>##UREF##9##12##</sup> (Table ##TAB##2##3##).</p>", "<title>Application of Generic Parts Technique to intangible resources</title>", "<p id=\"Par22\">In order to refine our resources beyond the IT artefacts of Blockchain, we applied the Generic Parts Technique to each IT artefact following the two-step iterative process described above. We used our results to continue to populate the Resources section of our BrainSwarming graph. Where it was not possible to decompose a resource further by decomposing words (i.e. ‘audit trail’ into ‘audit’ and ‘trail’), we used a dictionary definition for that word in order to decompose the resource (e.g. ‘contract’ into ‘enforceable’ and ‘agreement’), choosing the definition germane to the context where relevant. When unable to complete this process, for example because use of a definition did not permit decomposition, the etymology of the word was used to refine the resource (e.g. ‘encryption’ into ‘hidden’ and ‘inside’).</p>", "<title>Application of BrainSwarming to conceptual goals and intangible resources</title>", "<p id=\"Par23\">Having fully defined our refined goals and broken down our resources into their component parts, and used these to populate our BrainSwarming graph, we began searching for solution paths connecting the upward vectors of component resources with the downward vectors representing refined goals. We identified 100 potential solutions and marked these with connecting lines.</p>" ]	[ "<title>Results</title>", "<title>Case study: facilitating bioethical goals using Blockchain technology</title>", "<p id=\"Par24\">Applying BrainSwarming and the GPT to our case study, we generated 100 solution paths representing potential uses of blockchain technologies to further ethical objectives in clinical and research contexts (Fig. ##FIG##1##2##, Extended Data Table ##SUPPL##0##1##), thereby demonstrating the efficacy of the application of these techniques to the novel context of conceptual goals and intangible resources. For reasons of space, we highlight 25 of these potential solutions alongside their respective BrainSwarm solution pathways in Table ##TAB##3##4## and provide more detailed explanations for five of these below. For ease of analysis and presentation, we further classified our solution pathways into ‘use concepts’—groups of solutions falling into thematic groups (Table ##TAB##4##5##). Several solutions were reached via multiple routes on the BrainSwarming graph—demonstrating the potential of these particular use cases to satisfy more than one bioethical goal.</p>", "<title>Examples of solution pathways</title>", "<p id=\"Par25\">Below we outline five separate and distinct examples of solution pathways identified during our BrainSwarming process. We present these solution pathways as evidence that the application of IETs to contexts involving conceptual goals and intangible resources is feasible, and can indeed lead to effective identification of potential solutions to the specified problem. As with any other method in innovation, solutions identified by BrainSwarming, which pertains principally to the idea generation stage of the innovation process, must still be subsequently and rigorously evaluated according to their own merits before ultimately being implemented. We briefly explain each of the five example solution pathways below, refraining from detailed normative, technical, economic, or political assessment of the kind that would be necessary to complete the process of developing and implementing an innovation.</p>", "<p id=\"Par121\">\n<italic>1. Conditional informed consent</italic>\n</p>", "<p id=\"Par26\">Informed consent is a cornerstone of everyday clinical and research practice. However, it is often seen as a burden, overlooked, or implemented in ways that might shield a research project from legal liability but do little to respect the ideal of fully informed consent. In particular, many current consent procedures are largely static (patients may always withdraw from studies but otherwise their preferences cannot easily be updated) and unconditional (patients either consent or they do not).</p>", "<p id=\"Par27\">Smart contracts are programs that execute on data contained in blockchains when specific conditions are met (e.g. ‘transfer payment sum if and only if title deeds are uploaded to property register’). If connected to a data oracle such that the relevant information is available to a blockchain-based smart contract, consents stored on a blockchain could be made conditional by automatically revoking consent should certain conditions arise. For example, consent for data use might be given for a fixed period or for certain uses only, or by certain individuals or groups. In addition to automaticity, a blockchain-based implementation of this use case would be transparent and tamper-proof.</p>", "<p id=\"Par122\">\n<italic>2. Blockchain managed advanced directives</italic>\n</p>", "<p id=\"Par28\">Advanced directives, also known as living wills, are documents expressing a person’s preferences towards future medical treatment and research participation decisions in the event of cognitive or other incapacity. Advanced directives suffer from several practical problems, including difficulty in accessing, verifying, and dating them, as well as the issues identified above relating to consent in general, such as being hard to update, unconditional, or insufficiently detailed or specific. Though issues relating to verification and dating can be addressed using professional services such as notarisation, these can be costly and time consuming. Dating advanced directives is of great importance, in part to assess whether they were made during a period of capacity but also because later versions of advanced directives are supposed to override earlier ones.</p>", "<p id=\"Par29\">A possible solution to these issues, identified by our BrainSwarm, is to submit hashes of advanced directives to a blockchain-based registry. This has the potential to address issues of cost and effort (due to automation and removing the need for a notary, other witness, or lawyer), access (hashes of, but not the advanced directives themselves, would be publicly available on-chain), and ascertaining time (through the timestamping function of blockchains).</p>", "<p id=\"Par125\">\n<italic>3. Tokenized incentives for treatment adherence/healthy behaviors</italic>\n</p>", "<p id=\"Par30\">One of the most effective means of improving treatment adherence and healthy behaviour is through cash incentives<sup>##REF##31271816##13##,##UREF##10##14##</sup>. However, these programs are not widely used in part due to ethical concerns over fairness, trade-offs, and opportunity costs<sup>##UREF##9##12##,##UREF##11##15##</sup>.</p>", "<p id=\"Par31\">A potentially novel instantiation of this idea would be to reward treatment adherence and healthy behaviours with cryptocurrency tokens. A government could issue such tokens directly to individuals or through an intermediary and could imbue them with value by allowing them to be used, for example, for tax payments or for other government fees. Such a scheme would have the potential to reap the benefits of cash payments while obviating some of the associated ethical concerns, notably surrounding trade-offs and opportunity costs, as tokens would be free to mint and would not detract from other state health expenditures. The scheme could be set up to be financially self-sustaining by ensuring that payments made are outweighed by the overall money saved through improved population health.</p>", "<p id=\"Par124\">\n<italic>4. Smart contract-based checklists for clinical trials</italic>\n</p>", "<p id=\"Par32\">A significant proportion of biomedical research suffers from methodological flaws and a lack of statistical power<sup>##REF##24411645##16##</sup>. Institutional and ethical review boards are supposed to review scientific merit in addition to legal compliance and ethical acceptability<sup>##UREF##12##17##</sup> and are well-positioned to do so, since most human subject research has to undergo such an ethical review process. In practice, there is significant variability in the extent to which review boards attempt and are successful at ensuring scientific merit<sup>##REF##24411645##16##,##UREF##13##18##</sup>.</p>", "<p id=\"Par33\">Checklists have long been used to increase consistency and reduce errors in safety–critical contexts such as aviation and are increasingly applied in medicine and surgery<sup>##UREF##14##19##</sup>. A potentially novel application of this identified in our BrainSwarm is a smart contract-based checklist for institutional and ethical review. IRB members would fill out a review checklist on a hypothetical web portal. Progression through stages of protocol review would be locked by smart contract and predicated on submission of each section of the template. Upon submission, responses would be encrypted and sent to a repository via smart contract, which would also timestamp the submission. These responses could then be subject to random or automated audits.</p>", "<p id=\"Par34\">The automated nature would alleviate potential concerns of ‘audit creep’ as it would not involve additional labour for IRBs. Timestamping could be used to document review process steps being taken in reasonable timeframes and in the correct sequence—not only providing incontrovertible evidence in cases of discrepancies or disputes, but also likely increasing the transparency of and trust placed in IRB review processes. Information on thoroughness and speed of reviews could also be used internally for quality improvement.</p>", "<p id=\"Par35\">While the progress lock may be configured to force an IRB to make some kind of statement about the methodological merits or otherwise of proposed research, it is not intended to obviate or replace current protocols and legal frameworks, but rather to augment their implementation. Given the fundamental importance of basic scientific merit checks for overall scientific progress, however, any innovation which leads to improvements in this process would be worth weighing against these concerns. Other methods to develop the quality of proposals prior to submission should also continue to be utilised.</p>", "<p id=\"Par123\">\n<italic>5. Ethical approvals released with published studies</italic>\n</p>", "<p id=\"Par36\">Institutional and ethical review board decisions demonstrate a large degree of variability in the interpretation of regulations, value judgments, level of review required (full, none, or expedited), time to reach a decision, and quality of reasoning between different review boards<sup>##UREF##12##17##,##UREF##15##20##,##REF##23289698##21##</sup>. Increasing transparency and accountability of ethical and institutional review, for example via publishing IRB decisions, has been proposed as a means of addressing these issues<sup>##REF##27774154##22##</sup>. Such calls are sometimes resisted on the basis that increased transparency would be expensive and risks making public confidential information<sup>##UREF##16##23##</sup>.</p>", "<p id=\"Par37\">A potentially novel means of addressing these concerns would be possible if ethics and institutional review are implemented on-chain, as outlined above. A smart contract could monitor trial publications and automatically decrypt and selectively publish review decisions relating to successfully published trials. The automated nature of this process would address expense concerns, while conditioning release of reviews on successful publication would partially address confidentiality concerns (as much of the potentially confidential information would be published anyway in the associated research paper) as well as contributing to transparency.</p>", "<p id=\"Par38\">While separate confidentiality concerns related to IRB meeting minutes, memos, and other internal documents (rather than research protocols or participant data) are not addressed by this proposal, the fundamental role of the ethical review process in facilitating or inhibiting scientific progress makes any improvement in process, however partial, equally fundamental. These separate concerns could be addressed by having a specific form of review intended for publication alongside successful projects, which includes key information on ethics and methods reasoning but not more IRB-specific information such as meeting notes.</p>" ]	[ "<title>Discussion</title>", "<p id=\"Par39\">We set out to test whether BrainSwarming and the GPT could be adapted for use in biomedical contexts, involving conceptual, and in our case normative, goals, and software-based intangible resources. We generated 100 possible solution paths using only a small proportion of the nodes on the BrainSwarm, demonstrating the applicability of these tools in this novel context. Some of our ideas appeared more than once on our BrainSwarming graph as connections between multiple nodes. Combining blockchain-enabled prosent requests (Porsdam Mann et al. 2020) with token payments, for example, was identified at various points as a solution path between the goals of autonomy, beneficence, and justice and smart contracts, consensus, and transparent register resources. We considered duplication a promising sign indicating the potential of these use cases to satisfy more than one goal, or to be repurposed for multiple goals.</p>", "<p id=\"Par40\">In adapting BrainSwarming and the GPT to normative goals and blockchain resources, we necessarily made operational choices which have influenced the solutions we defined. For example, to refine our normative goals, we needed to disambiguate and define abstract ethical concepts. We chose a pragmatic way forward by basing our initial analysis of these concepts on their canonical description in Principles of Biomedical Ethics by Beauchamp & Childress and supplemented these with our own understanding of the concepts involved. This is a notoriously difficult and controversial task, and others repeating the exercise may well have chosen differently. While other principles or approaches to biomedical ethics may have been equally valid and useful, we considered our choice justified given the simplicity and widespread use of these principles in bioethical scholarship and practice. The refined goals in our BrainSwarm could for example have been defined to include the 15 principles described in UNESCO’s Universal Declaration of Bioethics and Human Rights, or to include the United Nations Sustainable Development Goals, although our approach here was sufficient for us to discover 100 solution pathways. Importantly, our aim of evaluating the usefulness of IETs was independent of agreement concerning our analyses and refinements of bioethical principles. Indeed, the ability of others to iteratively refine these concepts in different ways to the ones employed here may well be a strength of such techniques, in that it may allow for the classification and description of goals and resources in ways that lead to the identification of further potential use cases.</p>", "<p id=\"Par41\">To refine our resources and so populate the ‘Resources’ section of our BrainSwarm, we applied the GPT to each blockchain component (‘IT artefact’). We note that this is just one possible way to apply the GPT to intangible tools: our goal in doing so was to describe aspects of our available resources in ways which highlight potentially useful facets thereof, rather than to provide a canonical partition of these features, which is unnecessary for the purposes of applying the IETs described here. Thus, colleagues carrying out a similar exercise may arrive at different definitions and component resources, and so other innovative solutions to the problem.</p>", "<p id=\"Par42\">A similar point applies not only to our breakdown of goals and resources but to the potential solutions we identified. Many of our identified solution pathways will be innovative, and potentially useful to clinical and research practice. As IETs are methods in idea generation and innovation, the solutions reached by a different group of individuals would differ from ours. Their usefulness and novelty will necessarily be influenced by the degrees of expertise of those carrying out the exercise.</p>", "<p id=\"Par43\">Of note, solution paths were identified at an unpredictable rate. It was not the case, as might have been expected, that the first few hours invested lead to disproportionate numbers of potential solution paths. Had we not chosen an arbitrary cut-off of 100 potential use cases, we suspect we would have been able to identify many more solution paths at deeper levels of node hierarchy.</p>", "<p id=\"Par44\">Finally, it should be noted that we deliberately chose normative goals and intangible resources at a high level of abstraction to test our hypotheses. This choice was motivated by the reasoning that if we were successful in repurposing the Generic Parts and BrainSwarming techniques to maximally abstract and intangible goals and resources, these techniques are also likely to be applicable to less abstract goals and less intangible resources.</p>" ]	[ "<title>Conclusions</title>", "<p id=\"Par45\">BrainSwarming and GPT were successful in helping us to discover innovative solutions to the abstract problem we chose. Our experience leads us to conclude that these innovation-enhancing techniques can usefully be applied and adapted to clinical and research contexts. We demonstrated their potential by applying them to a case study involving the use of blockchain technologies to facilitate ethical goals in biomedicine. Many of the solutions identified are novel, though they necessarily reflect our knowledge and skill sets.</p>", "<p id=\"Par46\">The vast potential of IETs in healthcare and research is highlighted by the fact that others with different background experience taking slightly different approaches towards adaptation of these techniques may come up with different but equally innovative solutions to the same problem.</p>" ]	[ "<p id=\"Par1\">Innovation in healthcare and biomedicine is in decline, yet there exist no widely-known alternatives to traditional brainstorming that can be employed for innovative idea generation. McCaffrey's Innovation Enhancing Techniques (IETs) were developed to enhance creative problem-solving by helping the solver to overcome common psychological obstacles to generating innovative ideas. These techniques were devised for engineering and design problems, which involve solving practical goals using physical materials. Healthcare and science problems however often involve solving abstract goals using intangible resources. Here we adapt two of McCaffrey’s IETs, BrainSwarming and the Generic Parts Technique, to effectively enhance idea generation for such problems. To demonstrate their potential, we apply these techniques to a case study involving the use of blockchain technologies to facilitate ethical goals in biomedicine, and successfully identify 100 potential solutions to this problem. Being simple to understand and easy to implement, these and other IETs have significant potential to improve innovation and idea generation in healthcare, scientific, and technological contexts. By catalysing idea generation in problem-solving, these techniques may be used to target the innovative stagnation currently facing the scientific world.</p>", "<title>Subject terms</title>" ]	[ "<title>Innovation-Enhancing Techniques</title>", "<title>BrainSwarming</title>", "<p id=\"Par6\">BrainSwarming graphs (see Fig. ##FIG##0##1##a for a worked example), originally known as bidirectional networks (bi-nets)<sup>##UREF##7##10##</sup>, were designed as a means of visualising problem solving and facilitating simultaneous idea generation in a problem-solving group<sup>##UREF##1##3##</sup>—where social dynamics, such as having to wait turns, or certain individuals dominating conversation, may hinder progress.</p>", "<p id=\"Par7\">In a BrainSwarming session, a short description of the problem to be solved, the goal, is placed at the top of a two-dimensional graph on any medium that allows adaptation and visualisation, such as digital mind-mapping software, a whiteboard, or a large sheet of paper. Resources available to solve the problem are placed at the bottom of the graph.</p>", "<p id=\"Par8\">Next, the goal is iteratively refined downwards by placing more detailed or nuanced expressions of the same goal underneath it (here referred to as ‘refined goals’). Similarly, resources are iteratively refined upwards into their parts and components.</p>", "<p id=\"Par9\">Finally, where a refined goal and resource could together form a solution a link is created between them. Goals and resources thus form networks that ultimately converge in interactions between resources and refined goals, each representing a potential solution to the problem.</p>", "<p id=\"Par10\">In this way, BrainSwarming allows for the visualisation of both top-down problem framing and bottom-up problem solving<sup>##UREF##7##10##</sup>. Pilot studies demonstrate an increased rate of idea-generation in less time for individuals performing BrainSwarming compared to traditional brainstorming (115 ideas in 15 min vs. 100 ideas in 60 min)<sup>##UREF##1##3##</sup>. While the speed and volume of ideas generated may not in itself reflect the quality of these ideas, they are important factors in any creative process. Other things being equal, a technique that improves these factors will translate into speedier progress towards the solution of goals.</p>", "<title>Generic Parts Technique</title>", "<p id=\"Par11\">The Generic-Parts Technique (GPT) (see Fig. ##FIG##0##1##b for a worked example) is a different IET designed to supply new information or to help re-interpret existing information about the resources involved in a creative or design task, by enabling the noticing of obscure features through decomposition and redescription. Used in conjunction with BrainSwarming, the GPT provides a systematic method of refining the resources placed on the bottom half of the graph.</p>", "<p id=\"Par12\">The GPT involves a two-step iterative process of refinement, at each stage of which the following questions are asked of a resource object:<list list-type=\"order\"><list-item><p id=\"Par13\">Can this object be broken-down further? If so, the problem solver should decompose the resource into its components and place these on a new leaf in a hierarchical diagram;</p></list-item><list-item><p id=\"Par14\">Does this description imply a use? If so, the solver should reframe the resource description neutrally to prevent functional fixedness following from use descriptions (see Table ##TAB##0##1##).</p></list-item></list></p>", "<p id=\"Par15\">In a test on eight insight problems, human subjects trained to use the GPT method reached a solution 67.4% more often than controls, (Cohen’s d = 1.59—a large effect size)<sup>##REF##22318998##2##</sup>.</p>", "<title>Supplementary Information</title>", "<p>\n</p>" ]	[ "<title>Supplementary Information</title>", "<p>The online version contains supplementary material available at 10.1038/s41598-023-50232-y.</p>", "<title>Acknowledgements</title>", "<p>The authors wish to thank Ms G. Neziri for illustrations. This research was funded in whole, or in part, by the Wellcome Trust (Grant number WT203132/Z/16/Z).</p>", "<title>Author contributions</title>", "<p>S.P.M. conceived the study topic. A.V. and S.P.M. investigated the study topic, carried out primary research, and completed manuscript drafting. T.M. gave helpful advice on the use of IETs, and reviewed and provided feedback on manuscript drafts. J.S. gave helpful advice on the ethical principles, and reviewed and provided feedback on manuscript drafts. The final manuscript was approved by all authors.</p>", "<title>Data availability</title>", "<p>All data generated or analysed during this study are included in this article and its supplementary information files. It should be noted that, because of the idiosyncratic nature of idea generation and innovation, replication of the study may lead to different, but equally valid results.</p>", "<title>Competing interests</title>", "<p id=\"Par47\">AV and SPM disclose no conflicts of interest. TM is the inventor of the BrainSwarming technique and has previously consulted on its use. JS is a Partner Investigator on an Australian Research Council grant LP190100841 which involves industry partnership from Illumina; he does not personally receive any funds from Illumina. JS is a Bioethics Committee consultant for Bayer. JS, through his involvement with the Murdoch Children's Research Institute, received funding through from the Victorian State Government through the Operational Infrastructure Support (OIS) Program.</p>" ]	[ "<fig id=\"Fig1\"><label>Figure 1</label><caption><p>Innovation-Enhancing Techniques. (<bold>a</bold>) BrainSwarming: (i) In this example, we use the <italic>Stuck Truck</italic> problem. A delivery truck was too tall for an underpass, and becomes wedged tightly beneath it. Without further damaging the truck or the underpass, and without assistance from others, how can the driver get the truck unstuck? (ii) The main goal (<italic>‘liberate truck from underpass’</italic>) is placed at the top of the diagram and is broken down into refined goals that grow downwards (<italic>e.g. ‘slide truck’, ‘lower truck’, </italic>etc<italic>.</italic>). (iii) Known resources (<italic>e.g. ‘truck’, ‘road’, ‘underpass’</italic>) are placed across the bottom of the diagram and are broken into features and parts, which grow upwards as solid lines. (iv) The resources and goals are interacted together, signified by dotted lines, to produce effects that help satisfy the refined goals and ultimately the top goal. Where the two directions meet, a candidate solution emerges. For example, if you stress the suspension with available heavy objects (<italic>e.g. rocks</italic>), you can lower the truck and possibly free the truck from the underpass. (<bold>b</bold>) Generic Parts Technique: Suppose we need to tie two things together and we have only a candle. Applying the Generic Parts Technique, the candle’s composition is described as consisting of wax and a wick. In this context, the descriptor ‘wick’ is associated with burning to emit light. A more generic description is ‘string’, which is closely associated with tying things together. Removing the wax to free the string gives us something to use for tying. For completeness, one even more generic description of a string with smaller parts is <italic>‘long interwoven fibrous strands’</italic>.</p></caption></fig>", "<fig id=\"Fig2\"><label>Figure 2</label><caption><p>Application of Innovation Enhancing Techniques to a problem involving intangible goals and resources. BrainSwarm demonstrating 100 possible use cases of Blockchain technology to further ethical goals in healthcare and research. Numbering relates to possible use cases, expanded in Extended Data Table ##SUPPL##0##1##. * indicates the use case is highlighted and expanded in Table ##TAB##3##4##.</p></caption></fig>" ]	[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Known psychological obstacles to creative problem-solving.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Obstacle</th><th align=\"left\">Description</th><th align=\"left\">References</th></tr></thead><tbody><tr><td align=\"left\">Functional fixedness</td><td align=\"left\">The habit of seeing an object for its designed use, and so being unable to use it in a new way</td><td align=\"left\">Duncker<sup>##UREF##3##5##</sup></td></tr><tr><td align=\"left\">Design fixation</td><td align=\"left\">After seeing possible solution(s), future attempts to create an innovative solution are shaped by the solution(s) already seen: the solver’s own solutions resemble the solution seen</td><td align=\"left\">Jansson & Smith<sup>##UREF##4##6##</sup></td></tr><tr><td align=\"left\">Goal fixedness</td><td align=\"left\">The solver stays close to the original phrasing of the problem, and so only considers certain kinds of solution</td><td align=\"left\">McCaffrey & Krishnamurty<sup>##UREF##2##4##</sup></td></tr><tr><td align=\"left\">Analogy blindness</td><td align=\"left\">Difficulty adapting a solution from one area to another area</td><td align=\"left\">Gick & Holyoak<sup>##UREF##5##7##,##UREF##6##8##</sup></td></tr><tr><td align=\"left\">Assumption blindness</td><td align=\"left\">The solver makes assumptions about the nature of the solution, and is unaware that those assumptions are being made</td><td align=\"left\">McCaffrey & Krishnamurty<sup>##UREF##2##4##</sup></td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Bioethical principles as goals.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Principle</th><th align=\"left\">Explanation</th><th align=\"left\">Examples of biomedical policy enacting this principle</th></tr></thead><tbody><tr><td align=\"left\">Beneficence</td><td align=\"left\">The ethical desirability or ideal of benefitting people. The goal is, broadly speaking, to improve people’s welfare, help them achieve their goals, satisfy their preferences, and to avoid harm and frustration</td><td align=\"left\"><p>Facilitating biomedical research</p><p>Improving access to and quality of medical treatment</p><p>Reducing costs and barriers associated with access to research and practice</p></td></tr><tr><td align=\"left\">Non-maleficence</td><td align=\"left\">This principle appeals to the idea that, in addition to benefitting people, there is a separate duty or ideal not to cause them harm, or frustrate their desires, satisfactions, or goals. This is famously summed up in the Hippocratic Oath as ‘first, do no harm’</td><td align=\"left\"><p>IRB/Ethics review</p><p>Policy of not communicating unactionable incidental findings</p><p>High standards for safety of devices used in biomedical research and practice</p></td></tr><tr><td align=\"left\">Justice</td><td align=\"left\">A separate set of concerns about the distribution of benefits and harms, the need for fairness in policy, experimentation, and practice, and the observation of the rule of law and relevant legislation</td><td align=\"left\"><p>Demographically representative sampling in biomedical studies</p><p>Laws against discrimination</p><p>Reporting conflicts of interest</p></td></tr><tr><td align=\"left\">Autonomy</td><td align=\"left\">The ideal of respecting people’s choices regarding their own life and actions</td><td align=\"left\"><p>Informed consent</p><p>Research informed by patient advocates</p><p>Confidentiality</p></td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab3\"><label>Table 3</label><caption><p>IT Artefacts of Blockchain as resources.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Artefact</th><th align=\"left\">Explanation</th></tr></thead><tbody><tr><td align=\"left\">Immutable Audit Trail</td><td align=\"left\">Every block in the chain is created using a hash of the previous block. A change in the content of a previous block would affect all subsequent blocks’ hash values, exposing any attempt at tampering. This preserves integrity of data stored in the chain</td></tr><tr><td align=\"left\">Consensus Mechanism</td><td align=\"left\">A way of ensuring each node contains an identical copy of blockchain data, and agrees on any additions</td></tr><tr><td align=\"left\">Encryption Mechanism</td><td align=\"left\">Asymmetric key cryptography: each entity interacting with a blockchain is issued two unique identifiers (keys)—a public key serving as a public address, and a private key serving as a password or signature, to prove authenticity</td></tr><tr><td align=\"left\">Distributed Ledger</td><td align=\"left\">A store of information, distributed to all nodes in the network</td></tr><tr><td align=\"left\">Smart Contracts</td><td align=\"left\">An algorithm (program) which is executed automatically when certain pre-defined criteria are satisfied</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab4\"><label>Table 4</label><caption><p>25 solution pathways identified by BrainSwarming to achieve ethical goals in biomedicine using blockchain technology.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">a</th><th align=\"left\">Refined goal</th><th align=\"left\">Refined resource</th><th align=\"left\">Description of potential use case</th></tr></thead><tbody><tr><td align=\"left\">1</td><td align=\"left\">Increase efficiency of recruitment</td><td align=\"left\">Advantage/benefit (+ Smart contract)</td><td align=\"left\">Token payment for clinical trial goals eg. recruitment, replication, pre-registration, appropriate reporting</td></tr><tr><td align=\"left\">3</td><td align=\"left\">Reducing fraud</td><td align=\"left\">Auditor/looker + encryption</td><td align=\"left\">Verified pseudonymous governance reporting system for health service employees (including 'whistleblowing')</td></tr><tr><td align=\"left\" rowspan=\"3\">6</td><td align=\"left\">Improving equality of representation of research users</td><td align=\"left\">Consensus/agreement</td><td align=\"left\" rowspan=\"3\">Individuals with a condition of interest pseudonymously and verifiably share information about their condition to assist research, with option for tokenised or other payment; may involve use of NFT for personal data sets</td></tr><tr><td align=\"left\">Express/communicate preference/choice</td><td align=\"left\">Transparent register</td></tr><tr><td align=\"left\">Maximizing benefits of research</td><td align=\"left\">Smart contract</td></tr><tr><td align=\"left\">7</td><td align=\"left\">Improving equality of representation of research users</td><td align=\"left\">Consensus/agreement</td><td align=\"left\">Tamper-proof system to enable pseudonymous voting on research priorities according to encoded rules—may include voting restrictions or permissions, and preferential weighting</td></tr><tr><td align=\"left\">11</td><td align=\"left\">Improving review</td><td align=\"left\">Enforceability</td><td align=\"left\">Smart contract-enforced protocols for funders, authors, reviewers, and others</td></tr><tr><td align=\"left\">13</td><td align=\"left\">Express/communicate preference/choice</td><td align=\"left\">Transparent register</td><td align=\"left\">Advanced directives stored as blockchain hashes to prevent tampering</td></tr><tr><td align=\"left\" rowspan=\"2\">17</td><td align=\"left\">Pre-registration of protocols</td><td align=\"left\">Transparent register</td><td align=\"left\" rowspan=\"2\">On-chain timestamped protocol registration; may include token incentivisation to register</td></tr><tr><td align=\"left\">Improving use of and access to protocols</td><td align=\"left\">Transparent register</td></tr><tr><td align=\"left\">20</td><td align=\"left\">Express/communicate preference/choice</td><td align=\"left\">Smart contract</td><td align=\"left\">Informed consent—consent given, but automatically revoked if certain conditions are met. ['Practical Implementation of Consent']</td></tr><tr><td align=\"left\" rowspan=\"2\">21</td><td align=\"left\">Improving patient engagement</td><td align=\"left\">Smart contract</td><td align=\"left\" rowspan=\"2\">Gamification: token payments for treatment adherence</td></tr><tr><td align=\"left\">Improving treatment adherence</td><td align=\"left\">Smart contract</td></tr><tr><td align=\"left\">30</td><td align=\"left\">Rewards for contributions to research</td><td align=\"left\">Smart contract</td><td align=\"left\">Incentivising healthcare professionals to develop innovations and improvements to clinical practise by allowing them to share in savings arising from those innovations and improvements, through smart contracts</td></tr><tr><td align=\"left\">33</td><td align=\"left\">Reducing error</td><td align=\"left\">Automation</td><td align=\"left\">Automatic flagging of drugs or devices found to be outdated, sub-standard, harmful, wasteful, etc.; with suggestions for alternatives. To include the equivalent of Field Safety Notices</td></tr><tr><td align=\"left\">38</td><td align=\"left\">Reducing waste</td><td align=\"left\">Community</td><td align=\"left\">DAO for hospital governance or clinical management, with option for pseudonymous input (eg voting) from relevant stakeholders</td></tr><tr><td align=\"left\" rowspan=\"2\">40</td><td align=\"left\">Maximizing benefits of research</td><td align=\"left\">Smart contract</td><td align=\"left\" rowspan=\"2\">Blockchain used to automate or manage innovation enhancing tools, such as a BrainSwarming tool; allowing pseudonymised editing (including to refinements, goals, and creation of new graphs); with embedded AI technology to suggest analogues; may include award of tokens for effective solution paths</td></tr><tr><td align=\"left\">Maximizing benefits of treatment</td><td align=\"left\">Automation</td></tr><tr><td align=\"left\">43</td><td align=\"left\">Express/communicate preference/choice</td><td align=\"left\">Smart contract</td><td align=\"left\">DAO managed funding pool for innovative startups—funders buy tokens and vote on proposals, winner(s) by vote receive funding; funders receive proportional share of IP</td></tr><tr><td align=\"left\">48</td><td align=\"left\">Maximizing benefits of research</td><td align=\"left\">Smart contract</td><td align=\"left\">Tokens in place of grant funding; use restricted to governance, research, or other relevant costs</td></tr><tr><td align=\"left\">59</td><td align=\"left\">Desert</td><td align=\"left\">Immutable audit trail</td><td align=\"left\">Minting NFTs that represent ownership, which can be traded or fractionalised; complement or alternative to patent system</td></tr><tr><td align=\"left\">60</td><td align=\"left\">Express/communicate preference/choice</td><td align=\"left\">Consensus mechanism</td><td align=\"left\">DAO-based voting as a method of determining scientific consensus</td></tr><tr><td align=\"left\">68</td><td align=\"left\">Reducing waste</td><td align=\"left\">Transparent register</td><td align=\"left\">Labour exchange for medical or research staffing, including option for certifications, ratings, and pseudonymity. Individuals prosent depending on factors such as salary; exchange is automatically updated and distributed</td></tr><tr><td align=\"left\">70</td><td align=\"left\">Express/communicate preference/choice</td><td align=\"left\">Transparent register</td><td align=\"left\">Practical implementation of meta-consent</td></tr><tr><td align=\"left\" rowspan=\"2\">72</td><td align=\"left\">Improving record keeping</td><td align=\"left\">Automation</td><td align=\"left\" rowspan=\"2\">Gamification: token payments for healthy lifestyle / behaviours</td></tr><tr><td align=\"left\">Prevent harm</td><td align=\"left\">Smart contract</td></tr><tr><td align=\"left\">73</td><td align=\"left\">Rewards for contributions to research</td><td align=\"left\">Smart contract</td><td align=\"left\">Micropayments to authors / scholars when their work is accessed or viewed</td></tr><tr><td align=\"left\">76</td><td align=\"left\">Maximizing benefits of treatment</td><td align=\"left\">Automation</td><td align=\"left\">Real-time update of data in online publications, may include journal articles</td></tr><tr><td align=\"left\">92</td><td align=\"left\">Improving replicability</td><td align=\"left\">Timestamping</td><td align=\"left\">Timestamped verified snapshot of data at specific or random stage of research, automatically delivered to pre-specified interested parties eg. funders</td></tr><tr><td align=\"left\">95</td><td align=\"left\">Reducing disproportionate governance/ethical review</td><td align=\"left\">Transparent register</td><td align=\"left\">IRB decisions stored on blockchain, decision relating to paper made public on paper acceptance/publication</td></tr><tr><td align=\"left\">99</td><td align=\"left\">Reducing fraud</td><td align=\"left\">Automation</td><td align=\"left\">Automated checks on suggested reviewers to exclude those eg. with known conflicts, from same organisation</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab5\"><label>Table 5</label><caption><p>Solution pathways classified into use concepts—groups of solutions with a common theme.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Use concept</th><th align=\"left\"/></tr></thead><tbody><tr><td align=\"left\">Internet of Things</td><td align=\"left\">21</td></tr><tr><td align=\"left\">Enforcing trial protocols</td><td align=\"left\">11, 76, 92, 95</td></tr><tr><td align=\"left\">Enforcing rules</td><td align=\"left\">13, 33, 99</td></tr><tr><td align=\"left\">Gamification / incentivisation</td><td align=\"left\">1, 6, 21, 30, 40, 72, 73</td></tr><tr><td align=\"left\">Other token use</td><td align=\"left\">6, 7, 59</td></tr><tr><td align=\"left\">Financing</td><td align=\"left\">43, 74</td></tr><tr><td align=\"left\">Supply chain management</td><td align=\"left\">68</td></tr><tr><td align=\"left\">Administration / governance</td><td align=\"left\">1, 7, 11, 30, 33, 60, 68, 92, 95</td></tr><tr><td align=\"left\">Consent</td><td align=\"left\">13, 20, 70</td></tr><tr><td align=\"left\">Prosent & knowledge provenance</td><td align=\"left\">6, 59, 68</td></tr><tr><td align=\"left\">Voting & Consensus</td><td align=\"left\">7, 43, 60</td></tr><tr><td align=\"left\">Pseudonymous verification</td><td align=\"left\">6, 7, 40, 68, 70</td></tr><tr><td align=\"left\">Data & knowledge verification</td><td align=\"left\">13, 76, 92, 95, 99</td></tr></tbody></table></table-wrap>" ]	[]	[]	[]	[]	[]	[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>" ]	[ "<fn-group><fn><p><bold>Publisher's note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>These authors contributed equally: Anuraag A. Vazirani and Sebastian Porsdam Mann.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"41598_2023_50232_Fig1_HTML\" id=\"MO1\"/>", "<graphic xlink:href=\"41598_2023_50232_Fig2_HTML\" id=\"MO2\"/>" ]	[ "<media xlink:href=\"41598_2023_50232_MOESM1_ESM.docx\"><caption><p>Supplementary Information.</p></caption></media>" ]	[{"label": ["1."], "mixed-citation": ["Weisberg, RW. Prolegomena to theories of insight problem solving: A taxonomy of problems. In "], "italic": ["The Nature of Insight"]}, {"label": ["3."], "mixed-citation": ["McCaffrey, T. "], "italic": ["Overcome any Obstacle to Creativity"]}, {"label": ["4."], "surname": ["McCaffrey", "Krishnamurty"], "given-names": ["T", "S"], "article-title": ["The obscure features hypothesis in design innovation"], "source": ["Int. J. Des. Creat. Innov."], "year": ["2014"], "volume": ["3"], "issue": ["1"], "fpage": ["1"], "lpage": ["28"], "pub-id": ["10.1080/21650349.2014.893840"]}, {"label": ["5."], "surname": ["Duncker"], "given-names": ["K"], "article-title": ["On problem-solving (Lees, L.S trans)"], "source": ["Psychol. Monogr."], "year": ["1945"], "volume": ["58"], "issue": ["5"], "fpage": ["113"], "pub-id": ["10.1037/h0093599"]}, {"label": ["6."], "surname": ["Jansson", "Smith"], "given-names": ["DG", "SM"], "article-title": ["Design fixation"], "source": ["Des. Stud."], "year": ["1991"], "volume": ["12"], "issue": ["1"], "fpage": ["3"], "lpage": ["11"], "pub-id": ["10.1016/0142-694X(91)90003-F"]}, {"label": ["7."], "surname": ["Gick", "Holyoak"], "given-names": ["ML", "KJ"], "article-title": ["Analogical problem solving"], "source": ["Cognit. Psychol."], "year": ["1980"], "volume": ["12"], "issue": ["3"], "fpage": ["306"], "lpage": ["355"], "pub-id": ["10.1016/0010-0285(80)90013-4"]}, {"label": ["8."], "surname": ["Gick", "Holyoak"], "given-names": ["ML", "KJ"], "article-title": ["Schema induction and analogical transfer"], "source": ["Cognit. Psychol."], "year": ["1983"], "volume": ["15"], "issue": ["1"], "fpage": ["1"], "lpage": ["38"], "pub-id": ["10.1016/0010-0285(83)90002-6"]}, {"label": ["10."], "surname": ["McCaffrey", "Krishnamurty", "Lin"], "given-names": ["T", "S", "X"], "article-title": ["Cahoots: a software platform for enhancing innovation and facilitating situation transfer"], "source": ["Res. Pract. Technol. Enhanced Learn."], "year": ["2014"], "volume": ["9"], "issue": ["1"], "fpage": ["145"], "lpage": ["163"]}, {"label": ["11."], "mixed-citation": ["Beauchamp, T.L., & Childress, J.F. "], "italic": ["Principles of Biomedical Ethics"]}, {"label": ["12."], "surname": ["Du", "Pan", "Leidner", "Ying"], "given-names": ["W", "SL", "DE", "W"], "article-title": ["Affordances, experimentation and actualization of FinTech: A blockchain implementation study"], "source": ["J. Strateg. Inform. Syst."], "year": ["2019"], "volume": ["28"], "fpage": ["50"], "lpage": ["65"], "pub-id": ["10.1016/j.jsis.2018.10.002"]}, {"label": ["14."], "surname": ["Hodson", "Majid", "Vlaev", "Singh"], "given-names": ["N", "M", "I", "SP"], "article-title": ["Can incentives improve antipsychotic adherence in major mental illness?"], "source": ["A Mixed Methods Syst. Rev. 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The Struggle to Make Human Research Safe"], "year": ["2015"], "publisher-name": ["Oxford University Press"]}, {"label": ["19."], "mixed-citation": ["Gawande, A. "], "italic": ["The Checklist Manifesto: How to Get Things Right."]}, {"label": ["20."], "surname": ["Abbott", "Grady"], "given-names": ["L", "C"], "article-title": ["A systematic review of the empirical literature evaluating IRBs: What we know and what we still need to learn"], "source": ["J. Empir. Res. Human Res. Ethics"], "year": ["2011"], "volume": ["6"], "issue": ["1"], "fpage": ["3"], "lpage": ["19"], "pub-id": ["10.1525/jer.2011.6.1.3"]}, {"label": ["23."], "surname": ["Lynch"], "given-names": ["HF"], "article-title": ["Opening closed doors: Promoting IRB transparency"], "source": ["J. Law Med. Ethics"], "year": ["2018"], "volume": ["46"], "issue": ["1"], "fpage": ["145"], "lpage": ["158"], "pub-id": ["10.1177/1073110518766028"]}]	{ "acronym": [], "definition": [] }	23	CC BY	no	2024-01-13 00:02:20	Sci Rep. 2024 Jan 10; 14:832	oa_package/3f/c5/PMC10781689.tar.gz
PMC10781690	38200040	[ "<title>Introduction</title>", "<p id=\"Par2\">Despite alloys being at the forefront of mankind’s progress for thousands of years and the myriad of applications from infrastructure to medical tools to transport, there is a burgeoning demand for novel high strength and high ductility materials. Such materials can facilitate developing newer and sustainable applications by reducing amounts of alloy materials required which can enhance energy efficiencies<sup>##REF##27279217##1##</sup>. Traditional mechanisms for increasing strength typically come at the cost of lowering ductility and vice-versa, which is referred to as the strength–ductility trade-off<sup>##REF##27279217##1##–##UREF##0##3##</sup>. Recent work has focused on mixing five or more elements to create high-entropy alloys (HEAs) that overcome this trade-off<sup>##REF##27279217##1##,##UREF##1##4##,##UREF##2##5##</sup>, but these alloys are not easy to manufacture when compared to ternary alloys and require many expensive starting materials along with specialized processing techniques.</p>", "<p id=\"Par3\">The microstructural morphology of our NiTi–Nb nanocomposite in Fig. ##FIG##0##1##a consists of Nb nanofibers dispersed in an NiTi matrix. Similar nanostructured alloys have leveraged the ability to elastically deform up to 4–7% strain under stresses larger than the yield strength (a significant fraction of their ideal strength) of bulk materials when deformed non-hydrostatically (for e.g., in tension)<sup>##UREF##3##6##</sup>. Thus, we envisage NiTi-Nb processing leveraging micro- and nano-composite design solutions<sup>##UREF##4##7##</sup>. Previous work has shown an example of this microstructure; continuous Nb nanowires coexist within an active NiTi matrix that undergoes a phase change due to the underling martensitic transformation (MT) that takes place via twinning and subsequent detwinning as stress/strain levels increase<sup>##UREF##5##8##</sup>. These embedded nanowires have large elastic strains like those of free-standing nanowires (~ 3.5%) that are higher than the elastic strain embedded in conventional metal matrices that deform by dislocation slip (~ 1.5%)<sup>##UREF##5##8##</sup>. Thus, using these nanostructured materials allows for elastic and inelastic deformation matching to control properties such as the martensitic phase transformation<sup>##UREF##3##6##</sup>.</p>", "<p id=\"Par4\">Novel nanocomposite materials have used this concept of lattice strain matching between uniform lattice distortion of the martensitic phase transformation in the matrix material and uniform ultra-large elastic/plastic strains of Nb nanoscale microconstituents<sup>##UREF##3##6##,##UREF##5##8##–##REF##26745016##14##</sup>. Hao et. al showed that in such materials, the large elastic strains of Nb nanowires couple with the superelastic/pseudoelastic shape memory strain in the active NiTi matrix<sup>##UREF##7##10##</sup>. This coupling resulted in remarkable mechanical behavior and properties; recoverable strain that exceeds 6%, a relatively low Young’s Modulus that is nearly 30 GPa, and a ultrahigh yield strength of 1.65 GPa<sup>##UREF##7##10##</sup>. The length of these Nb nanowires ranged between 1 and 100 µm with an aspect ratio exceeding 100<sup>##UREF##7##10##</sup>. A synergistic effect of strain matching produced inhomogeneous elastic deformation in Nb nanowires, with nearly 8% strain induced in the Nb nanowire regions near the NiTi undergoing the stress-induced MT, and much lower strains in regions near untransformed austenitic NiTi matrix<sup>##REF##26745016##14##</sup>. Our NiTi–Nb material is comprised of discontinuous Nb nanofibers embedded in an NiTi matrix (as shown in the represented volume element in Fig. ##FIG##0##1##a.i that is recreated using the SEM images in the rolling and transverse directions in Fig. ##FIG##0##1##a.ii, a.iii respectively), with mean lengths of less than 500 nm and a lower aspect ratio (< 10) that results in a material with both high strength and ductility, and has the ability to recover imparted deformation via the shape memory effect (SME).</p>" ]	[ "<title>Methods</title>", "<p id=\"Par13\">A Ni<sub>47.7</sub>Ti<sub>43.5</sub>Nb<sub>8.8</sub> at.% alloy in rolled strip form (6 mm wide and 0.25 mm thick) was supplied by Medical Metals LLC. The material was prepared via multiple thickness reductions through cold rolling of an ingot made via vacuum induction melting, followed by annealing near recrystallization temperature (850 °C). Previous differential scanning calorimetry analysis did not reveal endothermic or exothermic peaks<sup>##UREF##14##18##,##UREF##30##35##</sup>. Consequently, the transformation temperatures were measured by applying a constant bias load (equivalent to 150 MPa stress) during thermal cycling: ; ; and <sup>##UREF##14##18##</sup>. Scanning electron microscopy (SEM) and transmission electron microscopy (TEM) were carried out at room temperature. For SEM, specimens were polished via SiC paper with grit size decreasing from 180 to 1200 and finally polished using 0.02 μm colloidal silica. The imaging was performed in a Philips XL30 ESEM, while EDX was performed in a Zeiss NVision 40 SEM. The fibril sizes were determined from the rolling direction micrograph in Fig. ##FIG##0##1##a.ii. The image was analyzed in ImageJ software by thresholding out the nanofibers<sup>##REF##22930834##36##</sup>. The length and the width of the nano-fibril was determined using the Feret’s diameter in the rolling and transverse direction respectively in the first micrograph in Fig. ##FIG##0##1##a.ii<sup>##UREF##31##37##</sup>. For TEM, specimens were mechanically polished to 10 μm thickness and made electron transparent using focused ion milling. The thinned specimen was attached to a molybdenum grid and ion milled from both sides in a Gatan Precision Ion Polishing System at a beam angle of 15° with an accelerating voltage of 3 kV. The imaging was performed in a Philips 420 transmission electron microscope operated at 120 kV using a single-tilt holder. The grain size was determined from Fig. ##FIG##0##1##b using the Mean Intercept Procedure per ASTM E112-12<sup>##UREF##32##38##</sup>.</p>", "<p id=\"Par14\">The tensile testing was performed in an MTS 810 servo hydraulic load frame at room temperature, and this frame was equipped with a custom induction coil heating set-up. The temperature was measured via a thermocouple attached to the specimen. Tensile specimens with dog-bone geometry were electro-discharge machined from the rolled strip such that the gage length was 10 mm and gage width was 3 mm, as shown in the inset of Fig. ##FIG##2##3##. A single specimen was deformed up to one of five pre-strain levels (5.0, 8.9, 9.9, 12.5 and 16.9%) so that five virgin specimens were pre-strained to investigate OWSME recovery. The specimens were loaded in displacement control at a rate of 0.0028 mm/s, which corresponds to an average strain rate of 1.7 × 10<sup>–4</sup> /s. After the strain during loading reached the desired level, the specimen was unloaded in force control at an equivalent stress rate of 1.2 MPa/s. The rates are in accordance with the ASTM E8 Standard Test Methods for Tension Testing of Metallic Alloys<sup>##UREF##33##39##</sup>. After unloading, the load was fixed at zero and the specimen was heated at a rate of 10–15 °C/min via induction.</p>", "<p id=\"Par15\">The axial strain measurements for the stress–strain curves were determined using a virtual digital extensometer feature in the digital image correlation software, referred to as an inspect extensometer (IE). The general steps for implementation of DIC analysis are specimen preparation, machine vision set-up and image acquisition, and image correlation. The correlation theory has been explained in the works of Sutton et al.<sup>##UREF##34##40##–##UREF##36##42##</sup>. A speckle pattern was applied on the specimen surface using an IWATA Micron-CMB airbrush. A very thin and uniform white coating of Golden Airbrush Titanium White (#8380) paint was applied as the background for a black micron speckle pattern of Golden Airbrush Carbon Black (#8040) paint. In-situ images of the spray-painted specimen surface were captured using a Grasshopper GRAS-20S4M/C CCD camera (1600 × 1200 pixels). Digital images had a resolution of 58.7 pix/mm. Image capture was synchronized with load and displacement data acquisition using Vic-Snap system (Correlated Solutions, Inc.). DIC numerical analysis was carried out using Vic-2D<sup>®</sup> software (Correlated Solutions, Inc.). The undeformed length of the IE is equivalent to the specimen gage length. Strain is calculated as the change in length divided by the undeformed length and is referred to as average/macroscale.</p>", "<p id=\"Par16\">In-situ full-field deformation measurements during pre-straining and OWSME recovery were calculated using digital image correlation (DIC). For the analysis, a region of interest (ROI) is selected and divided into subsets. The ROI in this work spans the specimen gage length and it measures 2.4 × 9.4 mm<sup>2</sup>, as shown in the inset of Fig. ##FIG##1##2##. DIC analysis can be considered as measuring strain over the specimen surface using micron sized strain gages; the size is defined by the subset size and spacing<sup>##UREF##37##43##</sup>, and was 270 μm for this work.</p>" ]	[ "<title>Results</title>", "<p id=\"Par5\">The microstructure of this anisotropic NiTi-Nb nanocomposite consists of discontinuous Nb nanofibers dispersed in an NiTi matrix, as shown in Fig. ##FIG##0##1##a. The SEM images are taken along the processing/rolling direction (Fig. ##FIG##0##1##a.ii) and perpendicular to it (Fig. ##FIG##0##1##a.iii) that were used to quantify the sizes of the Nb nanofibers. The mean length of the fibers is 452 nm, with a standard deviation of 273 nm and a range of 5 to 940 nm. The mean width of the fibers is 190 nm with a standard deviation of 105 nm and a range of 2 nm to 375 nm. Figure ##FIG##0##1##b is a TEM image showing the grain structure of the NiTi matrix (a few grains are highlighted in white dashes, and some Nb nanofibers in yellow dashes), and the representative grains are highlighted with sizes approaching 300 nm, with the smallest grains close to 100 nm and the larger grains approaching 400 nm. The light and dark contrast in the grains is related to the amount of electrons being transmitted through the structure, and is related to the different orientations of the grains. Many of the grains appear lighter potentially due to preferential orientation in the rolling direction. The higher magnification SEM image in Fig. ##FIG##0##1##c shows discontinuous Nb nanofibers are oriented in the rolling direction and coexist with nano-spheroids. An energy dispersive X-ray analysis (EDX) line scan in the high magnification micrograph in Fig. ##FIG##1##2## shows that the nanofibers have a much larger Nb concentration and much lower Ni/Ti concentration than the surrounding NiTi matrix.</p>", "<p id=\"Par6\">The uniaxial room temperature tensile stress–strain (σ–ε) plot in Fig. ##FIG##2##3## shows the mechanical response to failure. The NiTi–Nb nanocomposite possesses striking mechanical properties; a low Young’s Modulus of 64.3 GPa, very high ultimate tensile strength of 980 MPa, a large ductility with a strain at fracture of 58%, and a high tensile toughness of 514 MJ/m<sup>3</sup>. Also shown are accompanying full-field strain maps from DIC analysis of axial deformation measurements within the specimen gage section, which is defined as the region of interest (ROI). When linear-elastic response ends, the stress drops, and the stress-induced martensite (SIM) volume fraction grows causing the σ-ε response to plateau at a transformation stress of approximately 590 MPa. The SIM produces a localized strain contour band in DIC image 4 in Fig. ##FIG##2##3##, analogous to a high-strain Lüders bands. As the band grows across the gage length in images 4–8, the maximum localized strains saturate around 9%. Beyond the plateau, the elastic/plastic deformation of the detwinned martensite takes place<sup>##UREF##11##15##</sup>. Note that the contours change color homogeneously in images 8–10, which is typical for elastic deformation. Images 10–15 are captured during a second linear-to-nonlinear transition. It is well known that martensite plastically deforms, which is expected to produce strain localization<sup>##UREF##11##15##</sup>. Apparently, corresponding local strains are undetectable using our DIC measurement length scales. Images 16–19 correspond to the necking response, where stress begins to decrease with increasing strain. A localized region of very high strain appears in the center of the specimen where the strains approach 120%, and the specimen eventually fractures at this location.</p>", "<p id=\"Par7\">The shape memory recovery behavior that shows the material’s active behavior is shown in Fig. ##FIG##3##4##. In Fig. ##FIG##3##4##a, strain recovery produces an initial linear-elastic unloading σ-ε response. A distinct non-linear response follows the linear-elastic unloading segment for 5.0% that is indistinguishable at 16.9%. The non-linearity is attributed to partial superelastic shape memory recovery as SIM reverts to the austenite parent structure. After unloading from the active region (subjected to < 20% strain), samples were heated, and strain recovery takes place due to the reversion of SIM that has been stabilized in the NiTi matrix by loading deformation. Stable SIM remaining after unloading is remarkable. Superelastic recovery would be expected at room temperature as the material is in its austenitic state and fulfills the criteria for pseudoelasticity<sup>##UREF##12##16##–##UREF##14##18##</sup>. The stabilized SIM can be considered as “atypical” with respect to the primary SIM that formed at the stress-plateau. As the pre-strain levels increase, higher temperatures are required for complete reversion of the atypical martensite via the shape memory effect (SME). Only a fraction of the applied deformation/strain was recovered during unloading and heating, during linear-elastic unloading, SE recovery, and SME recovery, and permanent residual strain always remained. Deforming to 10% results in the largest recovered strain of 5% and the highest relative recovery (ratio of recovered strain via heating to the permanent deformation that remained).</p>" ]	[ "<title>Discussion</title>", "<p id=\"Par8\">Figure ##FIG##4##5## shows that our multifunctional NiTi-Nb metallic nanocomposite outperforms all the best performing 316L stainless steels<sup>##UREF##15##19##–##UREF##19##24##</sup>. The processing needed to produce these high strength and high ductility steels are solution nitriding<sup>##UREF##15##19##</sup>, ultrashort annealing at different temperatures to produce ultrafine grains<sup>##UREF##16##20##</sup>, multi-step dynamic compression to produce nanotwinned grains<sup>##UREF##17##21##</sup>, additively manufactured hierarchical microstructure<sup>##REF##29115290##23##</sup>, and additively manufactured textured microstructure<sup>##UREF##19##24##</sup>.</p>", "<p id=\"Par9\">Incredibly, the ultimate tensile strength and ductility of this nanocomposite material is among the best performing high-entropy alloys, as shown in Fig. ##FIG##4##5##<sup>##UREF##20##25##–##UREF##25##30##</sup>. Gao et al. studied an AlCoCrFeNi<sub>2.1</sub> eutectic HEA produced having modulated lamellar structure<sup>##UREF##20##25##</sup>. Li et al. investigated an Al<sub>0.3</sub>CoCrFeNi HEA that possessed nanosized particles reinforcing an austenitic matrix<sup>##UREF##21##26##</sup>. FeMnCoCr and CoCrFeMnNi HEAs showed high strength and high ductility at a cryogenic temperature of 77 K<sup>##UREF##22##27##,##UREF##25##30##</sup>. Zhang et al. analyzed a dual-phase FeCoCrNiMn HEA, with one phase contribution to high strength and the other to high ductility<sup>##UREF##23##28##</sup>. Jo et al. examined an FeCMnSiMoV HEA reinforced by segregated Mn bands<sup>##UREF##24##29##</sup>.</p>", "<p id=\"Par10\">Our nanocomposite alloy is also multifunctional; it has an active region when deformed to strains less than 20%, as shown in Fig. ##FIG##3##4##. This partial recovery is due to the shape memory effect in the NiTi matrix. The austenitic NiTi in the matrix transforms to stress-induced martensite (SIM) that is stable after unloading. This SIM is transformed back to austenite upon heating and the material and recovers residual strain due to the shape memory effect (SME)<sup>##UREF##26##31##</sup>. However, there is only partial recovery as the stress-induced transformation in the matrix is accompanied by the permanent plastic deformation of the Nb nano fibers. These nanofibers will not recover deformation upon heating, and likely affect surrounding stress-induced martensite to not recover as well (as shown schematically in Fig. ##FIG##0##1##d), resulting in permanent deformation not recovered via heating.</p>", "<p id=\"Par11\">Plausible mechanisms for the high strength of the material are related to the microstructural features that act as obstacles for dislocation motion<sup>##UREF##27##32##</sup>. For our NiTi-Nb nanocomposite material, these include (i) high grain boundary densities attributed to the nanosized grains and (ii) the presence of Nb nanofibers. Additionally, the strain-induced martensitic transformations are facilitated by much higher stresses compared to SIM. Rather than slip dislocations coalescing into a crack that grows until ductile fracture occurs, the strain-induced martensite curtails macro-scale necking. We postulate that the SIM and strain-induced martensite become stable so that only a fraction of martensite reverts via superelastic recovery. For SMAs, stabilization typically refers to martensite that becomes trapped and requires heating to higher temperatures above the characteristic phase transformation temperature<sup>##UREF##28##33##</sup>. Further recovery takes place during heating via SME, albeit a fraction of the applied strain is recoverable.</p>", "<p id=\"Par12\">Possible mechanisms for the high ductility of the materials are related to microstructural features that can accommodate large deformations prior to fracture. The Nb nanofibers can elongate extensively, withstanding the highest levels of plastic deformation<sup>##UREF##7##10##</sup>. Our microstructural analysis shows that the Nb nanofibers possess a length to width aspect ratio that ranges between 0.6 and 8.5. These nanofibers can further plastically deform to a much larger aspect ratio, closer to the higher aspect ratios > 100 achieved by Nb nanowires in the NiTi-Nb nanocomposite material studied by Hao et.al.<sup>##UREF##7##10##</sup>. Additionally, the dotted boundaries encompassing fibers in Fig. ##FIG##0##1##d depict a localized region of active NiTi adjacent to the fibers that can inherit the plastic deformation once the externally applied stress/strain level reaches a critical level. In local regions near these grains and/or in the heavily plastically deformed regions adjacent to the Nb nanofibers, the active matrix deforms further as SIM undergoes subsequent transitions to strain-induced martensite<sup>##UREF##29##34##</sup>. As a result, remarkably high strain levels are achieved prior to and during necking deformation. Thus, the microstructural features of highly pliable Nb nanofibers, undeformed grains and an NiTi matrix that undergoes the stress-induced martensitic transformation results in this active nanocomposite material that overcomes the strength/ductility tradeoff.</p>" ]	[]	[ "<p id=\"Par1\">The actualization of high strength and ductility in alloys, in addition to providing strong, formable materials, can lead to reduced weights in practical applications. However, increasing strength typically comes at the cost of lowering the ductility and vice-versa, referred to as the strength–ductility trade-off. In this work, we investigate the thermo-mechanical response of a 3-element multifunctional NiTi–Nb nanocomposite material that overcomes this trade-off, as it exhibits a high strength of 980 MPa and an ultrahigh ductility of 58% at fracture. The remarkable properties are attributed to the underlying microstructure of Nb nanofibers dispersed in an NiTi matrix. Deformation is accommodated via the shape memory transformation of the active NiTi matrix in concert with elastoplastic deformation of Nb nanofibers embedded within the matrix. Consequently, the material exhibits multifunctionality and recovers deformation during heating via the reversion of the stress-induced martensitic transformation in the NiTi matrix. The high strength and high ductility of this 3-element nanocomposite material puts it amongst the best performing high-entropy alloys (HEAs) that are typically made up of five or more elements.</p>", "<title>Subject terms</title>" ]	[]	[ "<title>Acknowledgements</title>", "<p>This study has been supported by the Mid-Atlantic Universities Transportation Center (MAUTC) Pooled Research Program issued by the Research and Innovative Technology administration of the US DOT (Grant No. DTRT12-G-UTC03). This material is based upon work supported by the National Science Foundation (NSF) under Grant No. 1538354. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. This study was also supported by the Maine Technology Institute (MTI) under project number 20220019. This study was also supported by the Maine Space Grants Consortium (MSGC's) Ideas Lab grant No. SG-22-23 funded by the National Aeronautics and Space Administration (NASA). The authors would also like to thank Prof. Hans J. Maier (Leibniz Universität Hannover, Hannover, Germany) for conducting TEM.</p>", "<title>Author contributions</title>", "<p>A.R.L. and R.F.H. designed and planned the research. A.R.L. performed the research, collected data, analyzed the data, and created the figures. A.R.L. and R.F.H. interpreted the results. A.N.M. performed literature review and data collection from the literature. A.R.L. wrote the paper with input from R.F.H. and A.N.M. E.S.P. and A.R.L. performed the energy dispersive X-ray analysis. R.F.G. supplied the material and provided input on materials processing.</p>", "<title>Data availability</title>", "<p>The datasets generated during and/or analyzed during the current study are available from the corresponding authors on reasonable request.</p>", "<title>Competing interests</title>", "<p id=\"Par17\">R.F.G is president of Medical Metals, the company that sells shape memory alloy materials that were used in this study.</p>" ]	[ "<fig id=\"Fig1\"><label>Figure 1</label><caption><p>The 3D schematic of the representative volume element (RVE) is shown in (<bold>a.i</bold>), which is reconstructed from the rolling direction microstructure in (<bold>a.ii</bold>) and the transverse direction in (<bold>a.iii</bold>). These SEM micrographs in (<bold>a.i,a.ii</bold>) show that the microstructure consists of white Nb nanofibers embedded in the darker NiTi matrix. The higher magnification image in (<bold>b</bold>) shows the grain structure of the NiTi matrix (NiTi grains highlighted in white dashes) and Nb nanofibers (highlighted in yellow dashes). The image in (<bold>c</bold>) at a similar magnification shows the geometry of individual white Nb nanofibers. The 2D schematic in (<bold>d</bold>) shows the Nb nanofibers in grey along with the region of plastic deformation highlighted via the darker dotted regions that form during deformation embedded in the orange NiTi matrix with the grain boundaries highlighted. The micrographs in (<bold>a,c</bold>) were obtained via SEM, and the micrograph in (<bold>b</bold>) was obtained via TEM.</p></caption></fig>", "<fig id=\"Fig2\"><label>Figure 2</label><caption><p>SEM-energy dispersive X-ray analysis (EDX) line going across a white Nb nanofibril clearly shows that is almost pure Nb, with the Nb concentration increasing and the Ni–Ti concentrations decreasing across the line. The whole area EDX results show that the at% distribution of Nb, Ti and Ni for the micrograph are close to the supplier’s concentration, thus validating EDX results.</p></caption></fig>", "<fig id=\"Fig3\"><label>Figure 3</label><caption><p>The room-temperature tensile stress–strain response of the NiTi-Nb nanocomposite model highlights the material’s outstanding strength and ductility. The accompanying DIC images show the morphology of localized strains in the loading direction at discrete points along the stress-stress response. The active region of 20% is delineated on the strain scale for the DIC images. The inset figure shows the sample geometry and location of region of interest for the DIC images.</p></caption></fig>", "<fig id=\"Fig4\"><label>Figure 4</label><caption><p>The active property of shape memory recovery of these NiT–Nb nanocomposite material is demonstrated in (<bold>b</bold>) after deforming different samples to different levels of increasing strains in (<bold>a</bold>). All specimens were heated to 150 °C from room temperature and then allowed to cool back down to room temperature.</p></caption></fig>", "<fig id=\"Fig5\"><label>Figure 5</label><caption><p>This plot shows the relative location of the ultimate tensile strength and ductility of the active NiTi–Nb nanocomposite material and those of high entropy alloys and stainless steels from literature. The green points represent the best-performing high entropy alloys (HEAs). The black points represent the best-performing 316L stainless steels. Our NiTi-Nb nanocomposite material is the red star. This red star lies amongst the best performing high-entropy alloys. It is also stronger than all the stainless steels and is on the higher ductility side of the steels as well. All the green and black data points are from references<sup>##UREF##15##19##–##UREF##25##30##</sup>.</p></caption></fig>" ]	[]	[ "<inline-formula id=\"IEq1\"><alternatives><tex-math id=\"M1\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$M_{s} \\, = \\, - 64 \\, ^\\circ {\\text{C}}$$\\end{document}</tex-math><mml:math id=\"M2\"><mml:mrow><mml:msub><mml:mi>M</mml:mi><mml:mi>s</mml:mi></mml:msub><mml:mspace width=\"0.166667em\"/><mml:mo>=</mml:mo><mml:mspace width=\"0.166667em\"/><mml:mo>-</mml:mo><mml:mn>64</mml:mn><mml:msup><mml:mspace width=\"0.166667em\"/><mml:mo>∘</mml:mo></mml:msup><mml:mtext>C</mml:mtext></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq2\"><alternatives><tex-math id=\"M3\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$M_{f} = \\, - 76 \\, ^\\circ {\\text{C}}$$\\end{document}</tex-math><mml:math id=\"M4\"><mml:mrow><mml:msub><mml:mi>M</mml:mi><mml:mi>f</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mspace width=\"0.166667em\"/><mml:mo>-</mml:mo><mml:mn>76</mml:mn><mml:msup><mml:mspace width=\"0.166667em\"/><mml:mo>∘</mml:mo></mml:msup><mml:mtext>C</mml:mtext></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq3\"><alternatives><tex-math id=\"M5\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$A_{s} \\, = \\, - 30 \\, ^\\circ {\\text{C}}$$\\end{document}</tex-math><mml:math id=\"M6\"><mml:mrow><mml:msub><mml:mi>A</mml:mi><mml:mi>s</mml:mi></mml:msub><mml:mspace width=\"0.166667em\"/><mml:mo>=</mml:mo><mml:mspace width=\"0.166667em\"/><mml:mo>-</mml:mo><mml:mn>30</mml:mn><mml:msup><mml:mspace width=\"0.166667em\"/><mml:mo>∘</mml:mo></mml:msup><mml:mtext>C</mml:mtext></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq4\"><alternatives><tex-math id=\"M7\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$A_{f} \\, = \\, - 7 \\, ^\\circ {\\text{C}}$$\\end{document}</tex-math><mml:math id=\"M8\"><mml:mrow><mml:msub><mml:mi>A</mml:mi><mml:mi>f</mml:mi></mml:msub><mml:mspace width=\"0.166667em\"/><mml:mo>=</mml:mo><mml:mspace width=\"0.166667em\"/><mml:mo>-</mml:mo><mml:mn>7</mml:mn><mml:msup><mml:mspace width=\"0.166667em\"/><mml:mo>∘</mml:mo></mml:msup><mml:mtext>C</mml:mtext></mml:mrow></mml:math></alternatives></inline-formula>" ]	[]	[]	[]	[]	[]	[ "<fn-group><fn><p><bold>Publisher's note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"41598_2023_50967_Fig1_HTML\" id=\"MO1\"/>", "<graphic xlink:href=\"41598_2023_50967_Fig2_HTML\" id=\"MO2\"/>", "<graphic xlink:href=\"41598_2023_50967_Fig3_HTML\" id=\"MO3\"/>", "<graphic xlink:href=\"41598_2023_50967_Fig4_HTML\" id=\"MO4\"/>", "<graphic xlink:href=\"41598_2023_50967_Fig5_HTML\" id=\"MO5\"/>", "<inline-graphic xlink:href=\"41598_2023_50967_Article_IEq1.gif\"/>", "<inline-graphic xlink:href=\"41598_2023_50967_Article_IEq2.gif\"/>", "<inline-graphic xlink:href=\"41598_2023_50967_Article_IEq3.gif\"/>", "<inline-graphic xlink:href=\"41598_2023_50967_Article_IEq4.gif\"/>" ]	[]	[{"label": ["3."], "surname": ["Wei"], "given-names": ["Y"], "article-title": ["Evading the strength\u2013ductility trade-off dilemma in steel through gradient hierarchical nanotwins"], "source": ["Nat. 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PMC10781691	38200104	[ "<title>Introduction</title>", "<p id=\"Par2\">With the lack of expression of estrogen receptor (ER), progesterone receptor (PR) and lack of amplification of human epidermal growth factor receptor 2 (HER2), chemotherapy is currently the main systemic therapeutic option for TNBC. Even though patients with TNBC generally showed a better response to chemotherapy than other BC subtypes, TNBC patients often exhibit a significantly different response towards conventional therapy due to heterogeneity and distinct differences in pathway activation<sup>##REF##17671126##1##,##REF##29365031##2##</sup>. The phosphoinositide 3-kinase (PI3K)/AKT pathway is among the most important intracellular signaling cascades in cancer and plays a pivotal role in linking receptor tyrosine kinases (RTKs), a transmembrane protein family with intrinsic tyrosine kinase activity, to cancer development and progression<sup>##REF##34916492##3##</sup>. With the frequent activation of PI3K/AKT signaling<sup>##REF##19435916##4##–##REF##23000897##6##</sup>, TNBC has been reported to be sensitive to the PI3K/mTOR inhibitor NVP-BEZ235 irrespective of PIK3CA mutation or PTEN deficiency, raising the possibility of targeting this axis for treatment<sup>##REF##21633166##7##</sup>. However, even though pre-clinical data indicate the potency of targeting the PI3K/AKT pathway in TNBC, intra-pathway feedback loops caused by single kinase inhibition along the PI3K/AKT axis and toxicity associated with PI3K/AKT/mTOR dual-blockade agents potentially limit their effectiveness in the clinic. Therefore, it is essential to identify novel therapeutic approaches to improve the prognosis of TNBC.</p>", "<p id=\"Par3\">BCL2-associated death promoter (BAD) is a BH3-only member of the BCL-2 family governing apoptosis and BAD phosphorylation is increased in various cancers<sup>##REF##29175460##8##</sup>. By phosphorylation at human Ser75, Ser99 and Ser118, BAD switches from pro-apoptotic functions to promotion of cell survival, by heterodimerizing with 14-3-3 protein instead of BCL-XL, BCL-2 or BCL-w<sup>##REF##29175460##8##,##REF##34113944##9##</sup>. In addition to its apoptotic function, a role of BAD in inhibiting G1 to S phase transition and CYCLIN D1 expression were previously reported<sup>##REF##17670745##10##</sup>. Being a core downstream molecule of the PI3K/AKT and MAPK pathways, BAD phosphorylation at the Serine 99 residue, and subsequently at Serine 118, is governed by the activation of PI3K/AKT whereas BAD phosphorylation at Serine 75 residue is predominantly achieved by the MAPK pathway<sup>##REF##10949026##11##</sup>. Not surprising given the aberrant activation of the PI3K/AKT pathway in TNBC, high pBADSer99 in TNBC has been reported to be associated with poor prognosis<sup>##REF##34113944##9##,##REF##31767884##12##</sup>. Therefore, targeting BAD phosphorylation at Ser99 independent of kinase activities<sup>##REF##30309962##13##</sup>, offers an alternate therapeutic approach for TNBC.</p>", "<p id=\"Par4\">Although targeted therapy has achieved advances in the understanding of cancer progression and cancer treatment, the intrinsic and acquired resistance of cancer cells has greatly limited the efficacy of a single or ‘one-target’ drug, often through the activation of compensatory signaling pathway<sup>##REF##26514196##14##</sup>. Conversely, combination therapy, by targeting multiple pathways, yields synergistic or additive therapeutic results which exhibit significant advantages in reducing dose-limiting toxicity and minimizing drug resistance, thus attracting considerable research and clinical interest<sup>##REF##28410237##15##</sup>. However, to date, a limited number of combination therapies are reported to be effective in the clinical setting for TNBC<sup>##REF##29686021##16##</sup>. As gene expression profiling reported increased expression of multiple RTKs in TNBC<sup>##REF##25025175##17##–##REF##17316758##19##</sup>, RTK inhibitors (TKIs) have been of interest in TNBC treatment. However, despite initial success in TKI treatment, acquired resistance due to acquisition of new mutations and bypass pathway activation limited therapeutic efficacy in TNBC, and other cancers<sup>##REF##21266357##20##–##REF##22665533##23##</sup>. Hence, effective synergistic combination approaches to improve the therapeutic efficacy of TKIs in TNBC is warranted. In this study, the chemical synthesis, and development of 2-((4-(2,3-dichlorophenyl)piperazin-1-yl)(pyridin-3-yl)methyl) phenol (NCK) as a more potent and orally bioavailable inhibitor of pBADSer99 when compared to NPB<sup>##REF##30309962##13##</sup> is reported. Furthermore, synergistic targets for rational drug combinations with pBADSer99 inhibitors in the treatment of TNBC were explored by combinatorial screening approaches. TKIs targeting VEGFR and c-MET, among other kinases, were identified as highly synergistic in combination with pBADSer99 inhibition. Their synergistic actions in the treatment of TNBC in vitro and ex vivo as well as in vivo, using orthotopic and intravenous TNBC and syngeneic models, and patient-derived xenograft models of TNBC was demonstrated.</p>", "<p id=\"Par5\">Collectively, these findings have provided proof of concept for therapeutic strategies for patients with TNBC and indicated the combined targeting of RTKs upstream and pBADSer99 downstream may be a promising avenue for TNBC therapy.</p>" ]	[ "<title>Methods</title>", "<title>Cell culture and reagent</title>", "<p id=\"Par28\">Human mammary carcinoma cell lines of the triple negative subtype MDA-MB-231, BT549, HCC1937 and Hs578T were purchased from the Procell Life Science Technology (Wuhan, China). MDA-MB-436, MDA-MB-468, HCC1937 and SUM159PT were purchased from BNBio Tech Co. Ltd (Beijing, China). SUM149PT and 4T1-luciferase cells were gifts from Tao Zhu’s laboratory (University of Science and Technology of China, China). All cell lines were maintained as per the manufacturer’s propagation instructions at 37 °C in a humidified incubator of 5% CO<sub>2</sub>. All in vitro cell based assays were performed in media containing a final concentration of 2% FBS. TNBC PDXs USTC-0 and USTC-1 were generated in the laboratory of Suling Liu (Fudan University, China) and have been previously described<sup>##REF##35296660##40##</sup>. OSI-930 and Crizotinib were purchased from Selleckchem (Houston, TX, USA). Lipofectamine 3000 used for plasmid transfection was purchased from Thermo Fischer Scientific (Waltham, MA, USA). siRNA plasmid targeting BAD was purchased from GENEWIZ, Azenta Life Sciences (South Plainfield, NJ, USA) (Supplementary Fig. ##SUPPL##0##20##). Cas9-gRNA vector and pSpCas9(BB)-2A-Puro (PX459) were a gift from Feng Zhang (Addgene plasmid # 48139). For homology directed repair (HDR) assay (Supplementary Fig. ##SUPPL##0##2##), the hBADS99A sequence was designed and carried out following the protocol of the laboratory of Feng Zhang<sup>##REF##24157548##62##</sup>. Antibodies used are listed in Supplementary Fig. ##SUPPL##0##21A##.</p>", "<title>Synthesis of piperazine based phenolic compounds</title>", "<p id=\"Par29\">The synthesis of piperazine based phenolic compounds were performed as previously described by using Petasis borono-Mannich multicomponent reaction<sup>##REF##30309962##13##,##REF##34681659##63##</sup>. The desired phenolic compound product was obtained by separation using column chromatography. The structure of NCK was characterized by LCMS, <sup>1</sup>H NMR, and <sup>13</sup>C NMR spectroscopic techniques (Supplementary Fig. ##SUPPL##0##4##).</p>", "<title>In silico DFT calculations and bioinformatic analyses</title>", "<p id=\"Par30\">The molecular structure of NCK was drawn using GaussView software<sup>##UREF##0##64##</sup>. The molecular geometry optimization of NCK was carried out by employing the density functional theory at B3LYP level and 6-31 G + (d,p) basis set by using Gaussian 09 software package<sup>##UREF##1##65##</sup>. The optimized structure of NCK has been used to calculate the molecular electrostatic potential (MEP), highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital energy (LUMO) (Supplementary Fig. ##SUPPL##0##5D, E##). For bioinformatic analyses, the Scripps Research Institute’s AutoDock 4.2 Tools (v1.5.6) (ADT) was used to generate grid and docking parameter files. The reported crystal structure of 14-3-3 complexed BAD protein was retrieved from Protein Data Bank (PDB ID: 7Q16). The protein and ligand preparations were done by using BIOVIA discovery studio Visualizer. Visualization of docking analysis was examined by using BIOVIA Discovery Studio Visualizer (v21.1.0.202298), Pymol.</p>", "<title>Cancer compound library screening</title>", "<p id=\"Par31\">The Cambridge Cancer Compound Library (L2300) was purchased from Selleckchem (Houston, TX, USA) and the detailed information of compounds are listed in Supplementary Fig. ##SUPPL##0##11A##. Cancer compound library screening was performed using IC<sub>25</sub> values of anti-cancer compounds (predicted with Genomics of Drug Sensitivity in Cancer (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.cancerrxgene.org/\">https://www.cancerrxgene.org/</ext-link>) and NCK at 3-point dilutions (0.1, 1, 10 μM) in medium containing 2% FBS to identify potential synergistic combinations. In detail, MDA-MB-231 cells (5 ×10<sup>3</sup>) were seeded in 96-well plates in 80 μl of culture medium (2% FBS) and allowed to settle overnight. 24-hour post-seeding, 10 μl of compounds dissolved in culture medium were prepared and added to the plates to reach a final concentration of IC<sub>25</sub> of the respective compound. 10 μl of NCK was also added into cell plates to yield a final concentration of 0.1, 1 or 10 μM. After 72-hours of treatment, cell viability was determined with AlamarBlue reagent and fluorescence was measured using a Tecan microplate reader, as previously described<sup>##REF##33344891##29##</sup>. A schematic of the high-throughput screening assay is included in Supplementary Fig. ##SUPPL##0##11B##.</p>", "<title>TNBC tissue microarrays and immunohistochemistry (IHC)</title>", "<p id=\"Par32\">The TNBC tissue microarray (ZL-Brc3N961) was obtained from Zhuoli Biotech Co., Ltd. (Shanghai, China). Consent for the use of the tissue samples and clinical data were obtained by Zhuoli Biotech Co., Ltd. (Shanghai, China). IHC staining and scoring were performed as previously described<sup>##REF##35332126##66##</sup>. The corresponding antibodies used here are listed in Supplementary Fig. ##SUPPL##0##21A##. The staining results were assessed and confirmed by two independent researchers blinded to the clinical data.</p>", "<title>RNA sequencing</title>", "<p id=\"Par33\">RNA was isolated from MDA-MB-231 cells following treatment of 0 and 5 μM of NCK or NPB using TRIzol reagent (Sigma-Aldrich, MO, USA), as previously described<sup>##REF##27032751##67##</sup>. RNA-seq was subsequently performed by BGI (Shenzhen, China). The cDNA library was prepared with the commercial Illumina library preparation kits (TruSeq Stranded RNA LT Ribo-Zero H/M/R Kit) according to the manufacturer’s protocols. Quality control was performed on the raw dNA-Seq reads (FastQC), and the adapters were cut (Trim Galore) and mapped with hg38 genome (HISAT2). Differentially expressed transcripts were defined with Cuffdiff tools and visualized using R software.</p>", "<title>Oncogenic and SPR analyses</title>", "<p id=\"Par34\">AlamarBlue® viability, total cell number, foci formation, growth in 3D Matrigel culture, apoptosis and cell cycle flow cytometry assays were performed as previously described<sup>##REF##37858772##68##</sup>. All in vitro based assays were performed in medium containing 2% FBS. Transwell migration assay was performed as previously described<sup>##REF##36915147##69##</sup>. Briefly, 1 × 10<sup>5</sup> cells suspended in serum-free medium containing 0.2% BSA were plated in the top chamber of 8 μm pore size (Corning, MA, USA). Medium with 10% serum was added to the lower chamber and cells were allowed to migrate for 24-48 hours (depending on cell line) before fixing with formalin, permeabilizing with methanol and staining with crystal violet. Real time migration assay was performed as previously reported by using xCELLigence system<sup>##REF##24935351##70##,##UREF##2##71##</sup>. Live/Dead cells staining was performed following manufacturer’s instruction using LIVE/DEAD™ Cell Imaging Kit (Thermo Fisher Scientific, MA, USA). CASPASE 3/7 assay (Biovision, CA, USA) was performed following the manufacturer’s protocol. Western blot analyses were performed as previously described<sup>##REF##30785766##72##</sup>. All blots were derived from the same experiment and were processed in parallel. The corresponding antibodies used are listed in Supplementary Fig. ##SUPPL##0##21A## and original blots are provided in Supplementary Fig. ##SUPPL##0##22##. Molecular interactions were analyzed by SPR using a BIAcore-2000 system (BIAcore AB, Uppsala, Sweden). Recombinant human BAD (Novoprotein, Suzhou, China) was immobilized on a sensor chip and analyzed as per the manufacturer’s protocol and as previously described<sup>##REF##36110371##73##</sup>. Combination index (CI) analysis was performed using the Chou-Talalay<sup>##REF##27073727##74##</sup> and SynergyFinder<sup>##REF##28379339##75##</sup> CI analysis method.</p>", "<title>In vivo studies</title>", "<p id=\"Par35\">All animal experiments were approved by the Institutional Animal Care and Use Committee of the Laboratory Animal Centre of Peking University Shenzhen Graduate School (permit YW; the permit from Tsinghua Shenzhen International Graduate School is “Ethical Development no. 37 (Year 2019)”. The schematic representation of in vivo studies is shown in Supplementary Fig. ##SUPPL##0##23##. Mice were housed in a controlled atmosphere (25 ± 1 °C at 50% relative humidity) under a 12-h light/12-h dark cycle. Animals had free access to food and water at all times. To establish the orthotopic xenograft model for single drug and combination studies, MDA-MB-231 (1 × 10<sup>7</sup> cells in 100 μL PBS containing growth factor reduced 25% Matrigel) or 4T1-luciferase cells (7 × 10<sup>4</sup> cells in 100 μL PBS containing growth factor reduced 25% Matrigel) were injected orthotopically into the right fourth mammary fat pad of 8-week-old female BALB/c-nude or BALB/c mice (Experimental Animal Center, Guangzhou, China). For the intravenous metastasis model, MDA-MB-231 (1 × 10<sup>6</sup> cells in 100 μL PBS) or 4T1-luciferase cells (1 × 10<sup>5</sup> cells in 100 μL PBS) were injected intravenously into the tail vein of 8-week-old female BALB/c-nude (MDA-MB-231) or BALB/c (4T1-luciferase) mice (Experimental Animal Center, Guangzhou, China). In vivo bioluminescent imaging was performed to determine the incidence and metastatic burden of luciferase-labeled 4T1 cells. Mice were injected i.p. with 10 mg/ml of D-luciferin (Gold Biotechnology, MO, USA) and imaged using a PerkinElmer IVIS Spectrum system. PDX were generated as previously described by transplanting fresh fragments orthotopically into 8-week-old female nonobese diabetic (NOD)/severe combined immunodeficient (SCID) mice (Beijing Vital River Laboratory Animal Technology Co., Beijing, China) (P1, passage 1)<sup>##REF##35296660##40##</sup>. The P1 xenografts were then fragmented, digested into single-cell suspension and implanted into mammary fat pad (100 μL PBS containing growth factor reduced 50% Matrigel) of NOD/SCID mice. Once orthotopic xenografts reached an approximate size of 100 mm<sup>3</sup>, the mice were randomly divided into three groups (<italic>n</italic> = 8) for single drug studies and six groups (<italic>n</italic> = 6) for combination studies and Kaplan-Meier (KM) analyses. For the single drug study, vehicle (4.6% DMSO, 14.3% PEG400, 9.7% water pH 5.0 and 71.4% N-saline) and NCK at 5 and 20 mg/kg were intraperitoneally injected daily for 3 weeks. In the combination and KM studies, vehicle, NCK (20 mg/kg) or OSI-930 (50 mg/kg) were intraperitoneally injected daily (<italic>q.d.</italic>) for 3 weeks, whereas Crizotinib (50 mg/kg) was injected intraperitoneally biweekly (<italic>b.i.w.</italic>) for 3 weeks. Animal body weight and xenograft size were measured daily using an electronic balance and a digital caliper, respectively. Xenograft volume (mm<sup>3</sup>) was calculated by using the formula: 0.52 × length × [width]<sup>2</sup>. Complete response (CR) was achieved if the average xenograft volume for the treatment group was unable to be determined for two or more consecutive measurements. Partial response (PR) was achieved if the average xenograft volume reduced to <50 mm<sup>3</sup> for two or more consecutive measurements. After 3-week drug treatment, all mice were sacrificed by CO<sub>2</sub> inhalation. Tumor and vital organs were weighed and isolated for further analysis. IHC analysis of xenograft histology sections and quantitative polymerase chain reaction (qPCR) were performed as previously described<sup>##REF##27816005##76##</sup>. The corresponding antibodies and the sequences of the oligonucleotide primers used are listed in Supplementary Fig. ##SUPPL##0##21A## and Supplementary Fig. ##SUPPL##0##21B## respectively. For PDX, xenograft growth was monitored daily by measurement of the xenograft volume until mice humane endpoint ( ~ 1000 mm<sup>3</sup>).</p>", "<title>Toxicity</title>", "<p id=\"Par36\">The toxicity study design was evaluated according to the previously published procedure<sup>##REF##26167454##77##</sup> with slight modification. In this model, ICR mice (Experimental Animal Center, Guangzhou, China) were injected i.p. with vehicle or NCK of 20 and 50 mg/kg continuously for 14 days (<italic>n</italic> = 8). Animals were given ad libitum access to food and water. The changes in body weight, food and water consumptions were recorded every day. Signs of toxicity, mortality and behavioral patterns were monitored daily after administration. At the end of the study, all mice were sacrificed by CO<sub>2</sub> inhalation, and the blood samples were collected to evaluate the hematological and biochemical parameters (Servicebio, Wuhan, China). All vital organs (heart, liver, spleen, lung, kidney, stomach, small intestinal and colon) were weighed and isolated at necropsy and histopathological examination performed.</p>", "<title>Statistical Analysis</title>", "<p id=\"Par37\">Two-tailed unpaired Student’s t test and one-way ANOVA analysis followed by Bonferroni’s posttest correction were used to calculate the statistical significance of two or multiple treatment groups, respectively. The level of significance was set as <italic>P</italic> < 0.05, <italic>P</italic> < 0.01, and **<italic>P</italic> < 0.001. All group data were presented as the means ± standard deviation (SD). All analyses were done using GraphPad Prism software (version 5.0).</p>", "<title>Reporting summary</title>", "<p id=\"Par38\">Further information on research design is available in the ##SUPPL##1##Nature Research Reporting Summary## linked to this article.</p>" ]	[ "<title>Results</title>", "<title>pBADSer99 is a therapeutic vulnerability in TNBC</title>", "<p id=\"Par6\">To determine whether pBADSer99 is a potential therapeutic vulnerability in TNBC, the level of pBADSer99 and expression of BAD in TNBC and adjacent normal (AD) tissue specimens were analyzed using immunohistochemistry (IHC) (Fig. ##FIG##0##1a##). TNBC specimens exhibited significantly increased expression of BAD and also increased pBADSer99 levels when compared to normal breast tissues specimens, as demonstrated by immunoreactive score (IRS) analysis (Fig. ##FIG##0##1a##, Supplementary Fig. ##SUPPL##0##1A, B##). Despite an increase in BAD expression, a higher pBADSer99/BAD ratio was observed in TNBC compared to normal breast tissues (Fig. ##FIG##0##1a##). A high level of pBADSer99 was observed in 67.4% (31/46) of cancer tissues, whereas in normal tissue, high expression was observed in 4.3% (2/46) of samples (Fig. ##FIG##0##1a##). In terms of subcellular localization, nuclear localization of pBADSer99 but cytoplasmic localization of BAD were observed in TNBC tissue specimens (Fig. ##FIG##0##1a##). This is consistent with a previous study which demonstrated that unlike BAD which is largely localized to cytoplasm, phosphorylated BAD, especially at Serine 99 (murine Serine 136) exhibits largely nuclear localization in cancer tissues of ER+PR+HER2+ , ER+PR+HER2- and ER-PR-HER2- BC patients<sup>##REF##31883527##24##</sup>. The study suggested that nuclear sequestration of phosphorylated BAD might be an alternative mechanism in addition to cytoplasmic sequestration by 14-3-3 to prevent BAD’s pro-apoptotic function at the mitochondria in BC<sup>##REF##34113944##9##,##REF##31883527##24##,##REF##29915393##25##</sup>.</p>", "<p id=\"Par7\">Furthermore, the correlation between pBADSer99 level and the pBADSer99/BAD ratio in TNBC specimens and their clinicopathologic characteristics were assessed. It was observed that the pBADSer99 level was not related to age nor lymph node metastasis, but was positively correlated with lower tumor grade and higher MKI67 labeling (Supplementary Fig. ##SUPPL##0##1C##). However, the pBADSer99/BAD ratio in TNBC specimens was positively correlated with higher tumor grade, a higher degree of lymph node metastasis and higher MKI67 labeling (Table ##TAB##0##1##), which are independent predictors of a poor outcome in TNBC<sup>##REF##31937819##26##–##REF##31822139##28##</sup>. Therefore, the high level of pBADSer99 and pBADSer99/BAD ratio in TNBC specimens and, the correlation of pBADSer99/BAD ratio with higher tumor grade and lymph node metastasis indicates an actionable vulnerability enabling targeting of BADSer99 phosphorylation in TNBC. To functionally correlate the association of pBADSer99 with clinicopathologic features of TNBC, homology-directed repair (HDR) was utilized to replace Ser99 with alanine generating hBADS99A (Supplementary Fig. ##SUPPL##0##2##) in MDA-MB-231 and BT549 cells, two TNBC cell lines. When compared to control cells, western blot analysis indicated that protein expression of total BAD was not altered but the pBADSer99 level and pBADSer99/BAD ratio were decreased (Fig. ##FIG##0##1b, c##, Supplementary Fig. ##SUPPL##0##3A##). Homology directed repair of hBAD to hBADS99A significantly reduced cell viability in MDA-MB-231 (Fig. ##FIG##0##1b##) and BT549 (Fig. ##FIG##0##1c##) cells when compared to vector control. In addition, consistent with the positive association of pBADSer99/BAD level with lymph node metastasis in TNBC specimens (Table ##TAB##0##1##), decreased migrative capacity was observed in hBADS99A-transfected MDA-MB-231 and BT549 cells when compared to the vector-transfected cells by transwell assay (Fig. ##FIG##0##1d##) and real-time migration assay (Supplementary Fig. ##SUPPL##0##3B, C##). Collectively, these results indicate that phosphorylation of BADSer99 is essential for TNBC cell survival and is a potential therapeutic target for this subtype of TNBC.</p>", "<title>Generation of a small molecule pBADSer99 inhibitor with improved potency</title>", "<p id=\"Par8\">The efficacy of a pBADSer99 small molecule inhibitor (NPB) in inducing apoptotic cell death in vitro in various human cancer cell lines and in vivo, independent of AKT signaling, was previously demonstrated<sup>##REF##30309962##13##</sup>. The efficacy of NPB in combination with cisplatin<sup>##REF##33344891##29##</sup> and PARP inhibitors in ovarian carcinoma (OC)<sup>##REF##35791346##30##</sup> and in PTEN-deficient endometrial carcinoma (EC)<sup>##REF##35725817##31##</sup> were also recently reported. Herein, the synthesis and characterization of a NPB analog, NCK, was carried out based on the Petasis borono-Mannich multicomponent reaction using 1-(2,3-dichlorophenyl)piperazine, salicylaldehyde, and 3-pyridine-boronic acid to generate a more potent and orally bioavailable pBADSer99 inhibitor (Fig. ##FIG##1##2a##, Supplementary Fig. ##SUPPL##0##4##, Supplementary Fig. ##SUPPL##0##5A##). Using NPB as reference, bioinformatic analysis of NCK was performed with the reported crystal structure of 14-3-3 complexed with BAD (PDB ID: 7Q16) retrieved from Protein Data Bank. When compared to NPB (binding affinity of -6.51 kcal/mol), the NCK molecule exhibited a higher binding affinity of -5.68 kcal/mol to BAD (Fig. ##FIG##1##2b, c##, Supplementary Fig. ##SUPPL##0##5B, C##). Additionally, the frontier molecular orbital’s (FMO) energy gap (∆E<sub>LUMO-HOMO</sub>) of NCK was 2.42 eV and the electrophilicity index (ψ), which demonstrates the binding ability of the compound to biomolecules, was 14.86 eV (Supplementary Fig. ##SUPPL##0##5D, E##).</p>", "<p id=\"Par9\">To compare the binding affinity of NCK and NPB towards BAD, we performed surface plasmon resonance (SPR) measurement by immobilizing BAD protein on a sensor chip with NCK or NPB as analyte. Representative reference-subtracted overlaid sensorgrams and the kinetic parameters are specified in Fig. ##FIG##1##2d## and Supplementary Fig. ##SUPPL##0##6A##. Notably, as demonstrated by the yielded dissociation equilibrium constant (K<sub>D</sub>), the affinity of NCK/BAD (K<sub>D</sub> = 4.81×10<sup>–8</sup> M) was observed to be higher than NPB/BAD (K<sub>D</sub> = 3.09×10<sup>–5</sup> M) (Fig. ##FIG##1##2d##, Supplementary Fig. ##SUPPL##0##6A##). Additionally, the pharmacological inhibition of BAD phosphorylation by NCK or NPB in TNBC cells was evaluated. By western blot analysis, starting at 0.1 μM, NCK inhibited BAD phosphorylation at Ser99, as demonstrated by a decreased pBADSer99/BAD ratio in MDA-MB-231 and BT549 cells. In contrast, compared to DMSO, NPB significantly inhibited pBADSer99/BAD protein levels at 1 μM and 10 μM in both TNBC cell lines (Fig. ##FIG##1##2e##, Supplementary Fig. ##SUPPL##0##6B##). Similar to NPB, NCK did not alter the levels of pBADSer75/BAD and pBADSer118/BAD (Fig. ##FIG##1##2e##, Supplementary Fig. ##SUPPL##0##6B##). Furthermore, the comparative potencies of NPB and NCK were evaluated in eight TNBC cell lines using total cell number assay in 2D culture and cell viability in 3D culture (Supplementary Fig. ##SUPPL##0##6C, D##). NCK was more potent than NPB in reducing 2D and 3D cell viability of all TNBC cell lines (Fig. ##FIG##1##2f##, Supplementary Fig. ##SUPPL##0##6D##). Specifically, NCK (IC<sub>50</sub> = 1.015 μM in MDA-MB-231 and 1.704 μM in BT549) demonstrated a more potent effect than NPB (IC<sub>50</sub> = 2.895 μM in MDA-MB-231 and 3.886 μM in BT549) in reducing the viability of TNBC cells ( ~ 3 fold in MDA-MB-231 and ~2 fold in BT549) in 2D culture. In 3D Matrigel, NCK demonstrated ~6 fold IC<sub>50</sub> difference (NCK IC<sub>50</sub> = 0.239 μM and NPB IC<sub>50</sub> = 1.374 μM) in MDA-MB-231 cells and ~8 fold IC<sub>50</sub> difference in viability (NCK IC<sub>50</sub> = 0.309 μM and NPB IC<sub>50</sub> = 2.584 μM) in BT549 cells (Fig. ##FIG##1##2f##). These results showed that NCK exhibits a more potent effect than NPB in reducing pBADSer99 and cell viability in TNBC cell lines in vitro and ex vivo.</p>", "<title>siRNA-mediated depletion of BAD expression hinders the effect of NCK</title>", "<p id=\"Par10\">To confirm the functional specificity of NCK to BAD, the effect of NCK treatment after siRNA-mediated depletion of BAD expression was examined in MDA-MB-231 cells. Western blot analysis demonstrated that the transient transfection of MDA-MB-231 cells with siRNA-BAD decreased levels of pBADSer99 and BAD expression compared to cells transfected with scrambled oligo (Fig. ##FIG##1##2g##). Consistent with previous findings<sup>##REF##17360431##32##–##REF##12011069##34##</sup>, no significant changes in cell viability nor CASPASE 3/7 activity were observed upon transfection of siRNA directed to the BAD transcript (Fig. ##FIG##1##2g–i##). NCK increased CASPASE 3/7 activity and decreased cell viability of MDA-MB-231 cells compared to the vehicle-treated cells. However, siRNA-mediated depletion of BAD expression abolished the effect of NCK on cell viability and CASPASE 3/7 activity (Fig. ##FIG##1##2g–i##), similar to that observed previously with NPB<sup>##REF##30309962##13##</sup>.</p>", "<title>Pharmacokinetics of NCK</title>", "<p id=\"Par11\">The pharmacokinetics of NCK were determined via intravenous (IV) and oral administration in Sprague-Dawley (SD) rats (Supplementary Fig. ##SUPPL##0##7##). Following a single 1 mg/kg IV dose, NCK showed a multiexponential disposition with high clearance of 58.3 mL/min·kg and a high volume of distribution at steady state (Vss) of 5.93 L/kg with a t<sub>1/2</sub> = 3.35 h. Following a single oral dose of 10 mg/kg, NCK showed rapid absorption followed by a multiexponential disposition with t<sub>max</sub> = 0.33 h, C<sub>max</sub> = 346 ng/mL, AUC<sub>inf</sub> = 1072 h·ng/ml, t<sub>1/2</sub> = 2.68 h and a moderate bioavailability of 37.2%, which is higher than the previously reported NPB (12.4%)<sup>##REF##30309962##13##</sup>.</p>", "<title>NCK enhances apoptosis and impedes cell-cycle progression in TNBC cells</title>", "<p id=\"Par12\">To delineate the biological processes commonly and differentially affected by NCK and NPB, RNA sequencing was performed whereby MDA-MB-231 cells treated with the two pharmacological inhibitors of pBADSer99, NCK or NPB, were analyzed. Hallmark analysis of the differentially expressed genes (DEGs) demonstrated that mitotic spindle, G2M checkpoint, apoptosis, UV response and early estrogen response were commonly affected after either NCK or NPB treatment (Fig. ##FIG##2##3a##) with 9 DEGs that were commonly upregulated and 31 that were downregulated after the treatment (Fig. ##FIG##2##3b##). In 6 of 9 DEGs upregulated, the magnitude of gene change was higher in the NPB-treated cells compared to NCK-treated cells; whereas in 19 of 31 DEGs, the magnitude of gene changes with NCK treatment was higher than that of NPB. Furthermore, gene set enrichment analysis (GSEA) demonstrated that MDA-MB-231 cells treated with NCK and NPB showed significant difference in enrichment of gene sets associated with “cell cycle checkpoints” but not with “apoptosis” (Fig. ##FIG##2##3c##, Supplementary Fig. ##SUPPL##0##8A##).This is consistent with the gene ontology (GO) annotations in the biological process that the cells treated with NCK, but not NPB, were annotated to categories of cell cycle, cell division and chromosome segregation (Supplementary Fig. ##SUPPL##0##8B, C##). In a panel of cell cycle related genes significantly affected by NCK treatment, the most downregulated gene was CDC20 (cell division cycle 20 homologue), a gene responsible for activating anaphase promoting complex (APC) for anaphase entry (Supplementary Fig. ##SUPPL##0##8D##). Given that the transcription of cell cycle related genes was affected by the treatment with NCK, cell cycle analysis by flow cytometry was performed following inhibition of pBADSer99 by NCK or NPB (Fig. ##FIG##2##3d##, Supplementary Fig. ##SUPPL##0##9A##). A 15.68% decrease in S-phase and a 17.75% increase in G1-phase was observed in NCK-treated MDA-MB-231 cells, which is consistent with a G0/G1 arrest. Similarly, treatment with NCK also resulted in a 27.12% decrease in S-phase and a 30.43% increase in G1-phase in NCK-treated BT549 cells. However, NPB treatment did not significantly result in cell cycle arrest in MDA-MB-231 and BT549 cells (Fig. ##FIG##2##3d##, Supplementary Fig. ##SUPPL##0##9A##). Subsequently, the effect of NCK or NPB in promoting apoptotic cell death in both MDA-MB-231 and BT549 cells was assessed using the Annexin V-propidium iodide (PI) assay. Consistently observed in both TNBC cell lines, NCK demonstrated a more potent effect than NPB in inducing apoptosis (early: PI − , FITC−Annexin V + ; late: PI + , FITC−Annexin V + ) (Fig. ##FIG##2##3e##, Supplementary Fig. ##SUPPL##0##9B##). Thus, NCK enhances apoptosis and impedes cell-cycle progression, more potently than NPB, in TNBC cells.</p>", "<title>Toxicity and in vivo efficacy of NCK</title>", "<p id=\"Par13\">To evaluate the tolerability of NCK for in vivo use, a toxicity study was carried out on mice at doses of 20 and 50 mg/kg NCK by intraperitoneal (i.p.) injection. 8 week old female Institute of Cancer Research (ICR) mice were injected i.p. with vehicle or a NCK dose of 20 or 50 mg/kg body weight continuously for 14 days. After the treatment period, NCK-treated mice did not show any significant differences in appearance or behaviour (Fig. ##FIG##3##4a##), body weight (Supplementary Fig. ##SUPPL##0##10A##), daily food consumption (Supplementary Fig. ##SUPPL##0##10B##) or water intake (Supplementary Fig. ##SUPPL##0##10C##) compared to the vehicle-treated mice. In addition, the relative weight of the liver, heart, spleen, stomach, lung, kidney, colon and small intestine were not significantly altered in mice treated with either 20 mg/kg or 50 mg/kg NCK as compared to the vehicle-treated group (Supplementary Fig. ##SUPPL##0##10D##). Histological analysis of the same organs did not demonstrate obvious pathology in mice receiving NCK at 20 mg/kg or 50 mg/kg i.p. (Fig. ##FIG##3##4b##, Supplementary Fig. ##SUPPL##0##10E##). There were also no significant effects of NCK treatment at 20 mg/kg or 50 mg/kg on levels of various standard haematological and serum parameters as compared to the vehicle group (Supplementary Fig. ##SUPPL##0##10F##).</p>", "<p id=\"Par14\">Next, the in vivo efficacy of NCK was examined in mice injected orthotopically with MDA-MB-231 cells to form xenografts. When the xenograft volume reached 100 mm<sup>3</sup>, mice were randomly grouped and injected i.p. with vehicle, NCK (5 mg/kg <italic>q.d</italic>.) or NCK (20 mg/kg <italic>q.d</italic>.) for 21 days. NCK treatment at both 5 mg/kg and 20 mg/kg significantly reduced xenograft volume (Fig. ##FIG##3##4c##) and weight (Fig. ##FIG##3##4d##) with no significant change in body weight (Fig. ##FIG##3##4e##) when compared to the vehicle treated group. Histological analyses of xenografts resected from mice treated with NCK showed significantly reduced pBADSer99 compared to vehicle-treated mice, accompanied by decreased MKI67 positivity and increased TUNEL scores (Fig. ##FIG##3##4f##).</p>", "<title>Tyrosine kinase inhibitors (TKIs) identified as the most synergistic compounds in combination with NCK to reduce MDA-MB-231 cell survival</title>", "<p id=\"Par15\">The Cambridge anti-cancer compound library was screened in combination with NCK in a TNBC cell line (MDA-MB-231) (Supplementary Fig. ##SUPPL##0##11##). Screening of 247 anti-cancer compounds, targeting a wide range of pathways including angiogenesis, apoptosis, PI3K/AKT/mTOR, MAPK, protein tyrosine kinases and metabolism, was performed on MDA-MB-231 cells to identify NCK-based synergistic combinations in TNBC cells (Fig. ##FIG##4##5a##). Among the 21 generalized drug groupings, “protein tyrosine kinase” was the grouping with the most compounds synergizing with NCK, followed by “angiogenesis” and the “MAPK pathway”. Among the protein tyrosine kinases, VEGFR and c-MET exhibited the highest target synergy with NCK. It is also noteworthy that some of the compounds targeting VEGFR were categorized under “angiogenesis” and “MAPK pathway” due to multi-target inhibition (polypharmacology) (Fig. ##FIG##4##5b, c##). Among the synergistic combinations of NCK with tyrosine kinases inhibitors (TKIs) identified, OSI-930 (dual VEGFR2 and c-KIT inhibitor) and Crizotinib (dual c-MET and ALK inhibitor) were selected for further in-depth investigation in combination with NCK in TNBC cell lines (Fig. ##FIG##4##5d##). These two compounds were chosen because of their low CI (high synergy) and targets on VEGFR and c-MET respectively. Notably, the inhibitors of RTKs usually inhibit multiple other kinases (Supplementary Fig. ##SUPPL##0##12A##). Therefore, the synergistic effect of TKIs with NCK may potentially be exerted through multi-target inhibition. Detailed FDA/ clinical trial related information of OSI-930 and Crizotinib are listed in Supplementary Fig. ##SUPPL##0##12B##.</p>", "<title>NCK synergizes with TKIs to reduce cell viability</title>", "<p id=\"Par16\">In order to further verify the synergistic effect of the pBADSer99 inhibitor NCK and TKIs obtained from the anti-cancer compound library screen, the effect of drug combinations (NCK-OSI-930 or NCK-Crizotinib) at 5 concentrations (0.01-100 μM) were evaluated by using cell viability assays in MDA-MB-231 and BT549 cells (Fig. ##FIG##5##6a##). In both cell lines, the combinatorial treatments exhibited synergistic effects, as demonstrated by the Chou-Talalay method, highest single agent (HSA) and bliss synergy analysis (Fig. ##FIG##5##6b##). Subsequently, the effect of NCK on the IC<sub>50</sub> of the two TKIs were determined. The potency and synergy of the NCK-OSI-930 and NCK-Crizotinib combinations were reflected by the marked reduction in IC<sub>50</sub> values of both TKIs in both TNBC cell lines (Fig. ##FIG##5##6c##). NCK significantly decreased the IC<sub>50</sub> of OSI-930 and Crizotinib by ~173 fold and ~96 fold in MDA-MB-231 cells respectively, and similarly decreased the TKI IC<sub>50</sub> by ~90 fold and ~57 fold in BT549 cells, respectively.</p>", "<title>NCK synergizes with TKIs to stimulate intrinsic apoptosis</title>", "<p id=\"Par17\">Flow cytometry results demonstrated that combinatorial treatment of NCK and OSI-930 or Crizotinib significantly promoted apoptotic cell death in both TNBC cell lines compared to NCK, OSI-930 or Crizotinib single treatment (Fig. ##FIG##6##7a##, Supplementary Fig. ##SUPPL##0##13A##). The combinatorial treatment of NCK and OSI-930 promoted apoptosis in a synergistic manner in both TNBC cell lines. The combined NCK-Crizotinib treatment synergistically and additively induced apoptosis in MDA-MB-231 and BT549 cells, respectively. Consistently, co-treatment of NCK with OSI-930 or Crizotinib in MDA-MB-231 cells and NCK with Crizotinib in BT549 cells synergistically increased CASPASE 3/7 activity; whereas co-treatment of NCK-OSI-930 increased CASPASE 3/7 activity in BT549 cells in an additive manner (Fig. ##FIG##6##7b##). Additionally, the effect of treatments on 2D foci formation and 3D colony growth in MDA-MB-231 and BT549 cells were evaluated. The treatment with NCK, OSI-930 or Crizotinib alone significantly attenuated the capacity for foci formation. Combined treatment of NCK with OSI-930 or Crizotinib elicited higher inhibition than single treatments on foci formation capacity in both cell lines (Fig. ##FIG##6##7c##, Supplementary Fig. ##SUPPL##0##13B##). For 3D ex vivo assays, similarly, NCK and single TKI treatment reduced cell growth in 3D Matrigel. Combined treatments of NCK and OSI-930 or NCK and Crizotinib markedly reduced ex vivo growth in 3D culture of both TNBC cell lines (Fig. ##FIG##6##7d##, Supplementary Fig. ##SUPPL##0##13C##). Western blot results demonstrated that NCK, OSI-930 or Crizotinib significantly decreased the level of pBADSer99/BAD in both MDA-MB-231 and BT549 cells. In MDA-MB-231 cells, combinatorial treatment of NCK and OSI-930 further significantly reduced pBADSer99/BAD levels compared to OSI-930, whereas co-treatment of NCK and OSI-930 or NCK and Crizotinib significantly reduced pBADSer99/BAD levels in BT549 cells when compared to OSI-930 or Crizotinib alone (Fig. ##FIG##6##7e##, Supplementary Fig. ##SUPPL##0##14A##). For the anti- and pro-apoptotic markers, NCK treatment significantly decreased the expression of BCL-2 in MDA-MB-231 and BT549, and increased the expression levels of BAX and BAK in BT549 cells (Fig. ##FIG##6##7e##, Supplementary Fig. ##SUPPL##0##14A##). When examining the ratio of anti-apoptotic and pro-apoptotic markers, NCK reduced the ratio of BCL-2/BAX and BCL-2/BAK in MDA-MB-231 cells, and reduced BCL-2/BAX, BCL-2/BAK, BCL-XL/BAX and BCL-XL/BAK in BT549 cells. The combinatorial treatment of NCK and OSI-930 significantly further reduced the BCL-2/BAK and BCL-XL/BAK ratios compared to OSI-930 treatment in MDA-MB-231 cells, whereas in BT549 cells, the combined treatment of NCK and OSI-930 reduced BCL-2/BAX, BCL-2/BAK, BCL-XL/BAX and BCL-XL/BAK ratios as compared to OSI-930 alone. For the combined NCK-Crizotinib treatment, the ratio of BCL-2/BAK, BCL-XL/BAX and BCL-XL /BAK were significantly reduced as compared to Crizotinib-treated cells in MDA-MB-231 cells. In BT549 cells, when compared to Crizotinib single treatment, BCL-2/BAX, BCL-2/BAK, BCL-XL/BAX and BCL-XL/BAK ratios were significantly decreased with combined NCK-Crizotinib treatment (Fig. ##FIG##6##7e##, Supplementary Fig. ##SUPPL##0##14A##).</p>", "<p id=\"Par18\">Next, western blot analyses were performed to determine potential alteration in expression or activity of the target proteins along the MAPK and PI3K/AKT pathways. Similar to NPB reported earlier in estrogen receptor (ER) + BC cell lines<sup>##REF##30309962##13##</sup>, western blot results showed that single treatment with NCK alone did not affect the levels of phosphorylated nor total protein of components of MAPK or PI3K/AKT pathways in TNBC cells (Fig. ##FIG##6##7f##, Supplementary Fig. ##SUPPL##0##14B##). In contrast, it was observed that OSI-930 or Crizotinib significantly inhibited the PI3K/AKT pathway in MDA-MB-231 cells by reducing the levels of p-PI3K (Tyr458)/PI3K and p-AKT (Ser473)/AKT. Additionally, OSI-930 treatment reduced the level of p-MEK1/2 (Ser218/222)/MEK and increased the level of p-ERK/ERK by reducing the expression of ERK in MDA-MB-231 cells. In BT549 cells, OSI-930 significantly reduced the level of p-AKT (Ser473)/AKT but increased the levels of p-MEK1/2 (Ser218/222)/MEK, whereas Crizotinib significantly reduced the level of p-AKT (Ser473)/AKT (Fig. ##FIG##6##7f##, Supplementary Fig. ##SUPPL##0##14B##).</p>", "<title>NCK synergizes with TKIs to suppress human MDA-MB-231 xenograft and mouse 4T1 homograft growth</title>", "<p id=\"Par19\">Given the synergism observed in vitro, it was reasoned that combined inhibition of pBADSer99 and OSI-930 or Crizotinib may also lead to synergism in vivo. The effect of NCK in combination with OSI-930 or Crizotinib against MDA-MB-231 cell generated xenograft and 4T1-luciferase cell generated homograft (syngeneic) growth in vivo were examined. When the xenografts/homografts became palpable (approximately 100 mm<sup>3</sup> in size), the mice were randomized and injected with vehicle, NCK (20 mg/kg <italic>q.d</italic>.), OSI-930 (20 mg/kg <italic>q.d</italic>.), Crizotinib (50 mg/kg <italic>b.i.w</italic>.), or the combination of NCK with OSI-930 or Crizotinib for 21/15 days (depending on humane endpoint). In both MDA-MB-231 xenograft and 4T1-luciferase homograft models, mice from all treated groups exhibited significant decreases in xenograft/homograft volume (Fig. ##FIG##7##8a##, Supplementary Fig. ##SUPPL##0##16A##) and weight (Supplementary Fig. ##SUPPL##0##15A##, Supplementary Fig. ##SUPPL##0##16B##) compared to the vehicle treated group. Additionally, mice receiving combined treatment of NCK and OSI-930 or NCK and Crizotinib demonstrated significant reductions in xenograft/homograft volume and weight compared to OSI-930 or Crizotinib single treatment groups respectively (Fig. ##FIG##7##8a##, Supplementary Fig. ##SUPPL##0##15A##, Supplementary Fig. ##SUPPL##0##16A, B##). In MDA-MB-231 xenografts, treatment with NCK alone resulted in the complete regression of 2/6 (33.33%) of the xenografts and treatment with Crizotinib alone resulted in complete regression of 1/6 (16.67%) xenografts; whereas combination treatments employing NCK with OSI-930 or NCK with Crizotinib resulted in complete regression of 5/6 (83.33%) and 3/6 (50.00%) xenografts respectively (Fig. ##FIG##7##8b, c##). All mice in the vehicle group exhibited progressive disease (xenograft volume >100 mm<sup>3</sup>) at the end of day 21 (Table ##TAB##1##2##). In 4T1-luciferase homografts, the results obtained from bioluminescence signals (Supplementary Fig. ##SUPPL##0##16C##) and homograft burden change (Supplementary Fig. ##SUPPL##0##16D, E##) were consistent with changes in homograft volume and weight.</p>", "<p id=\"Par20\">Consistent with the previous xenograft (Fig. ##FIG##3##4f##), NCK treatment reduced the level of pBADSer99 in the resected MDA-MB-231 xenograft compared to vehicle-treated mice, whereas BAD protein was not significantly different. Mice treated with NCK exhibited significantly reduced MKI67 positivity and increased TUNEL scores in the MDA-MB-231 xenograft compared to mice treated with vehicle. When compared to MDA-MB-231 xenografts resected from mice receiving OSI-930 or Crizotinib, reduction in the level of pBADSer99 by combination treatments were accompanied by decreased MKI67 positivity. Additionally, mice receiving combined treatment of NCK and OSI-930 exhibited significantly increased TUNEL staining when compared to MDA-MB-231 xenografts of OSI-930 treated mice (Fig. ##FIG##7##8d##, Supplementary Fig. ##SUPPL##0##15B##). Similar effects on the pBADS99/BAD ratio, MKI67 positivity and TUNEL scores were observed in 4T1-luciferase homografts after the respective treatments (Supplementary Fig. ##SUPPL##0##16F##). In terms of metastasis, no macroscopic colony was visible in lung samples of MDA-MD-231 cell engrafted mice from all treatment groups, yet IHC staining of human HPRT demonstrated that cells of human origin were detectable in lung sections of MDA-MB-231-engrafted mice receiving vehicle or Crizotinib treatment but not in the lung sections of mice treated with NCK, OSI-930, NCK-OSI-930 or NCK-Crizotinib (Supplementary Fig. ##SUPPL##0##15C##). The results were further verified by the determination of human <italic>HPRT</italic> (<italic>hHPRT</italic>) gene expression in lung relative to mouse <italic>gapdh</italic> (<italic>mgapdh</italic>) mRNA using real-time qPCR to identify the burden of lung metastases in each treatment group (Fig. ##FIG##7##8e##)<sup>##REF##11242036##35##,##REF##29730818##36##</sup>. When compared to vehicle-treated group, in which 3 out of 6 mice were positive for <italic>hHPRT</italic> mRNA, the treatment with NCK reduced lung metastasis incidence to 1/6 mice as determined by detectable lung <italic>hHPRT</italic> mRNA. Additionally, lung metastasis incidence was significantly reduced by treatment with OSI-930 in which none of the mice had lung metastasis as detected by qPCR in both OSI-930 and NCK-OSI-930 groups. Even though the incidence of lung metastasis in mice treated with Crizotinib was the same as vehicle (3/6 mice with detectable <italic>hHPRT</italic>), only 1 out of 6 mice receiving the combinatorial treatment of NCK and Crizotinib had detectable metastatic human cells in lung as determined by <italic>hHPRT</italic> mRNA expression (Fig. ##FIG##7##8e##).</p>", "<p id=\"Par21\">All mice tolerated the treatment regimens with no adverse impact on weight (Supplementary Fig. ##SUPPL##0##15D##, Supplementary Fig. ##SUPPL##0##16A##) or other noticeable toxic effects as determined by serum biochemical parameters (Supplementary Fig. ##SUPPL##0##15E##), suggesting that the drug combinations were well tolerated in vivo. Organ weights were not significantly different between the treatment groups except for spleen, which was significantly higher in the vehicle-treated mice (0.20 ± 0.06 g) when compared to mice receiving single (NCK, OSI-930 or Crizotinib) or double (NCK-OSI-930 or NCK-Crizotinib) treatments (0.09 ± 0.03 to 0.13 ± 0.05 g) in MDA-MB-231 xenografts (Supplementary Fig. ##SUPPL##0##15F##); and was significantly higher in the vehicle-treated mice (0.62 ± 0.11 g) when compared to mice receiving NCK or combination treatments (0.24 ± 0.05 to 0.42 ± 0.09 g) in 4T1-luciferase homografts (Supplementary Fig. ##SUPPL##0##16G##). This phenomenon of increased spleen weight has been previously observed in TNBC xenografts, which is associated with myeloid cell recruitment to the spleen after MDA-MB-231<sup>##REF##30737233##37##</sup> or 4T1<sup>##REF##16919266##38##</sup> cell inoculation.</p>", "<title>NCK in combination with TKIs suppresses TNBC lung metastasis</title>", "<p id=\"Par22\">Since metastasis is the primary contributor to mortality and poor prognosis for TNBC patients, the therapeutic potential of NCK, TKIs or combined NCK-TKI treatment on TNBC metastasis was further evaluated with an experimental lung metastasis model<sup>##REF##23991292##39##</sup>. The lung metastases were established by intravenous injection of MDA-MB-231 or 4T1-luciferase cells into the tail vein of BALB/c-nude or BALB/c mice respectively and were similarly given vehicle, NCK (20 mg/kg <italic>q.d</italic>.), OSI-930 (20 mg/kg <italic>q.d</italic>.), Crizotinib (50 mg/kg <italic>b.i.w</italic>.) or the combination treatments. In the MDA-MB-231 cell generated metastasis model, although no macro-metastases were observed, reduced relative lung weight was observed in the mice receiving NCK (0.17 ± 0.01 g), NCK-OSI-930 (0.14 ± 0.01 g) or NCK-CRI (0.15 ± 0.01 g) compared to vehicle-treated mice (0.19 ± 0.02 g) (Fig. ##FIG##7##8f##). NCK-OSI-930 combination treatment (0.14 ± 0.01 g) further significantly reduced the relative lung weight as compared to OSI-930 treatment (0.16 ± 0.01 g) (Fig. ##FIG##7##8f##). Subsequent histological analysis (H&E) for micro-metastatic nodules in the lung supported reduced lung TNBC cell colonization in mice receiving treatment compared to vehicle-treated mice (Fig. ##FIG##7##8g##). The quantitative measurement of micro-metastatic nodules by H&E staining demonstrated that treatment with NCK, OSI-930, NCK-OSI-930 or NCK-Crizotinib significantly reduced lung colonization compared to vehicle treatment. Furthermore, mice receiving NCK-Crizotinib treatment exhibited a significantly lower number of micro-metastatic nodules in lung compared to Crizotinib-treated mice (Fig. ##FIG##7##8g##). In the 4T1-luciferase cell generated metastasis model, mice receiving NCK or combination treatments (0.22 ± 0.08 to 0.29 ± 0.07 g) exhibited significantly lower relative lung weight as compared to mice receiving vehicle treatment (0.51 ± 0.10 g) (Fig. ##FIG##7##8f##). NCK-OSI-930 treatment (0.23 ± 0.08 g) further reduced the relative lung weight as compared to OSI-930 treatment (0.46 ± 0.18 g) significantly (Fig. ##FIG##7##8f##). The metastatic burden of mice intravenously injected with 4T1-luciferase cells was further determined by bioluminescent imaging (Fig. ##FIG##7##8h##) and counting of macroscopic nodules (Fig. ##FIG##7##8i##, Supplementary Fig. ##SUPPL##0##17A##) in lung. Whereas mice in the control group exhibited abundant lung metastasis as demonstrated by bioluminescent signals, NCK, CRI or combination treatments (NCK-OSI-930 and NCK-CRI) significantly abrogated lung metastases derived from tail vein injected 4T1-luciferase cells (Fig. ##FIG##7##8h##). Consistent results were obtained by counting of macro-metastatic nodules in the lungs of the same treatment groups (Fig. ##FIG##7##8i##, Supplementary Fig. ##SUPPL##0##17A##). Additionally, NCK-OSI-930 and NCK-CRI combinations significantly reduced lung metastatic colonization of 4T1-luciferase cells compared to single treatment with OSI-930 and CRI, respectively, as demonstrated similarly by counting of macro-metastatic nodules (Fig. ##FIG##7##8i##). Similar effects of NCK, TKIs or combination treatments on the level of pBADSer99 and on BAD expression were observed by IHC in both MDA-MB-231 and 4T1-luciferase cell generated metastasis models (Supplementary Fig. ##SUPPL##0##17B##, Supplementary Fig. ##SUPPL##0##18A##). No significant difference was observed in host animal body weight (Supplementary Fig. ##SUPPL##0##17C##, Supplementary Fig. ##SUPPL##0##18B##) and the weight of other vital organs (Supplementary Fig. ##SUPPL##0##17D##, Supplementary Fig. ##SUPPL##0##18C##) between the treatment groups, indicative of the tolerability of the treatments.</p>", "<title>NCK in combination with TKIs suppresses patient-derived xenograft (PDX) growth and extends the survival of the PDX-engrafted mice</title>", "<p id=\"Par23\">The effect of drug treatments on two patient-derived xenograft (PDX) models of TNBC, USTC-0 and USTC-1 were next examined. The use of the USTC-1 PDX was previously reported<sup>##REF##35296660##40##</sup>. USTC-0 and USTC-1 exhibit differential phosphorylation of BADSer99 (expressed as pBADSer99/BAD), as assessed by IHC (Fig. ##FIG##8##9a, b##). In detail, USTC-0 exhibited a higher pBADSer99 level (Supplementary Fig. ##SUPPL##0##19A, B##) and pBADSer99/BAD ratio (Fig. ##FIG##8##9b##) than USTC-1. Similar to the MDA-MB-231 xenograft, USTC-0- or USTC-1-bearing mice were treated with NCK (20 mg/kg <italic>q.d</italic>.), OSI-930 (20 mg/kg <italic>q.d</italic>.), Crizotinib (50 mg/kg <italic>b.i.w</italic>.), or the combination of NCK with OSI-930 or Crizotinib for 21 days. In both USTC-0 and USTC-1-bearing mice, when compared to the vehicle treated groups, NCK, OSI-930 and Crizotinib treatment significantly reduced the xenograft volume (Fig. ##FIG##8##9c, d, g, h##). Additionally, the combinatorial treatments of NCK-OSI-930 and NCK-Crizotinib were significantly more effective in reducing the PDX volumes than treatment with either OSI-930 or Crizotinib alone. However, consistent with the lower pBADSer99 level and pBADSer99/BAD ratio in USTC-1, similar but less pronounced effects were observed when compared to mice bearing USTC-0 (<italic>P</italic> < 0.01) (Supplementary Fig. ##SUPPL##0##19C##). Treatment of USTC-1-bearing mice with NCK resulted in a 45.6% reduction in xenograft burden compared to those treated with vehicle, as compared to 66.0% reduction in mice bearing USTC-0 (<italic>P</italic> < 0.01). Consistently, the combination treatment of NCK and OSI-930 (<italic>P</italic> < 0.001) or Crizotinib (<italic>P</italic> < 0.05) reduced USTC-0 xenograft burden by 60.04% and 66.58% as compared to 85.33% and 85.44% in the USTC-1 xenograft. The drug combination regimens were well tolerated in mice, according to body weight (Fig. ##FIG##8##9e, i##).</p>", "<p id=\"Par24\">Consistent with the suppression of PDX growth, NCK, OSI-930 and Crizotinib markedly extended the survival of USTC-0-bearing mice when compared to the vehicle-treated group. The combinatorial treatments of NCK-OSI-930 and NCK-Crizotinib demonstrated significant prolongation in survival (<italic>P</italic> < 0.001) when compared to OSI-930 or Crizotinib alone (Table ##TAB##2##3##). Specifically, the median survival days of each treatment group were vehicle (25.5), NCK (42.0), OSI-930 (34.0), Crizotinib (34.5), NCK-OSI-930 (67.0) and NCK- Crizotinib (74.0) (Fig. ##FIG##8##9f##). In mice bearing USTC-1, the median survival days of each treatment group were vehicle (22.5), NCK (34.5), OSI-930 (29.5), Crizotinib (27.0), NCK-OSI-930 (41.5) and NCK-Crizotinib (43.5) (Fig. ##FIG##8##9j##). No PDX in any group reached the humane termination endpoint during the 21-day treatment period.</p>" ]	[ "<title>Discussion</title>", "<p id=\"Par25\">Despite tremendous efforts, there remains no effective targeted therapy for TNBC to date. TNBC frequently harbors mutations with enhanced activation of the PI3K/AKT signaling pathway and is associated with poor prognosis<sup>##REF##19435916##4##,##REF##27893038##41##,##REF##30679171##42##</sup>. Therefore, targeting kinases along the PI3K/AKT pathway may represent a rational therapeutic opportunity to treat this aggressive subtype of BC<sup>##REF##27893038##41##</sup>. Given that BAD phosphorylation at Ser99 is also predominantly mediated by kinases along the PI3K/AKT pathway, which as stated is frequently activated in TNBC<sup>##REF##29175460##8##,##REF##31767884##12##</sup>, targeting BADSer99 phosphorylation represents a viable mechanistic based therapeutic strategy. Consistent with previous studies<sup>##REF##31767884##12##,##REF##25653146##43##</sup>, it was confirmed herein that TNBC tissues exhibit a higher pBADSer99/BAD ratio when compared to normal tissues and which is correlated with higher grade, higher MKI67 labeling and increased lymph node metastasis. Additionally, the inhibition of cell survival and migrative capacity by homology directed repair of BAD to BADS99A demonstrated that phosphorylation of BAD Serine 99 is an actionable target in TNBC. Previously, proof of concept studies using the pBADSer99 inhibitor, NPB, were reported in various cancer models including ER+BC, cisplatin-sensitive or resistant OC and PTEN-deficient EC<sup>##REF##30309962##13##,##REF##35791346##30##,##REF##35725817##31##</sup>. Herein, a more potent inhibitor of pBADSer99 with enhanced oral bioavailability that significantly increased apoptotic cell death in TNBC was generated. Given that molecular docking analyses have identified that NPB may interact with other proteins in addition to BAD<sup>##REF##30309962##13##</sup>, as is common with other “targeted” small molecule compounds<sup>##REF##31178407##44##,##REF##32066817##45##</sup>, there exists the possibility of NCK polypharmacology especially at higher concentrations. Determination of genes/gene functions specifically responsive to BADSer99 phosphorylation and determining if NCK also affects expression of these genes, or a subset of these genes, or exerts effects separately, may be of utility to determine any potential polypharmacology of NCK. This approach will, however, be complicated by the observation that both non-phosphorylated and phosphorylated BAD exert independent cellular functions<sup>##REF##29175460##8##</sup> and heterodimerization of BAD has been observed with multiple cellular proteins in addition to BCL-2 family members, such as hexokinase<sup>##REF##12931191##46##,##REF##24506868##47##</sup>, c-Jun<sup>##REF##17670745##10##</sup>, p53<sup>##REF##17000778##48##</sup> and androgen receptor<sup>##REF##37686282##49##</sup>. The RNA-sequencing and functional analyses contained herein do however suggest that NCK impacts both cancer cell survival and cell cycle, as might be expected<sup>##REF##17670745##10##,##REF##8929531##50##–##REF##11494146##52##</sup>. Regardless of any potential polypharmacology, NCK exhibited potent effects on in vivo models of TNBC, was well tolerated and synergized with TKIs. Different to NPB, pharmacological inhibition of BADSer99 phosphorylation by NCK also markedly impacted cell cycle related gene expression and prevented cell cycle progression. A role for BAD in regulating cell cycle has been previously suggested to be exerted through inhibiting AP1-mediated CYCLIN D1 expression and S phase entry in ER+BC, leading to G0/G1 growth arrest<sup>##REF##17670745##10##</sup>. This activity was also reported to be dependent on BAD phosphorylation at Ser75 and Ser99. However, anchorage independent growth of mouse derived cells has been reported to be dependent on Ser136 (human Ser99) and correlated to Bad binding to 14-3-3<sup>##REF##11526496##53##</sup>. Another study has demonstrated that BAD maintains cell cycle progression in low serum conditions (as herein) although this effect was reported to be independent of BAD phosphorylation requiring heterodimerization with BCL-XL<sup>##REF##11494146##52##</sup>. Herein, it was observed that a higher pBADSer99/BAD ratio was significantly associated with MKI67 labeling in TNBC samples indicative of a proliferative function for BADSer99 phosphorylation. Hence, the precise mechanism by which BAD and NCK perturbation of the pBADSer99/BAD ratio impacts on TNBC cell cycle progression needs further delineation. It was further observed that NCK significantly suppressed TNBC xenograft growth at 5 mg/kg and 20 mg/kg despite no significant toxicity being observed at 50 mg/kg and hence possesses a good therapeutic window. Furthermore, even though NCK is effective in combination therapies, it exhibits relatively potent single agent activity and hence could be of utility in patients with specific molecular or mutational indications, such as cancers with a high BAD phosphorylation ratio or mutations of the PI3K/AKT pathway.</p>", "<p id=\"Par26\">In the present study, pharmacological inhibition of RTKs, specifically VEGFR and c-MET exhibited the most synergistic effects in combination with NCK in reducing cell viability of MDA-MB-231 cells. VEGFR and c-MET are two RTKs with pivotal roles in regulating cell proliferation, angiogenesis and metastasis<sup>##REF##21633166##7##,##REF##14685170##54##</sup>. Previous work has demonstrated the role of VEGFR in increasing the TNBC cancer stem cell (CSC) population and metastasis<sup>##REF##25151964##55##</sup> and the association of c-MET expression with poor prognosis in TNBC<sup>##REF##24970481##56##</sup>. Among the VEGFR and c-MET inhibitors, OSI-930 and Crizotinib showed the highest synergy in combination with NCK. OSI-930 is an orally selective TKI targeting VEGFR2 (9 nM) and Kit (80 nM), which is currently being evaluated in phase 1 clinical trial<sup>##REF##16424037##57##</sup>. Crizotinib, an ATP-competitive, small molecule TKI targeting c-MET (11 nM) and ALK (24 nM), was approved by the FDA in 2011 to treat Anaplastic Lymphoma Kinase-rearranged (ALK + ) non-small cell lung cancer (NSCLC). Therefore, through multi-kinase inhibition (polypharmacology), OSI-930 and Crizotinib, the two TKIs can potentially target increased cell survival and metastasis of TNBC<sup>##REF##21363918##58##,##REF##27057482##59##</sup>. However, acquired drug resistance is frequently linked to activation of downstream PI3K/AKT signaling and remains a major obstacle limiting the clinical efficacy of TKIs<sup>##REF##21266357##20##,##REF##23319457##60##</sup>. Hence, combinatorial targeting of an aberrantly activated PI3K/AKT pathway in TNBC by a pBADSer99 inhibitor and TKIs could potentially abrogate the development of drug resistance and ameliorate the outcomes in TNBC. Herein, it was observed that the combination of NCK and OSI-930 or Crizotinib promoted apoptosis in a synergistic or additive manner through inhibition of BADSer99 phosphorylation. By inhibiting a core downstream effector of the PI3K/AKT and other pathways involved in acquired resistance, NCK synergizes with TKIs in reducing xenograft progression and TNBC lung metastasis in vivo. Additionally, both single agent treatments and combinatorial treatments extend the survival of the two TNBC PDX models examined herein. The effect of NCK and combinatorial treatments of NCK-OSI-930 or NCK-Crizotinib were observed to be more significant in the USTC-0 PDX which exhibited a higher pBADSer99/BAD ratio than USTC-1, and hence a BAD phosphorylation ratio may be a useful marker of potential drug efficacy. Given the robust heterogeneity of TNBC<sup>##REF##33088912##61##</sup>, it is important to acknowledge that two PDX models might not fully reflect the complexity observed clinically. Whereas the findings herein provide valuable proof-of-concept, a greater number of PDX models, with detailed information of genetic and molecular characteristics, should be incorporated in future studies to observe potential benefit of the treatment regimens for TNBC patients with different molecular subtypes.</p>", "<p id=\"Par27\">In summary, NCK was identified as a potent inhibitor of BADSer99 phosphorylation with high efficacy in reducing TNBC cell survival, xenograft growth and metastasis. Combination of NCK, with the dual VEGFR2 and c-Kit inhibitor, OSI-930, or dual c-Met and ALK inhibitor, Crizotinib, promoted apoptotic cell death by inhibiting BAD phosphorylation. Additionally, the combination of NCK with OSI-930 or Crizotinib demonstrated synergistic activity, yielding TNBC xenograft regression, abrogating lung metastasis and extending median survival in mice carrying TNBC PDXs. Hence, the present work identifies a highly synergistic and viable mechanistic based combination that targets BAD as a core effector of TNBC cell survival.</p>" ]	[]	[ "<p id=\"Par1\">Aberrant activation of the PI3K/AKT signaling axis along with the sustained phosphorylation of downstream BAD is associated with a poor outcome of TNBC. Herein, the phosphorylated to non-phosphorylated ratio of BAD, an effector of PI3K/AKT promoting cell survival, was observed to be correlated with worse clinicopathologic indicators of outcome, including higher grade, higher proliferative index and lymph node metastasis. The structural optimization of a previously reported inhibitor of BAD-Ser99 phosphorylation was therefore achieved to generate a small molecule inhibiting the phosphorylation of BAD at Ser99 with enhanced potency and improved oral bioavailability. The molecule 2-((4-(2,3-dichlorophenyl)piperazin-1-yl)(pyridin-3-yl)methyl) phenol (NCK) displayed no toxicity at supra-therapeutic doses and was therefore assessed for utility in TNBC. NCK promoted apoptosis and G0/G1 cell cycle arrest of TNBC cell lines in vitro, concordant with gene expression analyses, and reduced in vivo xenograft growth and metastatic burden, demonstrating efficacy as a single agent. Additionally, combinatorial oncology compound library screening demonstrated that NCK synergized with tyrosine kinase inhibitors (TKIs), specifically OSI-930 or Crizotinib in reducing cell viability and promoting apoptosis of TNBC cells. The synergistic effects of NCK and TKIs were also observed in vivo with complete regression of a percentage of TNBC cell line derived xenografts and prevention of metastatic spread. In patient-derived TNBC xenograft models, NCK prolonged survival times of host animals, and in combination with TKIs generated superior survival outcomes to single agent treatment. Hence, this study provides proof of concept to further develop rational and mechanistic based therapeutic strategies to ameliorate the outcome of TNBC.</p>", "<title>Subject terms</title>" ]	[ "<title>Supplementary information</title>", "<p>\n\n\n</p>" ]	[ "<title>Supplementary information</title>", "<p>The online version contains supplementary material available at 10.1038/s41698-023-00489-3.</p>", "<title>Acknowledgements</title>", "<p>The authors would like to thank Xinxin Huang for technical support on the CRISPR-Cas9 system construction. This research was supported by the National Natural Science Foundation of China (82172618 and 82102768), China; the Shenzhen Key Laboratory of Innovative Oncotherapeutics (ZDSYS20200820165400003) (Shenzhen Science and Technology Innovation Commission), China; Shenzhen Development and Reform Commission Subject Construction Project ([2017]1434), China; Universities Stable Funding Key Projects (WDZC20200821150704001), China; Guangdong Basic and Applied Basic Research Foundation (2020A1515111064), China; The Shenzhen Bay Laboratory, Oncotherapeutics (21310031), China; Overseas Research Cooperation Project (HW2020008) (Tsinghua Shenzhen International Graduate School), China; Research Fund, Kaohsiung Medical University (KMU-Q112002), Taiwan and China Postdoctoral Science Foundation (2022M721894), China.</p>", "<title>Author contributions</title>", "<p>Y.Q.T. and Y.C. contributed equally to this work. P.E.L., V.P. and Y.Q.T. designed the study; P.E.L., V.P. and B.B. supervised the study; B.B. synthesized the chemicals; S.L. and T.Z. contributed the PDX samples and provided scientific supports; Y.Q.T., Y.C., H.G., S.Z., X.H., D.D., A.M.K. and S.B. performed the experiments and analyzed the data; Y.Q.T. and Y.C. wrote the draft; P.E.L. and Y.Q.T. revised the paper. All authors read and approved the final manuscript.</p>", "<title>Data availability</title>", "<p>The data generated and analyzed during this study are available from 10.5281/zenodo.10129841. All other data supporting the findings of this study are available within the paper and its Supplementary Figure or from the corresponding author upon reasonable request.</p>", "<title>Competing interests</title>", "<p id=\"Par39\">The authors declare the following competing interests: B.B., V.P. and P.E.L. are listed as inventors on a patent application and derivatives thereof for NPB and NCK which is used in this work (WO/2019/194520). P.E.L. is an equity holder in Sinotar Pharmaceuticals Ltd which currently holds the license for this patent. All other authors have no competing interests to declare.</p>" ]	[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>pBADSer99 is a potential therapeutic target in TNBC.</title><p><bold>a</bold> pBADSer99 levels and BAD expression were determined using immunohistochemistry (IHC) in adjacent normal (AD) and TNBC tissue specimens. Representative IHC images of pBADSer99 in TNBC and AD tissues (up). Scale bar, 20 μm. Analysis of the pBADSer99/BAD ratio and pBADSer99 staining (%) in AD and TNBC tissue specimens (down). For pBADSer99, the immunoreactive score (IRS) 0 to 4 was categorized as negative and IRS 5 to 12 as positive. For the pBADSer99/BAD ratio, the IRS ratio higher than 0.75 was regarded as positive<sup>##REF##22996377##78##</sup>. Cell survival of MDA-MB-231 (<bold>b</bold>) and BT549 (<bold>c</bold>) cells after transfection with pBADS99A knock in plasmid or vector control. Data represent means ± SD (<italic>n</italic> = 3). <italic>P</italic> < 0.05, <italic>P</italic> < 0.01, and *<italic>P</italic> < 0.001. Corresponding immunoblots displaying levels of pBADSer99 and BAD. The sizes of detected bands in kDa are shown on the left. <bold>d</bold> Transwell analysis was performed to determine the effect of pBADS99A knock in on cell migration of MDA-MB-231 and BT549 cells. The TNBC cells were transfected with pBADS99A plasmid or vector control. Scale bar, 50 μm. Data represent means ± SD (<italic>n</italic> = 5). <italic>P</italic> < 0.05, <italic>P</italic> < 0.01, and <italic>P</italic> < 0.001.</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>pBADSer99 inhibitor NCK demonstrates improved potency over NPB.</title><p><bold>a</bold> Petasis reaction, a three component boronic Mannich-type reaction which utilizes boronic acids as a potential nucleophilic species, salicylaldehyde, and substituted piperazines to form the new C–C bond of the formula I compound, was utilized to synthesize NCK (C<sub>22</sub>H<sub>21</sub>Cl<sub>2</sub>N<sub>3</sub>O). <bold>b</bold> 3D surface and enlarged view of the docked compounds NPB (red) & NCK (black) with the BAD protein (dim grey). The yellow color indicates the site of the Serine 99 residue. <bold>c</bold> 2D structure representation of NCK interacting with BAD protein residues. <bold>d</bold> Sensorgrams obtained by SPR analysis of NCK with the BAD protein. BAD protein was immobilized on the surface of a CM5 sensor chip. A solution of NCK at variable concentrations (1.25–160 μM) was injected to generate the binding responses (RU) recorded as a function of time (s). The results were analyzed using BIA evaluation 4.1. <bold>e</bold> Western Blot analysis was used to assess the level of BAD phosphorylation at Ser99, Ser75 and Ser118 in TNBC cells after treatment with NCK and NPB. β-ACTIN was used as input control for cell lysate. The sizes of detected bands in kDa are shown on the left. <bold>f</bold> Dose-dependent effect of NCK and NPB in 2D and 3D culture on MDA-MB-231 and BT549 TNBC cells measured by using total cell number and AlamarBlue assay respectively (<italic>n</italic> = 3). <bold>g</bold> Western Blot analysis was used to assess the level of BAD phosphorylation at Ser99 in TNBC cells after transfection with siRNA-BAD. β-ACTIN was used as input control for cell lysate. The sizes of detected bands in kDa are shown on the left. <bold>h</bold> CASPASE3/7 activities of MDA-MB-231 cells after transfecting with siRNA targeting BAD transcript or scrambled control and treated with 5 μM NCK were evaluated using the Biovision Caspase 3/7 DEVD Assay Kit. Data represent means ± SD (<italic>n</italic> = 3). <italic>P</italic> < 0.05, <italic>P</italic> < 0.01, and <italic>P</italic> < 0.001. <bold>i</bold> Cell survival of MDA-MB-231 cells after transfecting with siRNA targeting BAD transcript or scrambled control and treated with 5 μM NCK. Data represent means ± SD (<italic>n</italic> = 3). <italic>P</italic> < 0.05, <italic>P</italic> < 0.01, and <italic>P</italic> < 0.001.</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><title>NCK enhances apoptosis and impedes cell-cycle progression in TNBC cells.</title><p><bold>a</bold> Hallmark analysis of the differential expressed genes (DEGs). Advanced bubble chart shows enrichment of hallmarks by DEGs commonly affected by NCK or NPB treatment. <bold>b</bold> Bar charts depicting DEGs commonly upregulated and downregulated by NCK or NPB treatment. <bold>c</bold> GSEA analyses of gene sets for cell cycle checkpoints. NES, normalized enrichment score. FDR, false discovery rate. Positive and negative NES indicate lower and higher expression in NCK when compared to NPB respectively. <bold>d</bold> Flow cytometry analysis of PI staining of cell cycle state of MDA-MB-231 and BT549 cells measured after treatment with 5 μM NCK or NPB using flow cytometry analysis as described in materials and methods. Data represent mean ± SD (<italic>n</italic> = 3). <italic>P</italic> < 0.05, <italic>P</italic> < 0.01, and <italic>P</italic> < 0.001. <bold>e</bold> Flow cytometry analysis of Annexin-V and propidium iodide (PI) staining of apoptotic cell death of MDA-MB-231 and BT549 cells measured after treatment with 5 μM NCK or NPB using flow cytometry analysis as described in materials and methods. Data represent means ± SD (<italic>n</italic> = 3). <italic>P</italic> < 0.05, <italic>P</italic> < 0.01, and <italic>P</italic> < 0.001.</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><title>NCK suppresses xenograft growth with no toxicity up to 50 mg/kg.</title><p><bold>a</bold> The morphology of mice receiving 0, 20 or 50 mg/kg NCK for 14 days. <bold>b</bold> Morphology of the internal organs was examined using H&E staining. Scale bar, 50 µm. <bold>c</bold> Xenograft volume (mm<sup>3</sup>) was measured every day and calculated by using the formula: 0.52 × length × [width]<sup>2</sup>. <bold>d</bold> Xenograft weight of each treatment group in all animals that were sacrificed after 21 days of treatment. <bold>e</bold> Animal weights of each treatment group are indicated. Animal weight was monitored every day. <bold>f</bold> Histological analyses and IRS scoring of pBAD at Ser99, BAD, MKI67, and TUNEL staining. The IRS scoring method is described in materials & methods. Scale bar, 20 µm. All data represent means ± SD (<italic>n</italic> = 8). <italic>P</italic> < 0.05, <italic>P</italic> < 0.01, and <italic>P</italic> < 0.001.</p></caption></fig>", "<fig id=\"Fig5\"><label>Fig. 5</label><caption><title>Tyrosine kinase inhibitors (TKIs) identified as the most synergistic compounds in combination with NCK to reduce MDA-MB-231 cell survival.</title><p><bold>a</bold> Heatmap plot depicts cell viability of MDA-MB-231 cells post-treatment as % Fraction affected (Fa) (Scale: Green to Red). Fa was calculated as 100 - cell viability (%). <bold>b</bold> Fold change (FC) of cell viability after treatments (compound X versus compound X + 10 μM NCK) and Fa of compound X alone were plotted. Compounds were marked in different colors, each representing the pathway targeted by the compound. Compounds with average CI < 1 when co-treated with NCK are shown. CI was calculated using the bliss independence method (CI= (E<sub>A</sub> + E<sub>B</sub>-E<sub>A</sub>E<sub>B</sub>)/E<sub>AB</sub>), where CI < 1 denotes synergy. <bold>c</bold> Top pathways and targets synergizing with NCK are plotted. <bold>d</bold> Cell viability of MDA-MB-231 cells after treatment with the highly ranked synergistic TKIs (at their respective IC<sub>25</sub>) with/without 10 μM NCK (from drug screening assay). Data represent means ± SD. <italic>P</italic> < 0.05, <italic>P</italic> < 0.01, and <italic>P</italic> < 0.001.</p></caption></fig>", "<fig id=\"Fig6\"><label>Fig. 6</label><caption><title>NCK synergizes with OSI-930 or Crizotinib to reduce TNBC cell survival.</title><p><bold>a</bold> The survival fraction of NCK, OSI-930 (OSI) and Crizotinib (CRI) or combination treatments were evaluated with total cell number assay (<italic>n</italic> = 3). <bold>b</bold> CI was measured with Chou-Talalay, where CI < 1 denotes synergy, CI = 1 denotes additivity, CI > 1 denotes antagonism. Synergy score was measured with HSA and bliss synergy analysis (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.synergyfinder.com\">www.synergyfinder.com</ext-link>), where CI > 0 denotes synergy, CI < 0 denotes antagonism. <bold>c</bold> Dose-response analysis of the shift in IC<sub>50</sub> of OSI-930 (OSI) and Crizotinib (CRI) in TNBC cells after co-treatment with NCK (2 μM) was evaluated with total cell number assay. Fold difference was calculated. Data represent means ± SD (<italic>n</italic> = 3).</p></caption></fig>", "<fig id=\"Fig7\"><label>Fig. 7</label><caption><title>NCK synergizes with OSI-930 or Crizotinib to stimulate caspase-mediated apoptotic cell death and reduce cell survival in vitro and ex vivo.</title><p><bold>a</bold> Representative flow cytometry plots using Annexin V FITC/PI staining for apoptotic cell death of MDA-MB-231 and BT549 cells measured after treatment with 5 μM NCK, 5 μM OSI-930 (OSI), 5 μM Crizotinib (CRI) or combinations using flow cytometry analysis at 72 hours as described in materials and methods (<italic>n</italic> = 3). <bold>b</bold> CASPASE 3/7 activities were evaluated in MDA-MB-231 and BT549 cells using the Biovision Caspase 3/7 DEVD Assay Kit after treatment with 5 μM NCK, 5 μM OSI-930 (OSI), 5 μM Crizotinib (CRI) or combinations. Data represent means ± SD (<italic>n</italic> = 3). <italic>P</italic> < 0.05, <italic>P</italic> < 0.01, and <italic>P</italic> < 0.001. <bold>c</bold> Crystal violet staining of foci in colonies generated by MDA-MB-231 cells and BT549 cells after exposure to 5 μM NCK, 5 μM OSI-930 (OSI), 5 μM Crizotinib (CRI) or combinations. <bold>d</bold> Representative images of MDA-MB-231 cells and BT549 cells cultured in 3D Matrigel after exposure to 5 μM NCK, 5 μM OSI-930 (OSI), 5 μM Crizotinib (CRI) or combinations. Scale bar, 100μm. <bold>e</bold> Western blot analysis was used to assess the level of various apoptotic proteins in TNBC cells after treatment with 1 μM NCK, 1 μM OSI-930 (OSI), 1 μM Crizotinib (CRI) or combinations. β-ACTIN was used as input control for cell lysate. The sizes of detected bands in kDa are shown on the left. <bold>f</bold> Western blot analysis was used to assess the expression/phosphorylation of various proteins of the PI3K/AKT and MAPK pathways after treatment with 1 μM NCK, 1 μM OSI-930 (OSI), 1 μM Crizotinib (CRI) or combinations. β-ACTIN was used as input control for cell lysate. The sizes of detected bands in kDa are shown on the left.</p></caption></fig>", "<fig id=\"Fig8\"><label>Fig. 8</label><caption><title>NCK synergizes with TKIs to suppress TNBC xenograft growth and lung metastasis.</title><p><bold>a</bold> MDA-MB-231 xenograft volume (mm<sup>3</sup>) was measured every day and calculated by using the formula: 0.52 × length × [width]<sup>2</sup>. <bold>b</bold> Resected MDA-MB-231 xenografts of each treatment group after sacrifice at the end of 21<sup>st</sup> day. <bold>c</bold> Xenograft burden change of MDA-MB-231 xenografts for each treatment group after sacrifice at the end of 21<sup>st</sup> day. <bold>d</bold> IHC images of pBAD at Ser99, BAD, MKI67 and TUNEL staining in xenografts. Scale bar, 20 µm. <bold>e</bold> Metastases were detected using human <italic>hypoxanthine-guanine phosphoribosyltransferase</italic> (<italic>hHPRT</italic>) mRNA per lung of BALB/c-nude mice orthotopically implanted with MDA-MB-231 cells by using qPCR. Mouse <italic>glyceraldehyde 3-phosphate dehydrogenase</italic> (<italic>mgapdh</italic>) was used as an internal control. Lung sample with CT value < 35 was regarded as metastatic and ≥ 35 was regarded as non-metastatic. <bold>f</bold> Relative lung weight (to body weight) of mice intravenously injected with MDA-MB-231 or 4T1-luciferase cells after treatment with vehicle, NCK, OSI-930 (OSI), Crizotinib (CRI) or combinations. <bold>g</bold> H&E staining and quantitative measurement of micro-metastatic nodules in lungs of MDA-MB-231 cell generated metastasis model. Scale bar: 200 μm. <bold>h</bold> Bioluminescence images and quantification of total flux in the lungs of 4T1-luciferase cell generated metastasis model for each treatment group at the end of the experiment. <bold>i</bold> Quantitative measurement of macro-metastatic nodules in the 4T1-luciferase metastasized lung of mice in each treatment group at the end of the experiment. All data represent means ± SD (<italic>n</italic> = 6). <italic>P</italic> < 0.05, <italic>P</italic> < 0.01, and <italic>P</italic> < 0.001.</p></caption></fig>", "<fig id=\"Fig9\"><label>Fig. 9</label><caption><title>NCK in combination with TKIs suppresses patient-derived xenograft (PDX) growth and extends the survival of the PDX-engrafted mice.</title><p><bold>a</bold> Representative IHC images of pBADSer99 level and BAD expression in USTC-0 and USTC-1. Scale bar, 20 µm. <bold>b</bold> IRS score of pBADSer99/BAD in USTC-0 and USTC-1. The immunoreactive score (IRS) 0 to 4 was categorized as negative and IRS 5 to 12 as positive. <bold>c</bold> Xenograft volume (mm<sup>3</sup>) of USTC-0 was measured every day and calculated by using the formula: 0.52 × length × [width]<sup>2</sup>. <bold>d</bold> Mean USTC-0 xenograft volume of each treatment group at the end of 21<sup>st</sup> day. <bold>e</bold> Animal weight (mean ± SD) of USTC-0 of each treatment group (<italic>n</italic> = 6). <bold>f</bold> Kaplan–Meier survival curves of USTC-0 treated with NCK (NCK), Crizotinib (CRI), OSI-930 (OSI) or combinations. <bold>g</bold> Xenograft volume (mm<sup>3</sup>) of USTC-1 was measured every day and calculated by using the formula: 0.52 × length × [width]<sup>2</sup>. <bold>h</bold> Mean USTC-1 xenograft volume of each treatment group at the end of 21<sup>st</sup> day. <bold>i</bold> Animal weight (mean ± SD) of USTC-1 of each treatment group (<italic>n</italic> = 6). <bold>j</bold> Kaplan–Meier survival curves of USTC-1 treated with NCK (NCK), Crizotinib (CRI), OSI-930 (OSI) or combinations. All data represent means ± SD (<italic>n</italic> = 6). <italic>P</italic> < 0.05, <italic>P</italic> < 0.01, and *<italic>P</italic> < 0.001.</p></caption></fig>" ]	[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Clinicopathological analysis.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th>Cohort</th><th>Total (N)</th><th>Positive (%)</th><th>Negative (%)</th><th><italic>P</italic>-value</th></tr></thead><tbody><tr><td colspan=\"4\"><bold>Age</bold></td><td><bold>0.647</bold></td></tr><tr><td><=55</td><td>18</td><td>33</td><td>67</td><td/></tr><tr><td>>55</td><td>28</td><td>29</td><td>71</td><td/></tr><tr><td><bold>Grade</bold></td><td/><td/><td/><td><bold><0.0001*</bold></td></tr><tr><td>I</td><td>19</td><td>32</td><td>68</td><td/></tr><tr><td>II</td><td>24</td><td>25</td><td>75</td><td/></tr><tr><td>III</td><td>3</td><td>67</td><td>33</td><td/></tr><tr><td colspan=\"4\"><bold>Lymph Node Metastasis</bold></td><td><bold><0.0001</bold></td></tr><tr><td>0</td><td>28</td><td>36</td><td>64</td><td/></tr><tr><td>1</td><td>6</td><td>0</td><td>100</td><td/></tr><tr><td>2</td><td>4</td><td>75</td><td>25</td><td/></tr><tr><td>3</td><td>3</td><td>0</td><td>100</td><td/></tr><tr><td colspan=\"4\"><bold>MKI67</bold></td><td><bold><0.008*</bold></td></tr><tr><td>Low</td><td>10</td><td>20</td><td>80</td><td/></tr><tr><td>Moderate</td><td>21</td><td>29</td><td>71</td><td/></tr><tr><td>Strong</td><td>15</td><td>40</td><td>60</td><td/></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Xenograft regression.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th>% Regression</th><th>VEH</th><th>NCK</th><th>OSI</th><th>N + O</th><th>CRI</th><th>N + C</th></tr></thead><tbody><tr><td>Complete Response (100%)</td><td>0</td><td>33</td><td>0</td><td>83</td><td>17</td><td>50</td></tr><tr><td>Partial Response (>50%)</td><td>0</td><td>17</td><td>33</td><td>17</td><td>17</td><td>50</td></tr><tr><td>Disease Progression</td><td>100</td><td>50</td><td>67</td><td>0</td><td>66</td><td>0</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab3\"><label>Table 3</label><caption><p>PDX host animal median survival.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th/><th>PDX</th><th>VEH</th><th>NCK</th><th>OSI</th><th>N + O</th><th>CRI</th><th>N + C</th></tr></thead><tbody><tr><td rowspan=\"2\">Median survival (Days)</td><td>USTC-0</td><td>25.5</td><td>42.0</td><td>34.0</td><td>67</td><td>34.5</td><td>74</td></tr><tr><td>USTC-1</td><td>22.5</td><td>34.5</td><td>29.5</td><td>41.5</td><td>27.0</td><td>43.5</td></tr></tbody></table></table-wrap>" ]	[]	[]	[]	[]	[]	[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>" ]	[ "<table-wrap-foot><p><italic>P</italic> < 0.05, <italic>P</italic> < 0.01, and **<italic>P</italic> < 0.001.</p><p>Correlation analysis between pBADSer99/BAD ratio and clinicopathological features of TNBC patient. </p></table-wrap-foot>", "<table-wrap-foot><p>Statistics of regression (%) of xenograft in mice receiving NCK (NCK), Crizotinib (CRI) and OSI-930 (OSI) or combinations group.</p></table-wrap-foot>", "<table-wrap-foot><p>Median survival time of mice receiving NCK (NCK), Crizotinib (CRI), OSI-930 (OSI) or combinations in TNBC PDX USTC-0 or USTC-1.</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>These authors contributed equally: Yan Qin Tan, Yi-Shiou Chiou.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"41698_2023_489_Fig1_HTML\" id=\"d32e496\"/>", "<graphic xlink:href=\"41698_2023_489_Fig2_HTML\" id=\"d32e852\"/>", "<graphic xlink:href=\"41698_2023_489_Fig3_HTML\" id=\"d32e1057\"/>", "<graphic xlink:href=\"41698_2023_489_Fig4_HTML\" id=\"d32e1129\"/>", "<graphic xlink:href=\"41698_2023_489_Fig5_HTML\" id=\"d32e1218\"/>", "<graphic xlink:href=\"41698_2023_489_Fig6_HTML\" id=\"d32e1270\"/>", "<graphic xlink:href=\"41698_2023_489_Fig7_HTML\" id=\"d32e1357\"/>", "<graphic xlink:href=\"41698_2023_489_Fig8_HTML\" id=\"d32e1497\"/>", "<graphic xlink:href=\"41698_2023_489_Fig9_HTML\" id=\"d32e1855\"/>" ]	[ "<media xlink:href=\"41698_2023_489_MOESM1_ESM.pdf\"><caption><p>Supplementary Information</p></caption></media>", "<media xlink:href=\"41698_2023_489_MOESM2_ESM.pdf\"><caption><p>Reporting Summary</p></caption></media>" ]	[{"label": ["64."], "mixed-citation": ["Dennington, R., Keith, T., Millam, J. Semichem Inc. Version 5.0. "], "italic": ["Shawnee Mission KS"]}, {"label": ["65."], "mixed-citation": ["Gaussian 16 Rev. C.01 (Wallingford, CT, 2016)."]}, {"label": ["71."], "surname": ["Bird", "Kirstein"], "given-names": ["C", "S"], "article-title": ["Real-time, label-free monitoring of cellular invasion and migration with the xCELLigence system"], "source": ["Nat. Methods"], "year": ["2009"], "volume": ["6"], "fpage": ["v"], "lpage": ["vi"], "pub-id": ["10.1038/nmeth.f.263"]}]	{ "acronym": [], "definition": [] }	78	CC BY	no	2024-01-13 00:02:20	NPJ Precis Oncol. 2024 Jan 10; 8:8	oa_package/f7/76/PMC10781691.tar.gz
PMC10781692	38199983	[ "<title>Introduction</title>", "<p id=\"Par2\">Suicide risk in patients with major depressive disorder (MDD) is 20 times more than that of the general population [##REF##27629598##1##, ##UREF##0##2##], and suicidal behavior exists at all times during major depressive episodes [##UREF##1##3##]. The lifetime prevalence of suicide attempts in patients with MDD is 31% [##REF##30178722##4##]. and more than half of patients experience suicidal ideation beforehand [##REF##14628986##5##]. Suicide-related costs account for about 5% of the total incremental costs of MDD adults [##REF##25742202##6##], representing a substantial burden to patients and their families.</p>", "<p id=\"Par3\">The general treatments for alleviating suicidal ideation include various antidepressants [##REF##18384467##7##–##REF##17453694##9##], lithium [##REF##16040577##10##, ##REF##23814104##11##], ketamine [##REF##25773961##12##, ##REF##29202655##13##], electric convulsive therapy (ECT) [##REF##27289303##14##, ##REF##32205736##15##], and cognitive behavioral therapy (CBT) [##REF##26535958##16##]. However, the antidepressants/lithium and psychotherapy usually require weeks to exert anti-suicide effects. Furthermore, antidepressants may increase suicide risk in children and adolescents, as well as adults in early-phase pharmacotherapy [##REF##17909125##17##, ##REF##27514302##18##]. ECT is an effective way to rapidly relieve suicidal ideation, but the tolerability and complex side effects limit its application [##REF##24091903##19##]. Evidence also suggests that ketamine may be a promising rapid-acting option, but its effects seem to be short-lived [##REF##25773961##12##]. The problems of current treatments motivate the search for safe and rapid relief interventions for suicidal ideation in patients with MDD.</p>", "<p id=\"Par4\">Recent evidence suggests that a non-invasive treatment option, i.e., repetitive transcranial magnetic stimulation (rTMS), may be a rapid and safe way in relieving both depression and suicidal ideation. As recommended by the “Evidence-based guidelines on the therapeutic use of repetitive transcranial magnetic stimulation (rTMS): An update (2014-2018)”, level A evidence (definite efficacy) is proposed for high-frequency (HF)-rTMS on the left dorsal lateral prefrontal cortex (DLPFC) in MDD [##REF##31901449##20##]. Actually, MRI-navigated rTMS has shown high efficacy and rapid action in the treatment of depression. Recently, an individualized accelerated, high-dose intermittent theta-burst stimulation (iTBS) protocol, i.e., Stanford Neuromodulation Therapy (SAINT), was proposed recently by Williams et al. [##REF##29415152##21##]. The safety, effectiveness, and rapid action of this protocol have been validated with both open-label and double-blind studies. Treatment with five days has shown a high response rate of 85.7% and remission rate of 78.6% for treatment-resistant depression [##UREF##2##22##]. SAINT has been approved by FDA as an effective way for the treatment of refractory depression. Notably, it has also been shown to be a potential way to rapidly reduce the severity of suicidal ideation [##REF##32252538##23##].</p>", "<p id=\"Par5\">Although SAINT appears to be highly effective, the neural mechanisms underwriting its rapid-acting antidepressant and suicide prevention effects remain unclear. The brain is a complex network comprising functionally specialized regions that flexibly interact to support a diverse repertoire of cognitive and behavioral functions [##REF##20176931##24##, ##REF##19190637##25##]. Characterizing the brain’s connectivity, which constitutes a functional connectome “fingerprint” [##REF##26457551##26##], may help to elucidate the neural mechanisms supporting the rapid-acting effects of SAINT. Indeed, accumulating evidence shows that the therapeutic efficacy of rTMS might be closely associated with the functional connectivity of its stimulation target on the DLPFC with, the subgenual anterior cingulate cortex (sgACC) [##REF##22658708##27##–##REF##32160765##31##]. However, the modulatory effects of rTMS are not only restricted to the DLPFC-sgACC connectivity, but also manifest in distributed brain networks associated with depression, such as the default mode network (DMN) [##REF##32726666##32##, ##REF##24629537##33##], affective network (AN) [##REF##28049085##34##], salience network (SN) [##REF##31668646##35##], reward network (RN) [##REF##26849183##36##], and visual network (VN) [##REF##31668646##35##]. How does the therapeutic intervention transmit from the stimulation target to distributed networks? Could the neural pathways conveying rTMS stimulation account for its rapid-acting antidepressant and suicide prevention effects?</p>", "<p id=\"Par6\">To address above-mentioned issues, we collected functional magnetic resonance imaging (fMRI) images in 32 MDD patients with suicidal ideation before and immediately after 5-day SAINT. We investigated the information flow from the rTMS target to core regions associated with depression and suicide ideation using effective connectivity analysis based on dynamic causal modelling (DCM). Effective connectivity differs from conventional functional connectivity simply computing the correlation among time courses of interacting regions. Instead, it could infer the causal influences from one region to another and depict the signal flow directions within a brain network. Our results showed that the rapid-acting antidepressant effects of SAINT were related to effective connections of the sgACC, while the suicide prevention effects were more associated with the effective connectivity of the insula (INS).</p>" ]	[ "<title>Methods</title>", "<title>Participants</title>", "<p id=\"Par7\">The study was approved by the Ethics Committee of the First Affiliated Hospital, Fourth Military Medical University, and was conducted in accordance with the Declaration of Helsinki (clinicaltrial.gov identifier: NCT04653337). Written informed consents were obtained from all the participants.</p>", "<p id=\"Par8\">All patients were recruited from the Department of Psychiatry at the First Affiliated Hospital, Fourth Military Medical University, from January 2021 to October 2021, according to the following criteria: (i) 18–60 years old; (ii) meeting the criteria of the Diagnostic and Statistical Manual of Mental Disorder, Fifth Edition (DSM-5) for patients with unipolar MDD assessed by Mini-Neuropsychiatric Interview (MINI); (iii) right handedness; (iv) with a score > 17 on the 17-item Hamilton Depression Rating Scale (HAMD-17) [##REF##29726344##37##]; (v) with a score ≥ 6 on the Beck Scale for Suicidal ideation-Chinese Version (BSI-CV) [##REF##14628986##5##, ##UREF##3##38##]; (vi) normal results on physical examination and electroencephalography. We excluded those patients with (i) received antidepressant treatment 2 months prior to the study; (ii) any other current or past psychiatric axis-I or axis-II disorders; (iii) severe physical illnesses; (iv) psychotic symptoms, alcohol or drug abuse; (v) a history of neurological disorders including seizure, cerebral trauma, or MRI evidence of structural brain abnormalities; (vi) contraindications to MRI and rTMS, such as metallic implants in the body, cardiac pacemakers, claustrophobia, etc.; (vii) acute suicide or self-injury behavior in need of immediate intervention; (viii) pregnancy, lactation, or a planned pregnancy for females.</p>", "<p id=\"Par9\">Thirty-four participants were enrolled in this study. Two patients withdrew from the study due to personal reasons after the first day of treatment. For ethical and safety reasons, venlafaxine (75 mg/d) or duloxetine (30 mg/d) were prescribed at the beginning of the treatment. Dexzopiclone or zolpidem was also used to improve the sleep quality of individuals who suffered from severe insomnia. Figure ##FIG##0##1## describes the workflow of the study, and the demographic characteristics of the patients are provided in the supplementary Table ##SUPPL##0##1##.</p>", "<title>Clinical assessments</title>", "<p id=\"Par10\">Suicidal ideation and depression symptoms were assessed by clinical and self-report scales at baseline, immediately after SAINT (after the last session of SAINT), 2 and 4 weeks after the whole SAINT. The severity of suicidal ideation was measured by BSI-CV, item 3 of the HAMD-17, and item 10 of the Montgomery-Asberg Depression Rating Scale (MADRS). Depression symptoms were assessed with HAMD-17 and MADRS. At the end of each day’s treatment, 6-item HAMD (HAMD-6) was also used to assess the depression symptoms. Potential neurocognitive side effects were assessed using a neuropsychological test battery before and immediately after SAINT, including Perceived Deficits Questionnaire-Depression (PDQ-D) [##REF##30802419##39##], Digital Span Test (DST) [##REF##21729426##40##], and Digit Symbol Substitution Test (DSST) [##REF##32748630##41##].</p>", "<p id=\"Par11\">BSI-CV scores were the main (clinical) outcomes of the study. The suicidal ideation response was defined as a reduction ≥ 50% on the BSI-CV, while the remission of suicidal ideation was defined as a reduction ≥ 50% and < 6 on the BSI-CV. Response to depression symptoms was defined as a reduction ≥ 50% on the HAMD-17, MADRS, and HAMD-6 scales. The remission of depression symptoms was defined as a score < 8 on the HAMD-17 [##REF##17258323##42##], a score < 11 on the MADRS [##REF##28068617##43##], a score < 5 on the HAMD-6 [##REF##27525966##44##], and a score < 13 on the BDI [##UREF##4##45##]. All statistical analyses of clinical data were conducted using SPSS, version 26 (IBM, Armonk, N.Y.). The level of statistical significance was set at <italic>p</italic> = 0.05. As one patient failed to participate the clinical assessment 4 weeks after SAINT, the mean value of all participants’ clinical score at that time point was used to replace missing data. Changes in BSI-CV, HAMD-17, HAMD-6, MADRS scores were assessed with repeated measures ANOVA, while changes in PDQ-D, DST, DSST were evaluated with paired <italic>t</italic> tests. The relevant results are displayed in Table ##TAB##0##1## and Fig. ##FIG##1##2##.</p>", "<title>Procedures of MRI-navigated rTMS</title>", "<p id=\"Par12\">The MRI-navigated rTMS treatment was delivered by a Black Dolphin Navigation Robot system (SmarPhin S-50, Solide Brain Control Medical Technology Co., Ltd., Xi’an, China). The individualized rTMS stimulation target is defined as the peak subunit on the DLPFC that was mostly negatively connected to the sgACC according to Cole et al. [##REF##32252538##23##]. Whereas the definition of the sgACC was slightly different from that of Cole et al. [##REF##32252538##23##]. In the current study, No. 187 and 188 atlases based on Brainnetome Atlas (BNA) (<ext-link ext-link-type=\"uri\" xlink:href=\"https://atlas.brainnetome.org/bnatlas.html\">https://atlas.brainnetome.org/bnatlas.html</ext-link>) [##REF##27230218##46##] were selected as the sgACC to improve the signal noise ratio and avoid mixing information comes from the corpus callosum. After the definition of the individualized stimulation target, 5-day sgACC FC-guided rTMS treatment, i.e., SAINT, was given for each patient [##UREF##2##22##, ##REF##32252538##23##]. Specifically, three consecutive iTBS were delivered at 90% of the resting motor threshold (RMT) for each session in 9 min 52 s. Ten sessions of iTBS (18,000 pulses), with a 50-min interval of each session, were delivered to the subject every day. The whole treatment lasted for 5 consecutive days and 90,000 pulses in total were received by each patient.</p>", "<title>Image acquisition</title>", "<p id=\"Par13\">High-resolution MRI data were acquired on a 3.0 T UNITED 770 scanner before and after treatment. Parameters for 3D-T1-weighted structural imaging were: slices = 192, repetition time = 7.24 ms, echo time = 3.10 ms, slice thickness = 1.0 mm, matrix size= 512 × 512, field of view = 256 × 256 mm<sup>2</sup>, flip angle = 10°. Parameters for resting-state fMRI with eye-closed were: slices =35, repetition time = 2000 ms, echo time = 30 ms, slice thickness = 4 mm, matrix size = 64 × 64, field of view = 224 × 224 mm<sup>2</sup>, flip angle = 90°. The pre-treatment (i.e., baseline) and post-treatment resting-state fMRI sessions lasted for about 12 minutes.</p>", "<title>Data preprocessing</title>", "<p id=\"Par14\">The MRI data were preprocessed with the statistical parametric mapping software package (SPM12, <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.fil.ion.ucl.ac.uk/spm/software/spm12/\">http://www.fil.ion.ucl.ac.uk/spm/software/spm12/</ext-link>) and the GRETNA toolbox (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.nitrc.org/projects/gretna/\">https://www.nitrc.org/projects/gretna/</ext-link>). After discarding the first 10 images due to magnetic field instability, slice timing correction was performed to correct differences in the acquisition time of slices within a volume. Next, realignment was used to correct head motion, and two subjects with translation larger than 3.5 mm or rotation larger than 3.5° were excluded. The images were then normalized to standard Montreal Neurological Institute (MNI) space and spatially smoothed with a Gaussian kernel filter (full width at half maximum (FWHM) = 6 mm). Temporal detrending was used to deal with low-frequency signal drift. Covariates including Friston-24 head motion parameters, signals from white matter, and CSF, were then regressed out. Furthermore, we performed global signal regression to remove spatially coherent confounds [##REF##22658708##27##, ##REF##33237320##29##, ##REF##19889849##47##]. Finally, the fMRI time series were temporally filtered with a bandpass filtering (0.01–0.1 Hz) for functional connectivity analyses. Functional connectivity (i.e., correlation) analyses were used to identify regions of interest (ROIs) for subsequent effective connectivity (i.e., DCM) modelling.</p>", "<p id=\"Par15\">It should be noted that only 28 of the 32 patients completed both the pretreatment and posttreatment MRI scanning. Among them, as 2 patients were excluded because of large head motion, 26 subjects were finally entered into the subsequent fMRI analyses.</p>", "<title>Functional connectivity profiles of the stimulation target</title>", "<p id=\"Par16\">Functional connectivity with the individualized stimulation targets was investigated first. In detail, a 6 mm radius spheric ROI centered at the DLPFC target’s MNI coordinates (Supplementary Table ##SUPPL##0##2##) for each participant was defined as the seed region. The Pearson correlations between the seed time series and time series of every voxel across the whole brain were then calculated, i.e., target-based functional connectivity (i.e., <italic>r</italic> map). To ensure the Gaussian distribution of residuals of the ensuing parametric tests, all <italic>r</italic> values were Fisher’s Z transformed. One-sample <italic>t</italic>-tests were then conducted on the transformed <italic>z</italic> maps and two signed maps of target-based functional connectivity were obtained, i.e., a positive functional connectivity map and a negative functional connectivity map (Fig. ##FIG##2##3##). Figure ##FIG##2##3a## represents regions that negatively correlated with DLPFC targets, while Fig. ##FIG##2##3b## shows regions that displayed positive correlations with the DLPFC targets (<italic>p</italic> < 0.05, uncorrected).</p>", "<title>Stimulation target-based effective connectivity analysis</title>", "<p id=\"Par17\">Effective connectivity analysis was confined to the left hemisphere regions. Thus, the left caudate (CAU), precuneus (PCUN), hippocampus (HIP), insular (INS) were included in the analysis (Fig. ##FIG##2##3c, d##). For midline regions, including the midline PFC (mPFC) and sgACC (Fig. ##FIG##2##3c, d##), the mean time courses of the bilateral cluster were extracted. For each brain region, a binary mask was generated according to the functional connectivity maps and the Brainnetome Atlas (BNA) [##REF##27230218##46##] (<ext-link ext-link-type=\"uri\" xlink:href=\"https://atlas.brainnetome.org/bnatlas.html\">https://atlas.brainnetome.org/bnatlas.html</ext-link>) (Supplementary Table ##SUPPL##0##2##, Fig. ##FIG##2##3a–d##). The mean time course of voxels within each mask was then extracted.</p>", "<p id=\"Par18\">Furthermore, to validate whether the depression functional circuit map of our study is consistent with the convergent network proposed by Siddiqi et al. [##REF##34239076##48##], partial correlations between target-based connectivity and depression score changes (including the changes of HAMD-17 and MADRS) with regressing out the baseline depression scores were conducted (Supplementary Fig. ##SUPPL##0##1##).</p>", "<p id=\"Par19\">Two DCMs were constructed based on ROIs showing positive and negative correlations with the target ROI. In brief, ROIs from the negative functional connectivity map were combined with the target (seed) ROI to create a fully inter-connected dynamic causal model, named the negative correlation effective connectivity model (NCECM), while the ROIs from the positive functional connectivity map were used to construct the corresponding positive correlation effective connectivity model (PCECM). Directed (i.e., causal) effective connectivity within the NCECM and PCECM were estimated using spectral dynamic causal modeling (spDCM) [##REF##24345387##49##] as follows.</p>", "<title>Effective connectivity analysis with spDCM</title>", "<p id=\"Par20\">The causal interactions among ROIs were modeled with random differential equations for the hidden neuronal states [##REF##24345387##49##]:</p>", "<p id=\"Par21\">Here, <italic>x</italic>(<italic>t</italic>)=[<italic>x</italic><sub><italic>1</italic></sub>(<italic>t</italic>) <italic>x</italic><sub><italic>2</italic></sub>(<italic>t</italic>) <italic>… x</italic><sub><italic>n</italic></sub>(<italic>t</italic>)] <sup>T</sup> denotes the hidden neuronal states that represent neuronal activity of the <italic>n</italic> interacting ROIs. <italic>A</italic> represents the effective connectivity characterizing the strength of directed connections among these ROIs, while <italic>v</italic>(<italic>t</italic>) models endogenous fluctuations, with a parameterized spectral profile. The neuronal model is then supplemented with standard hemodynamic state equations that model the translation from unobserved neuronal activity to observed BOLD signals from the ROIs [##REF##12948688##50##]. The model was then inverted, and model parameters were estimated in the frequency domain by fitting complex cross spectra through a Variational Laplace procedure [##REF##24345387##49##]. These (spectral) data features were evaluated prior to the temporal filtering used to identify ultra-slow functional connectivity.</p>", "<p id=\"Par22\">For each subject, a fully connected model with reciprocal connections between all pairs of ROIs was first defined for NCECM and PCECM, respectively. Each fully connected model was then optimized to maximize model evidence (as scored by variational free energy). The posterior probability of the parameters of this fully connected model — from each subject — was then entered into a second-level group analysis. The parametric empirical Bayes (PEB) framework [##REF##26569570##51##, ##REF##31226492##52##] was used to obtain second-level (i.e., between subject and session) commonalities and differences in effective connectivity with a General Linear Model (GLM). The advantage of the PEB framework over classical statistics is that both the posterior expectations and covariance of the parameters are considered when estimating effects at the group level.</p>", "<p id=\"Par23\">In summary, group-level effects were modeled with the following hierarchical model according to [##REF##31226492##52##]:</p>", "<p id=\"Par24\">Here, <italic>Y</italic><sub><italic>i</italic></sub> represents the observed BOLD data features of subject <italic>i</italic>. At first level, <italic>Y</italic><sub><italic>i</italic></sub> is modeled with a DCM with parameters , a GLM of confounding (and nuisance) effects with design matrix and parameter , and observation noise . At the second level, DCM parameters are modeled with a second GLM with design matrix <italic>X</italic> and group-level parameters which parameterize commonalities and differences in effective connectivity over subjects. The second level GLM included a constant term modelling group means (i.e., commonalities) and differences due to (i) pre-and post- treatment effects, response in terms of (ii) suicidal ideation and (iii) depression (see Fig. ##FIG##0##1c##—middle panel). models random between-subject effects that are not modelled by the GLM. The second-level parameters are assumed to have a prior expectation <italic>η</italic> and residuals . To optimize the ensuing PEB model, Bayesian Model Reduction (BMR) [##REF##26569570##51##] was used to search over all reduced PEB models. Finally, Bayesian Model Averaging (BMA) was employed to summarize connectivity over all plausible (reduced) PEB models.</p>", "<p id=\"Par25\">The ensuing Bayesian model averages of effective connectivity at the second level were used to identify commonalities (i.e., group means) that describe the functional architecture that was conserved over subjects and sessions. The Bayesian model averages of effective connectivity at the first level were used to test for correlations with clinical scores. These Bayesian model averages represent the most efficient estimates of connectivity because they inherit constraints from the second-level GLM.</p>", "<title>Correlations between fMRI connections and clinical scores</title>", "<p id=\"Par26\">To explore whether (functional, and effective) connectivity estimates could predict rTMS treatment effects—in mitigating depression and suicidal ideation symptoms—we calculated the correlations between connectivity estimates and depression (i.e., HAMD-17, MADRS) and suicidal ideation score changes (i.e., BSI-CV), respectively. Furthermore, the correlations between the change percentage in connections and clinical scores (i.e., HAMD-17, MADRS, and BSI-CV) were conducted for exploring the rTMS treatment effect. The post-treatment connectivity estimates were also correlated with clinical scores to explore the after-effect of 5-day treatment.</p>" ]	[ "<title>Results</title>", "<p id=\"Par27\">For all 26 patients, the MNI coordinates of the stimulation targets, the corresponding superficial depths, resting motor thresholds (RMT) and relevant clinical outcomes were displayed in Table ##TAB##1##2##. No severe adverse events occurred during the whole trial and the most common side effect was headache (supplementary materials, Supplementary Table ##SUPPL##0##3##). All side effects were mild, well tolerated, and resolved rapidly after stimulation.</p>", "<title>Suicidal ideation</title>", "<p id=\"Par28\">Changes in suicidality scale scores were assessed with a repeated measures ANOVA. After 5 days of treatment, there was significant decrease in the BSI-CV (<italic>F</italic> = 81.34, <italic>df</italic> = 2, 61, <italic>p</italic> < 0.001; Fig. ##FIG##1##2a, b##), item 3 of the HAMD-17 (<italic>F</italic> = 317.90, <italic>df</italic> = 2, 66, <italic>p</italic> < 0.001; Fig. ##FIG##1##2a, c##), item 10 of the MADRS (<italic>F</italic> = 314.72, <italic>df</italic> = 2, 64, <italic>p</italic> < 0.001; Fig. ##FIG##1##2a, d##) at follow-up. The mean BSI-CV score immediately after SAINT reduced by 65.23%. Bonferroni-corrected post-hoc comparisons revealed a significant difference in scores of BSI-CV between 0 and 4 weeks after treatment, while there was no significant difference between 0 and 2 weeks after treatment. Remission and response rates of suicidal ideation after treatment were 56.25% and 65.63% (0 weeks), 59.38% and 81.25% (2 weeks), 75.00% and 93.33% (4 weeks), respectively (Table ##TAB##0##1##).</p>", "<title>Depression symptoms</title>", "<p id=\"Par29\">Statistical analysis revealed a significant effect of time (weeks) on mean HAMD-17 scores (<italic>F</italic> = 267.30, <italic>df</italic> = 3, 93, <italic>p</italic> < 0.001; Fig. ##FIG##1##2a, e##) and a significant effect of day on mean HAMD-6 scores (<italic>F</italic> = 102.67, <italic>df</italic> = 3, 95, <italic>p</italic> < 0.001; Fig. ##FIG##1##2a, h##), with scores at all follow-up time points being significantly lower than at baseline (Bonferroni-corrected pairwise comparisons, <italic>p</italic> < 0.001). These results were recapitulated for the MADRS (<italic>F</italic> = 351.73, <italic>df</italic> = 2, 68, <italic>p</italic> < 0.001; Fig. ##FIG##1##2a, f##) and the BDI (<italic>F</italic> = 67.99, <italic>df</italic> = 3, 93, <italic>p</italic> < 0.001; Fig. ##FIG##1##2##a, g). After 5 days of treatment, the mean HAMD-17 score reduced by 66.39%, and the reduction of MADRS was 58.95%. Bonferroni-corrected post-hoc comparisons demonstrated a significant difference in scores of HAMD-17 between 0 and 4 weeks after treatment, while there was no significant difference between 0 and 2 weeks after treatment. The remission rate (HAMD-17 score < 8) and response rate (a reduction ≥ 50% from baseline in HAMD-17) after treatment were 53.13% and 81.25% (0 weeks); 56.25% and 90.63% (2 weeks); 81.25% and 96.88% (4 weeks), respectively (Table ##TAB##0##1##).</p>", "<title>Functional connectivity with the stimulation target</title>", "<p id=\"Par30\">The stimulation target-based functional connectivity pattern of the pre-treatment is shown in Fig. ##FIG##2##3## (a, b; <italic>p</italic> < 0.05 without correction). No significant differences were detected between the pre- and post-treatment (FDR correction, <italic>p</italic> < 0.05).</p>", "<p id=\"Par31\">However, the baseline (pre-treatment) functional connectivity between the DLPFC and PCUN—and between the DLPFC and mPFC—was negatively correlated with the reduction in HAMD-17 (<italic>p</italic> = 0.037, <italic>p</italic> = 0.039) (Fig. ##FIG##2##3e, f##), respectively. These functional anticorrelations strengthened after rTMS treatment for the PCUN (<italic>p</italic> = 0.033) and for the mPFC (<italic>p</italic> = 0.029), respectively (Fig. ##FIG##2##3g, h##). Moreover, the MADRS were also negatively correlated with the PCUN connectivity after treatment (<italic>p</italic> = 0.015) (Fig. ##FIG##2##3k##). We did not find any significant correlations between connectivity and suicidality.</p>", "<title>Stimulation target-based effective connectivity analysis</title>", "<title>Treatment effect for all subjects</title>", "<p id=\"Par32\">In the NCECM (Fig. ##FIG##3##4a, b##), the stimulation target (DLPFC) exerted inhibitory influences on the PCUN and INS. Signals from the INS are then sent to the sgACC, HIP, and INS itself. Since the connections from DLPFC to INS are inhibitory and connections from the INS to sgACC, INS and HIP are excitatory, these influences together resulted in inhibition and the negative functional connectivity with DLPFC seen in the sgACC, INS and HIP. These inhibitory influences then propagate from the sgACC to PCUN, and from the HIP to sgACC, and INS.</p>", "<p id=\"Par33\">For the PCECM, we only found significant excitatory influences from DLPFC to CAU, followed by inhibitory influences on the DLPFC and mPFC, inhibiting the responses of these two brain regions (Fig. ##FIG##3##4c, d##).</p>", "<p id=\"Par34\">After 5-day treatment, the self-connection of the INS and the connection from the HIP to INS was significantly increased, while decreases were observed in the connectivity from the HIP to sgACC (Fig. ##FIG##3##4e, f##). More importantly, the reduction of the BSI-CV scores was negatively correlated with the strength of the connection from the HIP to INS with <italic>p</italic> = 0.001 (Fig. ##FIG##3##4g##) after rTMS treatment. The MADRS reduction also correlated negatively with the effective connectivity from the HIP to INS (<italic>p</italic> = 0.021) following 5-days of treatment (Fig. ##FIG##3##4h##).</p>", "<title>Responders and non-responders to suicidal ideation</title>", "<p id=\"Par35\">The distributions of the individualized stimulation targets of the responders and non-responders to suicidal ideation were displayed in Fig. ##FIG##4##5a##.</p>", "<p id=\"Par36\">In the NCECM, the responders to suicidal ideation showed significantly increased connectivity from the HIP to DLPFC, whereas connectivity of the PCUN, INS, HIP, and the connection from HIP to sgACC (Fig. ##FIG##5##6a##) decreased. Meanwhile, differences in connectivity were observed in the connection between the CAU-DLPFC, and CAU self-connection in suicidal ideation responders, compared to non-responders (Fig. ##FIG##5##6b##).</p>", "<title>Responders and non-responders to depression</title>", "<p id=\"Par37\">The distributions of individualized stimulation targets of the responders and non-responders to depression were displayed in Fig. ##FIG##4##5b##.</p>", "<p id=\"Par38\">In contrast to the suicidal ideation pattern, the depression responders showed increased connections in NCECM the from HIP to sgACC and INS, as well as self-connection of the DLPFC after rTMS treatment, with decreased connectivity from the sgACC to HIP, as well as the self-connection of the sgACC (Fig. ##FIG##5##6c##). In PCECM, depression responders showed decreased connectivity in the CAU itself following rTMS treatment (Fig. ##FIG##5##6d##).</p>", "<p id=\"Par39\">For depression responders, the baseline (pre-treatment) self-connection of the sgACC was negatively correlated with MADRS reduction (<italic>p</italic> = 0.033) (Fig. ##FIG##5##6e##). The effective connectivity from the HIP to sgACC was also negatively correlated with MADRS scores after rTMS treatment (<italic>p</italic> = 0.040) (Fig. ##FIG##5##6f##).</p>" ]	[ "<title>Discussion</title>", "<p id=\"Par40\">In the current study, we examined the feasibility and clinical efficacy of SAINT in the relief of suicidal ideation in patients with MDD. Our results showed that SAINT rapidly reduced the severity of suicidal ideation with a high response rate of up to 65.63% with only 5 days of treatment. Moreover, stimulation of the DLPFC targets induced changes in a brain network of regions that had negative functional connectivity (i.e., correlations) with the target region. In addition, by comparing responders and non-responders, we found that distinct changes in connectivity may contribute to the rapid effects of SAINT on the relief of suicidal ideation and amelioration of depression severity, respectively. These findings suggest that SAINT has great promise for the treatment of suicidal ideation associated with depression. More importantly, the current study could also extend our understanding of the neurobiological underpinnings of SAINT, which could further facilitate optimization of its clinical efficacy.</p>", "<p id=\"Par41\">The current study demonstrated that SAINT is a safe and feasible way that could rapidly and effectively alleviate suicidal ideation in patients with MDD. High suicide risk in MDD is a serious public health issue, yet an effective treatment strategy which can rapidly and safely relieve suicidality in these patients remains elusive. Currently, available treatment options such as antidepressants, lithium, and psychotherapy have failed to show rapid and effective prevention effects, whereas some may even increase suicidal thoughts in early-phase pharmacotherapy [##REF##27289303##14##]. There is a growing interest in the use of rTMS to reduce suicidal ideation. However, studies have shown inconsistent benefits of rTMS on suicidal ideation [##REF##32251917##53##]. Earlier sham-controlled rTMS studies have reported a reduction in suicidal ideation, but the improvements were independent of active or sham stimulation [##REF##24731434##54##–##REF##27729854##56##]. After the stimulation protocol was optimized and MRI-guided precision targeting strategy was employed, suicide prevention effects of rTMS seem to have been enhanced. Pan and colleagues [##UREF##5##57##] reported that MRI-navigated high-dose rTMS treatment significantly reduced suicidal ideation relative to sham stimulation.</p>", "<p id=\"Par42\">Recent studies have also suggested that SAINT is an effective way to relieve depression, but its suicidal prevention effects remain unaddressed [##REF##32252538##23##]. In this study, we found that SAINT could effectively alleviate suicidal ideation in MDD patients, with a high response rate of up to 65.63%. Moreover, the response rate reached 78.13% and 90.63% respectively for 2 weeks and 4 weeks after SAINT. These findings could promote the development of safe and rapid suicide prevention strategies and reduce the suicide risk in patients of MDD.</p>", "<p id=\"Par43\">The current study identified the neural pathways that might support the rapid suicide prevention and antidepressant effects of SAINT. We studied the signal propagation pathways from the rTMS targets to other rTMS responsive regions by using effective connectivity analysis, which describes directed information flow within a brain network. It is thought that the propagation from the stimulation target (DLPFC) of rTMS may be an accurate biomarker for its clinical efficacy [##REF##30156743##58##]. In this study, for the first time, we identified the pathways using effective analysis from the DLPFC target to core brain systems implicated in depression. Specifically, stimulation of the DLPFC might first inhibit the activity of the PCUN and INS, from which influences were then relayed to the sgACC, resulted in suppression of enhanced limbic activation in depressed patients. These findings provide crucial support for the hypothesis that rTMS may induce its antidepressant effects through remote normalization of hyperactivity in the sgACC and other limbic regions [##REF##31668646##35##].</p>", "<p id=\"Par44\">It is worth noting that, instead of a direct inhibitory connection from the target to the sgACC, the results suggest that the stimulation effects might first propagate to the INS which then relies on the sgACC, and other core brain regions implicated in MDD. Intriguingly, although the basic idea behind SAINT is to improve the treatment efficacy by targeting the region that is most negatively functionally connected with sgACC [##UREF##2##22##, ##REF##32252538##23##], we did not find any significant correlations between the DLPFC-sgACC connectivity and depression score reductions in the current study. According to recent studies, the proximity (distance) between the actual target and the optimal DLPFC target was anticorrelated with the SGC-based functional connectivity strengths [##REF##33237320##29##, ##REF##33820629##59##]. Here, the optimal potential stimulation target has already been selected as the spot with the highest anticorrelation with the sgACC, and the actual stimulation target and the optimal stimulation target should be 0 mm, which may be one of the reasons for no significant correlation were witnessed between DLPFC-sgACC functional connectivity and depression score changes.</p>", "<p id=\"Par45\">Indeed, it is the INS acts as a hub node in the network. This is in line with the functional anatomy of the insula and sgACC. Anatomically, previous studies have reported the absence of direct anatomical connections between BA46 (DLPFC) and BA25 (sgACC) [##REF##10103094##60##]. In contrast, tracer studies and in vivo fiber tracking studies have consistently identified structural connectivity of the INS with frontal, temporal, and limbic regions in the macaque monkey and human brains [##REF##7174906##61##–##REF##28644199##64##]. Functionally, the INS has been considered to be a crucial functional hub [##REF##23937691##65##]. It is involved in a wide range of function including emotion regulation, salience detection, attentional control, etc. [##REF##23937691##65##]. More importantly, the INS was thought to initial the switching between large-scale task negative and task positive networks [##REF##20512370##66##–##REF##21908230##68##].</p>", "<p id=\"Par46\">Our findings also suggest that different neural mechanisms may contribute to the rapid-acting effects of SAINT on relief of suicidal ideation and amelioration of depression severity, respectively. By comparing the effective connectivity of responders and non-responders, we found that relief of suicidal ideation was specifically associated with effective connectivity of the INS and HIP, while mitigation in the severity of depression was related to connectivity of the sgACC. Consistent evidence have related the antidepressant effects of rTMS with connectivity of the sgACC [##REF##22658708##27##, ##REF##29274805##28##, ##REF##24150516##69##–##REF##25744500##71##]. An earlier study found that better treatment outcomes were associated with more negative functional connectivity between the target and sgACC [##REF##22658708##27##]. This finding was further replicated in research from other groups [##REF##29274805##28##, ##REF##24150516##69##, ##REF##25744500##71##]. This region thus was suggested as a possible neurobiological marker for the assessment of the clinical efficacy of antidepressant treatments [##REF##25744500##71##]. Baseline sgACC metabolic activity and connectivity were found to be predictable of anti-depressive response [##REF##29274805##28##, ##REF##24150516##69##, ##REF##25744500##71##], which is also replicated in our results.</p>", "<p id=\"Par47\">Resting-state DLPFC-sgACC functional connectivity profiles also reliably differentiated responders and non-responders [##REF##31668646##35##]. Furthermore, the depression circuit maps of those responders in our current study were to some extent similar by visually inspection with the convergent network proposed by Siddiqi, Schaper [##REF##34239076##48##] (Supplementary Figure ##SUPPL##0##1##). On the other hand, the differences between the connectivity maps of our current study and the convergent network from Siddiqi et al. [##REF##34239076##48##] were may attributed to the nature of the samples included in the manuscript were MDD patients with suicidal ideation, which in agree with our finding that different neural mechanisms may contribute to the rapid-acting effects on suicidal ideation and amelioration of depression severity.</p>", "<p id=\"Par48\">Regarding the neural pathways contributing to the suicide prevention effects of SAINT, the current study extended previous studies by showing that the effective connectivity of the INS and HIP predicts the rapid-acting effects of SAINT on suicidal ideation. The INS is one of the core regions in the brain’s salience network, which is crucial for cognitive control [##REF##26849183##36##]. Among individuals with borderline personality disorder, a disorder defined partially by recurrent suicidal behavior, the suicide attempters demonstrated decreased grey matter concentrations in the INS compared with healthy controls and non-attempters [##REF##22336640##72##]. Reduced cortical thickness in INS was also reported in depressed patients with suicidal ideation [##REF##25963377##73##]. A recent MEG study reported reduced gamma power which reflected imbalance in excitation-inhibition in the INS in MDD patients [##REF##31928949##74##].In the current study, we showed that the self-connection which is reflective of the excitatory of this INS was reduced by SAINT. Our findings suggest that SAINT may rapidly alleviate suicidal ideation through modulating the excitatory of the INS. More importantly, it may also be possible to optimize the clinical efficacy of SAINT for suicide prevention by selecting a stimulation target which demonstrates the most negative functional/effective connectivity with INS.</p>", "<p id=\"Par49\">We need to consider some limitations when interpreting our results. This study aimed to explore the feasibility of the Stanford Accelerated Intelligent Neuromodulation Therapy (SAINT) in rapidly relieving suicidal ideation with an open-label design without sham groups according to the original SAINT study [##REF##32252538##23##], we could not rule out possible confounding effects from drugs. A double-blind, randomized, sham-controlled trial is required for further investigation to better interpreting the therapy’s underlying mechanisms and benefiting it in alleviating suicide ideation and depression. In addition, a real-time target tracking and following robot system was used to ensure that the DLPFC subregion—which was most negatively functionally connected with sgACC—received the stimulation. Thus, we were unable to collecting fMRI data simultaneously when the patients were receiving rTMS stimulation, due to the difficulty of placing the robot system in an MRI scanner. Future studies may need to replicate the findings with concurrent TMS-fMRI or TMS-EEG. Another limitation is that the multiple corrections were not performed on the correlations between connectivity and clinical score because small sample size was used in this study. This study is for the first time to explore the underlying mechanism of SAINT, we should be cautious in interpreting these results and studies with large sample sizes are better to be conducted for further exploring the neural mechanism of the SAINT. Moreover, the limitation of the effectivity should be addressed here, DCM is constructed based on Bayesian model comparison or reduction, which depends upon data itself. This procedure would simplify model itself with sacrificing data complexity [##REF##28219774##75##]. In addition, considering safety issue for all patients, rTMS treatment were combined with antidepressants (i.e., Venlafaxine/Duloxetine) as previous studies [##UREF##2##22##, ##REF##32252538##23##, ##REF##29726344##37##]. Another limitation is that the recruited patients are not persons with treatment-refractory depression, the results could not be generalized to this population group.</p>" ]	[]	[ "<p id=\"Par1\">High suicide risk represents a serious problem in patients with major depressive disorder (MDD), yet treatment options that could safely and rapidly ameliorate suicidal ideation remain elusive. Here, we tested the feasibility and preliminary efficacy of the Stanford Accelerated Intelligent Neuromodulation Therapy (SAINT) in reducing suicidal ideation in patients with MDD. Thirty-two MDD patients with moderate to severe suicidal ideation participated in the current study. Suicidal ideation and depression symptoms were assessed before and after 5 days of open-label SAINT. The neural pathways supporting rapid-acting antidepressant and suicide prevention effects were identified with dynamic causal modelling based on resting-state functional magnetic resonance imaging. We found that 5 days of SAINT effectively alleviated suicidal ideation in patients with MDD with a high response rate of 65.63%. Moreover, the response rates achieved 78.13% and 90.63% with 2 weeks and 4 weeks after SAINT, respectively. In addition, we found that the suicide prevention effects of SAINT were associated with the effective connectivity involving the insula and hippocampus, while the antidepressant effects were related to connections of the subgenual anterior cingulate cortex (sgACC). These results show that SAINT is a rapid-acting and effective way to reduce suicidal ideation. Our findings further suggest that distinct neural mechanisms may contribute to the rapid-acting effects on the relief of suicidal ideation and depression, respectively.</p>", "<title>Subject terms</title>" ]	[ "<title>Supplementary information</title>", "<p>\n\n</p>" ]	[ "<title>Supplementary information</title>", "<p>The online version contains supplementary material available at 10.1038/s41398-023-02707-9.</p>", "<title>Acknowledgements</title>", "<p>This work was supported by the National Natural Science Foundation of China (61976248, 81974215), Clinical Research Project of Fourth Military Medical University (2021XB023), and Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions (NYKFKT2020001).</p>", "<title>Author contributions</title>", "<p>All authors contributed extensively to the work presented in this paper. BL and NZ conceptualized the work, analyzed the imaging data, and wrote the main paper; NT conceived the study with HW and L-BC and administered the experiment; KJF give technical support and conceptual advice; WZ, DW, JL, YC, MY, YQ, WL, WS, ML, PZ, LG, SQ administered the experiment and collected the data; L-BC, HW supervised its analysis and edited the manuscript.</p>", "<title>Data availability</title>", "<p>Data supporting the findings of this study are available from the corresponding author.</p>", "<title>Competing interests</title>", "<p id=\"Par50\">The authors declare no competing interests.</p>" ]	[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>Workflow for the whole study.</title><p><bold>a</bold> Flowchart of the trial. <bold>b</bold> Target-based functional connectivity profiles and ROIs selection for the following effective connectivity analysis. <bold>c</bold> Illustrations of dynamic causal modeling for effective connectivity analysis.</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>Changes in scale score during and after SAINT in MDD patients with suicidal ideation.</title><p><bold>a</bold>, <bold>b</bold> Significant decrease in the BSI-CV (<italic>F</italic> = 81.34, <italic>df</italic> = 2, 61, <italic>p</italic> < 0.001). <bold>a</bold>, <bold>c</bold> Significant decrease in item 3 of the HAMD-17 (<italic>F</italic> = 317.90, <italic>df</italic> = 2, 66, <italic>p</italic> < 0.001). <bold>a</bold>, <bold>d</bold> Significant decrease in item 10 of the MADRS (<italic>F</italic> = 314.72, <italic>df</italic> = 2, 64, <italic>p</italic> < 0.001). <bold>a</bold>, <bold>e</bold> Significant decrease in HAMD-17 (<italic>F</italic> = 267.30, <italic>df</italic> = 3, 93, <italic>p</italic> < 0.001). <bold>a</bold>, <bold>f</bold> Significant decrease in MADRS (<italic>F</italic> = 351.73, <italic>df</italic> = 2, 68, <italic>p</italic> < 0.001). <bold>a</bold>, <bold>g</bold> Significant decrease in BDI (<italic>F</italic> = 67.99, <italic>df</italic> = 3, 93, <italic>p</italic> < 0.001). <bold>a</bold>, <bold>h</bold> A significant effect of day on mean HAMD-6 scores (<italic>F</italic> = 102.67, <italic>df</italic> = 3, 95, <italic>p</italic> < 0.001). ***<italic>p</italic> < 0.001; BSI-CV: Chinese Version of the Beck Scale for Suicidal Ideation. HAMD-3: the 3rd item of HAMD-17; MADRS-10: the 10th item of Montgomery–Asberg Depression Rating Scale; HAMD-17: 17-item HAMD; MADRS: Montgomery–Asberg Depression Rating Scale; BDI: Beck Depression Inventory; HAMD-6: 6-item Hamilton Depression Rating Scale.</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><title>Target-based functional connectivity profiles and the correlations between functional connectivity and clinical scores.</title><p><bold>a</bold> Positive functional connectivity profiles of stimulation target. <bold>b</bold> Negative functional connectivity profiles of stimulation target. <bold>c</bold> Selected negative connectivity ROIs based on BNA templates. <bold>d</bold> Selected positive functional connectivity ROIs based on BNA templates. <bold>e</bold>, <bold>f</bold> Correlations between the baseline functional connectivity of the PCUN and mPFC and reductions in HAMD-17 scores. <bold>g</bold>, <bold>h</bold> Correlations between the reductions in HAMD-17 and the functional connectivity of the PCUN (<italic>p</italic> = 0.033) and mPFC (<italic>p</italic> = 0.030) after treatment. <bold>K</bold> Correlation between the reduction in MADRS and the functional connectivity of the PCUN (<italic>p</italic> = 0.015); to directly visualize the differences of the correlations between responders and non-responders, the correlation distributions of responders and non-responders were also plotted, respectively. BNA: Brainnetome Atlas.</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><title>The commonalities and differences of effective connectivity and correlations between effective connectivity and clinical scores; negative value represents inhibitory effects, while positive value indicates excitatory influences.</title><p><bold>a</bold>, <bold>b</bold> Commonalities of the effective connectivity between the pre- and pot-treatment in NCECM. <bold>c</bold>, <bold>d</bold> Commonalities of the effective connectivity between the pre- and post-treatment in PCECM. <bold>e</bold>, <bold>f</bold> Significant differences of the effective connectivity between the pre- and post-treatment in NCECM. <bold>g</bold> Anti-correlation between the reduction in BSI-CV scores and the effective connectivity from the HIP to the INS (<italic>p</italic> = 0.001). <bold>h</bold> Anti-correlation between the reduction in MADRS scores and the effective connectivity from the HIP to the INS (<italic>p</italic> = 0.021); same as Fig. ##FIG##2##3##, to directly visualize the differences of the correlations between responders and non-responders, the correlation distributions of responders and non-responders were also plotted, respectively (<bold>g</bold>, <bold>h</bold>).</p></caption></fig>", "<fig id=\"Fig5\"><label>Fig. 5</label><caption><title>The distribution of the stimulation targets.</title><p><bold>a</bold> The distribution of the stimulation targets for responders and non-responders to suicidal ideation. <bold>b</bold> The distribution of the stimulation targets for responders and non-responders to depression. Color orange represents the stimulation targets of responders, while the blue indicates non-responders.</p></caption></fig>", "<fig id=\"Fig6\"><label>Fig. 6</label><caption><title>Differences in effective connectivity between responders and non-responders to suicidal ideation and depression symptoms, respectively and the correlations between effective connectivity and clinical scores.</title><p><bold>a</bold>, <bold>b</bold> Differences in effective connectivity of the responders and non-responders to suicidal ideation in NCECM and PCECM. <bold>c</bold>, <bold>d</bold> Correlations between the reductions in MADRS score and the effective connectivity after 5-day treatment. <bold>e</bold>, <bold>f</bold> Correlations between the reduction in MADRS scores and the baseline self-connection of the sgACC (<italic>p</italic> = 0.033) and the connection of HIP-sgACC (<italic>p</italic> = 0.040); same as Fig. ##FIG##2##3## and Fig. ##FIG##3##4##, to directly visualize the differences of the correlations between responders and non-responders, the correlation distributions of responders and non-responders were also plotted, respectively (<bold>e</bold>, <bold>f</bold>).</p></caption></fig>" ]	[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Clinical measurements of the patients at baseline and follow up.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\">Measurement</th><th colspan=\"2\">Baseline</th><th colspan=\"4\">Post-SAINT</th><th colspan=\"4\">2 weeks after SAINT</th><th colspan=\"4\">4 weeks after SAINT</th></tr><tr><th>Mean</th><th>SD</th><th>Mean</th><th>SD</th><th>Response No. (%)</th><th>Remission No. (%)</th><th>Mean</th><th>SD</th><th>Response No. (%)</th><th>Remission No. (%)</th><th>Mean</th><th>SD</th><th>Response No. (%)</th><th>Remission No. (%)</th></tr></thead><tbody><tr><td colspan=\"15\"><italic>Suicidal ideation</italic></td></tr><tr><td>BSI-CV</td><td>17.63</td><td>7.06</td><td>6.13</td><td>5.98</td><td>21 (65.63)</td><td>18 (56.25)</td><td>5.81</td><td>6.01</td><td>25 (78.13)</td><td>19 (59.38)</td><td>3.39</td><td>4.53</td><td>29 (90.63)</td><td>24 (75.00)</td></tr><tr><td>HAMD, item 3</td><td>3.00</td><td>0.00</td><td>0.30</td><td>0.50</td><td>–</td><td>–</td><td>0.30</td><td>0.50</td><td>–</td><td>–</td><td>0.20</td><td>0.40</td><td>–</td><td>–</td></tr><tr><td>MADRS, item 10</td><td>3.90</td><td>0.30</td><td>0.50</td><td>0.80</td><td>–</td><td>–</td><td>0.50</td><td>0.80</td><td>–</td><td>–</td><td>0.30</td><td>0.70</td><td>–</td><td>–</td></tr><tr><td colspan=\"15\"><italic>Depression symptoms</italic></td></tr><tr><td>HAMD-17</td><td>27.91</td><td>4.31</td><td>9.36</td><td>5.43</td><td>26 (81.25)</td><td>17 (53.13)</td><td>7.84</td><td>4.57</td><td>29 (90.63)</td><td>18 (56.25)</td><td>5.72</td><td>4.24</td><td>30 (93.75)</td><td>26 (81.25)</td></tr><tr><td>MADRS</td><td>36.69</td><td>4.49</td><td>15.06</td><td>7.24</td><td>25 (78.13)</td><td>9 (28.13)</td><td>11.28</td><td>5.67</td><td>29 (90.63)</td><td>18 (56.25)</td><td>8.45</td><td>5.17</td><td>31 (96.88)</td><td>25 (78.13)</td></tr><tr><td>BDI</td><td>35.75</td><td>9.17</td><td>21.41</td><td>10.92</td><td>13 (40.63)</td><td>8 (25.00)</td><td>19.44</td><td>10.33</td><td>20 (62.50)</td><td>10 (31.25)</td><td>14.33</td><td>7.75</td><td>23 (71.88)</td><td>15 (46.88)</td></tr><tr><td>HAMD-6</td><td>13.63</td><td>2.17</td><td>4.41</td><td>2.66</td><td>26 (81.25)</td><td>19 (59.38)</td><td>–</td><td/><td>–</td><td>–</td><td>–</td><td/><td>–</td><td>–</td></tr><tr><td colspan=\"15\"><italic>Neurocognitive test</italic></td></tr><tr><td>PDQ-D</td><td>42.13</td><td>15.65</td><td>30.22</td><td>17.27</td><td>–</td><td>–</td><td>–</td><td/><td>–</td><td>–</td><td>–</td><td/><td>–</td><td>–</td></tr><tr><td>DST</td><td>13.50</td><td>2.31</td><td>14.88</td><td>2.54</td><td>–</td><td>–</td><td>–</td><td/><td>–</td><td>–</td><td>–</td><td/><td>–</td><td>–</td></tr><tr><td>DSST</td><td>55.19</td><td>11.60</td><td>63.63</td><td>11.89</td><td>–</td><td>–</td><td>–</td><td/><td>–</td><td>–</td><td>–</td><td/><td/><td/></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>MNI coordinates of the stimulation targets for all 26 patients, the corresponding superficial depths, resting motor thresholds (RMT) and relevant clinical outcomes.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\">Patients</th><th colspan=\"3\">MNI coordinates</th><th/><th/><th/><th/><th/></tr><tr><th><italic>x</italic></th><th><italic>y</italic></th><th><italic>z</italic></th><th>Superficial Depth (mm)</th><th>RMT (%)</th><th>BSI-CV (%)</th><th>HAMD-17(%)</th><th>MADRS (%)</th></tr></thead><tbody><tr><td>Patient 1</td><td>−26</td><td>45</td><td>28</td><td>20.49</td><td>65</td><td>0.22</td><td>0.64</td><td>0.54</td></tr><tr><td>Patient 2</td><td>−26</td><td>48</td><td>25</td><td>22.76</td><td>55</td><td>1.00</td><td>0.76</td><td>0.68</td></tr><tr><td>Patient 3</td><td>−29</td><td>49</td><td>32</td><td>24.86</td><td>25</td><td>1.00</td><td>0.59</td><td>0.62</td></tr><tr><td>Patient 4</td><td>−36</td><td>44</td><td>34</td><td>17.48</td><td>70</td><td>0.45</td><td>0.68</td><td>0.63</td></tr><tr><td>Patient 5</td><td>−27</td><td>50</td><td>23</td><td>22.34</td><td>30</td><td>1.00</td><td>0.79</td><td>0.78</td></tr><tr><td>Patient 6</td><td>−25</td><td>53</td><td>20</td><td>19.11</td><td>65</td><td>1.00</td><td>0.84</td><td>0.73</td></tr><tr><td>Patient 7</td><td>−40</td><td>52.5</td><td>4</td><td>21.53</td><td>45</td><td>0.72</td><td>0.74</td><td>0.60</td></tr><tr><td>Patient 8</td><td>−42</td><td>52</td><td>13</td><td>25.24</td><td>55</td><td>0.90</td><td>0.89</td><td>0.75</td></tr><tr><td>Patient 9</td><td>−32</td><td>21</td><td>40</td><td>21.15</td><td>55</td><td>0.50</td><td>0.48</td><td>0.46</td></tr><tr><td>Patient 0</td><td>−41</td><td>53</td><td>3</td><td>29.46</td><td>60</td><td>0.84</td><td>0.68</td><td>0.66</td></tr><tr><td>Patient 11</td><td>−26</td><td>48</td><td>27</td><td>20.67</td><td>60</td><td>0.83</td><td>0.83</td><td>0.83</td></tr><tr><td>Patient 12</td><td>−31</td><td>50</td><td>33</td><td>24.75</td><td>60</td><td>0.43</td><td>0.78</td><td>0.64</td></tr><tr><td>Patient 13</td><td>−40</td><td>50</td><td>1</td><td>21.92</td><td>70</td><td>0.36</td><td>0.81</td><td>0.84</td></tr><tr><td>Patient 14</td><td>−26</td><td>55</td><td>28</td><td>24.63</td><td>70</td><td>0.56</td><td>0.89</td><td>0.69</td></tr><tr><td>Patient 15</td><td>−30</td><td>48</td><td>34</td><td>17.79</td><td>35</td><td>0.47</td><td>0.86</td><td>0.78</td></tr><tr><td>Patient 16</td><td>−29</td><td>45</td><td>32</td><td>17.93</td><td>30</td><td>0.88</td><td>0.66</td><td>0.83</td></tr><tr><td>Patient 17</td><td>−41</td><td>52</td><td>8</td><td>17.91</td><td>60</td><td>0.44</td><td>0.59</td><td>0.26</td></tr><tr><td>Patient 18</td><td>−26</td><td>44</td><td>30</td><td>22.35</td><td>45</td><td>0.88</td><td>0.71</td><td>0.53</td></tr><tr><td>Patient 19</td><td>−30</td><td>48</td><td>35</td><td>23.17</td><td>70</td><td>1.00</td><td>0.77</td><td>0.83</td></tr><tr><td>Patient 20</td><td>−28</td><td>52</td><td>25</td><td>20.38</td><td>45</td><td>0.60</td><td>0.41</td><td>0.33</td></tr><tr><td>Patient 21</td><td>−28</td><td>48</td><td>32</td><td>20.45</td><td>70</td><td>0.33</td><td>0.69</td><td>0.59</td></tr><tr><td>Patient 22</td><td>−40</td><td>53</td><td>6</td><td>25.88</td><td>70</td><td>0.39</td><td>0.25</td><td>0.20</td></tr><tr><td>Patient 23</td><td>−30</td><td>38</td><td>30</td><td>17.59</td><td>75</td><td>0.44</td><td>0.75</td><td>0.50</td></tr><tr><td>Patient 24</td><td>−27</td><td>54</td><td>24</td><td>20.75</td><td>55</td><td>0.52</td><td>0.46</td><td>0.51</td></tr><tr><td>Patient 25</td><td>−36</td><td>33</td><td>37</td><td>16.78</td><td>60</td><td>0.33</td><td>0.59</td><td>0.58</td></tr><tr><td>Patient 26</td><td>−39</td><td>53</td><td>−1</td><td>17.95</td><td>45</td><td>0.28</td><td>0.34</td><td>0.31</td></tr></tbody></table></table-wrap>" ]	[ "<disp-formula id=\"Equa\"><alternatives><tex-math 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id=\"M9\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\varGamma }_{i}$$\\end{document}</tex-math><mml:math id=\"M10\"><mml:msub><mml:mrow><mml:mi>Γ</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq2\"><alternatives><tex-math id=\"M11\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\theta }_{i}$$\\end{document}</tex-math><mml:math id=\"M12\"><mml:msub><mml:mrow><mml:mi>θ</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq3\"><alternatives><tex-math id=\"M13\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${X}_{0}$$\\end{document}</tex-math><mml:math id=\"M14\"><mml:msub><mml:mrow><mml:mi>X</mml:mi></mml:mrow><mml:mrow><mml:mn>0</mml:mn></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq4\"><alternatives><tex-math id=\"M15\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\beta }_{i}$$\\end{document}</tex-math><mml:math id=\"M16\"><mml:msub><mml:mrow><mml:mi>β</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq5\"><alternatives><tex-math id=\"M17\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{\\varepsilon }_{i}}^{(1)}$$\\end{document}</tex-math><mml:math id=\"M18\"><mml:msup><mml:mrow><mml:msub><mml:mrow><mml:mi>ε</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:mn>1</mml:mn><mml:mo>)</mml:mo></mml:mrow></mml:msup></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq6\"><alternatives><tex-math id=\"M19\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\theta }_{i}$$\\end{document}</tex-math><mml:math id=\"M20\"><mml:msub><mml:mrow><mml:mi>θ</mml:mi></mml:mrow><mml:mrow><mml:mi>i</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq7\"><alternatives><tex-math id=\"M21\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\theta }^{(2)}$$\\end{document}</tex-math><mml:math id=\"M22\"><mml:msup><mml:mrow><mml:mi>θ</mml:mi></mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:mn>2</mml:mn><mml:mo>)</mml:mo></mml:mrow></mml:msup></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq8\"><alternatives><tex-math id=\"M23\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\varepsilon }^{(2)}$$\\end{document}</tex-math><mml:math id=\"M24\"><mml:msup><mml:mrow><mml:mi>ε</mml:mi></mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:mn>2</mml:mn><mml:mo>)</mml:mo></mml:mrow></mml:msup></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq9\"><alternatives><tex-math id=\"M25\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\theta }^{(2)}$$\\end{document}</tex-math><mml:math id=\"M26\"><mml:msup><mml:mrow><mml:mi>θ</mml:mi></mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:mn>2</mml:mn><mml:mo>)</mml:mo></mml:mrow></mml:msup></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq10\"><alternatives><tex-math id=\"M27\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\varepsilon }^{(3)}$$\\end{document}</tex-math><mml:math id=\"M28\"><mml:msup><mml:mrow><mml:mi>ε</mml:mi></mml:mrow><mml:mrow><mml:mo>(</mml:mo><mml:mn>3</mml:mn><mml:mo>)</mml:mo></mml:mrow></mml:msup></mml:math></alternatives></inline-formula>" ]	[]	[]	[]	[]	[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>" ]	[ "<table-wrap-foot><p><italic>N</italic> the number of participants, <italic>mean</italic> the mean value of measurement scores, <italic>SD</italic> standard deviation, <italic>HAMD-6</italic> 6 item Hamilton Depression Rating Scale (6-item HAMD), <italic>HAMD-17</italic> 17 item HAMD, <italic>MADRS</italic> Montgomery–Asberg Depression Rating Scale, <italic>BDI</italic> Beck Depression Inventory, <italic>BSI-CV</italic> Chinese Version of the Beck Scale for Suicidal Ideation, <italic>PDQ-D</italic> Perceived Deficits Questionnaire-Depression, <italic>DST</italic> Digital Span Test, <italic>DSST</italic> Digit-symbol Substitution Test.</p></table-wrap-foot>", "<table-wrap-foot><p><italic>RMT</italic> resting motor threshold, <italic>BSI-CV</italic> Chinese Version of the Beck Scale for Suicidal Ideation, <italic>HAMD-17</italic> 17 item HAMD, <italic>MADRS</italic> Montgomery–Asberg Depression Rating Scale.</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>These authors contributed equally: Baojuan Li, Na Zhao, Nailong Tang.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"41398_2023_2707_Fig1_HTML\" id=\"d32e528\"/>", "<graphic xlink:href=\"41398_2023_2707_Fig2_HTML\" id=\"d32e1116\"/>", "<graphic xlink:href=\"41398_2023_2707_Fig3_HTML\" id=\"d32e1248\"/>", "<graphic xlink:href=\"41398_2023_2707_Article_Equa.gif\" position=\"anchor\"/>", "<graphic xlink:href=\"41398_2023_2707_Article_Equb.gif\" position=\"anchor\"/>", "<graphic xlink:href=\"41398_2023_2707_Article_Equc.gif\" position=\"anchor\"/>", "<graphic xlink:href=\"41398_2023_2707_Article_Equd.gif\" position=\"anchor\"/>", "<inline-graphic xlink:href=\"41398_2023_2707_Article_IEq1.gif\"/>", "<inline-graphic xlink:href=\"41398_2023_2707_Article_IEq2.gif\"/>", "<inline-graphic xlink:href=\"41398_2023_2707_Article_IEq3.gif\"/>", "<inline-graphic xlink:href=\"41398_2023_2707_Article_IEq4.gif\"/>", "<inline-graphic xlink:href=\"41398_2023_2707_Article_IEq5.gif\"/>", "<inline-graphic xlink:href=\"41398_2023_2707_Article_IEq6.gif\"/>", "<inline-graphic xlink:href=\"41398_2023_2707_Article_IEq7.gif\"/>", "<inline-graphic xlink:href=\"41398_2023_2707_Article_IEq8.gif\"/>", "<inline-graphic xlink:href=\"41398_2023_2707_Article_IEq9.gif\"/>", "<inline-graphic xlink:href=\"41398_2023_2707_Article_IEq10.gif\"/>", "<graphic xlink:href=\"41398_2023_2707_Fig4_HTML\" id=\"d32e2469\"/>", "<graphic xlink:href=\"41398_2023_2707_Fig5_HTML\" id=\"d32e2512\"/>", "<graphic xlink:href=\"41398_2023_2707_Fig6_HTML\" id=\"d32e2565\"/>" ]	[ "<media xlink:href=\"41398_2023_2707_MOESM1_ESM.docx\"><caption><p>Supplementary materials</p></caption></media>" ]	[{"label": ["2."], "surname": ["Otte", "Gold", "Penninx", "Pariante", "Etkin", "Fava"], "given-names": ["C", "SM", "BW", "CM", "A", "M"], "article-title": ["Major depressive disorder"], "source": ["Nat Rev Dis Prim"], "year": ["2016"], "volume": ["2"], "fpage": ["1"], "lpage": ["20"]}, {"label": ["3."], "mixed-citation": ["Association AP. Diagnostic and statistical manual of mental disorders. 5th ed. Arlington, VA: American Psychiatric Publishing; 2013."]}, {"label": ["22."], "mixed-citation": ["Cole EJ, Phillips AL, Bentzley BS, Stimpson KH, Nejad R, Barmak F, et al. Stanford Neuromodulation Therapy (SNT): a double-blind randomized controlled trial. Am J Psychiatry. 2021;179:132\u201341."]}, {"label": ["38."], "mixed-citation": ["Liming W, Yanli S, Zhiqun L, Zhiyi L, Kerang Z. The reliability and validity of the Chinese version of the Beck Suicidal Ideation Scale in evaluating patients with depression. Chin J Health Psychol. 2012;159\u201360."]}, {"label": ["45."], "surname": ["Wang", "Gorenstein"], "given-names": ["YP", "C"], "article-title": ["Psychometric properties of the Beck Depression Inventory-II: a comprehensive review"], "source": ["Rev Brasileira psiquiatria (Sao Paulo, Braz : 1999)"], "year": ["2013"], "volume": ["35"], "fpage": ["416"], "lpage": ["31"], "pub-id": ["10.1590/1516-4446-2012-1048"]}, {"label": ["57."], "surname": ["Pan", "Shen", "Jiao", "Chen", "Li", "Lu"], "given-names": ["F", "Z", "J", "J", "S", "J"], "article-title": ["Neuronavigation-guided rTMS for the treatment of depressive patients with suicidal ideation: a double-blind, randomized, sham-controlled trial"], "source": ["Clin Pharm Ther"], "year": ["2020"], "volume": ["108"], "fpage": ["826"], "lpage": ["32"], "pub-id": ["10.1002/cpt.1858"]}]	{ "acronym": [], "definition": [] }	75	CC BY	no	2024-01-13 00:02:20	Transl Psychiatry. 2024 Jan 10; 14:21	oa_package/c6/b6/PMC10781692.tar.gz
PMC10781693	38200077	[ "<title>Introduction</title>", "<p id=\"Par2\">Type 2 diabetes mellitus (T2DM) and hypertension are closely related with metabolic syndrome, and interact synergistically to promote cardiovascular diseases and kidney dysfunction<sup>##REF##37328862##1##,##REF##28348018##2##</sup>. The use of the renin-angiotensin (Ang) system (RAS)-blocking agents for blood pressure control is recommended to prevent the development of chronic kidney disease and cardiovascular events in DM patients<sup>##REF##34979961##3##</sup>. Diabetic peripheral neuropathy (DPN) is one of the most frequent complications in DM patients, and the incidence of DPN is approximately 31.5% and 17.5% in T2DM and T1DM patients, respectively, according to a meta-analysis study<sup>##REF##31917119##4##</sup>. Clinically, the relationship between DPN and RAS is not necessarily clear, and the clinical efficacy of RAS inhibitors in reducing DPN is still open to question.</p>", "<p id=\"Par3\">Ang I is formed from angiotensinogen by renin, and converted into Ang II by angiotensin-converting enzyme (ACE), which activates AT<sub>1</sub> and AT<sub>2</sub> receptors. The peripheral RAS plays an important role in the regulation of blood pressure and fluid volume, and AT<sub>1</sub> receptor blockers (angiotensin receptor blockers; ARBs) and ACE inhibitors (ACEIs) are clinically used to treat or prevent hypertension, diabetic nephropathy and chronic heart failure. Most interestingly, accumulating evidence suggests that an Ang system like peripheral RAS is present in the brain and spinal cord, where angiotensinogen, Ang I, Ang II, ACE, AT<sub>1</sub> and AT<sub>2</sub> receptors, and cathepsin D, a renin-like enzyme, are detectable<sup>##REF##37237567##5##</sup>. In the CNS, supraspinal Ang II is considered to promote nociception via activation of AT<sub>1</sub> and/or AT<sub>2</sub> receptors, although AT<sub>1</sub> receptor activation in a certain brain area appears to suppress nociception<sup>##REF##37237567##5##</sup>. The functional upregulation of the spinal Ang II/AT<sub>1</sub> receptor system is involved in T1DM-related DPN<sup>##REF##27401876##6##</sup>. In contrast, the MAS receptors activated by Ang (1–7) formed from Ang II by ACE2 might function to rather reduce DPN in a T2DM model<sup>##REF##31987854##7##</sup>. In the primary afferents including the dorsal root ganglion (DRG) and sciatic nerves, the Ang II/AT<sub>1</sub> receptor system may contribute to the development of neuropathic pain<sup>##REF##37237567##5##</sup>. It is to be noted that clinical and preclinical studies have suggested the involvement of the Ang II/AT<sub>1</sub> receptor system in the chemotherapy-induced peripheral neuropathy<sup>##REF##35629066##8##–##REF##32485058##10##</sup>. Activation of AT<sub>2</sub> receptors expressed in macrophages is also implicated in neuropathic pain<sup>##REF##29976627##11##,##REF##30082378##12##</sup>, and the effectiveness of AT<sub>2</sub> receptor antagonists against neuropathic pain has been demonstrated in clinical and preclinical studies<sup>##REF##24507377##13##–##REF##29200998##15##</sup>. Collectively, the peripheral and/or central Ang II is considered to play a role in the development of neuropathic pain including DPN. It is thus likely that, of DM patients undergoing antihypertensive pharmacotherapy, ones receiving RAS blockers such as ACEIs and ARBs might have a lower risk than ones receiving the other (non-ACEI, non-ARB) antihypertensive agents. To test this hypothesis, in the present study, we conducted a retrospective cohort study by collecting medical information of T2DM patients undergoing antihypertensive medications at three different hospitals in Japan over a period of approximately a decade. On the basis of the results from the clinical study, we also performed a reverse translational study to ask whether ACEIs and ARBs could prevent the development of DPN in leptin-deficient <italic>ob</italic>/<italic>ob</italic> mice, a model of T2DM.</p>" ]	[ "<title>Methods</title>", "<title>Laboratory animals employed and ethical approval of the experimental procedures in a reverse translational study</title>", "<p id=\"Par4\">Leptin-deficient <italic>ob</italic>/<italic>ob</italic> mice exhibit obesity and the resulting metabolic abnormalities, such as insulin resistance, hyperinsulinemia, and hyperglycemia, with a phenotype similar to human T2DM<sup>##REF##5278387##16##,##REF##7984236##17##</sup>. Male <italic>ob</italic>/<italic>ob</italic> mice, the T2DM model, and age-matched lean (<italic>ob</italic>/+ or +/+) mice are obtained from CLEA Japan, Inc (Tokyo, Japan), and housed in standard shoebox cages located in a room maintained at a temperature of 22 ± 2 °C under a 12/12-h light/dark cycle (lights on: 07:00), with free access to food and tap water. All procedures for animal experiments conformed to National Institutes of Health Guide for the Care and Use of Laboratory Animals, and were approved by Ethics Committee of Animal Experiment at Tohoku Medical and Pharmaceutical University. The present study is reported in accordance with the ARRIVE guidelines (<ext-link ext-link-type=\"uri\" xlink:href=\"https://arriveguidelines.org\">https://arriveguidelines.org</ext-link>).</p>", "<title>Protocol of animal experiments</title>", "<p id=\"Par5\">Nociceptive sensitivity in <italic>ob</italic>/<italic>ob</italic> and the control lean mice was determined from the age of 5 until 12 weeks old. Mechanical nociceptive sensitivity was evaluated by the up-down method<sup>##REF##7990513##18##</sup> using a set of 8 calibrated von Frey filaments (Stoelting Touch Test Sensory Evaluator Kit #2 to #9: ranging from ≈ 0.018 to ≈ 1.4 g of force). Mice were placed within Plexiglas cubicles (9 × 5 × 5 cm high) that were positioned atop a perforated metal floor. The von Frey filaments were applied perpendicularly against the plantar surface of hindpaw until the fibers bowed, and then held for 3 s. A withdrawal from the filament, or lack thereof, was observed within the 3-s time window. At every time point, withdrawal threshold was measured once in each of the left and right hind paws, and the obtained two values were averaged. Thermal nociceptive sensitivity was measured using a plantar analgesia meter (Model 390; IITC Life Sciences, Los Angeles, CA, USA). Mice were placed in the above mentioned cubicles (9 × 5 × 5 cm high) positioned atop a 0.5-cm-think glass plate and habituated for at least 1 h before testing. A mobile high-intensity halogen lamp beam placed under the glass floor was focused on the plantar hind paw. The device was set to 20% of the maximum available heat intensity of the device (≈ 35 W/mm<sup>2</sup>). Withdrawal latencies were measured three times in each of the left and right hind paws, and the obtained six values were averaged. The assay of mechanical and thermal nociceptive sensitivity was carried out before drug administration on each day.</p>", "<p id=\"Par6\">The minimum necessary amount of blood was withdrawn from the tail vein with a 27G needle after disinfection of the mouse tail with 80% ethanol every week, and glucose levels were measured with a FreeStyle Precision Neo-Blood Glucose Monitoring Meter (Abbott Japan, Tokyo, Japan).</p>", "<p id=\"Par7\">Three different antihypertensive agents, i.e. perindopril erbumine (Sigma-Aldrich, St. Louis, MO, USA), an ACEI, telmisartan (FUJIFILM Wako Pure Chemical, Osaka, Japan), an ARB, and amlodipine besylate (Tokyo Chemical Industry, Tokyo, Japan), an L-type calcium channel blocker (CaB), were used to test their effects on the development of DPN in the <italic>ob</italic>/<italic>ob</italic> mice. The appropriate doses of these drugs in mice were decided according to the previous reports<sup>##REF##18235039##19##–##REF##16940709##21##</sup>. Perindopril and amlodipine were dissolved in saline, and telmisartan was in saline containing 5% DMSO, 5% Tween-80 and 20% polyethylene glycol 300. Perindopril at 2 mg/kg, telmisartan at 5 mg/kg and amlodipine at 3 mg/kg, in a volume of 0.1 mg/10 g body weight, were administered i.p. to <italic>ob</italic>/<italic>ob</italic> mice once a day for 6 weeks (i.e. until the age of 12 weeks), starting at the age of 6 weeks. All mice used in the present study were euthanized by CO<sub>2</sub> inhalation after the experiments.</p>", "<title>Data analysis for animal experiments</title>", "<p id=\"Par8\">The data obtained from animal experiments are shown as means ± SEM. Statistical significance was evaluated by analysis of variance followed by Tukey’s test for multiple comparisons of parametric data, and by Kruskal–Wallis <italic>H</italic>-test followed by a least significant difference-type test for multiple comparisons of non-parametric data.</p>", "<title>T2DM patients enrolled and the inclusion/exclusion criteria in a clinical retrospective cohort study</title>", "<p id=\"Par9\">We collected medical record information of 9478 ambulatory patients with T2DM who received antihypertensive medications at Kansai Medical University Hospital, Kindai University Nara Hospital and Seichokai Fuchu Hospital from April 2011 to December 2020. The observation period was from the first medication for T2DM (i.e. sulfonylureas, glinides, biguanides, α-glucosidase inhibitors, sodium-glucose transporter 2 inhibitors, dipeptidyl peptidase 4 inhibitors, glucagon-like peptides 1 receptor agonists, thiazolidinediones) at each hospital until the diagnosis of DPN or the last hospital visit. Exclusion criteria (Supplementary Fig. ##SUPPL##0##1##) were as follows: (1) DM medications for less than 7 days (n = 1647), (2) diagnosis of neuropathy prior to DM diagnosis (n = 327), and (3) under 20 years of age (n = 40). DPN in T2DM patients was diagnosed by a physician at intervals of 2–12 weeks, according to the diagnostic criteria of diabetic neuropathies<sup>##REF##20876709##22##</sup> and patient’s complaints. Thus, 7464 patients who met inclusion criteria were enrolled in this study, and the collected information from them included antihypertensive medications listed in the latest prescriptions, i.e. ARBs, ACEIs, CaBs, β-blockers or thiazide/thiazide-like diuretics, and the latest laboratory test values including hemoglobin A1c-national glycohemoglobin standardization program (HbA1c-NGSP), serum creatinine (Scr) and C-reactive protein (CRP) during the observation period.</p>", "<title>Clinical research design and statistical analysis in the retrospective cohort study</title>", "<p id=\"Par10\">The enrolled T2DM patients with hypertension were divided into three groups according to the prescribed antihypertensive medications: (1) ACEIs, (2) ARBs and (3) others, i.e. non-ACEI, non-ARB antihypertensive agents including CaBs, β-blockers and thiazide/thiazide-like diuretics. The time-related incidence of DPN in the three groups during the observation period was examined, compared by generating Kaplan–Meier curves and analyzed by Log-rank test. Bonferroni’s test was used for multiple comparisons between the three groups. Sub-analyses of the data obtained at each of the three hospitals and of the data from the two patient groups receiving blood–brain-barrier (BBB)-permeable and BBB-impermeable ACEI or ARB, respectively, were also performed. It is to be noted that the patients receiving both ACEI and ARB (n = 25) were not included in this analysis.</p>", "<p id=\"Par11\">Univariate and multivariate analyses using a Cox proportional hazard model were conducted to statistically evaluate the association of variables including the initial age, the latest values of CRP, HbA1c-NGSP, Scr (the median and over) during the observation period, gender (female), and the prescription of ACEIs or ARBs, CaBs, β-blockers and thiazide/thiazide-like diuretics with the time-related DPN development. The association of patient information including smoking history, serum lipid profile and blood pressure values, which could be obtained only from Kansai Medical University and Kindai University hospitals, with DPN development was evaluated by sub-analysis of the two hospitals’ medical data. Collinearity was examined with a variance inflation factor (VIF). The variable we used for all multivariate analyses was VIF < 5. HR for each variable is shown with 95% CI.</p>", "<p id=\"Par12\">A <italic>p</italic> value less than 0.05 was considered statistically significant in clinical analysis. EZR<sup>##REF##23208313##23##</sup>, a pilot user interface for R (version 1.61), was employed for statistical analysis of clinical data.</p>", "<title>Ethical approval of the clinical study</title>", "<p id=\"Par13\">The clinical study protocol was in accordance with the relevant guidelines and regulations including the Declaration of Helsinki, and approved by Ethics Committees of Kansai Medical University (approval number 2021347), Kindai University Nara Hospital (approval number 667) and Seichokai Fuchu Hospital (approval number 2022002). Considering the retrospective nature of this study, the need for informed consent was waived by each of the above-described three Ethics Committees, which approved the use of an opt-out procedure concerning patient consent, i.e. study patients were enrolled in the clinical analysis unless the individual patient requested an exemption. The official website of each hospital was used for communication of the research information with the patients. Any of our research members named in the author list did not have access to the information necessary for identifying individuals when analyzing the data.</p>" ]	[ "<title>Results</title>", "<title>Reverse translational analysis of the effect of the Ang system inhibition on DPN development in leptin-deficient <italic>ob</italic>/<italic>ob</italic> mice, a model for T2DM</title>", "<p id=\"Par14\">On the basis of the clinical evidence for the negative association of the prescription of ACEIs and ARBs, but not CaBs, with DPN development in the above-described retrospective cohort study, we conducted a reverse translational analysis using leptin-deficient <italic>ob</italic>/<italic>ob</italic> mice, a model for T2DM. Perindopril, an ACEI, at 2 mg/kg, telmisartan, an ARB, at 5 mg/kg, and amlodipine, a CaB, at 3 mg/kg, were administered i.p. daily to <italic>ob</italic>/<italic>ob</italic> mice for 6 weeks, starting at 6 weeks of age. The <italic>ob</italic>/<italic>ob</italic> mice at the age of 6 weeks already had maximal hyperglycemia, i.e. blood glucose levels higher than 300 mg/dL (Fig. ##FIG##0##1##E), but not DPN, compared to the control lean mice (Fig. ##FIG##0##1##A, C). Thereafter, they clearly developed DPN, i.e. decreased paw-withdrawal threshold (g) and shortened latency (s) in response to mechanical and thermal nociceptive stimuli, respectively, from the age of 9 weeks at least until 12 weeks (Fig. ##FIG##0##1##A, C). Interestingly, the <italic>ob</italic>/<italic>ob</italic> mice subjected to daily i.p. treatment with the ARB or ACEI developed neither mechanical nor thermal nociceptive hypersensitivity between the age of 9 and 12 months (Fig. ##FIG##0##1##A–D), although their blood glucose levels remained maximally elevated during and after treatment with ARB or ACEI (Fig. ##FIG##0##1##E). In contrast, the <italic>ob</italic>/<italic>ob</italic> mice receiving daily CaB administration had DPN development at the age of 9–12 weeks, in addition to the antecedent hyperglycemia, as the vehicle-treated <italic>ob</italic>/<italic>ob</italic> mice did (Fig. ##FIG##0##1##).</p>", "<title>Patient characteristics in a clinical retrospective cohort study</title>", "<p id=\"Par15\">Of 9478 patients with T2DM undergoing antihypertensive pharmacotherapy at Kansai Medical University Hospital, Kindai University Nara Hospital and Seichokai Fuchu Hospital in Japan for nearly a decade, from April 2011 to December 2020, 7464 in consideration of inclusion/exclusion criteria were enrolled in this study (Table ##TAB##0##1##). The overall DPN incidence was 12% after the observation periods, and the median (range) of the initial age (years) and the latest values of HbA1c-NGSP (%), Scr and CRP (mg/dL) during the observation periods were 73 (23–106), 6.8 (2.9–17), 0.9 (0.17–18) and 0.23 (0–39), respectively (Table ##TAB##0##1##). It is to be noted that the median of baseline HbA1c-NGSP was 7.2%, and that the median of observation periods was 373 days. The major categories of antihypertensive agents in the order of prescription frequency were CaBs > ARBs > β-blockers > ACEIs > thiazide/thiazide-like diuretics (Table ##TAB##0##1##).</p>", "<title>Association of the prescription of ACEIs or ARBs with the development of DPN in T2DM patients undergoing antihypertensive medications at the three hospitals</title>", "<p id=\"Par16\">Study patients were divided into three groups according to the pharmacological classes of the prescribed antihypertensive agents, i.e. ARB (ARB alone or in combination with non-ACEI antihypertensive agents), ACEI (ACEI alone or in combination with non-ARB antihypertensive agents) and “others” (non-ARB and non-ACEI antihypertensive agents), and the differences of the time-related incidence of DPN between the 3 groups were statistically analyzed. Kaplan–Meier curves and the log-rank test showed clearly and significantly (<italic>p</italic> < 0.001) different development of DPN among the 3 groups (Fig. ##FIG##1##2##). Bonferroni’s multiple comparison test indicated significantly delayed development of DPN in the patient groups treated with ARBs or ACEIs, compared to “others (non-ARB and non-ACEI antihypertensives)”, and no significant difference of DPN development between ARB and ACEI groups (Fig. ##FIG##1##2##). Sub-analysis of the data of each hospital showed a similar tendency, i.e. delayed development of DPN in ARB and ACEI groups in comparison with “others” (Fig. ##FIG##2##3##). These results indicate that the incidence of DPN in T2DM patients may be suppressed or delayed by ACEIs or ARBs.</p>", "<p id=\"Par17\">To ask whether the central or peripheral Ang II/AT<sub>1</sub> receptor system is involved in the DPN development, a sub-analysis of the data of the T2DM patients receiving ARBs or ACEIs was conducted in terms of their BBB permeability. ACEs and ARBs were divided into 2 classes according to their BBB permeability, i.e. BBB-permeable ARB/ACEI (candesartan, valsartan, telmisartan, azilsartan, captopril, perindopril, lisinopril, temocapril) and BBB-impermeable ARB/ACEI (losartan, irbesartan, olmesartan, enalapril, quinapril, imidapril)<sup>##REF##28445961##24##–##REF##19597068##30##</sup>. Kaplan–Meier curves and the log-rank test showed no difference of DPN development between the two DM groups receiving BBB-permeable and BBB-impermeable ARB/ACEI (Supplementary Fig. ##SUPPL##0##2##).</p>", "<p id=\"Par18\">We then used univariate and multivariate Cox proportional hazard regression models to evaluate the effect of diverse factors including the use of ARB or ACEI on the development of DPN in patients with T2DM. The univariate analysis detected significant positive or negative association of ages ≥ 73 years (median) [hazard ratio (HR), 1.48; 95% confidence interval (CI), 1.30–1.70; <italic>p</italic> < 0.001], female (HR, 1.41; 95% CI, 1.23–1.62; <italic>p</italic> < 0.001), CRP ≥ 0.23 mg/dL (median) (HR, 1.30; 95% CI; 1.13–1.49; <italic>p</italic> < 0.001), Scr ≥ 0.9 mg/dL (median) (HR, 0.95; 95% CI 0.91–1.00; <italic>p</italic> = 0.042), and the prescription of ACEI or ARB (HR, 0.63; 95% CI 0.55–0.73; <italic>p</italic> < 0.001), β-blocker (HR, 0.66; 95% CI 0.56–0.78; <italic>p</italic> < 0.001) and thiazide/thiazide-like diuretics (HR, 0.75; 95% CI 0.59–0.94; <italic>p</italic> = 0.014) with the development of DPN (Table ##TAB##1##2##). Then, the multivariate analysis indicated independently significant positive association of age ≥ 73 years (HR, 1.38; 95% CI 1.18–1.61; <italic>p</italic> < 0.001) and CRP ≥ 0.23 mg/dL (HR, 1.28; 95% CI 1.10–1.49; <italic>p</italic> = 0.002), and independently significant negative association of the prescription of ACEIs or ARBs (HR, 0.64; 95% CI 0.55–0.76; <italic>p</italic> < 0.001) and β-blocker (HR, 0.69; 95% CI 0.57–0.82;<italic> p</italic> < 0.001) with DPN development (Table ##TAB##1##2##). In contrast, there was no impact of the prescription of CaBs or thiazide/thiazide-like diuretics on DPN development (Table ##TAB##1##2##). Considering the β-blocker-induced suppression of sympathetically mediated renin release, the inhibition of the renin-angiotensin system has something to do with the prevention or delay of DPN development in T2DM patients.</p>", "<p id=\"Par19\">Patient information such as smoking history, serum lipids and blood pressure was available from the medical data of Kansai Medical University Hospital and Kindai University Hospital, but not Seichokai Fuchu Hospital. We thus conducted a sub-analysis of the data in the two hospitals, using patient information including the history of smoking and the latest values of serum lipids and blood pressure during the observation periods. The univariate Cox proportional analysis detected the use of ARB or ACEI as a factor that significantly reduced the risk of DPN, and showed that low-HDL cholesterol and diastolic blood pressure values were slightly, but significantly, associated with DPN development (Supplementary Table ##SUPPL##0##1##). On the other hand, multivariate Cox proportional analysis demonstrated that the use of ARB or ACEI, but not the other variables, was significantly associated with the decreased development of DPN, which was independent of the history of smoking, abnormal serum lipid profile or blood pressure values (Supplementary Table ##SUPPL##0##1##).</p>" ]	[ "<title>Discussion</title>", "<p id=\"Par20\">Our data from the clinical retrospective cohort study in T2DM patients undergoing antihypertensive pharmacotherapy at 3 hospitals in Japan demonstrated significant negative association of the prescription of ACEIs or ARBs as well as β-blockers with the development of DPN. This clinical finding was supported by our preclinical study, in which daily treatment with ACEI or ARB, but not CaB, prevented the development of DPN without affecting the hyperglycemia in leptin-deficient <italic>ob</italic>/<italic>ob</italic> mice, a T2DM model. Thus, our study suggests that pharmacological inhibition of the β-adrenoceptor/renin/Ang pathway is beneficial to prevent the development of DPN, in addition to diabetic nephropathy and cardiomyopathy<sup>##REF##34979961##3##,##REF##33550672##31##,##REF##34967935##32##</sup>, in T2DM patients.</p>", "<p id=\"Par21\">A randomized double-blind controlled trial showed that treatment with trandolapril, an ACEI, for 6–12 months tended to improve peripheral neuropathy in 41 normotensive patients with T1DM or T2DM<sup>##REF##9872248##33##</sup>. However, no further information about clinical effectiveness of ACEIs or ARBs on DPN development is available. Several studies reported that hypertension itself might increase the risk of neuropathy<sup>##REF##15673800##34##–##REF##31013342##36##</sup>, whereas, in randomized controlled trials in DM patients, more aggressive anti-hypertensive treatment did not prevent or delay the progression of neuropathy, compared with less strict blood pressure controls<sup>##REF##11849464##37##,##REF##17765963##38##</sup>. In the present study, T2DM patients undergoing antihypertensive pharmacotherapy were enrolled in the retrospective analysis, in order to minimize the effect of hypertension itself on DPN development. The findings of particular interest are that T2DM patients receiving antihypertensive medications other than ACEIs and ARBs had significantly earlier development of DPN than ones receiving ACEIs or ARBs, as analyzed by Bonferroni’s test (see Figs. ##FIG##1##2## and ##FIG##2##3##), and that, unlike ACEIs/ARBs or β-blockers, the prescription of CaBs or thiazide/thiazide-like diuretics did not have significant impact on DPN development in T2DM patients, as evaluated by Cox proportional multivariate analysis (see Table ##TAB##1##2##). It is also noteworthy that the multivariate sub-analysis of the patient information including blood pressure in the two hospitals clearly indicated that the preventive effects of ACEI or ARB on DPN development is independent of systolic and diastolic blood pressure values in addition to the history of smoking and abnormal serum lipid profile (see Supplementary Table ##SUPPL##0##1##). These clinical results are essentially in agreement with the findings from the animal experiments that daily treatment with the ACEI or ARB, but not CaB, prevented the development of DPN in the <italic>ob</italic>/<italic>ob</italic> mice, a model for T2DM (see Fig. ##FIG##0##1##).</p>", "<p id=\"Par22\">The D allele of the ACE insertion/deletion (I/D) gene variant, which is associated with higher ACE activity, could be related to increased risk of DPN as well as diabetic nephropathy in Caucasian<sup>##UREF##0##39##,##REF##24475405##40##</sup>. Angiotensinogen (AGT) M235T variant may be associated with myocardial infarction and brain infarction in East Asian group<sup>##REF##23933419##41##</sup>. Most interestingly, a study in the Japanese population has provided controversial evidence that the D allele of the ACE I/D has a protective effect on polyneuropathy, while there is no association between AGT gene polymorphism and polyneuropathy<sup>##REF##12449516##42##</sup>. This discrepancy is still open to question.</p>", "<p id=\"Par23\">The molecular mechanisms underlying the ACEI/ARB-induced suppression of DPN development accompanying T2DM in the clinical and preclinical studies are still open to question. Accumulating evidence suggests involvement of both central and peripheral Ang systems in pain processing<sup>##REF##37237567##5##,##REF##18976642##43##</sup>. However, the Ang system in the CNS, if any, is considered to play a relatively minor role in DPN development accompanying T2DM in the present study, because there was no difference of DPN development between two T2DM patient groups receiving BBB-permeable and BBB-impermeable ACEIs/ARBs, respectively, in the clinical study (see supplementary Fig. ##SUPPL##0##2##). There is evidence that Ang II-induced activation of both peripheral AT<sub>1</sub> and AT<sub>2</sub> receptors participates in the development of neuropathic pain<sup>##REF##37237567##5##,##REF##31037647##9##–##REF##33675631##14##</sup>. In the present clinical study, however, the prescription of ARBs, i.e. AT<sub>1</sub> receptor antagonists, had significant impact on DPN development that was equal to or greater than ACEIs in T2DM patients (see Figs. ##FIG##1##2## and ##FIG##2##3##), suggesting the essential role of the Ang II/AT<sub>1</sub> receptor system in DPN development. The downstream signals of AT<sub>1</sub> receptor activation involved in DPN development remain to be investigated in future studies, although stimulation of AT<sub>1</sub> receptors is known to cause oxidative stress<sup>##REF##33835385##44##</sup> which could be involved in the pathogenesis of DPN<sup>##REF##32832011##45##</sup>. In our clinical study, the number of T2DM patients receiving aliskiren, a renin inhibitor, was quite limited, so that the effect of aliskiren on DPN development could not be analyzed. Nonetheless, it is to be noted that an animal study demonstrated the beneficial effect of aliskiren in attenuating DPN in streptozotocin-induced diabetic rats<sup>##REF##33855212##46##</sup>. Considering the significant impact of β-blockers on DPN development in the clinical study (see Table ##TAB##1##2##), the peripheral β-adrenoceptor/renin/Ang/AT<sub>1</sub> receptor system appears to participate in DPN development accompanying T2DM, and may serve as therapeutic targets for DPN.</p>", "<p id=\"Par24\">An animal model of T1DM is created by single or a few administrations of streptozocin (STZ) in mice, and widely used in fundamental studies on complications of T1DM. However, it has been reported that STZ activates transient receptor potential (TRP) channels at the peripheral nerve level in a hyperglycemia-independent manner, implying that this DM model might not be suitable for studying DPN<sup>##REF##18089839##47##,##REF##25903127##48##</sup>. Also, in the clinical study, we did not determine the effects of ACEIs or ARBs on DPN accompanying T1DM, because of the insufficient number of T1DM patients.</p>", "<p id=\"Par25\">This work had some strengths and limitations. Information from medical records of 7464 T2DM patients undergoing antihypertensive pharmacotherapy at 3 hospitals was subjected to statistical analysis, and the obtained clinical evidence was then confirmed by a reverse translational study using an animal model for T2DM. The limitation of our clinical study includes the retrospective nature and the lack of detailed analysis of the effects of time-related blood pressure control and dosage of antihypertensive agents on DPN development. In future, prospective studies and/or meta-analysis should be conducted to ascertain the present findings.</p>", "<p id=\"Par26\">In conclusion, the present combined clinical and reverse translational approaches unveiled the essential role of the peripheral β-adrenoceptor/renin/Ang/AT<sub>1</sub> receptor system in the development of DPN accompanying T2DM, suggesting the therapeutic usefulness of its inhibition for prevention of DPN development. The prescription of ACEIs, ARBs or β-blockers, rather than CaBs or diuretics, is thus recommended to prevent DPN in T2DM patients.</p>" ]	[]	[ "<p id=\"Par1\">Given possible involvement of the central and peripheral angiotensin system in pain processing, we conducted clinical and preclinical studies to test whether pharmacological inhibition of the angiotensin system would prevent diabetic peripheral neuropathy (DPN) accompanying type 2 diabetes mellitus (T2DM). In the preclinical study, the nociceptive sensitivity was determined in leptin-deficient <italic>ob</italic>/<italic>ob</italic> mice, a T2DM model. A clinical retrospective cohort study was conducted, using the medical records of T2DM patients receiving antihypertensives at three hospitals for nearly a decade. In the <italic>ob</italic>/<italic>ob</italic> mice, daily treatment with perindopril, an angiotensin-converting enzyme inhibitor (ACEI), or telmisartan, an angiotensin receptor blocker (ARB), but not amlodipine, an L-type calcium channel blocker (CaB), significantly inhibited DPN development without affecting the hyperglycemia. In the clinical study, the enrolled 7464 patients were divided into three groups receiving ACEIs, ARBs and the others (non-ACEI, non-ARB antihypertensives). Bonferroni’s test indicated significantly later DPN development in the ARB and ACEI groups than the others group. The multivariate Cox proportional analysis detected significant negative association of the prescription of ACEIs or ARBs and β-blockers, but not CaBs or diuretics, with DPN development. Thus, our study suggests that pharmacological inhibition of the angiotensin system is beneficial to prevent DPN accompanying T2DM.</p>", "<title>Subject terms</title>" ]	[ "<title>Supplementary Information</title>", "<p>\n</p>" ]	[ "<title>Supplementary Information</title>", "<p>The online version contains supplementary material available at 10.1038/s41598-024-51572-z.</p>", "<title>Author contributions</title>", "<p>S.I., W.N., K.T. and A.K. designed this study. S.I., T.M., T.H., M.T., K.U., T.M., M.F., Y.K. and A.H. collected and analyzed the clinical data. W.N. and K.T. obtained and analyzed the data form animal experiments. S.I., W.N., T.M., T.H., M.T., F.S., K.T. and A.K. contributed to interpretation of the clinical and fundamental research data and manuscript preparations. All authors reviewed the manuscript and approved the final version.</p>", "<title>Data availability</title>", "<p>The data used in the present study are available form the corresponding author on reasonable request.</p>", "<title>Competing interests</title>", "<p id=\"Par27\">The authors declare no competing interests.</p>" ]	[ "<fig id=\"Fig1\"><label>Figure 1</label><caption><p>Effects of daily treatment with representative ARB, ACEI and CaB on the development of DPN in leptin-deficient <italic>ob</italic>/<italic>ob</italic> mice. Tactile (<bold>A</bold> and <bold>B</bold>) and thermal (<bold>C</bold> and <bold>D</bold>) nociceptive sensitivities and blood glucose levels (<bold>E</bold>) were determined in <italic>ob</italic>/<italic>ob</italic> mice and the control age-matched lean mice between 5 and 12 weeks of age. Telmisartan at 5 mg/kg, perindopril at 2 mg/kg or amlodipine at 3 mg/kg was administered i.p. to <italic>ob</italic>/<italic>ob</italic> mice once daily for 6 weeks (i.e. until the age of 12 weeks), starting at the age of 6 weeks. AUC values between 9 and 12 weeks of age, (<bold>B</bold>) and (<bold>D</bold>), were calculated from the time-related changes in paw-withdrawal threshold (<bold>A</bold>) and paw-withdrawal latency (<bold>C</bold>). Values represent the means ± SEM for 8 mice per group.</p></caption></fig>", "<fig id=\"Fig2\"><label>Figure 2</label><caption><p>Kaplan–Meier curves for the development of DPN in T2DM patients undergoing different antihypertensive pharmacotherapies at the three hospitals. The patients were divided into 3 groups according to the prescribed antihypertensives, i.e. (1) ACEI, (2) ARB and (3) Others (non-ACEI, non-ARB antihypertensives). In this analysis, 25 patients receiving both ACEI and ARB were not included. Statistical significance was analyzed by Log-rank test, followed by Bonferroni’s test for multiple comparisons.</p></caption></fig>", "<fig id=\"Fig3\"><label>Figure 3</label><caption><p>Sub-analysis of the association of the prescribed antihypertensives with the development of DPN in T2DM patients at each of the three hospitals. Kaplan–Meier curves of the three patient groups receiving ACEI, ARB and the others (non-ACEI, non ARB antihypertensives) were drawn for each of Kansai Medical (Med) University (Univ) Hospital (Hosp) (<bold>A</bold>), Kindai Univ Nara Hosp (<bold>B</bold>) or Seichokai Fuchu Hosp (<bold>C</bold>). Statistical significance was analyzed by Log-rank test, followed by Bonferroni’s test for multiple comparisons.</p></caption></fig>" ]	[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Characteristics of the enrolled T2DM patients undergoing antihypertensive pharmacotherapy who met inclusion criteria.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Characteristics</th><th align=\"left\">Numerical data</th></tr></thead><tbody><tr><td align=\"left\" colspan=\"2\">Patients who met inclusion criteria</td></tr><tr><td align=\"left\"> Total, n</td><td align=\"left\">7464</td></tr><tr><td align=\"left\"> Kansai Med Univ Hosp, n (%)</td><td align=\"left\">3945 (53)</td></tr><tr><td align=\"left\"> Kindai Univ Nara Hosp, n (%)</td><td align=\"left\">2047 (27)</td></tr><tr><td align=\"left\"> Seichokai Fuchu Hosp, n (%)</td><td align=\"left\">1472 (20)</td></tr><tr><td align=\"left\">Initial age (years) during the observation period, median (range)</td><td align=\"left\">73 (23–106)</td></tr><tr><td align=\"left\">Gender, female/male, n (%)</td><td align=\"left\">2509 (34)/4955 (66)</td></tr><tr><td align=\"left\">DPN development during the observation period, n (%)</td><td align=\"left\">868 (12)</td></tr><tr><td align=\"left\" colspan=\"2\">Latest laboratory data during the observation period</td></tr><tr><td align=\"left\"> HbA1c-NGSP (%), median (range), n</td><td align=\"left\">6.8 (2.9–17), 6383</td></tr><tr><td align=\"left\"> Scr (mg/dL), median (range), n</td><td align=\"left\">0.9 (0.17–18), 7194</td></tr><tr><td align=\"left\"> CRP (mg/dL), median (range), n</td><td align=\"left\">0.23 (0–39), 6438</td></tr><tr><td align=\"left\" colspan=\"2\">Prescribed antihypertensive agents</td></tr><tr><td align=\"left\"> ARB, n (%)</td><td align=\"left\">3983 (53)</td></tr><tr><td align=\"left\"> ACEI, n (%)</td><td align=\"left\">976 (13)</td></tr><tr><td align=\"left\"> CaB, n (%)</td><td align=\"left\">5213 (70)</td></tr><tr><td align=\"left\"> β-blocker, n (%)</td><td align=\"left\">1833 (25)</td></tr><tr><td align=\"left\"> Thiazide/thiazide-like diuretics, n (%)</td><td align=\"left\">703 (10)</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Cox proportional univariate and multivariate analyses of the association with variables including the latest laboratory test results and prescribed antihypertensive agents with DPN development.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\">Variables</th><th align=\"left\" colspan=\"2\">Univariate analysis</th><th align=\"left\" colspan=\"2\">Multivariate analysis</th></tr><tr><th align=\"left\">Hazard ratio (95% CI)</th><th align=\"left\"><italic>p</italic> value</th><th align=\"left\">Hazard ratio (95% CI)</th><th align=\"left\"><italic>p</italic> value</th></tr></thead><tbody><tr><td align=\"left\">Age, ≥ 73 years (median)</td><td char=\"(\" align=\"char\">1.48 (1.30–1.70)</td><td char=\".\" align=\"char\"><bold> < 0.001</bold></td><td char=\"(\" align=\"char\">1.38 (1.18–1.61)</td><td char=\".\" align=\"char\"><bold> < 0.001</bold></td></tr><tr><td align=\"left\">Gender, female</td><td char=\"(\" align=\"char\">1.41 (1.23–1.62)</td><td char=\".\" align=\"char\"><bold> < 0.001</bold></td><td char=\"(\" align=\"char\">0.98 (0.83–1.16)</td><td char=\".\" align=\"char\">0.83</td></tr><tr><td align=\"left\">HbA1c-NGSP, ≥ 6.8% (median)</td><td char=\"(\" align=\"char\">0.96 (0.83–1.11)</td><td char=\".\" align=\"char\">0.6</td><td char=\"(\" align=\"char\">0.88 (0.76–1.03)</td><td char=\".\" align=\"char\">0.12</td></tr><tr><td align=\"left\">Scr, ≥ 0.9 mg/dL (median)</td><td char=\"(\" align=\"char\">0.95 (0.91–1.00)</td><td char=\".\" align=\"char\"><bold>0.042</bold></td><td char=\"(\" align=\"char\">0.89 (0.75–1.04)</td><td char=\".\" align=\"char\">0.14</td></tr><tr><td align=\"left\">CRP, ≥ 0.23 mg/dL (median)</td><td char=\"(\" align=\"char\">1.30 (1.13–1.49)</td><td char=\".\" align=\"char\"><bold> < 0.001</bold></td><td char=\"(\" align=\"char\">1.28 (1.10–1.49)</td><td char=\".\" align=\"char\"><bold>0.002</bold></td></tr><tr><td align=\"left\">ARB or ACEI</td><td char=\"(\" align=\"char\">0.63 (0.55–0.73)</td><td char=\".\" align=\"char\"><bold> < 0.001</bold></td><td char=\"(\" align=\"char\">0.64 (0.55–0.76)</td><td char=\".\" align=\"char\"><bold> < 0.001</bold></td></tr><tr><td align=\"left\">CaB</td><td char=\"(\" align=\"char\">1.08 (0.93–1.25)</td><td char=\".\" align=\"char\">0.33</td><td char=\"(\" align=\"char\">0.87 (0.73–1.03)</td><td char=\".\" align=\"char\">0.12</td></tr><tr><td align=\"left\">β-blocker</td><td char=\"(\" align=\"char\">0.66 (0.56–0.78)</td><td char=\".\" align=\"char\"><bold> < 0.001</bold></td><td char=\"(\" align=\"char\">0.69 (0.57–0.82)</td><td char=\".\" align=\"char\"><bold> < 0.001</bold></td></tr><tr><td align=\"left\">Thiazide/thiazide-like diuretics</td><td char=\"(\" align=\"char\">0.75 (0.59–0.94)</td><td char=\".\" align=\"char\"><bold>0.014</bold></td><td char=\"(\" align=\"char\">0.82 (0.63–1.07)</td><td char=\".\" align=\"char\">0.14</td></tr></tbody></table></table-wrap>" ]	[]	[]	[]	[]	[]	[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>" ]	[ "<table-wrap-foot><p>The data were collected from medical records of the patients at Kansai Medical (Med) University (Univ) Hospital (Hosp), Kindai Univ Nara Hosp or Seichokai Fuchu Hosp. The median of the observation period (from the first medication for T2DM until the diagnosis of DPN or the last hospital visit) was 373 days, and the median of the initial HbA1c-NGSP was 7.2% (range: 3.3–18.3). DPN, diabetic peripheral neuropathy; HbA1c-NGSP, hemoglobin A1c-national glycohemoglobin standardization program; Scr, serum creatinine; CRP, C-reactive protein; ARB, angiotensin receptor blocker; ACEI, angiotensin-converting enzyme inhibitor; CaB, L-type calcium channel blocker.</p></table-wrap-foot>", "<table-wrap-foot><p>CI, confidence interval. HbA1c-NGSP, hemoglobin A1c-national glycohemoglobin standardization program; Scr, serum creatinine; CRP, C-reactive protein; ARB, angiotensin receptor blocker; ACEI, angiotensin-converting enzyme inhibitor; CaB, L-type calcium channel blocker.</p><p>Significant values are in bold.</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher's note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>These authors contributed equally: Shiori Iwane and Wataru Nemoto.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"41598_2024_51572_Fig1_HTML\" id=\"MO1\"/>", "<graphic xlink:href=\"41598_2024_51572_Fig2_HTML\" id=\"MO2\"/>", "<graphic xlink:href=\"41598_2024_51572_Fig3_HTML\" id=\"MO3\"/>" ]	[ "<media xlink:href=\"41598_2024_51572_MOESM1_ESM.pdf\"><caption><p>Supplementary Information.</p></caption></media>" ]	[{"label": ["39."], "surname": ["Stephens", "Dhamrait", "Acharya", "Humphries", "Hurel"], "given-names": ["JW", "SS", "J", "SE", "SJ"], "article-title": ["A common variant in the ACE gene is associated with peripheral neuropathy in women with type 2 diabetes mellitus"], "source": ["J. Diabetes Complicat."], "year": ["2006"], "volume": ["20"], "fpage": ["317"], "lpage": ["321"], "pub-id": ["10.1016/j.jdiacomp.2005.07.010"]}]	{ "acronym": [], "definition": [] }	48	CC BY	no	2024-01-13 00:02:20	Sci Rep. 2024 Jan 10; 14:1039	oa_package/c9/33/PMC10781693.tar.gz
PMC10781694	37950093	[ "<title>Introduction</title>", "<p id=\"Par6\">Pancreatic cancer (PC) is a highly aggressive cancer with a 5-year survival rate of approximately 5–10% upon diagnosis under the standard of care therapy [##UREF##0##1##]. Regrettably, the incidence of PC is on the rise [##REF##29803839##2##, ##REF##33865174##3##]. The high mortality rate of PC is mainly due to the fact that only a minority of patients (11%) are diagnosed with localized disease, while 52% of PC cases are diagnosed in the late stage, at which point few patients can receive curative intervention [##REF##32147593##4##, ##REF##33433946##5##]. Therefore, early detection of PC plays a critical role in improving overall outcomes.</p>", "<p id=\"Par7\">Minimally invasive methods such as liquid biopsy-based cancer biomarker evaluation promise hope for early-stage PC detection, resolution of unclear clinical imaging-detected lesions or prevention of cancer progression in patients with minor pancreatic abnormalities [##REF##35398344##6##]. The widely applied serological marker for pancreatic tumors in clinical practice is Carbohydrate Antigen 19-9 (CA 19-9), but its unequivocal power in terms of specificity, sensitivity, and predictive value for outcomes such as overall survival (OS) has been challenged in recent studies [##REF##23456571##7##, ##REF##22693400##8##]. Moreover, in 10% of the population, the lack of Lewis antigens precludes the CA 19-9 level from being informative [##REF##33333055##9##, ##REF##31676359##10##]. Other recently discovered blood biomarkers, such as circulating tumor cells (CTCs), exosomes [##REF##32628360##11##], circulating tumor DNA (ctDNA), or cell-free DNA (cfDNA), have shown promising insights, but there are major obstacles regarding their clinical applicability and verification as biomarkers. For instance, one of the major challenges of ctDNA/cfDNA application in PC is the extremely low levels of peripheral DNA, which can have a significant impact on sensitivity [##REF##33081107##12##], especially in the case of early-stage disease. In addition, clinically relevant and repeatable assays involving the use of DNA methylation markers in ctDNA have emerged and been shown to provide information on disease manifestation including minimal residual disease status of many cancers. Although very powerful and promising, sample preparation for DNA methylation analysis requires many resources especially compared to those required for free RNA detection, which limits stringent clinical application of this technology in routine use [##REF##33926918##13##, ##REF##34785539##14##].</p>", "<p id=\"Par8\">In contrast, mature miRNAs are highly stable in body fluids and exhibit exceptional specificity across different cancer types, making them as promising non-invasive biomarkers for cancer [##REF##29074454##15##, ##REF##26199650##16##]. Screening of tumor miRNA markers in body fluids holds great clinical value in assisting in the early diagnosis of disease and monitoring therapy success longitudinally. For liquid biopsy assays, the fraction of the sample needed to serve as a template for the serum, plasma and whole blood content of the same donor has been shown to vary significantly [##UREF##1##17##]. In this study, we focused on the serum of patients diagnosed with PC.</p>", "<p id=\"Par9\">In addition to the discussed blood-based biomarkers, strategies employing artificial intelligence are being developed in parallel to improve the efficacy of early screening, and this approach is being highly emphasized [##REF##35398344##6##, ##REF##33835956##18##, ##REF##31492412##19##]. We propose the integration of machine learning algorithms with real-world and public database analyses for the early screening of circulating miRNAs in PC. We propose that our results might assist the further development of early detection strategies by using minimally invasive screening tools for PC diagnosis and prognosis.</p>" ]	[ "<title>Materials and methods</title>", "<title>Patient cohort enrollment and miRNA sequencing</title>", "<p id=\"Par10\">We collected patient serum samples before the onset of any therapy (including neoadjuvant chemotherapy and surgery). The PC tissues and paired para-cancerous tissue, defined as healthy tissues (HT), were collected from operation specimens as defined by the existence of tumor cells detected by histopathological assessment through a German board-certified pathologist. A total of 26 patients from our hospital were enrolled, and the baseline clinical information of these patients is shown in Table ##TAB##0##1##. Ethical approval to conduct this study was granted by the ethics committee of the medical faculty of Magdeburg (33/01, amendment 43/14). Next-generation sequencing was conducted in contract-based cooperation at the genome analytics lab at Helmholtz-Center for Infection Research (HZI) Brunswick, Germany. The tissue miRNA sequence of our lab was defined as OLMS-T, while the serum miRNA sequence of our lab was defined as OLMS-S. The sequenced offline data were subjected to quality control to filter low-quality data while high-quality miRNAs were quantified. This process was completed via miRDeep 2.0.1.2. The workflow is shown in Fig. ##FIG##0##1##.</p>", "<title>Public data retrieval and total data pre-processing</title>", "<p id=\"Par11\">A total of eleven datasets are enrolled in our study, including one patient-matching tissue miRNA sequencing dataset of our own laboratory miRNA sequencing tumor dataset (OLMS-T, 13 PC vs. 13 HT samples) and The Cancer Genome Atlas Program-Pancreatic Adenocarcinoma dataset (TCGA-PAAD) (177 PC samples), and nine serum miRNA sequencing datasets, including OLMS-S (13 PC samples), GSE112264 (50 PC vs. 41 HT samples), GSE109319 (24 PC vs. 21 HT samples), GSE113486 (40 PC vs.100 HT samples), GSE59856 (100 PC vs. 150 HT samples), GSE106817 (2759 PC vs. 115 HT samples), GSE85589 (88 PC samples), GSE128508 (10 pancreatitis samples) and GSE128425 (4 pancreatitis samples). For our setup, we defined OLMS-T, GSE112264, and GSE109319 as group one to identify differentially expressed miRNAs between tumors and non-tumor tissues and set GSE113486 and GSE59856 as group two to train the predicted model and test the model robustness. OLMS-S and GSE106817 were used to test the predicted model in independent real-world studies. For group four, the GSE128508, GSE128425 and GSE85589 datasets were used to test the model to identify pancreatitis and PC. The TCGA-PAAD dataset, as group five, was used to explore the clinical value of miRNAs and to predict target genes and potential molecular functions. In addition, because the above data were retrieved from different platforms using different methods, we used the R package sva for batch effect removal in each group. The effect of batch effect removal is presented in Suppl. Fig S1.</p>", "<title>Analysis of differentially expressed miRNAs (DEMs)</title>", "<p id=\"Par12\">We used the limma package to identify differentially expressed miRNAs between normal tissue and cancer tissue from our laboratory sequence data (OLMS-T), healthy serum, and patient serum (GSE112264 and GSE109319). The criteria were set as \|Log Fold Change \| å 1 and <italic>p</italic> value < 0.01.</p>", "<title>Candidate DEM screening and validation computation in OLM-S sequences</title>", "<p id=\"Par13\">We put three DEM lists into robust rank aggregation (RRA) algorithms to order the importance of DEMs. Variables with a final p value of less than 0.05 were identified as significant DEMs. Considering that these DEMs were enriched according to public datasets, we used our serum dataset (OLMS-S) to validate the expression status of selected miRNAs.</p>", "<title>Machine learning algorithms to build a prediction model using candidate DEMs</title>", "<p id=\"Par14\">We aimed to validate whether the DEMs can identify pancreatic tumors from normal pancreatic tissues. First, according to the 70% vs. 30% ratio, we split the merged datasets from GSE113486 and GSE59856 into a training set and a test set, respectively. Then, four machine learning algorithms, random forest (RF), classification regression tree (CART), support vector machine (SVM), and logistic regression (LR), were used to build the PC risk prediction model in the training set. To test the model robustness, we also combined our laboratory serum (OLMS-S) with public data (GSE106817), and then a new dataset was obtained. Based on this new dataset, we randomly select 50% of the samples to simulate a real-world study to test the model accuracy.</p>", "<title>Testing the pancreatic cancer risk prediction model to discriminate pancreatitis</title>", "<p id=\"Par15\">To compare pancreatitis serum samples (GSE128425 and GSE128508) and GSE85589 containing PC serum samples, bias reduction was implemented to control for differences in clinical and quantity weighing between the three datasets (GSE128425 and GSE85589 using the same platform). Setting GSE128425 and GSE128508 as the control group and GSE85589 as the treatment group, we used propensity score matching (PSM) to control mixed variables between groups and selected 14 samples from the treatment group, which had a comparable age and sex distribution to the control group. After the enrollment of the samples, we validated our model to predict the accuracy between pancreatitis and PC in this new dataset by random forest algorithms.</p>", "<title>Exploring the clinical value of hsa-miR-205-5p</title>", "<p id=\"Par16\">Although the relevance of miR-205 for PC development has been revealed, the work is mostly focused on experimental model systems or ignores to analyze any age, ethnicity, or gender effects on its clinical prognostic value. Moreover, microRNA-205 has been described to be useful as a liquid biopsy diagnostic marker for PC. However, to the best of our knowledge, no report on testing the utility of miR-205 transcript levels as a blood biomarker for PC has been published. To fill these gaps in knowledge, we focused on this marker from the model for further analysis. We used TCGA dataset to validate the clinical value of hsa-miR-205 in tumor samples. Baseline characteristic variables such as sex, age, and race were selected for research to validate hsa-miR-205 expression in different groups. In addition, we also analyzed the association of hsa-miR-205 serum levels with clinical factors, including tumor stage, OS, disease-specific survival (DSS), and progression-free interval (PFI).</p>", "<title>Predicting relevant hsa-miR-205-5p target genes and upstream regulators in our patient cohort data</title>", "<p id=\"Par17\">We used starBase (<ext-link ext-link-type=\"uri\" xlink:href=\"http://starbase.sysu.edu.cn/\">http://starbase.sysu.edu.cn/</ext-link>), an online tool, to predict candidate targets of hsa-miR-205-5p, followed by gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis (conducted by same name R packages) to explore the molecular mechanism of this miRNA. Applying a strict condition for further data processing (clip dataset to level 3, degradome with level 3, expression in six cancer types, and validation by three programs) limited the computed number of putative candidate genes. We used the Pearson method to analyze the correlation between hsa-mir-205-5p and predicted candidate targets to identify a bona fide key target. The online tool KOBAS (KEGG Orthology-Based Annotation System; <ext-link ext-link-type=\"uri\" xlink:href=\"http://kobas.cbi.pku.edu.cn/genelist/\">http://kobas.cbi.pku.edu.cn/genelist/</ext-link>) was used to conduct the pathway enrichment analysis of the chosen target genes based on the KEGG and Reactome databases.</p>" ]	[ "<title>Results</title>", "<title>Upregulated miRNAs in pancreatic cancer serum and tissue samples compared to adjacent non-tumor tissues</title>", "<p id=\"Par18\">We analyzed a panel of miRNAs between PC normal and tumor tissue in our lab tissue dataset (OLMS-T) and serum datasets from GSE112264 and GSE109319, respectively. The results showed 66 upregulated DEMs in PC tissue in the OLMS-T dataset and 418 and 267 upregulated DEMs in PC serum in the GSE112264 and GSE109319 datasets, respectively (Fig. ##FIG##1##2a–c##). Merging the above results showed no intersection of miRNAs in either tissue or serum samples. Then, we chose the RRA method to select key miRNAs that showed elevated expression in both tumor tissue and serum. We identified four miRNAs for further processing, including hsa-mir-6819-5p, hsa-mir-1246, hsa-mir-205-5p, and hsa-mir-191-5p (Fig. ##FIG##1##2d##). However, when we validated the expression of these four miRNAs in patient serum from our own lab, we found that hsa-mir-6819-5p was not detectable and therefore excluded it from the following analysis (Fig. ##FIG##1##2e##). The work process is depicted in Fig. ##FIG##1##2f##.</p>", "<title>Serum miRNAs can predict pancreatic cancer presence with high accuracy</title>", "<p id=\"Par19\">In the training set, the RF algorithm showed that three miRNAs (hsa-mir-1246, hsa-mir-205-5p, and hsa-mir-191-5p) could predict PC with a high accuracy of 95.8% (Fig. ##FIG##2##3a##). CART, SVM, and LR algorithms also showed a high prediction accuracy. The area under the curve values were 0.868, 0.830, and 0.823, respectively (Fig. ##FIG##2##3b–d##). In the test dataset, these three miRNAs also showed a better prediction accuracy for PC than traditional serum markers. The accuracy of our prediction model of the four methods is 94.4%, 84.9%, 82.3%, and 83.3%, respectively (Fig. ##FIG##2##3e–h##). In a real-world study, this miRNA signature could distinguish PC patients from healthy patients based on serum samples with an accuracy of 82.3% according to the random forest algorithm (Fig. ##FIG##2##3i##); the accuracy was 83.5% with CART, 79.0% with SVM, and 82.2% with LR (Fig. ##FIG##2##3j–l##).</p>", "<title>Serum miRNA signature discriminates pancreatic cancer from chronic pancreatitis</title>", "<p id=\"Par20\">A total of three PC and chronic pancreatitis datasets were enrolled in this study, and Fig. ##FIG##3##4a## shows the baseline information of these participants. Before PSM, the predictive accuracy of the serum miRNA signature for discriminating between PC and pancreatitis was 84.5% (Fig. ##FIG##3##4b##). After the propensity score matching study (PSM), all pancreatitis patients (<italic>n</italic> = 14) from the GSE128425 and GSE128508 datasets were successfully harmonized in terms of the baseline clinical characteristics of PC patients (<italic>n</italic> = 14) in the GSE85589 dataset (Fig. ##FIG##3##4c##). After harmonization, using random forest analysis, the predictability of the miRNA triplet to discriminate pancreatitis from PC in patient serum increased to an accuracy of 91.5% (Fig. ##FIG##3##4d##).</p>", "<title>High expression of serum hsa-miR-205-5p is associated with a poor clinical outcome and incomplete tumor resection</title>", "<p id=\"Par21\">When we analyzed hsa-miR-205-5p expression in the TCGA-PAAD cohorts, we found that there was no significant difference (Fig. ##FIG##4##5a–c##) among sex, age, or race. Although there was no statistical significance, we detected an increase in transcript abundance in patients with elevated T stage or advanced histologic grading stage of the disease (Fig. ##FIG##4##5d, e##). The R0 resection margin of the tumor specimen is considered an envisioned outcome of curative-intent oncological surgery. We found that serum hsa-miR-205-5p expression was higher in those with R1/2 resection than in those with R0 resection (Fig. ##FIG##4##5f##). These results demonstrate that hsa-miR-205-5p could be used as a predictive marker for more advanced disease. Moreover, in the survival analysis, patients with high hsa-miR-205-5p expression always had a worse prognosis than patients with low hsa-miR-205-5p expression in terms of OS, DSS, and PFI (<italic>p</italic> = 0.05, <italic>p</italic> = 0.011, and <italic>p</italic> = 0.002, respectively) (Fig. ##FIG##4##5g–i##).</p>", "<title>Predicting upstream and downstream targets of hsa-miR-205-5p in pancreatic cancer</title>", "<p id=\"Par22\">After limiting the stringency of CLIP (crosslinking-immunoprecipitation) data to more than three, a total of 1616 candidate targets of hsa-miR-205-5p were predicted (Supplementary Table ##SUPPL##2##1##), and gene ontology enrichment analysis showed that these targets could be enriched in epithelial cell migration, autophagy and cell‒cell adherens junctions (Fig. ##FIG##5##6a–c##). Future study of putative signaling pathway analysis indicates that the above targets may be involved in the TGF-beta signaling pathway as well as phylogenetically conserved stem cell signaling pathways such as Wnt and Hedgehog signaling (Fig. ##FIG##5##6d##).</p>", "<p id=\"Par23\">To identify putative downstream targets, a total of 13 target genes were enrolled. Strikingly, after correlation analysis between hsa-miR-205-5p and its targets in PC, we found that only the expression of BMP and Activin Membrane-Bound Inhibitor (BAMBI) was significantly (negatively) associated with the expression level of hsa-miR-205-5p (Supplementary Figs. ##SUPPL##1##2## and 6e). Therefore, BAMBI was identified as a candidate target for further analysis. When we explored the potential mechanism of the BAMBI gene in PC, the pathway enrichment results indicated that this gene may be involved in the Wnt signaling pathway and TGF-beta signaling pathway (Fig. ##FIG##5##6f##). Considering that hsa-miR-205-5p is also enriched in the same pathways, we could infer that hsa-miR-205-5p may target BAMBI to activate the TGF-beta pathway, which in turn promotes pancreatic carcinogenesis.</p>", "<title>High mRNA expression of BAMBI is associated with better clinical outcomes for patients with pancreatic cancer</title>", "<p id=\"Par24\">Since BAMBI might be regulated by hsa-miR-205-5p, its expression trend should be negatively correlated with the expression of hsa-miRNA 205-5p. To demonstrate this hypothesis, we used the TCGA-PAAD database to conduct relevant data assessment. In concordance, we found that as the disease stage increased, the expression level of this gene decreased (Fig. ##FIG##5##6g–i##). Moreover, survival analysis results showed that high mRNA expression of BAMBI in PC indicated a better outcome, including prolonged OS, DSS and PFI (<italic>p</italic> = 0.024, <italic>p</italic> = 0.049, and <italic>p</italic> = 0.007, respectively) (Fig. ##FIG##5##6j–l##).</p>" ]	[ "<title>Discussion</title>", "<p id=\"Par25\">Early detection of disease manifestation or progression is a clinically feasible type of prevention scheme for malignant diseases. Current science policymakers show an increased focus on funding programs to develop better disease detection modalities [##REF##35760861##20##]. A large amount of evidence from clinical studies indicates the power of early cancer detection based on quantifying CTCs or ctDNAs in liquid biopsies. However, for PC, those approaches seem inefficient or at least infrequently reported because of their extreme rarity in the blood and the difficulty in detection, enumeration and characterization [##REF##30932020##21##]. Currently, the detection rate of ctDNA in early stage (stage I and II) PC does not exceed 30% [##REF##28874546##22##]. Moreover, it is difficult to detect ctDNA due to the low concentration of ctDNA in the blood of patients in early staged of PC. Therefore, the quantification of ctDNA for the early diagnosis of PC is still challenging [##REF##32485509##23##]. In recent years, the early diagnostic value of exosomal miRNA has also received attention, but the difficulty of exosome isolation and purification hinders its further promotion at this point. In contrast, assessing circulating miRNAs does not have the above limitations and can be fundamental for the development of applicable assays with high detection sensitivity and specificity. Our work associates with this field of research using patient-matched multisample collection and thereby supports non-targeted RNA biomarker discovery.</p>", "<p id=\"Par26\">Based on the evidence of the association between miRNAs and various cancers, in recent years many miRNAs present in body fluids (e.g., plasma, cerebrospinal fluid, saliva, urine, semen) have been proposed as potential cancer biomarkers for diagnosis and prognosis [##REF##32792861##24##]. Of note, our goal was to seek diagnostic miRNAs that can specifically address the unmet needs in the clinical diagnosis of PC patients, namely, the difficulty in discriminating tumors from pancreatitis and sensitively detecting the degree of resection of the tumor. The main result of our work is the development of a three-miRNA (hsa-miR-1246, hsa-miR-205-5p, and hsa-miR-191-5p) signature that addresses these issues. The generation and validation in multicenter, large-scale datasets was conducted by unsupervised computational methods (four machine learning algorithms, namely, RF, CART, SVM and LR). Our selective approach contrasts with that of other previously published works on related matters, and we hypothesize that it is of higher clinical translational relevance. Previous studies aimed at developing miRNA-based PC diagnostics are technologically limited, as they did not integrate the real-world datasets [##REF##35795552##25##–##REF##34094925##27##] or they relied on somewhat outdated computational algorithms [##REF##30365134##28##–##REF##34434493##32##]. Moreover, others have suggested a circulating three microRNA panel (miR-642b, miR-885-5p, and miR-22) for blood-based detection of PC [##REF##28074846##33##]. Our sequencing analysis could not confirm their candidate biomarkers. Although it is well known that the biological characteristics of cancers are complex and cannot be attributed to a single factor, recent statistical power calculations across multiple translational PC studies suggest that a single circulating microRNA can hold significant clinical predictive value [##REF##29922178##34##]. In this regard, the hsa-miRNA 205-5p proposed here has previously been reported to regulate PC cell biology, surprisingly with mostly tumor suppressive functions [##REF##28719220##35##, ##REF##28536008##36##], but also pro-tumorigenic functions described [##REF##31391772##37##]. We extend the evidence on this marker, supporting its potential as a new approach for the early diagnostics of PC.</p>", "<p id=\"Par27\">To the best of our knowledge, another novel aspect of our work is the discovery of transcriptional activation of BAMBI as a potential target for PC, and this could be a consequence of epigenetic dysregulation related to the disease. BAMBI was identified as a repressor gene of TGF-beta signaling and was regulated by hsa-miR-205-5p. Since the TGF-beta signaling pathway plays fundamental roles in tumor progression [##REF##28794854##38##], our work reveals new opportunities to investigate the biological function of hsa-miR-205-5p activity in PC. Of note, a study [##UREF##2##39##] from a decade ago proposed BAMBI to promote pancreatic tumor metastasis in TGF-intact tumors; however, there is no report of its mechanism. Wet lab experiments focusing on deciphering the biological role of transcriptional activation of BAMBI in PC are ongoing and might help to verify our hypothesis regarding this novel therapeutic target in the near future.</p>", "<p id=\"Par28\">We acknowledge the limitations of our study: 1. Our discovery cohort has a relatively low number of patients enrolled from a single center, and the clinical outcome data rely on public datasets only. To address this issue, a dedicated prospective clinical trial benchmarking the value of the three candidate microRNAs in PC patients with clinical follow-up is needed. The design of this trial might include a subgroup analysis discriminating between peri-operative chemotherapy of the patient or different surgery types that might be applied, i.e., robotic vs. laparoscopic. In this context, the most relevant clinical development would be the development of a minimally invasive, sensitive, and specific detection tool to identify pancreatic intraepithelial neoplasia or intraductal papillary mucinous neoplasms to detect PC development in the early stage. A possible trial shall include the option to enroll those patients and analyze their blood, as the disease is currently mostly identified by coincidental diagnosis. Our conducted literature search did not identify any microRNA dataset for this disease in the public domain that could be interrogated in our study. Moreover, due to insufficient depth of available data on the DNA mutation status of our analyzed tumor samples, we cannot confirm any utility of our test to discriminate among the genetic tumor subtypes. 2. Biomarker analysis is based on ultrasensitive next-generation sequencing data acquisition. The execution of a wet lab confirmatory trial, ideally on prospectively collected samples as described above, using targeted RNA quantification, such as RT‒qPCR or <italic>CRISPR/Cas</italic> diagnostics, is useful to validate our discovery [##REF##34680210##40##].</p>" ]	[ "<title>Conclusions</title>", "<p id=\"Par29\">In this study, a panel of three miRNAs (hsa-miR-1246, hsa-miR-205-5p, and hsa-miR-191-5p) was used to predict PC independent of the disease stage. Quantifying the transcript levels of hsa-miRNA 205-5p in the serum of patients might help to improve the stratification of tumor patients from patients with pancreatitis and can help to inform surgeons of the completeness of resection of the tumor area in a perioperative setting. The underlying mechanism and the utility of the proposed strategy to identify tumor subtypes need to be evaluated in follow-up studies.</p>" ]	[ "<title>Introduction</title>", "<p id=\"Par1\">Pancreatic cancer is a highly aggressive cancer, and early diagnosis significantly improves patient prognosis due to the early implementation of curative-intent surgery. Our study aimed to implement machine-learning algorithms to aid in early pancreatic cancer diagnosis based on minimally invasive liquid biopsies.</p>", "<title>Materials and methods</title>", "<p id=\"Par2\">The analysis data were derived from nine public pancreatic cancer miRNA datasets and two sequencing datasets from 26 pancreatic cancer patients treated in our medical center, featuring small RNAseq data for patient-matched tumor and non-tumor samples and serum. Upon batch-effect removal, systematic analyses for differences between paired tissue and serum samples were performed. The robust rank aggregation (RRA) algorithm was used to reveal feature markers that were co-expressed by both sample types. The repeatability and real-world significance of the enriched markers were then determined by validating their expression in our patients’ serum. The top candidate markers were used to assess the accuracy of predicting pancreatic cancer through four machine learning methods. Notably, these markers were also applied for the identification of pancreatic cancer and pancreatitis. Finally, we explored the clinical prognostic value, candidate targets and predict possible regulatory cell biology mechanisms involved.</p>", "<title>Results</title>", "<p id=\"Par3\">Our multicenter analysis identified hsa-miR-1246, hsa-miR-205-5p, and hsa-miR-191-5p as promising candidate serum biomarkers to identify pancreatic cancer. In the test dataset, the accuracy values of the prediction model applied via four methods were 94.4%, 84.9%, 82.3%, and 83.3%, respectively. In the real-world study, the accuracy values of this miRNA signatures were 82.3%, 83.5%, 79.0%, and 82.2. Moreover, elevated levels of these miRNAs were significant indicators of advanced disease stage and allowed the discrimination of pancreatitis from pancreatic cancer with an accuracy rate of 91.5%. Elevated expression of hsa-miR-205-5p, a previously undescribed blood marker for pancreatic cancer, is associated with negative clinical outcomes in patients.</p>", "<title>Conclusion</title>", "<p id=\"Par4\">A panel of three miRNAs was developed with satisfactory statistical and computational performance in real-world data. Circulating hsa-miRNA 205-5p serum levels serve as a minimally invasive, early detection tool for pancreatic cancer diagnosis and disease staging and might help monitor therapy success.</p>", "<p id=\"Par5\">\n\n</p>", "<title>Subject terms</title>" ]	[ "<title>Supplementary information</title>", "<p>\n\n\n\n\n</p>" ]	[ "<title>Supplementary information</title>", "<p>The online version contains supplementary material available at 10.1038/s41416-023-02488-4.</p>", "<title>Acknowledgements</title>", "<p>We thank the pathology department of University of Magdeburg for continuous support.</p>", "<title>Author contributions</title>", "<p>Conceptualization, YZ and UDK; methodology, WS and TW; formal analysis, WS, SA and SAM; data preparation, WS, VKA, CB and RS. writing—original draft, WS, TW and UDK; supervision, YZ and UDK; writing- revision, AP, CK, AL and MV; project administration and funding acquisition, UDK. All authors have reviewed and approved the final version of the manuscript.</p>", "<title>Funding</title>", "<p>The project was partly supported by funds from the State Saxony-Anhalt, Autonomy in Aging, project “potential biomarkers” given to CB. Open Access funding enabled and organized by Projekt DEAL.</p>", "<title>Data availability</title>", "<p>All data can be obtained from the corresponding author.</p>", "<title>Code availability</title>", "<p>All code with analysis can be obtained from the corresponding author.</p>", "<title>Competing interests</title>", "<p id=\"Par30\">The authors declare no competing interests.</p>", "<title>Ethics approval and consent to participate</title>", "<p id=\"Par31\">The Ethics Committee of University Magdeburg approved this study.</p>" ]	[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>Schematic presentation of the workflow of our study.</title><p>Own laboratory miRNA sequencing (OLMS) from patient-matched bio samples of from tumor (T), healthy pancreas tissue (HT) and serum (S) presents the real-world dataset (upper left panel). Computational assessment of this data together with nine publically available, context-related dataset featuring tissue and serum RNA-sequencing data (upper right panel) identified a microRNA candidate signature for identifying pancreatic cancer in patient serum. With the use of four machine learning algorithms, the specificity, sensitivity of those and selected candidate microRNA 205-5p were tested. Our data reveal a new possible strategy to identify pancreatic cancer, predict the clinical course of the patient and verify tumor resection completeness (lower panel).</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>Differential gene expression analysis and feature miRNA identification.</title><p>Analysis of upregulated miRNAs in our lab data (<bold>a</bold>) and public datasets (<bold>b</bold>, <bold>c</bold>). No merged results of upregulated miRNAs (<bold>d</bold>). RRA algorithms identified a four-feature miRNA panel (<bold>e</bold>), which after validation in our lab dataset was reduced to a three-feature miRNA panel (<bold>f</bold>).</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><title>Four machine-learning algorithms validate the predictive efficacy of microRNA panel.</title><p>Application of the three-miRNA panel to predict pancreatic cancer occurrence in the serum of patients via four machine-learning algorithms (RF, CART, SVM and LR) in the training datasets (<bold>a</bold>–<bold>d</bold>), test datasets (<bold>e</bold>–<bold>h</bold>) and real-world study (<bold>i</bold>–<bold>l</bold>).</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><title>The panel of three serum miRNAs can distinguish pancreatitis from pancreatic cancer.</title><p>Baseline clinical information of the enrolled patient groups (<bold>a</bold>). The feature miRNA panel prediction ability of 84.5% (<bold>b</bold>), which eliminates baseline differences between groups using propensity score matching (<bold>c</bold>), is increased to very indicative (91.5%, with a sensitivity of 94.5% and specificity of 80.0%) (<bold>d</bold>).</p></caption></fig>", "<fig id=\"Fig5\"><label>Fig. 5</label><caption><title>Association of the novel serum biomarker hsa-miR-205 with clinical factors.</title><p>Subgroup analysis to determine whether the hsa-miR-205 expression difference is independent of other factors: subgroups based on sex, age and race of the patient (<bold>a</bold>–<bold>c</bold>), subgroups based on T stage, histologic grade, and residual tumor status (<bold>d</bold>–<bold>f</bold>). Survival analysis of serum hsa-miR-205-5p expression level as an independent prognostic factor revealed that high expression is associated with significantly poorer clinical outcomes, namely, reduced OS, DSS or PFI (<bold>g</bold>–<bold>i</bold>).</p></caption></fig>", "<fig id=\"Fig6\"><label>Fig. 6</label><caption><title>Prediction of the mechanistic function of hsa-miR-205 and its candidate target BAMBI by interrogating mRNA and small RNA datasets.</title><p>The results of gene ontology analyses and pathway enrichment analyses for hsa-miR-205-related genes (<bold>a</bold>–<bold>d</bold>), which led to the identification BAMBI transcription as a putative target of hsa-miR-205 (<bold>e</bold>). Prediction of biological pathways involving BAMBI transcription in pancreatic cancer (<bold>f</bold>), as well as the distribution of tumor grading features including T stage (<bold>g</bold>), histologic grade (<bold>h</bold>) and residual tumor stage (<bold>i</bold>) in patients with different values of BAMBI transcription levels. Survival analysis of colon cancer patients with different BAMBI mRNA levels in their tumors, revealing that high BAMBI expression was associated with better clinical outcomes in terms of OS, DSS and PFI (<bold>j</bold>–<bold>l</bold>).</p></caption></fig>" ]	[ "<table-wrap id=\"Tab1\"><label>Table1</label><caption><p>Baseline characteristics of enrolled patients.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th rowspan=\"2\">Characteristics<sup>a</sup></th><th colspan=\"2\">Overall (<italic>n</italic> = 26)</th></tr><tr><th>Number</th><th>%</th></tr></thead><tbody><tr><td colspan=\"3\">Age(years)</td></tr><tr><td> ≤65</td><td>7</td><td>27</td></tr><tr><td> >65</td><td>19</td><td>73</td></tr><tr><td colspan=\"3\">Sex</td></tr><tr><td> Female</td><td>16</td><td>62</td></tr><tr><td> Male</td><td>10</td><td>38</td></tr><tr><td colspan=\"3\">BMI (Kg/m<sup><bold>2</bold></sup><bold>)</bold><sup>b</sup></td></tr><tr><td> ≤25</td><td>8</td><td>31</td></tr><tr><td> >25</td><td>18</td><td>69</td></tr><tr><td colspan=\"3\">Neoadjuvant therapy</td></tr><tr><td> No</td><td>15</td><td>28</td></tr><tr><td> Yes</td><td>11</td><td>72</td></tr><tr><td colspan=\"3\">Tumor size(mm)</td></tr><tr><td> ≤40</td><td>10</td><td>38</td></tr><tr><td> >40</td><td>16</td><td>62</td></tr><tr><td colspan=\"3\">Surgery</td></tr><tr><td> PPPD</td><td>20</td><td>77</td></tr><tr><td> Others</td><td>6</td><td>23</td></tr><tr><td colspan=\"3\">Histological type</td></tr><tr><td> PDAC</td><td>24</td><td>92</td></tr><tr><td> IPMN<sup>c</sup></td><td>1</td><td>4</td></tr><tr><td> NET<sup>d</sup></td><td>1</td><td>4</td></tr><tr><td colspan=\"3\">Grade</td></tr><tr><td> 1</td><td>4</td><td>15</td></tr><tr><td> 2</td><td>9</td><td>35</td></tr><tr><td> 3</td><td>13</td><td>50</td></tr><tr><td colspan=\"3\">Operation complications</td></tr><tr><td> No</td><td>16</td><td>62</td></tr><tr><td> Yes</td><td>10</td><td>38</td></tr></tbody></table></table-wrap>" ]	[]	[]	[]	[]	[]	[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM3\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM4\"></supplementary-material>" ]	[ "<table-wrap-foot><p><sup>a</sup>All enrolled patients were PDAC.</p><p><sup>b</sup>BMI body mass index.</p><p><sup>c</sup>IPMN intraductal papillary mucinous neoplasm.</p><p><sup>d</sup>NET pancreatic neuroendocrine tumor.</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>These authors contributed equally: Wenjie Shi, Thomas Wartmann.</p></fn></fn-group>" ]	[ "<graphic position=\"anchor\" xlink:href=\"41416_2023_2488_Figa_HTML\" id=\"d32e270\"/>", "<graphic xlink:href=\"41416_2023_2488_Fig1_HTML\" id=\"d32e585\"/>", "<graphic xlink:href=\"41416_2023_2488_Fig2_HTML\" id=\"d32e670\"/>", "<graphic xlink:href=\"41416_2023_2488_Fig3_HTML\" id=\"d32e718\"/>", "<graphic xlink:href=\"41416_2023_2488_Fig4_HTML\" id=\"d32e762\"/>", "<graphic xlink:href=\"41416_2023_2488_Fig5_HTML\" id=\"d32e816\"/>", "<graphic xlink:href=\"41416_2023_2488_Fig6_HTML\" id=\"d32e866\"/>" ]	[ "<media xlink:href=\"41416_2023_2488_MOESM1_ESM.tif\"><caption><p>Supplement Figure 1</p></caption></media>", "<media xlink:href=\"41416_2023_2488_MOESM2_ESM.tif\"><caption><p>Supplement Figure 2</p></caption></media>", "<media xlink:href=\"41416_2023_2488_MOESM3_ESM.docx\"><caption><p>Supplementary File Legends</p></caption></media>", "<media xlink:href=\"41416_2023_2488_MOESM4_ESM.docx\"><caption><p>Supplement Table 1</p></caption></media>" ]	[{"label": ["1."], "collab": ["Hepatology TLG."], "article-title": ["Pancreatic cancer: a state of emergency?The lancet"], "source": ["Gastroenterol Hepatol"], "year": ["2021"], "volume": ["6"], "fpage": ["81"]}, {"label": ["17."], "mixed-citation": ["Wang K, Yuan Y, Cho J-H, McClarty S, Baxter D, Galas DJ .Comparing the MicroRNA spectrum between serum and plasma. PLoS One. 2012;7:e4156."]}, {"label": ["39."], "surname": ["Brosnan", "Yachida", "Iacobuzio-Donahue"], "given-names": ["JA", "S", "CA"], "article-title": ["BAMBI Is overexpressed in metastatic pancreatic cancers with genetically Intact TGF-\u03b2 pathways: a potential mechanism to escape TGF-\u03b2 signaling during metastasis formation"], "source": ["Cancer Res"], "year": ["2011"], "volume": ["71"], "fpage": ["2438"], "lpage": ["2438"], "pub-id": ["10.1158/1538-7445.AM2011-2438"]}]	{ "acronym": [], "definition": [] }	40	CC BY	no	2024-01-13 00:02:20	Br J Cancer. 2024 Jan 31; 130(1):125-134	oa_package/d7/03/PMC10781694.tar.gz
PMC10781695	38200122	[ "<title>Introduction</title>", "<p id=\"Par2\">Organisms that live in social groups experience many benefits to their fitness and survival. For example, social animals have additional defenses against predation, improved foraging success, increased access to potential mates, and in some species cooperative care of offspring<sup>##UREF##0##1##</sup>. However, group living is also inherently linked to an increased risk of infectious disease due to high concentrations of susceptible hosts<sup>##UREF##1##2##</sup>, frequent close contacts<sup>##UREF##1##2##,##UREF##2##3##</sup>, and often high intragroup relatedness<sup>##UREF##1##2##,##UREF##3##4##</sup>. To combat this, social organisms supplement the immunological and cellular processes that compose an individual’s immune response with group-level adaptations for reduced disease transmission, often called social immunity<sup>##REF##17714663##5##</sup>. The social immune response consists of specific actions across several individuals, which collectively protect the social group from infection<sup>##REF##17714663##5##,##REF##31163158##6##</sup>, including behavioral and physiological mechanisms, genetic and morphological defenses, and variation in spatial organization<sup>##REF##17714663##5##</sup>. In social insects, which live in especially complex and interactive environments, the social immune response is highly adapted and specialized to different types of parasites and even different stages of infection within a colony<sup>##REF##17714663##5##,##UREF##4##7##–##UREF##6##9##</sup>.</p>", "<p id=\"Par3\">Social immune responses are well documented in honey bees (<italic>Apis mellifera</italic>) and are suspected to compensate for the reduced number of immunity-related genes found in honey bees compared to other sequenced insects<sup>##REF##17714663##5##,##REF##36430757##10##,##REF##17069638##11##</sup>. Hygienic behaviors, typically observed as the selective removal of dead, damaged, or diseased brood, are an effective defense against bacterial infections and have been selected for in honey bee breeding programs<sup>##REF##19909975##12##</sup>. Workers also collect and produce compounds with antimicrobial activity<sup>##REF##19619221##13##,##REF##19021816##14##</sup> and can share immunological memory to the larvae<sup>##REF##34424968##15##</sup>. Changes in behavior are also observed upon exposure to a parasite or pathogen, such as forced and altruistic removal from the colony<sup>##UREF##7##16##</sup> and reduced social interactions<sup>##REF##32341145##17##</sup>.</p>", "<p id=\"Par4\">Throughout a larval honey bee’s development, it will receive nearly 10,000 visits consisting of cell inspections and provisioning by nurse bees<sup>##UREF##8##18##</sup>. This high amount of activity around a single larva, let alone the thousands of other larvae, provides an excellent opportunity for pathogens to spread through the oral secretions produced to feed larvae<sup>##REF##20105559##19##</sup>. The ectoparasitic mite <italic>Varroa destructor</italic>, which vectors viruses such as Israeli acute paralysis virus (IAPV)<sup>##REF##26667378##20##–##REF##20926637##22##</sup>, also utilizes brood care behavior, using nurses as vehicles to introduce mites to new larval hosts<sup>##REF##26667378##20##,##REF##35137134##21##,##UREF##9##23##,##REF##27302644##24##</sup>. Viruses can also be spread through the oral-gut pathway from workers to the brood. Deformed wing virus (DWV) has been detected in larval diets produced by infected worker bees and is infectious to larvae that consume contaminated food<sup>##REF##17622639##25##</sup>. The transmission of IAPV through the larval diet is less studied in comparison to DWV but is likely to occur based on its detection in colony materials such as honey, pollen, and royal jelly<sup>##REF##25079600##26##</sup>. Additionally, the hypopharyngeal glands, which synthesize the royal jelly that is fed to larvae, contain the third highest accumulation of IAPV particles after the gut and nerve tissue<sup>##REF##25079600##26##</sup>.</p>", "<p id=\"Par5\">IAPV infection in adults can occur through consuming contaminated food, a common method of experimental infection<sup>##REF##32341145##17##,##REF##30891288##27##,##REF##33066263##28##</sup>, or through social interactions like trophallaxis<sup>##REF##30626038##29##</sup>. Once adult bees are exposed, IAPV presents as a systemic infection, leading to shivering and often lethal paralysis<sup>##REF##25079600##26##,##REF##18024913##30##</sup>, although sublethal and asymptomatic infections have been observed as well<sup>##REF##25079600##26##</sup>. Previously studied social immune responses to IAPV are not unlike responses to other pathogens; infected workers engage in fewer social behaviors such as trophallaxis<sup>##REF##32341145##17##</sup>, and queens may have a preference (albeit not statistically significant) for interacting with uninfected workers as opposed to infected workers<sup>##REF##30626038##29##</sup>. At the same time, however, these responses are context dependent, and IAPV infection can also result in behavioral changes that can increase virus transmission<sup>##REF##32341145##17##</sup>, in other words, host manipulation<sup>##REF##19289190##31##</sup>. For example, foragers experimentally infected with IAPV are more likely to be accepted into a foreign colony than uninfected bees, increasing pathogen transmission between colonies<sup>##REF##32341145##17##</sup>.</p>", "<p id=\"Par6\">Given the high potential for IAPV transmission through brood care and the highly adapted nature of the social immune response in honey bees, we tested the hypothesis that nurse bees exposed to IAPV exhibit a social immune response in an effort to reduce virus transmission. Under this social immunity hypothesis, we predicted that the frequency of virus infection in the social environment (e.g. the percentage of nurse bees exposed to IAPV in a given social group, or simply the percentage-exposed) influences the group’s larval care response, specifically that groups of workers with a higher percentage of exposed bees will reduce larval contact in order to avoid virus movement to larvae. Similarly, we predicted that, at the individual level, nurse bees exposed to IAPV will reduce their brood care behavior relative to uninfected counterparts when housed together in the same social environment. An alternative hypothesis is that IAPV manipulates the behavior of its host to increase transmission, as it can in some contexts<sup>##REF##32341145##17##</sup>. Thus, under this host manipulation hypothesis, we predicted that IAPV exposure would lead to more contact with larvae to facilitate transmission.</p>" ]	[ "<title>Materials and methods</title>", "<title>Cage set-up and experimental infection</title>", "<p id=\"Par7\">The following methods were designed to generate experimentally infected and uninfected populations of age-matched honey bee workers that can be assayed when the prime age for observing nursing behavior coincides with the peak of IAPV infection: 7 days after emergence<sup>##REF##26569402##32##</sup> and 48 h after exposure to the virus<sup>##REF##32341145##17##,##REF##30891288##27##,##REF##26923109##33##</sup>, respectively. Thus, newly emerged worker bees were sourced from three colonies across two days at the University of Illinois Urbana-Champaign Bee Research Facility (Urbana, IL 61801, USA) in late July 2022. Newly emerged bees were marked with one of ten colors using a paint pen (Sharpie) for individual recognition and placed in acrylic cube cages in groups of 35<sup>##REF##33066263##28##,##REF##27832169##34##</sup> according to color marking. This process was repeated using the same ten colors, ultimately resulting in two sets of ten cages, each containing 35 newly emerged bees all marked with the same color. Cages were provisioned ad libitum with 30% sucrose solution via drip feeder and artificial protein supplement (MegaBee). Artificial protein supplement was used to limit potential variation in nutrition due to pollen diets or pathogen contamination present in natural pollens. Artificial supplement does not interfere with the development of the hypopharyngeal glands when compared to natural pollen diets<sup>##REF##20346950##35##</sup>, so no effect on nursing behavior from the diet was expected. All cages were kept within a walk-in incubator kept at 34 °C and 50% relative humidity<sup>##REF##33066263##28##,##REF##26569402##32##,##REF##27832169##34##</sup>.</p>", "<p id=\"Par8\">Five days after the initial cage set-up and two days prior to behavioral observations, the sucrose feeders and protein supplements were removed from each cage. Each cage in one of the two sets of ten received a sublethal dose of IAPV, diluted in 600 µl of 30% sucrose solution (approximately 126,507 genome equivalents per cage, or 3614 genome equivalents per bee)<sup>##REF##32341145##17##,##REF##30891288##27##,##REF##32925874##36##</sup> for oral exposure. The other cages in each set received only 600 µl of 30% sucrose solution. After 24 h, the sucrose feeders and protein supplements were returned.</p>", "<p id=\"Par9\">Behavioral observations were initiated two days after IAPV exposure, when IAPV levels within the adults are most likely to peak, as well as when previous work has observed behavioral differences due to covert infection<sup>##REF##32341145##17##,##REF##30891288##27##,##REF##26923109##33##</sup>, and seven days after the initial cage set-up, when nursing behavior is best observed using this assay<sup>##REF##26569402##32##</sup>. Prior to beginning the assay, one adult bee of each of the ten color markings was transferred from its respective cube cage into a vertically oriented Petri dish (100 × 20 mm) with a beeswax foundation sheet pressed against the base of the dish to mimic in-hive conditions<sup>##REF##26569402##32##</sup>, ultimately resulting in ten uniquely marked individuals per dish. Adult bees were pulled either from the virus exposed cages or the strictly sucrose-fed cages in order to create three different social environments: dishes with 0% of individuals exposed to IAPV, dishes with 100% of individuals exposed, and dishes with 50% of individuals exposed (Fig. ##FIG##0##1##). Each dish was supplied with a 2.0 ml drip feeder containing 30% sucrose solution, inserted through a hole in the top of the dish. Following the dish assembly, all adult bees were left in the walk-in incubator with the lights on for 1 h before beginning the recording process.</p>", "<p id=\"Par10\">Twenty-four larvae were grafted into commercial plastic queen rearing cups (JZ-BZ) over 2 days from a colony different to the colonies from where the workers were sourced. The larvae were then transferred and raised in a queenless colony, during which the colony would build a wax cell around the rearing cup (also referred to as a “queen cell”). Queen cells were removed from the colony four days after grafting<sup>##REF##26569402##32##</sup> for immediate use in the behavioral assay.</p>", "<title>Behavioral recordings</title>", "<p id=\"Par11\">Dishes were recorded using a VIXIA HF R800 camcorder (Canon Inc.) in a haphazardly selected order. Prior to starting the recording, the number of dead bees in the assay dish, if any, were recorded (0% exposed: 1 dish with 1 dead bee; 50% exposed: 4 dishes with 1 dead bee, 1 dish with 2 dead bees; 100% exposed: 7 dishes with 1 dead bee, 1 dish with 2 dead bees). All deaths were observed to be due to aggression in the initial acclimation period. Upon starting the recording, the sucrose feeder was removed and replaced with a queen cell, after which the nurse bees’ interactions with the queen cell and each other were filmed. The recording was stopped 5 minutes after the queen cell was first inserted<sup>##REF##26569402##32##</sup>. Queen cells with larvae were labeled individually for use per group-type and so larval identity could be recorded. On each recording day, the three experimental groups were each assigned a set of four larvae. The larvae were rotated between dishes in a haphazard order, such that the same larva would not be shared across different experimental groups (e.g. between the 50% and 0% exposed communities), and that the same larva was not used in an assay twice in a row. Recordings were uploaded to cloud data storage for later behavioral analysis. Following the recordings, all dishes and nurse bees were immediately frozen at − 80 °C for whole bee sample processing.</p>", "<p id=\"Par12\">At the completion of the experiments, video recordings of the dishes were reviewed and analyzed. Behavioral interactions of interest included antennation around the opening of the queen cell (“external-antennation”) and inserting the head inside the queen cell (“insertion”), as both of these are indicative of the brood care response<sup>##REF##26569402##32##,##REF##31239296##37##</sup>. Upon observing one of these interactions, the color identity of the nurse, its associated treatment (exposed or unexposed), and the duration of the behavior were recorded to the nearest 1.0 s. The total number of interactions for each behavior and the number of unique nurses which entered the queen cell at least once (“responders”) were also recorded.</p>", "<title>Behavioral statistical analyses</title>", "<p id=\"Par13\">For both types of recorded behaviors, the numbers of interactions were standardized by taking the respective sums for each dish and dividing by the number of living adult bees in the dish at the time of recording. Likewise, the same process was used to standardize the duration of interactions for both behaviors as well as the number of responders. The durations of external-antennation behaviors were log-transformed prior to statistical analysis. Analyses were conducted in R using the lme4 package<sup>##UREF##10##38##</sup>. Linear mixed models were constructed for the response variables (the number and duration of each interaction type as well as the mumber of responding workers) and using the percentage of bees exposed to IAPV as a fixed effect. The observer, date, and the identity of the larva used in the assay were used as random effects and included when model selection (based on AIC values) deemed it necessary. Model diagnoses were assessed using the performance package<sup>##UREF##11##39##</sup>. Final model specifics can be found in the Supplementary Material. Significance of the percentage-exposed effect was determined using likelihood ratio tests against the null models.</p>", "<p id=\"Par14\">Within the 50% exposed dishes, the two treatments present were compared directly to each other for the same metrics that were used in the dish-level analysis (duration and number of external-antennation and insertion behaviors). Only responding workers (bees which interacted with the queen cell at least once, hereafter described as “nurses”) were used in these analyses (see Supplementary Tables ##SUPPL##0##S1##, ##SUPPL##0##S2##). This choice was made to limit the scope of our study to just nurse bees. Previous studies have used larval-response as a method of identifying nurse bees<sup>##REF##35202460##40##</sup>. In single cohort groups, workers will begin differentiating their behaviors outside their normal temporal patterns<sup>##UREF##12##41##</sup>, thus, for example, some of the adults in our study may have been precocious foragers and would not have responded to the larva at all.</p>", "<p id=\"Par15\">Generalized linear mixed models for external-antennation and insertion behaviors were constructed using the duration and number of interactions as the response variables and the treatment (either exposed or unexposed to IAPV) as a fixed effect. Poisson or negative binomial families were chosen based on AIC values, as were the inclusion of the following random effects: dish, date, observer, and larval identity<sup>##REF##32341145##17##</sup>. Analyses were performed in R using the lme4 package<sup>##UREF##10##38##</sup> and model diagnoses were assessed using the performance package<sup>##UREF##11##39##</sup>. Final model specifics can be found in the Supplementary Material. The significance of an individual nurse's treatment as a fixed effect was determined using likelihood ratio tests against the null models. When treatment proved to have a significant effect, estimated marginal means were used to compare the IAPV-exposed nurses to the unexposed nurses using the emmeans package<sup>##UREF##13##42##</sup>.</p>", "<p id=\"Par16\">Similar analyses were used to compare the unexposed nurses between the 50% and 0% groups. As with the previous comparisons, only responding bees were used in the analysis (Tables ##SUPPL##0##S3##, ##SUPPL##0##S4##). Using the percentage-exposed as a fixed effect, the same list of random effects used in the prior individual level of analysis were either included or excluded based on AIC values. Final model specifics can be found in the Supplementary Material. The significance of the percentage-exposed effect was determined through likelihood ratio tests against the null models.</p>", "<p id=\"Par17\">For all of the dish and individual behavioral responses, post hoc power analyses were conducted using the pwr package in R<sup>##UREF##14##43##</sup> for an estimated medium effect (<italic>d</italic> = 0.50) and an alpha of 0.05. Plots were constructed in R with ggplot2<sup>##UREF##15##44##</sup>, followed by legend and color adjustments in Adobe Illustrator (Adobe Systems).</p>", "<title>Dry head mass and virus quantification</title>", "<p id=\"Par18\">We investigated whether our treatments had an effect on the worker hypopharyngeal glands, as these organs are closely associated with nursing behavior and produce royal jelly. Dry head mass was used as a proxy for hypopharyngeal gland development, as dry mass has been used previously as a measurement for nutritional physiology<sup>##UREF##16##45##</sup>. Individual adult bees were selected via random number generator from the 50% infected group and then separated by treatment (<italic>N</italic> = 30 bees per treatment). Heads were removed from the rest of the body and placed in a drying oven (60 °C for 48 h) before removing the antennae and weighing to the nearest 0.1 mg<sup>##UREF##16##45##</sup>. Bodies were set aside for RNA extraction and subsequent virus quantification. Head masses between treatments were compared with a two-sample t-test in R.</p>", "<p id=\"Par19\">To confirm whether the experimental infection was successful, a subset of workers of each treatment (exposed <italic>N</italic> = 15 bees; unexposed <italic>N</italic> = 14 bees) used in the dry head mass study were randomly selected using a random number generator. An additional randomly selected subset of workers from the 0% (<italic>N</italic> = 14 bees) and 100% (<italic>N</italic> = 15 bees) exposed groups were also included and decapitated. Whole body RNA was extracted using TRIzol Reagent (Invitrogen) and treated with DNase I (New England Biolabs) before diluting to 100 ng/µl, quantified via nanodrop. IAPV RNA was quantified from the whole body samples in triplicate with one step RT-qPCR using the Power SYBR Green RNA-to CT 1-Step Kit (Applied Biosystems), as performed in previous studies<sup>##REF##33066263##28##</sup>. Previously established primers were selected for IAPV (Forward: TGCAAGTGAACGCCCCAAAAACG; Reverse: TGCCACAGTTCCGACAACATCTGC)<sup>##REF##26923109##33##</sup>, and initial quantities were calculated using a serially diluted standard curve (1:10) of viral RNA<sup>##REF##33066263##28##,##REF##26923109##33##,##REF##27832169##34##</sup>. Initial quantities were log-transformed prior to statistical analysis<sup>##UREF##17##46##</sup>. Differences in viral RNA quantities between treatments were determined in R using pairwise Wilcoxon rank sum tests, followed by a Benjamini–Hochberg adjustment. Figures for the virus quantification and head mass comparisons were created with ggplot2<sup>##UREF##15##44##</sup> in R, followed by legend and color adjustments in Adobe Illustrator (Adobe Systems).</p>" ]	[ "<title>Results</title>", "<p id=\"Par20\">In total, we observed 64 unique social groups (or 64 unique dishes), each with one of three percentages of individuals exposed to IAPV (0% exposed <italic>N</italic> = 23 dishes, 50% exposed <italic>N</italic> = 22 dishes, 100% exposed <italic>N</italic> = 19 dishes). The whole social group’s average number and duration of external antennation and insertion behaviors were compared between the three percentages of infection, as well as the number of responding workers to the queen cell. For individual level behaviors within the social groups with 50% of individuals exposed to IAPV, only the responding workers for each type of behavior were included in the analysis (external-antennation: exposed <italic>N</italic> = 76 nurses, unexposed <italic>N</italic> = 81 nurses; insertion: exposed <italic>N</italic> = 42 nurses; unexposed <italic>N</italic> = 39 nurses). For these nurses, the average number and duration of both types of interaction were compared between the two treatments. For each behavioral comparison, post hoc power analyses confirmed that the experimental designs each achieved an estimated power greater than 0.80<sup>##UREF##14##43##</sup>.</p>", "<title>Group level behaviors</title>", "<p id=\"Par21\">Varying the percentage of bees exposed to IAPV within a social group does not have a significant effect on the whole group’s time spent antennating around the opening of the queen cell (Fig. ##FIG##1##2##a, likelihood ratio test against the null model: <italic>X</italic><sup>2</sup> = 0.984, <italic>df</italic> = 2, <italic>p</italic> = 0.611) or the number of external-antennation behaviors (Fig. ##FIG##1##2##b, likelihood ratio test against the null model: <italic>X</italic><sup>2</sup> = 0.128, <italic>df</italic> = 2, <italic>p</italic> = 0.938). Likewise, the percentage of IAPV-exposed workers had no significant effect on the duration of time spent inside the queen cell (Fig. ##FIG##1##2##c, likelihood ratio test against the null model: <italic>X</italic><sup>2</sup> = 1.11, <italic>df</italic> = 2, <italic>p</italic> = 0.575), or the number of insertion interactions (Fig. ##FIG##1##2##d, likelihood ratio test against the null model: <italic>X</italic><sup>2</sup> = 4.08, <italic>df</italic> = 2, <italic>p</italic> = 0.130). Similarly, the percentage of exposed workers had no significant effect on the number of responding individuals in each dish (Fig. ##FIG##1##2##e, likelihood ratio test against the null model: <italic>X</italic><sup>2</sup> = 2.59, <italic>df</italic> = 2, <italic>p</italic> = 0.273).</p>", "<title>Individual level behaviors in the 50% exposed group</title>", "<p id=\"Par22\">Similar to the analyses performed at the group level, we observed no significant effect of individual nurse treatment on the duration of external-antennation (Fig. ##FIG##2##3##a, likelihood ratio test against the null model: <italic>X</italic><sup>2</sup> = 1.62, <italic>df</italic> = 1, <italic>p</italic> = 0.203), the number of external-antennation interactions (Fig. ##FIG##2##3##b, likelihood ratio test against the null model: <italic>X</italic><sup>2</sup> = 0.814, <italic>df</italic> = 1, <italic>p</italic> = 0.367), or the duration of time spent inside the queen cell (Fig. ##FIG##2##3##c, likelihood ratio test against the null model: <italic>X</italic><sup>2</sup> = 0.921, <italic>df</italic> = 1, <italic>p</italic> = 0.337). However, treatment did have a significant effect on the number of insertion interactions (Fig. ##FIG##2##3##d, likelihood ratio test against the null model: <italic>X</italic><sup>2</sup> = 6.99, <italic>df</italic> = 1, <italic>p</italic> = 8.16 × 10<sup>–3</sup>), with the IAPV exposed nurses entering the cell more than unexposed bees (estimated marginal means with Tukey HSD: <italic>SE</italic> = 0.222, <italic>Z-ratio</italic> = 2.63, <italic>p</italic> = 8.7 × 10<sup>–3</sup>).</p>", "<p id=\"Par23\">As we saw a difference in the number of insertion interactions between the exposed and unexposed bees in the 50% exposed environment, we questioned whether the unexposed bees were decreasing their level of attentiveness relative to completely unexposed environments as a means of avoiding self-contamination. We observed no differences in nursing attentiveness between the unexposed nurses from the 50% and 0% exposed groups. The presence of virus-exposed nurses did not have a significant effect on the duration of external-antennation (likelihood ratio test against the null model: <italic>X</italic><sup>2</sup> = 0.069, <italic>df</italic> = 1, <italic>p</italic> = 0.792) or the number of external-antennation interactions (likelihood ratio test against the null model: <italic>X</italic><sup>2</sup> = 0.0096, <italic>df</italic> = 1, <italic>p</italic> = 0.922). Likewise, no significant effect was observed on the duration of insertion interactions (likelihood ratio test against the null model: <italic>X</italic><sup>2</sup> = 0.828, <italic>df</italic> = 1, <italic>p</italic> = 0.363) or the number of insertion interactions (likelihood ratio test against the null model: <italic>X</italic><sup>2</sup> = 2.11, <italic>df</italic> = 1, <italic>p</italic> = 0.147).</p>", "<title>Dry head mass and virus quantification</title>", "<p id=\"Par24\">There were no observed differences in dry head mass between the two types of treatment (two-sample <italic>t</italic> test: <italic>t</italic> = 0.210, <italic>df</italic> = 58, <italic>p</italic> = 0.835, Fig. ##SUPPL##0##S1##), indicating that IAPV exposure 2 days prior to the behavioral observations did not cause anatomical differences that may affect the brood care response, such as the shrinking of hypopharyngeal glands<sup>##UREF##18##47##</sup>. Quantitative PCR for viral RNA confirmed that the adults in both the 50% and 100% exposed environments had significantly higher levels of IAPV transcripts relative to the unexposed adult bees in the 50% and 0% exposed environments (Table ##SUPPL##0##S5##). The two types of exposed groups did not differ in viral titers, nor did the two unexposed groups (Fig. ##SUPPL##0##S2##). The non-zero level of viral RNA detection in the uninfected bees can be attributed to regular background virus levels in the source colony<sup>##REF##26923109##33##</sup>, and our detected levels are similar to the quantities found in untreated bees in other published studies on the effects of IAPV infection on behavior<sup>##REF##32341145##17##</sup>.</p>" ]	[ "<title>Discussion</title>", "<p id=\"Par25\">Our results showed that exposure to IAPV did not affect overall group-level nursing responses; when comparing across group-level treatments where 100%, 50%, or 0% of nurses were exposed to IAPV, larvae always received the same amount of total care. However, while each larva received the same amount of attention no matter the makeup of nurse group, we observed differences in the nursing frequency between exposed and unexposed nurses within the groups that contained a mixture of control and IAPV-treated adult bees, with the IAPV-exposed nurses contacting larvae more frequently than controls.</p>", "<p id=\"Par26\">Our experiment investigated two alternate hypotheses: first, that a social immune response will be present in the nurse bees’ response to a larva, and second, that IAPV manipulates host behavior to increase virus transmission. The uniformity in group-level responses does not clearly support either of our hypotheses but may be explained by trade-offs between a social immune response and an essential social behavior. For example, if avoidance behaviors are over-expressed, the larva may receive inadequate feeding and care<sup>##REF##34714677##48##</sup>, resulting in detrimental effects on the colony’s health, as malnourished larvae are often culled<sup>##UREF##19##49##,##UREF##20##50##</sup> or, if the larvae are reared to adulthood, develop into impaired adults<sup>##REF##34234217##51##</sup>, ultimately acting as a less effective workforce<sup>##UREF##19##49##,##UREF##21##52##</sup>. These results are also likely not unique to IAPV. Similar responses have been observed when testing other bee pathogens and parasites, such as <italic>Varroa destructor</italic><sup>##REF##34714677##48##</sup>. Nurse bees, while under <italic>Varroa</italic> infestation, do not abandon the brood, despite nurse bees being the primary vehicle for <italic>Varroa</italic> dispersal<sup>##REF##35137134##21##,##UREF##9##23##,##REF##27302644##24##</sup>. This was accompanied, though, by a social immune response at the spatial organizational level; nurse bees were still observed near the brood, whereas foragers were found further away<sup>##REF##34714677##48##</sup>. While our experiment created a controlled environment to observe nursing behavior, it is not a representative situation of a real colony, where cohort sizes are much higher than those used in our experimental arenas. It is possible that our experiment only captures a small slice of the social immune responses and, similar to the response towards <italic>Varroa</italic><sup>##REF##34714677##48##</sup>, an organizational response requiring a larger group size may also occur.</p>", "<p id=\"Par27\">Within the group environment where 50% of nurses were exposed to IAPV, we found that IAPV-treated nurses interacted significantly more with larvae than controls. Additionally, we found no significant differences in the responses of the unexposed nurses in the 50% and 0% exposed groups, indicating that the unexposed bees in the 50% exposure group were not reducing their own responses as a means to limit self-contamination.</p>", "<p id=\"Par28\">This result supports the hypothesis that IAPV infection results in behavioral manipulation of the nurse host that could spread the virus rather than a social immune response that could suppress transmission. Our investigation of dry head mass indicates that these differences were not due to anatomical differences in hypopharyngeal gland sizes which may influence nursing behavior<sup>##UREF##16##45##,##UREF##18##47##</sup>. While honey bees show social immune responses to virus infection, IAPV can also manipulate adult cuticular hydrocarbon (CHC) profiles and increase the likelihood that infected bees are accepted into foreign colonies, likely spreading infections<sup>##REF##32341145##17##</sup>. Therefore, it is possible that IAPV may be altering the brood care-system in order to benefit its own transmission, as more frequent interactions with the larva may lead to a higher risk of viral transmission. If the observed increase in the number of interactions is due to IAPV, it is not unlikely that other parasites, such as <italic>Varroa</italic>, may exploit IAPV’s effect on brood care behavior. <italic>Varroa</italic>-parasitized bees may be more likely to be infected with IAPV<sup>##REF##20926637##22##</sup>, thus increasing the likelihood that a <italic>Varroa</italic>-parasitized bee enters a brood cell and fulfilling the mite’s dispersal needs. IAPV benefits again from this effect on behavior, both from the direct transmission through oral secretions but also through increased vectored transmission. If this hypothesis is correct, future studies should incorporate <italic>Varroa</italic> parasitization and movement into their designs, as well as investigating whether <italic>Varroa</italic> prefer infected hosts to uninfected hosts.</p>", "<p id=\"Par29\">While these findings seem to support only the hypothesis that IAPV is manipulating the host to increase transmission, there is another facet of social immunity that may instead be triggering the increased larval contact behaviors. Exposure to a parasite or pathogen promotes the bees’ natural hygienic response, during which honey bee workers inspect brood and remove dead, infected, or otherwise damaged individuals<sup>##UREF##22##53##</sup>. This is due to parasite-induced changes in the worker’s CHC profile, for example those caused by bacterial infection<sup>##UREF##23##54##</sup>, viral infection<sup>##REF##32341145##17##</sup>, and <italic>Varroa</italic> parasitism<sup>##REF##11272645##55##</sup>, which ultimately stimulate a hygienic response in the surrounding workers<sup>##UREF##23##54##,##REF##32346037##56##</sup>; infection status can also induce self-grooming<sup>##UREF##24##57##</sup>. Because they have been stimulated by a sublethal virus infection, the experimentally infected workers in our experiments may be more alert to the presence of a pathogen within their environment than their unexposed nestmates, priming them to perform hygienic behaviors. These bees may then inspect the larva more frequently to determine if the larva also shows signs of infection. While our experiment was not designed to measure the worker’s hygiene thresholds, our data provide some circumstantial support for this hypothesis. In our comparison of exposed and control individuals, IAPV-treated nurses performed more “insertion” behaviors than controls – i.e. inserting their head into the cell to contact the larva. However, for an individual bee over the course of five minutes, the total time spent performing insertion behaviors did not differ between the exposed and unexposed nurses in the 50% exposed dishes. Thus, the higher number of visits performed by the exposed bees was likely accompanied by individually <italic>shorter</italic> visit times. Longer visits are more indicative of feeding events, during which the larva is presented food secretions, whereas shorter visits typically involve larval inspections or cell inspections and maintenance<sup>##UREF##8##18##,##REF##26569402##32##,##UREF##25##58##</sup>. While it is well known that bees remove diseased or damaged brood<sup>##UREF##22##53##</sup>, to our knowledge, it is still unknown whether infected adults exhibit differences in their threshold or ability to detect or remove compromised larvae and pupae. Future work is necessary to understand how infection status of nurse bees affects hygienic responses at a colony level. This response may also have implications for parasite transmission; behaviors that are meant to stem the spread of certain parasites, such as allogrooming and hygienic behaviors, may instead facilitate transmission of others due to the increased number of intimate contacts<sup>##UREF##26##59##,##REF##33903723##60##</sup>. However, whether this is host manipulation or simply the virus exploiting an existing behavior remains to be studied.</p>", "<p id=\"Par30\">Using IAPV as a model virus for studying social immunity, we investigated how brood care, an essential behavior, is affected by viral exposure. However, further study with this system is required to determine whether the observed changes are supported by one or both of the discussed hypotheses, particularly in a larger social context that is more comparable to a full-sized colony. The group sizes used in this study are just one of the limitations. The assay design we used, as described by Shpigler & Robinson<sup>##REF##26569402##32##</sup>, only records behaviors over the course of five minutes. It is possible that longer contact periods between the workers and the larvae may elicit a stronger response. Since the virus’ first description in 2007<sup>##REF##18024913##30##</sup>, IAPV has been associated with colony losses<sup>##REF##17823314##61##</sup> and in many ways is not unlike an emerging infectious disease in other biological systems. Increased study on IAPV, especially its transmission through and interactions with host behavior, is imperative to better characterize the disease dynamics inside the highly specialized and interactive environment of a honey bee colony.</p>" ]	[]	[ "<p id=\"Par1\">To protect themselves from communicable diseases, social insects utilize social immunity—behavioral, physiological, and organizational means to combat disease transmission and severity. Within a honey bee colony, larvae are visited thousands of times by nurse bees, representing a prime environment for pathogen transmission. We investigated a potential social immune response to Israeli acute paralysis virus (IAPV) infection in brood care, testing the hypotheses that bees will respond with behaviors that result in reduced brood care, or that infection results in elevated brood care as a virus-driven mechanism to increase transmission. We tested for group-level effects by comparing three different social environments in which 0%, 50%, or 100% of nurse bees were experimentally infected with IAPV. We investigated individual-level effects by comparing exposed bees to unexposed bees within the mixed-exposure treatment group. We found no evidence for a social immune response at the group level; however, individually, exposed bees interacted with the larva more frequently than their unexposed nestmates. While this could increase virus transmission from adults to larvae, it could also represent a hygienic response to increase grooming when an infection is detected. Together, our findings underline the complexity of disease dynamics in complex social animal systems.</p>", "<title>Subject terms</title>" ]	[ "<title>Supplementary Information</title>", "<p>\n</p>" ]	[ "<title>Supplementary Information</title>", "<p>The online version contains supplementary material available at 10.1038/s41598-023-50585-4.</p>", "<title>Acknowledgements</title>", "<p>This research was supported in part by US Department of Agriculture grant 2019-67013-29300 and the School of Integrative Biology at the University of Illinois Urbana-Champaign. We thank N. Beach for apiary management and bee supply. We thank members of the Dolezal lab for discussion and assistance with field work.</p>", "<title>Author contributions</title>", "<p>L.N.T. and A.G.D. designed the experiments. L.N.T. performed the experiments. L.N.T. wrote the manuscript with consultation from A.G.D.</p>", "<title>Data availability</title>", "<p>The data supporting the findings of this study are openly available in “Dryad” at <ext-link ext-link-type=\"uri\" xlink:href=\"https://datadryad.org/stash/share/xoYSq34OktvhUNQC3140H0c4sejYWWrichzKRwyL_QA\">https://datadryad.org/stash/share/xoYSq34OktvhUNQC3140H0c4sejYWWrichzKRwyL_QA</ext-link>, <ext-link ext-link-type=\"uri\" xlink:href=\"https://doi.org/\">https://doi.org/</ext-link>10.5061/dryad.k98sf7mcj.</p>", "<title>Competing interests</title>", "<p id=\"Par31\">The authors declare no competing interests.</p>" ]	[ "<fig id=\"Fig1\"><label>Figure 1</label><caption><p>Visualization of cage and assay set-up. Day-old bees were placed in groups of 35 into two sets of ten acrylic cages. Each cage of bees received a different color-marking. After 5 days, one of the sets of ten cages was experimentally infected with a sublethal dose of IAPV diluted in sucrose. The other group received untreated sucrose. Two days after exposure, bees were transferred individually from the cages into the assay dishes, such that each dish had ten uniquely marked individuals. Bees were drawn from either the exposed cages or the unexposed cages in order to make the three different social environments: 0% of bees exposed, 50% of bees exposed, and 100% of bees exposed.</p></caption></fig>", "<fig id=\"Fig2\"><label>Figure 2</label><caption><p>Response to the queen cell does not differ between three types of social group. Dishes containing workers with varying percentages of individuals exposed to IAPV were observed for 5 minutes. All group-level responses for a given dish were standardized by dividing by the number of living bees in the dish at the time of recording. The average duration (<bold>a</bold>) and number (<bold>b</bold>) of external-antennation interactions to the queen cell do not differ between the three types of social group (duration: LRT, <italic>p</italic> = 0.611; number: LRT, <italic>p</italic> = 0.938). The average duration (<bold>c</bold>) and number (<bold>d</bold>) of insertion-interactions do not differ between the three types of social group (duration: LRT, <italic>p</italic> = 0.575; number: LRT, <italic>p</italic> = 0.130). (<bold>e</bold>) The average percentage of bees that interacted with the queen cell at least once (responders) does not differ between the three types of social group (LRT, <italic>p</italic> = 0.273). Displayed values are means ± s.e.m.</p></caption></fig>", "<fig id=\"Fig3\"><label>Figure 3</label><caption><p>Average durations and counts of external-antennation and insertion-interactions per individual bee within the 50% exposed group. Nurse bees in dishes where 50% of the population were exposed to IAPV were observed for five minutes. Treatment (IAPV-exposed or unexposed) did not have a significant effect on the average total duration (<bold>a</bold>) and average number (<bold>b</bold>) of external-antennation interactions per bee (duration: LRT, <italic>p</italic> = 0.203; number: LRT, <italic>p</italic> = 0.367). (<bold>c</bold>) Treatment did not have an effect on the average total duration of insertion interactions (LRT, <italic>p</italic> = 0.337). (<bold>d</bold>) An individual bee’s treatment had a significant effect on the average number of insertion-interactions (LRT, <italic>p</italic> = 8.16 × 10<sup>–3</sup>), and exposed bees entered inside the queen cell more often than unexposed bees (Tukey HSD, <italic>p</italic> = 8.7 × 10<sup>–3</sup>). Displayed values are means ± s.e.m.</p></caption></fig>" ]	[]	[]	[]	[]	[]	[]	[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>" ]	[ "<fn-group><fn><p><bold>Publisher's note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"41598_2023_50585_Fig1_HTML\" id=\"MO1\"/>", "<graphic xlink:href=\"41598_2023_50585_Fig2_HTML\" id=\"MO2\"/>", "<graphic xlink:href=\"41598_2023_50585_Fig3_HTML\" id=\"MO3\"/>" ]	[ "<media xlink:href=\"41598_2023_50585_MOESM1_ESM.pdf\"><caption><p>Supplementary Information.</p></caption></media>" ]	[{"label": ["1."], "surname": ["Silk"], "given-names": ["JB"], "article-title": ["The adaptive value of sociality in mammalian groups"], "source": ["Philos. Trans. R. Soc. B Biol. 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Ecol. Sociobiol."], "year": ["2020"], "volume": ["74"], "fpage": ["108"], "pub-id": ["10.1007/s00265-020-02887-0"]}, {"label": ["58."], "surname": ["Noonan"], "given-names": ["KC"], "article-title": ["Recognition of queen larvae by worker honey bees ("], "italic": ["Apis", "mellifera"], "source": ["Ethology"], "year": ["1986"], "volume": ["73"], "fpage": ["295"], "lpage": ["306"], "pub-id": ["10.1111/j.1439-0310.1986.tb00811.x"]}, {"label": ["59."], "surname": ["Drewe"], "given-names": ["JA"], "article-title": ["Who infects whom? Social networks and tuberculosis transmission in wild meerkats"], "source": ["Proc. R. Soc. B Biol. Sci."], "year": ["2009"], "volume": ["277"], "fpage": ["633"], "lpage": ["642"], "pub-id": ["10.1098/rspb.2009.1775"]}]	{ "acronym": [], "definition": [] }	61	CC BY	no	2024-01-13 00:02:20	Sci Rep. 2024 Jan 10; 14:991	oa_package/d0/7e/PMC10781695.tar.gz
PMC10781696	38200166	[ "<title>Introduction</title>", "<p id=\"Par2\">A number of gradient-based optimization strategies have been proposed in the recent years by researchers to address a variety of problems. These strategies are found to be competitive, but they have a number of shortcomings. The two main drawbacks are parameter tuning issues and local optima stagnation. These algorithms either stop moving towards the overall optimal solution or get stuck in a particular local optimal solution. Additionally, because the solutions produced by these algorithms are dependent on assumptions, they are ineffective for dealing with computationally expensive problems<sup>##UREF##0##1##</sup>. The same context led to the development of nature-inspired algorithms (NIA), which were created to overcome the problems with conventional optimization methods. Nature has served as an important source of motivation for humans to overcome difficulties in the real world for millions of years. Due to their speed and adaptability, NIA has become well-known in almost every area of research<sup>##UREF##1##2##</sup>. The global solution can be found using these population-based techniques without the need for gradient information. Their widespread appeal stems from this essential factor, which does not call for an initial assessment. Evolutionary algorithms (EA) and swarm intelligence (SI) algorithms make up the majority of the many algorithms that have been proposed in this field.</p>", "<p id=\"Par3\">The goal of EA is to identify the most suitable individual among all potential solutions and pass it on to the upcoming generation. This is because there is a higher chance that the best solutions from the previous generation will lead to more optimal solutions. A well-known evolutionary algorithm is the genetic algorithm (GA)<sup>##REF##1411454##3##</sup> which was developed based on Darwin’s theory of evolution. To address the shortcomings of GA, the differential evolution (DE)<sup>##UREF##2##4##</sup> technique was later developed. The other optimization methods in this group are moth flame optimization (MFO)<sup>##UREF##3##5##</sup>, evolutionary strategy (ES)<sup>##UREF##4##6##</sup> and biogeography-based optimization (BBO)<sup>##UREF##5##7##</sup>.</p>", "<p id=\"Par4\">The social behaviour of swarms of insects such as ants, bees and birds, served as the inspiration for SI, a form of meta-heuristic algorithm. This method is used by swarms to interact with one another and their environment while searching for food or prey. Both self-organization and task division are essential elements of these strategies. Particle swarm optimization (PSO)<sup>##UREF##6##8##</sup> mimics the behaviour of flocks of birds. Other algorithms in this category include the cuckoo search (CS)<sup>##UREF##7##9##</sup>, salp swarm algorithm (SSA)<sup>##UREF##8##10##,##UREF##9##11##</sup>, grey wolf optimization (GWO)<sup>##UREF##10##12##</sup>, artificial rabbits optimization<sup>##UREF##11##13##</sup>, bat algorithm (BA)<sup>##UREF##12##14##</sup> and naked mole-rat algorithm (NMRA)<sup>##UREF##13##15##</sup>. Recenty introduced optimization algorithms include Diversity-maintained multi-trial vector differential evolution algorithm for non-decomposition large-scale global optimization (DMDE)<sup>##UREF##14##16##</sup>, Binary approaches of quantum-based avian navigation optimizer to select effective features from high-dimensional medical data<sup>##UREF##15##17##</sup>, MFO-SFR: an enhanced moth-flame optimization algorithm using an effective stagnation finding and replacing strategy<sup>##UREF##16##18##</sup> and Quantum-based avian navigation optimizer algorithm (QANA)<sup>##UREF##17##19##</sup> that prove their worth for solving different optimization problems.</p>", "<p id=\"Par5\">NMRA is another recently proposed SI optimization technique that mimics the natural breeding habits of mole-rats. This algorithm requires tuning of only two parameters such mating factor and breeding probability (<italic>bp</italic>). NMRA is used to address some actual optimization issues, including the location of nodes in wireless sensor networks that are<sup>##UREF##18##20##</sup> and the design of ultra wide band antennas with DE hybridization<sup>##UREF##19##21##</sup>. Despite being a competitive algorithm, NMRA suffers from local optimal stagnation problem, which spurs the researcher to create an improved version of the classical NMRA.</p>", "<p id=\"Par6\">In the present work, utilizing both the global worst and global best solutions<sup>##UREF##20##22##</sup> to implement the attraction and repulsion strategy in the breeder phase (exploitation phase) of the proposed algorithm named as ARNMRA. In order to find the best solution, this method enables the search agents (breeder rats) to wander arbitrary under the effect of both attraction and repulsion. At this point, the implementation of the global worst solution can help increase population diversity and issue of premature convergence is handled. The main contribution of this paper can be summed up as follows:<list list-type=\"bullet\"><list-item><p id=\"Par7\">A self-adaptive attraction and repulsion-based NMRA is proposed with improved exploration and exploitation properties.</p></list-item><list-item><p id=\"Par8\">The working efficiency of proposed ARNMRA is tested for CEC 2005 and CEC 2019 numerical problems.</p></list-item><list-item><p id=\"Par9\">The statistical results of ARNMRA have been compared with other state-of-the-art algorithms and validated by two statistical tests, namely the rank-sum (p-rank) test and Friedman (f-rank) test.</p></list-item><list-item><p id=\"Par10\">A clustering protocol for mobile WSN is developed using ARNMRA for optimal selection of CH.</p></list-item><list-item><p id=\"Par11\">To achieve an extended stability period, a clustering solution inspired by the optimization method ARNMRA is taken into consideration.</p></list-item></list>This article is partitioned into various sections, with an introduction to the article is discussed in “<xref rid=\"Sec1\" ref-type=\"sec\">Introduction</xref>” section and a mathematical model of classical NMRA is presented in “<xref rid=\"Sec2\" ref-type=\"sec\">Mathematical model of NMRA</xref>” section. The proposed approach for enhancing the effectiveness of the fundamental NMRA is presented in “<xref rid=\"Sec6\" ref-type=\"sec\">Proposed algorithm: attraction and repulsion based naked mole-rat algorithm</xref>” section and the statistical findings for the CEC 2005 and CEC 2019 numerical benchmark problems are covered in “<xref rid=\"Sec9\" ref-type=\"sec\">Results and discussion</xref>” section. In “<xref rid=\"Sec13\" ref-type=\"sec\">Real time application: energy-efficient ARNMRA based routing protocol (EARNRP) for mobile wireless sensor networks</xref>” section details the clustering algorithm for energy efficient mobile WSN. Finally, “<xref rid=\"Sec21\" ref-type=\"sec\">Conclusion and future scope</xref>” section includes the article’s conclusion and future scope.</p>" ]	[]	[ "<title>Results and discussion</title>", "<p id=\"Par20\">Three sets of numerical benchmark problems, including the CEC 2005 numerical benchmark problems<sup>##UREF##22##24##</sup>, CEC 2019<sup>##UREF##23##25##</sup>, and CEC 2020<sup>##UREF##24##26##</sup>, are used to evaluate the effectiveness of the proposed ARNMRA. For these test problems, statistical results are produced and are then contrasted with other competitive meta-heuristic methods. The following subsections discuss the statistical outcomes of the proposed optimization technique ARNMRA for these numerical problems.</p>", "<title>Statistical results for CEC 2005 numerical benchmark problems</title>", "<p id=\"Par21\">The statistical findings for the proposed ARNMRA and other competing algorithms are analysed in this subsection. The 12 numerical problems from the CEC 2005 test suite are chosen, and descriptions of these problems are provided in<sup>##UREF##9##11##</sup> to evaluate the efficiency of ARNMRA. The numerical problems utilized here can be broadly categorised into two groups: uni-modal problems ( to ) and multi-modal problems ( to ). Here, all of the statistical findings are attained with a 30 dimension size, 50 mole-rats population and 500 iterations.</p>", "<p id=\"Par22\">The statistical findings of ARNMRA and various competitive algorithms including traditional NMRA, SHADE, OB-L-EO, SOGWO and IWOA are presented in Table ##TAB##0##1##. For 51 runs of the algorithm, the statistical findings are presented as mean and standard deviation (std) values. According to these findings, ARNMRA outperforms all other algorithms for problems , \n, , and . SHADE performs the best for problem and . For numerical problems and , the four approaches (OB-L-EO, SOGWO, NMRA and ARNMRA) achieve global minimal values While NMRA and ARNMRA provide identical results for . In case of problem , working efficiency of OB-L-EO is found to be superior in comparison with other optimization techniques.</p>", "<p id=\"Par23\">To assess the operational efficiency of ARNMRA, the study employs two statistical tests: the rank-sum (p-rank) test<sup>##UREF##25##27##</sup> and the Friedman (f-rank) test<sup>##UREF##26##28##</sup>. In the p-rank test, the variables <italic>win</italic>(<italic>w</italic>)/<italic>loss</italic>(<italic>l</italic>)/<italic>tie</italic>(<italic>t</italic>) are used to evaluate the performance of ARNMRA compared to other optimization techniques. A “ ”symbol indicates that the compared technique outperforms ARNMRA <italic>win</italic>(<italic>w</italic>), a “−” symbol denotes that it performs worse <italic>loss</italic>(<italic>l</italic>), and “” represents an equal performance <italic>tie</italic>(<italic>t</italic>). The results in Table ##TAB##0##1## demonstrate that ARNMRA outperforms the majority of the tested problems. Additionally, the f-rank test assigns a rank to each optimization method being evaluated. The fourth row of Table ##TAB##0##1## displays the rank of each technique for each numerical test problem. After assigning f-ranks to all algorithms, the average f-rank value and overall f-rank are calculated (last two rows of Table ##TAB##0##1##). These results indicate that ARNMRA is a statistically significant optimization technique, consistently ranking at the top among all other algorithms. Here, both the p-rank and f-rank tests confirm the superior performance of ARNMRA. The p-rank test demonstrates that ARNMRA outperforms other techniques in most test problems, while the f-rank test reinforces its statistical significance and consistent top ranking among the evaluated algorithms.</p>", "<p id=\"Par24\">For each numerical problem in the CEC 2005 test suite, convergence profiles of the basic NMRA and purposed ARNMRA are also drawn after the simulated results. The convergence graphs shown in Fig. ##FIG##1##1## confirm that the proposed ARNMRA converges to the optimal solution more quickly than the conventional NMRA.</p>", "<title>Statistical results for CEC 2019 numerical benchmark test suite</title>", "<p id=\"Par25\">In this subsection, the proposed ARNMRA’s performance is evaluated for the 100-digit challenge (CEC 2019). This test suite contains 10 numerical problems, and an explanation of every test problem can be found in the reference<sup>##UREF##23##25##</sup>. Here, statistical findings for 51 runs of the algorithms for the original NMRA and the suggested ARNMRA are presented as average, best, median, worst, and standard deviation (std) values. The number of mole-rats should be set to 50, the problem’s dimension should match the description, and a maximum iterations of 500 should be used for generating the results.</p>", "<p id=\"Par26\">Table ##TAB##1##2## contains the statistical findings produced for these numerical benchmark functions. For the benchmark problem , ARNMRA has a better operating capacity than classical NMRA. The suggested technique ARNMRA is the best for all performance measures for the benchmark problems , , , , and test functions. For problem , the results are compared for standard values and ARNMRA provides the best performance. In case of problems and , NMRA’s performance is better as compared to proposed ARNMRA. Overall, it is determined that the suggested optimization technique ARNMRA is shown superior performance for majority of these numerical benchmark problems.</p>", "<p id=\"Par27\">After obtaining simulated results for each numerical problem in the CEC 2019 test suite, convergence profiles for both the basic NMRA and the proposed ARNMRA are depicted. The convergence graphs, as illustrated in Fig. ##FIG##2##2##, demonstrate that the ARNMRA converges to the optimal solution at a faster rate compared to the original NMRA.</p>", "<title>Statistical results for CEC 2020 numerical benchmark test suite</title>", "<p id=\"Par28\">The performance of the proposed ARNMRA has been accessed for CEC 2020 test suite in this subsection. This test suite comprises 10 numerical problems, each with detailed explanations available in the referenced work<sup>##UREF##24##26##</sup>. The statistical results for 51 runs of both the original NMRA and the proposed ARNMRA are presented, including mean, best, worst and standard deviation (<italic>Std</italic>) values. The parameter configuration involves setting the function evaluations , ensuring the problem’s dimension (<italic>D</italic>) is 20 for result generation.</p>", "<p id=\"Par29\">The statistical results presented in Table ##TAB##2##3## highlight the performance of the ARNMRA on various numerical test problems. In the case of numerical problems , and , ARNMRA exhibits superior operational capabilities compared to the classical NMRA. Notably, the proposed technique, ARNMRA, outperforms classical NMRA across all performance metrics for benchmark problems , , , , , and . Conversely, for problem , NMRA performs better than the proposed ARNMRA. The suggested optimization technique, ARNMRA, demonstrates superior performance for most of these numerical test problems.</p>" ]	[ "<title>Results and discussion</title>", "<p id=\"Par20\">Three sets of numerical benchmark problems, including the CEC 2005 numerical benchmark problems<sup>##UREF##22##24##</sup>, CEC 2019<sup>##UREF##23##25##</sup>, and CEC 2020<sup>##UREF##24##26##</sup>, are used to evaluate the effectiveness of the proposed ARNMRA. For these test problems, statistical results are produced and are then contrasted with other competitive meta-heuristic methods. The following subsections discuss the statistical outcomes of the proposed optimization technique ARNMRA for these numerical problems.</p>", "<title>Statistical results for CEC 2005 numerical benchmark problems</title>", "<p id=\"Par21\">The statistical findings for the proposed ARNMRA and other competing algorithms are analysed in this subsection. The 12 numerical problems from the CEC 2005 test suite are chosen, and descriptions of these problems are provided in<sup>##UREF##9##11##</sup> to evaluate the efficiency of ARNMRA. The numerical problems utilized here can be broadly categorised into two groups: uni-modal problems ( to ) and multi-modal problems ( to ). Here, all of the statistical findings are attained with a 30 dimension size, 50 mole-rats population and 500 iterations.</p>", "<p id=\"Par22\">The statistical findings of ARNMRA and various competitive algorithms including traditional NMRA, SHADE, OB-L-EO, SOGWO and IWOA are presented in Table ##TAB##0##1##. For 51 runs of the algorithm, the statistical findings are presented as mean and standard deviation (std) values. According to these findings, ARNMRA outperforms all other algorithms for problems , \n, , and . SHADE performs the best for problem and . For numerical problems and , the four approaches (OB-L-EO, SOGWO, NMRA and ARNMRA) achieve global minimal values While NMRA and ARNMRA provide identical results for . In case of problem , working efficiency of OB-L-EO is found to be superior in comparison with other optimization techniques.</p>", "<p id=\"Par23\">To assess the operational efficiency of ARNMRA, the study employs two statistical tests: the rank-sum (p-rank) test<sup>##UREF##25##27##</sup> and the Friedman (f-rank) test<sup>##UREF##26##28##</sup>. In the p-rank test, the variables <italic>win</italic>(<italic>w</italic>)/<italic>loss</italic>(<italic>l</italic>)/<italic>tie</italic>(<italic>t</italic>) are used to evaluate the performance of ARNMRA compared to other optimization techniques. A “ ”symbol indicates that the compared technique outperforms ARNMRA <italic>win</italic>(<italic>w</italic>), a “−” symbol denotes that it performs worse <italic>loss</italic>(<italic>l</italic>), and “” represents an equal performance <italic>tie</italic>(<italic>t</italic>). The results in Table ##TAB##0##1## demonstrate that ARNMRA outperforms the majority of the tested problems. Additionally, the f-rank test assigns a rank to each optimization method being evaluated. The fourth row of Table ##TAB##0##1## displays the rank of each technique for each numerical test problem. After assigning f-ranks to all algorithms, the average f-rank value and overall f-rank are calculated (last two rows of Table ##TAB##0##1##). These results indicate that ARNMRA is a statistically significant optimization technique, consistently ranking at the top among all other algorithms. Here, both the p-rank and f-rank tests confirm the superior performance of ARNMRA. The p-rank test demonstrates that ARNMRA outperforms other techniques in most test problems, while the f-rank test reinforces its statistical significance and consistent top ranking among the evaluated algorithms.</p>", "<p id=\"Par24\">For each numerical problem in the CEC 2005 test suite, convergence profiles of the basic NMRA and purposed ARNMRA are also drawn after the simulated results. The convergence graphs shown in Fig. ##FIG##1##1## confirm that the proposed ARNMRA converges to the optimal solution more quickly than the conventional NMRA.</p>", "<title>Statistical results for CEC 2019 numerical benchmark test suite</title>", "<p id=\"Par25\">In this subsection, the proposed ARNMRA’s performance is evaluated for the 100-digit challenge (CEC 2019). This test suite contains 10 numerical problems, and an explanation of every test problem can be found in the reference<sup>##UREF##23##25##</sup>. Here, statistical findings for 51 runs of the algorithms for the original NMRA and the suggested ARNMRA are presented as average, best, median, worst, and standard deviation (std) values. The number of mole-rats should be set to 50, the problem’s dimension should match the description, and a maximum iterations of 500 should be used for generating the results.</p>", "<p id=\"Par26\">Table ##TAB##1##2## contains the statistical findings produced for these numerical benchmark functions. For the benchmark problem , ARNMRA has a better operating capacity than classical NMRA. The suggested technique ARNMRA is the best for all performance measures for the benchmark problems , , , , and test functions. For problem , the results are compared for standard values and ARNMRA provides the best performance. In case of problems and , NMRA’s performance is better as compared to proposed ARNMRA. Overall, it is determined that the suggested optimization technique ARNMRA is shown superior performance for majority of these numerical benchmark problems.</p>", "<p id=\"Par27\">After obtaining simulated results for each numerical problem in the CEC 2019 test suite, convergence profiles for both the basic NMRA and the proposed ARNMRA are depicted. The convergence graphs, as illustrated in Fig. ##FIG##2##2##, demonstrate that the ARNMRA converges to the optimal solution at a faster rate compared to the original NMRA.</p>", "<title>Statistical results for CEC 2020 numerical benchmark test suite</title>", "<p id=\"Par28\">The performance of the proposed ARNMRA has been accessed for CEC 2020 test suite in this subsection. This test suite comprises 10 numerical problems, each with detailed explanations available in the referenced work<sup>##UREF##24##26##</sup>. The statistical results for 51 runs of both the original NMRA and the proposed ARNMRA are presented, including mean, best, worst and standard deviation (<italic>Std</italic>) values. The parameter configuration involves setting the function evaluations , ensuring the problem’s dimension (<italic>D</italic>) is 20 for result generation.</p>", "<p id=\"Par29\">The statistical results presented in Table ##TAB##2##3## highlight the performance of the ARNMRA on various numerical test problems. In the case of numerical problems , and , ARNMRA exhibits superior operational capabilities compared to the classical NMRA. Notably, the proposed technique, ARNMRA, outperforms classical NMRA across all performance metrics for benchmark problems , , , , , and . Conversely, for problem , NMRA performs better than the proposed ARNMRA. The suggested optimization technique, ARNMRA, demonstrates superior performance for most of these numerical test problems.</p>" ]	[ "<title>Conclusion and future scope</title>", "<p id=\"Par94\">This study introduces the attraction and repulsion-based naked mole-rat algorithm (ARNMRA) as an enhancement to the original NMRA. The primary issue with NMRA is its susceptibility to premature convergence and getting trapped in local optima. To overcome this problem, the ARNMRA incorporates an attraction and repulsion strategy along with self-adaptation of the mating factor. The algorithm’s effectiveness is evaluated on benchmark problems from the CEC 2005 and CEC 2019 test suites, and its performance is compared to other algorithms including SHADE, OB-L-EO, SOGWO, IWOA, and NMRA. Furthermore, a mobility-based energy-aware routing protocol (EARNRP) for WSNs with mobile nodes is proposed in this paper. The protocol enables the selection of cluster heads (CHs) based on parameters such as connection time and mobility. Non-CH nodes aim to establish stable connections with CHs during the clustering process, considering the estimated connection time. The TDMA schedule is designed to assign timeslots for data transmission, with the order of timeslots based on the estimated connection time. This scheduling approach ensures efficient utilization of network resources and minimizes collisions among nodes.Simulations are conducted to evaluate the performance of the proposed algorithm and protocol. One key metric analyzed is the network lifetime, which is influenced by factors like the percentage of mobile nodes and varying speeds. Increased mobility and speed can lead to more frequent collisions among nodes, resulting in network congestion. The impact of this congestion on network lifetime and overall performance is investigated. By studying the network lifetime under different mobility and speed conditions, the research provides insights into the effectiveness of the proposed protocol in dynamic network environments. The findings emphasize the importance of considering mobility and speed factors in the design of routing protocols for WSNs with mobile nodes, as they directly impact network performance and longevity.</p>", "<p id=\"Par95\">As a future prospect, the performance of the proposed strategies can be further examined in engineering design optimization problems such as antenna design, robot control, filter optimization, and load dispatch issues. Additionally, the application of the ARNMRA algorithm can be extended to address multi-objective optimization problems, such as feature selection, web-based clustering, and other complex optimization challenges.</p>" ]	[ "<p id=\"Par1\">Naked mole-rat algorithm (NMRA) is a swarm intelligence-based algorithm that draws inspiration from the mating behaviour of mole rats (workers and breeders). This approach, which is based on the ability of breeders to reproduce with the queen, has been utilized to tackle optimization problems. The algorithm, however, suffers from local optima stagnation problem and a slower rate of convergence in order to provide gobal optimal solution. This study suggests attraction and repulsion strategy based NMRA (ARNMRA) along with self-adaptive properties to avoid trapping of solution in local optima. This strategy is utilized to create new breeder rat solutions and mating factor is made self-adaptive using simulated annealing (<italic>sa</italic>) based mutation operator. ARNMRA is evaluated on CEC 2005 numerical benchmark problems and found to be superior to other algorithms, including well-known ones like selective operation based GWO (SOGWO), opposition based laplacian equilibrium optimizer (OB-L-EO), improved whale optimization algorithm (IWOA), success-history based adaptive DE (SHADE) and original NMRA. Further, according to experimental results, the performance of ARNMRA is likewise superior to the NMRA for the CEC 2019 and CEC 2020 numerical problems. Convergence profiles and statistical tests (rank-sum test and Friedman test) are employed further to validate the experimental results. Moreover, this article extends the application of ARNMRA to address the data gathering aspect in mobile wireless sensor networks (MWSNs) with the goal of prolonging network lifetime and enhancing energy efficiency. In this MWSN-based protocol, a sensor node is elected as a cluster head based on factors like mobility, residual energy, and connection time. The protocol aims to maximize the system lifetime by efficiently collecting data from all sensors and transmitting it to the base station. The study emphasizes the significance of considering dynamic node densities and speed when designing effective data-gathering protocols for MWSNs.</p>", "<title>Subject terms</title>" ]	[ "<title>Mathematical model of NMRA</title>", "<p id=\"Par12\">One of the most well-known swarm intelligent meta-heuristic approaches is NMRA, which was proposed by<sup>##UREF##13##15##</sup>. This method divides the mole-rat population into worker rats and breeder rats based on the swarm intelligence behaviour of mole-rats found in nature. Breeder rats are useful for performing the exploitation phase of the algorithm, whereas worker rats are primarily incharge of doing the exploration phase. These mole-rats often reside in colonies approximately 70 in size, and the queen of each colony serves as its leader. The following phases are used to define the NMRA mathematical model (exploitation).</p>", "<title>Population initialization</title>", "<p id=\"Par13\">The initial distribution of mole-rats (<italic>MR</italic>) in the search space with dimension (<italic>d</italic>), where (<italic>d</italic>) is the variable count in the problem to be optimised. Each mole rat is initialised using the following equation:where <italic>p</italic>\n\n, <italic>q</italic>\n\n, presents <italic>p</italic>th solution of mole rat for <italic>q</italic>th dimension, and specify the lower and upper bounds of objective function. The random number <italic>rand</italic> has a uniform distribution between 0 and 1.</p>", "<title>Exploration phase (worker phase)</title>", "<p id=\"Par14\">In order to increase their chances of becoming breeding rats and eventually mating with the queen, worker rats often improve their fitness during this phase. As a result, the new worker rat generates a solution based on its own past and local information. If the new mole rat’s fitness for mating is superior, the old solution is disregarded, and the new solution is memorized. If not, the previous solution will be applied. The overall fitness of each rat is recorded once they have all finished the search. To determine the solution to the new worker rat’s use this equation:where designates the <italic>p</italic> thworker’s solution produced in the <italic>t</italic>th iteration, designates a new worker mole rat’s solution, parameter corresponds to the factor dealing with queen’s mating and its value is determined at random between 0 and 1, and and are two worker’s solutions chosen at random from the population.</p>", "<title>Exploitation phase (breeder phase)</title>", "<p id=\"Par15\">Breeder mole-rats in this phase must also maintain themselves updated in order to be chosen for mating and to continue to be breeders. Based on the overall initial best solution and breeding probability (<italic>bp</italic>), the rats in the breeder’s pool are updated. This <italic>bp</italic> defines at random in the [0,1] range. Some breeder rats might not be able to maintain their fitness and might end up in the worker’s group. The breeding rat solution is generated as follows:where is the <italic>p</italic>th breeder’s solution for the <italic>t</italic>th iteration, the factor regulates the frequency of breeder rats mating with the queen, and additional breeder rats or the solution has been produced depending on this frequency. The value of <italic>bp</italic> is initially set at 0.5.</p>", "<title>Proposed algorithm: attraction and repulsion based naked mole-rat algorithm</title>", "<p id=\"Par16\">NMRA has drawn the interest of many researchers due to its success in solving a wide range of practical problems. Since it was developed, the basic, linear approach has been utilized to resolve optimization problems, but it still has drawback of trapping in local optimal solution. Because of this, we present an enhanced version of NMRA in this study that is based on the attraction and repulsion strategy<sup>##UREF##20##22##</sup>. This suggested algorithm also contains a simulated annealing based mutation operation<sup>##UREF##21##23##</sup> to confirm the mating factor applicability to NMRA. Here, it is important to make sure that the attraction and repulsion strategy has been used to modify the Eq. (##FORMU##17##3##) and simulated annealing mutation operator is applied to parameter of the classical NMRA.</p>", "<title>Adoption of attraction and repulsion strategy</title>", "<p id=\"Par17\">The global optimal solution directs the breeder rats movement in the direction of the ideal solution (mating with queen). However, if the solution is trapped in the local optimal and unable to escape, the entire population is likely to stagnate. This proposed strategy ARNMRA uses the attraction–repulsion method to address this issue. Here, breeder rat solution Eq. (##FORMU##17##3##) has been modified with the help of global best and worst solution using the attraction–repulsion principle moving at random due to the effects of attraction and repulsiveness to discover the best optimal solution. The population’s diversity will be dramatically diminished as the algorithm’s iterative process progresses, which will lead to the premature occurrence. The introduction of the global worst solution may contribute to a rise in population diversity. It solves the issue of early convergence and broadens the population’s scope in the local search process. The following equation is used to obtain attraction–repulsion strategy based breeder rat solution:where is the breeder mole rat solution for the current iteration <italic>t</italic>, is the global worst position, is the global optimum solution. The values of and are considered as 0.5 and 0.4 respectively. The incorporation of attraction and repulsion during the mating process in the breeding mole rats within the ARNMRA algorithm leads to several advantages. Firstly, this interaction enhances the influence on the breeder mole rats, allowing them to be more strongly guided towards the global best solution, represented by the queen. By being influenced by both attraction and repulsion, the breeders are able to explore the solution space more effectively, increasing the likelihood of finding better solutions. Secondly, the interaction of attraction and repulsion promotes a higher level of diversity within the population. The attraction component encourages convergence towards promising regions of the search space, while the repulsion component discourages the population from clustering around local optima. As a result, the population maintains a greater diversity of solutions, which is beneficial for avoiding premature convergence and increasing the chances of finding the global optimum.</p>", "<title>Parameter adaptation</title>", "<p id=\"Par18\">The suggested optimization technique ARNMRA heavily relies on the original NMRA mating factor . To achieve better results, this parameter must be altered from its default definition in the basic NMRA. As a result, this option has been altered so that no changes at the user level are necessary and that the parameter is implemented using <italic>sa</italic> based mutation, which yields the best randomization outcomes. The <italic>sa</italic> based mutation technique is carried out using the generalised equation:where <italic>d</italic> is fixed at 0.95 and enhances the convergence speed of the algorithm, , and <italic>s</italic> are produced at random from values between [0,1].</p>", "<p id=\"Par19\">Overall, the ARNMRA optimization algorithm is developed using attraction and repulsion strategy along with adaption of important parameter . The major purpose of these modifications is to increase mole-rats diversity, which will enhance exploitation properties and convergence activities. The pseudo-code of the proposed ARNMRA is given in Algorithm 1. </p>", "<title>Real time application: energy-efficient ARNMRA based routing protocol (EARNRP) for mobile wireless sensor networks</title>", "<p id=\"Par30\">Wireless sensor networks (WSNs) consist of compact, energy-efficient sensor nodes that communicate without the need for wired connections. These nodes are designed to monitor and gather data from the surrounding physical environment using wireless communication technology<sup>##UREF##31##33##</sup>. These networks are typically used for applications such as environmental monitoring, industrial automation, healthcare, and smart cities. Mobile WSNs (MWSNs) introduce additional challenges compared to traditional static WSNs due to the mobility of sensor nodes. The movement of sensor nodes introduces dynamic changes in network topology, which can disrupt network connectivity and routing paths. Nodes may join or leave the network, leading to frequent topology reconfigurations. Efficient mechanisms for node tracking, localization, and adaptability to node mobility are required. In MWSNs, sensor nodes are typically powered by batteries, and energy efficiency is crucial for prolonging the network’s lifetime. However, node mobility can cause energy imbalances due to varying distances, resulting in some nodes depleting their energy faster than others. Strategies for energy-efficient routing, power management, and dynamic energy replenishment become essential.</p>", "<p id=\"Par31\">Maintaining connectivity in a mobile environment is challenging. Nodes may move out of each other’s communication range, leading to link failures and frequent disconnections. Reliable and robust communication protocols need to be employed to handle intermittent connections, link quality variations, and node mobility-induced network partitions. Mobile sensor nodes often generate large volumes of data. Efficient data fusion and aggregation techniques are required to reduce redundancy and minimize the amount of data transmitted. However, due to node mobility, data fusion becomes more challenging as nodes move in and out of each other’s sensing ranges. Routing in MWSNs becomes complex due to dynamic topology changes caused by node mobility. Traditional routing protocols designed for static WSNs may not be suitable. Adaptive routing algorithms that can dynamically adjust to changing network conditions, consider node mobility, and provide efficient path planning are necessary. Scalability is a concern in MWSNs, especially when large numbers of mobile nodes are involved. Efficient protocols and mechanisms are needed to handle the increased network size, dynamic topology changes, and frequent node movements without sacrificing network performance and resource utilization.</p>", "<p id=\"Par32\">Addressing these challenges requires the development of specialized algorithms, protocols, and system designs tailored for Mobile WSNs. Researchers continue to explore innovative solutions to overcome these obstacles and unlock the full potential of mobile sensing applications.</p>", "<p id=\"Par33\">Clustering in sensor networks is utilized to reduce energy consumption. However, existing clustering protocols for mobile sensor nodes encounter difficulties in maintaining energy efficiency because they do not adequately consider node movement after clustering. While mobile sensor nodes can offer improved network coverage and connectivity compared to static nodes, effectively managing their operation in line with specific application requirements is a complex task. In various mobile scenarios, existing clustering protocols for mobile sensor nodes often struggle to adequately tackle the challenges related to energy efficiency. One of the key reasons behind this is their failure to account for node movement after clustering. To address the energy efficiency challenges in mobile scenarios, it becomes crucial to develop clustering protocols that can adapt to node mobility. These protocols should take into consideration the movement patterns of nodes and incorporate energy-efficient strategies. By incorporating both node mobility and energy constraints into the design of clustering protocols, it becomes possible to develop more effective solutions that significantly enhance the energy efficiency of mobile sensor networks.</p>", "<p id=\"Par34\">The LEACH-M protocol, an extension of the LEACH protocol<sup>##UREF##32##34##</sup>, has been specifically designed to accommodate the mobility of sensor nodes. It maintains the same cluster head (CH) formation and selection process as LEACH but introduces membership declaration to ensure the inclusion of sensor nodes during the steady-state phase. LEACH-M improves the rate of successful packet delivery but at the cost of increased control overhead. E-LEACH, an enhanced version of LEACH<sup>##UREF##33##35##</sup>, incorporates the remoteness mobility metric in its protocol. It aims to enhance the performance of LEACH by considering node mobility. By taking into account node mobility, E-LEACH strives to optimize the routing decisions and improve the overall efficiency of the protocol. CBR<sup>##UREF##34##36##</sup> is another protocol proposed for mobile sensor nodes. CBR utilizes adaptive TDMA scheduling and employs round-free cluster heads. This allows the cluster head to receive data not only from its cluster members but also from nodes that enter the cluster. CBR aims to increase the packet delivery rate by enabling efficient data collection within the clusters. While LEACH-M, E-LEACH, and CBR all demonstrate improvements in terms of packet delivery rate, it is important to note that these enhancements come at the expense of increased control overhead and higher energy consumption. These trade-offs must be carefully considered when selecting a clustering protocol for mobile sensor networks.</p>", "<p id=\"Par35\">This section is organized as follows: in “<xref rid=\"Sec14\" ref-type=\"sec\">Radio model</xref>” section provides an overview of the challenges and requirements in wireless sensor networks with mobile nodes. In “<xref rid=\"Sec15\" ref-type=\"sec\">Random waypoint mobility model</xref>” section describes the mobility model used in the study. This sub-section explains the characteristics and behaviour of the wireless communication and mobility of sensor nodes. In “<xref rid=\"Sec16\" ref-type=\"sec\">System assumptions</xref>” and “<xref rid=\"Sec17\" ref-type=\"sec\">Protocol description</xref>” sections present the details of the proposed protocol, including its design, operation, and key features. This section explains how the protocol addresses the challenges and improves energy efficiency in WSNs with mobile nodes. In “<xref rid=\"Sec18\" ref-type=\"sec\">CH election in proposed EARNRP protocol</xref>” section deals with CH selection using EARNRP protocol. In “<xref rid=\"Sec19\" ref-type=\"sec\">Operation of proposed protocol</xref>” section, the proposed protocol’s operation has been discussed. The simulation setup and methodology used to evaluate the performance of the proposed protocol in “<xref rid=\"Sec20\" ref-type=\"sec\">Analysis of results</xref>” section. It discusses the experimental results, performance metrics, and comparisons with other protocols. The section provides analysis and discussions on the effectiveness of the proposed protocol.</p>", "<title>Radio model</title>", "<p id=\"Par36\">The first-order radio communication model<sup>##UREF##35##37##</sup> is a widely used model in Wireless Sensor Networks (WSNs) to estimate energy consumption during communication. It considers the energy dissipated by the radio electronics and power amplifier when transmitting a packet from a sender to a receiver. The energy consumption is influenced by factors such as the packet size, distance between the sender and receiver, path loss, and fading effects. Figure ##FIG##3##3## illustrates the components involved in the energy dissipation process. To characterize signal propagation in wireless communication, two commonly used models are the free space model and the multipath fading model. These models define how the signal strength and quality vary with the transmitter and receiver separation (<italic>d</italic>). The free space model assumes that the signal propagates through a clear and unobstructed space, whereas the multipath fading model accounts for the effects of reflections, diffractions, and scattering caused by objects and the environment. By utilizing these models, the first-order radio communication model enables the estimation of energy consumption based on the distance between communicating nodes and other relevant factors. It serves as a valuable tool for assessing and optimizing energy efficiency in WSNs.</p>", "<p id=\"Par37\">In the free space model, the assumption is made that wireless signals propagate through unobstructed space without encountering obstacles or interference. This model is typically applicable when the distance between the transmitter and receiver is relatively small, below a certain threshold distance (). According to the free space model, the received signal power diminishes in proportion to the square of the distance, as described by the inverse square law.</p>", "<p id=\"Par38\">On the other hand, the multipath fading model considers the effects of signal reflections, diffractions, and scattering caused by obstacles in the propagation environment. It is used for longer distances where the signal experiences multiple paths due to reflections and diffractions. The multipath fading model considers the constructive and destructive interference of these multiple signal paths, resulting in fluctuations in the received signal power. By using these models based on the transmitter and receiver separation, wireless communication systems can better understand and adapt to the characteristics of the propagation environment, leading to improved performance and reliability.</p>", "<p id=\"Par39\">The energy consumed for transmission () using the above said radio model can be calculated using the following equation:where is the energy dissipation of the radio electronics to run the transmitter and receiver circuitry. It is a device-specific parameter and is set to 50 nJ/bit [1]. is the transmit amplification energy, <italic>k</italic> is the packet size in bits, and <italic>d</italic> is the sender and receiver separation.</p>", "<p id=\"Par40\">It’s important to note that Eq. (##FORMU##121##7##) only accounts for the energy consumption of the receiver circuitry, as the receiver does not require amplification energy for reception.The first-order radio communication model is a simplified representation of the energy consumption in WSNs and does not capture all the complexities of real-world wireless communication. However, it provides a useful approximation for estimating energy consumption in WSNs.</p>", "<title>Random waypoint mobility model</title>", "<p id=\"Par41\">This mobility model is an extensively employed model for simulating node movement in wireless sensor networks (WSNs). It is widely recognized and adopted due to its ability to provide a realistic depiction of the mobility patterns of nodes within the network</p>", "<p id=\"Par42\">In the random waypoint model<sup>##UREF##36##38##</sup>, each node in the network follows a random movement pattern. The nodes have a predefined simulation area or region in which they can move. The model assumes that nodes pause for a fixed duration at specific locations and then select a random destination and speed to move towards. Once they reach the destination, they pause again and repeat the process for the duration of the simulation as shown in Fig. ##FIG##4##4##.</p>", "<p id=\"Par43\">The key parameters in the random waypoint model include:</p>", "<p id=\"Par44\">Pause time (): The duration for which a node remains stationary at a particular location before selecting a new destination.</p>", "<p id=\"Par45\">Minimum velocity () and maximum velocity (): The range of speeds at which nodes can move between different locations in the simulation area.</p>", "<p id=\"Par46\">Direction range: The possible range of directions in which nodes can move. Typically represented as an angle or a range of angles (e.g., between 0 and ).</p>", "<p id=\"Par47\">These parameters govern the movement behavior of nodes in the random waypoint model, allowing researchers to study various aspects of WSNs, such as network connectivity, routing protocols, and energy consumption. By using the random way point mobility model, researchers can evaluate the performance of WSNs under realistic node movement scenarios and develop strategies to optimize network protocols and algorithms to better adapt to dynamic environments.</p>", "<p id=\"Par48\">In the context of our proposed work, we establish several assumptions concerning the random waypoint mobility model. These assumptions form the basis for our research and guide our approach in simulating node movement:<list list-type=\"bullet\"><list-item><p id=\"Par49\">The simulation area is two-dimensional.</p></list-item><list-item><p id=\"Par50\"> represents the percentage of static nodes in the network.</p></list-item><list-item><p id=\"Par51\"> denotes the pause time for each node.</p></list-item><list-item><p id=\"Par52\"> and correspond to the minimum and maximum velocities of each individual node, respectively. These parameters determine the range within which the velocities of the nodes can vary during simulation.</p></list-item><list-item><p id=\"Par53\">The direction of each node lies within the range of 0 to , covering a full circle of possible directions.</p></list-item></list></p>", "<title>System assumptions</title>", "<p id=\"Par54\">The proposed clustering protocol assumes the following:</p>", "<p id=\"Par55\">Homogeneous and mobile sensors: The sensors in the network are homogeneous, meaning they have the same physical characteristics and capabilities. Additionally, these sensors are mobile, capable of movement within the network.</p>", "<p id=\"Par56\">Location and velocity awareness: Each sensor in the network has awareness of its own location and velocity. This information is crucial for making informed decisions during the clustering process.</p>", "<p id=\"Par57\">Time synchronization: Sensor nodes in the network are assumed to be time synchronized with each other. Time synchronization ensures coordinated operations and facilitates efficient communication among the sensors.</p>", "<p id=\"Par58\">Transmission time calculation: Each sensor is capable of calculating the time it takes for data transmission. This information is utilized during the clustering process and other communication activities within the network.</p>", "<p id=\"Par59\">Single stationary sink: A single sink node is deployed at the center of the network, and it remains stationary. The sink acts as a central point of data collection and aggregation for the sensor nodes.</p>", "<p id=\"Par60\">These assumptions provide a foundation for the proposed clustering protocol, enabling efficient and coordinated operation of the mobile sensor network.</p>", "<title>Protocol description</title>", "<p id=\"Par61\">Existing protocols such as M-LEACH and CBR<sup>##UREF##37##39##</sup> consider residual energy for cluster head (CH) election to maximize network lifetime. However, these protocols select CHs based on the minimum distance between nodes, which can lead to packet loss when mobile nodes move out of the cluster. Join request messages are used to mitigate this issue, but they consume additional energy and introduce control overhead.</p>", "<p id=\"Par62\">In order to overcome the challenges mentioned, our proposed protocol, EARNRP, focuses on establishing stable links between cluster heads (CHs) and their member nodes. The protocol aims to improve reliable packet delivery rates by actively searching for stable connections between non-CH nodes and CHs. During the steady state phase, sensor nodes send advertisement messages when they become disconnected from a CH, mitigating packet loss. The selection of new clusters is based on a value associated with each CH, which indicates the suitability of the connection. This value helps in making informed decisions regarding cluster formation. Additionally, after the transmission process, both the CH and its members assess whether a node should remain in the cluster. If a node is deemed unsuitable, the CH removes it from the TDMA (Time Division Multiple Access) schedule and allocates the slot to an alternative node. This approach aims to improve packet delivery rates while minimizing control overhead, thereby enhancing the overall efficiency of the protocol.</p>", "<title>CH election in proposed EARNRP protocol</title>", "<p id=\"Par63\">Unlike the LEACH protocol, which overlooks factors such as remaining energy and node location during cluster head (CH) election, our proposed protocol takes these considerations into account. This is crucial to prevent unbalanced energy consumption and premature depletion of nodes with low residual energy. In our protocol, the CH election process incorporates multiple parameters to make informed decisions. These parameters include the count of previous CHs, the remaining energy of the nodes, the distance between nodes and the sink, the proximity to existing CHs, node mobility, and the duration of their connection time. By considering these factors, we aim to ensure a fair and efficient selection of CHs in the network. Integrating these additional parameters enhances the CH election process, leading to a more balanced distribution of energy consumption and prolonging the overall network lifetime. By factoring in the remaining energy and node location, our proposed protocol addresses the limitations of the LEACH protocol and contributes to improved energy efficiency and longevity of the network.</p>", "<p id=\"Par64\">The combined threshold parameter T(EARNRP(n)) is calculated using the formula:whereis the weighted sum of five sub-threshold parameters, and the weights (, , , , and ) are assigned to balance their contributions. The five sub-thresholds are calculated as follows:where current speed of the node presented by () and the maximum speed of the node by (). We also take into account the average energy of the nodes () and the current energy of the specific node (). The distance between node <italic>i</italic> and cluster head <italic>j</italic> is denoted as , while represents the transmission range of the nodes. To prevent the factor from exceeding 1, we ensure that it remains within a valid range. Furthermore, we estimate the connection time () between node <italic>i</italic> and CH <italic>j</italic>. This estimation helps in determining the duration of the connection, which influences the CH selection process. Additionally, we consider the number of rounds that a node has been a CH so far (), as well as the alive nodes count in the current round (<italic>N</italic>). These parameters are instrumental in evaluating the history and status of the nodes during CH election. By taking all of these factors into account, our proposed protocol ensures a comprehensive and informed CH election process. It allows for effective selection of CHs based on factors such as node speed, energy levels, distance to the CH, connection time estimation, and historical information of CH participation.</p>", "<p id=\"Par65\">To optimize the parameters such as , , , , and , which are bounded within the range of [0, 1], we encounter an NP-hard problem. Consequently, evolutionary algorithms are considered the most suitable choice for solving such optimization problems.</p>", "<p id=\"Par66\">In our paper, we employ the ARNMRA algorithm to tackle this optimization problem and obtain optimal results. The fitness function used in the EARNRP protocol is based on multiple thresholds. To evaluate the performance of the proposed algorithm, a multi-objective fitness function is calculated:</p>", "<p id=\"Par67\">Maximize:Subject to:In our proposed fitness function, we incorporate five fixed and equal weighting parameters, denoted as , , , , and . These parameters are utilized to adjust the relative importance of the five objective sub-threshold parameters within the fitness function.</p>", "<p id=\"Par68\">Equations (##FORMU##138##10##) to (##FORMU##142##14##) are employed to calculate the specific values of these sub-threshold parameters. These equations capture the relationship between the objectives and define the sub-threshold values based on certain criteria or metrics. By incorporating the five weighting parameters and utilizing the sub-threshold equations, our fitness function provides a comprehensive evaluation of the multiple objectives being considered in the optimization problem.</p>", "<title>Operation of proposed protocol</title>", "<p id=\"Par69\">The proposed protocol operates in two phases: the setup and the steady phase.</p>", "<p id=\"Par70\"><italic>Set up phase</italic> During this phase, each node in the network generates a random number within the range of 0 to 1. If the generated random number is less than the adaptive threshold , the node declares itself as a CH. The threshold is calculated using Eq. (##FORMU##131##8##), which determines the likelihood of a node becoming a CH. Once a CH is selected, it initiates the broadcasting of an advertisement message to the member nodes in its vicinity. This message contains relevant information such as the CH’s location and velocity. The advertisement is transmitted using the CSMA/CA MAC protocol. Upon receiving the advertisement message, the member nodes assess their options and decide which cluster to join. This decision is based on evaluating the minimum distance between nodes, which is determined by the received signal strength of the advertisement message. To establish a stable link between the CH and its members, and to minimize packet loss and energy consumption, a value is assigned to each CH. This value is calculated using Eq. (##FORMU##132##9##), which indicates the stability of the link.</p>", "<p id=\"Par71\"><italic>TDMA schedule creation</italic> The process of creating a TDMA schedule involves the following steps:<list list-type=\"bullet\"><list-item><p id=\"Par72\">Advertisement message: When a CH receives advertisement messages from nodes expressing their interest in joining the cluster, it initiates the process of creating a TDMA schedule.</p></list-item><list-item><p id=\"Par73\">Determining time slots: The TDMA schedule is prepared based on the number of nodes in the cluster. time slots are allotted for data transmission.</p></list-item><list-item><p id=\"Par74\">Sequential transmission: It is assumed that a total of <italic>n</italic> data frames are sent consecutively. Each node, represented by its sequence number , initiates transmission at time , where k ranges from 0 to n. Here, represents the duration of a time slot.</p></list-item><list-item><p id=\"Par75\">Order of : The TDMA schedule is organized in increasing order of , which is the time interval between the start of transmission for node <italic>i</italic> and the arrival of data at node <italic>j</italic>. This organization ensures that the constraint \n is satisfied, maximizing the number of successfully transmitted data packets within the cluster.</p></list-item></list>By following these steps, the TDMA schedule is created in such a way that data transmission among the nodes is coordinated, allowing for efficient utilization of timeslots and maximizing the number of successfully transmitted data packets within each cluster.</p>", "<p id=\"Par76\"><italic>Steady state phase</italic> In the protocol, the following actions and mechanisms are in place to ensure efficient communication and avoid packet loss:<list list-type=\"bullet\"><list-item><p id=\"Par77\">Data packet loss: If the CH does not receive data packets from a sensor node, it considers those packets as lost. As a result, the CH eliminates the corresponding member node from its TDMA schedule.</p></list-item><list-item><p id=\"Par78\">Join request: If member nodes do not receive a data request message from the CH, they send join request messages to CHs in other clusters, expressing their intention to join. This allows member nodes to find alternative clusters to transmit their data.</p></list-item><list-item><p id=\"Par79\">ACK message: Once the CH successfully receives a data packet, it sends an acknowledgment (ACK) message to the member nodes, indicating that the data packet was received.</p></list-item><list-item><p id=\"Par80\">Cluster join request: Upon receiving a cluster join request message from a member node, the CH sends an advertisement message to that node, similar to the setup phase. This process eliminates the need for membership declarations and reducing overhead.</p></list-item><list-item><p id=\"Par81\">Estimated connection time: Both the CH and member nodes maintain information based on estimated connection time. They periodically check whether a node intends to stay in the cluster. If a node is planning to join a new cluster, it sends a join request message to avoid potential packet loss before disconnecting from the current CH. The CH, in turn, removes the membership declaration of the node.</p></list-item></list>By implementing these actions and mechanisms, the protocol ensures that data packets are not lost due to communication failures. It allows member nodes to switch clusters if necessary and reduces unnecessary overhead by eliminating membership declarations. The use of estimated connection time helps in managing the cluster membership effectively and avoiding packet loss during transitions.</p>", "<p id=\"Par82\">Figure ##FIG##5##5##a illustrates the process of a node leaving its old cluster and joining a new cluster. In this scenario, Node 9 decides to leave Cluster J, while Node 6 joins the cluster instead. Consequently, the cluster head (CH) of Cluster J modifies the time division multiple access (TDMA) schedule accordingly. Node 6 is included in the schedule, while Node 9 is removed. The adjustment of the TDMA schedule is based on the estimated connection time (), which is organized in ascending order among the member nodes and the CH node. This ordering allows for a systematic arrangement of the nodes within the schedule, ensuring efficient utilization of time slots and effective communication within the cluster.</p>", "<p id=\"Par83\">In Fig. ##FIG##5##5##b, the updated adaptive TDMA schedule is depicted after the adjustments have been made. Notably, the timeslot previously assigned to Node 9 has been replaced with the timeslot assigned to Node 6. This adaptive TDMA scheduling, facilitated by the adjustments, brings several benefits to the system. Firstly, it improves the rate of successful packet delivery, ensuring that communication between nodes within the cluster is reliable and efficient. By reassigning the timeslot to Node 6, which has joined the cluster, the scheduling enhances the utilization of the channel resources. Furthermore, the adaptive scheduling ensures that the communication resources are efficiently utilized even when nodes join or leave the cluster. This seamless accommodation of node mobility and changes in cluster membership contributes to the overall performance of the system. It allows for effective utilization of available resources while maintaining the desired level of communication quality and efficiency.</p>", "<title>Analysis of results</title>", "<p id=\"Par84\">MATLAB is used to analyze the performance of the EARNRP. The simulations are conducted on a network comprising 100 nodes within a 100 m 100 m area, with the sink positioned at the center. Figure ##FIG##6##6## illustrates the evolution of the count of alive nodes over multiple rounds in all the protocols, excluding the EARNRP protocol, specifically for stationary nodes. This depiction offers valuable insights into the overall behavior and stability of the network nodes over time.</p>", "<p id=\"Par85\">In Fig. ##FIG##6##6##, the alive nodes count is also depicted considering the presence of mobile nodes. The count of alive nodes is measured against the variation in node mobility factor (M) between 20 and 100, and the maximum fixed speed (FS) is set to 2 m/s. Here, the EARNRP with all stationary nodes are designated as EARNRP-FS0-M0 (i.e. EARNRP with fixed speed of 0 m/s and mobility factor 0). Similarly, EARNRP-FS2-M100 represents EARNRP with FS 2 m/s and mobility factor 100). The purpose of this analysis is to estimate the network’s lifetime by observing the alive nodes count in each round. In Fig. ##FIG##7##7##, we can observe the average remaining energy of the network during the communication rounds. This figure indicates the consumption of overall energy as compared to other competitive methods. Moreover, EARNRP exhibits a consistent and steady energy absorption pattern in each communication round.</p>", "<p id=\"Par86\">To evaluate the performance of the EARNRP protocol against other protocols, several metrics are considered: the first node dies (FND), the half node dies (HND), and the last node dies (LND) as shown in Table ##TAB##3##4##. These metrics provide insights into the resilience and longevity of the network under different scenarios.</p>", "<p id=\"Par87\">The research work encompasses three cases for simulations:</p>", "<p id=\"Par88\">Case 1: A fixed speed is assumed while varying node densities. The speed is set to 2 m/s, and simulations are conducted with a dynamic mobility rate ranging from 20 to 100 as shown in Fig. ##FIG##8##8##. The node count is varied, and its effect on the network’s lifetime is analyzed. Ten simulations are carried out in this scenario, considering a range of 20–100 mobile nodes. The observation reveals that as the mobile nodes count increases, the network lifetime decreases because of the presence of node mobility.</p>", "<p id=\"Par89\">Case 2: The dynamic speeds are assumed while maintaining fixed mobility, as illustrated in Fig. ##FIG##9##9##. Ten simulations are conducted with fixed mobility (M = 50) by varying the velocity of nodes from 0.2 to 20 m/s. The analysis focuses on the impact of increasing node velocity on the network’s lifetime.</p>", "<p id=\"Par90\">Case 3: The third case assumes fixed mobility (M = 100) with variation in speeds, as shown in Fig. ##FIG##10##10##. Ten simulations are conducted with fixed mobility while varying the node count. This scenario considers 100% fixed mobility, and the results are plotted by varying the speed in the range of 0.2–20 m/s.</p>", "<p id=\"Par91\">These three cases provide a comprehensive evaluation of the EARNRP protocol under different scenarios, considering node densities, speeds, and mobility factors. The simulations enable us to assess the network’s performance, lifetime, and stability in various settings.</p>", "<p id=\"Par92\">In the simulations, the performance of the proposed algorithm is evaluated by considering various factors, including the network lifetime. The network lifetime is an important metric that reflects the longevity of the network and its ability to function effectively over time. One of the factors that impact the network lifetime is the percentage of mobile nodes in the network. As the mobile nodes ,count increases, the network becomes more dynamic, which can introduce challenges such as node mobility, connectivity, and energy consumption. The proposed algorithm aims to optimize the network’s performance in the presence of mobile nodes, ensuring efficient resource allocation and minimizing energy consumption to extend the network’s lifetime. Another factor that affects the network lifetime is the speed of the nodes. Higher speeds can result in increased collisions among nodes, leading to network congestion. This congestion can have a negative impact on the network’s performance, reducing its lifetime. The proposed algorithm takes into account the node speeds and dynamically adjusts the network parameters to mitigate congestion and optimize resource allocation, thus improving the network’s lifetime. By conducting simulations and analyzing the network lifetime under different scenarios, the performance of the proposed algorithm has been assessed. The results of these simulations provide insights into the algorithm’s effectiveness in addressing challenges related to mobile nodes, node mobility, and network congestion, ultimately contributing to the improvement of overall network performance and longevity.</p>", "<p id=\"Par93\">\n\n\n\n</p>" ]	[ "<title>Author contributions</title>", "<p>Conceptualization, S.S.; methodology, S.S. and U.S.; software, S.S. and N.M.; validation, N.M.; writing—original draft preparation, S.S. and N.M.; writing—review and editing, N.M., F.G.</p>", "<title>Data availability</title>", "<p>The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.</p>", "<title>Competing interests</title>", "<p id=\"Par96\">The authors declare no competing interests.</p>" ]	[ "<fig position=\"anchor\" id=\"Figa\"><label>Algorithm 1</label><caption><p><bold>Pseudo-code of proposed ARNMRA</bold></p></caption></fig>", "<fig id=\"Fig1\"><label>Figure 1</label><caption><p>Convergence profiles of NMRA and ARNMRA for CEC 2005 numerical benchmark problems.</p></caption></fig>", "<fig id=\"Fig2\"><label>Figure 2</label><caption><p>Convergence profiles of NMRA and ARNMRA for CEC 2019 numerical problems.</p></caption></fig>", "<fig id=\"Fig3\"><label>Figure 3</label><caption><p>Radio communication model.</p></caption></fig>", "<fig id=\"Fig4\"><label>Figure 4</label><caption><p>Random waypoint model.</p></caption></fig>", "<fig id=\"Fig5\"><label>Figure 5</label><caption><p>For EARNRP, new adaptive TDMA scheduling creation.</p></caption></fig>", "<fig id=\"Fig6\"><label>Figure 6</label><caption><p>Number of alive nodes as a function of communication rounds for EARNRP in the presence and absence of mobility.</p></caption></fig>", "<fig id=\"Fig7\"><label>Figure 7</label><caption><p>Average energy as a function of communication rounds for EARNRP in the presence and absence of mobility.</p></caption></fig>", "<fig id=\"Fig8\"><label>Figure 8</label><caption><p>Network lifetime versus percentage of mobile nodes for varying speed.</p></caption></fig>", "<fig id=\"Fig9\"><label>Figure 9</label><caption><p>Network lifetime versus speed (m/s) for 50% node mobility.</p></caption></fig>", "<fig id=\"Fig10\"><label>Figure 10</label><caption><p>Network lifetime versus speed (m/s) for 100% node mobility.</p></caption></fig>" ]	[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Simulation results of ARNMRA in comparison with other algorithms for CEC 2005 benchmark test suite.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Problem</th><th align=\"left\"/><th align=\"left\">SHADE<sup>##UREF##27##29##</sup></th><th align=\"left\">OB-L-EO<sup>##UREF##28##30##</sup></th><th align=\"left\">SOGWO<sup>##UREF##29##31##</sup></th><th align=\"left\">IWOA<sup>##UREF##30##32##</sup></th><th align=\"left\">NMRA</th><th align=\"left\">ARNMRA</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"4\"></td><td align=\"left\">Mean</td><td align=\"left\">1.421E−09</td><td align=\"left\">6.758E−212</td><td align=\"left\">6.050E−77</td><td align=\"left\">8.131E−146</td><td align=\"left\">1.539E−87</td><td align=\"left\"><bold>3.246E−292</bold></td></tr><tr><td align=\"left\">Std</td><td align=\"left\">3.090E−09</td><td align=\"left\"><bold>0</bold></td><td align=\"left\">1.487E−76</td><td align=\"left\">4.358E−145</td><td align=\"left\">1.097E−86</td><td align=\"left\"><bold>0</bold></td></tr><tr><td align=\"left\">p-rank</td><td align=\"left\">−</td><td align=\"left\">−</td><td align=\"left\">−</td><td align=\"left\">−</td><td align=\"left\">−</td><td align=\"left\"/></tr><tr><td align=\"left\">f-rank</td><td align=\"left\">6</td><td align=\"left\">2</td><td align=\"left\">5</td><td align=\"left\">3</td><td align=\"left\">4</td><td align=\"left\">1</td></tr><tr><td align=\"left\" rowspan=\"4\"></td><td align=\"left\">Mean</td><td align=\"left\">8.700E−03</td><td align=\"left\">1.931E−108</td><td align=\"left\">1.179E−44</td><td align=\"left\">2.379E−102</td><td align=\"left\">2.134E−45</td><td align=\"left\"><bold>2.155E−150</bold></td></tr><tr><td align=\"left\">Std</td><td align=\"left\">2.132E−02</td><td align=\"left\">8.000E−108</td><td align=\"left\">1.341E−44</td><td align=\"left\">6.577E−102</td><td align=\"left\">8.585E−45</td><td align=\"left\"><bold>1.164E−149</bold></td></tr><tr><td align=\"left\">p-rank</td><td align=\"left\">−</td><td align=\"left\">−</td><td align=\"left\">−</td><td align=\"left\">−</td><td align=\"left\">−</td><td align=\"left\"/></tr><tr><td align=\"left\">f-rank</td><td align=\"left\">6</td><td align=\"left\">2</td><td align=\"left\">5</td><td align=\"left\">3</td><td align=\"left\">4</td><td align=\"left\">1</td></tr><tr><td align=\"left\" rowspan=\"4\"></td><td align=\"left\">Mean</td><td align=\"left\">1.542E+01</td><td align=\"left\">6.931E−187</td><td align=\"left\">6.821E−87</td><td align=\"left\">1.540E+04</td><td align=\"left\">4.503E−104</td><td align=\"left\"><bold>6.686E−250</bold></td></tr><tr><td align=\"left\">Std</td><td align=\"left\">9.940E+00</td><td align=\"left\"><bold>0</bold></td><td align=\"left\">3.420E−86</td><td align=\"left\">7.421E+03</td><td align=\"left\">3.029E−103</td><td align=\"left\"><bold>0</bold></td></tr><tr><td align=\"left\">p-rank</td><td align=\"left\">−</td><td align=\"left\">−</td><td align=\"left\">−</td><td align=\"left\">−</td><td align=\"left\">−</td><td align=\"left\"/></tr><tr><td align=\"left\">f-rank</td><td align=\"left\">6</td><td align=\"left\">2</td><td align=\"left\">4</td><td align=\"left\">5</td><td align=\"left\">3</td><td align=\"left\">1</td></tr><tr><td align=\"left\" rowspan=\"4\"></td><td align=\"left\">Mean</td><td align=\"left\">9.786E−01</td><td align=\"left\">4.732E−103</td><td align=\"left\">1.131E−45</td><td align=\"left\">1.311E+01</td><td align=\"left\">5.526E−45</td><td align=\"left\"><bold>8.827E−145</bold></td></tr><tr><td align=\"left\">Std</td><td align=\"left\">7.989E−01</td><td align=\"left\">1.600E−102</td><td align=\"left\">3.976E−45</td><td align=\"left\">1.612E+01</td><td align=\"left\">2.212E−44</td><td align=\"left\"><bold>6.294E−144</bold></td></tr><tr><td align=\"left\">p-rank</td><td align=\"left\">−</td><td align=\"left\">−</td><td align=\"left\">−</td><td align=\"left\">−</td><td align=\"left\">−</td><td align=\"left\"/></tr><tr><td align=\"left\">f-rank</td><td align=\"left\">5</td><td align=\"left\">2</td><td align=\"left\">3</td><td align=\"left\">6</td><td align=\"left\">4</td><td align=\"left\">1</td></tr><tr><td align=\"left\" rowspan=\"4\"></td><td align=\"left\">mean</td><td align=\"left\">2.441E+01</td><td align=\"left\">2.579E+01</td><td align=\"left\">2.887E+01</td><td align=\"left\">2.651E+01</td><td align=\"left\">2.897E+01</td><td align=\"left\"><bold>2.176E+01</bold></td></tr><tr><td align=\"left\">std</td><td align=\"left\">1.120E+01</td><td align=\"left\">1.556E−01</td><td align=\"left\">1.879E−02</td><td align=\"left\">6.600E−01</td><td align=\"left\">1.840E−02</td><td align=\"left\">4.070E−02</td></tr><tr><td align=\"left\">p-rank</td><td align=\"left\">−</td><td align=\"left\">−</td><td align=\"left\">−</td><td align=\"left\">−</td><td align=\"left\">−</td><td align=\"left\"/></tr><tr><td align=\"left\">f-rank</td><td align=\"left\">2</td><td align=\"left\">3</td><td align=\"left\">5</td><td align=\"left\">4</td><td align=\"left\">6</td><td align=\"left\">1</td></tr><tr><td align=\"left\" rowspan=\"4\"></td><td align=\"left\">Mean</td><td align=\"left\"><bold>5.311E−10</bold></td><td align=\"left\">9.091E−05</td><td align=\"left\">6.776E+00</td><td align=\"left\">3.630E−02</td><td align=\"left\">6.610E+00</td><td align=\"left\">1.510E+00</td></tr><tr><td align=\"left\">Std</td><td align=\"left\"><bold>6.352E−10</bold></td><td align=\"left\">5.977E−05</td><td align=\"left\">5.788E−01</td><td align=\"left\">6.947E−02</td><td align=\"left\">5.673E−01</td><td align=\"left\">5.178E−01</td></tr><tr><td align=\"left\">p-rank</td><td align=\"left\">+</td><td align=\"left\">+</td><td align=\"left\">−</td><td align=\"left\">+</td><td align=\"left\">−</td><td align=\"left\"/></tr><tr><td align=\"left\">f-rank</td><td align=\"left\">1</td><td align=\"left\">2</td><td align=\"left\">6</td><td align=\"left\">3</td><td align=\"left\">5</td><td align=\"left\">4</td></tr><tr><td align=\"left\" rowspan=\"4\"></td><td align=\"left\">Mean</td><td align=\"left\">2.352E−02</td><td align=\"left\">4.700E−04</td><td align=\"left\">5.932E−04</td><td align=\"left\">1.851E−03</td><td align=\"left\">6.230E−04</td><td align=\"left\"><bold>3.053E−04</bold></td></tr><tr><td align=\"left\">Std</td><td align=\"left\">8.800E−03</td><td align=\"left\">3.041E−04</td><td align=\"left\">4.957E−04</td><td align=\"left\">2.358E−03</td><td align=\"left\">5.151E−04</td><td align=\"left\"><bold>3.296E−04</bold></td></tr><tr><td align=\"left\">p-rank</td><td align=\"left\">−</td><td align=\"left\">−</td><td align=\"left\">−</td><td align=\"left\">−</td><td align=\"left\">−</td><td align=\"left\"/></tr><tr><td align=\"left\">f-rank</td><td align=\"left\">6</td><td align=\"left\">2</td><td align=\"left\">3</td><td align=\"left\">5</td><td align=\"left\">4</td><td align=\"left\">1</td></tr><tr><td align=\"left\" rowspan=\"4\"></td><td align=\"left\">Mean</td><td align=\"left\">8.531E+00</td><td align=\"left\"><bold>0</bold></td><td align=\"left\"><bold>0</bold></td><td align=\"left\"><bold>0</bold></td><td align=\"left\"><bold>0</bold></td><td align=\"left\"><bold>0</bold></td></tr><tr><td align=\"left\">Std</td><td align=\"left\">2.190E+00</td><td align=\"left\"><bold>0</bold></td><td align=\"left\"><bold>0</bold></td><td align=\"left\"><bold>0</bold></td><td align=\"left\"><bold>0</bold></td><td align=\"left\"><bold>0</bold></td></tr><tr><td align=\"left\">p-rank</td><td align=\"left\">−</td><td align=\"left\"></td><td align=\"left\"></td><td align=\"left\"></td><td align=\"left\"></td><td align=\"left\"/></tr><tr><td align=\"left\">f-rank</td><td align=\"left\">6</td><td align=\"left\">1</td><td align=\"left\">1</td><td align=\"left\">1</td><td align=\"left\">1</td><td align=\"left\">1</td></tr><tr><td align=\"left\" rowspan=\"4\"></td><td align=\"left\">Mean</td><td align=\"left\">3.952E−01</td><td align=\"left\"><bold>8.881E−16</bold></td><td align=\"left\"><bold>8.881E−16</bold></td><td align=\"left\">3.731E−15</td><td align=\"left\"><bold>8.881E−16</bold></td><td align=\"left\"><bold>8.881E−16</bold></td></tr><tr><td align=\"left\">Std</td><td align=\"left\">5.857E−01</td><td align=\"left\"><bold>0</bold></td><td align=\"left\"><bold>0</bold></td><td align=\"left\">2.168E−01</td><td align=\"left\"><bold>0</bold></td><td align=\"left\"><bold>0</bold></td></tr><tr><td align=\"left\">p-rank</td><td align=\"left\">−</td><td align=\"left\"></td><td align=\"left\"></td><td align=\"left\">−</td><td align=\"left\"></td><td align=\"left\"/></tr><tr><td align=\"left\">f-rank</td><td align=\"left\">6</td><td align=\"left\">1</td><td align=\"left\">1</td><td align=\"left\">5</td><td align=\"left\">1</td><td align=\"left\">1</td></tr><tr><td align=\"left\" rowspan=\"4\"></td><td align=\"left\">Mean</td><td align=\"left\">4.800E−03</td><td align=\"left\"><bold>0</bold></td><td align=\"left\"><bold>0</bold></td><td align=\"left\">2.641E−03</td><td align=\"left\"><bold>0</bold></td><td align=\"left\"><bold>0</bold></td></tr><tr><td align=\"left\">Std</td><td align=\"left\">7.700E−03</td><td align=\"left\"><bold>0</bold></td><td align=\"left\"><bold>0</bold></td><td align=\"left\">1.100E−02</td><td align=\"left\"><bold>0</bold></td><td align=\"left\"><bold>0</bold></td></tr><tr><td align=\"left\">p-rank</td><td align=\"left\">−</td><td align=\"left\"></td><td align=\"left\"></td><td align=\"left\">−</td><td align=\"left\"></td><td align=\"left\"/></tr><tr><td align=\"left\">f-rank</td><td align=\"left\">5</td><td align=\"left\">1</td><td align=\"left\">1</td><td align=\"left\">6</td><td align=\"left\">1</td><td align=\"left\">1</td></tr><tr><td align=\"left\" rowspan=\"4\"></td><td align=\"left\">Mean</td><td align=\"left\">3.458E−02</td><td align=\"left\"><bold>6.287E−06</bold></td><td align=\"left\">5.611E−02</td><td align=\"left\">9.300E−03</td><td align=\"left\">1.118E+00</td><td align=\"left\">1.295E−01</td></tr><tr><td align=\"left\">Std</td><td align=\"left\">8.751E−02</td><td align=\"left\"><bold>4.351E−06</bold></td><td align=\"left\">1.421E−02</td><td align=\"left\">2.561E−02</td><td align=\"left\">2.598E−01</td><td align=\"left\">9.180E−02</td></tr><tr><td align=\"left\">p-rank</td><td align=\"left\">+</td><td align=\"left\">+</td><td align=\"left\">+</td><td align=\"left\">+</td><td align=\"left\">−</td><td align=\"left\"/></tr><tr><td align=\"left\">f-rank</td><td align=\"left\">3</td><td align=\"left\">1</td><td align=\"left\">4</td><td align=\"left\">2</td><td align=\"left\">6</td><td align=\"left\">5</td></tr><tr><td align=\"left\" rowspan=\"4\"></td><td align=\"left\">Mean</td><td align=\"left\"><bold>7.321E−04</bold></td><td align=\"left\">3.832E−02</td><td align=\"left\">3.531E−01</td><td align=\"left\">1.600E−01</td><td align=\"left\">2.991E+00</td><td align=\"left\">8.406E−01</td></tr><tr><td align=\"left\">Std</td><td align=\"left\"><bold>2.800E−03</bold></td><td align=\"left\">9.751E−02</td><td align=\"left\">1.276E−01</td><td align=\"left\">1.365E−01</td><td align=\"left\">4.150E−02</td><td align=\"left\">3.230E−01</td></tr><tr><td align=\"left\">p-rank</td><td align=\"left\">+</td><td align=\"left\">+</td><td align=\"left\">+</td><td align=\"left\">+</td><td align=\"left\">−</td><td align=\"left\"/></tr><tr><td align=\"left\">f-rank</td><td align=\"left\">1</td><td align=\"left\">2</td><td align=\"left\">4</td><td align=\"left\">3</td><td align=\"left\">6</td><td align=\"left\">5</td></tr><tr><td align=\"left\"><italic>w</italic>/<italic>l</italic>/<italic>t</italic></td><td align=\"left\"/><td align=\"left\">3/9/0</td><td align=\"left\">3/6/3</td><td align=\"left\">2/7/3</td><td align=\"left\">3/8/1</td><td align=\"left\">0/9/3</td><td align=\"left\"/></tr><tr><td align=\"left\">f-rank value</td><td align=\"left\"/><td align=\"left\">4.41</td><td align=\"left\">2.00</td><td align=\"left\">3.50</td><td align=\"left\">3.83</td><td align=\"left\">3.75</td><td align=\"left\">1.91</td></tr><tr><td align=\"left\">Overall f-rank</td><td align=\"left\"/><td align=\"left\">6</td><td align=\"left\">2</td><td align=\"left\">3</td><td align=\"left\">5</td><td align=\"left\">4</td><td align=\"left\">1</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Simulation results for CEC 2019 numerical benchmark problems.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Problem</th><th align=\"left\">Algorithm</th><th align=\"left\">Best</th><th align=\"left\">Median</th><th align=\"left\">Mean</th><th align=\"left\">Worst</th><th align=\"left\">Std</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"2\"></td><td align=\"left\">NMRA</td><td align=\"left\">8.320E+04</td><td align=\"left\">2.551E+05</td><td align=\"left\">3.221E+05</td><td align=\"left\">8.397E+05</td><td align=\"left\">2.071E+05</td></tr><tr><td align=\"left\">ARNMRA</td><td align=\"left\">5.003E+04</td><td align=\"left\">1.168E+05</td><td align=\"left\">1.350E+05</td><td align=\"left\">4.848E+05</td><td align=\"left\">7.839E+04</td></tr><tr><td align=\"left\" rowspan=\"2\"></td><td align=\"left\">NMRA</td><td align=\"left\">1.759E+01</td><td align=\"left\">1.858E+01</td><td align=\"left\">1.855E+01</td><td align=\"left\">1.971E+01</td><td align=\"left\">6.112E−01</td></tr><tr><td align=\"left\">ARNMRA</td><td align=\"left\">1.739E+01</td><td align=\"left\">1.778E+01</td><td align=\"left\">1.784E+01</td><td align=\"left\">1.866E+01</td><td align=\"left\">3.432E−01</td></tr><tr><td align=\"left\" rowspan=\"2\"></td><td align=\"left\">NMRA</td><td align=\"left\">1.270E+01</td><td align=\"left\">1.270E+01</td><td align=\"left\">1.270E+01</td><td align=\"left\">1.270E+01</td><td align=\"left\">2.956E−07</td></tr><tr><td align=\"left\">ARNMRA</td><td align=\"left\">1.270E+01</td><td align=\"left\">1.270E+01</td><td align=\"left\">1.270E+01</td><td align=\"left\">1.270E+01</td><td align=\"left\">3.245E−08</td></tr><tr><td align=\"left\" rowspan=\"2\"></td><td align=\"left\">NMRA</td><td align=\"left\">9.163E+01</td><td align=\"left\">3.663E+02</td><td align=\"left\">4.741E+02</td><td align=\"left\">1.636E+03</td><td align=\"left\">3.660E+02</td></tr><tr><td align=\"left\">ARNMRA</td><td align=\"left\">9.600E+01</td><td align=\"left\">3.014E+02</td><td align=\"left\">3.528E+02</td><td align=\"left\">1.297E+03</td><td align=\"left\">2.361E+02</td></tr><tr><td align=\"left\" rowspan=\"2\"></td><td align=\"left\">NMRA</td><td align=\"left\">1.331E+00</td><td align=\"left\">1.978E+00</td><td align=\"left\">1.985E+00</td><td align=\"left\">2.624E+00</td><td align=\"left\">2.792E−01</td></tr><tr><td align=\"left\">ARNMRA</td><td align=\"left\">1.407E+00</td><td align=\"left\">1.939E+00</td><td align=\"left\">1.939E+00</td><td align=\"left\">2.589E+00</td><td align=\"left\">3.127E−01</td></tr><tr><td align=\"left\" rowspan=\"2\"></td><td align=\"left\">NMRA</td><td align=\"left\">9.074E+00</td><td align=\"left\">1.121E+01</td><td align=\"left\">1.115E+01</td><td align=\"left\">1.255E+01</td><td align=\"left\">8.049E−01</td></tr><tr><td align=\"left\">ARNMRA</td><td align=\"left\">9.124E+00</td><td align=\"left\">1.039E+01</td><td align=\"left\">1.036E+01</td><td align=\"left\">1.179E+01</td><td align=\"left\">6.533E−01</td></tr><tr><td align=\"left\" rowspan=\"2\"></td><td align=\"left\">NMRA</td><td align=\"left\">− 1.243E+02</td><td align=\"left\">1.981E+02</td><td align=\"left\">1.533E+02</td><td align=\"left\">4.042E+02</td><td align=\"left\">1.227E+02</td></tr><tr><td align=\"left\">ARNMRA</td><td align=\"left\">− 2.958E+02</td><td align=\"left\">1.019E+01</td><td align=\"left\">− 2.860E+00</td><td align=\"left\">2.191E+02</td><td align=\"left\">1.192E+02</td></tr><tr><td align=\"left\" rowspan=\"2\"></td><td align=\"left\">NMRA</td><td align=\"left\">4.974E+00</td><td align=\"left\">6.003E+00</td><td align=\"left\">5.919E+00</td><td align=\"left\">6.534E+00</td><td align=\"left\">3.304E−01</td></tr><tr><td align=\"left\">ARNMRA</td><td align=\"left\">4.565E+00</td><td align=\"left\">5.663E+00</td><td align=\"left\">5.594E+00</td><td align=\"left\">6.151E+00</td><td align=\"left\">3.227E−01</td></tr><tr><td align=\"left\" rowspan=\"2\"></td><td align=\"left\">NMRA</td><td align=\"left\">3.079E+00</td><td align=\"left\">4.179E+00</td><td align=\"left\">4.354E+00</td><td align=\"left\">1.016E+01</td><td align=\"left\">1.041E+00</td></tr><tr><td align=\"left\">ARNMRA</td><td align=\"left\">3.061E+00</td><td align=\"left\">5.180E+00</td><td align=\"left\">1.126E+01</td><td align=\"left\">1.097E+02</td><td align=\"left\">1.932E+01</td></tr><tr><td align=\"left\" rowspan=\"2\"></td><td align=\"left\">NMRA</td><td align=\"left\">1.951E+01</td><td align=\"left\">2.049E+01</td><td align=\"left\">2.048E+01</td><td align=\"left\">2.065E+01</td><td align=\"left\">1.628E−01</td></tr><tr><td align=\"left\">ARNMRA</td><td align=\"left\">2.017E+01</td><td align=\"left\">2.037E+01</td><td align=\"left\">2.037E+01</td><td align=\"left\">2.057E+01</td><td align=\"left\">7.670E−02</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab3\"><label>Table 3</label><caption><p>Simulation results for CEC 2020 numerical test problems.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Problem</th><th align=\"left\">Algorithm</th><th align=\"left\">Best</th><th align=\"left\">Mean</th><th align=\"left\">Worst</th><th align=\"left\">Std</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"2\"></td><td align=\"left\">NMRA</td><td align=\"left\">6.443E+02</td><td align=\"left\">2.444E+03</td><td align=\"left\">9.561E+03</td><td align=\"left\">1.838E+03</td></tr><tr><td align=\"left\">ARNMRA</td><td align=\"left\">2.960E+02</td><td align=\"left\">7.197E+02</td><td align=\"left\">9.956e+02</td><td align=\"left\">2.087E+02</td></tr><tr><td align=\"left\" rowspan=\"2\"></td><td align=\"left\">NMRA</td><td align=\"left\">5.611E+02</td><td align=\"left\">8.675E+02</td><td align=\"left\">1.232E+03</td><td align=\"left\">1.751E+02</td></tr><tr><td align=\"left\">ARNMRA</td><td align=\"left\">4.718E+02</td><td align=\"left\">7.848E+02</td><td align=\"left\">9.462E+02</td><td align=\"left\">1.548E+02</td></tr><tr><td align=\"left\" rowspan=\"2\"></td><td align=\"left\">NMRA</td><td align=\"left\">6.795E+01</td><td align=\"left\">1.350E+02</td><td align=\"left\">1.792E+02</td><td align=\"left\">2.308E+01</td></tr><tr><td align=\"left\">ARNMRA</td><td align=\"left\">8.612E+01</td><td align=\"left\">1.095E+02</td><td align=\"left\">1.311E+02</td><td align=\"left\">1.517E+01</td></tr><tr><td align=\"left\" rowspan=\"2\"></td><td align=\"left\">NMRA</td><td align=\"left\">6.300E+00</td><td align=\"left\">1.115E+01</td><td align=\"left\">1.379E+01</td><td align=\"left\">2.104E+00</td></tr><tr><td align=\"left\">ARNMRA</td><td align=\"left\">8.133E+00</td><td align=\"left\">1.060E+01</td><td align=\"left\">1.417E+01</td><td align=\"left\">1.798E+00</td></tr><tr><td align=\"left\" rowspan=\"2\"></td><td align=\"left\">NMRA</td><td align=\"left\">6.716E+03</td><td align=\"left\">3.966E+04</td><td align=\"left\">9.197E+04</td><td align=\"left\">2.341E+04</td></tr><tr><td align=\"left\">ARNMRA</td><td align=\"left\">9.335E+03</td><td align=\"left\">2.980E+04</td><td align=\"left\">6.069E+04</td><td align=\"left\">1.684E+04</td></tr><tr><td align=\"left\" rowspan=\"2\"></td><td align=\"left\">NMRA</td><td align=\"left\">2.269E+02</td><td align=\"left\">2.268E+02</td><td align=\"left\">2.268E+02</td><td align=\"left\">9.245E−13</td></tr><tr><td align=\"left\">ARNMRA</td><td align=\"left\">4.491E+02</td><td align=\"left\">4.491E+02</td><td align=\"left\">4.491E+02</td><td align=\"left\">4.769E−13</td></tr><tr><td align=\"left\" rowspan=\"2\"></td><td align=\"left\">NMRA</td><td align=\"left\">3.775E+03</td><td align=\"left\">1.064E+04</td><td align=\"left\">1.961E+04</td><td align=\"left\">4.588E+03</td></tr><tr><td align=\"left\">ARNMRA</td><td align=\"left\">2.973E+03</td><td align=\"left\">6.344E+03</td><td align=\"left\">9.450E+03</td><td align=\"left\">2.044E+03</td></tr><tr><td align=\"left\" rowspan=\"2\"></td><td align=\"left\">NMRA</td><td align=\"left\">1.041E+02</td><td align=\"left\">5.513E+02</td><td align=\"left\">2.621E+03</td><td align=\"left\">9.240E+02</td></tr><tr><td align=\"left\">ARNMRA</td><td align=\"left\">1.000E+02</td><td align=\"left\">3.097E+02</td><td align=\"left\">2.359E+03</td><td align=\"left\">6.798E+02</td></tr><tr><td align=\"left\" rowspan=\"2\"></td><td align=\"left\">NMRA</td><td align=\"left\">1.014E+02</td><td align=\"left\">2.008E+02</td><td align=\"left\">6.776E+02</td><td align=\"left\">1.745E+02</td></tr><tr><td align=\"left\">ARNMRA</td><td align=\"left\">1.011E+02</td><td align=\"left\">1.395E+02</td><td align=\"left\">2.105E+02</td><td align=\"left\">5.193E+01</td></tr><tr><td align=\"left\" rowspan=\"2\"></td><td align=\"left\">NMRA</td><td align=\"left\">3.993E+02</td><td align=\"left\">4.021E+02</td><td align=\"left\">4.060E+02</td><td align=\"left\">2.301E+00</td></tr><tr><td align=\"left\">ARNMRA</td><td align=\"left\">3.992E+02</td><td align=\"left\">4.033E+02</td><td align=\"left\">4.066E+02</td><td align=\"left\">3.248E+00</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab4\"><label>Table 4</label><caption><p>Network lifetime comparison for homogeneous setup with =1<italic>J</italic> in terms of dead nodes round history.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Dead Nodes (%)</th><th align=\"left\">LEACH</th><th align=\"left\">ERP</th><th align=\"left\">DRESEP</th><th align=\"left\">SAERP</th><th align=\"left\">HSSTERP</th><th align=\"left\">DESTERP</th><th align=\"left\">EARNRP-FS0-M0</th><th align=\"left\">EARNRP-FS2-M100</th></tr></thead><tbody><tr><td align=\"left\">1 (FND)</td><td align=\"left\">1805.1</td><td align=\"left\">2113.2</td><td align=\"left\">4101.9</td><td align=\"left\">2437.8</td><td align=\"left\">5635.8</td><td align=\"left\">5699.2</td><td align=\"left\"><bold>5742.9</bold></td><td align=\"left\">5720.6</td></tr><tr><td align=\"left\">10</td><td align=\"left\">2020.5</td><td align=\"left\">2275.8</td><td align=\"left\">4503.7</td><td align=\"left\">2444.2</td><td align=\"left\">5654.6</td><td align=\"left\">5711.6</td><td align=\"left\"><bold>5779.1</bold></td><td align=\"left\">5726.3</td></tr><tr><td align=\"left\">20</td><td align=\"left\">2067.8</td><td align=\"left\">2364.9</td><td align=\"left\">4770.2</td><td align=\"left\">2445.4</td><td align=\"left\">5667.1</td><td align=\"left\">5719.1</td><td align=\"left\"><bold>5795.5</bold></td><td align=\"left\">5727.8</td></tr><tr><td align=\"left\">30</td><td align=\"left\">2140.5</td><td align=\"left\">2437.6</td><td align=\"left\">4880.5</td><td align=\"left\">2447.6</td><td align=\"left\">5670.5</td><td align=\"left\">5721.8</td><td align=\"left\"><bold>5805.3</bold></td><td align=\"left\">5729.2</td></tr><tr><td align=\"left\">40</td><td align=\"left\">2170.6</td><td align=\"left\">2510.3</td><td align=\"left\">4984.1</td><td align=\"left\">2448.3</td><td align=\"left\">5676.3</td><td align=\"left\">5722.9</td><td align=\"left\"><bold>5810.2</bold></td><td align=\"left\">5734.4</td></tr><tr><td align=\"left\">50 (HND)</td><td align=\"left\">2213.8</td><td align=\"left\">2580.5</td><td align=\"left\">5127.0</td><td align=\"left\">2449.9</td><td align=\"left\">5680.2</td><td align=\"left\">5726.3</td><td align=\"left\"><bold>5812.9</bold></td><td align=\"left\">5762.3</td></tr><tr><td align=\"left\">60</td><td align=\"left\">2281.4</td><td align=\"left\">2651.2</td><td align=\"left\">5293.8</td><td align=\"left\">2451.1</td><td align=\"left\">5681.9</td><td align=\"left\">5727.1</td><td align=\"left\"><bold>5815.4</bold></td><td align=\"left\">5780.1</td></tr><tr><td align=\"left\">70</td><td align=\"left\">2345.8</td><td align=\"left\">2745.3</td><td align=\"left\">5396.3</td><td align=\"left\">2451.8</td><td align=\"left\">5688.2</td><td align=\"left\">5728.9</td><td align=\"left\"><bold>5816.3</bold></td><td align=\"left\">5784.2</td></tr><tr><td align=\"left\">80</td><td align=\"left\">2393.5</td><td align=\"left\">2836.9</td><td align=\"left\">5620.9</td><td align=\"left\">2452.2</td><td align=\"left\">5690.4</td><td align=\"left\">5733.1</td><td align=\"left\"><bold>5820.2</bold></td><td align=\"left\">5787.8</td></tr><tr><td align=\"left\">90</td><td align=\"left\">2484.8</td><td align=\"left\">2983.8</td><td align=\"left\">5771.5</td><td align=\"left\">2453.6</td><td align=\"left\">5692.8</td><td align=\"left\">5735.9</td><td align=\"left\"><bold>5823.3</bold></td><td align=\"left\">5788.3</td></tr><tr><td align=\"left\">100 (LND)</td><td align=\"left\">2763.8</td><td align=\"left\">3305.7</td><td align=\"left\"><bold>6400.8</bold></td><td align=\"left\">2454.7</td><td align=\"left\">5715.9</td><td align=\"left\">5738.6</td><td align=\"left\">5834.7</td><td align=\"left\">5789.6</td></tr></tbody></table></table-wrap>" ]	[ "<inline-formula id=\"IEq1\"><alternatives><tex-math id=\"M1\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$(\\lambda )$$\\end{document}</tex-math><mml:math id=\"M2\"><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>λ</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq2\"><alternatives><tex-math id=\"M3\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$(\\lambda )$$\\end{document}</tex-math><mml:math id=\"M4\"><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>λ</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:math></alternatives></inline-formula>", "<disp-formula id=\"Equ1\"><label>1</label><alternatives><tex-math id=\"M5\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\begin{aligned} MR_{p,q} = MR_{min,q} + rand \\times \\left( MR_{max,q}-N_{min,q}\\right) \\end{aligned}$$\\end{document}</tex-math><mml:math id=\"M6\" display=\"block\"><mml:mrow><mml:mtable><mml:mtr><mml:mtd 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id=\"M7\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\varepsilon$$\\end{document}</tex-math><mml:math id=\"M8\"><mml:mi>ε</mml:mi></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq4\"><alternatives><tex-math id=\"M9\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$[1,2,\\ldots MR]$$\\end{document}</tex-math><mml:math id=\"M10\"><mml:mrow><mml:mo stretchy=\"false\">[</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mn>2</mml:mn><mml:mo>,</mml:mo><mml:mo>…</mml:mo><mml:mi>M</mml:mi><mml:mi>R</mml:mi><mml:mo stretchy=\"false\">]</mml:mo></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq5\"><alternatives><tex-math id=\"M11\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\varepsilon$$\\end{document}</tex-math><mml:math id=\"M12\"><mml:mi>ε</mml:mi></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq6\"><alternatives><tex-math id=\"M13\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$[1,2\\ldots ,d]$$\\end{document}</tex-math><mml:math id=\"M14\"><mml:mrow><mml:mo stretchy=\"false\">[</mml:mo><mml:mn>1</mml:mn><mml:mo>,</mml:mo><mml:mn>2</mml:mn><mml:mo>…</mml:mo><mml:mo>,</mml:mo><mml:mi>d</mml:mi><mml:mo stretchy=\"false\">]</mml:mo></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq7\"><alternatives><tex-math id=\"M15\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$MR_{p,q}$$\\end{document}</tex-math><mml:math id=\"M16\"><mml:mrow><mml:mi>M</mml:mi><mml:msub><mml:mi>R</mml:mi><mml:mrow><mml:mi>p</mml:mi><mml:mo>,</mml:mo><mml:mi>q</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq8\"><alternatives><tex-math id=\"M17\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$MR_{min,q}$$\\end{document}</tex-math><mml:math id=\"M18\"><mml:mrow><mml:mi>M</mml:mi><mml:msub><mml:mi>R</mml:mi><mml:mrow><mml:mi>m</mml:mi><mml:mi>i</mml:mi><mml:mi>n</mml:mi><mml:mo>,</mml:mo><mml:mi>q</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq9\"><alternatives><tex-math id=\"M19\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$MR_{max,q}$$\\end{document}</tex-math><mml:math id=\"M20\"><mml:mrow><mml:mi>M</mml:mi><mml:msub><mml:mi>R</mml:mi><mml:mrow><mml:mi>m</mml:mi><mml:mi>a</mml:mi><mml:mi>x</mml:mi><mml:mo>,</mml:mo><mml:mi>q</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math></alternatives></inline-formula>", "<disp-formula id=\"Equ2\"><label>2</label><alternatives><tex-math id=\"M21\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\begin{aligned} MW_p^{t+1}=MW_p^t+\\lambda \\left( MW_u^t-MW_v^t\\right) \\end{aligned}$$\\end{document}</tex-math><mml:math id=\"M22\" display=\"block\"><mml:mrow><mml:mtable><mml:mtr><mml:mtd columnalign=\"right\"><mml:mrow><mml:mi>M</mml:mi><mml:msubsup><mml:mi>W</mml:mi><mml:mi>p</mml:mi><mml:mrow><mml:mi>t</mml:mi><mml:mo>+</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mi>M</mml:mi><mml:msubsup><mml:mi>W</mml:mi><mml:mi>p</mml:mi><mml:mi>t</mml:mi></mml:msubsup><mml:mo>+</mml:mo><mml:mi>λ</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mi>M</mml:mi><mml:msubsup><mml:mi>W</mml:mi><mml:mi>u</mml:mi><mml:mi>t</mml:mi></mml:msubsup><mml:mo>-</mml:mo><mml:mi>M</mml:mi><mml:msubsup><mml:mi>W</mml:mi><mml:mi>v</mml:mi><mml:mi>t</mml:mi></mml:msubsup></mml:mfenced></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:math></alternatives></disp-formula>", "<inline-formula id=\"IEq10\"><alternatives><tex-math id=\"M23\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$MW_p^t$$\\end{document}</tex-math><mml:math id=\"M24\"><mml:mrow><mml:mi>M</mml:mi><mml:msubsup><mml:mi>W</mml:mi><mml:mi>p</mml:mi><mml:mi>t</mml:mi></mml:msubsup></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq11\"><alternatives><tex-math id=\"M25\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$MW_p^{t+1}$$\\end{document}</tex-math><mml:math id=\"M26\"><mml:mrow><mml:mi>M</mml:mi><mml:msubsup><mml:mi>W</mml:mi><mml:mi>p</mml:mi><mml:mrow><mml:mi>t</mml:mi><mml:mo>+</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msubsup></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq12\"><alternatives><tex-math id=\"M27\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\lambda$$\\end{document}</tex-math><mml:math id=\"M28\"><mml:mi>λ</mml:mi></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq13\"><alternatives><tex-math id=\"M29\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$MW_u^t$$\\end{document}</tex-math><mml:math id=\"M30\"><mml:mrow><mml:mi>M</mml:mi><mml:msubsup><mml:mi>W</mml:mi><mml:mi>u</mml:mi><mml:mi>t</mml:mi></mml:msubsup></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq14\"><alternatives><tex-math id=\"M31\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$MW_v^t$$\\end{document}</tex-math><mml:math id=\"M32\"><mml:mrow><mml:mi>M</mml:mi><mml:msubsup><mml:mi>W</mml:mi><mml:mi>v</mml:mi><mml:mi>t</mml:mi></mml:msubsup></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq15\"><alternatives><tex-math id=\"M33\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$(MR_{best})$$\\end{document}</tex-math><mml:math id=\"M34\"><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>M</mml:mi><mml:msub><mml:mi>R</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">best</mml:mi></mml:mrow></mml:msub><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:math></alternatives></inline-formula>", "<disp-formula id=\"Equ3\"><label>3</label><alternatives><tex-math id=\"M35\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\begin{aligned} MB_p^{t+1}=(1-\\lambda )MB_p^t+\\lambda \\left( MR_{best}-MB_p^t\\right) \\end{aligned}$$\\end{document}</tex-math><mml:math id=\"M36\" display=\"block\"><mml:mrow><mml:mtable><mml:mtr><mml:mtd columnalign=\"right\"><mml:mrow><mml:mi>M</mml:mi><mml:msubsup><mml:mi>B</mml:mi><mml:mi>p</mml:mi><mml:mrow><mml:mi>t</mml:mi><mml:mo>+</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>1</mml:mn><mml:mo>-</mml:mo><mml:mi>λ</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mi>M</mml:mi><mml:msubsup><mml:mi>B</mml:mi><mml:mi>p</mml:mi><mml:mi>t</mml:mi></mml:msubsup><mml:mo>+</mml:mo><mml:mi>λ</mml:mi><mml:mfenced close=\")\" open=\"(\"><mml:mi>M</mml:mi><mml:msub><mml:mi>R</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">best</mml:mi></mml:mrow></mml:msub><mml:mo>-</mml:mo><mml:mi>M</mml:mi><mml:msubsup><mml:mi>B</mml:mi><mml:mi>p</mml:mi><mml:mi>t</mml:mi></mml:msubsup></mml:mfenced></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:math></alternatives></disp-formula>", "<inline-formula id=\"IEq16\"><alternatives><tex-math id=\"M37\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$MB_p^t$$\\end{document}</tex-math><mml:math id=\"M38\"><mml:mrow><mml:mi>M</mml:mi><mml:msubsup><mml:mi>B</mml:mi><mml:mi>p</mml:mi><mml:mi>t</mml:mi></mml:msubsup></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq17\"><alternatives><tex-math id=\"M39\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\lambda$$\\end{document}</tex-math><mml:math id=\"M40\"><mml:mi>λ</mml:mi></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq18\"><alternatives><tex-math id=\"M41\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$MB_p^{t+1}$$\\end{document}</tex-math><mml:math id=\"M42\"><mml:mrow><mml:mi>M</mml:mi><mml:msubsup><mml:mi>B</mml:mi><mml:mi>p</mml:mi><mml:mrow><mml:mi>t</mml:mi><mml:mo>+</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msubsup></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq19\"><alternatives><tex-math id=\"M43\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$(\\lambda )$$\\end{document}</tex-math><mml:math id=\"M44\"><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>λ</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq20\"><alternatives><tex-math id=\"M45\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\lambda$$\\end{document}</tex-math><mml:math id=\"M46\"><mml:mi>λ</mml:mi></mml:math></alternatives></inline-formula>", "<disp-formula id=\"Equ4\"><label>4</label><alternatives><tex-math id=\"M47\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\begin{aligned} MB_p^{t+1}=(1-\\lambda )MB_p^t+\\lambda \\left[ \\beta _1 \\left( MR_{best}-MB_p^t \\right) -\\beta _2 \\left( MR_{worst}-MB_p^t \\right) \\right] \\end{aligned}$$\\end{document}</tex-math><mml:math id=\"M48\" display=\"block\"><mml:mrow><mml:mtable><mml:mtr><mml:mtd columnalign=\"right\"><mml:mrow><mml:mi>M</mml:mi><mml:msubsup><mml:mi>B</mml:mi><mml:mi>p</mml:mi><mml:mrow><mml:mi>t</mml:mi><mml:mo>+</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:msubsup><mml:mo>=</mml:mo><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>1</mml:mn><mml:mo>-</mml:mo><mml:mi>λ</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mi>M</mml:mi><mml:msubsup><mml:mi>B</mml:mi><mml:mi>p</mml:mi><mml:mi>t</mml:mi></mml:msubsup><mml:mo>+</mml:mo><mml:mi>λ</mml:mi><mml:mfenced close=\"]\" open=\"[\"><mml:msub><mml:mi>β</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mfenced close=\")\" open=\"(\"><mml:mi>M</mml:mi><mml:msub><mml:mi>R</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">best</mml:mi></mml:mrow></mml:msub><mml:mo>-</mml:mo><mml:mi>M</mml:mi><mml:msubsup><mml:mi>B</mml:mi><mml:mi>p</mml:mi><mml:mi>t</mml:mi></mml:msubsup></mml:mfenced><mml:mo>-</mml:mo><mml:msub><mml:mi>β</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mfenced close=\")\" open=\"(\"><mml:mi>M</mml:mi><mml:msub><mml:mi>R</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">worst</mml:mi></mml:mrow></mml:msub><mml:mo>-</mml:mo><mml:mi>M</mml:mi><mml:msubsup><mml:mi>B</mml:mi><mml:mi>p</mml:mi><mml:mi>t</mml:mi></mml:msubsup></mml:mfenced></mml:mfenced></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:math></alternatives></disp-formula>", "<inline-formula id=\"IEq21\"><alternatives><tex-math id=\"M49\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$MB_p^t$$\\end{document}</tex-math><mml:math id=\"M50\"><mml:mrow><mml:mi>M</mml:mi><mml:msubsup><mml:mi>B</mml:mi><mml:mi>p</mml:mi><mml:mi>t</mml:mi></mml:msubsup></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq22\"><alternatives><tex-math id=\"M51\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$MR_{worst}$$\\end{document}</tex-math><mml:math id=\"M52\"><mml:mrow><mml:mi>M</mml:mi><mml:msub><mml:mi>R</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">worst</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq23\"><alternatives><tex-math id=\"M53\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$MR_{best}$$\\end{document}</tex-math><mml:math id=\"M54\"><mml:mrow><mml:mi>M</mml:mi><mml:msub><mml:mi>R</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">best</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq24\"><alternatives><tex-math id=\"M55\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\beta _1$$\\end{document}</tex-math><mml:math id=\"M56\"><mml:msub><mml:mi>β</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq25\"><alternatives><tex-math id=\"M57\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\beta _2$$\\end{document}</tex-math><mml:math id=\"M58\"><mml:msub><mml:mi>β</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq26\"><alternatives><tex-math id=\"M59\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$(\\lambda )$$\\end{document}</tex-math><mml:math id=\"M60\"><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>λ</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq27\"><alternatives><tex-math id=\"M61\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\lambda$$\\end{document}</tex-math><mml:math id=\"M62\"><mml:mi>λ</mml:mi></mml:math></alternatives></inline-formula>", "<disp-formula id=\"Equ5\"><label>5</label><alternatives><tex-math id=\"M63\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\begin{aligned} \\beta _k= \\beta _{min}+\\left( \\beta _{max}-\\beta _{min}\\right) \\times d^{(s-1)} \\end{aligned}$$\\end{document}</tex-math><mml:math id=\"M64\" display=\"block\"><mml:mrow><mml:mtable><mml:mtr><mml:mtd columnalign=\"right\"><mml:mrow><mml:msub><mml:mi>β</mml:mi><mml:mi>k</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mi>β</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">min</mml:mi></mml:mrow></mml:msub><mml:mo>+</mml:mo><mml:mfenced close=\")\" open=\"(\"><mml:msub><mml:mi>β</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">max</mml:mi></mml:mrow></mml:msub><mml:mo>-</mml:mo><mml:msub><mml:mi>β</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">min</mml:mi></mml:mrow></mml:msub></mml:mfenced><mml:mo>×</mml:mo><mml:msup><mml:mi>d</mml:mi><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>s</mml:mi><mml:mo>-</mml:mo><mml:mn>1</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msup></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:math></alternatives></disp-formula>", "<inline-formula id=\"IEq28\"><alternatives><tex-math id=\"M65\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\beta _{max}$$\\end{document}</tex-math><mml:math id=\"M66\"><mml:msub><mml:mi>β</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">max</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq29\"><alternatives><tex-math id=\"M67\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\beta _{min}$$\\end{document}</tex-math><mml:math id=\"M68\"><mml:msub><mml:mi>β</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">min</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq30\"><alternatives><tex-math id=\"M69\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\lambda$$\\end{document}</tex-math><mml:math id=\"M70\"><mml:mi>λ</mml:mi></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq38\"><alternatives><tex-math id=\"M71\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$p_1$$\\end{document}</tex-math><mml:math id=\"M72\"><mml:msub><mml:mi>p</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq39\"><alternatives><tex-math id=\"M73\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$p_7$$\\end{document}</tex-math><mml:math id=\"M74\"><mml:msub><mml:mi>p</mml:mi><mml:mn>7</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq40\"><alternatives><tex-math id=\"M75\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$p_8$$\\end{document}</tex-math><mml:math id=\"M76\"><mml:msub><mml:mi>p</mml:mi><mml:mn>8</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq41\"><alternatives><tex-math id=\"M77\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$p_{12}$$\\end{document}</tex-math><mml:math id=\"M78\"><mml:msub><mml:mi>p</mml:mi><mml:mn>12</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq42\"><alternatives><tex-math id=\"M79\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$p_1$$\\end{document}</tex-math><mml:math id=\"M80\"><mml:msub><mml:mi>p</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq43\"><alternatives><tex-math id=\"M81\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$p_2$$\\end{document}</tex-math><mml:math id=\"M82\"><mml:msub><mml:mi>p</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq44\"><alternatives><tex-math id=\"M83\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$p_3$$\\end{document}</tex-math><mml:math id=\"M84\"><mml:msub><mml:mi>p</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq45\"><alternatives><tex-math id=\"M85\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$p_4$$\\end{document}</tex-math><mml:math id=\"M86\"><mml:msub><mml:mi>p</mml:mi><mml:mn>4</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq46\"><alternatives><tex-math id=\"M87\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$p_5$$\\end{document}</tex-math><mml:math id=\"M88\"><mml:msub><mml:mi>p</mml:mi><mml:mn>5</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq47\"><alternatives><tex-math id=\"M89\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$p_7$$\\end{document}</tex-math><mml:math id=\"M90\"><mml:msub><mml:mi>p</mml:mi><mml:mn>7</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq48\"><alternatives><tex-math id=\"M91\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$p_6$$\\end{document}</tex-math><mml:math id=\"M92\"><mml:msub><mml:mi>p</mml:mi><mml:mn>6</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq49\"><alternatives><tex-math id=\"M93\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$p_{12}$$\\end{document}</tex-math><mml:math id=\"M94\"><mml:msub><mml:mi>p</mml:mi><mml:mn>12</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq50\"><alternatives><tex-math id=\"M95\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$p_8$$\\end{document}</tex-math><mml:math id=\"M96\"><mml:msub><mml:mi>p</mml:mi><mml:mn>8</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq51\"><alternatives><tex-math id=\"M97\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$p_{10}$$\\end{document}</tex-math><mml:math id=\"M98\"><mml:msub><mml:mi>p</mml:mi><mml:mn>10</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq52\"><alternatives><tex-math id=\"M99\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$p_9$$\\end{document}</tex-math><mml:math id=\"M100\"><mml:msub><mml:mi>p</mml:mi><mml:mn>9</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq53\"><alternatives><tex-math id=\"M101\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$p_{10}$$\\end{document}</tex-math><mml:math id=\"M102\"><mml:msub><mml:mi>p</mml:mi><mml:mn>10</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq54\"><alternatives><tex-math id=\"M103\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$+$$\\end{document}</tex-math><mml:math id=\"M104\"><mml:mo>+</mml:mo></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq55\"><alternatives><tex-math id=\"M105\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$=$$\\end{document}</tex-math><mml:math id=\"M106\"><mml:mo>=</mml:mo></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq56\"><alternatives><tex-math id=\"M107\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$p_1$$\\end{document}</tex-math><mml:math id=\"M108\"><mml:msub><mml:mi>p</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq57\"><alternatives><tex-math id=\"M109\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$p_2$$\\end{document}</tex-math><mml:math id=\"M110\"><mml:msub><mml:mi>p</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq58\"><alternatives><tex-math id=\"M111\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$p_3$$\\end{document}</tex-math><mml:math id=\"M112\"><mml:msub><mml:mi>p</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq59\"><alternatives><tex-math id=\"M113\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$p_4$$\\end{document}</tex-math><mml:math id=\"M114\"><mml:msub><mml:mi>p</mml:mi><mml:mn>4</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq60\"><alternatives><tex-math id=\"M115\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$p_5$$\\end{document}</tex-math><mml:math id=\"M116\"><mml:msub><mml:mi>p</mml:mi><mml:mn>5</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq61\"><alternatives><tex-math id=\"M117\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$p_6$$\\end{document}</tex-math><mml:math id=\"M118\"><mml:msub><mml:mi>p</mml:mi><mml:mn>6</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq62\"><alternatives><tex-math id=\"M119\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$p_7$$\\end{document}</tex-math><mml:math id=\"M120\"><mml:msub><mml:mi>p</mml:mi><mml:mn>7</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq63\"><alternatives><tex-math id=\"M121\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$p_8$$\\end{document}</tex-math><mml:math id=\"M122\"><mml:msub><mml:mi>p</mml:mi><mml:mn>8</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq64\"><alternatives><tex-math id=\"M123\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$=$$\\end{document}</tex-math><mml:math id=\"M124\"><mml:mo>=</mml:mo></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq65\"><alternatives><tex-math id=\"M125\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$=$$\\end{document}</tex-math><mml:math id=\"M126\"><mml:mo>=</mml:mo></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq66\"><alternatives><tex-math id=\"M127\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$=$$\\end{document}</tex-math><mml:math id=\"M128\"><mml:mo>=</mml:mo></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq67\"><alternatives><tex-math id=\"M129\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$=$$\\end{document}</tex-math><mml:math id=\"M130\"><mml:mo>=</mml:mo></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq68\"><alternatives><tex-math id=\"M131\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$p_9$$\\end{document}</tex-math><mml:math id=\"M132\"><mml:msub><mml:mi>p</mml:mi><mml:mn>9</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq69\"><alternatives><tex-math id=\"M133\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$=$$\\end{document}</tex-math><mml:math id=\"M134\"><mml:mo>=</mml:mo></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq70\"><alternatives><tex-math id=\"M135\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$=$$\\end{document}</tex-math><mml:math id=\"M136\"><mml:mo>=</mml:mo></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq71\"><alternatives><tex-math id=\"M137\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$=$$\\end{document}</tex-math><mml:math id=\"M138\"><mml:mo>=</mml:mo></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq72\"><alternatives><tex-math id=\"M139\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$p_{10}$$\\end{document}</tex-math><mml:math id=\"M140\"><mml:msub><mml:mi>p</mml:mi><mml:mn>10</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq73\"><alternatives><tex-math id=\"M141\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$=$$\\end{document}</tex-math><mml:math id=\"M142\"><mml:mo>=</mml:mo></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq74\"><alternatives><tex-math id=\"M143\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$=$$\\end{document}</tex-math><mml:math id=\"M144\"><mml:mo>=</mml:mo></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq75\"><alternatives><tex-math id=\"M145\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$=$$\\end{document}</tex-math><mml:math id=\"M146\"><mml:mo>=</mml:mo></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq76\"><alternatives><tex-math id=\"M147\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$p_{11}$$\\end{document}</tex-math><mml:math id=\"M148\"><mml:msub><mml:mi>p</mml:mi><mml:mn>11</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq77\"><alternatives><tex-math id=\"M149\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$p_{12}$$\\end{document}</tex-math><mml:math id=\"M150\"><mml:msub><mml:mi>p</mml:mi><mml:mn>12</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq78\"><alternatives><tex-math id=\"M151\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$b_1$$\\end{document}</tex-math><mml:math id=\"M152\"><mml:msub><mml:mi>b</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq79\"><alternatives><tex-math id=\"M153\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$b_2$$\\end{document}</tex-math><mml:math id=\"M154\"><mml:msub><mml:mi>b</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq80\"><alternatives><tex-math id=\"M155\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$b_4$$\\end{document}</tex-math><mml:math id=\"M156\"><mml:msub><mml:mi>b</mml:mi><mml:mn>4</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq81\"><alternatives><tex-math id=\"M157\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$b_5$$\\end{document}</tex-math><mml:math id=\"M158\"><mml:msub><mml:mi>b</mml:mi><mml:mn>5</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq82\"><alternatives><tex-math id=\"M159\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$b_6$$\\end{document}</tex-math><mml:math id=\"M160\"><mml:msub><mml:mi>b</mml:mi><mml:mn>6</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq83\"><alternatives><tex-math id=\"M161\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$b_8$$\\end{document}</tex-math><mml:math id=\"M162\"><mml:msub><mml:mi>b</mml:mi><mml:mn>8</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq84\"><alternatives><tex-math id=\"M163\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$b_{10}$$\\end{document}</tex-math><mml:math id=\"M164\"><mml:msub><mml:mi>b</mml:mi><mml:mn>10</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq85\"><alternatives><tex-math id=\"M165\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$b_3$$\\end{document}</tex-math><mml:math id=\"M166\"><mml:msub><mml:mi>b</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq86\"><alternatives><tex-math id=\"M167\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$b_7$$\\end{document}</tex-math><mml:math id=\"M168\"><mml:msub><mml:mi>b</mml:mi><mml:mn>7</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq87\"><alternatives><tex-math id=\"M169\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$b_9$$\\end{document}</tex-math><mml:math id=\"M170\"><mml:msub><mml:mi>b</mml:mi><mml:mn>9</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq88\"><alternatives><tex-math id=\"M171\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$b_1$$\\end{document}</tex-math><mml:math id=\"M172\"><mml:msub><mml:mi>b</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq89\"><alternatives><tex-math id=\"M173\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$b_2$$\\end{document}</tex-math><mml:math id=\"M174\"><mml:msub><mml:mi>b</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq90\"><alternatives><tex-math id=\"M175\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$b_3$$\\end{document}</tex-math><mml:math id=\"M176\"><mml:msub><mml:mi>b</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq91\"><alternatives><tex-math id=\"M177\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$b_4$$\\end{document}</tex-math><mml:math id=\"M178\"><mml:msub><mml:mi>b</mml:mi><mml:mn>4</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq92\"><alternatives><tex-math id=\"M179\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$b_5$$\\end{document}</tex-math><mml:math id=\"M180\"><mml:msub><mml:mi>b</mml:mi><mml:mn>5</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq93\"><alternatives><tex-math id=\"M181\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$b_6$$\\end{document}</tex-math><mml:math id=\"M182\"><mml:msub><mml:mi>b</mml:mi><mml:mn>6</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq94\"><alternatives><tex-math id=\"M183\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$b_7$$\\end{document}</tex-math><mml:math id=\"M184\"><mml:msub><mml:mi>b</mml:mi><mml:mn>7</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq95\"><alternatives><tex-math id=\"M185\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$b_8$$\\end{document}</tex-math><mml:math id=\"M186\"><mml:msub><mml:mi>b</mml:mi><mml:mn>8</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq96\"><alternatives><tex-math id=\"M187\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$b_9$$\\end{document}</tex-math><mml:math id=\"M188\"><mml:msub><mml:mi>b</mml:mi><mml:mn>9</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq97\"><alternatives><tex-math id=\"M189\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$b_{10}$$\\end{document}</tex-math><mml:math id=\"M190\"><mml:msub><mml:mi>b</mml:mi><mml:mn>10</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq98\"><alternatives><tex-math id=\"M191\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$10^4 \\times D$$\\end{document}</tex-math><mml:math id=\"M192\"><mml:mrow><mml:msup><mml:mn>10</mml:mn><mml:mn>4</mml:mn></mml:msup><mml:mo>×</mml:mo><mml:mi>D</mml:mi></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq99\"><alternatives><tex-math id=\"M193\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$t_1$$\\end{document}</tex-math><mml:math id=\"M194\"><mml:msub><mml:mi>t</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq100\"><alternatives><tex-math id=\"M195\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$t_4$$\\end{document}</tex-math><mml:math id=\"M196\"><mml:msub><mml:mi>t</mml:mi><mml:mn>4</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq101\"><alternatives><tex-math id=\"M197\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$t_5$$\\end{document}</tex-math><mml:math id=\"M198\"><mml:msub><mml:mi>t</mml:mi><mml:mn>5</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq102\"><alternatives><tex-math id=\"M199\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$t_2$$\\end{document}</tex-math><mml:math id=\"M200\"><mml:msub><mml:mi>t</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq103\"><alternatives><tex-math id=\"M201\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$t_3$$\\end{document}</tex-math><mml:math id=\"M202\"><mml:msub><mml:mi>t</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq104\"><alternatives><tex-math id=\"M203\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$t_7$$\\end{document}</tex-math><mml:math id=\"M204\"><mml:msub><mml:mi>t</mml:mi><mml:mn>7</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq105\"><alternatives><tex-math id=\"M205\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$t_8$$\\end{document}</tex-math><mml:math id=\"M206\"><mml:msub><mml:mi>t</mml:mi><mml:mn>8</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq106\"><alternatives><tex-math id=\"M207\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$b_9$$\\end{document}</tex-math><mml:math id=\"M208\"><mml:msub><mml:mi>b</mml:mi><mml:mn>9</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq107\"><alternatives><tex-math id=\"M209\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$t_{10}$$\\end{document}</tex-math><mml:math id=\"M210\"><mml:msub><mml:mi>t</mml:mi><mml:mn>10</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq108\"><alternatives><tex-math id=\"M211\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$t_6$$\\end{document}</tex-math><mml:math id=\"M212\"><mml:msub><mml:mi>t</mml:mi><mml:mn>6</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq109\"><alternatives><tex-math id=\"M213\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$t_1$$\\end{document}</tex-math><mml:math id=\"M214\"><mml:msub><mml:mi>t</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq110\"><alternatives><tex-math id=\"M215\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$t_2$$\\end{document}</tex-math><mml:math id=\"M216\"><mml:msub><mml:mi>t</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq111\"><alternatives><tex-math id=\"M217\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$t_3$$\\end{document}</tex-math><mml:math id=\"M218\"><mml:msub><mml:mi>t</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq112\"><alternatives><tex-math id=\"M219\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$t_4$$\\end{document}</tex-math><mml:math id=\"M220\"><mml:msub><mml:mi>t</mml:mi><mml:mn>4</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq113\"><alternatives><tex-math id=\"M221\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$t_5$$\\end{document}</tex-math><mml:math id=\"M222\"><mml:msub><mml:mi>t</mml:mi><mml:mn>5</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq114\"><alternatives><tex-math id=\"M223\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$t_6$$\\end{document}</tex-math><mml:math id=\"M224\"><mml:msub><mml:mi>t</mml:mi><mml:mn>6</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq115\"><alternatives><tex-math id=\"M225\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$t_7$$\\end{document}</tex-math><mml:math id=\"M226\"><mml:msub><mml:mi>t</mml:mi><mml:mn>7</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq116\"><alternatives><tex-math id=\"M227\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$t_8$$\\end{document}</tex-math><mml:math id=\"M228\"><mml:msub><mml:mi>t</mml:mi><mml:mn>8</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq117\"><alternatives><tex-math id=\"M229\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$t_9$$\\end{document}</tex-math><mml:math id=\"M230\"><mml:msub><mml:mi>t</mml:mi><mml:mn>9</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq118\"><alternatives><tex-math id=\"M231\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$t_{10}$$\\end{document}</tex-math><mml:math id=\"M232\"><mml:msub><mml:mi>t</mml:mi><mml:mn>10</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq119\"><alternatives><tex-math id=\"M233\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$d_0$$\\end{document}</tex-math><mml:math id=\"M234\"><mml:msub><mml:mi>d</mml:mi><mml:mn>0</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq120\"><alternatives><tex-math id=\"M235\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$E_{Tx}$$\\end{document}</tex-math><mml:math id=\"M236\"><mml:msub><mml:mi>E</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">Tx</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<disp-formula id=\"Equ6\"><label>6</label><alternatives><tex-math id=\"M237\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\begin{aligned} E_{Tx}(d) = E_{elec}(k)+ E_{amp}* k * d \\end{aligned}$$\\end{document}</tex-math><mml:math id=\"M238\" display=\"block\"><mml:mrow><mml:mtable><mml:mtr><mml:mtd columnalign=\"right\"><mml:mrow><mml:msub><mml:mi>E</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">Tx</mml:mi></mml:mrow></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>d</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:msub><mml:mi>E</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">elec</mml:mi></mml:mrow></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>k</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>+</mml:mo><mml:msub><mml:mi>E</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">amp</mml:mi></mml:mrow></mml:msub><mml:mrow/><mml:mo>∗</mml:mo><mml:mi>k</mml:mi><mml:mrow/><mml:mo>∗</mml:mo><mml:mi>d</mml:mi></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:math></alternatives></disp-formula>", "<inline-formula id=\"IEq121\"><alternatives><tex-math id=\"M239\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$E_{elec}$$\\end{document}</tex-math><mml:math id=\"M240\"><mml:msub><mml:mi>E</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">elec</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq122\"><alternatives><tex-math id=\"M241\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$E_{amp}$$\\end{document}</tex-math><mml:math id=\"M242\"><mml:msub><mml:mi>E</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">amp</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<disp-formula id=\"Equ7\"><label>7</label><alternatives><tex-math id=\"M243\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\begin{aligned} E_{Rx} (k)=E_{elec}* k \\end{aligned}$$\\end{document}</tex-math><mml:math id=\"M244\" display=\"block\"><mml:mrow><mml:mtable><mml:mtr><mml:mtd columnalign=\"right\"><mml:mrow><mml:msub><mml:mi>E</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">Rx</mml:mi></mml:mrow></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>k</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:msub><mml:mi>E</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">elec</mml:mi></mml:mrow></mml:msub><mml:mrow/><mml:mo>∗</mml:mo><mml:mi>k</mml:mi></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:math></alternatives></disp-formula>", "<inline-formula id=\"IEq123\"><alternatives><tex-math id=\"M245\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$T_p$$\\end{document}</tex-math><mml:math id=\"M246\"><mml:msub><mml:mi>T</mml:mi><mml:mi>p</mml:mi></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq124\"><alternatives><tex-math id=\"M247\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$V_{min}$$\\end{document}</tex-math><mml:math id=\"M248\"><mml:msub><mml:mi>V</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">min</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq125\"><alternatives><tex-math id=\"M249\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$V_{max}$$\\end{document}</tex-math><mml:math id=\"M250\"><mml:msub><mml:mi>V</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">max</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq126\"><alternatives><tex-math id=\"M251\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$2{\\pi }$$\\end{document}</tex-math><mml:math id=\"M252\"><mml:mrow><mml:mn>2</mml:mn><mml:mi>π</mml:mi></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq127\"><alternatives><tex-math id=\"M253\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$P_s$$\\end{document}</tex-math><mml:math id=\"M254\"><mml:msub><mml:mi>P</mml:mi><mml:mi>s</mml:mi></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq128\"><alternatives><tex-math id=\"M255\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$T_p$$\\end{document}</tex-math><mml:math id=\"M256\"><mml:msub><mml:mi>T</mml:mi><mml:mi>p</mml:mi></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq129\"><alternatives><tex-math id=\"M257\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$V_{min}$$\\end{document}</tex-math><mml:math id=\"M258\"><mml:msub><mml:mi>V</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">min</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq130\"><alternatives><tex-math id=\"M259\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$V_{max}$$\\end{document}</tex-math><mml:math id=\"M260\"><mml:msub><mml:mi>V</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">max</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq131\"><alternatives><tex-math id=\"M261\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$2{\\pi }$$\\end{document}</tex-math><mml:math id=\"M262\"><mml:mrow><mml:mn>2</mml:mn><mml:mi>π</mml:mi></mml:mrow></mml:math></alternatives></inline-formula>", "<disp-formula id=\"Equ8\"><label>8</label><alternatives><tex-math id=\"M263\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\begin{aligned} T_{EARNRP(n)} = {\\left\\{ \\begin{array}{ll} Z(n) &{}\\quad if \\; E(n) \\hspace{2pt} \\ge \\frac{1}{N} {\\sum }_{i=1}^N E(i) \\\\ 0 &{}\\quad if \\; E(n) \\hspace{2pt} < \\frac{1}{N} {\\sum }_{i=1}^N E(i) \\end{array}\\right. } \\end{aligned}$$\\end{document}</tex-math><mml:math id=\"M264\" display=\"block\"><mml:mrow><mml:mtable><mml:mtr><mml:mtd columnalign=\"right\"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mrow><mml:mi>E</mml:mi><mml:mi>A</mml:mi><mml:mi>R</mml:mi><mml:mi>N</mml:mi><mml:mi>R</mml:mi><mml:mi>P</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>n</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mfenced open=\"{\"><mml:mrow><mml:mtable><mml:mtr><mml:mtd columnalign=\"left\"><mml:mrow><mml:mi>Z</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>n</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mtd><mml:mtd columnalign=\"left\"><mml:mrow><mml:mrow/><mml:mspace width=\"1em\"/><mml:mi>i</mml:mi><mml:mi>f</mml:mi><mml:mspace width=\"0.277778em\"/><mml:mi>E</mml:mi><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>n</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mspace width=\"2.0pt\"/><mml:mo>≥</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mi>N</mml:mi></mml:mfrac><mml:msubsup><mml:mo>∑</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>N</mml:mi></mml:msubsup><mml:mi>E</mml:mi><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mrow></mml:mtd></mml:mtr><mml:mtr><mml:mtd columnalign=\"left\"><mml:mrow><mml:mrow/><mml:mn>0</mml:mn></mml:mrow></mml:mtd><mml:mtd columnalign=\"left\"><mml:mrow><mml:mrow/><mml:mspace width=\"1em\"/><mml:mi>i</mml:mi><mml:mi>f</mml:mi><mml:mspace width=\"0.277778em\"/><mml:mi>E</mml:mi><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>n</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mspace width=\"2.0pt\"/><mml:mo><</mml:mo><mml:mfrac><mml:mn>1</mml:mn><mml:mi>N</mml:mi></mml:mfrac><mml:msubsup><mml:mo>∑</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>N</mml:mi></mml:msubsup><mml:mi>E</mml:mi><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:mfenced></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:math></alternatives></disp-formula>", "<disp-formula id=\"Equ9\"><label>9</label><alternatives><tex-math id=\"M265\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\begin{aligned} Z(n)=a_1T_1(n)+a_2T_2(n)+a_3T_3(n)+a_4T_4(n)+a_5T_5(n) \\end{aligned}$$\\end{document}</tex-math><mml:math id=\"M266\" display=\"block\"><mml:mrow><mml:mtable><mml:mtr><mml:mtd columnalign=\"right\"><mml:mrow><mml:mi>Z</mml:mi><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>n</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>=</mml:mo><mml:msub><mml:mi>a</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:msub><mml:mi>T</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>n</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>+</mml:mo><mml:msub><mml:mi>a</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:msub><mml:mi>T</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>n</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>+</mml:mo><mml:msub><mml:mi>a</mml:mi><mml:mn>3</mml:mn></mml:msub><mml:msub><mml:mi>T</mml:mi><mml:mn>3</mml:mn></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>n</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>+</mml:mo><mml:msub><mml:mi>a</mml:mi><mml:mn>4</mml:mn></mml:msub><mml:msub><mml:mi>T</mml:mi><mml:mn>4</mml:mn></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>n</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>+</mml:mo><mml:msub><mml:mi>a</mml:mi><mml:mn>5</mml:mn></mml:msub><mml:msub><mml:mi>T</mml:mi><mml:mn>5</mml:mn></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>n</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:math></alternatives></disp-formula>", "<inline-formula id=\"IEq132\"><alternatives><tex-math id=\"M267\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$a_1$$\\end{document}</tex-math><mml:math id=\"M268\"><mml:msub><mml:mi>a</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq133\"><alternatives><tex-math id=\"M269\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$a_2$$\\end{document}</tex-math><mml:math id=\"M270\"><mml:msub><mml:mi>a</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq134\"><alternatives><tex-math id=\"M271\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$a_3$$\\end{document}</tex-math><mml:math id=\"M272\"><mml:msub><mml:mi>a</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq135\"><alternatives><tex-math id=\"M273\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$a_4$$\\end{document}</tex-math><mml:math id=\"M274\"><mml:msub><mml:mi>a</mml:mi><mml:mn>4</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq136\"><alternatives><tex-math id=\"M275\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$a_5$$\\end{document}</tex-math><mml:math id=\"M276\"><mml:msub><mml:mi>a</mml:mi><mml:mn>5</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<disp-formula id=\"Equ10\"><label>10</label><alternatives><tex-math id=\"M277\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\begin{aligned} T_1(n)= & {} \\frac{V_{max} - v_{n_{current}}}{V_{max}} \\end{aligned}$$\\end{document}</tex-math><mml:math id=\"M278\" display=\"block\"><mml:mrow><mml:mtable><mml:mtr><mml:mtd columnalign=\"right\"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>n</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>=</mml:mo></mml:mrow></mml:mtd><mml:mtd columnalign=\"left\"><mml:mrow><mml:mrow/><mml:mfrac><mml:mrow><mml:msub><mml:mi>V</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">max</mml:mi></mml:mrow></mml:msub><mml:mo>-</mml:mo><mml:msub><mml:mi>v</mml:mi><mml:msub><mml:mi>n</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">current</mml:mi></mml:mrow></mml:msub></mml:msub></mml:mrow><mml:msub><mml:mi>V</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">max</mml:mi></mml:mrow></mml:msub></mml:mfrac></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:math></alternatives></disp-formula>", "<disp-formula id=\"Equ11\"><label>11</label><alternatives><tex-math id=\"M279\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\begin{aligned} T_2(n)= & {} \\frac{E_{n_{current}}}{E_{avg}} \\end{aligned}$$\\end{document}</tex-math><mml:math id=\"M280\" display=\"block\"><mml:mrow><mml:mtable><mml:mtr><mml:mtd columnalign=\"right\"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>n</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>=</mml:mo></mml:mrow></mml:mtd><mml:mtd columnalign=\"left\"><mml:mrow><mml:mrow/><mml:mfrac><mml:msub><mml:mi>E</mml:mi><mml:msub><mml:mi>n</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">current</mml:mi></mml:mrow></mml:msub></mml:msub><mml:msub><mml:mi>E</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">avg</mml:mi></mml:mrow></mml:msub></mml:mfrac></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:math></alternatives></disp-formula>", "<disp-formula id=\"Equ12\"><label>12</label><alternatives><tex-math id=\"M281\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\begin{aligned} T_3(n)= & {} \\frac{R_{tran}-d_{ij}}{R_{tran}} \\end{aligned}$$\\end{document}</tex-math><mml:math id=\"M282\" display=\"block\"><mml:mrow><mml:mtable><mml:mtr><mml:mtd columnalign=\"right\"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mn>3</mml:mn></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>n</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>=</mml:mo></mml:mrow></mml:mtd><mml:mtd columnalign=\"left\"><mml:mrow><mml:mrow/><mml:mfrac><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">tran</mml:mi></mml:mrow></mml:msub><mml:mo>-</mml:mo><mml:msub><mml:mi>d</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">ij</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">tran</mml:mi></mml:mrow></mml:msub></mml:mfrac></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:math></alternatives></disp-formula>", "<disp-formula id=\"Equ13\"><label>13</label><alternatives><tex-math id=\"M283\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\begin{aligned} T_4(n)= & {} \\frac{\\Delta t_{ij}}{t_{frame}} \\end{aligned}$$\\end{document}</tex-math><mml:math id=\"M284\" display=\"block\"><mml:mrow><mml:mtable><mml:mtr><mml:mtd columnalign=\"right\"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mn>4</mml:mn></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>n</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>=</mml:mo></mml:mrow></mml:mtd><mml:mtd columnalign=\"left\"><mml:mrow><mml:mrow/><mml:mfrac><mml:mrow><mml:mi mathvariant=\"normal\">Δ</mml:mi><mml:msub><mml:mi>t</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">ij</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:msub><mml:mi>t</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">frame</mml:mi></mml:mrow></mml:msub></mml:mfrac></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:math></alternatives></disp-formula>", "<disp-formula id=\"Equ14\"><label>14</label><alternatives><tex-math id=\"M285\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\begin{aligned} T_5(n)= & {} \\frac{N_{ch}(n)}{N \\sum _{i=1}^{N} N_{ch}(i)} \\end{aligned}$$\\end{document}</tex-math><mml:math id=\"M286\" display=\"block\"><mml:mrow><mml:mtable><mml:mtr><mml:mtd columnalign=\"right\"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mn>5</mml:mn></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>n</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>=</mml:mo></mml:mrow></mml:mtd><mml:mtd columnalign=\"left\"><mml:mrow><mml:mrow/><mml:mfrac><mml:mrow><mml:msub><mml:mi>N</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">ch</mml:mi></mml:mrow></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>n</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mrow><mml:mrow><mml:mi>N</mml:mi><mml:msubsup><mml:mo>∑</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>N</mml:mi></mml:msubsup><mml:msub><mml:mi>N</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">ch</mml:mi></mml:mrow></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>i</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mrow></mml:mfrac></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:math></alternatives></disp-formula>", "<inline-formula id=\"IEq137\"><alternatives><tex-math id=\"M287\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$v_{n_{current}}$$\\end{document}</tex-math><mml:math id=\"M288\"><mml:msub><mml:mi>v</mml:mi><mml:msub><mml:mi>n</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">current</mml:mi></mml:mrow></mml:msub></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq138\"><alternatives><tex-math id=\"M289\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$V_{max}$$\\end{document}</tex-math><mml:math id=\"M290\"><mml:msub><mml:mi>V</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">max</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq139\"><alternatives><tex-math id=\"M291\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$E_{avg}$$\\end{document}</tex-math><mml:math id=\"M292\"><mml:msub><mml:mi>E</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">avg</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq140\"><alternatives><tex-math id=\"M293\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$E_{n_{current}}$$\\end{document}</tex-math><mml:math id=\"M294\"><mml:msub><mml:mi>E</mml:mi><mml:msub><mml:mi>n</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">current</mml:mi></mml:mrow></mml:msub></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq141\"><alternatives><tex-math id=\"M295\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$d_{ij}$$\\end{document}</tex-math><mml:math id=\"M296\"><mml:msub><mml:mi>d</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">ij</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq142\"><alternatives><tex-math id=\"M297\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$R_{tran}$$\\end{document}</tex-math><mml:math id=\"M298\"><mml:msub><mml:mi>R</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">tran</mml:mi></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq143\"><alternatives><tex-math id=\"M299\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$R_{tran} - d_{ij}$$\\end{document}</tex-math><mml:math id=\"M300\"><mml:mrow><mml:msub><mml:mi>R</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">tran</mml:mi></mml:mrow></mml:msub><mml:mo>-</mml:mo><mml:msub><mml:mi>d</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">ij</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq144\"><alternatives><tex-math id=\"M301\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\Delta t_{ij}$$\\end{document}</tex-math><mml:math id=\"M302\"><mml:mrow><mml:mi mathvariant=\"normal\">Δ</mml:mi><mml:msub><mml:mi>t</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">ij</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq145\"><alternatives><tex-math id=\"M303\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$N_{ch(n)}$$\\end{document}</tex-math><mml:math id=\"M304\"><mml:msub><mml:mi>N</mml:mi><mml:mrow><mml:mi>c</mml:mi><mml:mi>h</mml:mi><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>n</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq146\"><alternatives><tex-math id=\"M305\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$a_1$$\\end{document}</tex-math><mml:math id=\"M306\"><mml:msub><mml:mi>a</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq147\"><alternatives><tex-math id=\"M307\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$a_2$$\\end{document}</tex-math><mml:math id=\"M308\"><mml:msub><mml:mi>a</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq148\"><alternatives><tex-math id=\"M309\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$a_3$$\\end{document}</tex-math><mml:math id=\"M310\"><mml:msub><mml:mi>a</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq149\"><alternatives><tex-math id=\"M311\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$a_4$$\\end{document}</tex-math><mml:math id=\"M312\"><mml:msub><mml:mi>a</mml:mi><mml:mn>4</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq150\"><alternatives><tex-math id=\"M313\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$a_5$$\\end{document}</tex-math><mml:math id=\"M314\"><mml:msub><mml:mi>a</mml:mi><mml:mn>5</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<disp-formula id=\"Equ15\"><label>15</label><alternatives><tex-math id=\"M315\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\begin{aligned} Fitness = a_1 T_1 (n)+a_2 T_2 (n)+a_3 T_3 (n)+a_4 T_4 (n)+a_5 T_5 (n) \\end{aligned}$$\\end{document}</tex-math><mml:math id=\"M316\" display=\"block\"><mml:mrow><mml:mtable><mml:mtr><mml:mtd columnalign=\"right\"><mml:mrow><mml:mi>F</mml:mi><mml:mi>i</mml:mi><mml:mi>t</mml:mi><mml:mi>n</mml:mi><mml:mi>e</mml:mi><mml:mi>s</mml:mi><mml:mi>s</mml:mi><mml:mo>=</mml:mo><mml:msub><mml:mi>a</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:msub><mml:mi>T</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>n</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>+</mml:mo><mml:msub><mml:mi>a</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:msub><mml:mi>T</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>n</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>+</mml:mo><mml:msub><mml:mi>a</mml:mi><mml:mn>3</mml:mn></mml:msub><mml:msub><mml:mi>T</mml:mi><mml:mn>3</mml:mn></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>n</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>+</mml:mo><mml:msub><mml:mi>a</mml:mi><mml:mn>4</mml:mn></mml:msub><mml:msub><mml:mi>T</mml:mi><mml:mn>4</mml:mn></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>n</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo>+</mml:mo><mml:msub><mml:mi>a</mml:mi><mml:mn>5</mml:mn></mml:msub><mml:msub><mml:mi>T</mml:mi><mml:mn>5</mml:mn></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>n</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:math></alternatives></disp-formula>", "<disp-formula id=\"Equ16\"><label>16</label><alternatives><tex-math id=\"M317\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\begin{aligned} \\sum _{i=1}^{5} a_i=1 \\end{aligned}$$\\end{document}</tex-math><mml:math id=\"M318\" display=\"block\"><mml:mrow><mml:mtable><mml:mtr><mml:mtd columnalign=\"right\"><mml:mrow><mml:munderover><mml:mo>∑</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mn>5</mml:mn></mml:munderover><mml:msub><mml:mi>a</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow></mml:mtd></mml:mtr></mml:mtable></mml:mrow></mml:math></alternatives></disp-formula>", "<inline-formula id=\"IEq151\"><alternatives><tex-math id=\"M319\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$a_1$$\\end{document}</tex-math><mml:math id=\"M320\"><mml:msub><mml:mi>a</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq152\"><alternatives><tex-math id=\"M321\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$a_2$$\\end{document}</tex-math><mml:math id=\"M322\"><mml:msub><mml:mi>a</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq153\"><alternatives><tex-math id=\"M323\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$a_3$$\\end{document}</tex-math><mml:math id=\"M324\"><mml:msub><mml:mi>a</mml:mi><mml:mn>3</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq154\"><alternatives><tex-math id=\"M325\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$a_4$$\\end{document}</tex-math><mml:math id=\"M326\"><mml:msub><mml:mi>a</mml:mi><mml:mn>4</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq155\"><alternatives><tex-math id=\"M327\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$a_5$$\\end{document}</tex-math><mml:math id=\"M328\"><mml:msub><mml:mi>a</mml:mi><mml:mn>5</mml:mn></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq156\"><alternatives><tex-math id=\"M329\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$T_{EARNRP}(n)$$\\end{document}</tex-math><mml:math id=\"M330\"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">EARNRP</mml:mi></mml:mrow></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>n</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq157\"><alternatives><tex-math id=\"M331\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$T_{EARNRP}(n)$$\\end{document}</tex-math><mml:math id=\"M332\"><mml:mrow><mml:msub><mml:mi>T</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">EARNRP</mml:mi></mml:mrow></mml:msub><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>n</mml:mi><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq158\"><alternatives><tex-math id=\"M333\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$n_i$$\\end{document}</tex-math><mml:math id=\"M334\"><mml:msub><mml:mi>n</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq159\"><alternatives><tex-math id=\"M335\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$(n_{i}+k(1/(p-1)))\\tau$$\\end{document}</tex-math><mml:math id=\"M336\"><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>n</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>+</mml:mo><mml:mi>k</mml:mi><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>1</mml:mn><mml:mo stretchy=\"false\">/</mml:mo><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>p</mml:mi><mml:mo>-</mml:mo><mml:mn>1</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo stretchy=\"false\">)</mml:mo><mml:mi>τ</mml:mi></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq160\"><alternatives><tex-math id=\"M337\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\tau$$\\end{document}</tex-math><mml:math id=\"M338\"><mml:mi>τ</mml:mi></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq161\"><alternatives><tex-math id=\"M339\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\Delta t_{ij}$$\\end{document}</tex-math><mml:math id=\"M340\"><mml:mrow><mml:mi mathvariant=\"normal\">Δ</mml:mi><mml:msub><mml:mi>t</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">ij</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq162\"><alternatives><tex-math id=\"M341\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\Delta t_{ij}$$\\end{document}</tex-math><mml:math id=\"M342\"><mml:mrow><mml:mi mathvariant=\"normal\">Δ</mml:mi><mml:msub><mml:mi>t</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">ij</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq163\"><alternatives><tex-math id=\"M343\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$(n_{i}+k(1/(p-1)))\\tau$$\\end{document}</tex-math><mml:math id=\"M344\"><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:msub><mml:mi>n</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>+</mml:mo><mml:mi>k</mml:mi><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mn>1</mml:mn><mml:mo stretchy=\"false\">/</mml:mo><mml:mrow><mml:mo stretchy=\"false\">(</mml:mo><mml:mi>p</mml:mi><mml:mo>-</mml:mo><mml:mn>1</mml:mn><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo stretchy=\"false\">)</mml:mo></mml:mrow><mml:mo stretchy=\"false\">)</mml:mo><mml:mi>τ</mml:mi></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq164\"><alternatives><tex-math id=\"M345\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\tau \\le \\Delta t_{ij}$$\\end{document}</tex-math><mml:math id=\"M346\"><mml:mrow><mml:mi>τ</mml:mi><mml:mo>≤</mml:mo><mml:mi mathvariant=\"normal\">Δ</mml:mi><mml:msub><mml:mi>t</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">ij</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq165\"><alternatives><tex-math id=\"M347\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\Delta t_{ij}$$\\end{document}</tex-math><mml:math id=\"M348\"><mml:mrow><mml:mi mathvariant=\"normal\">Δ</mml:mi><mml:msub><mml:mi>t</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">ij</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math></alternatives></inline-formula>", "<inline-formula id=\"IEq166\"><alternatives><tex-math id=\"M349\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$\\times$$\\end{document}</tex-math><mml:math id=\"M350\"><mml:mo>×</mml:mo></mml:math></alternatives></inline-formula>", "<inline-formula 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PMC10781697	38200107	[ "<title>Introduction</title>", "<p id=\"Par3\">Intestinal vagal afferents are the rapid information superhighway through which chemosensory information is relayed from the gut to the brain. Moreover, perhaps because of its huge sensory surface, the gut supplies the majority of vagal afferent fibres to the brain<sup>##REF##1706359##1##</sup>. Despite this, little is known about how vagal afferent function alters in the aged although dystrophic anatomical changes have been observed in vagal afferents innervating the intestine<sup>##REF##19665435##2##</sup>.</p>", "<p id=\"Par4\">The primary target of vagal innervation in the murine gut is the myenteric plexus, while direct vagal innervation of the mucosa and submucosa are sparse or absent<sup>##REF##1706359##1##</sup>. Indeed the densest innervation of the intestinal epithelial layer cells<sup>##REF##2436086##3##,##REF##6199254##4##</sup> is supplied by the enteric nervous system, which provides more than 90% of sensory neuropeptide containing fibres to the mucosal layer. While there are several functional classes of neurons within the myenteric plexus, only one class, the intrinsic primary afferents (IPANs) is both chemo- and mechanosensitive serving as an intramural sensory gatekeeper relaying the signals originating from luminal contents to the afferent vagus nerve<sup>##REF##24719355##5##</sup>.</p>", "<p id=\"Par5\">It has been proposed, based on age-related changes in calcium-binding proteins and neurotransmitter content, that IPANs are most susceptible to neurodegeneration when aged are compared to young animals<sup>##REF##15065999##6##,##REF##12181159##7##</sup>. Given the crucial gatekeeper role that the IPANs have in relaying information to the afferent vagus, any study of age-related changes in vagal function must be accompanied by a study of age-related functional changes in myenteric IPANs.</p>", "<p id=\"Par6\">Background mesenteric nerve fibre discharge is diminished in aged compared to young adult humans<sup>##UREF##0##8##</sup> and this finding has been replicated in C57BL/6 mice for which constitutive mesenteric multiunit spiking was significantly reduced in aged (18–24 mo) compared to young (3 mo) animals<sup>##REF##26592729##9##</sup>. Also, we have previously reported that the background vagal afferent firing rate is reduced in aged CD-1 mice by 62% compared to young CD-1 and that intraluminal infusion of the aminosterol squalamine dilactate could return vagal firing rates to those seen young animals<sup>##REF##31551703##10##</sup>. Since electrical vagal stimulation has been used clinically to treat depressive disorders<sup>##UREF##1##11##</sup> it is possible that a diminished or altered resting vagal afferent firing rate and pattern in the elderly contributes to the significant occurrence of behavioural depression in this group<sup>##REF##22244025##12##</sup>.</p>", "<p id=\"Par7\">The class of neuron most likely responsible for the reduction in resting vagus basal firing rate with ageing is the IPAN. With respect to how IPANs signal to the afferent vagus, we have previously reported<sup>##REF##24719355##5##</sup> that, for young mice, neurotransmission is via an IPAN soma to vagal afferent terminal nicotinic synapse that we have termed the intramural sensory synapse.</p>", "<p id=\"Par8\">The aged murine enteric nervous system displays dystrophic neurons<sup>##UREF##2##13##</sup> and degenerating sensory neuropeptide containing nerve fibres<sup>##REF##23537898##14##</sup>. Aged enteric neurons also accumulate lipofuscin in their somata<sup>##REF##23537898##14##</sup>. However despite this evidence for anatomical and neurochemical changes there have been no physiological recordings made from aged IPANs or other enteric neurons. It is thus hardly possible to know how aged IPANs are functionally altered compared to young IPANs. In view of the increased human median age in the developed world and the importance of the enteric nervous system in gut-to-brain transmission it is critical to investigate an animal model of the aged enteric nervous system. It is also important to identify drugs whose ingestion might reduce the functional effects of ageing.</p>", "<p id=\"Par9\">The constitutive firing rate of vagal fibres innervating the small intestine is lower for aged compared to young mice (see above) but vertebrate neurons encode information by firing patterns and intervals not just firing rates. Despite this, it is not known whether there exists a specific age-associated vagal firing pattern (ageing code). <sup>15</sup>Vagal firing patterns can be described by 4 parameters, mean interspike interval (MII), burst duration (BD), gap duration (GD) and intraburst interval (IBI), that describe firing intervals with respect to action potential bursts and the overall mean interspike interval<sup>##REF##34702901##15##</sup>. This raises the question whether an ageing code incorporating these parameters can be determined by comparing vagal firing patterns between old and young animals and whether the addition of luminal squalamine to the small intestine of aged mice can reduce or eliminate the ageing code. This would be important because the afferent vagus projects monosynaptically via the nucleus of the solitary tract to the hypothalamic arcuate nucleus<sup>##REF##679038##16##</sup> that releases growth hormone releasing hormone which programs somatic ageing<sup>##REF##23636330##17##</sup>, thus removal of the gut-derived vagus ageing code by squalamine might alter the functional effects of somatic ageing.</p>", "<p id=\"Par10\">In addition, acute treatment of major depressive disorders with selective serotonin reuptake inhibitor (SSRI) antidepressants such as sertraline may reduce therapeutic efficacy in the aged<sup>##REF##22244025##12##,##REF##27389296##18##</sup>.To discover whether peripheral vagal mechanisms might contribute to this effect we applied intraluminal sertraline to the aged intestine to establish whether the SSRI can still facilitate afferent vagal firing as it does in young mice<sup>##REF##34702901##15##</sup>. We also tested if squalamine, which evokes an antidepressant vagal code in young animals<sup>##REF##34702901##15##</sup>, is more resistant to ageing in this regard than sertraline.</p>", "<p id=\"Par11\">For the present article, mixed extracellular action potentials (multiunit responses) were recorded from the jejunal mesenteric nerve bundles, and extracellular discharge from individual single vagal fibres (single units) identified by their unique action potenial shape and amplitude<sup>##REF##34702901##15##,##UREF##3##19##</sup>.</p>", "<p id=\"Par12\">Patch clamp whole-cell recordings were made from the myenteric plexus using ex vivo segments of mouse jejunum as described in Mao et al. <sup>##REF##16899648##20##</sup>, and passive and active physiological properties of IPANs examined.</p>", "<p id=\"Par13\">We identified the defining temporal characteristics of an ageing code for both IPANs and afferent vagal fibres when neurons from aged (18–24 mo) were compared to those from young (3 mo) animals. We also determined that the vagal ageing code was suppressed by application of squalamine.</p>" ]	[ "<title>Methods</title>", "<title>Animals</title>", "<p id=\"Par39\">Six to eight-week-old female and male CD-1 or male Swiss Webster (SW) mice were purchased from Charles River (Montreal, QC, Canada). Young animals (3 mo) were allowed to habituate to the animal facility for 1 week while older animals were housed until they were 18–24 mo of age when they were used for experimentation. Animals were housed 4/cage and under controlled conditions (21◦C) on a 12-h light/dark cycle (lights on at 5:00 a.m.) and fed ad libitum. All experiments were carried out in accordance with the guidelines of the Canadian Council on Animal Care and ARRIVE Guidelines and were approved by McMaster University’s Animal Research Ethics Board (Animal Utilisation Protocols: 16-08-30 & 20-05-21). Mice were euthanized by cervical dislocation and all action potential recordings performed ex vivo.</p>", "<title>Enteric nervous system</title>", "<p id=\"Par40\">A 2 cm segment of ileum was removed from freshly euthanized mice and the tissue was placed in a 2 ml recording petri dish whose inside base was lined with sylgard (170 silicone elastomer, Dow Corning, Midland, MI, USA) and filled with Krebs buffer of the following composition (mM): NaCl 118.1, KCl 4.8, NaHCO<sub>3</sub> 25, NaH<sub>2</sub>PO<sub>4</sub> 1.0, MgSO<sub>4</sub> 1.2, glucose 11.1, CaCl<sub>2</sub> 2.5; the buffer was continuously bubbled with carbogen (95% O<sub>2</sub>–5% CO<sub>2</sub>). Nicardipine (3 μM) (Sigma-Aldrich, Oakville, ON, Canada) was routinely added to the saline to prevent spontaneous muscle contraction. The segment was opened along a line parallel to the mesenteric attachment and pinned flat, under moderate tension, mucosa uppermost. The myenteric plexus was exposed by dissecting away the mucosa, submucosa, and circular muscle. The recording dish was then mounted on an inverted microscope (Nikon Eclipse TE 2000-S,Melville, NY, USA) and imaged via a PC computer using a Rolera-XR camera (Surrey, BC, Canada) and the tissue continuously superfused (4 ml min<sup>-1</sup>) with carbogenated Krebs warmed to 34 <sup>o</sup>C. A ganglion was prepared for patch clamping as described previously<sup>##REF##10896726##53##</sup>; briefly, the selected ganglion was exposed by gravity flow for 15 min to 3 ml of 0.02% protease type XIV in Krebs saline (Sigma-Aldrich), then the upper surfaces of myenteric neurons were revealed by cleaning part of the ganglion with a fine hair until individual neuron soma became just visible. As noted previously<sup>##REF##10896726##53##</sup> there was no evidence of cell swelling after this gentle treatment.</p>", "<p id=\"Par41\">Signals were measured in voltage recording (current clamp) mode using an Axon Instruments Multiclamp 700 A computer amplifier (Molecular Devices, San Jose, CA, USA), and a Digidata 1322 A (Axon Instruments) digitizer was used for A/D conversion. Signals were low pass, 4-point Bessel filtered at 5 kHz, and then digitized at 20 kHz. Data were stored on computer and analyzed offline using Pclamp software (Molecular Devices). Voltage or current commands were delivered to the amplifier under computer control using Clampex 9 (Molecular Devices) software. Patch pipettes were pulled on a Flaming-Brown-P97 (Sutter Instrument, Novato, CA, USA) electrode puller to produce micropipettes with resistances 4–6 MΩ. The patch pipettes were made from thick-walled borosilicate glass (Sutter Instrument) and filled with a solution of the following composition in mM: KMeSO<sub>4</sub> 110-115, NaCl 9, CaCl<sub>2</sub> 0.09, MgCl<sub>2</sub> 1.0, HEPES 10, Na<sub>3</sub>GTP 0.2, BAPTA.K<sub>4</sub> 0.2 with 0.2 % neurobiotin (Vector Laboratories, Newark, CA, US) 14 mL KOH to bring the pH to 7.3. The online program Maxchelator (Maxchelator:<ext-link ext-link-type=\"uri\" xlink:href=\"https://somapp.ucdmc.ucdavis.edu/pharmacology/bers/maxchelator\">https://somapp.ucdmc.ucdavis.edu/pharmacology/bers/maxchelator</ext-link>) gives a predicted value free [Ca<sup>2+</sup>] of 0.18 μM at 34 <sup>o</sup>C<sup>##REF##8201981##54##</sup> for this intracellular solution. This value is close to resting free [Ca<sup>2+</sup>] as estimated using Ca<sup>2+</sup>-sensitive dyes in guinea pig Dogiel type II neurons (IPANs)<sup>##REF##11110808##55##,##REF##3251597##56##</sup>.</p>", "<p id=\"Par42\">With the amplifier in voltage-clamp recording, about 50 hPa positive pressure was internally applied to the pipette before its tip entered the Krebs buffer superfusing the myenteric plexus preparation; the pressure was maintained until the tip was in close apposition to a neuron membrane. Only recordings with seal resistances ≥ 4 GΩ were used for analysis. After gigaseal formation, the amplifier was switched to current clamp recording and whole cell recording mode entered by further suction. During the recording period, depolarising or hyperprolarising current pulses could be injected, under computer control, via the patch pipette using Pclamp 9 Clampex software (Molecular Devices). Access resistance and cell membrane resistance, capacitance and time constants, were periodically monitored by software programmed switching to the Pclamp membrane test protocol which injects square wave pulses oscillating about the holding potential.</p>", "<p id=\"Par43\">At the end of each recording, neurons were ionophoretically loaded with neurobiotin by passing 40 × 500 ms duration +0.1 nA current pulses via the patch pipette. The tissue was fixed in Zamboni’s fixative (2% v/v picric acid, 4% paraformaldehyde in 0.1 M Na<sub>2</sub>HPO<sub>4</sub>/NaH<sub>2</sub>PO<sub>4</sub> buffer, pH = 7.0) overnight at 4 <sup>o</sup>C, and then cleared using 3 ×10 min washes of DMSO followed by 3 × 10 min washes with PBS. The tissue was then exposed to streptavidin-Texas Red (Vector Laboratories), diluted 1:50, to reveal neurobiotin. After final rinsing, the tissue was mounted in PBS containing 80% glycerol and 0.1% NaN<sub>3</sub> and viewed under fluorescence epi-illumination on an inverted microscope (Nikon Eclipse TE 2000-S,Melville, NY, USA) and imaged using a Rolera-XR camera (Surrey, BC, Canada) Texas Red (596 nm & 620 nm excitation and emission peaks). Shapes of fluorescing neurons were traced using Inkscape 1.2 (Inkscape Project, available from: <ext-link ext-link-type=\"uri\" xlink:href=\"https://inkscape.org\">https://inkscape.org</ext-link>).</p>", "<title>Mesenteric nerve recording</title>", "<p id=\"Par44\">Mice were sacrificed by cervical dislocation and all action potential recordings performed ex vivo. Short (2.5 cm) segments of proximal jejunum with attached mesenteric arcade containing a single neuromuscular bundle were immediately removed and placed in a sylgard lined recording petri dish filled with Krebs buffer. The segment was emptied of contents using a syringe filled with Krebs, then both ends were cannulated with silicone tubing. The gut and mesenteric tissue were pinned to the sylgard using pins cut from 0.25 mm diameter tungsten wire and the mesenteric nerve bundle exposed by microdissection under a stereomicroscope. The preparation was then transferred to a Nikon Eclipse TE 2000 inverted microscope and the lumen gravity perfused at 1 ml/min with room temperature (22 °C) carbogenated Krebs or Krebs plus 30 μM squalamine dilactate using several Mariotte bottles<sup>##REF##17743173##57##</sup> attached to a plastic manifold. The serosal compartment was separately perfused at 5 ml/min with prewarmed (34°C) Krebs solution to which 3 µM nicardipine had been added to isolate vagal chemosensory responses by preventing active muscle contractions but not vagal responses to distension<sup>##REF##23139216##58##</sup>.</p>", "<p id=\"Par45\">The cleaned nerve was sucked into a glass-recording pipette attached to a patch-clamp electrode holder, and extracellular nerve recordings made with pClamp software using a Multi-Clamp 700B amplifier and Digidata 1440 A signal converter (Molecular Devices). The nerve bundle within the pipette was isolated from the Krebs within the recording dish by gently pressing the tip into fat tissue adherent to the uncleaned parts mesenteric arcade. Electrical signals were bandpass-filtered at 0.1–2 kHz, sampled at 20 kHz, and displayed and stored on a personal computer<sup>##REF##23139216##58##</sup>.</p>", "<p id=\"Par46\">Baseline recording with Krebs buffer in the gut lumen was performed for 15 min to verify that the resting firing rate was stationary using Pclamp software; samples with non-stationary discharge (windup or rundown) were discarded. Then recording continued for 30 min which constituted the test period for young vs aged comparisons. For experiments where squalamine or sertraline were added to the luminal perfusate, the Krebs buffer only control recording period was 30 min and this was followed by another 30 min of recording in the presence of either drug. Rundown of constitutive vagal discharge in this system is not evident until >90 min of recording<sup>##REF##23139216##58##</sup>. Only one luminal test additive was applied once per animal to avoid possible signal rundown. After recording responses to luminal test substances and to allow post-hoc identification of vagal single units, we applied 0.2 ml CCK to the serosal surface of the jejunum using a handheld micropipette. Finally we distended the intestine by raising the intraluminal pressure to 14 hPa to demonstrate that the isolated single units could still respond to distension. Testing for the response of each of the isolated single units to CCK is a well-established method for identifying vagal fibres within the mesenteric nerve bundle<sup>##REF##23139216##58##,##REF##8961188##59##</sup>. Cholecystokinin (25–33) sulphated (AnaSpec, Fremont, CA, USA) was dissolved in dimethyl sulfoxide (DMSO) to make a 1 mM stock solution. Aliquots were diluted on the day of the experiment to a working concentration of 0.1 µM in Krebs buffer, with a final DMSO concentration ≤0.0001%.</p>", "<p id=\"Par47\">We tested the following psychoactive agents: 10 µM sertraline hydrochloride<sup>##REF##34702901##15##</sup> (MilliporeSigma, Burlington, MA, USA). Squalamine dilactate was provided by Dr Michael Zasloff, Georgetown University (Washington, DC, United States). Squalamine dilactate powder was dissolved in 90% ethanol to make a stock solution, then aliquoted and stored at -20 °C until use. Stock solution was diluted in Krebs buffer to a working concentration of 30 µM for in vitro experiments<sup>##REF##34702901##15##</sup>. These concentrations activate young adult vagal fibres by approximately the same intensity of ≈20% above baseline firing rates.</p>", "<title>Analysis of single-unit firing patterns</title>", "<p id=\"Par48\">Each analysed vagal single unit was discriminated from others in the multiunit recording using principal component analysis of their action potential shape, amplitude and width using the Dataview program<sup>##REF##23493818##60##</sup> for extracellular action potential analysis. Single units belonging to each vagal axon were converted into a single event point processes and displayed and used for further analysis<sup>##REF##34702901##15##</sup>.</p>", "<p id=\"Par49\">For each single unit event channel in Dataview point processes intervals vs time were displayed as using the event parameter histogram plot option and the mean interspike interval (MII) read from the descriptive statistics panel.</p>", "<p id=\"Par50\">Event bursts were detected by the Poisson surprise method<sup>##REF##34702901##15##,##REF##3998798##61##</sup>. For each control or treatment event channel being measured in Dataview the Event analyse: Histograms/statistics option of the programme calculated the gap (GD) and burst (BD) durations<sup>##REF##34702901##15##</sup>. For intraburst intervals (IBI) and the Krebs and treatment bursts event channels that were created by the Poisson surprise method were logically combined using the AND gate function, thus extracting only the bursts from the point process events for either the control or treatment recording periods.</p>", "<title>Statistics and reproducibility</title>", "<p id=\"Par51\">Descriptive statistics were calculated in GraphPad Prism ver. 8.3 (GraphPad Software, San Diego, USA) are given as mean ± standard errors. When a statistical test was performed, the <italic>P</italic> value given is the probability of the test statistic being at least as extreme as the one observed if the null hypothesis of no difference is admitted. The partial eta squared statistic <italic>η</italic><sup>2</sup><sub>p</sub><sup>##UREF##9##62##</sup><sup>, pp. 70-71</sup> gives the effect size for differences calculated in the t-test module within GraphPad. According to Cohen’s guidelines<sup>##UREF##10##63##</sup> for interpreting <italic>η</italic><sup>2</sup><sub>p</sub>, 0.01 indicates a small, 0.06 a medium and 0.14 a large effect size. Fractional changes in measured parameters, each given as mean ± standard error, were performed in GraphPad, which also calculated the propagated standard error for the fractional changes.</p>", "<title>Reporting summary</title>", "<p id=\"Par52\">Further information on research design is available in the ##SUPPL##4##Nature Portfolio Reporting Summary## linked to this article.</p>" ]	[ "<title>Results</title>", "<title>Young and old IPANs differ physiologically</title>", "<p id=\"Par14\">We have previously reported<sup>##REF##31551703##10##</sup> that colonic migrating motor complexes for aged CD-1 mice have reduced velocity and frequency compared to similar motor complexes recorded from young mice. It is well-known that such colon and small intestine propulsive motor patterns are generated by the enteric nervous system<sup>##UREF##4##21##</sup><sup>, pp. 81-89</sup> and disappear when the enteric nervous system is absent<sup>##UREF##5##22##</sup>, when IPANs are selectively silenced pharmacologically<sup>##REF##19788711##23##</sup> or by point mutation-induced inhibition of protein kinase A in IPANs<sup>##REF##16329126##24##</sup>.</p>", "<p id=\"Par15\">In the current study the electrical characteristics of the IPANs of aged mice were compared with those of young ones. We identified all IPANs electrophysiologically as myenteric neurons that possessed a hump on the descending phase of their action potential and had a post-action potential slow afterhyperpolarisation (sAHP)<sup>##REF##16899648##20##,##REF##8027515##25##</sup> For passive cell membrane characteristics, the resting membrane potential (<italic>V</italic><sub>m</sub>,) input resistance (<italic>R</italic><sub><italic>in</italic></sub>,) and leak conductance (<italic>g</italic><sub>leak</sub>,) differed between young (green) and aged (brown) myenteric IPANs (Fig. ##FIG##0##1a##). However, neither membrane capacitance (<italic>C</italic><sub><italic>m</italic></sub>) nor action potential (AP) width at half height above baseline (<italic>AP</italic><sub><italic>1/2width</italic></sub>) differed statistically between young and aged IPANs. <italic>V</italic><sub>m</sub> increased by 24% from -55 m<italic>V</italic> (effect size <italic>η</italic><sup>2</sup><sub>p</sub> = 0.46), <italic>R</italic><sub>in</sub> decreased by 35% from 265 MΩ (<italic>η</italic><sup>2</sup><sub>p</sub> = 0.31) and <italic>g</italic><sub>leak</sub> increased by 76% from 4.1 nS (<italic>η</italic><sup>2</sup><sub>p</sub> = 0.50) when aged were compared to young neurons (Fig. ##FIG##0##1a##). For active membrane characteristics young IPANs showed greater excitability than young ones. The rheobase (threshold current for evoking a single action potential (AP) increased 136% from 45 pA (<italic>η</italic><sup>2</sup><sub>p</sub> = 0.44), average no. APs evoked by a stimulus pulse injected at twice rheobase intensity decreased by 36% from 2.8 (<italic>η</italic><sup>2</sup><sub>p</sub> = 0.38), and the magnitude (area under the curve) of the inhibitory slow afterhyperpolarisation (<italic>sAHP</italic><sub>AUC</sub>) evoked by 3 APs increased by 46% from -59 mV.s (<italic>η</italic><sup>2</sup><sub>p</sub> = 0.35) (Fig. ##FIG##0##1a##). Representative traces of IPAN action potentials are shown in Fig. ##FIG##0##1b## (young) and Fig. ##FIG##0##1d## (aged), 1st order time differentials of the APs demonstrated that both young and aged IPANs possessed a hump on the AP descending phase indicating that Ca<sup>2+</sup> influx contributed to the APs<sup>##REF##16899648##20##,##REF##11790812##26##</sup>. Fig. f & g illustrate AP firing at twice rheobase stimulus intensity for young and aged neurons, respectively. Figure ##FIG##0##1h & i## show traces of the sAHP for young (h) and aged (i) IPANs. 12 young and 8 aged IPANs were recorded from, of these 9 young and 7 aged were filed with neurobiotin marker dye and their shape recovered immunohistologically. All 16 had Dogiel type II morphology with smooth round or oval somas and long circumferentially-directed axons. Figure ##FIG##0##1j & k## show traces of young and aged IPANs revealing typical Dogiel type II morphology. In summary, the sAHP current increased with age. Ageing was also associated with hyperpolarised Vm, increased plasma membrane permeability (decreased <italic>R</italic><sub>in</sub> & increased <italic>g</italic><sub>leak</sub>), and a reduction in the number of APs discharged; all of which would be associated with a larger background sAHP current. Contrariwise, cell capacitance, action potential shape and the action potential hump were unaltered by ageing.</p>", "<title>Aged jejunum mesenteric nerve bundles discharge multiunit spikes at a slower rate than those from young mice</title>", "<p id=\"Par16\">Using a suction recording electrode we recorded the resting mixed (multiunit) AP extracellular spikes discharge from mesenteric nerve bundles attached to short jejunal segments. The average multiunit firing rated was 61% slower for aged compared to young mice (Fig. ##FIG##1##2a##). Representative examples in Fig. ##FIG##1##2## of young vs aged multiunit spikes are given in panel b and c, respectively.</p>", "<title>Aged IPANs exposed to squalamine displayed a young physiological phenotype</title>", "<p id=\"Par17\">We recorded from aged IPANs (see above) and 30 minutes after completing the first sets of physiological measurements, the Krebs buffer superfusate bathing the myenteric plexus preparation was switched to one that contained 30 μM squalamine lactate<sup>##REF##31551703##10##</sup>; measurements were then repeated. Addition of squalamine had a general excitatory effect increasing the electroresponsiveness of IPANs to resemble that for young IPANs. The onset latency for depolarisation and reduction in <italic>sAHP</italic><sub>AUC</sub> ranged from 4 to 5 min. For passive cell membrane characteristics: <italic>V</italic><sub>m</sub> depolarized by 21% to -54 mV (<italic>η</italic><sup>2</sup><sub>p</sub> = 0.61), <italic>R</italic><sub>in</sub> increased by 65% to 283 MΩ (<italic>η</italic><sup>2</sup><sub>p</sub> = 0.54), <italic>g</italic><sub>leak</sub> decreased by 31% to 5.0 nS (<italic>η</italic><sup>2</sup><sub>p</sub> = 0.32). For active membrane characteristics: rheobase decreased by 45% to 58 pA (<italic>η</italic><sup>2</sup><sub>p</sub> = 0.34), the average number of AP fired at 2x rheobase increased by 67% to 3.0 (<italic>η</italic><sup>2</sup><sub>p</sub> = 0.47), and <italic>sAHP</italic><sub>AUC</sub> decreased by 26% to -64 mV.s (<italic>η</italic><sup>2</sup><sub>p</sub> = 0.29) (Fig. ##FIG##2##3a##). <italic>C</italic><sub><italic>m</italic></sub> and <italic>AP</italic><sub><italic>1/2width</italic></sub> were not altered by squalamine. Representative traces of IPAN action potentials in the presence of squalamine are shown in Fig. ##FIG##2##3b-e##. Figure ##FIG##2##3b## shows an AP and 3c gives the 1st order time differentials of the AP confirming that the IPAN AP possessed a hump on its descending phase indicating that Ca<sup>2+</sup> influx contributed to the AP. Figure ##FIG##2##3d & e## illustrate the increased AP firing duration at twice rheobase stimulus intensity for an aged neurons in the presence of squalamine (cf. Fig. ##FIG##0##1g##). Figure ##FIG##2##3e## depicts the reduced sAHP in the presence of squalamine (cf. Fig. ##FIG##0##1i##).</p>", "<title>Comparing young to aged vagal single unit resting discharge revealed an ageing code which was eliminated by adding squalamine to the lumen of aged jejunal segments</title>", "<p id=\"Par18\">Single units were extracted from multiunit vagal nerve recordings using principle component analysis<sup>##REF##34702901##15##</sup>. We measured several distinct parameters that fully describe the firing patterns observed within the 30 min recording periods used for each test sample. The parameters were: mean interspike interval (MII), average burst duration (BD), average gap duration between bursts (GD) and the average intraburst interval (IBI) (Fig. ##FIG##3##4a##). 70% of single vagal units recorded from young mice discharged ≥ 1 burst. Significantly fewer such events were detected for aged single units (Fig. ##FIG##3##4b##). When young were compared to aged mice, ageing increased MII, GD, IBI but not BD. MII increased by 427% (<italic>η</italic><sup>2</sup><sub>p</sub> = 0.20), GD by 38% (<italic>η</italic><sup>2</sup><sub>p</sub> = 0.19) and IBI by 46% (<italic>η</italic><sup>2</sup><sub>p</sub> = 0.22) (Fig. ##FIG##3##4c##).</p>", "<p id=\"Par19\">Addition of 30 μM squalamine to the Krebs buffer perfusing the lumen reduced the sample mean differences for MII, BD, GD and IBI to statistically insignificant 8%, -4%, -5%, and 2%, respectively (Fig. ##FIG##3##4d##). Plotting fractional changes for aged compared to young mice for each of the 4 parameters revealed a unique, hitherto unknown, ageing code (Fig. ##FIG##3##4e##). The heat map in Fig. ##FIG##3##4f## demonstrates that the ageing code is categorically different for those previously calculated for prodepressant (LPS) or antidepressant (JB-1, fluoxetine or sertraline) luminal stimuli acting on the vagus<sup>##REF##34702901##15##</sup>. The ageing code was absent when squalamine was present in the lumen (Fig. ##FIG##3##4g##) for which the standard error of the mean spanned the zero line for fractional differences.</p>", "<title>The ageing code was conserved across sexes</title>", "<p id=\"Par20\">The vagal code evoked by young vs. aged CD1 female mice was qualitatively similar for that for male mice (Fig. ##FIG##4##5##). Thus, both sexes revealed the canonical ageing code of increased MII, increased GD, and increased IBI.</p>", "<title>Swiss Webster mice</title>", "<p id=\"Par21\">Swiss Webster mice also exhibited the ageing code. We repeated the comparison between 17 young and 12 aged male mice for Swiss Webster (SW) mice and the effects of squalamine on the code to demonstrate that the ageing code is not strain specific. All protocols and the calculation of the ageing code were performed in the same manner as for CD-1 mice.</p>", "<p id=\"Par22\">Clearly, SW mice also exhibited an ageing code when young were compared to aged mice (Supplementary Fig. ##SUPPL##1##1##). Additionally, as was the case for CD1 mice, addition of 10 µM intraluminal squalamine altered parameter levels for aged vagal fibres to levels seen in young mice on the ageing code disappeared in the presence of squalamine (Supplementary Fig. ##SUPPL##1##1##).</p>", "<p id=\"Par23\">All of 9 young SW and 7 aged IPANs that were iontophoretically injected with Neurobiotin marker dye revealed the multipolar shapes characteristic of IPANs. Examples of SW mouse IPAN shapes revealed after intracellular Neurobiotin dye filling are shown in Supplementary Fig. ##SUPPL##1##2##.</p>", "<title>Squalamine but not sertraline augmented afferent aged vagus spike firing</title>", "<p id=\"Par24\">We have published<sup>##REF##34702901##15##</sup> that for young mice squalamine evokes a vagal code closely resembling that of sertraline. In the current study we compare the effects of 10 μM sertraline with those of 30 μM squalamine<sup>##REF##34702901##15##</sup> on the vagal code. These concentrations were the same as we have used previously<sup>##REF##34702901##15##</sup> for ex vivo vagal single unit recording and were chosen because we had shown that they activate vagal single units by ~20% above resting firing rates for young mice. When this study was conducted with preparations from aged mice, sertraline increased MII for most single units whereas squalamine decreased MII all units (Fig. ##FIG##5##6a##), with a greater proportion of single units discharging with ≥1 burst for units exposed to squalamine than to sertraline (Fig. ##FIG##5##6b##). The changes occurred due to an increase in MII for sertraline (Fig. ##FIG##5##6c##), and a decrease in MII and IBI for squalamine (Fig. ##FIG##5##6d##). These data suggest that, unlike for young mice, sertraline and squalamine have opposing effects for aged vagus single unit firing rates. There was also a reduced repertoire for complex discharge patterns involving action potential bursting when sertraline was compared to squalamine.</p>", "<p id=\"Par25\">Representative event markers and sequential rate histograms depicting the 2 different representative single units from aged animals show that while intraluminal squalamine increased (Fig. ##FIG##6##7a-c##), sertraline decreased the firing rate (Fig. ##FIG##6##7d-f##). In contrast, sertraline increased the single-unit firing rate for a representative recording taken from a young vagal fibres (Fig. ##FIG##6##7g-i##). We have previously published that intraluminal squalamine increases the firing rate for young vagal fibres<sup>##REF##34702901##15##</sup>. In register with this increase in young fibre firing rates, mucosal application of 30 μM squalamine (Fig. ##FIG##6##7k##) or 10 μM sertraline (Fig. ##FIG##6##7i##) increased the number of IPAN action potentials evoked by intracellular injection of 500 ms duration depolarising current pulses from 2.9 ± 0.2 to 5.4 ± 0.3 (<italic>P</italic> < 0.0001, paired t test, <italic>N</italic> = 11, 3 mice) for squalamine and from 2.9 ± 0.2 to 7.8 ± 0.2 (<italic>P</italic> < 0.0001, paired t test for sertraline, <italic>N</italic> = 12, 3 mice).</p>" ]	[ "<title>Discussion</title>", "<p id=\"Par26\">Our findings show IPANs of aged mice differed neurophysiologically from those of young mice. Using ex vivo patch clamp recordings from in situ myenteric IPANs we found that aged IPANs are less excitable than young ones. Aged IPANs had more hyperpolarised <italic>V</italic><sub><italic>m</italic></sub>, a smaller <italic>R</italic><sub><italic>in</italic></sub> and a greater <italic>g</italic><sub>leak</sub>. For active membrane characteristics, aged IPANs had a greater <italic>Rheobase</italic>, a smaller No. APs 2 x Rheobase (smaller number of APs discharged at twice <italic>Rheobase</italic> stimulus intensity) and a greater <italic>sAHP</italic><sub>AUC</sub>. Addition of squalamine to the Krebs buffer perfusing the gut lumen returned aged IPANs to a young electroresponsive phenotype.</p>", "<p id=\"Par27\">Using extracellular AP recordings, we found that the average firing rate of jejunal mesenteric multiunit spikes was lower for aged than for young CD-1 mice confirming and extending a similar previous finding for which only a trend that did not quite reached statistical significance was reported<sup>##REF##31551703##10##</sup>.</p>", "<p id=\"Par28\">Single unit recordings revealed that APs recorded from young mouse tissue showed a greater number of bursts than those recorded using aged mice. Aged mice had a larger single unit mean interspike interval (MII), firing gap duration (GD) and within burst intraburst interval (IBI) while burst duration (BD) did not differ statistically. Plotting these parameters as fractional differences aged vs young revealed a unique sensory vagus ageing code. Addition of squalamine to the superfusate bathing the myenteric plexus abolished the ageing code changing the parameters measured from aged vagal fibres to values seen for young fibres. Interestingly, the ageing code, like the antidepressant code<sup>##REF##34702901##15##</sup>, was not confined to only one mouse strain but was also present in Swiss Webster mice.</p>", "<p id=\"Par29\">The vagus-stimulating action of sertraline, but not squalamine, was lost for aged mice. Aged vagal single units exposed to the antidepressant sertraline exhibited fewer bursts than those exposed to squalamine. Sertraline increased MII (slowing the firing rate) while having no statistical effects on the other parameters; in contrast squalamine enhanced vagal firing by decreasing MII and IBI.</p>", "<p id=\"Par30\">Previous studies of ENS and intestinal vagal ageing have been confined only to anatomical changes, however a recent paper using 8-9 mo old hSNCAA53T constructs as a model for Parkinson’s disease showed reduced excitability for IPANs<sup>##REF##32925094##27##</sup>. Colonic migrating motor complexes in aged (24 mo) mice are significantly slower than those in young (3 mo) ones<sup>##REF##31551703##10##</sup> and this is also consistent with decreased function of the aged ENS. Normal ageing has been associated with a plethora of intracellular molecular changes, including abnormal reactive oxygen species and Ca<sup>2+</sup> levels that trigger mitochondrial dysfunction. In this regard, IPAN mitochondria influence the resting membrane potential and importantly play a role in the post-action potential uptake of Ca<sup>2+</sup> released from intracellular stores<sup>##REF##12177194##28##</sup>. Indeed, experimental impairment of mitochondria in young adult IPANs has been shown to lead to the exaggeration and prolongation of the sAHP, exactly as has been demonstrated in the present paper for aged IPANs<sup>##REF##12177194##28##</sup>.</p>", "<p id=\"Par31\">We confirmed our earlier results showing a trend that resting CD-1 mesenteric nerve multiunit firing rates were lower for aged than young mice<sup>##REF##31551703##10##</sup>, and that intraluminal squalamine could restore the decreased firing rates for aged mice. Our mesenteric multiunit results were also consistent with studies that reported mesenteric nerve multiunit activity was lower for aged (64 y) than younger (47 y) humans and that there were fewer single unit bursts for aged humans.</p>", "<p id=\"Par32\">In the present paper we reveal a canonical ageing code of increased MII, increased GD, and increased IBI for aged vs. young vagal single unit firing patterns. This code was present for both male and female CD-1 mice. Thus, if there are differences across the sexes in afferent vagal signalling, they are likely to be manifest in the central nervous system rather than at the level of the subdiaphragmatic vagus. We had previously published<sup>##REF##31551703##10##</sup> that the single unit mean interspike interval is larger for aged than for young CD-1 mice, and that addition of squalamine to the Krebs buffer perfusing the lumen of the aged jejunum returned MII to values recorded from young mice. However, no neurons in any animal nervous system seem to encode information solely by firing rate, rather firing patterns and intervals must be considered to understand how neurons encode information.</p>", "<p id=\"Par33\">A simpler vagal code analysis has determined that for young (7-12 wks) male rats single unit spike burst frequencies increase in relation to eating<sup>##REF##35200374##29##</sup>. A recent publication<sup>##UREF##6##30##</sup> using only young 8-16 wk old mice used density-spaced clustering algorithms of spike shapes contained within cervical multiunit signals, recorded in vivo. This method revealed that injected cytokines could be identified by increases in the vagal single unit firing rate that the cytokines elicited<sup>##UREF##6##30##</sup>. Overall, and to the best of our knowledge, no analogous ageing code to the one being offered in the present paper has yet been published for any nervous system. As we have mentioned in the Introduction, some aged individuals are relatively resistant to the antidepressant effects of SSRIs, but a direct comparison between sertraline and squalamine on the firing rates of aged afferent vagal fibres also has not yet been revealed.</p>", "<p id=\"Par34\">Why does the electrical excitability of IPANs and firing pattern of afferent vagal fibres change with old age? Some insight might be had from our current understanding of the potential mechanisms by which squalamine might be acting. We have previously shown that local administration of squalamine restores electrical activity in IPANs from Parkinson’s disease mouse models genetically engineered to accumulate aggregates of alpha-synuclein within the enteric neurons<sup>##REF##32925094##27##</sup>. Normal peristalsis was restored and brain-directed vagal afferent is stimulated. In addition, orally administered squalamine successfully restored gut motility and several neurological symptoms in elderly patients with Parkinson’s disease-associated constipation in two a recently completed Phase 2b clinical trials<sup>##REF##36343348##31##,##UREF##7##32##</sup>, demonstrating the translatability of the preclinical observations to humans.</p>", "<p id=\"Par35\">In aqueous solution squalamine exists as a zwitterion with a net positive charge. As a consequence of its chemical structure it is highly amphiphilic, it is both highly water soluble and membrane active, and will bind electrostatically to membranes that contain anionic phospholipids and subsequently embed within the membrane<sup>##REF##33150350##33##–##REF##36421008##39##</sup>. Furthermore, previous studies have demonstrated that squalamine can effectively both displace proteins that are bound electrostatically to neuronal membranes and additionally, prevent their initial aggregation on the membrane surface<sup>##REF##28096355##40##</sup>. For example, studies in C. elegans, engineered to express an aggregating human mutation in alpha-synuclein, develop paralysis as aggregates of alpha-synuclein accumulate within their excitable muscle cells<sup>##REF##28096355##40##</sup>. Exposure of these worms to increasing concentrations of squalamine results in a proportional reduction in the number of protein aggregates and a dose-dependent increase in motility<sup>##REF##28096355##40##</sup> .Recent studies have demonstrated that misfolded proteins, defined as those that resist proteinase digestion, accumulate with ageing in all organs of the mouse<sup>##REF##36778589##41##</sup>. These ageing-associated misfolded proteins could include alpha-synuclein, since numerous studies have reported that alpha-synuclein increases with age in older rats, monkeys, and humans<sup>##REF##22647852##42##–##REF##37403161##45##</sup>. These data are not surprising, as age is a key risk factor for many neurodegenerative disorders. Based on these observations we speculate that squalamine might improve the electrical excitability of the IPANs in the aged mouse through displacement of misfolded proteins from cellular membranes involved in neuronal electrical activity.</p>", "<p id=\"Par36\">Numerous studies have shown that once integrated into a membrane the spatial organization of lipids within the membrane, fluidity, and tensile strength are altered<sup>##REF##37433124##37##</sup>. Because squalamine electrostatically reduces the overall surface charge of the membrane, the function of membrane proteins positioned by electrostatic forces can be affected. For example, the application of squalamine to a mouse cortical neuron ex vivo activates the synaptic AMPA receptor<sup>##REF##20547132##46##</sup>, and in other experimental settings inhibits the Type 3 sodium hydrogen exchanger, which regulates intracellular pH<sup>##REF##21245831##47##</sup>. Thus, it is also possible that squalamine could enhance electrical excitability of the aged IPAN as a consequence of the modulation of the activity of membrane-associated proteins. The precise mechanism by which squalamine restores the electrical activity of the aged IPAN to a more youthful phenotype, however, remains to be determined.</p>", "<p id=\"Par37\">Squalamine does have antibiotic activity and could perhaps alter gut propulsive activity either directly by stimulating IPANs (as shown here) or by lessening of Parkinsonian intestinal dysbiosis<sup>##REF##36376318##48##,##REF##35967873##49##</sup>. However, antibiotics may have conflicting effects on gut motility: bacitracin, neomycin, and penicillin V increase colonic propulsion<sup>##REF##29104530##50##,##UREF##8##51##</sup> while vancomycin or ampicillin decrease faecal output<sup>##REF##28086815##52##</sup>. It is not clear that squalamine’s antimicrobial activity, per se, can explain its therapeutic effects on constipation in Parkinson’s disease.</p>", "<p id=\"Par38\">In conclusion, our findings show that ageing is associated with decreased excitability of intrinsic primary afferent neurons in the enteric nervous system and a specific afferent jejunal gut-brain axis ageing code deduced from the resting firing pattern of vagal single unit fibres. The ageing code could be suppressed by intraluminal squalamine. We also showed that whilst sertraline decreased the firing rate of vagal afferent single units, squalamine retained the ability to excite vagal fibres for the aged vagus. These findings suggest targeting the vagus nerve using pharmacological or electroceutical approaches may be a productive research area for age related disorders involving the gut-brain axis.</p>" ]	[]	[ "<p id=\"Par1\">Vagus nerve signaling is a key component of the gut-brain axis and regulates diverse physiological processes that decline with age. Gut to brain vagus firing patterns are regulated by myenteric intrinsic primary afferent neuron (IPAN) to vagus neurotransmission. It remains unclear how IPANs or the afferent vagus age functionally. Here we identified a distinct ageing code in gut to brain neurotransmission defined by consistent differences in firing rates, burst durations, interburst and intraburst firing intervals of IPANs and the vagus, when comparing young and aged neurons. The aminosterol squalamine changed aged neurons firing patterns to a young phenotype. In contrast to young neurons, sertraline failed to increase firing rates in the aged vagus whereas squalamine was effective. These results may have implications for improved treatments involving pharmacological and electrical stimulation of the vagus for age-related mood and other disorders. For example, oral squalamine might be substituted for or added to sertraline for the aged.</p>", "<p id=\"Par2\">Ex vivo electrophysiology reveals that vagus firing patterns differ between aged and young mice, and that the aminosterol, squalamine, changed aged neuronal firing to a younger neuron phenotype. Squalamine, but not sertraline, increased firing rates in the aged vagus.</p>", "<title>Subject terms</title>" ]	[ "<title>Supplementary information</title>", "<p>\n\n\n\n\n\n</p>" ]	[ "<title>Supplementary information</title>", "<p>The online version contains supplementary material available at 10.1038/s42003-023-05623-2.</p>", "<title>Acknowledgements</title>", "<p>This work was supported by grants RGPIN-2019-05982 & RGPIN-2021-03816 from the Natural Sciences and Engineering Research Council of Canada Discovery Grant Program awarded to WK. It was also supported by a Clifton W. Sherman Scholarship and a Queen Elizabeth II Graduate Scholarship in Science & Technology to CW.</p>", "<title>Author contributions</title>", "<p>K.M.N., C.W., Y.M. performed the experiments. W.K. designed the study. and wrote the initial manuscript draft. K.M., A.S., D.B. & M.Z. contributed to study design and helped with the initial draft. P.F., M.A., H.H., E.I. helped with revisions and drafting. This paper is dedicated to the memory of John Bienenstock, Distinguished University Professor of Pathology and Molecular Medicine at McMaster University. John was the Founding Director of the Brain-Body Institute and a mentor to innumerable graduate students, post-doctoral fellows and visiting scientists. He was a personal friend and the inspiration behind the work described in the present paper.</p>", "<title>Peer review</title>", "<title>Peer review information</title>", "<p id=\"Par53\"><italic>Communications Biology</italic> thanks Ibrahim Javed, Wenfei Han and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editors: Karli Montague-Cardoso and George Inglis. ##SUPPL##0##Peer reviewer reports## are available.</p>", "<title>Data availability</title>", "<p>The authors declare that the data supporting the findings of this study are available within the paper and its supplementary information files. Source data underlying figures are provided in Supplementary Data ##SUPPL##3##1##.</p>", "<title>Competing interests</title>", "<p id=\"Par54\">The authors declare no competing interests.</p>" ]	[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>Jejunal enteric nervous system intrinsic primary afferent neuron IPAN physiology differs between old and young mice.</title><p><bold>a</bold> For this and all other bar graphs in the present paper, mean values given at base of bar graphs, open circles represent values measured from individual neurons. Resting membrane potentials (<italic>V</italic><sub>m</sub>) of aged are more polarized than those of young mice. <italic>C</italic><sub>m</sub> (membrane capacitance = measure of cell surface membrane area) was not different. Input resistance (<italic>R</italic><sub>in</sub>), a measure of number of open background channels = reciprocal of cell membrane leakiness)-aged IPANs had lower <italic>R</italic><sub>in</sub>. Background leak conductance (<italic>g</italic><sub>leak</sub>) was lower for young than for old IPANs. <italic>Rheobase</italic> (threshold current required to evoke one action potential 50% of the time) was lower for young than old IPANs. The width of the action potential at half height of its positive amplitude (<italic>AP</italic><sub><italic>1/2</italic></sub>\n<italic>width</italic>) was not statistically different for young vs aged IPANs. The number of action potentials evoked at 2 x rheobase intensity (<italic>No. APs 2 x rheobase</italic>) was greater for young compared to aged IPANs. The area under the curve of the postaction potential slow afterhyperpolarisation (<italic>sAHP AUC</italic>) was greater for aged than young IPANs. <bold>b, d</bold> Representative action potential shapes for young and aged IPANs (colour-code: green indicates young, brown indicates aged). <bold>c, e</bold> First order time derivative of action potential showing that both old and young spikes have a hump (arrowhead) on their descending phase, indicating that calcium influx (which contributes significantly to AP<sub>1/2</sub> width) did not differ notably between young and aged IPANs. <bold>f, g</bold> Example traces of action potentials evoked at 2x rheobase for young and aged IPANs. <bold>h, i</bold> Traces of post-action potential sAHP from young vs aged IPANs. Positions of truncated action potentials indicated by filled circles. <bold>j, k</bold> Examples of IPAN shapes revealed after intracellular neurobiotin dye filling. IPANs had smooth oval cell bodies with multiple long axonal processes running circumferentially within the myenteric plexus (Dogiel type II cell morphology). The full extent of these processes are not shown. Statistics: <italic>N</italic><sub>young</sub> = 12 (from 6 mice), <italic>N</italic><sub>aged</sub> = 8 (from 4 mice). All comparisons made using unpaired t tests except for <italic>No. APs 2 x rheobase</italic> for which a Mann-Whitney test was applied. All tests were two-tailed. All error bars represent the standard error of the mean.</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>Young compared to aged jejunum mesenteric nerve bundle (multiunit) spike firing rates.</title><p><bold>a</bold> Multiunit spike firing rates were statistically slower for recording made from aged compared to young mice. <bold>b</bold> Representative multiunit trace recorded from young animal. <bold>c</bold> Multiunit trace from aged animal. Statistics: <bold>a</bold> Comparison made using unpaired t test, two-tailed. Nerve fibre bundles: <italic>N</italic><sub>young</sub> = 8 and <italic>N</italic><sub>aged</sub> = 22. Means given at base of bar graphs, error bars are standard errors.</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><title>Addition of 30 μM squalamine to IPANs changes IPAN physiology from aged to young phenotype.</title><p><bold>a</bold> Squalamine depolarised <italic>V</italic><sub>m</sub>, increased <italic>R</italic><sub>in</sub>, reduced <italic>g</italic><sub>leak</sub> decreased <italic>Rheobase</italic>, increased <italic>No. APs 2 x rheobase</italic>, and decreased <italic>sAHP AUC</italic>. <italic>C</italic><sub>m</sub> and <italic>AP</italic><sub><italic>1/2 width</italic></sub> were not statistically different. <bold>b, c</bold> Traces of IPAN action potential (<bold>b</bold>) and first order time derivative (<bold>c</bold>) in the presence of squalamine. <bold>d</bold> Example of IPAN action potential discharge at 2 x rheobase stimulus intensity. <bold>e</bold> Representative post-action potential sAHP. Position of truncated action potential indicated by filled circle. Statistics: All experiments were paired comparing before (luminal Krebs buffer only) and after addition of squalamine to Krebs. <italic>N</italic> = 9 (from 5 mice). All comparisons made using paired t tests except for <italic>No. APs 2 x rheobase</italic> for which a Wilcoxon test was applied. All tests were two-tailed. All error bars represent the standard error of the mean.</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><title>The ageing code, male CD-1. MII, GD, and IBI, but not BD were all significantly increased in aged compared to young single units.</title><p><bold>a</bold> Diagram of stylised single unit discharge illustrating the 4 parameters measured to quantify individual spike (single unit) firing patterns. The parameters were: mean interspike interval (MII), burst duration (BD), gap duration (GD) and intraburst interval (IBI). <bold>b</bold> Bar graphs showing that the number of vagal fibre single units displaying more than 1 spike burst during the 30 min recording period was greater for young than old mice. <bold>c</bold> Dot plots with superimposed bar graphs (mean ± s.e.m.) showing values for all 4 single unit firing parameters. Nine young mice were compared to 19 aged ones. MII, GD and IBI reached statistical significance. <bold>d</bold> No statistical difference was discernible for any of 4 firing pattern parameters when recordings taken from the 19 young mice luminally perfused with only Krebs buffer were compared to those taken from 10 aged mice whose lumen was perfused with Krebs containing 30 μM squalamine. <bold>e</bold> The ageing code revealed by plotting fractional differences (mean ± s.e.m.) of aged vs. young mice for each of the 4 firing parameters. All of parameters except for BD contributed to the code. <bold>f</bold> Heat map of the parameter fractional differences showing that the ageing code is categorically different from the prodepressant (LPS) or antidepressant (JB-1, fluoxetine, sertraline) codes. <bold>g</bold> The ageing code was eliminated when the lumen of aged animals was perfused with Krebs with added squalamine and compared to young mice whose jejunal lumen was perfused with Krebs only. Statistics: <bold>b</bold> Contingency table analysis by two-sided Fisher’s exact test. <bold>c</bold>, <bold>d</bold> Comparisons of parameter means for young vs. aged or young vs. aged + squalamine made using Dunnett’s T3 multiple comparisons t tests.</p></caption></fig>", "<fig id=\"Fig5\"><label>Fig. 5</label><caption><title>The ageing code, female CD-1. MII, GD, and IBI, but not BD were all significantly increased in aged single units.</title><p><bold>a</bold> Dot plots with superimposed bar graphs (mean ± s.e.m.) showing values for all 4 single unit firing parameters. Five young mice were compared to 8 aged ones. MII, GD and IBI reached statistical significance. <bold>b</bold> No statistical difference was discernible for any of parameters when recordings taken from the 5 young mice perfused with only Krebs buffer were compared to those taken from 5 aged mice whose lumen was perfused with Krebs containing 30 μM squalamine. <bold>c</bold> The ageing code revealed by plotting fractional differences (mean ± s.e.m.) of aged vs young mice. All of parameters except for BD contributed to the code. <bold>d</bold> The ageing code was eliminated when the lumen of aged animals was perfused with squalamine. <bold>c,d</bold>, Comparisons of parameter means for young vs. aged or young vs. aged + squalamine made using Dunnett’s T3 multiple comparisons t tests.</p></caption></fig>", "<fig id=\"Fig6\"><label>Fig. 6</label><caption><title>Squalamine increased the number of spike bursts and decreased MII & IBI in vagal single units for aged mice; sertraline was ineffective or had opposing effects.</title><p><bold>a</bold> Squalamine decreased MII for all aged single units tested, sertraline increased MII for more than 70% of units. <bold>b</bold> The proportion of aged single units with more than 1 burst in their firing pattern was higher for units exposed to intraluminal squalamine than for ones exposed to sertraline. <bold>c</bold> Sertraline increased MII for single units from aged mice with no statistically discernable effects on BD, GD or IBI. <bold>d</bold> Squalamine decreased MII and IBI for aged mice. Statistics: <bold>a, b</bold> Contingency table analysis by two-sided Fisher’s exact tests. <bold>c, d</bold> Comparisons of parameter means for aged vs. aged + sertraline or aged vs. aged + squalamine made using paired t tests. There were not enough single unit bursts (0 or 1) for aged mice to make statistical comparisons for GD. All error bars represent the standard error of the mean.</p></caption></fig>", "<fig id=\"Fig7\"><label>Fig. 7</label><caption><title>Representative action potential event markers of individual single unit responses to intraluminal application of squalamine vs sertraline.</title><p><bold>a</bold> Event markers showing the occurrence of an individual single unit during its 30 min recording period for intraluminal squalamine test for aged mouse. <bold>b</bold> Superimposed traces of 26 single units used to generate event markers for <bold>a</bold>. <bold>c</bold> Binned sequential rate histogram showing evolution of excitatory response to squalamine. <bold>d</bold> Single unit event markers showing occurrence of single unit during sertraline test for aged vagus. <bold>e</bold> 25 superimposed traces of single units used to generate event markers for <bold>d</bold>. <bold>f</bold> Binned sequential rate histogram showing reduction in single unit firing in response to intraluminal application of sertraline. <bold>g</bold> Event markers for single unit from young mouse. <bold>h</bold> 20 superimposed traces of single unit used for generation of event markers in <bold>g</bold>. <bold>i</bold> Histogram showing excitatory response for intraluminal sertraline in young mouse. <bold>k</bold> For young mice mucosal application of 30 μM squalamine increased the number of IPAN action potentials evoked by injection of 500 ms depolarising current pulse at 2x threshold intensity. <bold>i</bold>, Sertraline (10 μM) increased the number of action potentials evoked by injection of a 500 ms depolarising pulse.</p></caption></fig>" ]	[]	[]	[]	[]	[]	[]	[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM3\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM4\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM5\"></supplementary-material>" ]	[ "<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"42003_2023_5623_Fig1_HTML\" id=\"d32e630\"/>", "<graphic xlink:href=\"42003_2023_5623_Fig2_HTML\" id=\"d32e669\"/>", "<graphic xlink:href=\"42003_2023_5623_Fig3_HTML\" id=\"d32e833\"/>", "<graphic xlink:href=\"42003_2023_5623_Fig4_HTML\" id=\"d32e905\"/>", "<graphic xlink:href=\"42003_2023_5623_Fig5_HTML\" id=\"d32e953\"/>", "<graphic xlink:href=\"42003_2023_5623_Fig6_HTML\" id=\"d32e1025\"/>", "<graphic xlink:href=\"42003_2023_5623_Fig7_HTML\" id=\"d32e1111\"/>" ]	[ "<media xlink:href=\"42003_2023_5623_MOESM1_ESM.pdf\"><caption><p>Peer Review File</p></caption></media>", "<media xlink:href=\"42003_2023_5623_MOESM2_ESM.pdf\"><caption><p>Supplementary Information</p></caption></media>", "<media xlink:href=\"42003_2023_5623_MOESM3_ESM.docx\"><caption><p>Description of Additional Supplementary Files</p></caption></media>", "<media xlink:href=\"42003_2023_5623_MOESM4_ESM.xlsx\"><caption><p>Supplementary Data 1</p></caption></media>", "<media xlink:href=\"42003_2023_5623_MOESM5_ESM.pdf\"><caption><p>Reporting summary</p></caption></media>" ]	[{"label": ["8."], "mixed-citation": ["Yu, Y. et al. Interplay between mast cells, enterochromaffin cells, and sensory signaling in the aging human bowel. "], "italic": ["Neurogastroenterol Motil"], "bold": ["28,"]}, {"label": ["11."], "mixed-citation": ["Pigato, G. et al. Vagus Nerve Stimulation in Treatment-Resistant Depression: A Case Series of Long-Term Follow-up. "], "italic": ["J. ECT"], "bold": ["39(1),"]}, {"label": ["13."], "mixed-citation": ["Gamage, P. P., Ranson, R. N., Patel, B. A., Yeoman, M. S. & Saffrey, M. J. Myenteric neuron numbers are maintained in aging mouse distal colon. "], "italic": ["Neurogastroenterol Motil"], "bold": ["25(7),"]}, {"label": ["19."], "mixed-citation": ["Nullens, S. et al. In Vitro Recording of Mesenteric Afferent Nerve Activity in Mouse Jejunal and Colonic Segments. "], "italic": ["J. Vis. Exp."], "bold": ["116,"]}, {"label": ["21."], "mixed-citation": ["Furness, J. B. "], "italic": ["The enteric nervous system"]}, {"label": ["22."], "mixed-citation": ["Kunze, W. A. & Furness, J. B. The enteric nervous system and regulation of intestinal motility. "], "italic": ["Annu. Rev. Physiol."], "bold": ["61,"]}, {"label": ["30."], "mixed-citation": ["Zanos, T. P. et al. Identification of cytokine-specific sensory neural signals by decoding murine vagus nerve activity. "], "italic": ["Proc. Natl. Acad. Sci."]}, {"label": ["32."], "surname": ["Hauser"], "given-names": ["RA"], "article-title": ["Targeting neurons in the gastrointestinal tract to treat Parkinson\u2019s disease"], "source": ["Clin. parkinsonism Relat. Disord."], "year": ["2019"], "volume": ["1"], "fpage": ["2"], "lpage": ["7"], "pub-id": ["10.1016/j.prdoa.2019.06.001"]}, {"label": ["51."], "mixed-citation": ["Forsythe, P., Kunze, W. & Bienenstock, J. Moody microbes or fecal phrenology: what do we know about the microbiota-gut-brain axis. in "], "italic": ["BMC Med."], "bold": ["14(1),"]}, {"label": ["62."], "mixed-citation": ["Motulsky, H. J. "], "italic": ["Analyzing Data with GraphPad Prism"], "ext-link": ["www.graphpad.com"]}, {"label": ["63."], "mixed-citation": ["Cohen, J. "], "italic": ["Statistical power analysis for the behavioral sciences"]}]	{ "acronym": [], "definition": [] }	63	CC BY	no	2024-01-13 00:02:20	Commun Biol. 2024 Jan 10; 7:80	oa_package/eb/f2/PMC10781697.tar.gz
PMC10781698	38200184	[ "<title>Introduction</title>", "<p id=\"Par3\">Neuroinflammation refers to inflammation of the brain and of the spinal cord. Acute neuroinflammation is a severe and rapidly fatal condition encountered in microbial or auto-immune encephalitis<sup>##REF##16556343##1##,##REF##19347024##2##</sup>. Neuroinflammation is a common feature and contributor of chronic inflammatory, autoimmune and neurodegenerative diseases affecting the brain and the spinal cord such as in multiple sclerosis (MS), Alzheimer’s disease and Parkinson’s disease<sup>##REF##17678964##3##</sup>. Chronic neuroinflammation is a major driver of pathogenesis in these conditions, and a better understanding of the underlying cellular and molecular mechanism is required to develop new treatment strategies. The mouse model of experimental cerebral malaria (ECM) induced by infection with <italic>Plasmodium berghei</italic> ANKA (PbA) has been used extensively to demonstrate the critical role of myeloid cells (neutrophils, dendritic cells, macrophages), CD4<sup>+</sup> and CD8<sup>+</sup> T cells and NK cells in the pathogenesis of acute neuroinflammation, and to establish the role of pro-inflammatory mediators (e.g., TNF, type I and type II IFN, IL1b, MIP1a, MIP1b/CCL4, CXCL1, CXCL9, CXCL10)<sup>##REF##29922917##4##</sup>. On the other hand, the mouse model of experimental autoimmune encephalomyelitis (EAE) has been used to study cell types and molecules involved in chronic neuroinflammation with associated autoimmunity<sup>##REF##31907274##5##</sup>.</p>", "<p id=\"Par4\">We have used genome wide mutagenesis in mice to identify novel genes that modulate neuroinflammation, and for which mutational inactivation protects against lethal encephalitis during infection with <italic>Plasmodium berghei</italic> ANKA<sup>##REF##25403443##6##–##REF##27721430##9##</sup>. One of the genes uncovered was <italic>Ccdc88b</italic>, a gene expressed exclusively in hematopoietic organs (spleen, bone marrow, lymph nodes and thymus), and in lymphoid and myeloid cells derived from them<sup>##REF##25403443##6##</sup>. Immunophenotyping studies indicated that abrogation of <italic>Ccdc88b</italic> function causes loss of T lymphocyte function, with decreased maturation, impaired activation and reduced cytokine production in response to pro-inflammatory signals or T-cell receptor engagement<sup>##REF##25403443##6##</sup>. In myeloid cells, loss of <italic>Ccdc88b</italic> impairs several aspects of dendritic cell (DC) function, suggesting that the CCDC88B protein is essential for cellular and molecular pathways common to these two cell types. Finally, we have shown that CCDC88B is required for in vitro mobility and in vivo migration of DCs, including their capacity to activate T lymphocytes<sup>##REF##32480428##10##</sup>.</p>", "<p id=\"Par5\">In humans, the <italic>CCDC88B</italic> gene maps on Chromosome 11q13 within a locus associated with vulnerability to several common inflammatory diseases including psoriasis, primary biliary cirrhosis, sarcoidosis, MS, and inflammatory bowel disease (IBD)<sup>##REF##25642632##11##–##REF##29030607##13##</sup>. In mouse models, we have shown that <italic>Ccdc88b</italic>-deficient mice are protected against DSS-induced intestinal colitis. Likewise, in a T cell transfer model, <italic>Ccdc88b</italic>-deficent T cells do not induce colitis in immunocompromised <italic>Rag1</italic><sup><italic>−/−</italic></sup> mice<sup>##REF##29030607##13##</sup>. Parallel studies have shown that the CCDC88B protein and mRNA are elevated in the colon of IBD patients. Furthermore, expression of <italic>CCDC88B</italic> mRNA is regulated by cis-acting variants in CD14<sup>+</sup> myeloid cells, with <italic>CCDC88B</italic> mRNA expression correlated positively with disease risk in a cohort of Crohn’s disease patients<sup>##REF##29030607##13##</sup>. Together, these findings have established a key role for CCDC88B in the pathogenesis of inflammatory conditions of the brain and of the gut.</p>", "<p id=\"Par6\">CCDC88B belongs to the Hook-related protein family that includes, CCDC88A (GIV, Girdin, HkRP1), CCDC88B (Gipie, HkRP3) and CCDC88C (DAPLE, HkRP2). These Hook proteins are defined by shared secondary structure motifs that include an N-terminal Hook-related microtubule-binding domain (MBD), a central coiled coil domain (CCD), and a C-terminal domain involved in binding different subcellular organelles<sup>##REF##15749703##14##</sup>. Whereas the MBD and CCD domains show sequence conservation, the divergent C-terminal domain is thought to confer protein-specific functions<sup>##REF##15749703##14##,##REF##15882442##15##</sup>. The study of these proteins has shown that they are cytoplasmic scaffold proteins that function in different physiological and biochemical pathways, some of which have been revealed by proteins they recruit to specific molecular scaffolds. CCDC88A binds to the Akt kinase and to the actin cytoskeleton and is involved in migration of cancer cells, and in angiogenesis<sup>##REF##20132219##16##</sup>. CCDC88C is a cytoplasmic scaffold protein essential for transducing Wnt signaling pathways<sup>##REF##14750955##17##</sup>, and mutations in <italic>CCDC88C</italic> have been linked to transformation and metabolism of cancer cells<sup>##REF##31431650##18##,##REF##26577606##19##</sup>; familial autosomal recessive truncations of CCDC88C was also found in severe congenital hydrocephalus<sup>##REF##23042809##20##</sup>. CCDC88B was shown to interact with the CDC42 guanine nucleotide exchange factor DOCK 8<sup>##REF##25762780##21##</sup>.</p>", "<p id=\"Par7\">Here, we have used a systematic proteomics approach to identify CCDC88B protein interactors in primary thymocytes and in the T cell line BI-141. These studies identified the Rho/Rac Guanine Nucleotide Exchange Factor 2 (ARHGEF2) and the RAS Protein Activator Like 3 (RASAL3) as specific CCDC88B interactors, and this was further validated by co-immunoprecipitation and by double immunofluorescence with confocal microscopy. In addition, mice defective in <italic>Rasal3</italic> or <italic>Arhgef2</italic> share a protective phenotype in mouse models of inflammation similar to that exhibited by <italic>Ccdc88b</italic> mutants: <italic>Arhgef2</italic> and <italic>Rasal3</italic> deficient mice are protected against neuroinflammation in the EAE model and, to a lesser extent, the ECM model. Interestingly, loss of <italic>Rasal3</italic> or <italic>Arhgef2</italic> also exacerbates the effect of DSS induced colitis. Finally, mutations in <italic>Rasal3</italic> and in <italic>Arhgef2</italic> affect the motility and migration of DCs, a function also affected in <italic>Ccdc88b</italic> mutant primary DCs<sup>##REF##32480428##10##</sup>. These studies identify the CCDC88B/RASAL3/ARHGEF2 molecular scaffold as playing a critical role in the migratory properties of DCs.</p>" ]	[ "<title>Materials and methods</title>", "<title>Mice</title>", "<p id=\"Par37\">Wild type C57BL/6J (B6) mice, 8–12 weeks of age, were obtained from the Jackson Laboratory (Bar Harbor, ME). Except were indicated, both male and female animals were used indiscriminately. The <italic>Ccdc88b</italic><sup><italic>m1PGrs</italic></sup> mutant mouse strain (referred to as <italic>Ccdc88b</italic><sup><italic>Mut</italic></sup>) was generated by genome-wide chemical mutagenesis<sup>##REF##25403443##6##</sup>. <italic>Rasal3</italic><sup><italic>−/−</italic></sup> mice were provided by Dr. H. Suzuki, from the National Center for Global Health and Medicine, Chiba (Japan), and is described elsewhere<sup>##REF##25793935##25##</sup>. <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> mice were generated by CRISPR/CAS9 by the Transgenic Core of the Life Sciences Complex at McGill University, using the experimental protocol and characterization strategy summarized in Supplementary Fig. ##SUPPL##0##2##. All mice were housed under specific pathogen-free conditions at the animal care facility of the Goodman Cancer Research Centre, McGill University, and the animal studies were conducted using protocols approved by the McGill Institutional Review Board (protocol number 5287) and following guidelines and regulations of the Canadian Council of Animal Care. We have complied with all relevant ethical regulations for animal use.</p>", "<title>Co-immunoprecipitation for SDS-PAGE and mass spectrometry analyses</title>", "<p id=\"Par38\">Initial anti-CCDC88B co-immunoprecipitations from mouse primary thymocytes (Fig. ##FIG##0##1a## and Supplementary Data ##SUPPL##2##1##: MS/MS Expt #1) were carried out essentially as previously described<sup>##UREF##0##49##</sup>, using: (1) 200 mg of cryo-milled thymocytes; (2) 20 mM HEPES, pH 7.4, 0.5% (v/v) Triton X-100, 200 mM NaCl; and (3) an affinity-purified rabbit anti-CCDC88B polyclonal anti-serum<sup>##REF##25403443##6##</sup> coupled to Dynabeads M270 Epoxy in-house (adapted from ref. <sup>##REF##21536766##50##</sup>). This strategy was maintained for subsequent co-immunoprecipitations with changes as described herein. To prepare thymocytes, ~3 g of mice thymi were harvested, passed through a 70 µm cell strainer using a syringe plunger and washed with PBS. Cell suspensions were then centrifuged inside a syringe, the supernatant was removed, and the pellet was injected directly into liquid nitrogen. Two additional duplicate anti-CCDC88B co-immunoprecipitations from thymi were analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis, prior to co-immunoprecipitation optimization screening. To further explore potential missing information in our IPs, an anti-CCDC88B co-immunoprecipitation based interaction screen<sup>##REF##25938370##51##</sup> was carried out on BI-141 T cells at ~50 mg per reaction, and the results are curated on <ext-link ext-link-type=\"uri\" xlink:href=\"http://www.copurifcation.org/\">www.copurifcation.org/</ext-link>. From this screen, three conditions were selected for further anti-CCDC88B co-immunoprecipitations, in triplicate, and label-free quantitative (LFQ) MS analysis from mouse primary thymocytes and BI-141 T cells: (1) 20 mM HEPES, pH 7.4, 200 mM NaCl, 0.5% (v/v) Triton X-100; (2) 20 mM HEPES, pH 7.4, 150 mM NaCl, 10 mM deoxy-Big CHAP; (3) 200 mM ammonium acetate, pH 7.0, 1% (v/v) Triton X-100. The criteria for selection of these conditions was based on visual inspection of the stained gel as follows: prominent staining of the band associated with CCDC88B and a relatively discrete pattern of other protein bands with some staining intensity differences between them.</p>", "<title>Sample workup for LC-MS/MS</title>", "<p id=\"Par39\">Co-immunoprecipitated fractions were eluted in 1.1x NuPAGE sample loading buffer (Thermo Fisher Scientific #NP007) at 70 °C, reduced with DTT, alkylated with iodoacetamide, and run ~ 1 h at 200 V on a 1 mm 4–12% Bis-Tris NuPAGE gel followed by Sypro Ruby staining and protein band excision (Fig. ##FIG##0##1a## and Supplementary Data ##SUPPL##2##1##: MS/MS Expt #1) or as 4–6 mm gel-plugs, prior to Coomassie G-250 staining, peptide workup and LC-MS-MS (essentially as described in refs. <sup>##REF##31106238##52##,##REF##17406544##53##</sup>). Gel bands or plugs were excised, cut into 1 mm cubes, de-stained, and digested overnight with 3.1 ng/μl trypsin (Promega, Madison, WI, #V5280) in 25 mM ammonium bicarbonate. Peptides were extracted from the gel in two incubations of 1 h each with 1.7% v/v formic acid, 67% v/v acetonitrile at room temperature with agitation. After partial evaporation by vacuum centrifugation to remove acetonitrile, digests were desalted on Stage Tips<sup>##REF##12585499##54##</sup>. Stage Tip eluates were concentrated by vacuum centrifugation, loaded onto an Easy-Spray column (ES800, Thermo Fisher Scientific) and gradient-eluted (Solvent A = 0.1% v/v formic acid in water, Solvent B = 0.1% v/v formic acid in acetonitrile, flow rate 300 nl/min) into an Orbitrap Fusion Tribrid mass spectrometer (Thermo Fisher Scientific) acquiring data-dependent CID fragmentation spectra.</p>", "<title>MS data analysis</title>", "<p id=\"Par40\">Files were submitted to MaxQuant<sup>##REF##19029910##55##</sup> for protein identification and LFQ (label-free quantification). Searches were performed against mouse protein sequences, exogenous contaminants, and a decoy database of reversed protein sequences. LFQ was performed separately for each of three buffer conditions. Non-mouse proteins and proteins that scored worse than any hit in the decoy database were removed from the MaxQuant output file “proteingroups.txt”. LFQ intensities were log2 transformed and samples were grouped by both cell type (thymocytes or BI-141 cells) and buffer condition. For each of the three immunoprecipitation conditions, respectively, proteins showing a LFQ intensity below three in at least one cell type were not considered. Missing values were imputed from a normal distribution with a down-shifted median relative to the measured data distribution, in order to simulate intensity near the detection limit. Triplicate immunoprecipitates from thymocytes and BI-141 cells in each immunoprecipitation condition were compared using two-sided Student’s <italic>t</italic> test as follow. Statistical significance was determined by a modified <italic>t</italic>-test that controls the relative importance of the Student’s <italic>t</italic> test <italic>p</italic> value and the fold change<sup>##UREF##1##56##,##REF##27348712##57##</sup> with a threshold for FDR (permutations) of 0.01. The RAW and MaxQuant processed files are available for download via ProteomeXchange with identifier PXD023779. Furthermore, a series of filters were used in a decision tree to prioritize possible CCDC88B interactors for further validation. First, candidate CCDC88B interactors were given priority if they were detected in at least 2 independent experiments, in different elution conditions, and if they were detected with immunoprecipitates from both primary thymus and BI-141 cells. Peptide coverage and abundance (peptide counts) in the extracts and relative intensity were also considered to prioritize interactors (Supplementary Fig. ##SUPPL##0##1##). Abundant proteins often found in co-immunoprecipitates (CRAPome v1.1) were excluded<sup>##REF##23921808##22##</sup>. Since CCDC88B is known to be a cytoplasmic protein with microtubule interacting domains<sup>##REF##25403443##6##,##REF##29030607##13##</sup>, all nuclear proteins and DNA-binding proteins were also excluded from the analysis. A ponderation score was established (Supplementary Data ##SUPPL##2##1## and Supplementary Fig. ##SUPPL##0##1##) based on these criteria and identified RASAL3 and ARHGEF2 as two prioritized CCDC88B interactors.</p>", "<title>Immunoprecipitation and immunoblotting</title>", "<p id=\"Par41\">HEK293T cells were transiently transfected with a combination of expression plasmids encoding HA-CCDC88B<sup>##REF##25403443##6##</sup>, RASAL3-FLAG (EX-Mm25984-M14, GeneCopoeia) or ARHGEF2-FLAG (EX-Mm19287-M14, GeneCopoeia) epitope tagged proteins, using the lipofectamine 2000 system (Thermo Fisher). Twenty-four hours later, transfected cell pellets were harvested and resuspended in RIPA lysis buffer (20 mM Tris-HCl, pH 7.5, 200 mM NaCl, 1 mM EDTA 1 mM EGTA 1% NP-40, 2.5 mM sodium pyrophosphate), supplemented with protease inhibitors (complete ultra protease inhibitor, Roche). Samples were clarified by centrifugation and incubated with monoclonal primary antibodies anti-FLAG (Sigma), or anti-HA (Santa Cruz Biotechnology), or IgG used as a negative control; immune complexes were recovered using protein G beads (Dynabeads). Beads were washed extensively, and protein eluted using boiling Laemmli buffer (2% SDS, 10% glycerol, 5% 2-mercaptoethanol) and analyzed by SDS-PAGE. In some experiments, bone marrow-derived dendritic cells (BMDCs) were generated from B6, <italic>Ccdc88b</italic><sup><italic>mut</italic></sup>, <italic>Rasal3</italic><sup><italic>−/−</italic></sup>, <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> bone marrow as described previously<sup>##REF##32480428##10##</sup>. These cells were lysed in 20 mM HEPES, pH7.4, 300 mM NaCl, 1% NP-40 (supplemented with protease inhibitors). Samples were clarified by centrifugation, pre-cleared and incubated overnight with affinity-purified rabbit anti-CCDC88B hyperimmune serum<sup>##REF##25403443##6##</sup>; immune complexes were recovered using Sera-Mag Speedbeads Protein A/G Magnetic Particles (Sigma) according to the manufacturer protocol. For immunoblotting, proteins were separated by SDS-PAGE and transferred onto nitrocellulose membrane (GE-Healthcare), followed by incubation with rabbit anti-CCDC88B hyperimmune serum, followed by detection with HRP anti-rabbit antibodies (GE-Healthcare) and ECL, as previously described<sup>##REF##25403443##6##</sup>. In some experiments, an affinity-purified rabbit anti-RASAL3 hyperimmune serum<sup>##REF##25793935##25##</sup> or anti-ARHGEF2 antibody (Cell Signaling Technology) were used (both at 1:300 dilution). Unprocessed original Western Blot analyses can be found in Supplementary Fig. ##SUPPL##0##6##.</p>", "<title>Immunofluorescence</title>", "<p id=\"Par42\">Transfected HEK293T cells expressing HA-CCDC88B, in combination with either RASAL3-FLAG or ARHGEF2-FLAG, were fixed for 5 min with methanol at −20 °C, followed by permeabilization for 4 min at −20 °C with 30% methanol/70% acetone. Fixed cells were blocked with 3% BSA in PBS for 20 min and incubated overnight with the primary antibody anti-CCDC88B (rabbit polyclonal<sup>##REF##25403443##6##</sup>, 1:200) or anti-HA (Santa Cruz Biotechnology, 1:1000) with either anti-RASAL3 (rabbit polyclonal, provided by Dr. Suzuki<sup>##REF##25793935##25##</sup>, 1:200], anti-ARHGEF2 (Cell Signaling Technology, 1:200) or anti-FLAG (Sigma, 1:1000). Bound antibodies were detected using either Alexa488-coupled anti-rabbit IgG (Invitrogen) and Alexa594-coupled anti-mouse IgG (Invitrogen) secondary antibodies at a 1:000 dilution. In some experiments, cells were also counterstained with 4′,6-diamidino-2-phenylindole (DAPI, Invitrogen) to visualize the nuclei. Images were acquired using a Zeiss LSM710 Meta Laser Scanning Confocal microscope (100x lens) and processed with ImageJ (Fiji) with a maximum intensity projection.</p>", "<title>Plasmodium berghei ANKA Infection</title>", "<p id=\"Par43\">Infections with <italic>P. berghei ANKA</italic> (PbA) were performed as we previously described<sup>##REF##27721430##9##</sup>. Parasites were originally provided by the Malaria Reference and Research Reagent Resource Center (MR4) and were kept frozen at −80 °C in RPMI 1640 with 15% glycerol. Freshly thawed parasites were passaged once into B6 mice (7 days) and parasitemia in freshly harvested blood was monitored on stained thin blood smears (Diff-Quick reagents staining; Fisher Scientific) to prepare the infectious inoculum. PbA infection was performed using 10<sup>6</sup> parasitized red blood cells (pRBCs) injected intravenously. Mice were then monitored for the appearance of neurological symptoms associated with cerebral malaria three times daily and euthanized when reaching clinical endpoints. All remaining animals were euthanized on day 19 post-infection (experimental endpoint).</p>", "<title>Experimental autoimmune encephalomyelitis</title>", "<p id=\"Par44\">Experimental autoimmune encephalomyelitis (EAE) was induced as described elsewhere<sup>##REF##27721430##9##</sup>. Briefly, female mice were injected subcutaneously with a short peptide of myelin oligodendrocyte glycoprotein (MOG, amino acids 35–55, 150 µg) emulsified in complete Freund’s adjuvant (50 μg/mouse). The same day and 2 days later, mice received 300 ng of pertussis toxin injected intraperitoneally. Mice were monitored daily for progressive paresis of tail and limbs; severely impaired animals were euthanized. Score was evaluated individually for the tail, each hind limb and each front limb on a scale from 0 to 3 (0, no symptoms; 1, weak; 2, full paresis; 3, no movement), for a possible maximum score of 10. The EAE incidence score was recorded as the highest score reached by each individual mouse over the course of the experiment. In some experiment, the mice were sacrificed at day 11, perfused, and the spinal cord harvested by flushing the spinal column. The spinal cord was then cut into small pieces and digested for 45 min in DNAseI (50 µg/ml, New England Biolabs) and collagenase V (1 mg/ml, Sigma). Single cells suspension was then prepared by passing through a 70 µm cell strainer using a syringe plunger and washed with PBS. Finally, the cells were stained with vital dye Zombie Aqua dye (1:400 dilution, Biolegend) then surface stained with the following fluorescently-labeled antibodies: 1:200 FITC anti-NK1.1 (clone PK136, eBioscience), 1:300 PerCP-Cy5.5 anti-Ly6G (clone 1A8, Biolegend), 1:300 APC anti-CD8α (clone 53-6.7, eBioscience), 1:400 APC-Fire 750 anti-CD45 (clone 30-F11, Biolegend), 1:1000 BV421 anti-Ly6C (clone HK1.4, Biolegend), 1:300 BV605 anti-CD11b (clone M1/70, Biolegend), 1:400 BV711 I-A/I-E anti-MHCII (clone M5/114.15.2, Biolegend), 1:300 BV785 anti-CD11c (clone N418, Biolegend), 1:400 PE anti-CD4 (clone RM4-5, Biolegend), 1:200 PE-Dazzle 594 anti-CD3 (clone 17A2, Biolegend) and 1:400 PE-Cy7 anti-F4/80 (clone BM8, Biolegend). Stained samples were processed using a Fortessa flow cytometer (BD Biosciences) and the results were analyzed using FlowJo software (Tree Star Inc). Gating strategy can be found in Supplementary Fig. ##SUPPL##0##3##, and each sample were normalized for 10 million living cells.</p>", "<title>Mouse model of intestinal colitis</title>", "<p id=\"Par45\">The mouse model of DSS induced intestinal colitis was performed as we previously described<sup>##REF##29030607##13##</sup>. Briefly, male mice were treated with 3% DSS (w/v, dextran sodium sulfate; MP Biomedicals) in their drinking water for 5 days, followed by 3 days of water. Mice were weighed daily throughout the experiments and euthanized at day 8. The entire colon was removed, measured and fixed in 10% neutral-buffered formalin overnight. Samples were embedded in paraffin, cut into 4 µm sections followed by staining with eosin and hematoxylin. To avoid bias, a trained pathologist scored each colon blindly on a scale of 36, with up to 4 points given in each of those categories: inflammatory cell infiltration, inflammatory cell depth, submucosal edema, increased mucosal thickening, surface epithelial degeneration, gland epithelial apoptosis, gland epithelial degeneration/abscesses, gland goblet/enterocyte ratio decrease and gland loss. In some experiments, colons from control and treated mice were instead dissociated with Lamina Propria Dissociation Kit (Miltenyi Biotec), according to the manufacturer instructions. Single-cell suspensions were prepared from colon, and stained with vital dye Zombie Aqua dye (1:400 dilution, Biolegend) then surface stained with the following fluorescently-labeled antibodies: 1:200 FITC anti-NK1.1 (clone PK136, eBioscience), 1:300 PerCP-Cy5.5 anti-CD4 (clone RM4-5, Biolegend), 1:200 PE anti-TCRγδ (clone GL3, eBioscience), 1:200 PE-Dazzle 594 anti-CD3 (clone 17A2, Biolegend), 1:200 PE-Cy7 anti-CD44 (clone IM7, eBioscience), 1:300 APC anti-CD62L (clone MEL-14, eBioscience), 1:200 AlexaFluor 700 anti-CD8α (clone 53-6.7, eBioscience), 1:400 APC-Fire 750 anti-CD45 (clone 30-F11, Biolegend) and 1:300 eFluor 450 anti-CD19 (clone eBio1D3, eBioscience). In some case, a myeloid panel was used instead: 1:300 FITC anti-CD64 (clone X54-5/7.1, Biolegend), 1:300 PerCP-Cy5.5 anti-Ly6G (clone 1A8, Biolegend), 1:200 PE anti-SiglecF (clone E50-2440, BDBioscience), 1:200 PE-Dazzle 594 anti-CD103 (clone 2E7, Biolegend), 1:400 PE-Cy7 anti-CD45 (30-F11, Biolegend), 1:200 APC anti-CD317 (clone 927, Biolegend), 1:800 APC-Cy7 I-A/I-E anti-MHCII (clone M5/114.15.2, Biolegend), 1:1000 BV421 anti-Ly6C (clone HK1.4, Biolegend), 1:300 BV605 anti-CD11b (clone M1/70, Biolegend), 1:300 BV785 anti-CD11c (clone N418, Biolegend) and 1:400 Zombie Aqua Fixable Viability (Biolegend). Stained samples were processed using a Fortessa flow cytometer (BD Biosciences) and the results were analyzed using FlowJo software (Tree Star Inc).</p>", "<title>Colon histology and immunochemistry</title>", "<p id=\"Par46\">The fixed colon tissues were embedded in paraffin after dehydration, and 4 μm paraffin sections were prepared. After dewaxing and hydration, H&E staining was performed. The paraffin sections were scored as previously described<sup>##REF##29030607##13##</sup>. To visualize goblet cells and mucin, an Alcian blue-PAS staining was performed. Sections were stained with Alcian blue at pH 2.5 (AB) followed by a staining step with the periodic acid-Schiff (PAS). For the IHC, paraffin-embedded tissue sections were de-waxed and rehydrated, incubated in Diva Decloaker antigen retrieval solution (Biocare) and boiled for 20 min in a pressure cooker; Enzyme Block (DAKO) was also used for 15 min to block peroxidase. Slides were stained with either 1:100 anti-Ki-67 (D3B5; Cell signaling), anti-CD68 (KP1, abcam), 1:250 anti-pSTAT3(Tyr705/D3A7, cell signaling), 1:100 anti-CD3 (SP7, Abcam) for 1 h. All antibodies were used in automated immunohistochemistry assays in accordance with the protocols recommended by the manufacturer (VENTANA BenchMark ULTRA instrument). Bound antibodies were detected using peroxidase-goat anti-rabbit secondary antibodies (AB_2307391, Jackson laboratories) used at a 1:500 dilution (VENTANA DISCOVERY series instruments). Slides were counter-stained with hematoxylin, then mounted. In some experiment, slides were then scanned and process using Aperio ImageScope (Version 12.4.3, Leica Biosystems Imaging Inc). The 2 most distal field of view (at 10x) were then systematically processed to quantify infiltration of immune cell populations using Fiji (Version 1.53f51<sup>##REF##22743772##58##</sup>) using the following main operators: Thresholding (Otsu Binary), Automatic Smoothing/Sharpening, Watersheding and Analyze particle.</p>", "<title>Mouse model of T cell specific colitis</title>", "<p id=\"Par47\">Colitis was induced as we previously described<sup>##REF##29030607##13##</sup>. Briefly, spleen from B6, <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> and <italic>Rasal3</italic><sup><italic>−/−</italic></sup> were enriched for naive CD4<sup>+</sup> CD45RB<sup>hi</sup> T cells (CD4<sup>+</sup> T Cell Isolation Kit; Miltenyi Biotec) and single-cell suspensions was prepared. Cells were then then surface stained with the following fluorescently-labeled antibodies: 1:300 FITC anti-CD4 (GK1.5, eBioscience), 1:250 PE anti-CD25 (PC61.5,eBioscience) and 1:1500 APC anti-CD45RB (C363.16A, eBioscience). Naive cells where then purified by cell sorting (FACSAriaII, BD Biosciences) and 5 × 10<sup>5</sup> CD4<sup>+</sup> CD45RB<sup>hi</sup> T cells were then injected intravenously into <italic>Rag1</italic><sup><italic>−/−</italic></sup> mice. Mice were sacrificed 7 weeks after injection, their colon harvested and processed as above.</p>", "<title>Gene expression and cytokine quantitation in colon tissues</title>", "<p id=\"Par48\">RNA from distal colons flash-frozen in liquid nitrogen was prepared and analyzed as described elsewhere<sup>##REF##29030607##13##</sup>. Briefly, RNA was purified using a FastPrep 24 homogenizer (MP Biomedicals) with lysing matrix D beads (MP Biomedicals) and RNeasy kits (QIAGEN). MMLV reverse transcriptase was used with a cocktail of Oligo dT and random hexamers (Invitrogen) for cDNA synthesis. The primer set (5′-CCGGGAGCTTCGAGGCCAAC-3′ and 5′-CCTATCTGGCAAGCGGGGC-3′) was used to quantify the relative expression of <italic>Ccdc88b</italic> mRNA. Cytokines and chemokines RNA transcripts were quantified by RT-qPCR using Perfecta SYBR Green PCR kit and the following primers sets: <italic>Mcp</italic>-1 (5′-AGGTGTCCCAAAGAAGCTGTA-3′ and 5′-TCTGGACCCATTCCTTCTTG-3′), <italic>Il-6</italic> (5′-GAAGTAGGGAAGGCCGTGG-3′ and 5′-GAAGTAGGGAAGGCCGTGG-3′), <italic>Emr1</italic> (F4/80) (5’-CTTTGGCTATGGGCTTCCAGTC-3’and 5’-GCAAGGAGGACAGAGTTTATCGTG-3), <italic>Rantes</italic> (5′-GCAAGTGCTCCAATCTTGCA-3′ and 5′-CTTCTCTGGGTTGGCACACA-3′). <italic>Mgl1</italic> (5’-GAGAAAGGCTTTAAGAACTGGG-3’ and 5’-GACCACCTGTAGTGATGTGGG-3’) and <italic>C4b</italic> (5’-GATGAGGTTCGCCTGCTATT-3’ and 5’-GACTTGGGTGATCTTGGACTC-3’) Gene expression was normalized to <italic>Hprt</italic> (5′-TCAGTCAACGGGGGACATAAA-3′ and 5′-GGGGCTGTACTGCTTAACCAG-3′) and relative expressions was calculated using the ΔΔCT method<sup>##REF##11846609##59##</sup>.</p>", "<title>Mouse model of colitis-associated colorectal cancer</title>", "<p id=\"Par49\">For the colitis-associated colorectal cancer model (CA-CRC), mice were injected intraperitoneally with azoxymethane (MP Biomedical; 7 mg/kg) followed by three 4-day cycles of 2% DSS in the drinking water, with each cycle 17 days apart. Mice were monitored at regular intervals and weighed weekly. Colons were collected after 14 weeks, fixed in formalin and analyzed for the number of tumors per colon, as well as their total tumor surface area (calculated from diameter of individual tumors). In some experiments, colon sections were also analyzed by immunohistochemistry as above.</p>", "<title>Single-cell RNA sequencing</title>", "<p id=\"Par50\">Colons from B6 mice were collected, cut into small pieces, and washed in gut buffer (1X Hank’s Balanced Salt Solution (HBSS) containing 2% heat-inactivated fetal bovine serum (FBS) and 15 mM of HEPES. Epithelial cells were removed by shaking in gut buffer with 5 mM EDTA for 30 min at 37 °C, with occasional vortexing. Finally, tissues were treated with collagenase IV (20 mg/ml) and DNase I (10 mg/ml) in RPMI-1640 supplemented with 5% FBS and 15 mM of HEPES for 20 min at 37 °C and passed through a 70 μM cell strainer to collect lamina propria cells. Cells were stained for viability using Zombie Viability Dye V500 (1:400), and surface stained with anti-CD45 APC (1:400, clone 30F11, Biolegend). Stained cell suspensions were sorted on a BD FACSAriaIII Cell Sorter (BD Biosciences) to obtain viable CD45<sup>+</sup> cells. Freshly sorted cells were washed and resuspended in 0.04% BSA in PBS for loading on the 10x Chromium chip. Single-cell capture and cDNA preparation was done according to the 10x Single-Cell 3’ (version 3.1) protocol, with 8000 cells targeted for capture per sample. Libraries were sequenced on the NovaSeq 6000 Sequencing System (Illumina). Raw sequencing data can be found on the Gene Expression Omnibus Repository (GEO)<sup>##REF##11752295##60##</sup> with the accession number GSE249342. Raw gene expression matrices were generated for each sample by the Cell Ranger, and the output was analyzed using the Seurat package. Low-quality reads were filtered based on three criteria: number of detected genes per cell, number of UMIs expressed per cell and mitochondrial content, using the following threshold parameters: nGene (between 200 and 7500), nUMI (between 500 and 75,000), and percentage of mitochondrial genes expressed (<7.5%). Doublets were then identified by finding cells expressing markers of two cell lineages simultaneously, as well as using the DoubletFinder package. After removal of low-quality cells and doublets, we captured 12,475 CD45<sup>+</sup> cells from B6 colonic lamina propria (total from 2 samples). Gene expression matrices were normalized using the NormalizeData function and scaled using the ScaleData function, regressing out effects of cell cycle and percentage of expressed mitochondrial genes. Identification of highly variable features, linear dimension reduction by PCA transformation, UMAP dimensionality reduction, and cell clustering were performed using standard Seurat package workflows. Myeloid cells were integrated for further sub-clustering. Normalization, scaling, PCA, UMAP and clustering were performed as described above.</p>", "<title>T cell homing assay</title>", "<p id=\"Par51\">Spleen were harvested, processed into single-cell suspensions and red blood cells lysed using ammonium chloride containing buffer (ACK; Fisher scientific). Splenocytes were stained with either 2 µM of CellTracker green (5-chloromethylfluorescein diacetate or CMFDA, ThermoFisher) or 20 µM of CellTracker orange (5-(and-6)-(((4-chloromethyl)benzoyl)amino)tetramethylrhodamine or CMTMR, Thermo Fisher) for 15 min at 37 °C. Stained cells were extensively washed with PBS, counted twice, and a mixture (1:1 ratio) of independently stained splenocytes (total of 1 × 10<sup>7</sup> cells) were injected intravenously into the tail vein of B6 mice. Inguinal lymph nodes (LNs) were harvested 6 h later and processed for analysis of cell composition by flow cytometry using the following fluorescently-labeled antibodies in addition to CMFDA and CMTMR: 1:300 PerCP-Cy5.5 anti-CD4 (clone RM4-5, Biolegend), 1:400 APC anti-CD45 (clone 30-F11, eBioscience), 1:300 APC-Fire anti-CD8a (clone 53-6.7, eBioscience), 1:200 PE-Dazzle 594 anti-CD3 (clone 17A2, Biolegend) and Zombie Aqua Fixable Viability (Biolegend). The relative amount of CMFDA<sup>+</sup> T cells compared to CMTMR<sup>+</sup> T cells was calculated as follows: (% of CMFDA<sup>+</sup> T cells in sample/% of CMFDA<sup>+</sup> T cells in injection mix)/(% of CMTMR<sup>+</sup> T cells in sample/% of CMTMR<sup>+</sup> T cells in injection mix), as we described<sup>##REF##32480428##10##</sup>.</p>", "<title>In vivo migration assay</title>", "<p id=\"Par52\">Migration assays were performed as previously described<sup>##REF##32480428##10##</sup>. LPS-treated bone marrow-derived dendritic cells (BMDC) were stained with either 2 µM of CellTracker green (CMFDA, Thermo Fisher) or 20 µM of CellTracker orange (CMTMR, Thermo Fisher) for 15 min at 37 °C, extensively washed with PBS, counted twice, and a mixture (1:1 ratio) of independently stained cells (total of 1 × 10<sup>6</sup> cells) were injected in the footpad of B6 mice. Popliteal LNs were harvested 48 h later, and the ratio of CMFDA and CMTMR labeled CD11c<sup>+</sup> cells was analyzed by flow cytometry. The relative amount of CMFDA<sup>+</sup> DCs compared to CMTMR<sup>+</sup> DCs was calculated as above.</p>", "<title>In vitro patrolling assay</title>", "<p id=\"Par53\">Patrolling assays were performed as previously described<sup>##REF##32480428##10##</sup>. BMDCs were cultured in Nunc Lab-Tek 8-wells cover-glass chambers (Thermo Fisher) for 3 h at 37 °C and 5% CO<sub>2</sub> in RPMI supplemented with 10% fetal bovine serum (HyClone) before imaging. Two representative fields of view per sample were acquired every minute for 90 min by brightfield microscopy using a Zeiss LSM700. Images were processed with ImageJ (Fiji<sup>##REF##22743772##58##</sup>) and analyzed using the built-in TrackMate plugin<sup>##REF##27713081##61##</sup> in addition to a homemade MatLab script (MathWorks). Single tracks produced by the TrackMate plugin were manually removed when tracking cellular debris or non-adherent cells.</p>", "<title>RHOA activation assay</title>", "<p id=\"Par54\">Bone marrow-derived dendritic cells (BMDCs) were produced as described previously<sup>##REF##32480428##10##</sup>, serum starved for 24 h and stimulated for 5 min at 37 °C in RPMI supplemented with 10% fetal bovine serum with 1 µg/ml LPS. BMDCs were then wash extensively with ice cold PBS and processed rapidly to a Rho Activation Assay Biochem Kit (Cytoskeleton Inc) accordingly to the manufacturer instructions. Briefly, BMDCs were lysed in 50 mM Tris pH 7.5, 10 mM MgCl2, 0.5 M NaCl, and 2% Igepal (Cytoskeleton Inc), and 800 µg of lysates submitted to immunoprecipitation with Rhotekin RBD beads IP for 1h15 at 4 °C with 50 µg beads. Lysate was then washed, eluted in 2x Laemmli sample buffer, boiled for 10 min and analyzed by SDS-PAGE and Western blot analysis using Anti-RHOA monoclonal IgM antibody (1:500 Cytoskeleton Inc), as described above. For this assay, positive control was also produced by saturating BMDCs lysate with 200 μM GTPγS and negative control was produced by saturating BMDCs with 1 mM GDP, before immunoprecipitation. Western blot images were scanned, processed with ImageJ (Fiji<sup>##REF##22743772##58##</sup>) and analyzed for the relative intensity of RHOA-GTP when compared to total RHOA in the input.</p>", "<title>Statistical analysis and reproducibility</title>", "<p id=\"Par55\">Results are presented as mean ± SEM, unless otherwise indicated. Prism5 (GraphPad) software was used for all statistical tests, using parametric tests when criteria for normality is satisfied and non-parametric otherwise, as indicated. Differences were considered statistically significant when <italic>p</italic> ≤ 0.05. <italic>p</italic> values are indicated by <italic>p</italic> ≤ 0.05, <italic>p</italic> < 0.01, **<italic>p</italic> < 0.001. Data are independent biological replicate, where each individual data point in each plots represent a single mouse or primary cell line derived from an individual mouse, except when otherwise indicated; number of independent experiments carried out with similar results are identified in each corresponding Figure legend and were all reproducible.</p>", "<title>Reporting summary</title>", "<p id=\"Par56\">Further information on research design is available in the ##SUPPL##3##Nature Portfolio Reporting Summary## linked to this article.</p>" ]	[ "<title>Results</title>", "<title>RASAL3 and ARHGEF2 physically interact with CCDC88B</title>", "<p id=\"Par8\">We previously established the cellular basis for the ECM and IBD protective effect of <italic>Ccdc88b</italic> inactivation<sup>##REF##29030607##13##</sup>. Yet, the underlying mechanistic basis of these effects remains unknown. Primary sequence analysis of CCDC88B points to several family-specific sequence motifs associated with protein-protein interaction and organelles binding or localization. To identify CCDC88B-dependent biochemical pathways important for the function of myeloid and lymphoid cells, we sought to identify proteins that physically and functionally interact with CCDC88B in these cells<sup>##REF##25403443##6##,##REF##32480428##10##,##REF##29030607##13##</sup>.</p>", "<p id=\"Par9\">We first performed an experiment where CCDC88B was immunoprecipitated from murine thymic extracts, followed by separation of captured proteins by SDS-PAGE. Nine Sypro stained bands were excised and analyzed by LC-MS/MS (Fig. ##FIG##0##1a## and Supplementary Data ##SUPPL##2##1##), and four different putative CCDC88B interactors were detected with a high spectral count: RAS Protein Activator Like 3 (RASAL3), Rho/Rac Guanine Nucleotide Exchange Factor 2 (ARHGEF2), Heat Shock Protein Family A Member 8 (HSPA8) and Nucleoporin 160 (NUP160). Of note, these proteins were excised at their corresponding molecular mass and were easily visible on SDS-PAGE. CCDC88B was also detected as the most abundant protein at its expected molecular mass (Fig. ##FIG##0##1a##).</p>", "<p id=\"Par10\">We next carried out co-immunoprecipitations of CCDC88B from cryo-milled BI-141 T cells and mouse thymi, followed by identification of pulled down proteins by LC-MS/MS (Fig. ##FIG##0##1b##); 253 proteins were detected (Supplementary Data ##SUPPL##2##1##) and filters taking into account abundance (number of peptides and protein coverage), relative intensity, retention of interaction under different co-immunoprecipitation conditions, and presence in both BI-141 cells and in primary thymic co-immunoprecipitates in independent tests were applied. Because CCDC88B is a cytoplasmic protein, nuclear proteins, DNA-binding proteins and species commonly found in protein:protein interaction studies<sup>##REF##23921808##22##</sup> were discarded. A priority score was assigned to the different species detected (Fig. ##FIG##0##1b##; Supplementary Data ##SUPPL##2##1## and Supplementary Fig. ##SUPPL##0##1##), resulting in the identification of RASAL3 and ARHGEF2 as the two top candidate CCDC88B interactors (Fig. ##FIG##0##1c##). RASAL3 is a 114.7 kDa cytosolic protein, member of the Ras GTPase-activating proteins (RasGAP) family with characteristic pleckstrin homology (PH), C2, and Ras GTPase-activation protein (RasGAP) domains. This protein family may act as a negative regulator of Ras signaling<sup>##REF##23443682##23##</sup>. ARHGEF2 (also known as GEF-H1) is a 133.7 kDa cytosolic protein member of the Rho/Rac Guanine Nucleotide Exchange Factor family known to activate Rho-GTPases by promoting the exchange of GDP for GTP<sup>##REF##27301673##24##</sup>.</p>", "<p id=\"Par11\">Physical interaction between CCDC88B, RASAL3 and ARHGEF2 was further investigated following pairwise transient expression of the 3 proteins in HEK293T cells (Fig. ##FIG##1##2##). Cell extracts were subjected to immunoprecipitation and immunoblotting with pairs of antibodies; CCDC88B (detected by immunoblotting with anti-HA) could be pulled down by either RASAL3 or ARHGEF2 when immunoprecipitated with anti-FLAG antibody (Fig. ##FIG##1##2a##). Conversely, CCDC88B immunoprecipitated both RASAL3 (Fig. ##FIG##1##2b##) and ARHGEF2 (Fig. ##FIG##1##2c##) detected by anti-FLAG. In addition, interaction between CCDC88B, RASAL3, and ARHGEF2 was analyzed by double immunofluorescence following expression of the proteins in HEK293T cells transfected as above. These experiments identified a strong overlapping staining between CCDC88B and RASAL3 (Fig. ##FIG##1##2d##) or ARGHEF2 (Fig. ##FIG##1##2e##). Staining for the 3 proteins expressed in HEK293T cells was very similar, appearing primarily associated with both the cell membrane and with intracellular punctate/filamentous compartment.</p>", "<p id=\"Par12\">Taken together, these studies strongly suggest that CCDC88B forms a cellular complex with RASAL3 and ARHGEF2 in T cells. This complex appears associated with the cell membrane and with an intracellular reticulated compartment. These findings are in agreement with our recent observation that CCDC88B co-localizes with intracellular actin filaments<sup>##REF##32480428##10##</sup> and suggest that the CCDC88B/ARHGEF2/RASAL3 complex may be associated with the cytoskeleton.</p>", "<title>Absence of RASAL3 and ARHGEF2 protects against neuroinflammation</title>", "<p id=\"Par13\">We next tested whether the proximity and physical interaction detected between CCDC88B, ARHGEF2 and RASAL3 translates into a functional relationship. Having previously shown that <italic>Ccdc88b</italic> inactivation protects against neuroinflammation (ECM model)<sup>##REF##25403443##6##</sup>, we evaluated a possible role for ARHGEF2 and RASAL3 in neuroinflammation using mouse mutants inactivated at each locus. For this, we created an <italic>Arhgef2</italic> loss-of-function mutant by CRISPR-Cas9 gene editing in a C57BL/6J (B6) genetic background (Supplementary Fig. ##SUPPL##0##2##). <italic>Rasal3</italic><sup><italic>−/−</italic></sup> mutant mice have been previously described<sup>##REF##25793935##25##</sup>. Control B6, along with <italic>Ccdc88b</italic><sup><italic>Mut</italic></sup>, <italic>Rasal3</italic><sup><italic>−/−</italic></sup> and <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> mutant mice were infected with PbA, monitored for appearance of neurological symptoms and survival was recorded (Fig. ##FIG##2##3a–c##). <italic>Ccdc88b</italic><sup><italic>Mut</italic></sup> mice were highly resistant to the cerebral phase of the disease (d5–d9), with greater than ~70% survival at d16 (Fig. ##FIG##2##3a##). On the other hand, <italic>Rasal3</italic><sup><italic>−/−</italic></sup> mice showed a moderate but significant level of resistance over controls, with ~20% survival beyond d16 (Fig. ##FIG##2##3b##). <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> mutant mice were as susceptible to ECM as controls (Fig. ##FIG##2##3c##).</p>", "<p id=\"Par14\">We also investigated <italic>Ccdc88b</italic><sup><italic>Mut</italic></sup>, <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> and <italic>Rasal3</italic><sup><italic>−/−</italic></sup> mutants in a non-microbial model of neuroinflammation (Fig. ##FIG##2##3d–f##). In EAE, neuroinflammation and axonal damage are induced by autoimmune response to myelin oligodendrocyte glycoprotein which is co-administered with pertussis toxin. Response to EAE is evaluated by appearance and severity of symptoms assessed by a clinical score and by overall survival. In the EAE model, <italic>Ccdc88b</italic><sup><italic>Mut</italic></sup> (Fig. ##FIG##2##3d##), <italic>Rasal3</italic><sup><italic>−/−</italic></sup> (Fig. ##FIG##2##3e##), and <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> (Fig. ##FIG##2##3f##) mutants showed significant protection when compared to B6 controls, including lower clinical scores and increased survival, as identified by the fraction of animals reaching clinical endpoint (Fig. ##FIG##2##3d–f##). Clinical scores for individual mice revealed that all mutants displayed a much milder phenotype than controls.</p>", "<p id=\"Par15\">To better characterize the basis of EAE resistance seen in <italic>Ccdc88b</italic><sup><italic>Mut</italic></sup>, <italic>Rasal3</italic><sup><italic>−/−</italic></sup> and <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> mice, we investigated the infiltration of pro-inflammatory cells in the spinal cord of mutant and controls at day 11, a time point that corresponds to appearance of neurological symptoms (Fig. ##FIG##2##3d–f##). Flow cytometry analysis (see Supplementary Fig. ##SUPPL##0##3##) of the cellular content of <italic>Rasal3</italic><sup><italic>−/−</italic></sup> and <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> spinal cord identified a significant reduction in the numbers of infiltrating CD45<sup>+</sup>CD3<sup>+</sup>CD4<sup>+</sup> T cells (Fig. ##FIG##2##3g##) and CD45<sup>+</sup>CD11c<sup>+</sup>MHCII<sup>+</sup> antigen presenting cells (Fig. ##FIG##2##3g##) compared to B6 controls. The decreased infiltration of antigen presenting cells was also noted in the spinal cord of <italic>Ccdc88b</italic><sup><italic>Mut</italic></sup> mice (Fig. ##FIG##2##3h##). These results strongly suggest that EAE resistance in <italic>Rasal3</italic><sup><italic>−/−</italic></sup>, <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> and <italic>Ccdc88b</italic><sup><italic>Mut</italic></sup> is associated with reduced/delayed infiltration of lymphoid and myeloid cells in the spinal cord, with possible concomitant dampening of pathological neuroinflammation.</p>", "<p id=\"Par16\">These results show that the <italic>Ccdc88b</italic><sup><italic>Mut</italic></sup>, <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> and <italic>Rasal3</italic><sup><italic>−/−</italic></sup> mutants display significant resistance in one or both models of neuroinflammation models tested. Hence, the physical interaction detected between the CCDC88B, ARHGEF2 and RASAL3 proteins appears to underlie functional relationship when tested in vivo during neuroinflammation.</p>", "<title>Rasal3<sup>−/−</sup> and Arhgef2<sup>−/−</sup> mice are highly susceptible to DSS-induced colitis</title>", "<p id=\"Par17\">We have previously shown that the protection of <italic>Ccdc88b</italic><sup><italic>Mut</italic></sup> mice against intestinal colitis is due to reduced intestinal inflammation and decreased leukocyte infiltration<sup>##REF##29030607##13##</sup>. We therefore tested the response of <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> and <italic>Rasal3</italic><sup><italic>−/−</italic></sup> mutant to DSS-induced colitis. We noticed that both mutant mice lost significantly more weight than the B6, an indicator of the pathology progression (Fig. ##FIG##3##4a##). Moreover, the colons from both <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> and <italic>Rasal3</italic><sup><italic>−/−</italic></sup> mice were significantly shorter than B6 controls (Fig. ##FIG##3##4b, c##). Histological examination of colon sections also showed a quantitatively higher pathology score in both <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> and <italic>Rasal3</italic><sup><italic>−/−</italic></sup> mice compared to B6, based on criteria that included inflammatory cell infiltration, submucosal edema and surface epithelial degeneration (Fig. ##FIG##3##4d##). Increased cellular infiltration of cells in inflamed colons of <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> and <italic>Rasal3</italic><sup><italic>−/−</italic></sup> mice (seen in the histological pathology scoring) was concomitant to increased expression of <italic>Ccdc88b</italic> mRNA (Fig. ##FIG##3##4e##), a marker of myeloid and lymphoid cells<sup>##REF##29030607##13##</sup>. Furthermore, expression of mRNAs for pro-inflammatory cytokines (IL6), chemoattractant (MCP1, RANTES), markers of cellular infiltration (MGL1, F4/80), and C4b, were significantly elevated in the colon of DSS-treated <italic>Rasal3</italic><sup><italic>−/−</italic></sup> and, to a lesser degree in <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> mice compared to B6 (Fig. ##FIG##3##4f##). Lastly, immunohistological studies of colon sections revealed that both <italic>Rasal3</italic><sup><italic>−/−</italic></sup> and <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> DSS-treated mice show reduced level of mucin (Fig. ##FIG##3##4g, h##). Quantification of cellular infiltrates (2 distalmost fields of view of each colon) revealed a significantly increased infiltration of CD3<sup>+</sup> T cells and CD68<sup>+</sup> mononuclear phagocytes, as well as a decreased number of KI-67<sup>+</sup> proliferating cells in DSS-treated <italic>Rasal3</italic><sup><italic>−/−</italic></sup> mice compared to B6, all in agreement with greater susceptibility in <italic>Rasal3</italic><sup><italic>−/−</italic></sup> mice (Fig. ##FIG##3##4g, h##). Interestingly, cellular infiltrates in the colons of DSS-treated <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> mice were quantitatively similar to those seen in B6 controls (Fig. ##FIG##3##4g, h##), and this despite a stronger inflammatory environment (Fig. ##FIG##3##4f##), suggesting possible differences in the mechanistic basis of DSS-susceptibility in <italic>Rasal3</italic><sup><italic>−/−</italic></sup> and <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> mutants.</p>", "<p id=\"Par18\">To further investigate the contribution of lymphoid cells to the exacerbated colitis phenotype seen in <italic>Rasal3</italic><sup><italic>−/−</italic></sup> and <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> mice, we tested the ability of B6, <italic>Rasal3</italic><sup><italic>−/−</italic></sup> and <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> naïve CD4<sup>+</sup> CD25<sup>−</sup>CD45RB<sup>Hi</sup> T cells to induce colitis upon adaptive transfer into lymphopenic <italic>Rag1</italic><sup><italic>−/−</italic></sup> mice. Seven weeks post-transfer, histology of colon sections revealed no difference between B6 and <italic>Arhgef2</italic><sup><italic>−/−</italic></sup>, including a similar CD3<sup>+</sup> T cells infiltration (Supplementary Fig. ##SUPPL##0##4a##) and pathology score (Supplementary Fig. ##SUPPL##0##4b##), suggesting that the exacerbated colitis response to DSS seen in <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> mice (Fig. ##FIG##3##4##) was not lymphoid in nature. Conversely, transfer of <italic>Rasal3</italic><sup><italic>−/−</italic></sup> naive T cells failed to induce colitis when compared to B6, exhibiting both a decrease in the amount of infiltrating CD3<sup>+</sup> T cells (Supplementary Fig. ##SUPPL##0##4a##) and a lower pathology score (Supplementary Fig. ##SUPPL##0##4b##). This suggests that RASAL3 is require for the proper function of T lymphocytes during colitis, in agreement with recently published studies<sup>##REF##25793935##25##,##REF##29291408##26##</sup>.</p>", "<p id=\"Par19\">To better understand the susceptibility of <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> and <italic>Rasal3</italic><sup><italic>−/−</italic></sup> mice to colitis, we conducted cellular immunophenotyping of colon cell populations by flow cytometry following DSS treatment. These showed a greater infiltration of CD45<sup>+</sup> cells in lamina propria of <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> and <italic>Rasal3</italic><sup><italic>−/−</italic></sup> mice (Fig. ##FIG##4##5a##), with an important contribution from CD11b<sup>+</sup>/Ly6G<sup>+</sup> neutrophils in both mutants compared to controls (Fig. ##FIG##4##5b##). Moreover, unsupervised tSNE visualization in <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> mice revealed a significantly reduced proportion and number of both CD4<sup>+</sup> and CD8<sup>+</sup> T cells (populations 1 to 6, Fig. ##FIG##4##5c, d##) in this infiltrate compared to B6 and <italic>Rasal3</italic><sup><italic>−/−</italic></sup> mice (Fig. ##FIG##4##5e, f##) This abnormal cellular infiltration leads to a higher granulocytes to lymphocytes ratio in <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> mice (Fig. ##FIG##4##5g##), suggesting further gene-specific effects of the mutation on the type of cellular infiltrate at the site of inflammation/tissue damage. Taken together, these results indicate an altered response of <italic>Rasal3</italic><sup><italic>−/−</italic></sup> and <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> mice to acute DSS-induced colitis, with higher susceptibility and increased pathology and enhanced inflammatory response. Furthermore, we note unique differences in the composition of the cellular infiltrates in the gut of <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> mutant mice. These results point to both a requirement for RASAL3 and ARHGEF2 in neuroinflammation (in the EAE model), while their absence is associated with increased inflammation in the gut following DSS-induced colitis.</p>", "<p id=\"Par20\">We have previously observed that in other single gene models of immunodeficiency, dysregulated inflammatory response causes not only protection against acute neuroinflammation but also causes increased susceptibility to colitis-associated colorectal cancer (CA-CRC)<sup>##REF##31827213##27##</sup>. Hence, we tested the impact of loss of function for <italic>Rasal3</italic> and <italic>Arhgef2</italic> on response to CA-CRC induced by combined treatment with DSS and the carcinogen azoxymethane (AOM). We note that as opposed to <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> mice that behaved like controls, <italic>Rasal3</italic><sup><italic>−/−</italic></sup> mice showed a significantly greater susceptibility to CA-CRC; this susceptibility was expressed both by an increase in the total number of tumors per colon (Supplementary Fig. ##SUPPL##0##5a, b##), and an increase total tumor surface area (Supplementary Fig. ##SUPPL##0##5c##). Immunohistological studies of colon sections also revealed an exacerbated response in <italic>Rasal3</italic><sup><italic>−/−</italic></sup> mice (as shown by H&E and pSTAT3 staining), with a higher cellular proliferation (identified by KI67<sup>+</sup> cells) and greater infiltration of CD3<sup>+</sup> T cells (Supplementary Fig. ##SUPPL##0##5d##). Overall, <italic>Rasal3</italic><sup><italic>−/−</italic></sup> mice display an enhanced susceptibility to AOM DSS-induced CA-CRC model when compared to B6, with an increase cellular proliferation and T cell infiltration in the gut which is not seen in <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> mice.</p>", "<p id=\"Par21\">Finally, to explore the co-expression of <italic>Ccdc88b</italic> with <italic>Rasal3</italic> or <italic>Arhgef2</italic> mRNAs in colon immune cells we performed single-cell RNA sequencing at steady state. In lymphoid cells (Fig. ##FIG##4##5h, i##), a large fraction of T cells expressing <italic>Ccdc88b</italic> also express <italic>Rasal3</italic> (22.8% of <italic>Ccdc88b</italic><sup>+</sup> cells, Fig. ##FIG##4##5h##) and <italic>Arhgef2</italic> (30.8% of <italic>Ccdc88b</italic><sup><italic>+</italic></sup> cells, Fig. ##FIG##4##5i##). In myeloid cells, an even larger proportion of cells that express <italic>Ccdc88b</italic> also express <italic>Rasal3</italic> (56.0% of <italic>Ccdc88b</italic><sup><italic>+</italic></sup> cells, Fig. ##FIG##4##5j##) and <italic>Arhgef2</italic> (55.9% of <italic>Ccdc88b</italic><sup><italic>+</italic></sup> cells, Fig. ##FIG##4##5k##). Co-expression of both <italic>Ccdc88b</italic> with <italic>Rasal3</italic> and <italic>Arhgef2</italic> in DCs is of particular interest, given that CCDC88B expression was shown to be essential for the mobility and inflammatory function of these cells, both in vitro and in vivo<sup>##REF##32480428##10##</sup>, raising the possibility that RASAL3 and ARHGEF2 may also be involved in migratory properties of these cells.</p>", "<title>Rasal3<sup>−/−</sup> and Arhgef2<sup>−/−</sup> dendritic cells exhibit altered mobility in vivo</title>", "<p id=\"Par22\">We have shown that CCDC88B is required for mobility of myeloid (DCs) and lymphoid cells in vivo<sup>##REF##32480428##10##</sup>. We thus assessed the loss of <italic>Rasal3</italic><sup><italic>−/−</italic></sup> and <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> function on mobility of T cells (Fig. ##FIG##5##6a##). These studies revealed that CD4<sup>+</sup> and CD8<sup>+</sup> T cells from <italic>Ccdc88b</italic><sup><italic>mut</italic></sup> and <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> mutants showed a reduced capacity to home to the inguinal LN. Surprisingly, CD4<sup>+</sup> and CD8<sup>+</sup> T cells from <italic>Rasal3</italic><sup><italic>−/−</italic></sup> showed a greater homing capacity than B6 cells in this model (Fig. ##FIG##5##6a##). Overall, these results suggest that a functional CCDC88B/ARHGEF2/RASAL3 complex is required for mobility of T lymphocytes in vivo.</p>", "<p id=\"Par23\">We also assessed the role of ARHGEF2 and RASAL3 in migration of DCs in vivo. For this, primary BMDCs from B6 controls and from <italic>Rasal3</italic><sup><italic>−/−</italic></sup> or <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> mutant mice were pulsed with LPS in vitro, followed by labeling with different fluorescent dyes (Fig. ##FIG##5##6b, c##). The ratio of labeled B6 to <italic>Rasal3</italic><sup><italic>−/−</italic></sup> DCs was found to be significantly reduced, suggesting enhanced migration of <italic>Rasal3</italic><sup><italic>−/−</italic></sup> DCs compared to B6 (Fig. ##FIG##5##6b, d##). Conversely, the ratio of B6 to <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> DCs was found to be significantly higher, in agreement with decreased migration of <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> DCs (Fig. ##FIG##5##6c, d##), similar to what we previously reported for <italic>Ccdc88b</italic><sup><italic>mut</italic></sup> DCs<sup>##REF##32480428##10##</sup>. Taken together, these results indicate that similarly to CCDC88B, both ARHGEF2 and RASAL3 are required for proper migration of DCs in vivo.</p>", "<p id=\"Par24\">Next, given that RASAL3, ARHGEF2 and CCDC88B all play a role in DCs migration and that they do physically interact in T lymphocytes, we evaluated if these proteins also interact together in DCs. Immunoprecipitation of CCDC88B in BMDCs extracts from the B6, <italic>Ccdc88b</italic><sup><italic>Mut</italic></sup>, <italic>Rasal3</italic><sup><italic>−/−</italic></sup> and <italic>Arhgef2</italic><sup><italic>−/−</italic></sup>showed that both RASAL3 (Fig. ##FIG##5##6e##) and ARHGEF2 (Fig. ##FIG##5##6f##) were pulled down, indicating that both proteins interact with CCDC88B in DCs. A similar immunoprecipitation using <italic>Ccdc88b</italic><sup><italic>Mut</italic></sup> DCs did not pull down either protein (Fig. ##FIG##5##6e, f## and Supplementary Fig. ##SUPPL##0##6a, b##), demonstrating the specificity of the interaction. These results show that the CCDC88B/ARHGEF2/RASAL3 complexes, initially identified in thymocytes and T cells, are required for proper migration of DCs and T cells.</p>", "<title>Rasal3 and Arhgef2 modulate dendritic cells motility in vitro through RHOA activation</title>", "<p id=\"Par25\">We next investigated whether the physical interaction detected between CCDC88B, RASAL3 and ARHGEF2 in DCs translates into functional interaction at the cellular level, including a possible role in DC cells mobility in vitro. For this, we used an in vitro patrolling assay at steady state (Fig. ##FIG##6##7a##); similar to the reduced mobility phenotype previously observed in <italic>Ccdc88b</italic><sup><italic>Mut</italic></sup> DCs<sup>##REF##32480428##10##</sup>, <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> DCs were found to travel a significantly shorter distance than control DCs (Fig. ##FIG##6##7a, b##). Conversely, <italic>Rasal3</italic><sup><italic>−/−</italic></sup> DCs displayed the opposite phenotype with an increased traveled distance compared to controls (Fig. ##FIG##6##7a, b##). This was further reflected by the higher mean speed and maximum speed of <italic>Rasal3</italic><sup><italic>−/−</italic></sup> DCs (5.0 ± 1.8 µm/min and 16.9 ± 6.5 µm/min, respectively) when compared to B6 DCs (4.3 ± 1.7 µm/min and 14.6 ± 5.8 µm/min, respectively); contrariwise, the mean and maximum speeds of <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> DCs were significantly slower (3.7 ± 1.6 µm/min and 13.0 ± 6.2 µm/min, respectively) (Fig. ##FIG##6##7c, d##). Likewise, the arrest coefficient, calculated as the fraction of time that the cell does not move using a threshold of 7 µm/min, was respectively reduced for <italic>Rasal3</italic><sup><italic>−/−</italic></sup> DCs (0.76 ± 0.17 and increased for <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> DCs (0.85 ± 0.15), when compared to B6 control cells (0.82 ± 0.16) (Fig. ##FIG##6##7e##).</p>", "<p id=\"Par26\">ARHGEF2 is known to be required for the regulation of the small GTPase RHOA, which is itself a master regulator of cytoskeleton rearrangement and proper cellular movement<sup>##REF##33126816##28##</sup>. Thus, we next tested the activation status of RHOA-GTP in B6, <italic>Ccdc88b</italic><sup><italic>Mut</italic></sup>, <italic>Rasal3</italic><sup><italic>−/−</italic></sup> and <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> DCs following stimulation with LPS, using Rhotekin-coupled beads (Fig. ##FIG##6##7f## and Supplementary Fig. ##SUPPL##0##6c##). Stimulation of B6 DCs with LPS leads to a rapid and strong activation of RHOA, indicated by the high level of RHOA-GTP (Fig. ##FIG##6##7f##). On the other hand, absence of ARHGEF2 leads to decrease level of activation of RHOA in DCs (Fig. ##FIG##6##7f, g##). Furthermore, <italic>Ccdc88b</italic><sup><italic>Mut</italic></sup> DCs completely failed to activate RHOA (Fig. ##FIG##6##7f, g##). Lastly, and in opposition to <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> DCs, <italic>Rasal3</italic><sup><italic>−/−</italic></sup> DCs exhibited an over-activation of RHOA, with a higher level of RHOA-GTP than B6 control (Fig. ##FIG##6##7f, g##). These results indicate that while CCDC88B is required for the activation of RHOA, proper level of activation is also regulated by ARHGEF2 and, inversely, RASAL3.</p>", "<p id=\"Par27\">Considering our previous demonstration of the requirement of CCDC88B for DC cells mobility<sup>##REF##32480428##10##</sup>, our results strongly suggest that CCDC88B, RASAL3 and ARHGEF2 form part of a multi-protein complex that plays an important role in DCs movement, where CCDC88B acts as a protein scaffold for the recruitment of ARHGEF2 and RASAL3 to the proper localization where they are required to modulate the motility of DCs and T-cells. In agreement with our in vivo data, the opposite effect of inactivation of <italic>Rasal3</italic> and <italic>Arhgef2</italic> on cell mobility raises the possibility that the 2 proteins play an opposite functional or regulatory role in the biochemical activity of this complex. This model is further supported by biochemical data showing that absence of ARHGEF2 (or CCDC88B) leads to reduced RHOA activation (possibly through loss of GTPase activity of ARHGEF2), while absence of RASAL3 leads to over-activation of RHOA (possibly through absence of the RasGAP activity of RASAL3).</p>" ]	[ "<title>Discussion</title>", "<p id=\"Par28\">CCDC88B was discovered as a gene which inactivation protects against acute neuroinflammation in models of cerebral malaria<sup>##REF##25403443##6##</sup>. Subsequently, we showed that (1) <italic>Ccdc88b</italic> inactivation also protects against inflammation in experimental colitis<sup>##REF##29030607##13##</sup>, (2) <italic>CCDC88B</italic> is the human gene underlying the 11q13 locus associated with susceptibility to several inflammatory conditions, including IBD<sup>##REF##25403443##6##,##REF##29030607##13##</sup>, and (3) <italic>CCDC88B</italic> mRNA is regulated by cis-acting elements in human myeloid cells, and <italic>CCDC88B</italic> mRNA expression is positively correlated with disease risk in Crohn’s disease patients<sup>##REF##25403443##6##,##REF##29030607##13##</sup>. Subsequently, we showed that CCDC88B contributes to normal inflammatory response by regulating mobility and migration of myeloid cells (DCs) to the site of inflammation in vivo, and in vitro<sup>##REF##32480428##10##</sup>. At the molecular level, the mechanism by which CCDC88B regulates leukocyte migration remains unknown.</p>", "<p id=\"Par29\">Using a proteomics approach with CCDC88B-expressing primary thymocytes and a T-lymphocyte cell line, we identified ARHGEF2 and RASAL3 as CCDC88B interactors (Fig. ##FIG##0##1##). The physical interaction between CCDC88B and ARHGEF2/RASAL3 was validated by reciprocal co-immunoprecipitation and by double immunofluorescence (Fig. ##FIG##1##2##). Their functional interaction was established in vivo (Figs. ##FIG##2##3## and ##FIG##3##4##): <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> and <italic>Rasal3</italic><sup><italic>−/−</italic></sup> mutants display resistance to neuroinflammation in EAE (alike <italic>Ccdc88b</italic><sup><italic>mut</italic></sup> mice), and show altered inflammatory response during DSS induced colitis. Furthermore, <italic>Ccdc88b</italic>, <italic>Rasal3</italic>,and <italic>Arhgef2</italic> are co-expressed in T cells and DCs (Fig. ##FIG##4##5##). Finally, mutations in <italic>Arhgef2</italic> and <italic>Rasal3</italic> have a dramatic but opposite effect on the migratory properties of myeloid and lymphoid cells in vivo (Fig. ##FIG##5##6##) and in vitro (Fig. ##FIG##6##7##). These studies establish that CCDC88B, ARHGEF2 and RASAL3 are critical for leukocytes mobility and activation of immune and inflammatory responses.</p>", "<p id=\"Par30\">Recent studies have established a close partnership between ARHGEF2 and the Rho family of small GTPases (RHOA) known to regulate contractibility and cell movements<sup>##REF##27738004##29##–##REF##20733052##32##</sup>. ARHGEF2 is a microtubule-associated RHOA-specific nucleotide exchange factor (stimulates conversion of RHOA-GDP to RHOA-GTP)<sup>##REF##9857026##33##</sup> that couples microtubule depolarization with Rho-mediated actin stress fiber formation and cell contraction<sup>##REF##11912491##34##,##REF##18287519##35##</sup>. The activation of ARHGEF2 in response to inflammatory, mitogenic, and morphogenic signals promotes activation of RHOA and stress fiber formation<sup>##REF##11912491##34##</sup>. ARHGEF2 has been implicated in migratory function of fibroblasts, neutrophils, epithelial cells, and T lymphocytes<sup>##REF##27738004##29##–##REF##20733052##32##,##REF##18394899##36##</sup>. In T cells, activation of ARHGEF2 by PPP2R2A is required for Th1/Th17 differentiation, and PPP2R2A deficiency in T cells dampens the ARHGEF2/RHOA/ROCK pathway activation, decreasing EAE pathogenesis<sup>##REF##33762326##37##</sup>. In neutrophils, loss of ARHGEF2 function leads to reduced spreading, crawling, and migration in response to sheer stress<sup>##REF##27738004##29##</sup>. More broadly, ARHGEF2-dependent regulation of cytoskeletal rearrangements have secondary effects on phagocytosis, intracellular pathogens recognition, interaction with bacterial effectors, response to viral RNAs, activation of IRF3, and production of type I interferon<sup>##REF##24270516##38##–##REF##28783414##40##</sup>. Finally, ARHGEF2 overexpression has been associated with tumor progression including hepatocellular carcinoma, high-grade melanomas and malignant megakaryocytes<sup>##REF##11912491##34##,##REF##22847784##41##,##REF##22387001##42##</sup>. Disruption of ARHGEF2/RHOA signaling (removal of BNIP-2 scaffold) affects breast carcinoma cells migration<sup>##REF##32789168##43##</sup>.</p>", "<p id=\"Par31\">In agreement with this published work, we observed that ARHGEF2 forms part of a protein complex with CCDC88B that is required for migration and inflammatory functions of DCs. Indeed, <italic>Arhgef2</italic> mutant DCs show a migratory defect in vivo (homing assay) and show reduced mobility in vitro. <italic>Arhgef2</italic> mutants also show resistance to neuroinflammation in EAE<sup>##REF##25403443##6##,##REF##32480428##10##</sup>. Conversely, <italic>Arhgef2</italic> deficiency causes susceptibility to colitis, expressed as increased pathology score, increased production of inflammatory cytokines (IL6, MCP1, RANTES, C4b), increased infiltration of CD45<sup>+</sup> cells in the lamina propria, with a marked abundance of CD11b<sup>+</sup>/Ly6G<sup>+</sup> neutrophils. Colitis susceptibility in <italic>Arhgef2</italic> mutants is opposite to colitis resistance seen in <italic>Ccdc88b</italic> mutants<sup>##REF##29030607##13##</sup>. The reason for this seemingly contradictory result can only be speculated on at the moment, but could involve different mutation-specific effects on cellular responses at the site of inflammation, with cellular infiltrates of different leukocytes composition including strong granulocytes content in <italic>Arhgef2</italic> vs. <italic>Ccdc88b</italic>. The compensatory granulocytic response during colitis in <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> mice is similar to other mouse mutants with DC-deficiency, including <italic>Irf1</italic> mutants<sup>##REF##31827213##27##</sup>.</p>", "<p id=\"Par32\">Finally, a locus on 1q22 containing 16 genes in linkage disequilibrium is associated with susceptibility to IBD<sup>##REF##23128233##44##</sup>. The reported top-ranking marker (SNP rs670523) in this interval maps close to and is in almost complete LD with SNPs within or proximal to <italic>ARHGEF2</italic> (<italic>r</italic><sup>2</sup> score >0.96). Annotation of this locus using a Myeloid Inflammation Score (MIS)<sup>##REF##25403443##6##,##REF##24973457##45##</sup> shows that <italic>Arhgef2</italic> has the highest MIS (3.4) in the locus interval (Fig. ##FIG##7##8##). These analyses together strongly suggest that <italic>ARHGEF2</italic> is the morbid gene at 1q22 locus (position 155 Mb). Hence, CCDC88B (11q13) and AHRGEF2 (1q22) physically and functionally interact, and are genetically linked to IBD susceptibility in humans.</p>", "<p id=\"Par33\">RASAL3, is a member of the family of RAS GTPase-activating proteins (GAP), which negatively regulates RAS signaling by stimulating hydrolysis of RAS-GTP to RAS-GDP<sup>##REF##23443682##23##</sup>. It has been shown to negatively regulate Ras/Erk signaling by stimulating p21RAS and Rac2 GTPase activity<sup>##REF##23443682##23##</sup>. <italic>Rasal3</italic><sup><italic>−/−</italic></sup> mutant show decreased T cells and NKT cells numbers, display increased Erk phosphorylation and reduced production of cytokines by NKT cells<sup>##REF##25652366##46##</sup>. In T cells, RASAL3 may repress TCR-signaling (ERK phosphorylation) which is required for T cells survival in vivo<sup>##REF##25793935##25##</sup>, and <italic>Rasal3</italic><sup><italic>−/−</italic></sup> mutants display dampened inflammatory allergic dermatitis<sup>##REF##29291408##26##</sup>.</p>", "<p id=\"Par34\">Here, we show that RASAL3 forms a complex with CCDC88B and ARHGEF2 that plays an important role in the migration of DCs. <italic>Rasal3</italic><sup><italic>−/−</italic></sup> mutant DCs show both increased migration in vivo and enhanced mobility in vitro. This is opposite of <italic>Ccdc88b</italic> and <italic>Arhgef2</italic> mutant DCs measured in a similar way, suggesting that RASAL3 may act to negatively regulate cell migration in the context of the CCDC88B/RASAL3/ARHGEF2 complex (Fig. ##FIG##8##9##). These findings are similar to studies of FAM49B/CYRI<sup>##REF##30250061##47##,##REF##31285585##48##</sup>; CYRI binds to RAC1 in the GTP bound form and negatively regulates RAC1 GTPase activity. Inactivating CYRI mutations cause (1) increased mobility/migration of myeloid cells in vitro, (2) increased resistance to <italic>Salmonella typhimurium</italic>, (3) increased susceptibility to <italic>Mycobacterium tuberculosis</italic> and <italic>Listeria monocytogenes</italic>. It is tempting to speculate that RASAL3 may play a role functionally similar to CYRI in Rho/Rac signaling in regulating cell mobility.</p>", "<p id=\"Par35\"><italic>Rasal3</italic> inactivation causes resistance to neuroinflammation in the ECM and EAE models (alike <italic>Ccdc88b</italic> and <italic>Arhgef2</italic> mutants) (Fig. ##FIG##2##3##). In addition, mutant <italic>Rasal3</italic><sup><italic>−/−</italic></sup> mice show enhanced susceptibility to DSS-colitis, which is similar to <italic>Arhgef2</italic> mutants (Fig. ##FIG##3##4##), including enhanced pathological scores, expression of inflammatory cytokine markers, and presence of CD45<sup>+</sup> cellular infiltrate in the lamina propria and dominated by CD11b<sup>+</sup>/Ly6G<sup>+</sup> granulocytes. Hence, although mutations in <italic>Arhgef2</italic> and <italic>Rasal3</italic> have opposite effects on DC migration, they cause similar pathological effects in DSS-colitis, albeit with different leukocytes infiltrates. Also, the <italic>Rasal3</italic> mutation causes a unique susceptibility to colitis-associated colorectal cancer phenotype not seen in the <italic>Ccdc88b</italic> or <italic>Arhgef2</italic> mutants (Supplementary Fig. ##SUPPL##0##5##). Such gene-specific effects could reflect unique function of the protein in T cells, DCs or other cells, or unique aspects of secondary response to inflammatory stimulus.</p>", "<p id=\"Par36\">We propose a model where CCDC88B, ARHGEF2 and RASAL3 form a protein complex functionally critical for migration of lymphoid and myeloid cells. Although RASAL3 and ARHGEF2 are expressed in many cell types, CCDC88B is expressed exclusively in leukocytes possibly providing cell-specific function. Based in part on (1) the known but opposite regulatory role of ARHGEF2 and RASAL3 in Ras/Rho/Rac GTPase and associated signaling, (2) the known role of Rho/Rac GTPase in dynamic cytoskeletal structure, and function in cell migration, and (3) the phenotypic expression and inverse impact of loss of function at either <italic>Arhgef2</italic>, and <italic>Rasal3</italic> on DC cell mobility (Figs. ##FIG##5##6## and ##FIG##6##7##), our findings suggest that ARHGEF2 and RASAL3 play an agonist and antagonist role in regulating the activity of this protein complex in cell movement by modulating the activation status of RHOA, and potentially other GTPases (Fig. ##FIG##8##9##). Finally, our findings in mouse models together with results from genetic association studies in humans for at least 2 of its components (<italic>CCDC88B</italic>, <italic>ARHGEF2</italic>) strongly suggest that this complex and genetic variants within its constituents may impact migration of inflammatory cells, and possibly impact genetic vulnerability to chronic inflammatory diseases in humans.</p>" ]	[]	[ "<p id=\"Par1\"><italic>CCDC88B</italic> is a risk factor for several chronic inflammatory diseases in humans and its inactivation causes a migratory defect in DCs in mice. CCDC88B belongs to a family of cytoskeleton-associated scaffold proteins that feature protein:protein interaction domains. Here, we identified the Rho/Rac Guanine Nucleotide Exchange Factor 2 (ARHGEF2) and the RAS Protein Activator Like 3 (RASAL3) as CCDC88B physical and functional interactors. Mice defective in <italic>Arhgef2 or Rasal3</italic> show dampened neuroinflammation, and display altered cellular response and susceptibility to colitis; <italic>ARHGEF2</italic> maps to a human Chromosome 1 locus associated with susceptibility to IBD. <italic>Arhgef2</italic> and <italic>Rasal3</italic> mutant DCs show altered migration and motility in vitro, causing either reduced (<italic>Arhgef2</italic>) or enhanced (<italic>Rasal3</italic>) migratory properties. The CCDC88B/RASAL3/ARHGEF2 complex appears to regulate DCs migration by modulating activation of RHOA, with ARHGEF2 and RASAL3 acting in opposite regulatory fashions, providing a molecular mechanism for the involvement of these proteins in DCs immune functions.</p>", "<p id=\"Par2\">CCDC88B physically interacts with ARHGEF2 and RASAL3; defective mice show dampened neuroinflammation, altered susceptibility to colitis and altered DCs motility by modulating RHOA, with ARHGEF2 and RASAL3 acting in opposite regulatory fashions.</p>", "<title>Subject terms</title>" ]	[ "<title>Supplementary information</title>", "<p>\n\n\n\n\n</p>" ]	[ "<title>Supplementary information</title>", "<p>The online version contains supplementary material available at 10.1038/s42003-023-05751-9.</p>", "<title>Acknowledgements</title>", "<p>We thank Geneviève Perreault, Suzan Gauthier, the Cell Vision Core Facility and the Advanced BioImaging Facility (ABIF) at McGill University. This work was supported in part by research grants to P.G. from the Canadian Institutes for Health Research (CIHR, Foundation grant), and the Canadian Cancer Research Society. P.G. is supported by a Distinguished James McGill Professorship award from McGill University. D.L. is supported by a research grant from the CIHR and a Junior Investigator award from the Fonds de Recherche du Québec Santé (FRQS). This work benefited from collaboration with the National Center for Dynamic Interactome Research (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.ncdir.org\">www.ncdir.org</ext-link>) leveraging funds from the National Institute of General Medical Sciences of the National Institutes of Health (NIH), grant P41GM109824; J.L. is funded in-part by NIH grant R01GM126170.</p>", "<title>Author contributions</title>", "<p>J.-F.O., D.L., J.L., P.G. and N.F. designed the study; J.-F.O., D.L., T.J., M.J.P., L.G., H.J., K.R.M., G.X., J.L. and N.F. performed the experiments; J.-F.O., D.L., T.J., R.C., H.J., J.L. and N.F. analyzed the data; J.-F.O., D.L., J.L., P.G. and N.F. wrote the initial draft and J.-F.O., D.L., R.C., H.S., J.L., P.G. and N.F. contributed to the final draft of the manuscript and provided expertise.</p>", "<title>Peer review</title>", "<title>Peer review information</title>", "<p id=\"Par57\">This manuscript has been previously reviewed at another Nature Portfolio journal. The manuscript was considered suitable for publication without further review at <italic>Communications Biology</italic>. Handling Editor: Christina Karlsson Rosenthal.</p>", "<title>Data availability</title>", "<p>The MS proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE<sup>##REF##30395289##62##</sup> partner repository with the dataset PXD023779. The protein interactions from this publication have been submitted to the IMEx (<ext-link ext-link-type=\"uri\" xlink:href=\"http://www.imexconsortium.org\">http://www.imexconsortium.org</ext-link>) consortium through IntAct<sup>##REF##24234451##63##</sup> and assigned the identifier IM-28785. The raw single-cell RNA sequencing data can be found on the Gene Expression Omnibus Repository (GEO)<sup>##REF##11752295##60##</sup> with the accession code GSE249342. Any other data that support the findings of this study, including additional MS/MS data (see Supplementary Data ##SUPPL##2##1##), are available within the article, its ##SUPPL##0##Supplementary Information## and from the corresponding author upon reasonable request.</p>", "<title>Competing interests</title>", "<p id=\"Par58\">The authors declare no competing interests.</p>" ]	[ "<fig id=\"Fig1\"><label>Fig. 1</label><caption><title>Identification of CCDC88B-interacting proteins by LC-MS/MS.</title><p><bold>a</bold> Co-immunoprecipitation of CCDC88B from whole thymus extract with anti-CCDC88B anti-serum analyzed by gel electrophoresis. The gel was stained with Sypro, nine bands were excised and submitted to LC-MS/MS (see “Results”). The four most abundant proteins (spectral count >20) are identified in orange. <bold>b</bold> Schematic representation of the experimental procedure used for proteomics analysis. Proteins were extracted from whole thymus or BI-141 T cell line using three different lysis buffers, then submitted to immunoprecipitation using an anti-CCDC88b polyclonal anti-serum. Eluates were subjected to LC-MS/MS to identify and quantify enriched proteins. The results were analyzed using multiple filtering parameters to identify the most probable CCDC88B interactors. An independent experiment was performed in duplicate on whole thymic extracts. <bold>c</bold> Graph presenting LC-MS/MS quantitative results. The percentage of protein length that was covered by MS/MS identified peptides is plotted against the number of unique peptides identified, whereas the bubble size is proportional to a score (arbitrary units) based on filtering parameters (see Supplementary Data ##SUPPL##2##1##); the two best candidates (along with CCDC88B) are drawn in orange. Parts of (<bold>b</bold>) were drawn by using pictures from Servier Medical Art. Servier Medical Art by Servier is licensed under a Creative Commons Attribution 3.0 Unported License (<ext-link ext-link-type=\"uri\" xlink:href=\"https://creativecommons.org/licenses/by/3.0/\">https://creativecommons.org/licenses/by/3.0/</ext-link>).</p></caption></fig>", "<fig id=\"Fig2\"><label>Fig. 2</label><caption><title>Validation of physical interaction between CCDC88B, ARHGEF2 and RASAL3.</title><p><bold>a</bold> Co-immunoprecipitation demonstrating interactions between CCDC88B, RASAL3 and ARHGEF2. HEK293T cells were transiently transfected with the indicated expression vectors and cells extracts were subjected to immunoprecipitation with an anti-FLAG monoclonal antibody. Immunoprecipitated proteins (IP FLAG) and total lysates (Input) were analyzed by gel electrophoresis (SDS-PAGE) and subjected to immunoblotting using an anti-HA monoclonal antibody to reveal HA-tagged CCDC88B. <bold>b</bold>, <bold>c</bold> HEK293T cells transfected with the indicated expression vectors were subjected to immunoprecipitation with an anti-CCDC88B polyclonal anti-serum. Immunoprecipitated proteins (IP CCDC88B) and total lysates (Input) were analyzed by gel electrophoresis and subjected to immunoblotting using an anti-FLAG monoclonal antibody to reveal FLAG tagged RASAL3 and ARHGEF2 (<bold>b</bold>, <bold>c</bold> respectively). <bold>d</bold>, <bold>e</bold> Double immunofluorescence of HA-CCDC88B and RASAL3-FLAG or ARHGEF2-FLAG co-expressed in transiently transfected HEK293T cells, respectively. Tagged proteins were revealed by combinations of anti-FLAG, anti-CCDC88B, anti-HA, and anti-ARHGEF2 (with corresponding fluorophores) as indicated in the boxes. Images were acquired by confocal microscopy, insert scale bar = 1 μm. Closeups in inserts show the extent of colocalization near the cell membrane.</p></caption></fig>", "<fig id=\"Fig3\"><label>Fig. 3</label><caption><title>Loss of RASAL3 and ARHGEF2 reduces susceptibility to neuroinflammation.</title><p>Survival of B6, <italic>Ccdc88b</italic><sup><italic>Mut</italic></sup> (<bold>a</bold>), <italic>Rasal3</italic><sup><italic>−/−</italic></sup> (<bold>b</bold>), and <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> (<bold>c</bold>) mutant mice following infection with <italic>Plasmodium berghei</italic> ANKA (PbA). The total number of mice in each group is indicated; animals surviving beyond day 8 are considered resistant to PbA-induced lethal neuroinflammation (n.s. non-significant, **<italic>p</italic> < 0.001, Gehan-Breslow-Wilcoxon test). <bold>d</bold>–<bold>f</bold> Experimental autoimmune encephalomyelitis (EAE) was induced by combined treatment with pertussis toxin and myelin oligodendrocyte protein and the extent of pathogenesis (clinical score) was assessed daily as described in “Materials and methods”. Clinical scores (left) and EAE incidence, representing the highest score reached by each individual mouse over the course of the experiment (right), of B6, <italic>Ccdc88b</italic><sup><italic>Mut</italic></sup>, <italic>Rasal3</italic><sup><italic>−/−</italic></sup> and <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> mice are shown, along with the number of mice used in each group (clinical scores shown as means ± SEM; <italic>p</italic> < 0.05, *<italic>p</italic> < 0.01; Mann–Whitney test). Data from (<bold>d</bold>) to (<bold>f</bold>) are representative of at least three independent experiments where each group were compared to the same controls within the same experiment. <bold>g</bold>, <bold>h</bold> EAE was induced in B6, <italic>Ccdc88b</italic><sup><italic>Mut</italic></sup>, <italic>Rasal3</italic><sup><italic>−/−</italic></sup> and <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> mice and the spinal cords harvested at day 11. Total CD45<sup>+</sup>CD3<sup>+</sup>CD4<sup>+</sup> T cells (<bold>g</bold>) and total CD45<sup>+</sup>CD11c<sup>High</sup>MHCII<sup>High</sup> antigen presenting cells (<bold>h</bold>) were analyzed by flow cytometry. Representative flow cytometry data (normalized for 10 million viable cells; left) and quantification of individual mice (right) for each genotype are shown; data from (<bold>g</bold>) are gated first on total viable CD45<sup>+</sup>CD3<sup>+</sup> cells and data from (<bold>h</bold>) are gated first on total viable CD45<sup>+</sup> cells (see Supplementary Fig. ##SUPPL##0##3##). Control: B6 mice without EAE induction.</p></caption></fig>", "<fig id=\"Fig4\"><label>Fig. 4</label><caption><title>Loss of RASAL3 or ARHGEF2 enhances pathogenesis of DSS-induced colitis.</title><p>Control B6, as well as <italic>Rasal3</italic><sup><italic>−/−</italic></sup> and <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> mutant mice were given 3% DSS for 5 days followed by 3 days of water and were then sacrificed. <bold>a</bold> Body weight loss, expressed as percent of initial weight, for each genotype (means ± SEM, <italic>p</italic> < 0.05, <italic>p</italic> < 0.01; two-tailed Student’s <italic>t</italic> test). <bold>b</bold> Representative images of colons from B6, <italic>Rasal3</italic><sup><italic>−/−</italic></sup> and <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> mice at day 8. <bold>b</bold> Quantification of colons length (means ± SEM. <italic>p</italic> < 0.001; two-tailed Student’s <italic>t</italic> test). Data from (<bold>a</bold>) to (<bold>c</bold>) are representative of three independent experiments. <bold>d</bold> Pathology scores evaluating inflammatory cell infiltration, submucosal edema, gland loss and surface epithelial erosion/ulceration (mean ± SEM; <italic>p</italic> < 0.05, *<italic>p</italic> < 0.01; two-tailed Student’s <italic>t</italic> test). qPCR data for the relative expression for <italic>Ccdc88b</italic> (<bold>e</bold>) as well as indicated genes coding for cytokines, chemokines, and myeloid cell markers (<bold>f</bold>) in distal colons of non-treated (Control) or at day 8 following DSS treatment (DSS). Data represent expression relative to <italic>Hprt</italic> which was used as an internal control (means ± SEM, <italic>p</italic> < 0.05, <italic>p</italic> < 0.01; <italic>p</italic> < 0.001; two-tailed Student’s <italic>t</italic> test). <bold>g</bold> Immunohistochemistry staining of colons of B6, <italic>Rasal3</italic><sup><italic>−/−</italic></sup> and <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> mice at day 8 following DSS treatment for the indicated markers. Insert bar: 100 µm. Data are representative of at least 5 colons per group. <bold>h</bold> Quantification of (<bold>g</bold>), for the average of the two most distal field of view of each colon (at 10X, using automated counting, see “Materials and methods”). Each data point represents an individual mouse (means ± SEM, n.s. non-significant; <italic>p</italic> < 0.05, *<italic>p</italic> < 0.01; two-tailed Student’s <italic>t</italic> test).</p></caption></fig>", "<fig id=\"Fig5\"><label>Fig. 5</label><caption><title>Cellular infiltrate in the lamina propria of <italic>RASAL3</italic><sup><italic>−/−</italic></sup> and <italic>ARHGEF2</italic><sup><italic>−/−</italic></sup> mice following DSS-induced colitis.</title><p>Control B6, as well as <italic>Rasal3</italic><sup><italic>−/−</italic></sup> and <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> mutant mice were given 3% DSS for 5 days followed by 3 days of water and were then sacrificed. <bold>a</bold>, <bold>b</bold> Percentage of total CD45<sup>+</sup> cells and neutrophils in the lamina propria from B6, <italic>Rasal3</italic><sup><italic>−/−</italic></sup> and <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> mice colon at day 8 following DSS treatment, respectively (mean ± SEM; n.s. non-significant, <italic>p</italic> < 0.05, <italic>p</italic> < 0.01, <italic>p</italic> < 0.001; two-tailed Student’s <italic>t</italic> test). <bold>c</bold> tSNE-based visualization of the different CD45<sup>+</sup> cell populations found in the lamina propria, colored and numbered as per the legend (pooled analysis from all genotypes). <bold>d</bold> Same visualization as in (<bold>c</bold>), but for B6 (left), <italic>Rasal3</italic><sup><italic>−/−</italic></sup> (middle) and <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> (right) mice colon at day 8 following DSS treatment, with the highest cellular density in red. Inserts depict regions of the tSNE plots corresponding to the CD8 and CD4 T cells clusters. <bold>e</bold>, <bold>f</bold> Percentage of CD4<sup>+</sup> and CD8<sup><bold>+</bold></sup> T cells from (<bold>d</bold>), respectively (mean ± SEM; <italic>p</italic> < 0.05, <italic>p</italic> < 0.01; two-tailed Student’s <italic>t</italic> test). <bold>g</bold> Ratio of total number of granulocytes vs. total number of lymphocytes from (<bold>d</bold>) (mean ± SEM; n.s. non-significant, <italic>p</italic> < 0.01; two-tailed Student’s <italic>t</italic> test). tSNE-based visualization of single-cell RNAseq data from B6 lamina propria cells obtained at steady state for indicated lymphoid (<bold>h</bold>, <bold>i</bold>) and myeloid (<bold>j</bold>, <bold>k</bold>) populations. Expression of <italic>Ccdc88b</italic> is indicated in green and level of expression of either <italic>Rasal3</italic> (<bold>h</bold>, <bold>j</bold>) or <italic>Arhgef2</italic> (<bold>i</bold>, <bold>k</bold>) in red, as indicated in the legend. Indicated percentages represent the fraction of cells co-expressing RNA for <italic>Rasal3</italic> or <italic>Arhgef2</italic> with <italic>Ccdc88b</italic>, for the indicated populations.</p></caption></fig>", "<fig id=\"Fig6\"><label>Fig. 6</label><caption><title>RASAL3 and ARHGEF2 are required for migration of dendritic cells in vivo.</title><p><bold>a</bold> Total spleen cells from control B6 and either <italic>Ccdc88b</italic><sup><italic>mut</italic></sup>, <italic>Rasal3</italic><sup><italic>−/−</italic></sup> or <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> mutants were stained with CMFDA or CMTMR dye, mixed at a 1:1 ratio and injected intravenously into B6 mice. Inguinal lymph nodes (LNs) were harvested 6 h later and analyzed by flow cytometry for number of CD4<sup>+</sup> or CD8<sup>+</sup> T cells. Numbers are expressed as a fold enrichment of dyed B6 T cells over dyed cells from <italic>Ccdc88b</italic><sup><italic>mut</italic></sup>, <italic>Rasal3</italic><sup><italic>−/−</italic></sup>, <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> mutants or control B6 (means ± SEM, <italic>p</italic> < 0.01, <italic>p</italic> < 0.001; two-tailed Student’s <italic>t</italic> test). <bold>b</bold>, <bold>c</bold> Control B6 and either <italic>Rasal3</italic><sup><italic>−/−</italic></sup> or <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> LPS pulsed BMDCs were stained with CMFDA or CMTMR dye, mixed together at a 1:1 ratio and injected sub-cutaneously in the footpad of B6 mice. Draining popliteal LNs were harvested 48 h later and analyzed by flow cytometry (plot gated on CMFDA<sup>+</sup> and CMTMR<sup>+</sup> dyed cells). <bold>d</bold> Quantification from (<bold>b</bold>) and (<bold>c</bold>), expressed as a fold enrichment of dyed B6 BMDCs over dyed <italic>Rasal3</italic><sup><italic>−/−</italic></sup>, <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> or control B6 BMDCs (means ± SEM, <italic>p</italic> < 0.01, <italic>p</italic> < 0.001; two-tailed Student <italic>t</italic> test). Data from (<bold>a</bold>) to (<bold>d</bold>) are representative of three independent experiments. <bold>e</bold>, <bold>f</bold> Lysates from B6, <italic>Ccdc88b</italic><sup><italic>Mut</italic></sup>, <italic>Rasal3</italic><sup><italic>−/−</italic></sup> and <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> BMDCs were immunoprecipitated with an anti<sup>−</sup>CCDC88B polyclonal anti-serum, and RASAL3 or ARHGEF2 proteins were detected by immunoblotting. Data from (<bold>e</bold>) and (<bold>f</bold>) are representative of two independent experiments.</p></caption></fig>", "<fig id=\"Fig7\"><label>Fig. 7</label><caption><title>Altered motility parameters of <italic>Rasal3</italic><sup><italic>−/−</italic></sup> and <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> dendritic cells.</title><p>BMDCs generated from B6, <italic>Rasal3</italic><sup><italic>−/−</italic></sup> and <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> mutants were imaged by bright-field time-lapse microscopy for 90 min and tracked using the TrackMate plugin from the ImageJ software, with manual correction. <bold>a</bold> Representative field of view of B6, <italic>Rasal3</italic><sup><italic>−/−</italic></sup> and <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> BMDC cultures with the tracking results over the course of the experiment. <bold>b</bold> Normalized Rose plot depicting 25 randomly chosen track of individual B6 controls (blue), <italic>Rasal3</italic><sup><italic>−/−</italic></sup> (green) or <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> (red) DCs. <bold>c</bold> Average velocity of individual BMDCs from control B6, <italic>Rasal3</italic><sup><italic>−/−</italic></sup> and <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> DCs mutants (means ± SD, <italic>p</italic> < 0.001; two-tailed Student’s <italic>t</italic> test). <bold>d</bold> Maximum speed reached by individual DCs (means ± SD, <italic>p</italic> < 0.001; two-tailed Student’s <italic>t</italic> test). <bold>e</bold> Arrest coefficient using a threshold of 7 µm/min for actively patrolling DCs (means ± SD, <italic>p</italic> < 0.01, ***<italic>p</italic> < 0.001; two-tailed Student’s <italic>t</italic> test). Data from (<bold>a</bold>) to (<bold>e</bold>) were processed using a homemade MatLab script and are representative of three independent experiments, taking at least two separated field of view (0.314 mm<sup>2</sup> per field of view) per DCs culture. <bold>f</bold> BMDCs from B6, <italic>Ccdc88b</italic><sup><italic>Mut</italic></sup>, <italic>Rasal3</italic><sup><italic>−/−</italic></sup> and <italic>Arhgef2</italic><sup><italic>−/−</italic></sup> mutants were stimulated with LPS and protein lysate analyzed for the level of active RhoA-GTP using Rhotekin-RBD (Rho Binding Domain) beads. As control, some BMDCs were loaded with high concentration of non-hydolyzable GTP (GTPγS, positive control) or GDP (negative control) prior to pull-down. <bold>g</bold> Quantification of (<bold>f</bold>), normalized to the total amount of RhoA expression, for two independent experiments.</p></caption></fig>", "<fig id=\"Fig8\"><label>Fig. 8</label><caption><title><italic>ARHGEF2</italic> containing IBD risk locus on chromosome 1 (1q22).</title><p>The single nucleotide polymorphism (SNP) rs670523 was previously associated to IBD by GWAS<sup>##REF##23128233##44##</sup>. The graph shows the linkage disequilibrium structure of the proxy SNPs to rs670523 (dark gray) calculated using LDlink<sup>##REF##26139635##64##</sup> as <italic>r</italic><sup>2</sup> values based on the 1000 Genomes phase 3v5 CEU + GBR European reference haplotypes (left <italic>y</italic> axis). The proxy SNPs are color-coded according to their <italic>r</italic><sup>2</sup> values and positioned at their chromosomal location (<italic>x</italic>-axis) over the genes found at this locus. The right <italic>y</italic>-axis shows the results of the MIS (Myeloid Inflammatory Score) epigenetic scoring method that we previously described<sup>##REF##25403443##6##</sup> and designed to prioritize candidate genes involved in inflammatory processes in myeloid cells based on functional genomics information. The MIS for each gene is shown by a blue bubble over its transcriptional start site with a diameter proportional to the MIS. <italic>ARHGEF2</italic> is found in strong LD with rs670523 and has the highest MIS within this IBD risk locus.</p></caption></fig>", "<fig id=\"Fig9\"><label>Fig. 9</label><caption><title>CCDC88B, ARHGEF2 and RASAL3 complex.</title><p>Proposed interaction of CCDC88B with ARHGEF2 and RASAL3 in the context of cellular mobility (left), or in the absence of ARGHEF2 (middle) or RASAL3 (right), and their downstream target, RhoA. GEF guanine nucleotide exchange factor, GAP GTPase-activating protein.</p></caption></fig>" ]	[]	[]	[]	[]	[]	[]	[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM2\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM3\"></supplementary-material>", "<supplementary-material content-type=\"local-data\" id=\"MOESM4\"></supplementary-material>" ]	[ "<fn-group><fn><p><bold>Publisher’s note</bold> Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>These authors contributed equally: Jean-Frederic Olivier, David Langlais.</p></fn><fn><p>These authors jointly supervised this work: John LaCava, Philippe Gros, Nassima Fodil.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"42003_2023_5751_Fig1_HTML\" id=\"d32e640\"/>", "<graphic xlink:href=\"42003_2023_5751_Fig2_HTML\" id=\"d32e724\"/>", "<graphic xlink:href=\"42003_2023_5751_Fig3_HTML\" id=\"d32e950\"/>", "<graphic xlink:href=\"42003_2023_5751_Fig4_HTML\" id=\"d32e1415\"/>", "<graphic xlink:href=\"42003_2023_5751_Fig5_HTML\" id=\"d32e1766\"/>", "<graphic xlink:href=\"42003_2023_5751_Fig6_HTML\" id=\"d32e2122\"/>", "<graphic xlink:href=\"42003_2023_5751_Fig7_HTML\" id=\"d32e2434\"/>", "<graphic xlink:href=\"42003_2023_5751_Fig8_HTML\" id=\"d32e2803\"/>", "<graphic xlink:href=\"42003_2023_5751_Fig9_HTML\" id=\"d32e2880\"/>" ]	[ "<media xlink:href=\"42003_2023_5751_MOESM1_ESM.pdf\"><caption><p>Supplementary Information</p></caption></media>", "<media xlink:href=\"42003_2023_5751_MOESM2_ESM.docx\"><caption><p>Description of Supplementary Materials</p></caption></media>", "<media xlink:href=\"42003_2023_5751_MOESM3_ESM.xlsx\"><caption><p>Supplementary Data 1</p></caption></media>", "<media xlink:href=\"42003_2023_5751_MOESM4_ESM.pdf\"><caption><p>Reporting Summary</p></caption></media>" ]	[{"label": ["49."], "mixed-citation": ["LaCava, J., Jiang, H. & Rout, M. P. Protein complex affinity capture from cryomilled mammalian cells. "], "italic": ["J. Vis. Exp."], "bold": ["118"]}, {"label": ["56."], "surname": ["Cox"], "given-names": ["J"], "article-title": ["Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, termed MaxLFQ"], "source": ["Mol. Cell Proteom."], "year": ["2014"], "volume": ["13"], "fpage": ["2513"], "lpage": ["2526"], "pub-id": ["10.1074/mcp.M113.031591"]}]	{ "acronym": [], "definition": [] }	64	CC BY	no	2024-01-13 00:02:20	Commun Biol. 2024 Jan 10; 7:77	oa_package/2d/ac/PMC10781698.tar.gz
PMC10781699	38200086	[ "<title>Introduction</title>", "<p id=\"Par2\">In developing countries, rapid industrial growth, economic expansion, and urbanization contribute significantly to increased environmental pollution, posing a national and international concern<sup>##REF##33858386##1##–##UREF##1##3##</sup>. Activities from industries like petrochemicals and heavy automotive production release pollutants, including toxic metals, organic pollutants, pesticides, microplastics, and emerging pollutants, threatening groundwater resources and human health, environmental services, and sustainable development<sup>##REF##34499897##4##</sup>. Recent studies worldwide report groundwater contamination with lead, iron, manganese, cadmium, copper, and chromium<sup>##REF##34499897##4##</sup>. Cadmium contamination, a global issue mainly affecting Asia and Africa, poses risks to food and water supplies. Cd, even at low concentrations, is highly toxic, leaching into the soil and bio-accumulating in ecosystems<sup>##UREF##2##5##</sup>.</p>", "<p id=\"Par3\">The global concern regarding the presence of heavy metals in the environment has significantly grown due to their potential negative impacts on human health. These metals are well-known toxins that can cause organ damage and exhibit teratogenic and carcinogenic effects<sup>##REF##37740101##6##,##UREF##3##7##</sup>. Groundwater contamination with heavy metals has been linked to severe health implications, including kidney damage, degenerative neurological conditions, respiratory and cardiovascular diseases, and cancer<sup>##UREF##4##8##,##UREF##5##9##</sup>. Because of their persistent nature, potential toxic elements (PTEs) tend to accumulate in groundwater, constituting a primary route of exposure for humans<sup>##REF##34890596##10##</sup>. Given these severe health risks, public authorities regularly monitor the concentration of various PTEs to mitigate potential hazards to public health. Building upon this backdrop of groundwater's significance and the challenges it confronts, the current study delves into the environmental and human health risks associated with heavy metal contamination in Siwa Oasis.</p>", "<p id=\"Par4\">While essential for metabolism, copper (Cu), zinc (Zn), iron (Fe), and manganese (Mn) can become hazardous when their levels in drinking water exceed permissible limits. Heavy metals can enter the human body through various pathways, including oral consumption, dermal contact, and inhalation<sup>##REF##28643849##11##–##UREF##7##13##</sup>. These contaminants can be found in drinking water sources, such as surface water and groundwater<sup>##UREF##8##14##,##UREF##9##15##</sup>, vegetables, and air<sup>##REF##35669798##16##</sup>. Metals in the environment can be attributed to industrial, agricultural, domestic, medical, and technological activities. When the levels of heavy metals in drinking water exceed the limits set by international organizations, it can lead to various health problems<sup>##UREF##4##8##</sup>. Ensuring the protection of the environment and human health is crucial, and this involves assessing water quality. The first step in this process is evaluating water quality and identifying pollutant sources to mitigate pollution levels. Effective methods for evaluating the environmental and human health risks associated with heavy metals include the heavy metal pollution index (HPI), metal index (MI), hazard quotient (HQ), hazard index (HI), and carcinogenic risk (CR), integrated with Monte Carlo simulation<sup>##UREF##0##2##–##REF##34499897##4##,##UREF##5##9##,##REF##35669798##16##–##UREF##11##20##</sup>. Furthermore, cluster analysis and principal component analysis (PCA) are valuable tools for classifying the sources of heavy metals and understanding hydrochemical processes in surface water and groundwater<sup>##UREF##8##14##,##UREF##12##21##–##UREF##14##23##</sup>. Contaminated water resources pose severe threats to humans and animals, giving rise to major biological and chemical concerns. Groundwater resources worldwide are increasingly affected by depletion and pollution, and this issue extends to Egyptian deserts, where Siwa Oasis heavily relies on groundwater for drinking and irrigation<sup>##UREF##15##24##</sup>. Statistics reveal that over one billion people lack access to safe drinking and irrigation water, resulting in approximately 25,000 annual deaths in developing countries<sup>##UREF##16##25##</sup>.</p>", "<p id=\"Par5\">Siwa Oasis, located in Egypt's northwestern desert, mainly relies on groundwater for drinking and irrigation<sup>##UREF##15##24##</sup>. Groundwater sources within the Oasis Oasis include groundwater from aquifers like the Nubian sandstone aquifer and shallow aquifers like the Tertiary carbonate aquifer. The groundwater supports agricultural activities and domestic use in Siwa Oasis<sup>##UREF##17##26##</sup>. Salt lakes within the OasisOasis serve as outlets for water originating from cultivated lands, natural springs, and artesian wells. The growing demand for groundwater in Siwa Oasis, driven by population growth, agriculture, and tourism, has led to the establishment of numerous wells. However, the random placement of these wells and excessive extraction may lead to decreased water pressure and quality. Unauthorized drilling further exacerbates these issues, emphasizing the need for comprehensive studies to monitor the quantity and quality of this limited water resource<sup>##UREF##18##27##</sup>.</p>", "<p id=\"Par6\">This study aims to comprehensively investigate the environmental and human health risks associated with eight heavy metals in various water resources of Siwa Oasis. The objectives are: (1) Recognize the potential sources using the Spearman correlation matrix, principal component analysis, cluster analysis, and kriging interpolation. (2) Determine the geochemical processes controlling the water chemistry. (3) Applying an innovative approach through integrating several water quality indices (HPI, MI, HQ, HI, and CR) with deterministic and probabilistic (Monte Carlo simulation) methods to assess carcinogenic and non-carcinogenic health risks associated with heavy metal contamination in Siwa Oasis. (4) Using Python programming code to facilitate Monte Carlo simulations, offering precision and efficiency. Extensive libraries and statistical capabilities enable the handling of uncertainty and variability in input parameters, giving more accurate risk estimations. This integration denotes a significant improvement in the assessment of heavy metal pollution.</p>" ]	[ "<title>Materials and methods</title>", "<title>Study area description</title>", "<p id=\"Par7\">Siwa Oasis, located in the western desert of Egypt, is a depression that the Mediterranean Sea surrounds to the north, the Libya-Egypt border to the west, and Cairo to the east (Fig. ##FIG##0##1##). It is positioned at latitude 29°12' N and longitude 25°43' E. The main economic activities in this OasisOasis include agriculture, with palm trees, olives, and various fruits and vegetables being cultivated. Industrial pursuits such as bottling mineral water and extracting olive oil are also prominent<sup>##UREF##19##28##</sup>. Siwa Oasis covers a land area of 1100 square kilometers and had a population of around 23,546 as of 2010. The climate in Siwa Oasis is characterized by dryness with a high evaporation rate of 16.8 mm per day, which decreases to around 5.4 mm per day during winter. Precipitation in the area is minimal, with a rainfall of about 10 mm<sup>##UREF##20##29##</sup>. This arid climate and its isolation and limited water resources present unique challenges and opportunities for residents and industries in Siwa Oasis<sup>##UREF##21##30##</sup>.</p>", "<title>Geology and water resources of Siwa depression</title>", "<p id=\"Par8\">The Siwa Oasis has a landscape with different layers of rocks. These include deposits like dunes and salt flats and older layers from the Middle Eocene period made up of limestone and shale (Fig. ##FIG##1##2##a,b). There are also layers from different geological eras, such as the Palaeozoic, Mesozoic, and Cainozoic<sup>##UREF##22##31##</sup>. Regarding water supply, Siwa has five aquifers ranging from ones in deposits to a deeper one called the Nubian sandstone aquifer (Fig. ##FIG##1##2##c). The primary irrigation and domestic use source come from the Miocene aquifer (Tertiary carbonate aquifer), while NSSA (deep aquifer) is mainly used for drinking. However, there are challenges in this region, including soil salinization and waterlogging that mainly occur near salt lakes like Zeitoun, Aghormi, Siwa, and Maraqi. Although these lakes receive water from the groundwater, they have salinity levels, which makes their water unsuitable for domestic or aquatic purposes. Proper management of water resources is crucial to address these issues and ensure the use of the oasis water reserves<sup>##UREF##23##32##</sup>.</p>", "<title>Sampling and analysis of physicochemical parameters and heavy metals</title>", "<p id=\"Par9\">In February 2022, a field trip was conducted where 133 water samples were collected, including 113 samples from the Tertiary carbonate aquifer (TCA), eight samples from springs, and 12 samples from lakes and drains in polyethylene bottles. These samples were then analyzed chemically at the Desert Research Center in Egypt and Miskolc University in Hungary. During the fieldwork, the pH and electrical conductivity (EC) were measured using portable meters with daily calibration. pH measurements were taken with a WTW model LF 538 pH meter. Electrical conductivity was measured using a YSI model 35 conductivity meter. The methods of Rainwater Thatcher and Friedman were employed. The alkali metal ions (Na<sup>+</sup> and K<sup>+</sup>) were measured using flame photometry using a standard curve. The hardness (TH) was determined through EDTA procedures, while CO<sub>3</sub><sup>2−</sup> and HCO<sub>3</sub><sup>−</sup> were analyzed volumetrically. By considering TH and Ca<sup>2+</sup> contents, the concentration of Mg<sup>2+</sup> was calculated. To estimate chloride levels accurately, AgNO<sub>3</sub> titration was employed. To uphold precision, every sample underwent duplicate analysis. In cases where the discrepancy between the total cations and anions surpassed 5%, the sample analysis was reiterated until a satisfactory percentage difference was achieved. The precision and dependability of the results were assured through the utilization of flame photometry and spectrophotometry methods. All chemical data were expressed in mg/L units with a measurement precision assessed through the ionic balance error (IBE) within ± 5%. The total dissolved solids (TDS) were calculated by adding the ions. The ionic balance (IB) was then determined by comparing the percentages of cations and anions with a range, for IB being within ± 5%. Lastly, ICP was utilized to measure the concentrations of heavy metals. Software including surfer 16.6.484 and ArcGIS Pro 2.8.8 were used to create the distribution maps of the sampling location and investigated parameters.</p>", "<title>Cluster analysis (CA)</title>", "<p id=\"Par10\">Cluster Analysis (CA) is a technique used for recognizing patterns in datasets from various sources without supervision. CA identifies features that differentiate groups within the dataset and clusters them accordingly. Both R mode (Row mode) and Q mode (Column mode) approaches have been employed to execute and construct CA. These approaches help create clusters of water samples with characteristics enabling the identification of spatial similarities and grouping of sampling stations based on their hydrogeochemical properties<sup>##UREF##25##34##</sup>. CA is a tool used in categorizing processes in groundwater (GW), particularly in hydrochemistry investigations, as it plays a crucial role in grouping collected water samples into meaningful geological and hydrogeological categories. A cluster dendrogram is often utilized to represent the clustering process and simplify the complexity of the data, providing a clear depiction of groupings and their relationships<sup>##UREF##25##34##</sup>.</p>", "<title>Principal component analysis (PCA)</title>", "<p id=\"Par11\">Principal Component Analysis (PCA) is a technique that handles complex multivariate datasets in a linear structure. Its purpose is to analyze data without losing any information while summarizing the dataset and estimating the number of variables needed to explain the variance<sup>##UREF##24##33##</sup>. By reducing the dimensionality of the data, PCA uncovers hidden patterns and relationships between variables that may not be immediately apparent. To assess groundwater (GW) contamination, the Kaiser Criterion uses eigenvalues from the scree plot to extract principal components. Additionally, the suitability of data for factor analysis is evaluated through tests like Kaiser Meyer Olkin (KMO) and Bartlett's tests, which indicate whether variables are adequate or inadequate within the model. KMO values falling within the ranges of 0.8 to 1, 0.5 to 0.8, and less than 0.5 indicate sufficient, reasonably adequate, and undesirable (or inadequate) data appropriateness, respectively. This comprehensive approach helps researchers gain valuable insights from complex datasets while ensuring that the data adequately represent the underlying relationships<sup>##REF##35395310##36##</sup>.</p>", "<title>Heavy metal pollution index (HPI) and metal index (MI)</title>", "<p id=\"Par12\">The Heavy Metal Pollution Index (HPI) is a tool applied for evaluating the contamination levels of water resources by heavy metals<sup>##UREF##27##37##</sup>. This index is especially effective in determining if the quality of water is suitable for consumption, considering the presence of heavy metals. The HPI is determined through a method that involves assigning ratings to parameters related to pollution and then calculating a weighted mean using those ratings. Each pollution parameter is given a weight in this process. The rating system usually ranges from 0 to 1. It is determined by considering the importance of each quality factor or by comparing values with recommended reference standards<sup>##UREF##28##38##</sup>. Specific equations are used to calculate the HPI, as described in Eqs. ##FORMU##0##1## and ##FORMU##1##2##.where Q<sub>i</sub> stands for the sub-index parameter; n is the number of parameters taken for analysis; w<sub>i</sub> depicts the weight of each parameter, evaluated as 1/S<sub><bold>i</bold></sub>; S<sub>i</sub> symbolizes the standard value of each parameter; Q<sub>i</sub> represents the sub-index of the boundary, determined by Eq. ##FORMU##1##2##.</p>", "<p id=\"Par13\">The HPI calculation is based on the levels of eight metals: chromium (Cr), copper (Cu), iron (Fe), manganese (Mn), nickel (Ni), lead (Pb), zinc (Zn), and cadmium (Cd). A modified scale with three categories is commonly used to provide an understanding of heavy metal pollution. These categories are classified as excellent water quality when HPI is below 25, good with HPI ranging from 26 to 50, poor quality with an HPI value between 51 to 75, very poor with HPI ranging from 76 to 100, and high pollution risk (unsuitable) when HPI exceeds 100<sup>##UREF##29##39##</sup>.</p>", "<p id=\"Par14\">On the other hand, the Metal Index (MI) for drinking water offers an assessment of the overall quality of drinking water by considering the potential impact of heavy metals on human health. The MI assumes that the toxicity levels of these metals have a correlation with their concentration, which means they can potentially cause chronic toxic effects on various organs in the human body. The calculation of MI involves an evaluation of the condition where the drinking water quality is compromised if the concentration of a metal exceeds its designated Upper Allowable Limit (UAL) value 38. The following equation (Eq. ##FORMU##2##3##) illustrates the heavy metal index.where: C<sub>ave</sub> signifies the average concentration of each studied HM; UALi stands for the upper allowable limit of the ith metal in the sample. Metal index (MI) has six classes: very clean (MI < 0.3); clean (0.3 < MI < 1); partly affected (1 < MI < 2); moderately affected (2 < MI < 4); heavily affected (4 < MI < 6); and severally affected (MI > 6)<sup>##UREF##31##41##</sup>.</p>", "<title>Human health risk assessment</title>", "<p id=\"Par15\">The assessment of health risks follows a model recommended by the United States Environmental Protection Agency<sup>##UREF##32##42##</sup>. Health risk assessment is an analysis examining how environmental pollutants impact health. These risks can be divided into two categories: Carcinogenic (CR) and non-carcinogenic (NCR)<sup>##REF##31675577##43##</sup>. Carcinogenic risks assess the likelihood of developing cancer as a result of prolonged exposure to a pollutant or a combination of contaminants. In contrast, non-carcinogenic risks primarily deal with exposure and include genetic and teratogenic effects<sup>##REF##34224706##44##</sup>. Heavy metals (HMs) found in drinking water sources primarily enter the body through consumption and contact with the skin. Therefore, this study conducts health risk assessments from direct drinking and skin contact, which can be expressed in Eqs. ##FORMU##3##5## and ##FORMU##4##6##:where CDI <sub>oral</sub> is the average daily direct intake dose, CDI <sub>Dermal</sub> is the average daily dose absorbed by the skin, C<sub>w</sub> is the contents of HMs in the water sample (mg/L), IR is the daily ingestion rate (L/d), EF is the exposure frequency, ED is the exposure duration, BW is the body weight, SA is the exposed skin area, Kp is the skin permeability coefficient, CF is the conversion factor and ET is the exposure time. These exposure parameters are presented in Table ##TAB##0##1##.</p>", "<p id=\"Par16\">RfD is the reference dose, and ABS is the digestive coefficient of the gastrointestinal tract (Table ##TAB##0##1##).</p>", "<p id=\"Par17\">The carcinogenic risk (CR) caused by direct digestion and skin contact can be expressed as follows:where CSF represents the conversion slope factor of HMs (Table ##TAB##0##1##).</p>", "<title>Monte Carlo simulation</title>", "<p id=\"Par18\">Monte Carlo simulation is a technique used in risk assessment to decrease uncertainty related to heavy metal (HM) concentrations and exposure parameters and predict the carcinogenic and non-carcinogenic risks. By adopting this method, researchers can obtain estimations of health risk values<sup>##UREF##5##9##,##REF##34363801##19##</sup>. Python software, version 3.9.7, is commonly employed for implementing Monte Carlo simulations. Therefore, in the current study, Python code was running for 10,000 iterations and utilized to conduct Monte Carlo simulations and calculate the probability risks associated with both carcinogenic and non-carcinogenic risks of heavy metals for adults and children.</p>" ]	[ "<title>Results and discussion</title>", "<title>Physicochemical parameters</title>", "<p id=\"Par19\">The hydrochemistry was evaluated based on the physicochemical parameters and heavy metals (Table ##TAB##1##2##).</p>", "<p id=\"Par20\">The total dissolved solids (TDS) values in the studied water samples ranged from 1120 mg/L in the TCA to 153,589 mg/L in the salt lakes, with an average value of 9834.1 mg/L. The pH values of the samples fell within the range of 6.8 to 8.7, indicating neutral to alkaline water conditions. Calcium concentrations ranged from 19.6 mg/L to 2508.8 mg/L, while magnesium (Mg<sup>2+</sup>) concentrations varied from 9 mg/L in the groundwater to 12,216.6 mg/L in surface lakes. Potassium (K<sup>+</sup>) ranged from a minimum of 3.5 mg/L to a maximum of 83 mg/L, and sodium concentrations spanned from 192 mg/L to 39,500 mg/L. Chloride (Cl<sup>−</sup>) concentrations ranged from 580 mg/L to 94,250 mg/L, while sulfate (SO<sub>4</sub>\n<sup>2−</sup>) concentrations varied from 5 mg/L to 5348.7 mg/L. Bicarbonate (HCO<sub>3</sub><sup>−</sup>) concentrations ranged from 83.7 mg/L to 328.8 mg/L. The water resources in Siwa Oasis were classified as follows: TCA, springs, and drains were categorized as brackish to saline water, while the surface lakes were classified as hypersaline based on their TDS values. According to WHO<sup>##UREF##35##50##</sup>, most of the physicochemical parameters in the majority of water samples exceeded the standard limits, rendering them unsuitable for drinking purposes. According to FAO standards<sup>##UREF##36##54##</sup>, TDS, Ca<sup>2+</sup>, Mg<sup>2+</sup>, Na<sup>+</sup>, K<sup>+</sup>, Cl<sup>-</sup>, and SO<sub>4</sub><sup>2-</sup> exceeded the limits for irrigation water in 92%, 24%, 99.2%, 42.1%, 2.2%, 91.7%, and 5.3% of the water samples, respectively. The high concentrations of Cl<sup>-</sup> and Mg<sup>2+</sup> have the potential to increase soil salinity and reduce plant production, necessitating further treatment of irrigation water.</p>", "<p id=\"Par21\">The average concentrations of heavy metals in the water samples were as follows: Cd (0.04 mg/L), Cr (0.6 mg/L), Cu (1.14 mg/L), Fe (2.16 mg/L), Mn (0.28 mg/L), Ni (0.1 mg/L), Pb (0.33 mg/L), and Zn (0.03 mg/L). These concentrations are ranked in descending order as follows: Fe > Cu > Cr > Pb > Mn > Ni > Cd > Zn. Notably, the mean concentrations of Fe, Cd, Cr, Pb, and Mn exceeded the standard limits set by WHO<sup>##UREF##35##50##</sup>, while the concentrations of the other heavy metals remained within the limits.</p>", "<title>Surface water and groundwater origin</title>", "<p id=\"Par22\">Figure ##FIG##2##3## illustrates the origin of surface water and groundwater samples in Siwa Oasis using the Sulin graph<sup>##UREF##37##55##</sup>. Firstly, a significant amount (31.5%) of the water samples in the TCA is located in an area associated with recent marine water origin and has a composition rich in MgCl<sub>2</sub>. On the other hand, most of the TCA water samples, water from springs, drains, and salt lakes, fall into the category of old marine water origin characterized by a CaCl<sub>2</sub> composition.</p>", "<p id=\"Par23\">The higher levels of sodium (Na<sup>+</sup>) and potassium (K<sup>+</sup>) in these samples indicate they may have come from meteoric water through upward flow from the deep Nubian sandstone aquifer (NSSA). However, it is important to note that all water samples share a common marine origin due to the geological makeup of the Tertiary carbonate aquifer. This aquifer predominantly consists of deposits like limestone and dolomite. The presence of these marine deposits (dolomite and limestone) indicates the old presence of seawater trapped within the aquifer system. The high salt content in the groundwater of the TCA area can be attributed to factors including the influence of marine activities and the dissolution of minerals like calcite and dolomite found in the geological formations. Despite this marine influence, the primary source of water supply to the TCA appears to be the upward flow from the deep NSSA with minimal contributions from rainfall in the arid Siwa Oasis region<sup>##UREF##38##56##</sup>. Furthermore, the contribution of freshwater from NSSA through fault planes does not significantly impact the origin of the TCA samples, as indicated in Fig. ##FIG##2##3##.</p>", "<title>Geochemical Processes controlling water chemistry</title>", "<p id=\"Par24\">The presence of clay minerals in the system can have an impact on the mineralization of groundwater by facilitating ion exchange processes. Clay minerals tend to balance their charge by adsorbing monovalent cations like Na<sup>+</sup> and K<sup>+</sup> while releasing Ca<sup>2+</sup> and Mg<sup>2+</sup> or vice versa. The Chloro alkaline index (CAI) serves as a tool for identifying ion exchange mechanisms between minerals in the aquifer and groundwater. A positive CAI-I value indicates a reverse ion exchange process, whereas a negative value suggests that ion exchange processes control the chemistry of water<sup>##UREF##39##57##,##UREF##40##58##</sup>. In this study, all samples showed positive CAI-I values (Fig. ##FIG##3##4##a), indicating reverse ion exchanges between K<sup>+</sup> and Na<sup>+</sup> ions in water and Mg<sup>2+</sup> and Ca<sup>2+</sup> ions in the surrounding rock.</p>", "<p id=\"Par25\">To gain insight into the mechanism and weathering type controlling the water chemistry, bivariate plots were used considering the ratio of Ca<sup>2+</sup>/Na<sup>+</sup> versus Mg<sup>2+</sup>/Na<sup>+</sup>. These plots (Fig. ##FIG##3##4##b) revealed that silicate weathering plays a significant role in surface water and groundwater composition in Siwa Oasis. However, specific samples fell within zones associated with evaporate dissolution. The study area is mainly characterized by limestone and dolomite in the TCA. These formations receive water from the Nubian aquifer, which comprises sandstone with shale and clay layers. It is worth noting that there are shale and clay layers present between the TCA and NSSA, indicating that alumina silicates might be involved in silicate weathering processes.</p>", "<p id=\"Par26\">Additionally, Pearson's correlation matrix of inter-elemental relationships provides valuable insights into the sources and routes of heavy metals and major ions. This analysis helps elucidate how various heavy metals are linked and provides information about their origins in the groundwater system. In the water samples collected from the Siwa area, it showed a very significant correlation of TDS–Na (r = 0.99), TDS–Cl (r = 1), TDS–Mg (r = 0.97), TDS–Ca (r = 0.86), TDS–SO<sub>4</sub> (r = 0.95), Na–Mg (r = 0.97), Na–Ca (r = 0.83), Na–Cl (r = 0.97), Na–SO<sub>4</sub> (r = 0.92), Mg–Cl (r = 0.96), Mg–SO<sub>4</sub> (r = 0.95), Mg–Ca (r = 0.82), Mg–SO<sub>4</sub> (r = 0.86), Cr–Cu (r = 0.81), Cr–Fe (r = 0.77), Cr–Ni (r = 0.59), Cr–Mn (r = 0.8), Cu–Fe (r = 0.85), Cu–Mn (r = 0.9), Cu–Ni (r = 0.65), Mn–Ni (r = 0.64), and Pb–Ni (r = 0.52) as shown in (Fig. ##FIG##3##4##c). The analysis of the relationship between Total Dissolved Solids (TDS) and major ions in both surface water and groundwater provides insights into the factors that contribute to increased salinity in the study areas' water resources. Correlations among ions were observed, indicating the presence of specific minerals and processes that affect water salinity.</p>", "<p id=\"Par27\">The strong correlation between sodium and chloride ions suggests the existence of halite salt in the aquifer system. This indicates that as it dissolves into groundwater, it can lead to increasing water salinity. The correlation between calcium and magnesium suggests the presence of carbonate minerals such as dolomite in the aquifer system. Dolomite dissolution can contribute to elevated Ca and Mg ion levels in water. Similarly, the correlation between calcium (Ca<sup>2+</sup>) and sulfate (SO<sub>4</sub><sup>2−</sup>) indicates the presence of gypsum minerals in the TCA. Gypsum dissolution can increase calcium and sulfate concentrations in water, thereby contributing to salinity levels. According to heavy metals, the analysis reveals a contribution from various human activities in the study area. These activities include agriculture practices, improper sanitation methods, and discharge from sources as organic decomposition. These activities carried out by humans result in the release of heavy metals into the water resources of Siwa Oasis.</p>", "<title>Cluster analysis of physicochemical parameters and heavy metals</title>", "<p id=\"Par28\">The analysis of groundwater samples using a combination of the Wards linkage method and Euclidean distance revealed three groups (Fig. ##FIG##4##5##) based on their chemical characteristics;</p>", "<p id=\"Par29\">Group 1 (G1): This group consisted of parameters such as total dissolved solids (TDS), sodium (Na<sup>+</sup>), calcium (Ca<sup>2+</sup>), magnesium (Mg<sup>2+</sup>), sulfate (SO<sub>4</sub><sup>2−</sup>), and chloride (Cl<sup>−</sup>). These parameters are related to carbonates and evaporite components. The strong correlation between sulfate and chloride indicates that chlorides and salts significantly contribute to groundwater salinity in this region. Moreover, the dominance of Mg<sup>2+</sup> and Ca<sup>2+</sup> suggests a connection between carbonate properties and the mineralization process in groundwater.</p>", "<p id=\"Par30\">Group 2 (G2): This group included metals like copper (Cu), chromium (Cr), iron (Fe), and manganese (Mn). These metals may have a common source and be influenced by similar redox environments potentially associated with varying geological compositions at different depths.</p>", "<p id=\"Par31\">Group 3: G3 comprised the remaining metals, nickel (Ni), zinc (Zn), lead (Pb), and cadmium (Cd). Similar to G2, G3 suggests that these heavy metals can exist in groundwater through sources possibly influenced by redox conditions based on geological factors.</p>", "<p id=\"Par32\">Group 4: G4 was identified by bicarbonate (HCO<sub>3</sub><sup>−</sup>) ions. Unlike G1, which was linked to the dissolution of carbonate minerals, G4 suggests that the bicarbonate found in the groundwater comes from a source possibly resulting from processes like silicate weathering. In essence, this analysis of clusters provides insights into the characteristics and origins of various components present in the water resources of the study areas. It highlights how factors such as mineralization processes and the existence of heavy metals influence the quality of water.</p>", "<title>Principal component analysis (PCA)</title>", "<p id=\"Par33\">The principal component analysis (PCA) was conducted to reduce the dimensionality of the dataset and identify underlying patterns in the water chemistry data. To determine if PCA could be applied, the Kaiser Meyer Olkin (KMO) was observed<sup>##UREF##26##35##</sup>, which resulted in a value of 0.6. This value is higher than 0.5. Additionally, Bartlett's test of sphericity showed a result (0.000, less than 0.05), indicating that the data was suitable for PCA. After conducting PCA, three components (PC1, PC2, and PC3) were extracted from a scree plot with an eigenvalue greater than 1 (Fig. ##FIG##5##6##a,b). These components explained the proportions of variability in the data; PC1 accounted for 40%, PC2 for 26.5%, and PC3 for 7.5% (Table ##TAB##2##3##). The variable loadings were examined to understand the strength of the relationship between these components and the original variables used in the analysis. Variables with loadings close to 1 had a strong link with the respective principal component.</p>", "<p id=\"Par34\">PC1 (salinization factor): The strong association between PC1 and variables such as Mg<sup>2+</sup>, Ca<sup>2+</sup>, Na<sup>+</sup>, Cl<sup>−</sup>, SO<sub>4</sub><sup>2−</sup>, and TDS can refer to PC1 as a salinization factor (Table ##TAB##2##3##). The salinity in the water is likely caused by processes such as the weathering of limestone, halite dissolution, gypsum, and the exchange of ions between groundwater and surrounding rock of TCA. Additionally, human activities like irrigation practices and fertilization may also contribute to the presence of calcium (Ca<sup>2+</sup>), sodium (Na<sup>+</sup>), and magnesium (Mg<sup>2+</sup>), where agriculture is the main activity in Siwa Oasis.</p>", "<p id=\"Par35\">Regarding PC2 (alkaline and contamination factor), there is a relationship between this component and variables such as manganese (Mn), iron (Fe), copper (Cu), nickel (Ni), and chromium (Cr) (Table ##TAB##2##3##). This suggests that PC2 represents factors related to alkaline conditions and contamination. The interaction between alkaline water, rocks, and soil, oxidation–reduction processes, and potential contamination may contribute to the presence of these metals. The concentrations of iron and manganese can vary depending on whether the groundwater oxidized or reduced. Geogenic processes influence groundwater elements like heavy metals affected by natural processes, including pH and mineral dissolution<sup>##UREF##0##2##,##UREF##5##9##</sup>. High levels of these metals could indicate contamination from discharges or natural mineralization processes. According to previous studies<sup>##UREF##41##59##</sup>, the tertiary carbonate rocks contain glauconite and Fe oxides detrital grains in the northwestern desert of Egypt. Moghra Formation in the study area contains about 1.6–36.1%, 0–0.6%, and 5–50 mg/l of Fe<sub>2</sub>O<sub>3</sub>, MnO, and Cu, respectively. This suggests the geogenic source of these metals in the water resources of Siwa Oasis.</p>", "<p id=\"Par36\">As for PC3 (anthropogenic metal source), it is associated with variables zinc (Zn), cadmium (Cd), and Lead (Pb) (Table ##TAB##2##3##). Unlike the previous components, these metals were not recorded in the geological formations, suggesting an anthropogenic source for Zn, Cd, and Pb in the studied water.</p>", "<p id=\"Par37\">High concentrations of the investigated heavy metals in irrigation water extracted from TCA can cause serious problems like mineralization and waterlogging through different processes such as ion exchange, cation imbalance, mineral precipitation, sodicity, and salinity. When heavy metals like cadmium, lead, and zinc displace cations on soil exchange sites, it can change the chemistry of the groundwater and the potential supersaturation of minerals. Furthermore, when heavy metals interact with ions in the soil water, it can cause minerals like gypsum to precipitate, reducing soil permeability and worsening waterlogging issues<sup>##UREF##42##60##</sup>. The formation of hardpans or cemented layers due to the precipitation of iron and aluminum oxides further limits water movement in the soil. Elevated concentrations of heavy metals can negatively impact soil microbial activity. Microorganisms play a crucial role in organic matter decomposition and nutrient cycling<sup>##UREF##43##61##</sup>. Reduced microbial activity can result in the accumulation of organic matter, further contributing to waterlogging issues.</p>", "<title>Heavy metal pollution index (HPI) and metal index (MI)</title>", "<p id=\"Par38\">The Heavy Metal Pollution Index (HPI) is a tool used for assessing the pollution level of heavy metals in both surface water and groundwater. It helps evaluate the impact of metals on water quality and aids in monitoring and managing health risks associated with exposure to these metals<sup>##REF##28643849##11##</sup>. The HPI values ranged from 111.7 to 7274.5 in the water samples. All the water samples collected were categorized as having high pollution risk and not suitable for drinking according to the HPI classification<sup>##UREF##28##38##</sup> (HPI > 100) (Table ##TAB##3##4##).</p>", "<p id=\"Par39\">The MI (Metal Index) method was used alongside the HPI index to understand how heavy metals affect water quality. This allowed us to assess the extent of metal contamination in water by comparing it with the maximum allowable limit values outlined in WHO guidelines<sup>##REF##21818637##48##</sup>. The average MI values were between 6.5 and 462 (Table ##TAB##3##4##). These results indicate high impact and contamination of heavy metals in Siwa Oasis water resources according to MI classification<sup>##UREF##30##40##</sup> 38. It highlights the need for monitoring and improving water quality in the OasisOasis. In general, both the HPI and MI evaluations bring attention to the presence of heavy metals in the water resources, which could threaten the environment and humans in Siwa Oasis. This underlines the urgency of tackling this pollution and safeguarding the well-being of the people living there and the surrounding environment. The distribution maps of HPI and MI by using the kriging method showed that the most vulnerable area with heavy metals is the central and western part of Siwa Oasis, which could be due to the over-pumping of groundwater for irrigation purposes (Fig. ##FIG##6##7##a,b).</p>", "<title>Health risk assessment</title>", "<p id=\"Par40\">The non-carcinogenic and carcinogenic risk hazard indices (HI) were assessed by calculating ingestion and dermal absorption pathways' hazard quotients (HQ). The outcomes reveal the combined potential health risks for humans from exposure to different heavy metals for both children and adults.</p>", "<title>Non-carcinogenic health risk</title>", "<p id=\"Par41\">The toxic elements cadmium (Cd), chromium (Cr), copper (Cu), iron (Fe), manganese (Mn), nickel (Ni), lead (Pb), and zinc (Zn) were evaluated to determine the non-carcinogenic risk in both child and adult. For adults, the hazard quotient (HQ) ingestion ranged from 1.12 to 11.58, 0.015 to 123.8, 0.0013 to 11.7, 0.0001 to 1.5, 0.0002 to 4.2, 0.00015 to 1.08, 0.03 to 28.04 and 2.01E-5 to 0.01 for Cd, Cr, Cu, Fe, Mn, Ni, Pb and Zn, respectively (Fig. ##FIG##7##8##a). The HQ ingestion for child ranged from 0.46 to 44.2, 0.06 to 472.9, 0.005 to 44.9, 0.0005 to 5.9, 0.001 to 16.17, 0.0006 to 4.1, 0.1 to 107.06 and 0.0001 to 0.04 for Cd, Cr, Cu, Fe, Mn, Ni, Pb and Zn, respectively (Fig. ##FIG##7##8##b). Based on HQ oral values, the human health risks associated with exposure to Cd, Cr, Cu, Fe, Mn, Ni, Pb, and Zn through ingestion are generally higher for children than adults. It is worth noting that HQ oral values for children and adults are within the permissible limit under 1 for Cu, Fe, Mn, Ni, and Zn. In contrast, the HQ oral value (adult) was more significant than 1 in 76.7%, 45.8%, and 79.7% of the water samples for Cd, Cr, and Pb, respectively. HQ oral value (child) was more significant than 1 in 95.5%, 86.9%, and 94% of the water samples for Cd, Cr, and Pb, respectively. These values are specific to the location and period studied, and the actual human health risks may vary depending on various factors such as exposure duration and frequency, individual susceptibility, and environmental conditions. Nonetheless, the HQ dermal values for adults were in the range of 0.01 to 1.09, 0.005 to 47.02, 2.15E-5 to 0.18, 2.96E-6 to 0.03, 2.98E-5 to 0.5, 3.58E-6 to 0.02, 7.15E-5 to 0.07, and 2.86E-7 to 0.00015 for Cd, Cr, Cu, Fe, Mn, Ni, Pb, and Zn, respectively (Fig. ##FIG##7##8##c). Moreover, for a child, the HQ dermal values were in the range of 0.03 to 3.24, 0.016 to 138.7, 6.33E-5 to 0.5, 8.74E-6 to 0.1, 8.79E-5 to 1.48, 1.05E-5 to 0.07, 0.0002 to 0.2, and 8.44E-7 to 0.0004 for Cd, Cr, Cu, Fe, Mn, Ni, Pb, and Zn, respectively (Fig. ##FIG##7##8##d). The HQ dermal values for adults are within the permissible limit under 1 for all heavy metal parameters. In contrast, the HQ dermal value (child) was more significant than 1 in 24% and 50.3% of the water samples for Cd and Cr, respectively, and the rest of the heavy metals fell within acceptable limits. Based on HQ oral and dermal values, the human health risks associated with exposure to Cd, Cr, Cu, Fe, Mn, Ni, Pb, and Zn through dermal exposure are generally higher for children than adults. It is concluded that through oral contact, Cd, Cr, and Pb are the most contributing metals to human health risk (adult and child). In the case of dermal contact, children are more vulnerable to Cd and Cr from the water resources in Siwa Oasis, while there is no risk for adults.</p>", "<p id=\"Par42\">The hazard index (HI) is a valuable indicator used to assess the overall potential health hazard posed by heavy metals in both surface water and groundwater of Siwa Oasis. It considers all possible exposure routes, including ingestion and dermal pathways. The hazard quotients (HQs) associated with each heavy metal and exposure route are summed up to calculate the HI. This comprehensive approach provides a more complete picture of the combined health risks associated with heavy metal contamination in the water resources of Siwa Oasis. The HI value is an essential indicator for assessing the overall health impact and safety of the water sources in the region. HI oral values ranged from 1.6 to 142.1 and 6.2 to 542.6 for adults and children, respectively (Table ##TAB##3##4##). Moreover, the HI dermal values ranged from 0.07 to 47.8 and 0.2 to 141 for adults and children, respectively (Table ##TAB##3##4##).</p>", "<p id=\"Par43\">It can be concluded that the HI oral values for adults and children were above safe levels (HI > 1) in 100% of the water samples and fell in the high-risk category of non-carcinogenic impact. HI value for adults showed that 80.6% of the water samples fell in the low-risk class, and 19.4% showed a high risk of dermal contact. HI, value for child indicated that 22.4% of the water samples fell in the low-risk class, and 77.6% of the samples showed high risk of dermal contact (Table ##TAB##3##4##). The result of the HI showed that the child is more vulnerable to oral and dermal contact with heavy metals than adults. However, it is essential to monitor the levels of these metals in the different water resources of Siwa Oasis and their potential health effects, where the groundwater in the study area is non-rechargeable and is the primary water resource for different uses. The distribution maps of the hazard index (HI) in adults and children through dermal and oral contact showed that the central and western parts of Siwa Oasis are the most vulnerable locations to the non-carcinogenic risk impact of heavy metals (Fig. ##FIG##8##9##).</p>", "<title>Carcinogenic health risk (CR)</title>", "<p id=\"Par44\">Carcinogenic risks assess the probability of developing cancer as a result of prolonged exposure to a pollutant or a combination of contaminants. The traditional calculation of CR was conducted to calibrate and compare its values with the predicted CR gained from the Monte Carlo simulation later. In the case of adults, the CR oral values fell in a range between 0.0003 and 0.03, 2.26E-05 and 0.18, 3.16E-05 and 0.03 for Cd, Cr, and Pb, respectively (Fig. ##FIG##9##10##a), while for a child, CR oral values were between 0.001 and 0.1, 8.63E-05 and 0.7, 0.0001 and 0.1 for Cd, Cr, and Pb respectively (Fig. ##FIG##9##10##b).</p>", "<p id=\"Par45\">Regarding oral contact with heavy metals from the water resources of Siwa Oasis, the high carcinogenic risk (CR > 1 × 10<sup>–4</sup>) for adults was found in 77.6%, 96.3%, and 98.5% of the water samples for Cd, Cr, and Pb respectively and for a child in 100%, 98.5%, and 100 of water samples for Cd, Cr, and Pb respectively (Table ##TAB##3##4##). On the other hand, for adults, the CR dermal values fell in a range between 0.002 and 0.2, 0.0002 and 1.7, 1.5E-05 and 0.01 for Cd, Cr, and Pb, respectively (Fig. ##FIG##9##10##c), while for child, CR dermal values were between 0.005 and 0.5, 0.0006 and 5.2, 4.43E-05 and 0.04 for Cd, Cr, and Pb respectively (Fig. ##FIG##9##10##d). Based on dermal contact with heavy metals from the water samples, the high carcinogenic risk (CR > 1 × 10<sup>–4</sup>) for adults was found in 100%, 100%, and 94.7% of the water samples for Cd, Cr, and Pb respectively and for a child in 100%, 100%, and 97.8% of water samples for Cd, Cr, and Pb respectively (Table ##TAB##3##4##). The current findings indicated that further treatment is required for all water resources in Siwa Oasis, where the carcinogenic risk from heavy metals is very high and threatens the human health of both children and adults.</p>", "<title>Monte Carlo simulation approach</title>", "<p id=\"Par46\">The Monte Carlo simulation was applied to predict the values of HQ (oral and dermal) of Cd, Cr, Cu, Fe, Mn, Ni, Pb, and Zn, as well as CR (oral and dermal) of Cd, Cr, and Pb for both adults and children.</p>", "<title>Non-carcinogenic health risk</title>", "<p id=\"Par47\">The findings, from the Monte Carlo simulation offer insights into the health hazards linked to exposure to heavy metals through various routes in Siwa Oasis. It is reassuring to note that according to the estimated dermal hazard quotient (HQ dermal), there are no indications of any metal exceeding limits (Fig. ##FIG##10##11##a,b). This suggests that the risk of health issues due to skin contact with water resources is unlikely for adults and children. However, when oral exposure routes are considered, the situation changes. While some heavy metals like Cu, Fe, Mn, Ni, and Zn have predicted HQ values within limits (low risk) for adults, Cd, Cr, and Pb showed estimated HQ values higher than 1 (high risk). This implies a health risk for adults consuming water contaminated with Cd, Cr, and Pb through ingestion. Similar patterns are also observed for children (Fig. ##FIG##10##11##d,d).</p>", "<p id=\"Par48\">While some heavy metals like Fe, Ni and Zn do not pose risks through oral exposure routes in children, Cd, Cr, Cu, Mn, and Pb show predicted HQ values greater than 1, indicating potential health risks associated with consuming water containing these metals. It is important to remember that these assessments consider assumptions and uncertainties stemming from data sources. Hence, it is crucial to monitor the levels of exposure and regularly update risk assessments to safeguard the water resources in the area and protect the population's health. Through the comparison between the calculated HQ (Fig. ##FIG##7##8##) and predicted HQ (Fig. ##FIG##10##11##) through oral and dermal contact with heavy metals, it was found that Cd, Cr, and Pb are the main parameters responsible for high non-carcinogenic impact for child and adult in Siwa Oasis. Monte Carlo simulation was an effective method to predict the HQ successfully.</p>", "<title>Carcinogenic health risk through oral contact</title>", "<p id=\"Par49\">The analysis of carcinogenic risk probabilities (CR) for oral measurements in children and adults reveals some critical patterns. Across all parameters (Cd et al.), the CR oral measurements are consistently higher in children than adults. For children, the 5th percentile CR oral values (the lower bounds of the estimated cancer risk) were 0.017, 0.019, and 0.012 for Cd, Cr, and Pb, respectively (Fig. ##FIG##11##12##a–c). On the other hand, at the percentile level 95th (the upper bounds of estimated risk), CR oral values were determined as 0.044, 0.045, and 0.0275 for Cd, Cr, and Pb, respectively (Fig. ##FIG##11##12##a–c) representing higher potential risks for children. In contrast to this pattern observed in children’s data, the estimated cancer risk levels were relatively lower in adults based on their percentiles. For adults, the lower bounds of estimated cancer risks (5th percentile CR oral values) stood at 0.0047, 0.005, and 0.003 for Cd, Cr, and Pb, respectively (Fig. ##FIG##11##12##d–f). Furthermore, it was found that at the 95th percentile range, the estimated CR were 0.011, 0.0118, and 0.0072 for Cd, Cr, and Pb, respectively (Fig. ##FIG##11##12##d–f), suggesting lesser potential risks compared to those observed in children. However, the predicted CR through oral contact showed that most water samples collected from Siwa Oasis have the probability of causing high risk for children and adults with (CR > 1 × 10<sup>−4</sup>).</p>", "<p id=\"Par50\">Through the comparison between the calculated CR (Fig. ##FIG##7##8##) and predicted CR (Fig. ##FIG##10##11##) through oral contact with heavy metals, it was found that the three metals (Cd et al.) would have a high carcinogenic impact on children and adults in all water resources of Siwa Oasis. Monte Carlo simulation was an effective method to predict the CR oral successfully.</p>", "<title>Carcinogenic health risk through dermal contact</title>", "<p id=\"Par51\">The analysis of carcinogenic risk probabilities (CR) due to skin contact in children and adults reveals that children consistently have higher CR values than adults for all parameters (Cd et al.). Regarding children, the estimated 5th percentile CR levels for developing cancer through skin contact were 0.071, 0.13, and 0.0041 for Cd, Cr, and Pb, respectively (Fig. ##FIG##12##13##a–c). On the other hand, the estimated 95th percentile CR risk levels for developing cancer through skin contact in children were 0.162, 0.33, and 0.0102 for Cd, Cr, and Pb, respectively (Fig. ##FIG##12##13##a–c). These values indicate the upper boundaries of potential risks from dermal exposure in children. In contrast to children’s results, adults had estimated CR levels for developing cancer through skin contact with values of 0.022, 0.047, and 0.0013 as their lowest percentile (5th) for Cd, Cr, and Pb, respectively (Fig. ##FIG##11##12##d–f). The 95th percentile CR dermal values for adults were 0.055, 0.112, and 0.0034 for Cd, Cr, and Pb, respectively (Fig. ##FIG##11##12##d–f), representing the upper bounds of the estimated cancer risk from adult dermal exposure. Overall, the findings indicate that both children and adults are exposed to a high risk of developing cancer due to exposure to Cd, Cr, and Pb found in water resources within Siwa Oasis. The predicted cancer risk levels from the Monte Carlo simulation exceed the acceptable risk level (CR > 1.0E-04) in the majority of water samples, which suggests that continuous exposure to these metals could potentially lead to the development of cancer in the future for both adults and children. These findings emphasize the need to minimize metal contamination in water sources, aiming to reduce carcinogenic health.</p>", "<p id=\"Par52\">Through the comparison between the calculated CR (Fig. ##FIG##9##10##) and predicted CR (Fig. ##FIG##12##13##) through dermal contact with heavy metals, it was found that the three metals (Cd et al.) would have a high carcinogenic impact on children and adults in the majority of water samples collected from Siwa Oasis. Monte Carlo simulation was an effective tool for predicting the CR dermal successfully.</p>", "<p id=\"Par53\">This research evaluates the contamination caused by metals in Siwa Oasis. The Heavy Metal Pollution Index (HPI) and Metal Index (MI) reveal surface water and groundwater pollution levels. The HPI values, ranging from 111.7 to 7274.5, classify all water samples as polluted, rendering them unsuitable for drinking. The MI method also emphasizes the impact and contamination of metals, with average MI values ranging from 6.5 to 462. Maps showing distribution patterns highlight western areas of Siwa Oasis as vulnerable potentially due to excessive groundwater pumping for irrigation purposes. Additionally, an analysis based on components reveals both human-related sources of heavy metal pollution. HQ and HI were calculated to fully understand the impact of the metals detected in Siwa Oasis on human health.</p>", "<p id=\"Par54\">The Hazard Quotient (HQ) values for cadmium (Cd), chromium (Cr), and lead (Pb) indicated non-carcinogenic risks to human health. Among these metals, Cd, Cr, and Pb pose risks to children. Long-term exposure to Cd can harm the kidneys and bones, while Cr exposure may cause skin problems. Even low levels of Pb exposure can have cognitive consequences for children. The increased Risk (CR) values for Cd, Cr, and Pb highlight long-term risks. Carcinogenic risk assessments and Monte Carlo simulations further emphasize the urgency for water treatment to mitigate long-term health consequences. These findings collectively emphasize the need for measures to tackle heavy metal pollution and ensure the well-being of the Siwa Oasis community, showcasing the valuable insights provided by this study. However, it is essential to note the limitations of this study, such as its specificity to a location and period, potential variations in health risks, uncertainties associated with data sources, and assumptions made during the Monte Carlo simulation. Despite these limitations, this study emphasizes the need to monitor and manage metal contamination in Siwa Oasis to safeguard the environment and public health. The current study hints at potential ecological consequences, including impacts on soil quality and water resources. The economic repercussions on local agriculture and industries also require attention. The persistence of heavy metals in the environment raises concerns about long-term effects on the ecosystem. Strategies for mitigation and remediation should not only prioritize human health but also aim to preserve the environmental integrity of Siwa Oasis. Regulatory measures and community involvement remain crucial for sustainable solutions. According to the current findings, it is recommended that desalination stations be established to enhance water quality for irrigation in the study area. Additionally, creating companies specializing in salt extraction could provide a dual benefit of addressing heavy metal pollution and utilizing the extracted salts in various industries. By recognizing the broader environmental context and implementing proactive measures, this study emphasizes the urgency of a comprehensive approach to address heavy metal pollution and promote sustainable environmental management in Siwa Oasis. For further research, it would be beneficial to thoroughly understand how heavy metal concentrations vary over time. Conducting studies that cover seasons and years could provide valuable insights into the dynamics of metal pollution. It would also be helpful to investigate water treatment technologies and their effectiveness in reducing levels of metals. Understanding the socio-impacts of metal contamination on communities and industries is crucial for developing holistic management strategies. Lastly, exploring the feasibility and impact of implementing suggested measures like desalination stations and salt extraction companies would provide insights into environmental management in Siwa Oasis.</p>" ]	[ "<title>Results and discussion</title>", "<title>Physicochemical parameters</title>", "<p id=\"Par19\">The hydrochemistry was evaluated based on the physicochemical parameters and heavy metals (Table ##TAB##1##2##).</p>", "<p id=\"Par20\">The total dissolved solids (TDS) values in the studied water samples ranged from 1120 mg/L in the TCA to 153,589 mg/L in the salt lakes, with an average value of 9834.1 mg/L. The pH values of the samples fell within the range of 6.8 to 8.7, indicating neutral to alkaline water conditions. Calcium concentrations ranged from 19.6 mg/L to 2508.8 mg/L, while magnesium (Mg<sup>2+</sup>) concentrations varied from 9 mg/L in the groundwater to 12,216.6 mg/L in surface lakes. Potassium (K<sup>+</sup>) ranged from a minimum of 3.5 mg/L to a maximum of 83 mg/L, and sodium concentrations spanned from 192 mg/L to 39,500 mg/L. Chloride (Cl<sup>−</sup>) concentrations ranged from 580 mg/L to 94,250 mg/L, while sulfate (SO<sub>4</sub>\n<sup>2−</sup>) concentrations varied from 5 mg/L to 5348.7 mg/L. Bicarbonate (HCO<sub>3</sub><sup>−</sup>) concentrations ranged from 83.7 mg/L to 328.8 mg/L. The water resources in Siwa Oasis were classified as follows: TCA, springs, and drains were categorized as brackish to saline water, while the surface lakes were classified as hypersaline based on their TDS values. According to WHO<sup>##UREF##35##50##</sup>, most of the physicochemical parameters in the majority of water samples exceeded the standard limits, rendering them unsuitable for drinking purposes. According to FAO standards<sup>##UREF##36##54##</sup>, TDS, Ca<sup>2+</sup>, Mg<sup>2+</sup>, Na<sup>+</sup>, K<sup>+</sup>, Cl<sup>-</sup>, and SO<sub>4</sub><sup>2-</sup> exceeded the limits for irrigation water in 92%, 24%, 99.2%, 42.1%, 2.2%, 91.7%, and 5.3% of the water samples, respectively. The high concentrations of Cl<sup>-</sup> and Mg<sup>2+</sup> have the potential to increase soil salinity and reduce plant production, necessitating further treatment of irrigation water.</p>", "<p id=\"Par21\">The average concentrations of heavy metals in the water samples were as follows: Cd (0.04 mg/L), Cr (0.6 mg/L), Cu (1.14 mg/L), Fe (2.16 mg/L), Mn (0.28 mg/L), Ni (0.1 mg/L), Pb (0.33 mg/L), and Zn (0.03 mg/L). These concentrations are ranked in descending order as follows: Fe > Cu > Cr > Pb > Mn > Ni > Cd > Zn. Notably, the mean concentrations of Fe, Cd, Cr, Pb, and Mn exceeded the standard limits set by WHO<sup>##UREF##35##50##</sup>, while the concentrations of the other heavy metals remained within the limits.</p>", "<title>Surface water and groundwater origin</title>", "<p id=\"Par22\">Figure ##FIG##2##3## illustrates the origin of surface water and groundwater samples in Siwa Oasis using the Sulin graph<sup>##UREF##37##55##</sup>. Firstly, a significant amount (31.5%) of the water samples in the TCA is located in an area associated with recent marine water origin and has a composition rich in MgCl<sub>2</sub>. On the other hand, most of the TCA water samples, water from springs, drains, and salt lakes, fall into the category of old marine water origin characterized by a CaCl<sub>2</sub> composition.</p>", "<p id=\"Par23\">The higher levels of sodium (Na<sup>+</sup>) and potassium (K<sup>+</sup>) in these samples indicate they may have come from meteoric water through upward flow from the deep Nubian sandstone aquifer (NSSA). However, it is important to note that all water samples share a common marine origin due to the geological makeup of the Tertiary carbonate aquifer. This aquifer predominantly consists of deposits like limestone and dolomite. The presence of these marine deposits (dolomite and limestone) indicates the old presence of seawater trapped within the aquifer system. The high salt content in the groundwater of the TCA area can be attributed to factors including the influence of marine activities and the dissolution of minerals like calcite and dolomite found in the geological formations. Despite this marine influence, the primary source of water supply to the TCA appears to be the upward flow from the deep NSSA with minimal contributions from rainfall in the arid Siwa Oasis region<sup>##UREF##38##56##</sup>. Furthermore, the contribution of freshwater from NSSA through fault planes does not significantly impact the origin of the TCA samples, as indicated in Fig. ##FIG##2##3##.</p>", "<title>Geochemical Processes controlling water chemistry</title>", "<p id=\"Par24\">The presence of clay minerals in the system can have an impact on the mineralization of groundwater by facilitating ion exchange processes. Clay minerals tend to balance their charge by adsorbing monovalent cations like Na<sup>+</sup> and K<sup>+</sup> while releasing Ca<sup>2+</sup> and Mg<sup>2+</sup> or vice versa. The Chloro alkaline index (CAI) serves as a tool for identifying ion exchange mechanisms between minerals in the aquifer and groundwater. A positive CAI-I value indicates a reverse ion exchange process, whereas a negative value suggests that ion exchange processes control the chemistry of water<sup>##UREF##39##57##,##UREF##40##58##</sup>. In this study, all samples showed positive CAI-I values (Fig. ##FIG##3##4##a), indicating reverse ion exchanges between K<sup>+</sup> and Na<sup>+</sup> ions in water and Mg<sup>2+</sup> and Ca<sup>2+</sup> ions in the surrounding rock.</p>", "<p id=\"Par25\">To gain insight into the mechanism and weathering type controlling the water chemistry, bivariate plots were used considering the ratio of Ca<sup>2+</sup>/Na<sup>+</sup> versus Mg<sup>2+</sup>/Na<sup>+</sup>. These plots (Fig. ##FIG##3##4##b) revealed that silicate weathering plays a significant role in surface water and groundwater composition in Siwa Oasis. However, specific samples fell within zones associated with evaporate dissolution. The study area is mainly characterized by limestone and dolomite in the TCA. These formations receive water from the Nubian aquifer, which comprises sandstone with shale and clay layers. It is worth noting that there are shale and clay layers present between the TCA and NSSA, indicating that alumina silicates might be involved in silicate weathering processes.</p>", "<p id=\"Par26\">Additionally, Pearson's correlation matrix of inter-elemental relationships provides valuable insights into the sources and routes of heavy metals and major ions. This analysis helps elucidate how various heavy metals are linked and provides information about their origins in the groundwater system. In the water samples collected from the Siwa area, it showed a very significant correlation of TDS–Na (r = 0.99), TDS–Cl (r = 1), TDS–Mg (r = 0.97), TDS–Ca (r = 0.86), TDS–SO<sub>4</sub> (r = 0.95), Na–Mg (r = 0.97), Na–Ca (r = 0.83), Na–Cl (r = 0.97), Na–SO<sub>4</sub> (r = 0.92), Mg–Cl (r = 0.96), Mg–SO<sub>4</sub> (r = 0.95), Mg–Ca (r = 0.82), Mg–SO<sub>4</sub> (r = 0.86), Cr–Cu (r = 0.81), Cr–Fe (r = 0.77), Cr–Ni (r = 0.59), Cr–Mn (r = 0.8), Cu–Fe (r = 0.85), Cu–Mn (r = 0.9), Cu–Ni (r = 0.65), Mn–Ni (r = 0.64), and Pb–Ni (r = 0.52) as shown in (Fig. ##FIG##3##4##c). The analysis of the relationship between Total Dissolved Solids (TDS) and major ions in both surface water and groundwater provides insights into the factors that contribute to increased salinity in the study areas' water resources. Correlations among ions were observed, indicating the presence of specific minerals and processes that affect water salinity.</p>", "<p id=\"Par27\">The strong correlation between sodium and chloride ions suggests the existence of halite salt in the aquifer system. This indicates that as it dissolves into groundwater, it can lead to increasing water salinity. The correlation between calcium and magnesium suggests the presence of carbonate minerals such as dolomite in the aquifer system. Dolomite dissolution can contribute to elevated Ca and Mg ion levels in water. Similarly, the correlation between calcium (Ca<sup>2+</sup>) and sulfate (SO<sub>4</sub><sup>2−</sup>) indicates the presence of gypsum minerals in the TCA. Gypsum dissolution can increase calcium and sulfate concentrations in water, thereby contributing to salinity levels. According to heavy metals, the analysis reveals a contribution from various human activities in the study area. These activities include agriculture practices, improper sanitation methods, and discharge from sources as organic decomposition. These activities carried out by humans result in the release of heavy metals into the water resources of Siwa Oasis.</p>", "<title>Cluster analysis of physicochemical parameters and heavy metals</title>", "<p id=\"Par28\">The analysis of groundwater samples using a combination of the Wards linkage method and Euclidean distance revealed three groups (Fig. ##FIG##4##5##) based on their chemical characteristics;</p>", "<p id=\"Par29\">Group 1 (G1): This group consisted of parameters such as total dissolved solids (TDS), sodium (Na<sup>+</sup>), calcium (Ca<sup>2+</sup>), magnesium (Mg<sup>2+</sup>), sulfate (SO<sub>4</sub><sup>2−</sup>), and chloride (Cl<sup>−</sup>). These parameters are related to carbonates and evaporite components. The strong correlation between sulfate and chloride indicates that chlorides and salts significantly contribute to groundwater salinity in this region. Moreover, the dominance of Mg<sup>2+</sup> and Ca<sup>2+</sup> suggests a connection between carbonate properties and the mineralization process in groundwater.</p>", "<p id=\"Par30\">Group 2 (G2): This group included metals like copper (Cu), chromium (Cr), iron (Fe), and manganese (Mn). These metals may have a common source and be influenced by similar redox environments potentially associated with varying geological compositions at different depths.</p>", "<p id=\"Par31\">Group 3: G3 comprised the remaining metals, nickel (Ni), zinc (Zn), lead (Pb), and cadmium (Cd). Similar to G2, G3 suggests that these heavy metals can exist in groundwater through sources possibly influenced by redox conditions based on geological factors.</p>", "<p id=\"Par32\">Group 4: G4 was identified by bicarbonate (HCO<sub>3</sub><sup>−</sup>) ions. Unlike G1, which was linked to the dissolution of carbonate minerals, G4 suggests that the bicarbonate found in the groundwater comes from a source possibly resulting from processes like silicate weathering. In essence, this analysis of clusters provides insights into the characteristics and origins of various components present in the water resources of the study areas. It highlights how factors such as mineralization processes and the existence of heavy metals influence the quality of water.</p>", "<title>Principal component analysis (PCA)</title>", "<p id=\"Par33\">The principal component analysis (PCA) was conducted to reduce the dimensionality of the dataset and identify underlying patterns in the water chemistry data. To determine if PCA could be applied, the Kaiser Meyer Olkin (KMO) was observed<sup>##UREF##26##35##</sup>, which resulted in a value of 0.6. This value is higher than 0.5. Additionally, Bartlett's test of sphericity showed a result (0.000, less than 0.05), indicating that the data was suitable for PCA. After conducting PCA, three components (PC1, PC2, and PC3) were extracted from a scree plot with an eigenvalue greater than 1 (Fig. ##FIG##5##6##a,b). These components explained the proportions of variability in the data; PC1 accounted for 40%, PC2 for 26.5%, and PC3 for 7.5% (Table ##TAB##2##3##). The variable loadings were examined to understand the strength of the relationship between these components and the original variables used in the analysis. Variables with loadings close to 1 had a strong link with the respective principal component.</p>", "<p id=\"Par34\">PC1 (salinization factor): The strong association between PC1 and variables such as Mg<sup>2+</sup>, Ca<sup>2+</sup>, Na<sup>+</sup>, Cl<sup>−</sup>, SO<sub>4</sub><sup>2−</sup>, and TDS can refer to PC1 as a salinization factor (Table ##TAB##2##3##). The salinity in the water is likely caused by processes such as the weathering of limestone, halite dissolution, gypsum, and the exchange of ions between groundwater and surrounding rock of TCA. Additionally, human activities like irrigation practices and fertilization may also contribute to the presence of calcium (Ca<sup>2+</sup>), sodium (Na<sup>+</sup>), and magnesium (Mg<sup>2+</sup>), where agriculture is the main activity in Siwa Oasis.</p>", "<p id=\"Par35\">Regarding PC2 (alkaline and contamination factor), there is a relationship between this component and variables such as manganese (Mn), iron (Fe), copper (Cu), nickel (Ni), and chromium (Cr) (Table ##TAB##2##3##). This suggests that PC2 represents factors related to alkaline conditions and contamination. The interaction between alkaline water, rocks, and soil, oxidation–reduction processes, and potential contamination may contribute to the presence of these metals. The concentrations of iron and manganese can vary depending on whether the groundwater oxidized or reduced. Geogenic processes influence groundwater elements like heavy metals affected by natural processes, including pH and mineral dissolution<sup>##UREF##0##2##,##UREF##5##9##</sup>. High levels of these metals could indicate contamination from discharges or natural mineralization processes. According to previous studies<sup>##UREF##41##59##</sup>, the tertiary carbonate rocks contain glauconite and Fe oxides detrital grains in the northwestern desert of Egypt. Moghra Formation in the study area contains about 1.6–36.1%, 0–0.6%, and 5–50 mg/l of Fe<sub>2</sub>O<sub>3</sub>, MnO, and Cu, respectively. This suggests the geogenic source of these metals in the water resources of Siwa Oasis.</p>", "<p id=\"Par36\">As for PC3 (anthropogenic metal source), it is associated with variables zinc (Zn), cadmium (Cd), and Lead (Pb) (Table ##TAB##2##3##). Unlike the previous components, these metals were not recorded in the geological formations, suggesting an anthropogenic source for Zn, Cd, and Pb in the studied water.</p>", "<p id=\"Par37\">High concentrations of the investigated heavy metals in irrigation water extracted from TCA can cause serious problems like mineralization and waterlogging through different processes such as ion exchange, cation imbalance, mineral precipitation, sodicity, and salinity. When heavy metals like cadmium, lead, and zinc displace cations on soil exchange sites, it can change the chemistry of the groundwater and the potential supersaturation of minerals. Furthermore, when heavy metals interact with ions in the soil water, it can cause minerals like gypsum to precipitate, reducing soil permeability and worsening waterlogging issues<sup>##UREF##42##60##</sup>. The formation of hardpans or cemented layers due to the precipitation of iron and aluminum oxides further limits water movement in the soil. Elevated concentrations of heavy metals can negatively impact soil microbial activity. Microorganisms play a crucial role in organic matter decomposition and nutrient cycling<sup>##UREF##43##61##</sup>. Reduced microbial activity can result in the accumulation of organic matter, further contributing to waterlogging issues.</p>", "<title>Heavy metal pollution index (HPI) and metal index (MI)</title>", "<p id=\"Par38\">The Heavy Metal Pollution Index (HPI) is a tool used for assessing the pollution level of heavy metals in both surface water and groundwater. It helps evaluate the impact of metals on water quality and aids in monitoring and managing health risks associated with exposure to these metals<sup>##REF##28643849##11##</sup>. The HPI values ranged from 111.7 to 7274.5 in the water samples. All the water samples collected were categorized as having high pollution risk and not suitable for drinking according to the HPI classification<sup>##UREF##28##38##</sup> (HPI > 100) (Table ##TAB##3##4##).</p>", "<p id=\"Par39\">The MI (Metal Index) method was used alongside the HPI index to understand how heavy metals affect water quality. This allowed us to assess the extent of metal contamination in water by comparing it with the maximum allowable limit values outlined in WHO guidelines<sup>##REF##21818637##48##</sup>. The average MI values were between 6.5 and 462 (Table ##TAB##3##4##). These results indicate high impact and contamination of heavy metals in Siwa Oasis water resources according to MI classification<sup>##UREF##30##40##</sup> 38. It highlights the need for monitoring and improving water quality in the OasisOasis. In general, both the HPI and MI evaluations bring attention to the presence of heavy metals in the water resources, which could threaten the environment and humans in Siwa Oasis. This underlines the urgency of tackling this pollution and safeguarding the well-being of the people living there and the surrounding environment. The distribution maps of HPI and MI by using the kriging method showed that the most vulnerable area with heavy metals is the central and western part of Siwa Oasis, which could be due to the over-pumping of groundwater for irrigation purposes (Fig. ##FIG##6##7##a,b).</p>", "<title>Health risk assessment</title>", "<p id=\"Par40\">The non-carcinogenic and carcinogenic risk hazard indices (HI) were assessed by calculating ingestion and dermal absorption pathways' hazard quotients (HQ). The outcomes reveal the combined potential health risks for humans from exposure to different heavy metals for both children and adults.</p>", "<title>Non-carcinogenic health risk</title>", "<p id=\"Par41\">The toxic elements cadmium (Cd), chromium (Cr), copper (Cu), iron (Fe), manganese (Mn), nickel (Ni), lead (Pb), and zinc (Zn) were evaluated to determine the non-carcinogenic risk in both child and adult. For adults, the hazard quotient (HQ) ingestion ranged from 1.12 to 11.58, 0.015 to 123.8, 0.0013 to 11.7, 0.0001 to 1.5, 0.0002 to 4.2, 0.00015 to 1.08, 0.03 to 28.04 and 2.01E-5 to 0.01 for Cd, Cr, Cu, Fe, Mn, Ni, Pb and Zn, respectively (Fig. ##FIG##7##8##a). The HQ ingestion for child ranged from 0.46 to 44.2, 0.06 to 472.9, 0.005 to 44.9, 0.0005 to 5.9, 0.001 to 16.17, 0.0006 to 4.1, 0.1 to 107.06 and 0.0001 to 0.04 for Cd, Cr, Cu, Fe, Mn, Ni, Pb and Zn, respectively (Fig. ##FIG##7##8##b). Based on HQ oral values, the human health risks associated with exposure to Cd, Cr, Cu, Fe, Mn, Ni, Pb, and Zn through ingestion are generally higher for children than adults. It is worth noting that HQ oral values for children and adults are within the permissible limit under 1 for Cu, Fe, Mn, Ni, and Zn. In contrast, the HQ oral value (adult) was more significant than 1 in 76.7%, 45.8%, and 79.7% of the water samples for Cd, Cr, and Pb, respectively. HQ oral value (child) was more significant than 1 in 95.5%, 86.9%, and 94% of the water samples for Cd, Cr, and Pb, respectively. These values are specific to the location and period studied, and the actual human health risks may vary depending on various factors such as exposure duration and frequency, individual susceptibility, and environmental conditions. Nonetheless, the HQ dermal values for adults were in the range of 0.01 to 1.09, 0.005 to 47.02, 2.15E-5 to 0.18, 2.96E-6 to 0.03, 2.98E-5 to 0.5, 3.58E-6 to 0.02, 7.15E-5 to 0.07, and 2.86E-7 to 0.00015 for Cd, Cr, Cu, Fe, Mn, Ni, Pb, and Zn, respectively (Fig. ##FIG##7##8##c). Moreover, for a child, the HQ dermal values were in the range of 0.03 to 3.24, 0.016 to 138.7, 6.33E-5 to 0.5, 8.74E-6 to 0.1, 8.79E-5 to 1.48, 1.05E-5 to 0.07, 0.0002 to 0.2, and 8.44E-7 to 0.0004 for Cd, Cr, Cu, Fe, Mn, Ni, Pb, and Zn, respectively (Fig. ##FIG##7##8##d). The HQ dermal values for adults are within the permissible limit under 1 for all heavy metal parameters. In contrast, the HQ dermal value (child) was more significant than 1 in 24% and 50.3% of the water samples for Cd and Cr, respectively, and the rest of the heavy metals fell within acceptable limits. Based on HQ oral and dermal values, the human health risks associated with exposure to Cd, Cr, Cu, Fe, Mn, Ni, Pb, and Zn through dermal exposure are generally higher for children than adults. It is concluded that through oral contact, Cd, Cr, and Pb are the most contributing metals to human health risk (adult and child). In the case of dermal contact, children are more vulnerable to Cd and Cr from the water resources in Siwa Oasis, while there is no risk for adults.</p>", "<p id=\"Par42\">The hazard index (HI) is a valuable indicator used to assess the overall potential health hazard posed by heavy metals in both surface water and groundwater of Siwa Oasis. It considers all possible exposure routes, including ingestion and dermal pathways. The hazard quotients (HQs) associated with each heavy metal and exposure route are summed up to calculate the HI. This comprehensive approach provides a more complete picture of the combined health risks associated with heavy metal contamination in the water resources of Siwa Oasis. The HI value is an essential indicator for assessing the overall health impact and safety of the water sources in the region. HI oral values ranged from 1.6 to 142.1 and 6.2 to 542.6 for adults and children, respectively (Table ##TAB##3##4##). Moreover, the HI dermal values ranged from 0.07 to 47.8 and 0.2 to 141 for adults and children, respectively (Table ##TAB##3##4##).</p>", "<p id=\"Par43\">It can be concluded that the HI oral values for adults and children were above safe levels (HI > 1) in 100% of the water samples and fell in the high-risk category of non-carcinogenic impact. HI value for adults showed that 80.6% of the water samples fell in the low-risk class, and 19.4% showed a high risk of dermal contact. HI, value for child indicated that 22.4% of the water samples fell in the low-risk class, and 77.6% of the samples showed high risk of dermal contact (Table ##TAB##3##4##). The result of the HI showed that the child is more vulnerable to oral and dermal contact with heavy metals than adults. However, it is essential to monitor the levels of these metals in the different water resources of Siwa Oasis and their potential health effects, where the groundwater in the study area is non-rechargeable and is the primary water resource for different uses. The distribution maps of the hazard index (HI) in adults and children through dermal and oral contact showed that the central and western parts of Siwa Oasis are the most vulnerable locations to the non-carcinogenic risk impact of heavy metals (Fig. ##FIG##8##9##).</p>", "<title>Carcinogenic health risk (CR)</title>", "<p id=\"Par44\">Carcinogenic risks assess the probability of developing cancer as a result of prolonged exposure to a pollutant or a combination of contaminants. The traditional calculation of CR was conducted to calibrate and compare its values with the predicted CR gained from the Monte Carlo simulation later. In the case of adults, the CR oral values fell in a range between 0.0003 and 0.03, 2.26E-05 and 0.18, 3.16E-05 and 0.03 for Cd, Cr, and Pb, respectively (Fig. ##FIG##9##10##a), while for a child, CR oral values were between 0.001 and 0.1, 8.63E-05 and 0.7, 0.0001 and 0.1 for Cd, Cr, and Pb respectively (Fig. ##FIG##9##10##b).</p>", "<p id=\"Par45\">Regarding oral contact with heavy metals from the water resources of Siwa Oasis, the high carcinogenic risk (CR > 1 × 10<sup>–4</sup>) for adults was found in 77.6%, 96.3%, and 98.5% of the water samples for Cd, Cr, and Pb respectively and for a child in 100%, 98.5%, and 100 of water samples for Cd, Cr, and Pb respectively (Table ##TAB##3##4##). On the other hand, for adults, the CR dermal values fell in a range between 0.002 and 0.2, 0.0002 and 1.7, 1.5E-05 and 0.01 for Cd, Cr, and Pb, respectively (Fig. ##FIG##9##10##c), while for child, CR dermal values were between 0.005 and 0.5, 0.0006 and 5.2, 4.43E-05 and 0.04 for Cd, Cr, and Pb respectively (Fig. ##FIG##9##10##d). Based on dermal contact with heavy metals from the water samples, the high carcinogenic risk (CR > 1 × 10<sup>–4</sup>) for adults was found in 100%, 100%, and 94.7% of the water samples for Cd, Cr, and Pb respectively and for a child in 100%, 100%, and 97.8% of water samples for Cd, Cr, and Pb respectively (Table ##TAB##3##4##). The current findings indicated that further treatment is required for all water resources in Siwa Oasis, where the carcinogenic risk from heavy metals is very high and threatens the human health of both children and adults.</p>", "<title>Monte Carlo simulation approach</title>", "<p id=\"Par46\">The Monte Carlo simulation was applied to predict the values of HQ (oral and dermal) of Cd, Cr, Cu, Fe, Mn, Ni, Pb, and Zn, as well as CR (oral and dermal) of Cd, Cr, and Pb for both adults and children.</p>", "<title>Non-carcinogenic health risk</title>", "<p id=\"Par47\">The findings, from the Monte Carlo simulation offer insights into the health hazards linked to exposure to heavy metals through various routes in Siwa Oasis. It is reassuring to note that according to the estimated dermal hazard quotient (HQ dermal), there are no indications of any metal exceeding limits (Fig. ##FIG##10##11##a,b). This suggests that the risk of health issues due to skin contact with water resources is unlikely for adults and children. However, when oral exposure routes are considered, the situation changes. While some heavy metals like Cu, Fe, Mn, Ni, and Zn have predicted HQ values within limits (low risk) for adults, Cd, Cr, and Pb showed estimated HQ values higher than 1 (high risk). This implies a health risk for adults consuming water contaminated with Cd, Cr, and Pb through ingestion. Similar patterns are also observed for children (Fig. ##FIG##10##11##d,d).</p>", "<p id=\"Par48\">While some heavy metals like Fe, Ni and Zn do not pose risks through oral exposure routes in children, Cd, Cr, Cu, Mn, and Pb show predicted HQ values greater than 1, indicating potential health risks associated with consuming water containing these metals. It is important to remember that these assessments consider assumptions and uncertainties stemming from data sources. Hence, it is crucial to monitor the levels of exposure and regularly update risk assessments to safeguard the water resources in the area and protect the population's health. Through the comparison between the calculated HQ (Fig. ##FIG##7##8##) and predicted HQ (Fig. ##FIG##10##11##) through oral and dermal contact with heavy metals, it was found that Cd, Cr, and Pb are the main parameters responsible for high non-carcinogenic impact for child and adult in Siwa Oasis. Monte Carlo simulation was an effective method to predict the HQ successfully.</p>", "<title>Carcinogenic health risk through oral contact</title>", "<p id=\"Par49\">The analysis of carcinogenic risk probabilities (CR) for oral measurements in children and adults reveals some critical patterns. Across all parameters (Cd et al.), the CR oral measurements are consistently higher in children than adults. For children, the 5th percentile CR oral values (the lower bounds of the estimated cancer risk) were 0.017, 0.019, and 0.012 for Cd, Cr, and Pb, respectively (Fig. ##FIG##11##12##a–c). On the other hand, at the percentile level 95th (the upper bounds of estimated risk), CR oral values were determined as 0.044, 0.045, and 0.0275 for Cd, Cr, and Pb, respectively (Fig. ##FIG##11##12##a–c) representing higher potential risks for children. In contrast to this pattern observed in children’s data, the estimated cancer risk levels were relatively lower in adults based on their percentiles. For adults, the lower bounds of estimated cancer risks (5th percentile CR oral values) stood at 0.0047, 0.005, and 0.003 for Cd, Cr, and Pb, respectively (Fig. ##FIG##11##12##d–f). Furthermore, it was found that at the 95th percentile range, the estimated CR were 0.011, 0.0118, and 0.0072 for Cd, Cr, and Pb, respectively (Fig. ##FIG##11##12##d–f), suggesting lesser potential risks compared to those observed in children. However, the predicted CR through oral contact showed that most water samples collected from Siwa Oasis have the probability of causing high risk for children and adults with (CR > 1 × 10<sup>−4</sup>).</p>", "<p id=\"Par50\">Through the comparison between the calculated CR (Fig. ##FIG##7##8##) and predicted CR (Fig. ##FIG##10##11##) through oral contact with heavy metals, it was found that the three metals (Cd et al.) would have a high carcinogenic impact on children and adults in all water resources of Siwa Oasis. Monte Carlo simulation was an effective method to predict the CR oral successfully.</p>", "<title>Carcinogenic health risk through dermal contact</title>", "<p id=\"Par51\">The analysis of carcinogenic risk probabilities (CR) due to skin contact in children and adults reveals that children consistently have higher CR values than adults for all parameters (Cd et al.). Regarding children, the estimated 5th percentile CR levels for developing cancer through skin contact were 0.071, 0.13, and 0.0041 for Cd, Cr, and Pb, respectively (Fig. ##FIG##12##13##a–c). On the other hand, the estimated 95th percentile CR risk levels for developing cancer through skin contact in children were 0.162, 0.33, and 0.0102 for Cd, Cr, and Pb, respectively (Fig. ##FIG##12##13##a–c). These values indicate the upper boundaries of potential risks from dermal exposure in children. In contrast to children’s results, adults had estimated CR levels for developing cancer through skin contact with values of 0.022, 0.047, and 0.0013 as their lowest percentile (5th) for Cd, Cr, and Pb, respectively (Fig. ##FIG##11##12##d–f). The 95th percentile CR dermal values for adults were 0.055, 0.112, and 0.0034 for Cd, Cr, and Pb, respectively (Fig. ##FIG##11##12##d–f), representing the upper bounds of the estimated cancer risk from adult dermal exposure. Overall, the findings indicate that both children and adults are exposed to a high risk of developing cancer due to exposure to Cd, Cr, and Pb found in water resources within Siwa Oasis. The predicted cancer risk levels from the Monte Carlo simulation exceed the acceptable risk level (CR > 1.0E-04) in the majority of water samples, which suggests that continuous exposure to these metals could potentially lead to the development of cancer in the future for both adults and children. These findings emphasize the need to minimize metal contamination in water sources, aiming to reduce carcinogenic health.</p>", "<p id=\"Par52\">Through the comparison between the calculated CR (Fig. ##FIG##9##10##) and predicted CR (Fig. ##FIG##12##13##) through dermal contact with heavy metals, it was found that the three metals (Cd et al.) would have a high carcinogenic impact on children and adults in the majority of water samples collected from Siwa Oasis. Monte Carlo simulation was an effective tool for predicting the CR dermal successfully.</p>", "<p id=\"Par53\">This research evaluates the contamination caused by metals in Siwa Oasis. The Heavy Metal Pollution Index (HPI) and Metal Index (MI) reveal surface water and groundwater pollution levels. The HPI values, ranging from 111.7 to 7274.5, classify all water samples as polluted, rendering them unsuitable for drinking. The MI method also emphasizes the impact and contamination of metals, with average MI values ranging from 6.5 to 462. Maps showing distribution patterns highlight western areas of Siwa Oasis as vulnerable potentially due to excessive groundwater pumping for irrigation purposes. Additionally, an analysis based on components reveals both human-related sources of heavy metal pollution. HQ and HI were calculated to fully understand the impact of the metals detected in Siwa Oasis on human health.</p>", "<p id=\"Par54\">The Hazard Quotient (HQ) values for cadmium (Cd), chromium (Cr), and lead (Pb) indicated non-carcinogenic risks to human health. Among these metals, Cd, Cr, and Pb pose risks to children. Long-term exposure to Cd can harm the kidneys and bones, while Cr exposure may cause skin problems. Even low levels of Pb exposure can have cognitive consequences for children. The increased Risk (CR) values for Cd, Cr, and Pb highlight long-term risks. Carcinogenic risk assessments and Monte Carlo simulations further emphasize the urgency for water treatment to mitigate long-term health consequences. These findings collectively emphasize the need for measures to tackle heavy metal pollution and ensure the well-being of the Siwa Oasis community, showcasing the valuable insights provided by this study. However, it is essential to note the limitations of this study, such as its specificity to a location and period, potential variations in health risks, uncertainties associated with data sources, and assumptions made during the Monte Carlo simulation. Despite these limitations, this study emphasizes the need to monitor and manage metal contamination in Siwa Oasis to safeguard the environment and public health. The current study hints at potential ecological consequences, including impacts on soil quality and water resources. The economic repercussions on local agriculture and industries also require attention. The persistence of heavy metals in the environment raises concerns about long-term effects on the ecosystem. Strategies for mitigation and remediation should not only prioritize human health but also aim to preserve the environmental integrity of Siwa Oasis. Regulatory measures and community involvement remain crucial for sustainable solutions. According to the current findings, it is recommended that desalination stations be established to enhance water quality for irrigation in the study area. Additionally, creating companies specializing in salt extraction could provide a dual benefit of addressing heavy metal pollution and utilizing the extracted salts in various industries. By recognizing the broader environmental context and implementing proactive measures, this study emphasizes the urgency of a comprehensive approach to address heavy metal pollution and promote sustainable environmental management in Siwa Oasis. For further research, it would be beneficial to thoroughly understand how heavy metal concentrations vary over time. Conducting studies that cover seasons and years could provide valuable insights into the dynamics of metal pollution. It would also be helpful to investigate water treatment technologies and their effectiveness in reducing levels of metals. Understanding the socio-impacts of metal contamination on communities and industries is crucial for developing holistic management strategies. Lastly, exploring the feasibility and impact of implementing suggested measures like desalination stations and salt extraction companies would provide insights into environmental management in Siwa Oasis.</p>" ]	[ "<title>Conclusion</title>", "<p id=\"Par55\">This study undertook a comprehensive assessment of heavy metals pollution and associated environmental and health risks in different water resources. The heavy metals included Fe, Mn, Zn, Cu, Ni, Cr, Pb, and Cd. In order to assess risks to both the environment and human health, indices included HPI, MI, HQ, HI, and CR for oral and dermal exposure routes were evaluated. The Monte Carlo method simulates carcinogenic risk assessments, providing a more comprehensive understanding and realistic potential health impacts. In terms of heavy metal concentrations, the mean values ranked as follows: Fe > Cu > Cr > Pb > Mn > Ni > Cd > Zn. Meanwhile, Fe, Cd, Cr, Pb, and Mn exceeded WHO standards. The total dissolved solids (TDS) exhibited significant variability, ranging from 1120 mg/L in the Tertiary Carbonate Aquifer (TCA) to 153,589 mg/L in salt lakes, averaging 9834.1 mg/L. The pH values, ranging from 6.8 to 8.7, indicated neutral to alkaline water conditions. Various ions, including calcium, magnesium, potassium, sodium, chloride, sulfate, and bicarbonate, surpassed recommended limits for irrigation according to FAO standards. The origin of water samples highlights a significant portion of the TCA with recent and old marine water origins. Geochemical processes, including ion exchange facilitated by clay minerals and silicate weathering, contribute to the complex water chemistry. Furthermore, the correlation between heavy metals and various human activities signifies anthropogenic contributions to heavy metal concentrations. The HPI and MI values revealed a risk of pollution across all water resources (HPI > 100 and MI > 6). Moreover, HI oral values were greater than one (HI > 1) in most water samples, indicating high risks associated with the non-carcinogenic effects of these metals on both adults and children. The health risks associated with dermal contact showed a higher risk for children, and 77.6% of water samples have HI > 1. It is still a concern for adults; 19.4% of water samples have an HI value greater than one (HI > 1). Regarding metals like Cd, Cr, and Pb, most water samples indicate that adults and children are vulnerable to carcinogenic effects. The CR values (oral and dermal) for these metals are greater than 1 × 10<sup>−4</sup> in most samples. The Monte Carlo method further confirmed the presence of carcinogenic impact, with the 5th and 95th percentile risk exposures indicating elevated risks for both children and adults. Additionally, statistical analyses such as cluster analysis and Principal Component Analysis (PCA) provide insights into groundwater composition. The clustering of variables reveals distinct groups based on physicochemical characteristics such as carbonates and evaporites, heavy metals, and bicarbonates, shedding light on the sources and controlling factors of water chemistry. PCA identifies three components where PC1, with 40% of the total variance, represents factors related to limestone weathering and ion exchange processes, which contribute to salinization. PC2, with 26.5 of the total variances, indicates the movement of alkaline water and potential contamination processes particularly associated with heavy metals. PC3, with 7.5 of the total variances, highlights the sources of Zn, Cd, and Pb. Considering these findings, immediate action must be taken to mitigate the dangers of metal pollution in Siwa Oasis. It is crucial to implement treatment strategies for all water resources to protect the environment and human health. Particular attention should be given to preventing carcinogenic and non-carcinogenic health impacts by decreasing exposure duration. This finding showed that the Monte Carlo method is an effective tool that should be applied alongside the traditional calculation of health risk indices to decrease uncertainty and increase the reliability of the results.</p>" ]	[ "<p id=\"Par1\">This study assessed the environmental and health risks associated with heavy metals in the water resources of Egypt's northwestern desert. The current approaches included the Spearman correlation matrix, principal component analysis, and cluster analysis to identify pollution sources and quality-controlling factors. Various indices (HPI, MI, HQ, HI, and CR) were applied to evaluate environmental and human health risks. Additionally, the Monte Carlo method was employed for probabilistic carcinogenic and non-carcinogenic risk assessment via oral and dermal exposure routes in adults and children. Notably, all water resources exhibited high pollution risks with HPI and MI values exceeding permissible limits (HPI > 100 and MI > 6), respectively. Furthermore, HI oral values indicated significant non-carcinogenic risks to both adults and children, while dermal contact posed a high risk to 19.4% of samples for adults and 77.6% of samples for children (HI > 1). Most water samples exhibited CR values exceeding 1 × 10<sup>–4</sup> for Cd, Cr, and Pb, suggesting vulnerability to carcinogenic effects in both age groups. Monte Carlo simulations reinforced these findings, indicating a significant carcinogenic impact on children and adults. Consequently, comprehensive water treatment measures are urgently needed to mitigate carcinogenic and non-carcinogenic health risks in Siwa Oasis.</p>", "<title>Subject terms</title>", "<p>Open access funding provided by University of Miskolc.</p>" ]	[]	[ "<title>Acknowledgements</title>", "<p>We would like to thank the desert research center for helping to collect the water samples. The current research work has been funded by the sustainable development and technologies national program of the Hungarian Academy of Sciences (FFT NP FTA).</p>", "<title>Author contributions</title>", "<p>M.H.E., M.E., E.A.M & T.M. designed the study; H.S.R., M.E., E.A.M and M.H.E. collected and prepared samples, performed field survey; M.H.E., M.E., H.S.R. and E.A.M performed laboratory work; M.H.E., A.K. & P.S. prepared maps; M.H.E., A.K., P.S. and T.M wrote, reviewed, and edited the manuscript. All authors contributed extensively to the discussions about the work and in reviewing and revising the manuscript.</p>", "<title>Funding</title>", "<p>Open access funding provided by University of Miskolc. The current research work has been funded by the sustainable development and technologies national program of the Hungarian Academy of Sciences (FFT NP FTA).</p>", "<title>Data availability</title>", "<p>The datasets utilized and/or analyzed during the current study are available upon request from the corresponding author.</p>", "<title>Competing interests</title>", "<p id=\"Par56\">The authors declare no competing interests.</p>" ]	[ "<fig id=\"Fig1\"><label>Figure 1</label><caption><p>Location and sampling map of Siwa depression.</p></caption></fig>", "<fig id=\"Fig2\"><label>Figure 2</label><caption><p>Surface geological map modified after<sup>##UREF##24##33##</sup> (<bold>a</bold>), geomorphological map modified after<sup>##UREF##24##33##</sup> (<bold>b</bold>), hydrogeological conceptual model and subsurface geological formations in Siwa Oasis (<bold>c</bold>).</p></caption></fig>", "<fig id=\"Fig3\"><label>Figure 3</label><caption><p>Sulin graph showing the origin and type of water samples in Siwa Oasis.</p></caption></fig>", "<fig id=\"Fig4\"><label>Figure 4</label><caption><p>Chloro alkaline index (<bold>a</bold>), bivariate plot of Ca<sup>2+</sup>/Na<sup>+</sup> versus Mg<sup>2+</sup>/Na<sup>+</sup> (<bold>b</bold>), and Pearson correlation matrix (<bold>c</bold>).</p></caption></fig>", "<fig id=\"Fig5\"><label>Figure 5</label><caption><p>Cluster analysis of the investigated parameters in water samples using dendrogram.</p></caption></fig>", "<fig id=\"Fig6\"><label>Figure 6</label><caption><p>Principal components extracted from scree plot (<bold>a</bold>) and its visualization on 3D plot (<bold>b</bold>).</p></caption></fig>", "<fig id=\"Fig7\"><label>Figure 7</label><caption><p>Distripution map of metal index (<bold>a</bold>) and heavy metal pollution index (<bold>b</bold>).</p></caption></fig>", "<fig id=\"Fig8\"><label>Figure 8</label><caption><p>Box plot of the hazard quotient (HQ oral) in adult (<bold>a</bold>), (HQ oral) in child (<bold>b</bold>), (HQ dermal) in adult (<bold>c</bold>), and (HQ dermal) in child.</p></caption></fig>", "<fig id=\"Fig9\"><label>Figure 9</label><caption><p>Distripution maps of Hazard index ina adult and child through oral and dermal contact.</p></caption></fig>", "<fig id=\"Fig10\"><label>Figure 10</label><caption><p>Box plot of the carcinogenic risk (CR) in adult and child through oral (<bold>a</bold> and <bold>b</bold>) and dermal (c and d) contact.</p></caption></fig>", "<fig id=\"Fig11\"><label>Figure 11</label><caption><p>The predicted hazard quotient (HQ dermal) in adult (<bold>a</bold>), (HQ dermal) in child (<bold>b</bold>), (HQ oral) in adult (<bold>c</bold>), and (HQ oral) in child.</p></caption></fig>", "<fig id=\"Fig12\"><label>Figure 12</label><caption><p>Predicted carcinogenic risk (CR) in adult (<bold>a</bold>, <bold>b</bold> and <bold>c</bold>) and child (<bold>d</bold>, <bold>e</bold> and <bold>f</bold>) through oral contact for Cd, Cr, and Pb respectively.</p></caption></fig>", "<fig id=\"Fig13\"><label>Figure 13</label><caption><p>Predicted carcinogenic risk (CR) in adult (<bold>a</bold>, <bold>b</bold> and <bold>c</bold>) and child (<bold>d</bold>, <bold>e</bold> and <bold>f</bold>) through dermal contact for Cd, Cr, and Pb respectively.</p></caption></fig>" ]	[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>The parameters for the calculation of HQ, HI, and CR.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">HM</th><th align=\"left\">Cd</th><th align=\"left\">Cr</th><th align=\"left\">Cu</th><th align=\"left\">Fe</th><th align=\"left\">Mn</th><th align=\"left\">Ni</th><th align=\"left\">Pb</th><th align=\"left\">Zn</th><th align=\"left\">References</th></tr></thead><tbody><tr><td align=\"left\">RfD Oral(mg/kg/day)</td><td align=\"left\">0.0005</td><td align=\"left\">0.003</td><td align=\"left\">0.04</td><td align=\"left\">0.7</td><td char=\".\" align=\"char\">0.024</td><td char=\".\" align=\"char\">0.02</td><td align=\"left\">0.0014</td><td align=\"left\">0.3</td><td align=\"left\"><sup>##REF##22805985##45##</sup></td></tr><tr><td align=\"left\">ABS</td><td align=\"left\">0.05</td><td align=\"left\">0.025</td><td align=\"left\">0.3</td><td align=\"left\">0.2</td><td char=\".\" align=\"char\">0.04</td><td char=\".\" align=\"char\">0.04</td><td align=\"left\">0.3</td><td align=\"left\">0.2</td><td align=\"left\"><sup>##UREF##33##46##</sup></td></tr><tr><td align=\"left\">Rfd Dermal (mg/kg/day)</td><td align=\"left\">0.000025</td><td align=\"left\">0.000075</td><td align=\"left\">0.012</td><td align=\"left\">0.14</td><td char=\".\" align=\"char\">0.00096</td><td char=\".\" align=\"char\">0.0008</td><td align=\"left\">0.00042</td><td align=\"left\">0.06</td><td align=\"left\"><sup>##UREF##34##47##</sup></td></tr><tr><td align=\"left\">CSF oral mg/kg/day</td><td align=\"left\">6.1</td><td align=\"left\">0.5</td><td align=\"left\"/><td align=\"left\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td align=\"left\">0.5</td><td align=\"left\"/><td align=\"left\"><sup>##REF##21818637##48##</sup></td></tr><tr><td align=\"left\">CSF dermal</td><td align=\"left\">6100</td><td align=\"left\">500</td><td align=\"left\"/><td align=\"left\"/><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td align=\"left\">500</td><td align=\"left\"/><td align=\"left\"><sup>##REF##21818637##48##</sup></td></tr><tr><td align=\"left\">Kp</td><td align=\"left\">0.001</td><td align=\"left\">0.002</td><td align=\"left\">0.001</td><td align=\"left\">0.001</td><td char=\".\" align=\"char\">0.001</td><td char=\".\" align=\"char\">0.0002</td><td align=\"left\">0.0001</td><td align=\"left\">0.0006</td><td align=\"left\"><sup>##REF##30391893##49##</sup></td></tr><tr><td align=\"left\">Si</td><td align=\"left\">0.003</td><td align=\"left\">0.05</td><td align=\"left\">3</td><td align=\"left\">0.3</td><td char=\".\" align=\"char\">0.05</td><td char=\".\" align=\"char\">0.07</td><td align=\"left\">0.01</td><td align=\"left\">1</td><td align=\"left\"><sup>##UREF##35##50##</sup></td></tr><tr><td align=\"left\">ET Adult (h/day)</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\">0.58</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"><sup>##REF##19165409##51##</sup></td></tr><tr><td align=\"left\">ET Child (h/day)</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\">1</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"><sup>##REF##19165409##51##</sup></td></tr><tr><td align=\"left\">SA Adult (cm2)</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\">18,000</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"><sup>##UREF##33##46##</sup></td></tr><tr><td align=\"left\">SA Child (cm2)</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\">6600</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"><sup>##UREF##33##46##</sup></td></tr><tr><td align=\"left\">CF (L/cm3)</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\">0.001</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"><sup>##REF##19165409##51##</sup></td></tr><tr><td align=\"left\">IR Adult (L/day)</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\">2.2</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"><sup>##UREF##34##47##</sup></td></tr><tr><td align=\"left\">IR Child (L/day)</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\">1.8</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"><sup>##UREF##34##47##</sup></td></tr><tr><td align=\"left\">EF (day/year)</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\">350</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"><sup>##UREF##32##42##</sup></td></tr><tr><td align=\"left\">ED Adult (year)</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\">70</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"><sup>##UREF##33##46##</sup></td></tr><tr><td align=\"left\">ED Child (year)</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\">6</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"><sup>##UREF##33##46##</sup></td></tr><tr><td align=\"left\">BW Adult (kg)</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\">70</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"><sup>##REF##25647791##52##</sup></td></tr><tr><td align=\"left\">BW Child (kg)</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\">15</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"><sup>##REF##25647791##52##</sup></td></tr><tr><td align=\"left\">AT Adult (day)</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\">25,550</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"><sup>##REF##28029481##53##</sup></td></tr><tr><td align=\"left\">AT Child (day)</td><td align=\"left\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\">2190</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/><td align=\"left\"/><td align=\"left\"/><td align=\"left\"><sup>##REF##28029481##53##</sup></td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Statistical properties of the investigated parameters in water resources of Siwa Oasis.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Parameters</th><th align=\"left\">Min</th><th align=\"left\">Max</th><th align=\"left\">Mean</th><th align=\"left\">SD</th></tr></thead><tbody><tr><td align=\"left\">pH</td><td align=\"left\">6.8</td><td align=\"left\">8.7</td><td char=\".\" align=\"char\">7.9</td><td align=\"left\">0.3</td></tr><tr><td align=\"left\">TDS</td><td align=\"left\">1120</td><td align=\"left\">153,589</td><td char=\".\" align=\"char\">9834.1</td><td align=\"left\">20,701.9</td></tr><tr><td align=\"left\">K<sup> + </sup></td><td align=\"left\">3.5</td><td align=\"left\">83</td><td char=\".\" align=\"char\">42.8</td><td align=\"left\">18.5</td></tr><tr><td align=\"left\">Na<sup> + </sup></td><td align=\"left\">192</td><td align=\"left\">39,500</td><td char=\".\" align=\"char\">2240.9</td><td align=\"left\">5531.6</td></tr><tr><td align=\"left\">Mg<sup>2 + </sup></td><td align=\"left\">9</td><td align=\"left\">12,216.6</td><td char=\".\" align=\"char\">676.6</td><td align=\"left\">1388.8</td></tr><tr><td align=\"left\">Ca<sup>2 + </sup></td><td align=\"left\">19.6</td><td align=\"left\">2508.8</td><td char=\".\" align=\"char\">366.5</td><td align=\"left\">401</td></tr><tr><td align=\"left\">Cl<sup>−</sup></td><td align=\"left\">580</td><td align=\"left\">94,250</td><td char=\".\" align=\"char\">5933.9</td><td align=\"left\">13,042.3</td></tr><tr><td align=\"left\">SO<sub>4</sub><sup>2−</sup></td><td align=\"left\">5</td><td align=\"left\">5348.7</td><td char=\".\" align=\"char\">486.6</td><td align=\"left\">652.4</td></tr><tr><td align=\"left\">HCO<sup>3−</sup></td><td align=\"left\">83.7</td><td align=\"left\">328.8</td><td char=\".\" align=\"char\">166.7</td><td align=\"left\">36.5</td></tr><tr><td align=\"left\">CO<sub>3</sub><sup>2−</sup></td><td align=\"left\">0</td><td align=\"left\">35.3</td><td char=\".\" align=\"char\">6.2</td><td align=\"left\">8.8</td></tr><tr><td align=\"left\">Cd</td><td align=\"left\">0.002</td><td align=\"left\">0.19</td><td char=\".\" align=\"char\">0.04</td><td align=\"left\">0.03</td></tr><tr><td align=\"left\">Cr</td><td align=\"left\">0.0015</td><td align=\"left\">12.3</td><td char=\".\" align=\"char\">0.6</td><td align=\"left\">1.63</td></tr><tr><td align=\"left\">Cu</td><td align=\"left\">0.002</td><td align=\"left\">15.6</td><td char=\".\" align=\"char\">1.14</td><td align=\"left\">3.004</td></tr><tr><td align=\"left\">Fe</td><td align=\"left\">0.003</td><td align=\"left\">36.2</td><td char=\".\" align=\"char\">2.16</td><td align=\"left\">5.35</td></tr><tr><td align=\"left\">Mn</td><td align=\"left\">0.0002</td><td align=\"left\">3.37</td><td char=\".\" align=\"char\">0.28</td><td align=\"left\">0.68</td></tr><tr><td align=\"left\">Ni</td><td align=\"left\">0.0001</td><td align=\"left\">0.72</td><td char=\".\" align=\"char\">0.1</td><td align=\"left\">0.12</td></tr><tr><td align=\"left\">Pb</td><td align=\"left\">0.002</td><td align=\"left\">2.23</td><td char=\".\" align=\"char\">0.33</td><td align=\"left\">0.34</td></tr><tr><td align=\"left\">Zn</td><td align=\"left\">0.0002</td><td align=\"left\">0.1</td><td char=\".\" align=\"char\">0.03</td><td align=\"left\">0.024</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab3\"><label>Table 3</label><caption><p>The principal component analysis of the physicochemical parameters and heavy metals in the water samples.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Parameters</th><th align=\"left\">PC1</th><th align=\"left\">PC2</th><th align=\"left\">PC3</th></tr></thead><tbody><tr><td align=\"left\">TDS</td><td align=\"left\"><bold>0.984</bold></td><td char=\".\" align=\"char\">0.068</td><td char=\".\" align=\"char\">0.076</td></tr><tr><td align=\"left\">Na</td><td align=\"left\"><bold>0.958</bold></td><td char=\".\" align=\"char\">0.079</td><td char=\".\" align=\"char\">0.122</td></tr><tr><td align=\"left\">Mg</td><td align=\"left\"><bold>0.962</bold></td><td char=\".\" align=\"char\">0.069</td><td char=\".\" align=\"char\">0.069</td></tr><tr><td align=\"left\">Ca</td><td align=\"left\"><bold>0.884</bold></td><td char=\".\" align=\"char\">−0.01</td><td char=\".\" align=\"char\">−0.064</td></tr><tr><td align=\"left\">Cl</td><td align=\"left\"><bold>0.978</bold></td><td char=\".\" align=\"char\">0.066</td><td char=\".\" align=\"char\">0.064</td></tr><tr><td align=\"left\">SO4</td><td align=\"left\"><bold>0.958</bold></td><td char=\".\" align=\"char\">0.033</td><td char=\".\" align=\"char\">0.002</td></tr><tr><td align=\"left\">HCO3</td><td align=\"left\">0.341</td><td char=\".\" align=\"char\">0.031</td><td char=\".\" align=\"char\">−0.415</td></tr><tr><td align=\"left\">Cd</td><td align=\"left\">0.091</td><td char=\".\" align=\"char\">0.476</td><td char=\".\" align=\"char\">0.238</td></tr><tr><td align=\"left\">Cr</td><td align=\"left\">−0.02</td><td char=\".\" align=\"char\"><bold>0.856</bold></td><td char=\".\" align=\"char\">−0.084</td></tr><tr><td align=\"left\">Cu</td><td align=\"left\">0.032</td><td char=\".\" align=\"char\"><bold>0.94</bold></td><td char=\".\" align=\"char\">−0.004</td></tr><tr><td align=\"left\">Fe</td><td align=\"left\">−0.063</td><td char=\".\" align=\"char\"><bold>0.885</bold></td><td char=\".\" align=\"char\">−0.013</td></tr><tr><td align=\"left\">Mn</td><td align=\"left\">0.005</td><td char=\".\" align=\"char\"><bold>0.914</bold></td><td char=\".\" align=\"char\">0.026</td></tr><tr><td align=\"left\">Ni</td><td align=\"left\">0.326</td><td char=\".\" align=\"char\">0.686</td><td char=\".\" align=\"char\">0.304</td></tr><tr><td align=\"left\">Pb</td><td align=\"left\">0.103</td><td char=\".\" align=\"char\">0.421</td><td char=\".\" align=\"char\"><bold>0.498</bold></td></tr><tr><td align=\"left\">Zn</td><td align=\"left\">0.173</td><td char=\".\" align=\"char\">0.015</td><td char=\".\" align=\"char\"><bold>0.794</bold></td></tr><tr><td align=\"left\">Eigenvalues</td><td align=\"left\">6</td><td char=\".\" align=\"char\">3.9</td><td char=\".\" align=\"char\">1.1</td></tr><tr><td align=\"left\">% of Variance</td><td align=\"left\">40</td><td char=\".\" align=\"char\">26.5</td><td char=\".\" align=\"char\">7.5</td></tr><tr><td align=\"left\">Cumulative %</td><td align=\"left\">40</td><td char=\".\" align=\"char\">66.5</td><td char=\".\" align=\"char\">74.1</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab4\"><label>Table 4</label><caption><p>The environmental and health risk indices.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\">Criteria</th><th align=\"left\">Min</th><th align=\"left\">Max</th><th align=\"left\">Mean</th><th align=\"left\">Range</th><th align=\"left\">Class</th><th align=\"left\">Samples (%)</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"6\">MI</td><td char=\".\" align=\"char\" rowspan=\"6\">6.5</td><td align=\"left\" rowspan=\"6\">462</td><td char=\".\" align=\"char\" rowspan=\"6\">72.3</td><td align=\"left\">MI < 0.3</td><td align=\"left\">Very clean</td><td align=\"left\">0 (0%)</td></tr><tr><td align=\"left\">0.3 < MI < 1</td><td align=\"left\">Clean</td><td align=\"left\">0 (0%)</td></tr><tr><td align=\"left\">1 < MI < 2</td><td align=\"left\">Partly affected</td><td align=\"left\">0 (0%)</td></tr><tr><td align=\"left\">2 < MI < 4</td><td align=\"left\">Moderately affected</td><td align=\"left\">0 (0%)</td></tr><tr><td align=\"left\">4 < MI < 6</td><td align=\"left\">Heavily affected</td><td align=\"left\">0 (0%)</td></tr><tr><td align=\"left\">MI > 6</td><td align=\"left\">Severely affected</td><td align=\"left\">133 (100%)</td></tr><tr><td align=\"left\" rowspan=\"5\">HPI</td><td char=\".\" align=\"char\" rowspan=\"5\">111.7</td><td align=\"left\" rowspan=\"5\">7274.5</td><td char=\".\" align=\"char\" rowspan=\"5\">1702.9</td><td align=\"left\"> < 25</td><td align=\"left\">Excellent</td><td align=\"left\">0 (0%)</td></tr><tr><td align=\"left\">26—50</td><td align=\"left\">Good</td><td align=\"left\">0 (0%)</td></tr><tr><td align=\"left\">51—75</td><td align=\"left\">Poor</td><td align=\"left\">0 (0%)</td></tr><tr><td align=\"left\">76—100</td><td align=\"left\">Very poor</td><td align=\"left\">0 (0%)</td></tr><tr><td align=\"left\"> > 100</td><td align=\"left\">Unsuitable</td><td align=\"left\">133 (100%)</td></tr><tr><td align=\"left\" rowspan=\"2\">HI Adult (Oral)</td><td char=\".\" align=\"char\" rowspan=\"2\">1.6</td><td align=\"left\" rowspan=\"2\">142.1</td><td char=\".\" align=\"char\" rowspan=\"2\">14.04</td><td align=\"left\"> < 1</td><td align=\"left\">Low risk</td><td align=\"left\">0 (0%)</td></tr><tr><td align=\"left\"> > 1</td><td align=\"left\">High risk</td><td align=\"left\">133 (100%)</td></tr><tr><td align=\"left\" rowspan=\"2\">HI Child (Oral)</td><td char=\".\" align=\"char\" rowspan=\"2\">6.2</td><td align=\"left\" rowspan=\"2\">542.6</td><td char=\".\" align=\"char\" rowspan=\"2\">53.6</td><td align=\"left\"> < 1</td><td align=\"left\">Low risk</td><td align=\"left\">0 (0%)</td></tr><tr><td align=\"left\"> > 1</td><td align=\"left\">High risk</td><td align=\"left\">133 (100%)</td></tr><tr><td align=\"left\" rowspan=\"2\">HI Adult (Dermal)</td><td char=\".\" align=\"char\" rowspan=\"2\">0.07</td><td align=\"left\" rowspan=\"2\">47.8</td><td char=\".\" align=\"char\" rowspan=\"2\">2.6</td><td align=\"left\"> < 1</td><td align=\"left\">Low risk</td><td align=\"left\">108 (80.6%)</td></tr><tr><td align=\"left\"> > 1</td><td align=\"left\">High risk</td><td align=\"left\">26 (19.4%)</td></tr><tr><td align=\"left\" rowspan=\"2\">HI Child (Dermal)</td><td char=\".\" align=\"char\" rowspan=\"2\">0.2</td><td align=\"left\" rowspan=\"2\">141</td><td char=\".\" align=\"char\" rowspan=\"2\">7.7</td><td align=\"left\"> < 1</td><td align=\"left\">Low risk</td><td align=\"left\">30 (22.4%)</td></tr><tr><td align=\"left\"> > 1</td><td align=\"left\">High risk</td><td align=\"left\">103 (77.6%)</td></tr><tr><td align=\"left\" rowspan=\"2\">CRCd Adult (Oral)</td><td char=\".\" align=\"char\" rowspan=\"2\">0.0003</td><td align=\"left\" rowspan=\"2\">0.03</td><td char=\".\" align=\"char\" rowspan=\"2\">0.007</td><td align=\"left\"> < 1 × 10–4</td><td align=\"left\">Acceptable</td><td align=\"left\">30 (22.4%)</td></tr><tr><td align=\"left\"> > 1 × 10–4</td><td align=\"left\">High risk</td><td align=\"left\">103 (77.6%)</td></tr><tr><td align=\"left\" rowspan=\"2\">CRCr Adult (Oral)</td><td char=\".\" align=\"char\" rowspan=\"2\">2.26E-05</td><td align=\"left\" rowspan=\"2\">0.18</td><td char=\".\" align=\"char\" rowspan=\"2\">0.009</td><td align=\"left\"> < 1 × 10–4</td><td align=\"left\">Acceptable</td><td align=\"left\">5 (3.7%)</td></tr><tr><td align=\"left\"> > 1 × 10–4</td><td align=\"left\">High risk</td><td align=\"left\">128 (96.3%)</td></tr><tr><td align=\"left\" rowspan=\"2\">CRPb Adult (Oral)</td><td char=\".\" align=\"char\" rowspan=\"2\">3.16E-05</td><td align=\"left\" rowspan=\"2\">0.03</td><td char=\".\" align=\"char\" rowspan=\"2\">0.005</td><td align=\"left\"> < 1 × 10–4</td><td align=\"left\">Acceptable</td><td align=\"left\">2 (1.5%)</td></tr><tr><td align=\"left\"> > 1 × 10–4</td><td align=\"left\">High risk</td><td align=\"left\">131 (98.5%)</td></tr><tr><td align=\"left\" rowspan=\"2\">CRCd Child (Oral)</td><td char=\".\" align=\"char\" rowspan=\"2\">0.001</td><td align=\"left\" rowspan=\"2\">0.1</td><td char=\".\" align=\"char\" rowspan=\"2\">0.03</td><td align=\"left\"> < 1 × 10–4</td><td align=\"left\">Acceptable</td><td align=\"left\">0 (0%)</td></tr><tr><td align=\"left\"> > 1 × 10–4</td><td align=\"left\">High risk</td><td align=\"left\">133 (100%)</td></tr><tr><td align=\"left\" rowspan=\"2\">CRCr Child (Oral)</td><td char=\".\" align=\"char\" rowspan=\"2\">8.63E-05</td><td align=\"left\" rowspan=\"2\">0.7</td><td char=\".\" align=\"char\" rowspan=\"2\">0.03</td><td align=\"left\"> < 1 × 10–4</td><td align=\"left\">Acceptable</td><td align=\"left\">2 (1.5%)</td></tr><tr><td align=\"left\"> > 1 × 10–4</td><td align=\"left\">High risk</td><td align=\"left\">131 (98.5%)</td></tr><tr><td align=\"left\" rowspan=\"2\">CRPb Child (Oral)</td><td char=\".\" align=\"char\" rowspan=\"2\">0.0001</td><td align=\"left\" rowspan=\"2\">0.1</td><td char=\".\" align=\"char\" rowspan=\"2\">0.02</td><td align=\"left\"> < 1 × 10–4</td><td align=\"left\">Acceptable</td><td align=\"left\">0 (0%)</td></tr><tr><td align=\"left\"> > 1 × 10–4</td><td align=\"left\">High risk</td><td align=\"left\">133 (100%)</td></tr><tr><td align=\"left\" rowspan=\"2\">CRCd Adult (Dermal)</td><td char=\".\" align=\"char\" rowspan=\"2\">0.002</td><td align=\"left\" rowspan=\"2\">0.2</td><td char=\".\" align=\"char\" rowspan=\"2\">0.04</td><td align=\"left\"> < 1 × 10–4</td><td align=\"left\">Acceptable</td><td align=\"left\">0 (0%)</td></tr><tr><td align=\"left\"> > 1 × 10–4</td><td align=\"left\">High risk</td><td align=\"left\">133 (100%)</td></tr><tr><td align=\"left\" rowspan=\"2\">CRCr Adult (Dermal)</td><td char=\".\" align=\"char\" rowspan=\"2\">0.0002</td><td align=\"left\" rowspan=\"2\">1.7</td><td char=\".\" align=\"char\" rowspan=\"2\">0.08</td><td align=\"left\"> < 1 × 10–4</td><td align=\"left\">Acceptable</td><td align=\"left\">0 (0%)</td></tr><tr><td align=\"left\"> > 1 × 10–4</td><td align=\"left\">High risk</td><td align=\"left\">133 (100%)</td></tr><tr><td align=\"left\" rowspan=\"2\">CRPb Adult (Dermal)</td><td char=\".\" align=\"char\" rowspan=\"2\">1.5E-05</td><td align=\"left\" rowspan=\"2\">0.01</td><td char=\".\" align=\"char\" rowspan=\"2\">0.002</td><td align=\"left\"> < 1 × 10–4</td><td align=\"left\">Acceptable</td><td align=\"left\">7 (5.3%)</td></tr><tr><td align=\"left\"> > 1 × 10–4</td><td align=\"left\">High risk</td><td align=\"left\">126 (94.7%)</td></tr><tr><td align=\"left\" rowspan=\"2\">CRCd Child (Dermal)</td><td char=\".\" align=\"char\" rowspan=\"2\">0.005</td><td align=\"left\" rowspan=\"2\">0.5</td><td char=\".\" align=\"char\" rowspan=\"2\">0.1</td><td align=\"left\"> < 1 × 10–4</td><td align=\"left\">Acceptable</td><td align=\"left\">0 (0%)</td></tr><tr><td align=\"left\"> > 1 × 10–4</td><td align=\"left\">High risk</td><td align=\"left\">133 (100%)</td></tr><tr><td align=\"left\" rowspan=\"2\">CRCr Child (Dermal)</td><td char=\".\" align=\"char\" rowspan=\"2\">0.0006</td><td align=\"left\" rowspan=\"2\">5.2</td><td char=\".\" align=\"char\" rowspan=\"2\">0.2</td><td align=\"left\"> < 1 × 10–4</td><td align=\"left\">Acceptable</td><td align=\"left\">0 (0%)</td></tr><tr><td align=\"left\"> > 1 × 10–4</td><td align=\"left\">High risk</td><td align=\"left\">133 (100%)</td></tr><tr><td align=\"left\" rowspan=\"2\">CRPb Child (Dermal)</td><td char=\".\" align=\"char\" rowspan=\"2\">4.43E-05</td><td align=\"left\" rowspan=\"2\">0.04</td><td char=\".\" align=\"char\" rowspan=\"2\">0.007</td><td align=\"left\"> < 1 × 10–4</td><td align=\"left\">Acceptable</td><td align=\"left\">3 (2.2%)</td></tr><tr><td align=\"left\"> > 1 × 10–4</td><td align=\"left\">High risk</td><td align=\"left\">130 (97.8%)</td></tr></tbody></table></table-wrap>" ]	[ "<disp-formula id=\"Equ1\"><label>1</label><alternatives><tex-math id=\"M1\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\text{HPI}}=\\frac{{\\sum }_{{\\text{i}}=1}^{{\\text{n}}}{{\\text{W}}}_{{\\text{i}}}{{\\text{Q}}}_{{\\text{i}}}}{\\sum_{{\\text{i}}=1}^{{\\text{n}}}{\\text{Wi}}}$$\\end{document}</tex-math><mml:math id=\"M2\" display=\"block\"><mml:mrow><mml:mtext>HPI</mml:mtext><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:msubsup><mml:mo>∑</mml:mo><mml:mrow><mml:mtext>i</mml:mtext><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mtext>n</mml:mtext></mml:msubsup><mml:msub><mml:mtext>W</mml:mtext><mml:mtext>i</mml:mtext></mml:msub><mml:msub><mml:mtext>Q</mml:mtext><mml:mtext>i</mml:mtext></mml:msub></mml:mrow><mml:mrow><mml:msubsup><mml:mo>∑</mml:mo><mml:mrow><mml:mtext>i</mml:mtext><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mtext>n</mml:mtext></mml:msubsup><mml:mtext>Wi</mml:mtext></mml:mrow></mml:mfrac></mml:mrow></mml:math></alternatives></disp-formula>", "<disp-formula id=\"Equ2\"><label>2</label><alternatives><tex-math id=\"M3\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${Q}_{i}={\\sum }_{i=1}^{n}100 \\times \\frac{{C}_{i}}{{S}_{i}}$$\\end{document}</tex-math><mml:math id=\"M4\" display=\"block\"><mml:mrow><mml:msub><mml:mi>Q</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:msubsup><mml:mo>∑</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>n</mml:mi></mml:msubsup><mml:mn>100</mml:mn><mml:mo>×</mml:mo><mml:mfrac><mml:msub><mml:mi>C</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:msub><mml:mi>S</mml:mi><mml:mi>i</mml:mi></mml:msub></mml:mfrac></mml:mrow></mml:math></alternatives></disp-formula>", "<disp-formula id=\"Equ3\"><label>3</label><alternatives><tex-math id=\"M5\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${M}_{i}={\\sum }_{i=1}^{i}\\frac{{C}_{ave}}{{UAL}_{i}}$$\\end{document}</tex-math><mml:math id=\"M6\" display=\"block\"><mml:mrow><mml:msub><mml:mi>M</mml:mi><mml:mi>i</mml:mi></mml:msub><mml:mo>=</mml:mo><mml:msubsup><mml:mo>∑</mml:mo><mml:mrow><mml:mi>i</mml:mi><mml:mo>=</mml:mo><mml:mn>1</mml:mn></mml:mrow><mml:mi>i</mml:mi></mml:msubsup><mml:mfrac><mml:msub><mml:mi>C</mml:mi><mml:mrow><mml:mi mathvariant=\"italic\">ave</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mrow><mml:mi mathvariant=\"italic\">UAL</mml:mi></mml:mrow><mml:mi>i</mml:mi></mml:msub></mml:mfrac></mml:mrow></mml:math></alternatives></disp-formula>", "<disp-formula id=\"Equ4\"><label>5</label><alternatives><tex-math id=\"M7\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{\\text{CDI}}}_{{\\text{oral}}}=\\frac{{{\\text{C}}}_{{\\text{w}}}\\times {\\text{IR}}\\times {\\text{EF}}}{{\\text{BW}}\\times {\\text{AT}}} \\times {\\text{ED}}$$\\end{document}</tex-math><mml:math id=\"M8\" display=\"block\"><mml:mrow><mml:msub><mml:mtext>CDI</mml:mtext><mml:mtext>oral</mml:mtext></mml:msub><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mtext>C</mml:mtext><mml:mtext>w</mml:mtext></mml:msub><mml:mo>×</mml:mo><mml:mtext>IR</mml:mtext><mml:mo>×</mml:mo><mml:mtext>EF</mml:mtext></mml:mrow><mml:mrow><mml:mtext>BW</mml:mtext><mml:mo>×</mml:mo><mml:mtext>AT</mml:mtext></mml:mrow></mml:mfrac><mml:mo>×</mml:mo><mml:mtext>ED</mml:mtext></mml:mrow></mml:math></alternatives></disp-formula>", "<disp-formula id=\"Equ5\"><label>6</label><alternatives><tex-math id=\"M9\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${{\\text{CDI}}}_{{\\text{dermal}}}=\\frac{{{\\text{C}}}_{{\\text{ave}}}\\times {\\text{ET}}\\times {\\text{EF}}\\times {\\text{Kp}}\\times {\\text{SA}}\\times {\\text{CF}}}{{\\text{BW}}\\times {\\text{AT}}} \\times {\\text{ED}}$$\\end{document}</tex-math><mml:math id=\"M10\" display=\"block\"><mml:mrow><mml:msub><mml:mtext>CDI</mml:mtext><mml:mtext>dermal</mml:mtext></mml:msub><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mtext>C</mml:mtext><mml:mtext>ave</mml:mtext></mml:msub><mml:mo>×</mml:mo><mml:mtext>ET</mml:mtext><mml:mo>×</mml:mo><mml:mtext>EF</mml:mtext><mml:mo>×</mml:mo><mml:mtext>Kp</mml:mtext><mml:mo>×</mml:mo><mml:mtext>SA</mml:mtext><mml:mo>×</mml:mo><mml:mtext>CF</mml:mtext></mml:mrow><mml:mrow><mml:mtext>BW</mml:mtext><mml:mo>×</mml:mo><mml:mtext>AT</mml:mtext></mml:mrow></mml:mfrac><mml:mo>×</mml:mo><mml:mtext>ED</mml:mtext></mml:mrow></mml:math></alternatives></disp-formula>", "<disp-formula id=\"Equ6\"><label>7</label><alternatives><tex-math id=\"M11\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${HQ}_{dermal/oral}=\\frac{{{\\text{CDI}}}_{{\\text{dermal}}}{/{\\text{CDI}}}_{{\\text{oral}}}}{{{\\text{RfD}}}_{{\\text{dermal}}}{/{\\text{RfD}}}_{{\\text{oral}}}}$$\\end{document}</tex-math><mml:math id=\"M12\" display=\"block\"><mml:mrow><mml:msub><mml:mrow><mml:mi mathvariant=\"italic\">HQ</mml:mi></mml:mrow><mml:mrow><mml:mi>d</mml:mi><mml:mi>e</mml:mi><mml:mi>r</mml:mi><mml:mi>m</mml:mi><mml:mi>a</mml:mi><mml:mi>l</mml:mi><mml:mo stretchy=\"false\">/</mml:mo><mml:mi>o</mml:mi><mml:mi>r</mml:mi><mml:mi>a</mml:mi><mml:mi>l</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mtext>CDI</mml:mtext><mml:mtext>dermal</mml:mtext></mml:msub><mml:msub><mml:mrow><mml:mo stretchy=\"false\">/</mml:mo><mml:mtext>CDI</mml:mtext></mml:mrow><mml:mtext>oral</mml:mtext></mml:msub></mml:mrow><mml:mrow><mml:msub><mml:mtext>RfD</mml:mtext><mml:mtext>dermal</mml:mtext></mml:msub><mml:msub><mml:mrow><mml:mo stretchy=\"false\">/</mml:mo><mml:mtext>RfD</mml:mtext></mml:mrow><mml:mtext>oral</mml:mtext></mml:msub></mml:mrow></mml:mfrac></mml:mrow></mml:math></alternatives></disp-formula>", "<disp-formula id=\"Equ7\"><label>8</label><alternatives><tex-math id=\"M13\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${RfD}_{dermal}={RfD}_{oral}\\times ABS$$\\end{document}</tex-math><mml:math id=\"M14\" display=\"block\"><mml:mrow><mml:msub><mml:mrow><mml:mi mathvariant=\"italic\">RfD</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"italic\">dermal</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mrow><mml:mi mathvariant=\"italic\">RfD</mml:mi></mml:mrow><mml:mrow><mml:mi mathvariant=\"italic\">oral</mml:mi></mml:mrow></mml:msub><mml:mo>×</mml:mo><mml:mi>A</mml:mi><mml:mi>B</mml:mi><mml:mi>S</mml:mi></mml:mrow></mml:math></alternatives></disp-formula>", "<disp-formula id=\"Equ8\"><label>9</label><alternatives><tex-math id=\"M15\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$HI=\\sum HQ$$\\end{document}</tex-math><mml:math id=\"M16\" display=\"block\"><mml:mrow><mml:mi>H</mml:mi><mml:mi>I</mml:mi><mml:mo>=</mml:mo><mml:mo>∑</mml:mo><mml:mi>H</mml:mi><mml:mi>Q</mml:mi></mml:mrow></mml:math></alternatives></disp-formula>", "<disp-formula id=\"Equ9\"><label>10</label><alternatives><tex-math id=\"M17\">\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym} \n\t\t\t\t\\usepackage{amsfonts} \n\t\t\t\t\\usepackage{amssymb} \n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$${\\text{CR}}=\\mathrm{CDI }\\times \\mathrm{ CSF}$$\\end{document}</tex-math><mml:math id=\"M18\" display=\"block\"><mml:mrow><mml:mtext>CR</mml:mtext><mml:mo>=</mml:mo><mml:mi mathvariant=\"normal\">CDI</mml:mi><mml:mo>×</mml:mo><mml:mi mathvariant=\"normal\">CSF</mml:mi></mml:mrow></mml:math></alternatives></disp-formula>" ]	[]	[]	[]	[]	[]	[ "<table-wrap-foot><p>The units of all chemical parameters were measured in mg/L except pH.</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher's note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"41598_2023_50000_Fig1_HTML\" id=\"MO1\"/>", "<graphic xlink:href=\"41598_2023_50000_Fig2_HTML\" id=\"MO2\"/>", "<graphic xlink:href=\"41598_2023_50000_Article_Equ1.gif\" position=\"anchor\"/>", "<graphic xlink:href=\"41598_2023_50000_Article_Equ2.gif\" position=\"anchor\"/>", "<graphic xlink:href=\"41598_2023_50000_Article_Equ3.gif\" position=\"anchor\"/>", "<graphic xlink:href=\"41598_2023_50000_Article_Equ4.gif\" position=\"anchor\"/>", "<graphic xlink:href=\"41598_2023_50000_Article_Equ5.gif\" position=\"anchor\"/>", "<graphic xlink:href=\"41598_2023_50000_Article_Equ6.gif\" position=\"anchor\"/>", "<graphic xlink:href=\"41598_2023_50000_Article_Equ7.gif\" position=\"anchor\"/>", "<graphic 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PMC10781700	38200135	[ "<title>Introduction</title>", "<p id=\"Par2\">Cochlear implants (CIs) are considered to be one of the most successful medical devices. The efficacy and cost effectiveness of CIs for life-changing rehabilitation of disabling hearing loss are well established, with approximately 750,000 implant recipients worldwide<sup>##REF##35416780##1##</sup>. With advances in implant technology and continually expanding clinical indications, the emphasis is increasingly shifting towards improving outcomes and enhancing patient experience.</p>", "<p id=\"Par3\">One of the main areas of interest in CI research is reduction of surgical trauma to the delicate internal structures of the cochlea during electrode insertion. Preservation of low frequency residual hearing may enable electro-acoustic stimulation, whose potential benefit includes improved speech understanding in background noise, music appreciation and sound localization<sup>##REF##15867642##2##–##REF##23992489##6##</sup>. Furthermore, insertion trauma has been linked to the development of intracochlear fibrosis and neo-ossification, which may have a negative impact on speech perception outcomes and revision surgery in the future<sup>##REF##24915284##7##–##REF##35015749##9##</sup>.</p>", "<p id=\"Par4\">A systematic review by Hoskison et al. found an overall 17.6% trauma rate, determined either radiologically or histologically, in adult CI recipients<sup>##REF##28534710##10##</sup>. It has been proposed that CI insertion could be improved with more accurate and consistent electrode insertion, for example in the form of robotic guidance<sup>##REF##28534710##10##,##REF##33902040##11##</sup>. Given that electrode insertion is performed essentially blindly beyond the round window or cochleostomy at present, it would seem a logical initial step to determine the anatomical factors which may be associated with increased insertion trauma. In this context, the most basic surgical aim would be to place the electrode within the scala tympani (ST) compartment of the cochlea without translocation to the scala media (SM) or scala vestibuli (SV), which has been shown to influence CI outcomes<sup>##REF##28894813##12##</sup>. A number of studies to date have examined various cochlear parameters as defined by pre-operative imaging and CI outcomes, the estimated cochlear duct length and angular insertion depth (AID) being the most studied parameters<sup>##REF##33459799##13##–##REF##36397263##15##</sup>. Identification of anatomical factors associated with increased likelihood of scalar translocation could be useful in two broad aspects. Firstly, such knowledge could help inform the electrode choice for individual patients even if the anatomical factor is a “fixed” characteristic that is not possible to manipulate; for example, the clinician may choose or avoid a particular type of electrode array depending on the size of the cochlea. Secondly, it may be possible to identify an anatomical feature that has the potential to be applied to the development of an individualised, imaging-based electrode insertion system.</p>", "<p id=\"Par5\">In this study, the authors explored the hypothesis that scalar translocation of a pre-curved CI electrode can be predicted by 1) the size of the cochlear basal turn and 2) the orientation of the inferior segment of the cochlear basal turn relative to the mastoid segment of the facial nerve, expressed as horizontal and vertical angles in the three-dimensional (3D) space.</p>" ]	[ "<title>Materials and methods</title>", "<title>Ethical considerations</title>", "<p id=\"Par6\">This retrospective study underwent local institutional review by the Clinical Research Analytics Governance group (CRAG) at Guy’s and St. Thomas’ NHS Foundation Trust and was approved, including waived informed consent (GSTT Electronic Record Research Interface, IRAS ID: 257283, Rec Reference: 20/EM/0112). The study was conducted strictly in accordance with the relevant guidelines and regulations.</p>", "<title>Study cohort</title>", "<p id=\"Par7\">The Auditbase, Picture Archiving and Communication System (PACS) Sectra and Electronic Patient Record databases were searched for all adult and paediatric patients who were implanted with the HiFocus™ Mid-Scala (MS) electrode array (Advance Bionics, Valence, CA, USA) at our institution between March 2013 and July 2018 and had post-operative cone beam computed tomography (CBCT). Exclusion criteria were: congenital cochlear anomalies, acquired pathologies affecting the patency of the cochlear lumen (e.g. labyrinthitis ossificans, otospongiosis, vestibular schwannoma extending into the cochlea) and electrode insertion via cochleostomy. Based on the electrode placement within the scalar chambers as assessed on post-operative CBCT, the study cohort was divided into two groups: those with the electrode entirely within the ST compartment of the cochlea (“ST group”) and those with translocation from ST to SV (“ST-SV group”).</p>", "<title>Angular insertion depth and scala position</title>", "<p id=\"Par8\">CBCT imaging was performed post-operatively using a 3D Accuitomo 170 (J Morita, Kyoto, Japan), model MCT-1, type EX 1/2 F17, with parameters: voltage 80 kV, current 10mA, and 0.125 (0.125 × 0.125 × 0.125). The assessment of scalar translocation and AID was performed by a neuroradiologist (SC) on the Picture Archiving and Communication System (PACS) Sectra software, as per the standard practice at our center<sup>##UREF##1##16##,##REF##19386728##17##</sup>. The position of the electrode array within the scala chambers was assessed at four locations within the basal turn of the cochlea (mid-inferior segment, ascending segment, mid-superior segment and descending segment), reflecting its antero (ST)—posterior (SV) relationship within the cochlea lumen (Fig. ##FIG##0##1##a–f). For evaluation of AID, the angle measurement tool of the PACS Sectra software was used on a double oblique coronal reformatted image through the basal turn with a 2mm average slab reconstruction designed to demonstrate the entire electrode array, with the round window as the 0° point. Where the electrode was inserted beyond the basal turn (360°) and into the middle turn of the cochlea, the angle between the round window and the distal electrode contact through the mid-modiolar point was added to 360° (Fig. ##FIG##1##2##).</p>", "<title>Cochlear dimensions</title>", "<p id=\"Par9\">The dimensions of the cochlear basal turn were measured in terms of distance A and distance B on a double-oblique paracoronal reformatted image as described by Escude et al.<sup>##REF##22770690##18##</sup>. Images were viewed with a window width/center of 4000/400 and reformatted at 1-mm thick reformat, such that the basal turn from the RW to the opposite outer cochlear wall was visualised on a single image. Distance A was measured as the largest distance from the mid-RW to the opposite wall of the basal turn through the mid-modiolar axis using the measurement tool. Distance B was measured as the distance perpendicular to distance A, joining the outer walls of the superior and inferior segments of the basal turn using the measurement tool and crosshairs as 90° reference (Fig. ##FIG##2##3##). Both distances were recorded to the nearest 0.1 mm independently by a neuroradiologist (SC) and an implant surgeon (IP).</p>", "<title>Inferior segment of the cochlear basal turn-facial nerve angles</title>", "<p id=\"Par10\">The orientation of the inferior segment of the cochlear basal turn in the 3D space was expressed as horizontal and vertical angles relative to a plane incorporating the mastoid segment of the facial nerve, the most critical structure in defining the facial recess and posterior tympanotomy in the standard transmastoid approach to the RW. These “facial nerve angles” were assessed using the 3D Slicer version 4.11.20210226 (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.slicer.org\">https://www.slicer.org</ext-link>), which is a free, open-source software for visualization, processing, segmentation, registration and analysis of medical images<sup>##UREF##2##19##–##REF##17063008##21##</sup>.</p>", "<p id=\"Par11\">The inferior segment of the cochlear basal turn was represented by a straight line running through the middle of the RW and the centre of the ST compartment at the mid-inferior segment of the cochlear basal turn, which was achieved by placing fiducials on the above locations in the 3D Slicer. For placement of the mid-ST/mid-inferior segment fiducial, the junction between the inferior and ascending segments was first determined by scrolling through both axial and coronal sections. A fiducial was placed at mid-point between the RW and the junction between the inferior and ascending segments, and then adjusted in the sagittal plane to the middle of the ST compartment. Another fiducial was placed in the centre of the RW, again using all three axial, coronal and sagittal planes. The two fiducials were then joined up with a straight line, hereinafter referred to as the “cochlear line”.</p>", "<p id=\"Par12\">In order to delineate the mastoid segment of the facial nerve in a manner consistent from case to case, fiducials were placed in the axial, coronal and sagittal planes on the mid-anterior surface of the facial nerve at the level of the umbo of the malleus and a half way between the umbo and the stylomastoid foramen, with a standard obliquity with respect to the long axis of the vestibule (“facial line”). In order to create a plane which incorporated the mastoid segment of the facial nerve and against which the angles of the cochlear line could be measured, a third fiducial was placed on the tip of the short process of the incus since it is one of the surgical landmarks used intraoperatively to determine the location of the second genu of the facial nerve, the starting point of the mastoid segment. A “facial plane” was then created by aligning all three fiducials in the same plane. The fiducials, lines, planes and angles are summarised in Table ##TAB##0##1## and illustrated in Figs. ##FIG##3##4## and ##FIG##4##5##. The process of placing the fiducials was performed independently by two observers, a neuroradiologist (SC) and an implant surgeon (IP).</p>", "<p id=\"Par13\">For the measurements of the facial nerve angles in 3D Slicer, a Python function (available from the Python Interactor Tool) was developed to read the coordinates of the placed fiducials shown in Fig. ##FIG##3##4## and to perform automatic calculation and visualisation of the horizontal and vertical angles as well as the auxiliary lines and planes shown in Fig. ##FIG##4##5##. In order to facilitate further research and to provide the broader research community with a valuable resource for experimentation and further advancements, our source code has been made available online (<ext-link ext-link-type=\"uri\" xlink:href=\"https://github.com/RViMLab/SciReports_Cochlear_Angle/tree/main\">https://github.com/RViMLab/SciReports_Cochlear_Angle/tree/main</ext-link>).</p>", "<title>Statistical analysis</title>", "<p id=\"Par14\">All statistical analyses were performed with SPSS v.28.0.1.1 (IBM Cord., Armonk, NY, USA). The inter-rater reliability of the measurements of cochlear dimensions and facial nerve angles was determined by use of the intraclass correlation coefficient (ICC). Since the Shapiro–Wilk test showed the data to be normally distributed, a two-tailed independent Student’s t test was employed to analyse the mean difference between the ST and ST-SV groups for continuous variables. The Chi-square test of independence was used for categorical variables. The threshold for statistical significance was set at alpha = 0.05. In addition, a backward stepwise binary logistic regression was performed to determine whether scalar translocation (the binary dependent variable) could be predicted from distance A, distance B, horizontal facial nerve angle and/or vertical facial nerve angle (the independent variables). At each step, the least correlated variable was removed (exit criterion <italic>p</italic> > 0.10), and the p-value threshold of 0.05 (entry criterion) was used to set a limit on the number of variables used in the final model. Area under the receiver-operating characteristic (ROC) curve was also calculated. Of note, the Bonferroni correction was not applied in view of the fact that the current study had a clear hypothesis, a type II error would be more likely than a type I error, and avoidance of a type I error could be said to be imperative<sup>##UREF##3##22##,##REF##24697967##23##</sup>.</p>" ]	[ "<title>Results</title>", "<title>Study population</title>", "<p id=\"Par15\">The search method identified a total of 51 patients who had undergone CI surgery at our institution during the study period and had post-operative CBCT. Of these, 11 cases were excluded from the study for the following reasons: electrode insertion via cochleostomy (n = 5), insufficient clinical data available (n = 4) and indeterminate electrode position with regard to the scalar chambers on CBCT (n = 2). The final analysis therefore included 40 patients that met the inclusion criteria, with a female preponderance (female 25, male 15) and mean age at implantation of 48.8 years (median 48.0, range 4.5–88.0). In all 40 cases, full insertion was achieved intra-operatively and the post-operative CBCT confirmed all 16 electrode contacts to be intra-cochlear. The mean AID was 404° ± 46 (standard deviation, SD) in the ST group (median 410, range 300–450) and 427° ± 46 in the ST-SV group (median 420, range 360–540).</p>", "<p id=\"Par16\">Based on post-operative CBCT, there were 24 patients in the ST group (60%) and 16 patients in the ST-SV group (40%). There was no difference between the two groups in the age at implantation (<italic>p</italic> = 0.43), gender (<italic>p</italic> = 0.74), mean AID (<italic>p</italic> = 0.13) or ear side implanted (<italic>p</italic> = 0.57), with all operating surgeons being right-handed.</p>", "<title>Cochlear dimensions</title>", "<p id=\"Par17\">The mean distance A was significantly smaller in the ST-SV group (mean 8.3mm ± 0.4, median 8.5, range 7.3–9.1) than the ST group (mean 8.9 mm ± 0.4, median 8.9, range 8.3–9.9) (<italic>p</italic> < 0.001). There was no statistically significant difference between the two groups in distance B (mean 6.5 mm ± 0.4, median 6.5, range 5.9–7.4 in the ST group; mean 6.4 ± 0.3, median 6.3, range 6.1–6.8 in the ST-SV group) (<italic>p</italic> = 0.36) (Fig. ##FIG##5##6##). The inter-rater reliability was excellent for both distance A (ICC 0.969, 95% confidence interval (CI) [0.942, 0.984]) and distance B (ICC 0.935 [95% CI 0.868, 0.967]).</p>", "<title>Facial nerve angles</title>", "<p id=\"Par18\">The mean horizontal facial nerve angle was significantly wider in the ST-SV group (mean 80.2° ± 7.6, median 82.9, range 68.1–95.7) than in the ST group (mean 74.9° ± 7.7, median 74.3, range 56.9–89.0) (<italic>p</italic> = 0.040). There was no statistically significant difference between the two groups in the vertical facial nerve angle (mean 74.9° ± 7.7, median 74.3, range 56.9–89.0 in the ST group; mean 86.0° ± 16.4, median 85.1, range 57.6–125.1 in the ST-SV group) (<italic>p</italic> = 0.33) (Fig. ##FIG##6##7##). The inter-rater reliability was excellent for both the horizontal facial nerve angle (ICC 0.986, [95% CI 0.973, 0.992]) and the vertical facial nerve angle (ICC 0.949 [95% CI 0.899, 0.974]).</p>", "<title>Prediction of translocation</title>", "<p id=\"Par19\">A backward stepwise binary logistic regression assessed the effect of distance A, distance B, horizontal facial nerve angle and vertical facial nerve angle on the presence of scalar translocation. The Variables in the Equation table is provided in Table ##TAB##1##2##. The overall model was statistically significant compared to the null model (χ<sup>2</sup> = 24.89, <italic>p</italic> < 0.001) and the Hosmer–Lemeshow goodness-of-fit test indicated a good logistic regression model fit (χ2 = 6.38, <italic>p</italic> = 0.61). Only distance A (<italic>p</italic> = 0.003) and horizontal facial nerve angle (<italic>p</italic> = 0.017) were statistically significant. The final model with these two independent variables explained between 44.0% (Cox & Snell <italic>R</italic><sup>2</sup>) and 59.9% (Nagelkerke <italic>R</italic><sup>2</sup>) of the variance in scalar translocation, indicating a strong relationship, and correctly classified 82.5% of cases. For distance A, every 1mm decrease was associated with a 99.2% increase in odds of translocation [95% CI 80.3%, 100%] whilst for horizontal facial nerve angle, every 1-degree increase was associated with 18.1% increase in odds of translocation [95% CI 3.0%, 35.5%]. The area under the curve (AUC) of the ROC curve for the final model was 0.901 [95% 0.796, 1.006], indicating an outstanding discrimination (Fig. ##FIG##7##8##).</p>" ]	[ "<title>Discussion</title>", "<p id=\"Par20\">Electrode placement in the scala tympani (ST) without translocation is the most fundamental prerequisite for preservation of the internal structures of the cochlea during cochlear implant (CI) surgery. In this study, we aimed to investigate the potential anatomical factors that may influence scalar translocation of a pre-curved electrode in normal cochleae via the round window (RW) approach. Our study findings showed that cochleae with scalar translocation of the Advanced Bionics Mid-Scala (MS) electrode array had a significantly smaller distance A (<italic>p</italic> < 0.001) and a wider horizontal angle between the inferior segment of the cochlear basal turn and the mastoid segment of the facial nerve (<italic>p</italic> = 0.04). Moreover, a backward logistic regression model found that both distance A (p = 0.003) and horizontal facial nerve angle (<italic>p</italic> = 0.017) were able to predict scalar translocation with an area under the curve (AUC) value of 0.901 for the final model.</p>", "<p id=\"Par21\">The importance of correct electrode placement within ST is widely recognised both for improved speech perception outcomes and for hearing preservation<sup>##REF##28894813##12##</sup>. The factors that have been found to be associated with higher rates of ST retention include lateral wall arrays<sup>##REF##33159469##24##,##REF##31467506##25##</sup>, electrode insertion via the RW<sup>##UREF##4##26##</sup> and prolonged insertion time<sup>##REF##29780962##27##</sup>. A review by Dhanasingh & Jolly from 2019 analysed 1,831 implanted ears reported from 25 peer-reviewed published articles and divided them into pre-curved electrode group (Slim-Modiolar, Contour Advance, Contour, Mid-Scala and Helix) and straight lateral wall group (Slim-Straight, 1J, Standard, Medium, Compressed and FLEX). The authors found the scalar translocation rate to be higher with pre-curved electrodes (32%) than with lateral wall electrode arrays (6.7%) and suggested that lateral wall electrodes be used for all cases without anatomical complications, reserving the use of perimodiolar electrodes for selected clinical situations<sup>##REF##31467506##25##</sup>.</p>", "<p id=\"Par22\">In addition to lower rates of scalar translocation, it has been proposed that lateral wall electrode arrays confer a wider dynamic range, are more likely to achieve preservation of residual hearing at least in the short term, and allow deeper insertion that provides more complete cochlear coverage leading to improved speech perception in noise<sup>##REF##35213473##28##</sup>. However, proponents of peri-modiolar electrodes would argue that there are distinct advantages conferred by having the electrode contacts closer to the neural elements in the modiolus, including narrower spread of excitation, reduced behavioural and electrically-evoked compound action potential thresholds, wider dynamic range and improved speech perception<sup>##REF##19308437##29##–##REF##27256964##32##</sup>. The debate surrounding the choice of CI electrodes is on-going, and the design of an ideal electrode array has yet to be established. Interest therefore still remains in identifying risk factors for scalar translocation with perimodiolar electrodes, and refining the surgical approach to minimise insertion trauma to the cochlea.</p>", "<p id=\"Par23\">The MS electrode, usually classified under peri-modiolar arrays, was designed to reduce the damage to the lateral wall of the cochlea by targeting the largest part of ST and aims to achieve more consistent AID in different size cochleae than electrode arrays that sit closer to the modiolus or in the lateral compartment of ST<sup>##REF##27246747##33##,##REF##27871074##34##</sup>. In our cohort, there was indeed no significant difference in the mean AID between the ST group and ST-SV group despite the mean distance A, representing the size of the cochlear basal turn, being significantly smaller in the ST-SV group. However, the high scalar translocation rate of 40% with the MS electrode array found in this study is also in keeping with previously reported figures which range from 20 to 57% for this electrode type<sup>##REF##31467506##25##</sup>.</p>", "<p id=\"Par24\">Whilst the marked individual variations in the size and morphology of the human cochlea and their potential implications in CI outcomes are well-documented<sup>##REF##18833017##35##–##REF##19415036##37##</sup>, specific anatomic factors that influence scalar translocation have yet to be fully established. In a recent study by Eisenhut et al.<sup>##REF##33648794##38##</sup>, the authors examined scalar translocation in patients implanted with a perimodiolar cochlear implant (CI512, Cochlear Ltd., Sydney NSW, Australia) and reported that it was associated with increased non-linear narrowing of the proximal segment of the cochlear basal turn. It is noted that their study did not find external cochlea diameters including distance A to be significant factors, whereas our findings indicate that scalar translocation of the MS electrode is more likely to occur in cochleae with a smaller distance A and that the current MS electrode design may be better suited to larger cochleae.</p>", "<p id=\"Par25\">In addition to identifying “cochlear factors” that are associated with greater likelihood of scalar translocation, which could aid the clinician to choose the most appropriate electrode array in individual cases, we examined the orientation of the inferior segment of the cochlear basal turn relative to the mastoid segment of the facial nerve as a potential factor that could be used to guide electrode insertion with less trauma. Our study found that the horizontal facial nerve angle varied by 68%, ranging from 56.9° to 95.7°, and the wider this angle, the higher the probability of scalar translocation. This is particularly interesting in view of the findings in a study by Daoudi et al., in which a high rate of translocations occurred with the MS electrode array regardless of whether insertion was manual or with a teleoperated robot<sup>##REF##33306661##39##</sup>. In a subsequent cadaveric study using the same robotic system, Torres et al. were able to reduce insertion trauma and scalar translocation rates of the MS array by adding navigation to robotic insertion in order to align the electrode to the axis of the basal turn of the ST and its subsequent coiling<sup>##REF##34284383##40##</sup>. It therefore seems plausible that, the process of calculating on pre-operative imaging the angulation of the inferior segment of the cochlear basal turn in relation to the plane of the mastoid segment of the facial nerve and the short process of the incus, which are important structures routinely encountered during CI surgery, could be automated and combined with a simple navigation system intra-operatively in order to guide electrode insertion. This is an important consideration, since, unlike other anatomic risk factors that cannot be altered, it is something that is open to surgical manipulation.</p>", "<p id=\"Par26\">In an earlier study by Breinbauer and Praetorius<sup>##REF##25634464##41##</sup>, the authors used the 3D Slicer to segment the cochlea and demonstrated the variability in ideal insertion vectors via cochleostomy and round window approaches in 100 ears, relative to the axial, coronal and sagittal planes. Since these angles are difficult for the operating surgeon to apply to the intraoperative setting, they went on to express the insertion vector as distances in mm to the tip of the short process of the incus and the mastoid segment of the facial nerve. In the current study, we used the same structures to construct a plane against which the orientation of the inferior segment of the cochlear basal turn could be described. Although there were some differences between the two studies in the methodology adopted, both explored the concept of utilising other anatomical structures relevant to and accessible during CI surgery to potentially inform and guide electrode insertion, as part of the effort to reduce intra-cochlear trauma.</p>", "<p id=\"Par27\">Another potential benefit of such an approach would be standardisation of electrode insertion, especially if performed with a mechanical system at a constant and consistent insertion speed. The evidence for factors that influence hearing preservation in CI surgery in the current literature is not conclusive. Various systematic reviews and meta-analyses to date have produced somewhat conflicting outcomes with regard to the effect(s) of the surgical approach (RW insertion vs cochleostomy), electrode insertion speed, choice of electrode type or length, and use of corticosteroids on hearing preservation rates<sup>##REF##23640087##42##–##REF##30624395##44##</sup>. The possibility that such discrepancies may be at least in part due to wide variations in the accuracy of the insertion trajectory cannot be excluded. If the process of electrode placement were to be optimised and made more reliably consistent as far as possible through mechanical insertion at a constant speed along a pre-determined ideal insertion trajectory, it would reduce the impact of a potentially significant variable.</p>", "<p id=\"Par28\">The authors acknowledge that this study has a number of limitations. Firstly, only one specific type of an electrode array was evaluated and the findings cannot be generalised to other types of electrodes. Our study did not include the other type of peri-modiolar electrodes, so-called “modiolus-hugging”, as they are rarely used in our centre. As for the lateral wall electrodes which have much lower scalar translocation rates as already discussed, a similar study on anatomic parameters will require a different outcome measure, for example hearing preservation rates. Secondly, the facial nerve angles measured in this study are not transferable to the intra-operative setting in the current formats, as the mastoid segment of the facial nerve is not routinely delineated along its entire length during CI surgery. Nonetheless, this does not detract from the main study findings, as the anatomical structures used in this study to define the relative angulation of the inferior segment of the cochlear basal turn are pertinent to CI surgery in terms of the access to the cochlea. Finally, the relative importance of the diameter of the cochlear basal turn and the horizontal facial nerve angle is not clear; in other words, it cannot be determined from the study findings whether aligning the insertion trajectory to the orientation of the inferior segment of the cochlear basal turn would necessarily reduce the scalar translocation rate in smaller cochleae. That said, the authors believe that the current study has successfully identified two important anatomic factors that are associated with insertion trauma and produced the evidence base to support the development of imaging-based navigation in CI surgery to optimise the insertion trajectory.</p>" ]	[ "<title>Conclusion</title>", "<p id=\"Par29\">The probability of scalar translocation of the pre-curved MS electrode array increases as distance A of the cochlear basal turn decreases and the angle between the inferior segment of the cochlear basal turn and the mastoid segment of the facial nerve increases.</p>" ]	[ "<p id=\"Par1\">Scalar translocation is a severe form of intra-cochlear trauma during cochlear implant (CI) electrode insertion. This study explored the hypothesis that the dimensions of the cochlear basal turn and orientation of its inferior segment relative to surgically relevant anatomical structures influence the scalar translocation rates of a pre-curved CI electrode. In a cohort of 40 patients implanted with the Advanced Bionics Mid-Scala electrode array, the scalar translocation group (40%) had a significantly smaller mean distance A of the cochlear basal turn (<italic>p</italic> < 0.001) and wider horizontal angle between the inferior segment of the cochlear basal turn and the mastoid facial nerve (<italic>p</italic> = 0.040). A logistic regression model incorporating distance A (<italic>p</italic> = 0.003) and horizontal facial nerve angle (<italic>p</italic> = 0.017) explained 44.0–59.9% of the variance in scalar translocation and correctly classified 82.5% of cases. Every 1mm decrease in distance A was associated with a 99.2% increase in odds of translocation [95% confidence interval 80.3%, 100%], whilst every 1-degree increase in the horizontal facial nerve angle was associated with an 18.1% increase in odds of translocation [95% CI 3.0%, 35.5%]. The study findings provide an evidence-based argument for the development of a navigation system for optimal angulation of electrode insertion during CI surgery to reduce intra-cochlear trauma.</p>", "<title>Subject terms</title>" ]	[]	[ "<title>Author contributions</title>", "<p>I.P., S.C., C.B., S.O. conceptualised the study; I.P., S.C., C.K. collected the data; I.P., S.C., C.K., C.B. analysed the data; I.P., S.C. prepared the figures; I.P., S.C., C.K. wrote the main manuscript; all five authors reviewed and revised the manuscript.</p>", "<title>Data availability</title>", "<p>The data from the current study are available from the corresponding author on reasonable request.</p>", "<title>Competing interests</title>", "<p id=\"Par30\">The authors declare no competing interests.</p>" ]	[ "<fig id=\"Fig1\"><label>Figure 1</label><caption><p>Assessment of scalar translocation. (<bold>a</bold>) Oblique coronal cone beam CT image showing the location of sagittal oblique sections within the descending segment (B), superior and inferior segments (C,E) and ascending segment (D,F). (<bold>b</bold>–<bold>d</bold>) images demonstrate a scala tympani (ST) location of the electrode array throughout without crossing. Arrows in (<bold>b</bold>–<bold>d</bold>) indicate the electrode array in the posterior aspect of the descending turn, superior segment and ascending segments respectively. Note the electrode array is also depicted within the posterior aspect (ST) of the inferior segment in (<bold>c</bold>). (<bold>e</bold>,<bold>f</bold>) images demonstrate crossing of the electrode array from the ST to scala vestibuli (SV) compartments. Arrow in (<bold>e</bold>) indicates the anterior position (SV) of the electrode array whereas the electrode array in the inferior segment is posteriorly positioned (ST). Arrow in (<bold>f</bold>) shows the electrode array passing from posteriorly to anteriorly within the ascending segment.</p></caption></fig>", "<fig id=\"Fig2\"><label>Figure 2</label><caption><p>Oblique coronal cone beam CT image illustrating the measurement of angular insertion depth. A 2 mm slab thickness average reconstruction is performed in order to include all electrode contacts within the section thickness. A line is placed in the axis of measurement A from the mid round window (white filled arrow) through the mid-modiolar axis (dot). This bisects the more distal cochlear at the 360° point. An angle (double headed arrow) is then measured between this point and the most distal electrode contact (open arrow). This angle is then added to 360° to determine the angular insertion depth.</p></caption></fig>", "<fig id=\"Fig3\"><label>Figure 3</label><caption><p>Oblique coronal cone beam CT image demonstrating the distance A and B measurements method. Distance A is indicated by a measurement through the mid-modiolar axis which passes from the round window (at the location of the reference electrode) to the diametrically opposite outer wall of the basal turn of cochlear. Distance B is measured perpendicular to distance A through the mid-modiolar axis, extending between the outer walls of the inferior and superior segments of the basal turn.</p></caption></fig>", "<fig id=\"Fig4\"><label>Figure 4</label><caption><p>Placements of fiducials in 3D Slicer. Four-up view of fiducial placements by two independent assessors (IP and SC) (<bold>a</bold>) tip of the short process of the incus (axial) (<bold>b</bold>) mastoid segment of the facial nerve at the level of the umbo (coronal) (<bold>c</bold>) mastoid segment of the facial nerve halfway between the upper fiducial and the stylomastoid foramen (<bold>d</bold>) 3D view of all fiducials placed by two assessors.</p></caption></fig>", "<fig id=\"Fig5\"><label>Figure 5</label><caption><p>Measurements of horizontal and vertical facial nerve angles in 3D Slicer. 3D volume view generated with 3D Slicer version 4.11.20210226 (<ext-link ext-link-type=\"uri\" xlink:href=\"https://www.slicer.org\">https://www.slicer.org</ext-link>), incorporating facial nerve angle measurements and segmentations of the cochlea, facial nerve and incus (3D Slicer). <italic>RW</italic> round window, <italic>SP</italic> short process, <italic>ST</italic> scala tympani.</p></caption></fig>", "<fig id=\"Fig6\"><label>Figure 6</label><caption><p>Box and whisker plots for comparison of distance A and distance B between ST and ST-SV groups.</p></caption></fig>", "<fig id=\"Fig7\"><label>Figure 7</label><caption><p>Box and whisker plots for comparison of horizontal and vertical facial nerve angles between ST and ST-SV groups.</p></caption></fig>", "<fig id=\"Fig8\"><label>Figure 8</label><caption><p>Receiver operating characteristic (ROC) curve of final model.</p></caption></fig>" ]	[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Fiducial placements for measurement of facial nerve angles.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\">Represents</th><th align=\"left\">Fiducial 1</th><th align=\"left\">Fiducial 2</th></tr></thead><tbody><tr><td align=\"left\">Cochlear line</td><td align=\"left\">Inferior segment of cochlear basal turn</td><td align=\"left\">Centre of round window</td><td align=\"left\">Centre of scala tympani (“mid-scala”) at mid-inferior segment</td></tr><tr><td align=\"left\">Facial line</td><td align=\"left\">Mastoid segment of facial nerve</td><td align=\"left\">At the level of umbo of malleus</td><td align=\"left\">Half way between umbo of malleus and stylomastoid foramen</td></tr><tr><td align=\"left\">Short process of incus</td><td align=\"left\">Tip of short process of incus</td><td align=\"left\">Tip of short process of incus</td><td align=\"left\">N/A</td></tr><tr><td align=\"left\">Facial plane</td><td align=\"left\" colspan=\"3\">Plane incorporating the facial line and short process of incus as determined above, in order to express the orientation of the inferior segment of the cochlear basal turn as angles in the 3D space</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Variable in the equation table from backward stepwise logistic regression.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\" rowspan=\"2\"/><th align=\"left\" rowspan=\"2\">B</th><th align=\"left\" rowspan=\"2\">S.E</th><th align=\"left\" rowspan=\"2\">Wald</th><th align=\"left\" rowspan=\"2\">df</th><th align=\"left\" rowspan=\"2\">Sig</th><th align=\"left\" rowspan=\"2\">Exp(B)</th><th align=\"left\" colspan=\"2\">95% CI for EXP(B)</th></tr><tr><th align=\"left\">Lower</th><th align=\"left\">Upper</th></tr></thead><tbody><tr><td align=\"left\" colspan=\"9\">Step 1<sup>a</sup></td></tr><tr><td align=\"left\"> Distance A</td><td align=\"left\">− 5.378</td><td char=\".\" align=\"char\">1.758</td><td char=\".\" align=\"char\">9.357</td><td align=\"left\">1</td><td char=\".\" align=\"char\">0.002</td><td align=\"left\">0.005</td><td char=\".\" align=\"char\">0.000</td><td char=\".\" align=\"char\">0.145</td></tr><tr><td align=\"left\"> Distance B</td><td align=\"left\">1.185</td><td char=\".\" align=\"char\">1.686</td><td char=\".\" align=\"char\">0.494</td><td align=\"left\">1</td><td char=\".\" align=\"char\">0.482</td><td align=\"left\">3.270</td><td char=\".\" align=\"char\">0.120</td><td char=\".\" align=\"char\">88.974</td></tr><tr><td align=\"left\"> Horizontal angle</td><td align=\"left\">0.218</td><td char=\".\" align=\"char\">0.101</td><td char=\".\" align=\"char\">4.656</td><td align=\"left\">1</td><td char=\".\" align=\"char\">0.031</td><td align=\"left\">1.243</td><td char=\".\" align=\"char\">1.020</td><td char=\".\" align=\"char\">1.516</td></tr><tr><td align=\"left\"> Vertical angle</td><td align=\"left\">0.046</td><td char=\".\" align=\"char\">0.053</td><td char=\".\" align=\"char\">0.766</td><td align=\"left\">1</td><td char=\".\" align=\"char\">0.381</td><td align=\"left\">1.047</td><td char=\".\" align=\"char\">0.944</td><td char=\".\" align=\"char\">1.161</td></tr><tr><td align=\"left\"> Constant</td><td align=\"left\">17.599</td><td char=\".\" align=\"char\">14.595</td><td char=\".\" align=\"char\">1.454</td><td align=\"left\">1</td><td char=\".\" align=\"char\">0.228</td><td align=\"left\">43,962,797.751</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\" colspan=\"9\">Step 2<sup>a</sup></td></tr><tr><td align=\"left\"> Distance A</td><td align=\"left\">− 5.048</td><td char=\".\" align=\"char\">1.685</td><td char=\".\" align=\"char\">8.972</td><td align=\"left\">1</td><td char=\".\" align=\"char\">0.003</td><td align=\"left\">0.006</td><td char=\".\" align=\"char\">0.000</td><td char=\".\" align=\"char\">0.175</td></tr><tr><td align=\"left\"> Horizontal angle</td><td align=\"left\">0.234</td><td char=\".\" align=\"char\">0.102</td><td char=\".\" align=\"char\">5.317</td><td align=\"left\">1</td><td char=\".\" align=\"char\">0.021</td><td align=\"left\">1.264</td><td char=\".\" align=\"char\">1.036</td><td char=\".\" align=\"char\">1.543</td></tr><tr><td align=\"left\"> Vertical angle</td><td align=\"left\">0.053</td><td char=\".\" align=\"char\">0.051</td><td char=\".\" align=\"char\">1.089</td><td align=\"left\">1</td><td char=\".\" align=\"char\">0.297</td><td align=\"left\">1.055</td><td char=\".\" align=\"char\">0.954</td><td char=\".\" align=\"char\">1.166</td></tr><tr><td align=\"left\"> Constant</td><td align=\"left\">20.551</td><td char=\".\" align=\"char\">13.797</td><td char=\".\" align=\"char\">2.219</td><td align=\"left\">1</td><td char=\".\" align=\"char\">0.136</td><td align=\"left\">841,775,701.163</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr><tr><td align=\"left\" colspan=\"9\">Step 3<sup>a</sup></td></tr><tr><td align=\"left\"> Distance A</td><td align=\"left\">− 4.772</td><td char=\".\" align=\"char\">1.605</td><td char=\".\" align=\"char\">8.835</td><td align=\"left\">1</td><td char=\".\" align=\"char\">0.003</td><td align=\"left\">0.008</td><td char=\".\" align=\"char\">0.000</td><td char=\".\" align=\"char\">0.197</td></tr><tr><td align=\"left\"> Horizontal angle</td><td align=\"left\">0.167</td><td char=\".\" align=\"char\">0.070</td><td char=\".\" align=\"char\">5.695</td><td align=\"left\">1</td><td char=\".\" align=\"char\">0.017</td><td align=\"left\">1.181</td><td char=\".\" align=\"char\">1.030</td><td char=\".\" align=\"char\">1.355</td></tr><tr><td align=\"left\"> Constant</td><td align=\"left\">27.798</td><td char=\".\" align=\"char\">12.272</td><td char=\".\" align=\"char\">5.131</td><td align=\"left\">1</td><td char=\".\" align=\"char\">0.023</td><td align=\"left\">1,182,294,718,040</td><td char=\".\" align=\"char\"/><td char=\".\" align=\"char\"/></tr></tbody></table></table-wrap>" ]	[]	[]	[]	[]	[]	[]	[ "<table-wrap-foot><p><italic>B</italic> unstandardized regression weight, <italic>S.E.</italic> standard error, <italic>Wald</italic> Wald test, <italic>df</italic> degrees of freedom, <italic>Sig.</italic> statistical significance of the test, <italic>Exp(B)</italic> odds ratio (predicted change in odds for a unit increase in the predictor).</p><p><sup>a</sup>Variable(s) entered on step 1: distance A, distance B, Horizontal angle, vertical angle.</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher's note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn><fn><p>These authors contributed equally: Irumee Pai and Steve Connor.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"41598_2023_47133_Fig1_HTML\" id=\"MO1\"/>", "<graphic xlink:href=\"41598_2023_47133_Fig2_HTML\" id=\"MO2\"/>", "<graphic xlink:href=\"41598_2023_47133_Fig3_HTML\" id=\"MO3\"/>", "<graphic xlink:href=\"41598_2023_47133_Fig4_HTML\" id=\"MO4\"/>", "<graphic xlink:href=\"41598_2023_47133_Fig5_HTML\" id=\"MO5\"/>", "<graphic xlink:href=\"41598_2023_47133_Fig6_HTML\" id=\"MO6\"/>", "<graphic xlink:href=\"41598_2023_47133_Fig7_HTML\" id=\"MO7\"/>", "<graphic xlink:href=\"41598_2023_47133_Fig8_HTML\" id=\"MO8\"/>" ]	[]	[{"label": ["3."], "mixed-citation": ["Gfeller, K. E., "], "italic": ["et al.", "Audiol. Neurootol."], "bold": ["11"]}, {"label": ["16."], "surname": ["Connor"], "given-names": ["SEJ"], "article-title": ["Round window electrode insertion potentiates retention in the scala tympani"], "source": ["J. Acta Oto-Laryngologica."], "year": ["2012"], "volume": ["132"], "fpage": ["932"], "lpage": ["937"], "pub-id": ["10.3109/00016489.2012.680493"]}, {"label": ["19."], "mixed-citation": ["Kikinis, R., Pieper, S.D., Vosburgh, K. 3D Slicer: A platform for subject-specific image analysis, visualization, and clinical support. "], "italic": ["Intraoperative imaging image-guided therapy"], "bold": ["3"]}, {"label": ["22."], "surname": ["Perneger"], "given-names": ["TV"], "article-title": ["What's wrong with Bonferroni adjustments"], "source": ["BMJ."], "year": ["1998"], "volume": ["18"], "fpage": ["1236"], "lpage": ["1238"], "pub-id": ["10.1136/bmj.316.7139.1236"]}, {"label": ["26."], "mixed-citation": ["Avasarala, V.S., Jinka, S.K. & Jeyakumar, A. Complications of cochleostomy versus round window surgical approaches: A systematic review and meta-analysis. "], "italic": ["Cureus."], "bold": ["14,"]}, {"label": ["43."], "mixed-citation": ["Santa Maria, P.L., Gluth, M.B., Yuan, Y., Atlas, M.D. & Blevins, N.H. Hearing preservation surgery for cochlear implantation: a meta-analysis. "], "italic": ["Otol. Neurotol."], "bold": ["35"]}]	{ "acronym": [], "definition": [] }	44	CC BY	no	2024-01-13 00:02:20	Sci Rep. 2024 Jan 10; 14:1024	oa_package/3f/c5/PMC10781700.tar.gz
PMC10781701	38200207	[ "<title>Introduction</title>", "<p id=\"Par2\">Musculoskeletal toxicity is a significant source of morbidity, noncompliance, and treatment discontinuation in patients taking adjuvant aromatase inhibitor (AI) therapy for estrogen receptor (ER) positive breast cancer, which can ultimately impact survival outcomes<sup>##REF##22689091##1##</sup>. Estrogen deprivation induced by AI therapy contributes to musculoskeletal toxicity by promoting osteoclastic bone resorption and accelerating bone loss, thus predisposing patients to an array of well-established skeletal complications<sup>##REF##20432458##2##</sup>. In addition, preliminary data from preclinical models and observational studies suggests AI-induced bone loss may also contribute to impairments in muscle function, including reductions in grip strength, muscle-specific force, and/or power generation, though relatively little is known about the molecular mechanism(s) underlying these functional impairments<sup>##REF##28039445##3##–##REF##21273342##6##</sup>.</p>", "<p id=\"Par3\">Our preclinical work has elucidated a mechanism implicating changes within the bone microenvironment as a source of muscle dysfunction in high bone turnover states<sup>##REF##26457758##7##,##REF##26593325##8##</sup>. In several different mouse models of osteolytic bone metastases, we discovered that bone destruction secondary to bone metastases results in decreased muscle force production. Increased bone turnover leads to excess resorption and subsequent release of cytokines stored in the mineralized bone matrix, including transforming growth factor-β (TGF-β). TGF-β mediates molecular crosstalk between the skeletal and muscular organ systems by signaling myocytes to upregulate NADPH oxidase 4 (Nox4), resulting in systemic oxidation of skeletal muscle proteins, including the ryanodine receptor/calcium (Ca<sup>2+</sup>) release channel, RyR1<sup>##REF##26457758##7##,##REF##26593325##8##</sup>. RyR1 is located on the sarcoplasmic reticulum and is normally bound to its stabilizing subunit, calstabin1, in a closed state. The RyR1-calstabin1 complex functions as a gatekeeper, sequestering Ca<sup>2+</sup> in the sarcoplasmic reticulum until signaled via membrane depolarization to dissociate from calstabin1 and destabilize, resulting in massive influx of Ca<sup>2+</sup> into the myoplasm—a key step in excitation–contraction coupling<sup>##REF##8390976##9##</sup>. In contrast, aberrant destabilization of the RyR1-calstabin1 complex triggered by Nox4-oxidation results in “leaky” RyR1 Ca<sup>2+</sup> channels, weaker intracellular signaling, and compromised muscle force production<sup>##REF##26457758##7##,##REF##26593325##8##,##REF##21803290##10##</sup>. Furthermore, inhibition along the TGF-β-Nox4-RyR1 pathway attenuates observed impairments in muscle function in preclinical studies, highlighting the potential for therapeutic exploitation of this axis to improve functional outcomes for patients<sup>##REF##26457758##7##</sup>. This biochemical signature of “leaky” RyR1 Ca<sup>2+</sup> channels—nitrosylation, oxidation, and decreased binding of calstabin1—has been identified in the skeletal muscle of mouse and human models of osteolytic bone metastases and Camurati-Engelmann disease, a non-malignant bone disorder associated with excess TGF-β and muscle weakness<sup>##REF##26457758##7##</sup>. Estrogen deprivation therapy with aromatase inhibition also results in states of bone turnover similar to these models; thus, these findings provide a theoretical basis for the hypothesis that estrogen deprivation induced by AI therapy stimulates osteoclastic bone resorption, secretion of excess TGF-β, and may potentiate muscle dysfunction through oxidation of RyR1 and resultant Ca<sup>2+</sup> leak in patients with early-stage breast cancer.</p>", "<p id=\"Par4\">Here, we aimed to investigate the impact of AI-induced bone resorption on the skeletal muscle ryanodine receptor RyR1, and to explore the relationship between changes in muscle function at the molecular and clinical levels in women with breast cancer. Establishing the underlying pathophysiology of AI-induced muscle dysfunction will be the obligate first step toward developing mechanistically-based interventions for muscular dysfunction associated with anti-estrogen therapies, thus improving quality of life, compliance, and outcomes for this patient population.</p>" ]	[ "<title>Materials and methods</title>", "<title>Participant recruitment and eligibility criteria</title>", "<p id=\"Par5\">Fifteen postmenopausal patients with stage I-III ER positive breast cancer planning to initiate an AI were recruited from the Indiana University Melvin and Bren Simon Comprehensive Cancer Center and Eskenazi Health in Indianapolis, IN. Postmenopausal status was defined as age ≥ 60 years, prior bilateral oophorectomy, absence of any menstrual periods in the last 12 months without surgical intervention, or FSH and estradiol in the postmenopausal range. All patients had completed primary therapy for breast cancer including surgery, radiation, and chemotherapy at least 14 days prior to study enrollment. Ongoing trastuzumab and/or pertuzumab therapy was allowed given no known impact on bone turnover or muscle physiology. Additional eligibility included age ≥ 18 years, body weight ≤ 350 lbs. (per dual-energy x-ray absorptiometry (DXA) scan weight limit), and ECOG performance status of 0–1 at the time of study enrollment. Patients with underlying osteoporosis or severe osteopenia (defined as a DXA T-score < 2.0), prior history of a non-traumatic fragility bone fracture, or other disorders affecting bone function or turnover were excluded from the study to more directly evaluate the effect of AI on bone turnover. Past history of vitamin D deficiency was allowed, though current deficiencies required correction to ≥ 20 ng/ml prior to study enrollment; those with vitamin D deficiencies refractory to supplementation were ineligible. Patients taking medications affecting bone metabolism, including bisphosphonates or denosumab, were also excluded.</p>", "<title>Study design</title>", "<p id=\"Par6\">This was a prospective, observational pilot study. Treatment with anastrozole (1 mg) once daily was initiated on day 1 and continued for the duration of the study. Anastrozole was chosen as the initial AI to minimize variability and because it was the most prescribed AI at our institution; however, any AI was allowed as there is no difference in mechanism or impact on bone turnover<sup>##REF##17692126##11##,##REF##18029171##12##</sup>. Patients underwent assessments at baseline prior to starting AI and after 6 months of AI exposure. Assessments included a quadriceps muscle biopsy, DXA measures of body composition and bone mineral density (BMD), isokinetic dynamometry, Short Physical Performance Battery (SPPB), grip strength, 6-min walk test, patient-reported outcome (PRO) questionnaires, and serum samples, as detailed below. Patients were compensated for their time to complete the assessments. The study was approved by the Institutional Review Board (IRB) at Indiana University and performed in accordance with the ethical standards of the Declaration of Helsinki. All patients provided written informed consent.</p>", "<p id=\"Par7\">Patients were contacted monthly for AI dosing information and an estimate of how many doses they missed per week on average. Adherence to AI therapy for the 14 days preceding muscle tissue biopsy collection was required; if on a treatment break during this timeframe, biopsies were delayed until 14 days after AI therapy was restarted. For AI-related toxicities, therapy could be held for ≤ 28 days. Dose adjustments were not permitted. If toxicity occurred, switching to an alternate AI was permitted within 28 days. If the AI was held for > 28 days or discontinued, the patient was removed from the study and not replaced. Adverse events were graded according to the NCI Common Toxicity Criteria (Version 4.0), documented, and reported to the Data Safety Monitoring Committee and/or IRB per study protocol.</p>", "<title>Data collection</title>", "<title>Demographics</title>", "<p id=\"Par8\">Patient demographics and clinical data, including date of initial diagnosis, prior breast cancer therapies, disease stage, concomitant medications, height/weight, and ECOG performance status, were recorded at baseline.</p>", "<title>Tissue collection and processing</title>", "<p id=\"Par9\">Muscle biopsies were performed at baseline and 6 months by a physician trained in the procedure (TB). Samples were obtained from the vastus lateralis using the modified Bergström technique as previously described<sup>##UREF##1##13##</sup>. Muscle tissue samples were immediately flash frozen and stored at – 80 °C. RyR1 was immunoprecipitated from the muscle lysate with 2 µg anti-RyR specific antibody (Santa Cruz Biotechnology, sc-376507) in 0.5 mL of a modified RIPA buffer (20 mM Tris–HCl (pH 7.5), 250 mM NaCl,1 mM EDTA, 1% NP-40, 1 mM Na<sub>3</sub>VO<sub>4</sub>, and Protease Inhibitor Cocktail (Cell Signaling Technology, #5871S) for 4 h at 4 °C. The immune complexes were incubated with Protein G Sepharose<sup>®</sup> 4 Fast Flow (GE Healthcare, #17-0618-01) overnight at 4°C, and the beads were washed three times with RIPA buffer. The immuno-precipitates were size-fractionated on 4–20% SDS-PAGE gels for RYR1 and 15% for calstabin1, and transferred onto PVDF membranes for 2.5 h at 200 mA. Immunoblots were developed using the following primary antibodies: anti-RyR and anti-calstabin1 (Santa Cruz Biotechnology, sc-133067). To determine channel oxidation, the carbonyl groups in the protein side chains were derivatized to 2,4-dinitrophenylhydrazone (DNP-hydrazone) by reaction with 2,4-dinitrophenylhydrazine (DNPH). The DNP signal was determined using a specific anti-DNP antibody (Millipore, MAB2223). All immunoblots were developed using the SuperSignal West Pico PLUS Chemiluminescent Substrate (Thermo Fisher Scientific, 34577) and detected using an Odyssey system (LI-COR, Inc.). The bait protein RyR1 acts as an internal control. Relative band intensity was quantified using ImageJ Software (NIH).</p>", "<title>Body composition</title>", "<p id=\"Par10\">Height (to nearest 0.1 cm) and mass (to nearest 0.1 kg) were measured without shoes using a calibrated stadiometer (Seca 264; Seca GmbH & Co., Hamburg, Germany) and scale (MS140-300; Brecknell, Fairmont, MN), respectively. Body mass index (BMI; kg/m<sup>2</sup>) was calculated as body mass relative to height squared. DXA (Norland Elite; Norland at Swissray, Fort Atkinson, WI) was performed to assess BMD and body composition measures (total body fat percentage, total lean mass, and total fat mass). The latter allowed for normalization of strength assessments to total and lean body mass, as previously described<sup>##UREF##2##14##–##REF##20861456##16##</sup>. Regional DXA scans were performed to obtain femoral neck and lumbar spine BMD and associated T-scores.</p>", "<title>Muscle performance</title>", "<p id=\"Par11\">Muscle contractile properties, fatigue resistance, and functional recovery of the knee extensors were assessed with a Biodex 4 isokinetic dynamometer (Biodex Medical Systems, Shirley, NY), as previously described<sup>##REF##25199856##17##,##REF##26179185##18##</sup>. Patients performed 3–4 maximal knee extension exercises with their dominant leg at angular velocities of 0, 1.57, 3.14, 4.71, and 6.28 rad/s. The maximal torque, or voluntary force production, generated at each velocity was recorded with 2-min rest periods between sets of contractions. To eliminate artifacts, data was “windowed” to isolate the isokinetic phase and smoothed using a 9-point weighted moving average filter using the manufacturer’s software. Peak power was calculated using the highest achieved torque at each velocity and the resulting power-velocity curve was fit with a parabolic function to determine maximal torque, speed, and power. Subsequently, patients underwent an “all out” 50 contraction fatigue test at 3.14 rad/s to evaluate fatigue resistance during repetitive, maximal activation, measured as a fatigue index comparing power in the first third of contractions to power in the last third. Recovery of muscle function was assessed by measuring restoration of torque during knee extensions performed periodically over the next 10 min. The half-life of torque recovery was calculated by fitting a monoexponential function to the latter data.</p>", "<p id=\"Par12\">Dominant hand grip strength (Jamar Plus + digital hand dynamometer; Sammons Preston, Bolingbrook, IL) and the time taken to complete 5 chair stands were assessed, as we have previously described<sup>##REF##34972866##19##</sup>. Dynamometer performance was confirmed weekly by applying known masses. In addition to raw values, grip strength and repeat chair stand outcomes were converted to age- and sex-matched z-scores relative to our published reference data<sup>##REF##34972866##19##</sup>. Time to walk 4-m from a stationary start at normal speed (usual gait speed) was measured with a stopwatch and converted to speed (m/s). Results from the repeat chair stand, usual gait speed, and a static balance test (ability to balance for 10 s with feet in side-by-side, semi-tandem, and tandem positions) were used to calculate the Short Physical Performance Battery (SPPB) score<sup>##REF##8126356##20##</sup>. A higher score out of 12 indicates better performance. Distance walked in 6 min was measured on a 20-m course.</p>", "<title>Patient-reported outcomes</title>", "<p id=\"Par13\">Patients completed the physical function domain of NIH Patient-Reported Outcomes Measurement Information System (PROMIS) computerized adaptive test (CAT) (PROMIS-CAT) (version 1.2) to provide a self-reported indication of functional health. PROMIS scores are standardized and expressed as T-scores with a population mean of 50 and standard deviation (SD) of 10<sup>##REF##17443116##21##</sup>.</p>", "<title>Bone turnover</title>", "<p id=\"Par14\">Serum samples were collected to measure markers of bone turnover. ELISA assays were performed for quantification of N-terminal crosslinked telopeptide of type 1 collagen (NTX1; Novus Biologicals) and TGF-β (isoform TGF-β1) using platelet-free plasma (R&D systems). ELISA was done in triplicate and results were quantified and averaged.</p>", "<title>Statistical analysis</title>", "<p id=\"Par15\">Categorical variables were reported as frequencies and percentages of the total enrolled population. Continuous variables were reported as mean ± SD. The primary endpoint was to compare the relative levels of calstabin1 bound to RyR1 channels in skeletal muscle pre- and post-initiation of AI therapy, measured by coimmunoprecipitation. Ratios of calstabin1to RyR1 were compared at baseline and 6 months post-AI exposure using paired t-tests, or Wilcoxon signed-rank tests if assumptions of paired t-tests were not met. A p value of < 0.05 was considered significant. Compliance with AI therapy, defined as self-report of an average ≥ 80% of daily doses on monthly nursing calls, and participation in baseline and 6-month procedures were required to be evaluable for the primary endpoint. It was estimated at least 12 patients would be needed for feasibility and precision around estimates pre- and post-AI exposure in this pilot study<sup>##UREF##3##22##</sup>. It was estimated at least 20% of patients would discontinue their AI in the 6 months of study duration and thus 15 patients were enrolled.</p>", "<p id=\"Par16\">Secondary endpoints and analyses included a comparison of changes in relative RyR1 oxidation levels pre- and post-AI exposure using paired t-tests or Wilcoxon signed-rank tests. Differences from baseline to 6-month follow-up in clinical muscle measures, including maximal torque, muscle recovery half-life, total SPPB score, and distance in the 6-min walk test, were compared using paired t-tests or Wilcoxon signed-rank tests. Correlation between changes in RyR1 biochemistry and changes in muscle function were assessed using Pearson correlation coefficients.</p>" ]	[ "<title>Results</title>", "<title>Patient demographics and clinical data</title>", "<p id=\"Par17\">Fifteen eligible patients (mean age ± SD = 59.9 ± 4.6 years) were enrolled (CONSORT, Fig. ##FIG##0##1##). Eleven identified as White race. All patients had early-stage breast cancer: 46.6% (n = 7) stage I, 33.3% (n = 5) stage II, and 20% (n = 3) stage III. Three of the patients had previously received chemotherapy and all had received radiation. All patients were initially treated with anastrozole. Four patients experienced AI toxicity of joint pain necessitating a treatment change to an alternate AI (letrozole-3, exemestane-1). All patients reported compliance with AI therapy.</p>", "<title>RyR1 complexes and oxidation in muscle tissue after AI exposure</title>", "<p id=\"Par18\">One patient opted to not proceed with the 6-month muscle biopsy and thus 14 patients were evaluable for this endpoint. No adverse events were reported as a result of the biopsy procedure. There was a 2.8-fold increase in oxidation of RyR1 channels after AI exposure between baseline and 6-month follow-up (0.23 ± 0.37 vs. 0.88 ± 0.80, p < 0.001). In addition, there was a more than 50% decrease in bound calstabin1 to RyR1 after AI exposure (1.69 ± 1.53 vs. 0.74 ± 0.85, p < 0.001), consistent with a biochemical signature of dysfunctional and leaking Ca<sup>2+</sup> channels (Fig. ##FIG##1##2##).</p>", "<title>Muscle contractile properties and physical function after AI exposure</title>", "<p id=\"Par19\">For those properties with established normative values, patients had normal hand grip strength (z-score = 0.28; 95% confidence interval [CI], − 0.30 to 0.86) and time to complete 5 chair stands (z-score = − 0.14; 95% CI − 0.57 to 0.28) at baseline. Their self-reported functional health was 0.32 SD (95% CI 0.03 to 0.60 SD) below normal, as assessed via the physical function domain of the PROMIS-CAT.</p>", "<p id=\"Par20\">There were no significant differences between baseline and 6-month follow-up in knee extensor muscle power, fatigue index after 50 “all out” contractions, or time to muscle recovery (Table ##TAB##0##1##). There was a 10.5% (95% CI 3.7% to 17.3%) decrease in grip strength over 6 months (26.2 ± 5.9 kg vs. 23.4 ± 5.9 kg, p < 0.01). There were no differences between baseline and 6 months in time to complete 5 chair stands (9.9 ± 1.8 s vs. 10.4 ± 1.1 s, p = 0.37), SPPB score (11.5 ± 0.9 vs. 11.7 ± 0.6, p = 0.38), PROMIS-CAT score (46.8 ± 5.6 vs. 47.3 ± 8.6, p = 0.40) or 6-min walk distance (468 ± 86 m vs. 476 ± 105 m, p = 0.53).</p>", "<p id=\"Par21\">When exploring whether increase in RyR1 oxidation or loss of calstabin1 correlated with muscle function changes by dynamometry at the individual level, we found a significant correlation between change in oxidized RyR1 and maximal muscle power (r = 0.60, p = 0.02) and % fatigue (r = 0.57, p = 0.03). No correlations were found between bound calstabin1 and muscle function changes (Table ##TAB##1##2##). In addition, there was no correlation between change in grip strength and change in oxidized RyR1 (r = − 0.14, p = 0.65) or bound calstabin1 (r = 0.12, p = 0.69).</p>", "<title>Body composition and bone density after AI exposure</title>", "<p id=\"Par22\">There were no significant differences between baseline and 6 months in body fat, lean muscle, or bone density with 6 months of AI exposure (<italic>see</italic> Table ##SUPPL##0##1##<italic>in supplemental data</italic>).</p>", "<title>Serologic bone turnover markers after AI exposure</title>", "<p id=\"Par23\">To determine whether muscle changes were correlated with early changes in bone turnover, we measured serum NTX-1 and TGF-β, both of which are increased in states of high bone turnover. We found 0.74 negative change in NTX-1 (p = 0.23) and 232 negative change in TGF-β (p = 0.64) from baseline to 6 months. When evaluating correlations between changes in bone turnover markers and change in RyR1 oxidation and loss of calstabin1 at the individual level, we found no significant associations (<italic>see</italic> Table ##SUPPL##0##2##<italic>in supplemental data)</italic>.</p>" ]	[ "<title>Discussion</title>", "<p id=\"Par24\">The skeletal complications of estrogen deprivation therapy in the treatment of breast cancer are long-term and well-established; in contrast, muscular complications are less well described and may have more immediate impact on physical function and quality of life. Based on compelling preclinical data that high bone turnover results in maladaptive changes in skeletal muscle and muscle weakness, we evaluated biochemical signatures and detailed clinical muscle function in women with breast cancer before and after AI therapy. As hypothesized, AI therapy resulted in a biochemical signature consistent with ‘leaky’ Ca<sup>2+</sup> channels, oxidation of RyR1 and loss of its stabilizing unit calstabin1. In addition, while there were no differences in physical function after 6 months of AI exposure, oxidized RyR1 correlated with peak muscle power and rates of muscle fatigue.</p>", "<p id=\"Par25\">Despite significant biochemical changes, there were no statistically significant changes in muscle function by dynamometry, SPPB, 6-min walk test, or self-reported functional health at the group level after 6 months of AI exposure. This is in contrast to a preclinical model of ovariectomized mice treated with AI versus placebo, which demonstrated a significant reduction in muscle-specific force of the extensor digitorum longus muscle in those treated with AI<sup>##REF##28039445##3##</sup>. In our study, we did observe a significant decrease in grip strength at 6 months, consistent with another clinical observational report of women taking AIs<sup>##REF##24816806##23##</sup>. However, this decrease in grip strength did not correlate with biochemical changes; this may be because decreases in grip strength have been primarily associated with joint pain in women on AIs, rather than reflective of actual muscle function<sup>##REF##24816806##23##,##REF##30553719##24##</sup>. Our study is the first to investigate muscle function in women on AIs with more comprehensive and specific endpoints beyond grip strength. Prior clinical observational studies have focused primarily on changes in muscle <italic>mass</italic> with AI exposure, with inconsistent results<sup>##REF##21046232##25##–##REF##26571369##28##</sup>. However, it is well-established that muscle mass is not a clear predictor of muscle function. Muscle function is a superior predictor of clinical outcomes including quality of life, activities of daily living, and functional independence, and is a significant independent predictor of mortality<sup>##REF##28431396##29##,##REF##10811541##30##</sup>. In a prospective study of older women with breast cancer, decline in physical function was associated with a 34% increased risk of death at 10 years<sup>##REF##23232922##31##</sup>. Prior observational studies have found decline in physical function specifically in those patients experiencing AI-induced musculoskeletal syndrome, defined by presence of joint pain<sup>##REF##30553719##24##</sup>. Increased joint pain has also been correlated with reduced physical activity levels, which in turn may lead to declines in physical function<sup>##REF##24165356##32##</sup>. However, our study is limited by 6-month follow-up and many patients develop pain that may limit function at later time points; both of the observational studies referenced here evaluated patients several years into their AI therapy.</p>", "<p id=\"Par26\">We did observe a correlation between molecular muscle changes and muscle power and fatigue resistance when evaluating changes in individual patients. The lack of significant changes in muscle function at the group level may be secondary to the limitations of small sample size or early evaluation of endpoints at 6 months. With only 6-month follow-up in our study, it is possible that we are seeing early biochemical changes in muscle associated with AI therapy that are still subclinical, and with longer follow-up, we may see more functional decline. Prior data analyzing skeletal complications associated with AI therapy suggest bone turnover starts rapidly as measured by serologic markers; however, it may be compensated and take significant time to cause additional systemic impact on muscle function that is visible at the clinical level<sup>##REF##16869719##33##</sup>.</p>", "<p id=\"Par27\">In this study, it is also possible that enrolling only a limited sample size of postmenopausal women at varying times after menopause impacted our ability to see muscle function changes. The gradual loss of estrogen with menopause and aging is associated with loss of muscle mass and function, likely related both to direct effects of estrogen signaling in muscle and a decline in physical activity with age. Hormone replacement therapy after menopause attenuates loss of muscle and may also improve muscle regeneration in response to exercise; thus, we would hypothesize that the complete estrogen deprivation with AI therapy would result in further decline in muscle function<sup>##REF##25610174##34##,##REF##22395277##35##</sup>. Future work analyzing muscular health in patients receiving endocrine therapy should include premenopausal women as many of those patients receive more rapid and severe estrogen decline with ovarian suppression and AI therapy, and may have more significant muscular impairment.</p>", "<p id=\"Par28\">In addition, patients who had received chemotherapy were included in this analysis. Our prior work evaluated muscle power generation on a stationary bicycle following primary therapy for breast cancer, finding a similar reduction in power at 6 months in patients receiving endocrine therapy alone as those who received chemotherapy, without any evidence of recovery of function in either group by 12 months<sup>##REF##30136009##36##</sup>. Based on this, we included patients regardless of prior chemotherapy and evaluated outcomes at 6 months in this analysis. However, this heterogeneity may have blunted baseline muscle function as preclinical work suggests cytotoxic chemotherapy induces protein degradation via activation of the NF-kB pathway and induction of the ubiquitin–proteasome system, while also reducing muscle protein synthesis<sup>##REF##29991992##37##,##REF##23497627##38##</sup>.</p>", "<p id=\"Par29\">Preclinical work indicates that it may be possible to preserve RyR1 function in muscle. Inhibition of bone turnover with the bisphosphonate zolendronic acid results in reduced RyR1 oxidation, stabilization of calstabin1, and corresponding preservation of muscle function in mouse models<sup>##REF##26457758##7##</sup>. In addition, mechanical signals delivered to bone either from exercise or vibrational stimulation can reduce bone turnover and improve muscle function in preclinical models of estrogen deprivation<sup>##REF##30814687##39##,##UREF##4##40##</sup>. Translating these findings to clinic, we are currently studying the role of low intensity vibration to preserve muscle function in women receiving AIs who do not or cannot participate in regular exercise. In this ongoing trial (NCT03712813), we are collecting longitudinal changes in muscle architecture, function, and biochemistry over a 2 year period, and including both pre- and post-menopausal patients receiving estrogen deprivation.</p>", "<p id=\"Par30\">To our knowledge, this study represents the only analysis of skeletal muscle tissue in the most common population of breast cancer patients. Repeated muscle biopsies were feasible and safe in this population, with only one of 15 patients declining a follow-up biopsy and no adverse events reported. This is a comprehensive assessment across molecular, subjective, and objective muscle function measures in a population where this was not previously explored, and is based on a mechanistic hypothesis that could be therapeutically exploited to improve patient outcomes. While we observed changes in RyR1, there may be other biochemical markers of muscle impairment associated with endocrine therapy worthy of investigation, and future work should more comprehensively analyze muscle tissue in these patients. Musculoskeletal complications are a major source of morbidity due to breast cancer and its therapy; however, little has been done to explore the ‘musculo’ aspect of musculoskeletal health in survivors. This may have significant implications for short- and long-term quality of life, medication adherence, physical activity participation, and physical function. More comprehensive assessments of <italic>musculo</italic>skeletal health are possible and needed across the cancer continuum.</p>" ]	[]	[ "<p id=\"Par1\">We evaluated biochemical changes in skeletal muscle of women with breast cancer initiating aromatase inhibitors (AI), including oxidation of ryanodine receptor RyR1 and loss of stabilizing protein calstabin1, and detailed measures of muscle function. Fifteen postmenopausal women with stage I–III breast cancer planning to initiate AI enrolled. Quadriceps muscle biopsy, dual-energy x-ray absorptiometry, isokinetic dynamometry, Short Physical Performance Battery, grip strength, 6-min walk, patient-reported outcomes, and serologic measures of bone turnover were assessed before and after 6 months of AI. Post-AI exposure, oxidation of RyR1 significantly increased (0.23 ± 0.37 vs. 0.88 ± 0.80, p < 0.001) and RyR1-bound calstabin1 significantly decreased (1.69 ± 1.53 vs. 0.74 ± 0.85, p < 0.001), consistent with dysfunctional calcium channels in skeletal muscle. Grip strength significantly decreased at 6 months. No significant differences were seen in isokinetic dynamometry measures of muscle contractility, fatigue resistance, or muscle recovery post-AI exposure. However, there was significant correlation between oxidation of RyR1 with muscle power (r = 0.60, p = 0.02) and muscle fatigue (r = 0.57, p = 0.03). Estrogen deprivation therapy for breast cancer resulted in maladaptive changes in skeletal muscle, consistent with the biochemical signature of dysfunctional RyR1 calcium channels. Future studies will evaluate longer trajectories of muscle function change and include other high bone turnover states, such as bone metastases.</p>", "<title>Subject terms</title>" ]	[ "<title>Supplementary Information</title>", "<p>\n</p>" ]	[ "<title>Supplementary Information</title>", "<p>The online version contains supplementary material available at 10.1038/s41598-024-51751-y.</p>", "<title>Acknowledgements</title>", "<p>Expedition Inspiration (PI: Ballinger). Indiana University Simon Comprehensive Cancer Center (PI: Ballinger). National Institutes of Health (NIH/NIAMS P30 AR072581) and the Indiana Clinical Translational Science Award/Institute (NCATS UL1TR002529-01).</p>", "<title>Author contributions</title>", "<p>T.B., A.C., and T.G. conceived of the idea. T.B., R.H., B.S., C.A., and A.C. collected data in the corresponding clinical trial. L.S. and K.R. carried out laboratory experiments. T.S., L.S., S.A., S.W., T.G., A.C., and T.B. analyzed and interpreted the data. T.S. and T.B. wrote the manuscript. All authors commented and provided feedback on the manuscript.</p>", "<title>Data availability</title>", "<p>The data generated in this study are available upon request from the corresponding author.</p>", "<title>Competing interests</title>", "<p id=\"Par31\">The authors declare no competing interests.</p>" ]	[ "<fig id=\"Fig1\"><label>Figure 1</label><caption><p>CONSORT.</p></caption></fig>", "<fig id=\"Fig2\"><label>Figure 2</label><caption><p>(<bold>a</bold>) Representative immunoprecipitation-Western blot analysis for RyR1 with oxidized thiol groups and their matched total content in homogenate (upper panels) and interaction between RyR1 and calstabin1 (lower panels). BL—baseline; 6 M—6 month follow up. (<bold>b</bold>,<bold>c</bold>) Quantification of relative oxidized RyR1 and the RyR-calstabin1. Results are expressed as mean with SEM. Wilcoxon signed-rank test.</p></caption></fig>" ]	[ "<table-wrap id=\"Tab1\"><label>Table 1</label><caption><p>Muscle contractile function, fatigue resistance, and recovery measured by isokinetic dynamometry before and after aromatase inhibitor exposure.</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\">Baseline</th><th align=\"left\">6 months</th><th align=\"left\">P value</th></tr></thead><tbody><tr><td align=\"left\" colspan=\"4\">Torque-velocity test</td></tr><tr><td align=\"left\"> Maximal torque (nm/kg)</td><td char=\"±\" align=\"char\">1.54 ± 0.31</td><td char=\"±\" align=\"char\">1.53 ± 0.34</td><td char=\".\" align=\"char\">0.94</td></tr><tr><td align=\"left\"> Maximal speed (rad/s)</td><td char=\"±\" align=\"char\">12.2 ± 2.4</td><td char=\"±\" align=\"char\">11.8 ± 1.1</td><td char=\".\" align=\"char\">0.62</td></tr><tr><td align=\"left\"> Maximal power (w/kg)</td><td char=\"±\" align=\"char\">3.45 ± 1.07</td><td char=\"±\" align=\"char\">3.61 ± 0.79</td><td char=\".\" align=\"char\">0.44</td></tr><tr><td align=\"left\" colspan=\"4\">Fatigue-recovery test</td></tr><tr><td align=\"left\"> Initial peak torque (nm/kg)</td><td char=\"±\" align=\"char\">0.84 ± 0.17</td><td char=\"±\" align=\"char\">0.84 ± 0.14</td><td char=\".\" align=\"char\">0.85</td></tr><tr><td align=\"left\"> Final peak torque (nm/kg)</td><td char=\"±\" align=\"char\">0.34 ± 0.06</td><td char=\"±\" align=\"char\">0.32 ± 0.07</td><td char=\".\" align=\"char\">0.31</td></tr><tr><td align=\"left\"> % Fatigue</td><td char=\"±\" align=\"char\">58 ± 10</td><td char=\"±\" align=\"char\">62 ± 10</td><td char=\".\" align=\"char\">0.42</td></tr><tr><td align=\"left\"> Recovery peak torque (nm/kg)</td><td char=\"±\" align=\"char\">0.83 ± 0.16</td><td char=\"±\" align=\"char\">0.86 ± 0.15</td><td char=\".\" align=\"char\">0.34</td></tr><tr><td align=\"left\"> Recovery half-life (s)</td><td char=\"±\" align=\"char\">72 ± 38</td><td char=\"±\" align=\"char\">62 ± 31</td><td char=\".\" align=\"char\">0.32</td></tr></tbody></table></table-wrap>", "<table-wrap id=\"Tab2\"><label>Table 2</label><caption><p>Correlations between change in RyR1 biochemistry and muscle contractile properties (n = 14).</p></caption><table frame=\"hsides\" rules=\"groups\"><thead><tr><th align=\"left\"/><th align=\"left\">Muscle function variable</th><th align=\"left\">Pearson correlation coefficient</th><th align=\"left\">P value</th></tr></thead><tbody><tr><td align=\"left\" rowspan=\"2\">Change in oxidized RyR1</td><td align=\"left\">Maximal power</td><td char=\".\" align=\"char\">0.60</td><td char=\".\" align=\"char\">0.02</td></tr><tr><td align=\"left\">% fatigue</td><td char=\".\" align=\"char\">0.58</td><td char=\".\" align=\"char\">0.03</td></tr><tr><td align=\"left\" rowspan=\"2\">Change in RyR1/bound calstabin1</td><td align=\"left\">Maximal power</td><td char=\".\" align=\"char\">0.22</td><td char=\".\" align=\"char\">0.44</td></tr><tr><td align=\"left\">% fatigue</td><td char=\".\" align=\"char\">− 0.10</td><td char=\".\" align=\"char\">0.75</td></tr></tbody></table></table-wrap>" ]	[]	[]	[]	[]	[]	[ "<supplementary-material content-type=\"local-data\" id=\"MOESM1\"></supplementary-material>" ]	[ "<table-wrap-foot><p>All data presented as mean ± SD. Contraction fatigue testing was performed at an angular velocity of 3.14 rad/s.</p></table-wrap-foot>", "<table-wrap-foot><p>Corr., correlation; Coeff., coefficient.</p></table-wrap-foot>", "<fn-group><fn><p><bold>Publisher's note</bold></p><p>Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.</p></fn></fn-group>" ]	[ "<graphic xlink:href=\"41598_2024_51751_Fig1_HTML\" id=\"MO1\"/>", "<graphic xlink:href=\"41598_2024_51751_Fig2_HTML\" id=\"MO2\"/>" ]	[ "<media xlink:href=\"41598_2024_51751_MOESM1_ESM.pdf\"><caption><p>Supplementary Information.</p></caption></media>" ]	[{"label": ["4."], "surname": ["Miller", "Sollars", "Althouse", "Han", "Ventura", "Sledge"], "given-names": ["KD", "JL", "SK", "LK", "SJ", "JS"], "article-title": ["Abstract P1-09-01: Beyond fatigue\u2014The impact of breast cancer treatment on body composition and energy expenditure"], "source": ["Cancer Res."], "year": ["2015"], "volume": ["75"], "issue": ["9_Supplement"], "fpage": ["P1-09-01"], "pub-id": ["10.1158/1538-7445.SABCS14-P1-09-01"]}, {"label": ["13."], "surname": ["Shanely", "Zwetsloot", "Triplett", "Meaney", "Farris", "Nieman"], "given-names": ["RA", "KA", "NT", "MP", "GE", "DC"], "article-title": ["Human skeletal muscle biopsy procedures using the modified Bergstrom technique"], "source": ["J. Vis. Exp."], "year": ["2014"], "volume": ["91"], "fpage": ["51812"]}, {"label": ["14."], "surname": ["Irwin", "McTiernan", "Manson", "Thomson", "Sternfeld", "Stefanick"], "given-names": ["ML", "A", "JE", "CA", "B", "ML"], "article-title": ["Physical activity and survival in postmenopausal women with breast cancer: Results from the women's health initiative"], "source": ["Cancer Prev. Res."], "year": ["2011"], "volume": ["4"], "issue": ["4"], "fpage": ["522"], "lpage": ["529"], "pub-id": ["10.1158/1940-6207.CAPR-10-0295"]}, {"label": ["22."], "surname": ["Julious"], "given-names": ["SA"], "article-title": ["Sample size of 12 per group rule of thumb for a pilot study"], "source": ["Pharm. Stat."], "year": ["2005"], "volume": ["4"], "issue": ["4"], "fpage": ["287"], "lpage": ["291"], "pub-id": ["10.1002/pst.185"]}, {"label": ["40."], "surname": ["Pagnotti", "Trivedi", "Wright", "John", "Murthy", "Pattyn"], "given-names": ["GM", "T", "LE", "SK", "S", "RR"], "article-title": ["Low-magnitude mechanical signals combined with zoledronic acid reduce musculoskeletal weakness and adiposity in estrogen-deprived mice"], "source": ["bioRxiv."], "year": ["2023"], "volume": ["2"], "fpage": ["1102"]}]	{ "acronym": [], "definition": [] }	40	CC BY	no	2024-01-13 00:02:20	Sci Rep. 2024 Jan 10; 14:1029	oa_package/84/90/PMC10781701.tar.gz

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